Before we jump in today, a reminder that our InvestorPlace offices are closed tomorrow in honor of Independence Day.

If you need help from our Customer Service team, they’ll be happy to assist you when we reopen on Monday at 9 a.m. Eastern.

Have a good evening,

Jeff Remsburg

As I write on Thursday morning, the Labor Department just reported 57,000 jobs added in June – badly missing the 115,000 consensus and a sharp step down from May’s downwardly revised 129,000.

The unemployment rate did tick down to 4.2%. But that’s not really good news…

The drop came almost entirely from a falling labor-force participation rate, which slid to 61.5%, its lowest level since March 2021. So, we can translate this number as “fewer people looking for work” rather than “more people finding it.”

Now, plenty of talking heads are already taking a familiar posture – the soft jobs report takes pressure off the Fed, rate hikes get kicked further down the road, onward and upward for the market.

But there’s a wrinkle…

The man running the Fed has already told you, in his own words, that he’s not grading today’s number the way Wall Street is.

So, how is he grading it?

Yesterday, Federal Reserve Chairman Kevin Warsh gave us a preview of how he’s thinking

Speaking Wednesday at the ECB’s forum on central banking in Sintra, Portugal, Warsh once again declined to signal anything about this month’s meeting. But he didn’t stay quiet on inflation.

Here’s Warsh:

We’re all in the price stability business… but if there was a common thing I heard over the last couple of days, it was open-mindedness on these questions of AI, open-mindedness on productivity, but we’ve all looked around, and we’ve seen that prices are too high.

Translation: whatever AI is doing to boost productivity isn’t fixing the inflation problem yet.

That’s the lens Warsh brought into this morning’s number – not “did payrolls beat consensus?” but “does anything here change my inflation math?”

It’s unlikely the answer is “yes.”

One more detail from yesterday is worth noting, given this morning’s data…

In that same Sintra appearance, Warsh described the labor market as “steady.” Twenty-four hours later, payrolls missed by more than half, and a chunk of the workforce simply stopped looking for jobs altogether.

That’s either an inconvenient coincidence or an early sign of the exact disconnect we’re about to walk through…

You see, yesterday, Warsh also gave us a glimpse of where he wants the Fed’s whole approach to data to go – and when:

My hope, my aspiration, is that nine-12 months from now we’re going to be using new technologies to understand what’s happening in the real economy in a contemporaneous real time way.

That’s not a Fed chair who trusts the data he’s handed by default. It’s a Fed chair actively building his own alternative to it.

We flagged this shift three weeks ago

Regular Digest readers will remember our June 16 issue, where we laid out the case that Warsh is dismantling forward guidance itself – the dot plot, the press-conference roadmapping, the whole architecture Bernanke built and Powell expanded.

At his Senate confirmation hearing, Warsh to this plainly:

Unlike many of my colleagues, past and present, I don’t believe in forward guidance.

I don’t believe that I should be previewing for you what a future decision might be.

Our takeaway was that if the Fed stops telling you where it’s going, the incoming data becomes the new dot plot. Every economic report gets bigger. Every release becomes a larger event that can rattle markets.

Today’s jobs report was the first real-world test of that thesis. So, we should still be careful not to rush to interpret it.

Warsh already told us what’s wrong with this morning’s number – just not what should replace it

In our June 25 Digest, we showed you that Warsh doesn’t lean primarily on headline PCE – he’s called the Fed’s conventional inflation gauge little more than a “rough swag.”

Instead, he watches the Dallas Fed’s trimmed mean PCE, a measure that lops off the pricing outliers on either side to focus on what’s happening in the middle.

Warsh hasn’t given us a parallel substitute he prefers to the jobs report. But at his very first FOMC press conference as chair, on June 17, he made clear he has the same discomfort with it.

A reporter pressed him on how much weight he puts on the initial payroll print. Here’s Warsh:

What we’re less interested in is echoes of history.

Some of the data that we receive – that we’re waiting on the first Friday after the month of payroll index or something else – that might be an echo of history that’s quite useful on its third revision.

We need to take those error bounds down because we have to make hard decisions in real time.

Translating that out of Fed-speak, Warsh is saying the number that hits your screen the moment it’s released isn’t the real number. It’s a rough first draft that gets rewritten twice more over the following two months – and Warsh is telling you, directly, that he doesn’t fully trust the first draft.

Today’s data proved his point…

May’s already-strong 172,000 print got cut by another 43,000, down to 129,000. And April came down another 31,000, to 148,000.

That’s two more months of the “echo of history” Warsh is describing, rewriting itself in real time.

So, what’s a better way to read this morning’s data?

Warsh hasn’t told us what replaces the headline print. His task forces on data quality are still being staffed – he said as much yesterday at Sintra, promising more detail “next week.”

So, until the Fed builds something better, investors are left to build their own workaround.

Here’s ours: rather than reacting to any single month’s headline number, watch the three-month average payroll gain, calculated using the most recently revised figures rather than each month’s initial estimate.

To be clear, this isn’t Warsh’s framework. But it’s offered in the spirit of the problem he’s flagged.

So, what does this morning’s number look like after smoothing?

Averaging the three most recently revised prints – April’s 148,000, May’s 129,000, and June’s initial 57,000 – puts the trailing three-month pace at roughly 111,000 jobs a month.

To keep pace with population growth and keep the unemployment rate steady, the U.S. economy historically needs to add roughly 150,000 jobs per month.

So, this average is somewhat weak.

Plus, it represents a real cooling trend – and a confirmed one, since all three months just got revised in the same direction: down.

So, even though our three-month framework doesn’t let us treat today’s softer headline print, on its own, as significant enough to move Fed policy, three consecutive downward revisions point toward a real signal.

Is that enough for Warsh to ease up on his hawkishness?

A critical detail to keep in mind

Every new Fed chair gets the same treatment from the media…

Cameras find one face, and that face’s mood becomes shorthand for the whole institution’s mood.

It happened with Bernanke. It happened with Powell. It’s happening now with Warsh.

But Warsh doesn’t run the Fed the way the coverage sometimes implies. He’s chairman, but on the FOMC, his vote counts the same as everyone else’s.

And right now, that committee is deeply split – even if the vote itself doesn’t show it. At Warsh’s first meeting on June 17, the Fed held rates unanimously. But look past the vote to the dot plot underneath it, and the picture changes…

Nine of his 18 colleagues penciled in higher rates before year-end, six of those wanting two separate hikes, while eight favored holding steady and just one wanted a cut.

Warsh himself declined to submit a projection at all.

A unanimous vote with that kind of split sitting underneath it isn’t a consensus. It’s a committee that agreed to disagree quietly for one more meeting.

So, everything we’ve walked through so far – the “echo of history” comment, the discomfort with headline noise, the instinct to look past a single month’s print – tells you how Warsh is likely reading this jobs report. It doesn’t tell you how the rest of the committee reads it, divided as it is.

What this means for how you trade between now and the fall

We got a confirmation of our June 16 thesis this morning – just not a clean, one-directional one.

The initial reaction to the jobs print was textbook…

Futures ripped higher within minutes, yields fell, and traders recalculated their rate hikes expectations. But that snap judgment didn’t hold…

As I write around lunchtime, the early gains have disappeared – though the Dow is still up, the S&P has gone negative, and the Nasdaq is off almost 1%.

This is a market split on how to read a soft-but-revision-heavy print: does cooling labor demand ease the pressure that’s been keeping the Fed hawkish, or is it an early sign the economy itself is cracking?

And without a Warsh press conference to smooth that disagreement into a tidy consensus, this is what price discovery looks like instead – fast, messy, and uncertain.

But the bigger picture hasn’t changed…

Warsh isn’t going to move on one number – he needs a sustained trend. And even if he gets one, he’ll still need to bring along a committee that doesn’t fully agree with him.

Until both of those things happen, expect exactly what we’ve been suggesting is today’s new normal: bigger reactions to smaller pieces of data – and, apparently, uncertain reactions to those reactions.

It’s the logical consequence of a Fed that’s more divided, and less communicative, than it’s been in years.

We’ll keep tracking this as the story develops.

Have a good evening,

Jeff Remsburg

The post The Real Story Behind the Disappointing Jobs Report appeared first on InvestorPlace.

It can get very philosophical. But I’ll let you in on a secret: At the end of the day, the culprit is almost always the same…

Emotions are what’s driving our behavior.

That’s true today, and it was true in 100,000 B.C.

Imagine you and your hunter/gatherer tribe are out and about… moving to a place with more fresh water.

On your way, you see three dozen terrified members of your neighboring tribe running for their lives. It’s a human stampede.

Your instincts will tell you to run like the wind. Your instincts will say there’s a good reason three dozen people are running for their lives. It doesn’t matter if you can’t see a saber-toothed tiger or a rival tribe with spears… you just know it’s time to run.

This reason – survival – is the core reason why humans find comfort in crowds. It’s how we survived in the wild and became the dominant species on Earth. To this day, we know having your own crowd – your family, friends, and coworkers – leads to longer, better lives.

However, the desire to be part of a crowd can kill your stock portfolio.

We need look no further than how Wall Street responded in early April 2025, when President Trump unveiled his “Liberation Day” tariff plan.

The crowd panicked.

On Thursday, April 3, the Dow dropped 1,679 points. The S&P 500 sank 4.8%. The NASDAQ fell 6%. It was the worst day for the major indexes since the COVID-19 crash.

However, I repeatedly told investors to stand pat and wait out the storm; the market would bounce back. The reality is Wall Street is a manic crowd and it likes to “react” first and “think” later.

This proved to be the right call.

Just days later, President Trump announced a 90-day pause on most reciprocal tariffs. Stocks exploded higher. The S&P 500 jumped 9.5%, its best day since 2008. The NASDAQ surged 12%, its best day in 24 years. And the Dow jumped nearly 3,000 points.

By late June, the S&P 500 and NASDAQ were back at all-time highs.

Investors who sold in the panic missed one of the fastest rebounds in market history.

Going your own way can save it.

The reality is that the human brain is a marvelous tool for creating art, music, language, and engineering feats, but it’s a terrible tool for investing.

The more you know about the workings of your own mind, the “bugs” inside it, and how they work against our investment performance, the more you can develop strategies to mitigate the negative effects of those bugs.

In today’s 蜜桃传媒 360, I’ll explain Crowd-Seeking Bias, how it works and how you can neutralize its negative effects. Then, I’ll show you how my Precursor Intelligence system can help you sidestep costly investing mistakes… and zero in on stocks with the best chance of delivering market-beating gains.

The Problem With Crowd-Seeking

A lot of you are probably fans of momentum investing. The truth is, I am, too. You always want to capitalize on a trend, and trends are made up of people.

But while following the crowd CAN result in great momentum plays… you don’t want to do so blindly.

The crowd-seeking I’m talking about – follow the herd, think later – is responsible for a lot of failed investments. It means you won’t pick up on a shift in the trend. So, you’ll get your timing all wrong. You’ll often end up buying near the highs and selling near the lows.

With Crowd-Seeking Bias, even the best investing ideas can become a losing proposition.

The flip side is to be a contrarian. In other words, to buy the dip and sell the highs.

As I mentioned, though, it goes against our instincts. That’s why everyone isn’t Warren Buffett. But you can get his level of returns (or better) by checking your emotions at the door – and sticking with a pattern that works.

The premise is simple.

Look Off the Beaten Path…

There’s an easy way to resist our tendency for crowd-seeking, and it’s to look for buys where nobody else is looking.

It’s a lot easier to go your own way when nobody else is there to influence your decisions.

In other words, look for a company that gets little to no mainstream attention.

A company that doesn’t get fawning coverage on CNBC or on the internet.

But one that’s still growing like crazy – in terms of sales, operating margins, and especially earnings.

Whenever its stock experiences a sell-off… then that’s a great opportunity. And those fundamental factors are exactly what I’ve designed my P.I. system to detect.

But again, you only want the highest-quality companies.

Then you can shift the engine into reverse, too. When an investment starts to slip on these factors, it’s time to sell. (Especially when the crowd hasn’t caught on yet.)

In total, there’s 8 factors to look for. Apply them to fundamentally superior stocks, and that’s the basis for my Precursor Intelligence system.

Once you have these 8 precursors in mind, the results can be phenomenal. For example…

Take Sezzle Inc. (SEZL), for example.

Most investors had never heard of it.

It’s a small buy-now-pay-later credit company. It wasn’t being covered by CNBC. It wasn’t one of the same crowded AI names everyone was chasing. And it certainly wasn’t the kind of stock most retail investors were talking about.

But in early September 2024, my P.I. system identified a major shift in the stock’s ownership structure.

The “elephants” were moving in.

In other words, large institutional investors were quietly accumulating shares before the crowd caught on.

So, after doing my own vetting, I recommended Sezzle to my members.

In less than a year, they had the chance to capture a 555% gain.

That’s the power of going where the smart money is moving before the crowd gets there.

Precursor Intelligence Presentation Now Available

Operating margins, sales metrics, earnings projections…it all sounds pretty boring, I know. That’s exactly the point.

These factors don’t activate your feelings. They do activate something much more important: the system behind Precursor Intelligence.

Not a lot of people take the time to assess stocks this way – much less all 8 factors. So, it’s a great way to beat the Crowd-Seeking Bias we discussed today…and end up with better gains in half the time.

If you want to learn more, go here for the Precursor Intelligence recording and transcript. Besides the glimpse at my system, I even reveal my No.1 stock pick. Click here for details.

Sincerely,

Louis Navellier

Editor, 蜜桃传媒 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Sezzle Inc. (SEZL)

The post How to Beat Wall Street’s Manic Crowd appeared first on InvestorPlace.

We wish you all a Happy Fourth of July here from InvestorPlace.

Tom Yeung here with today’s Smart Money.

I used to look forward to upgrading my smartphone.

Every new model felt revolutionary. The iPhone 3… iPhone 6… iPhone 8… every new generation was miles ahead of the one before.

Then something changed.

By the late 2010s, smartphones had become “good enough.” (I now use a Google Pixel with a version number I don’t know.)

Most people stopped upgrading every two years because the improvements simply weren’t worth it. That shift reshaped the entire industry.

Former smartphone giants like HTC, BlackBerry, and Nokia were soon replaced by low-cost manufacturers. Meanwhile, Apple Inc. (AAPL) kept winning – not because it always had the best hardware, but because it owned the ecosystem.

AI may be reaching a similar turning point.

The biggest winners of the next phase may not be the companies building the fastest chips or the largest AI models. Instead, they may be the companies building products people rely on every day.

And a little-known Chinese startup may have just given us the clearest sign yet.

Let’s take a look…

A Free AI That’s Almost as Good

Last month, Chinese startup Z.ai released GLM 5.2, an open-source AI model that ranks among the world’s best.

Unlike models from OpenAI, Anthropic, or Google, GLM 5.2 is completely free. Anyone can download it, modify it, and even use it commercially.

Even more impressive, it’s good enough to run complicated tasks. I’ve taken the model for a test-drive, and can say it’s almost on par with America’s leading AI systems.

And Z.ai isn’t alone.

It’s one of China’s “Six AI Tigers,” a group of startups just months behind the best U.S. companies. They’re giving away capable models and monetizing cloud computing instead.

In other words, cheap, “good enough” AI is arriving much sooner than many investors expected.

Today’s fourth-best AI model can already perform many real-world business tasks, and it doesn’t require Nvidia’s newest chips to do it. Instead, it runs well on the last generation’s hardware that is available to Chinese firms.

That makes AI a lot like smartphones. When technology becomes good enough, buyers become less willing to pay premium prices for cutting-edge hardware unless you own the whole ecosystem like Apple.

In fact, this has happened with almost every new technology. TVs… digital cameras… PCs… solar panels… When “good enough” versions start showing up, price becomes more important than owning the latest model.

This doesn’t mean AI is slowing down, but it does mean that the companies capturing the biggest profits will change.

Why This Changes the Investment Story

Rather than flowing primarily to hardware makers, more value could shift toward businesses that build indispensable AI-powered products, software, and ecosystems.

That’s why Eric has been cautious about chasing the hottest semiconductor stocks after their enormous gains. No one wants to be caught holding the next Blackberry.

Instead, he continues focusing on what we call AI Appliers: the companies using AI to create products customers can’t easily replace.

Several months ago, Eric and I warned that parts of the AI market were becoming overheated. Just as smartphones evolved from breakthrough hardware into everyday commodities, AI may be entering its own “iPhone Moment.”

If that’s the case, the biggest investment opportunity won’t necessarily be building better AI. It will be owning the companies that put increasingly cheap, increasingly capable AI to work.

You can learn more about Eric’s recommended AI Applier companies at Fry’s Investment Report.

Simply click here to learn more.

Until next time,

Thomas Yeung, CFA

蜜桃传媒 Analyst, InvestorPlace

The post Why AI Becoming “Good Enough” Changes Everything for Investors appeared first on InvestorPlace.

When the first American railroads began reporting revenue in the 1840s, the critics who had called the whole enterprise an overbuilt fantasy found themselves with less and less to say.

Something similar is happening in AI right now.

Exponential View just published the most comprehensive accounting of the AI economy we’ve yet seen — its State of the AI Economy 2026 report — with real revenue, utilization, and capex payback math. 

The numbers don’t leave much room for the bear narrative.

The Revenue Bears Have Run Out of Excuses — $175 Billion Says So 

Exponential View’s report estimates the global ex-China Generative AI (GenAI) economy is producing $175 billion in annualized revenue. And before anyone accuses Exponential View of creative accounting — this figure excludes chips, AI ad uplift, legacy software “AI features,” and financing.

In other words, it is only reflecting real customer demand.

Now, $175 billion in run-rate revenue sounds massive — and it is. But let’s contextualize that number. 

One hundred seventy-five billion dollars represents just 0.5% of total U.S. GDP. The broader ‘digital economy’ sector makes up about 10% of GDP. Total U.S. corporate profits — which surged to a record $4.426 trillion in Q1 of 2026 — are 25x larger than the entire GenAI revenue pool. AI revenue is enormous in absolute terms and almost minuscule in relative terms.

Therein lies the opportunity…

Because here’s the thing those relative numbers don’t capture: speed. AI revenue relative to GDP is already up 10x from Q1 2024. GenAI is scaling 3x faster than prior IT waves — faster than the internet and mobile booms. In 2023, the AI economy needed 180 days to add $1 billion of cumulative revenue. Today it needs less than two days. That is a 90x acceleration in the speed of revenue generation. Recent quarter-over-quarter growth is running ~35%, which annualizes to more than 3x.

This is the setup every long-term investor dreams about. Big enough to validate the thesis. Small enough that the runway is virtually unlimited. The penetration curve is in the very earliest innings of a generational platform shift — and the data proves it.

The CapEx Math Is Actually Working

According to the bears, while the hyperscalers are spending a combined ~$2 trillion cumulatively through 2026 on AI infrastructure — the largest technology buildout in history — there’s no possible way the economics ever pencil out. It’s a capex bubble about to burst.

Except… the math is starting to work.

The AI economy is now generating enough revenue to cover depreciation: the ongoing cost of using up the infrastructure built to run it. Not with room to spare, but the gap has closed, and the direction is positive.

For every dollar of AI infrastructure that depreciates, roughly $1.19 in hyperscaler and neocloud revenue is coming in to cover it — and $1.32 when you count the full GenAI economy. A year ago, that ratio was below 1. Now it’s above it.

This is still a race, of course. But the critics who insisted AI would never generate sufficient revenue to justify the buildout are already being proven wrong. And we are still in the early stages of the utilization ramp.

The Jevons Paradox: Why Falling Token Prices Are Bullish for AI

One of the more sophisticated bear arguments has to do with token cost. Some believe that as token prices continue to collapse — with blended pricing falling from ~$17 per million tokens to ~$2 —AI companies are destroying the economics of the industry.

‘Margins are going to zero. The boom is over.’

But that argument confuses price with value — and ignores how technology adoption actually works. 

For technologies with elastic demand, falling prices create value; cheaper tokens = more use cases.

Better models expand what AI can actually do. Reasoning models consume more tokens as they think through complex problems. So the very thing bears are pointing to as a headwind — price compression — is actually the accelerant for the next leg of volume growth.

More apps, more agents, more inference, more memory, more networking, more storage, more power, more cooling, more data centers… 

The Jevons paradox — the observation that efficiency improvements in resource use lead to increased total consumption — is playing out in real time across the AI infrastructure stack.

Bears are worried about price compression. Bulls are focused on volume elasticity. The data says volume wins.

Why AI Feels Slower Than It Is — and Why That’s Exactly What the Data Predicts

Here is one nuance worth understanding, because it explains why AI’s impact can feel underwhelming in GDP statistics even as it is very real inside companies.

Seven in 10 GenAI claims from companies in the S&P 500 focus on cost savings, time savings, throughput, or quality improvement. Explicit revenue gains are only ~6% of claims. The first killer enterprise AI app is not “create a magical new business line.” It’s “do the same work faster, cheaper, better.”

This is actually the normal pattern for platform shifts. The efficiency wave always comes first. Productivity gains show up in margins and labor leverage before they show up in GDP or revenue. The internet’s first decade was dominated by cost reduction and efficiency. Revenue came later — and when it came, it was enormous.

AI is following the same script. Efficiency now. Revenue later. And if the efficiency wave alone is already generating $175 billion in run-rate demand, imagine what happens when the revenue wave hits.

What the Revenue Inflection Means for AI Infrastructure Stocks Right Now

The macro data on AI has never been more bullish. The micro data — real company revenues, utilization trends, and capex payback — is inflecting positively. And yet AI stocks have been choppy, volatile, and in some cases well off their highs.

That combination — improving fundamentals, weak stock prices — is the definition of a buying opportunity.

The names best positioned to benefit from this data are across the full AI Builder stack, detailed most recently here:

• Chips and semiconductors
• Memory
• Networking and optics
• Servers and infrastructure
• Power and cooling

The Bottom Line: The Direction Changed

For the past two years, the race between AI capex and AI revenue has been the central question of this trade. This quarter, for the first time, the revenue side pulled ahead.

That doesn’t mean the race is over. The capex curve will keep rising. But the direction has changed — and in markets, direction matters more than destination.

The AI trade is alive, the fundamentals are inflecting, and the market is handing you a discount on one of the most compelling long-term growth stories in history.

That doesn’t happen often. Act accordingly.

Here’s one way to do that.

The infrastructure data in this piece tells you the AI buildout is real and accelerating. What it doesn’t tell you is where the most sophisticated private capital has already been positioning — months before this quarter’s numbers made the bull case undeniable.

Peter Thiel’s answer? A wholesale exit from public markets and a move into the physical substrate of the AI economy — the hard assets that get paid regardless of which model, which hyperscaler, or which application layer ultimately wins.

Most of those positions aren’t available to retail investors. Seven of them have a publicly traded equivalent.

Here’s what that portfolio looks like — and the thesis behind every position.

The post The AI Capex Bear Case Just Lost Its Best Argument appeared first on InvestorPlace.

Last week, Jeremy Grantham, British investor, billionaire, and GMO co-founder, went full bear:

This is the most expensive market in American history…

My guess is sometime between two weeks ago, two weeks from now, two months, two quarters and conceivably two years – the timing is always terribly uncertain – the market’s going to peak out and drop back to trend.

And getting back to trend from here is closer to a 70% decline than a 50% decline

Do you find this helpful?

At some point… between two weeks ago and two years from now… we’ll have a huge crash.

No disrespect to Grantham – he’s a legendary investor – but, to me, this comment is useless. Worse, it can be financially damaging.

On January 24, 2023, Grantham released his official 2023 outlook letter titled “After a Timeout, Back to the Meat Grinder!” where he warned of a potential 50% crash.

Then in April of 2023, he told the We Study Billionaires podcast that the modern “superbubble” was on the verge of popping.

And in July of 2023, he put a 70% probability on a crash matching the patterns of 1929, 2000, and 2021.

Not only has no such crash occurred since Grantham’s first January 2023 call, but stocks have surged since then. The Nasdaq 100 is up more than 150%.

If you’d sat out of stocks based on Grantham’s call, you’d have missed your account more than doubling. That’s not a rounding error – that’s a potential multi-year retirement delay.

The reality is that we will eventually have a market crash. But I don’t think it’ll be tomorrow, next month, or even this year. And today, I want to highlight one major reason why.

To be clear, I’m not saying the next 6-12 months will be smooth, or even that we won’t suffer a 10%-15% haircut somewhere along the way. But I believe “the crash” remains farther out on the horizon, which means one thing…

It’s still time to be invested and make money before the eventual pain arrives.

A magazine cover, a stock chart, and a lesson about tops

Older investors like me will recall 1991 when the U.S. economy was clawing its way out of a recession.

Auto sales had collapsed to a level that would mark the low point for the next 16 years. Investors were bearish on Detroit, and they had reason to be – TIME even ran a cover story that November asking a blunt question: “Can GM survive in today’s world?”

Thirteen months later, the mood had entirely flipped…

The economy was strengthening, auto sales had bounced back, and TIME ran a new cover featuring the CEOs of the Big Three automakers. But this one wasn’t despairing. It was triumphant: “The Big Three – How Detroit is shifting into high gear.”

Same company, same industry, 13 months apart.

So, which would have been the better time to buy GM stock? At the point of despair or the point of hope?

In November 1992, during the “despair” cover, GM shares traded around $28.

By December 1993, during the “hope” cover, they’d climbed to just above $55, nearly doubling.

And then, just 12 months after that triumphant cover ran, GM shares had fallen by about a third, back to $35.

Despair preceded the gains. Hope preceded the losses.

This teaches us a critical lesson about investment peaks and valleys that we’d be wise to remember today…

Whether on a stock-specific basis or across broad markets, “tops” tend to form when investors are wildly confident, bullish, and greedy, while bottoms are typically carved out when investors are despairing and hopeless.

Alan Greenspan coined the phrase “irrational exuberance” to describe exactly this dynamic during the dot-com run-up. And Newsweek‘s famous 1999 cover capturing that era’s FOMO ran just months before the Nasdaq collapsed.

So, here’s the question…

Does this market feel irrationally exuberant?

The sentiment data tells a different story than the headlines

If euphoria and rabid FOMO are the preconditions for a top, today’s numbers don’t support the “we’re there” thesis nearly as cleanly as Grantham’s bubble framing suggests.

Yes, some pockets of the market are experiencing FOMO, but as we’ll get to, it’s somewhat justified by earnings. More on that shortly…

First, zeroing in on sentiment, let’s start with retail investors…

The latest American Association of Individual Investors Sentiment Survey from last week shows bullish sentiment at 44.9%. That’s above the historical average of 37.5%, so it’s not nothing. But it’s well below the 60% to 70%-plus readings that marked the actual dot-com peak.

Retail investors look more like accumulators than blind speculators right now – surveys on AI-focused investors show the overwhelming majority plan to hold or add to positions, with only a small minority looking to reduce exposure.

Now look at wealthier investors…

A recent Janus Henderson survey of affluent and high-net-worth investors found that 67% are actively worried about an AI bubble bursting within the next 12 months.

That’s not complacency or “YOLO” risk-taking. That’s a market grinding higher while two-thirds of its most sophisticated participants are looking over their shoulder.

Finally, on the institutional side, Bank of America’s Global Fund Manager Survey shows funds remain structurally long tech, but positioning has eased meaningfully. Managers are taking profits on the most extended hardware names and rotating into broader equities rather than doubling down.

Put it together, and you get a market climbing the proverbial “wall of worry” – high conviction paired with persistent, widespread caution.

That combination has historically been a feature of ongoing bull markets, not a signature of imminent tops.

A true top tends to require something close to universal agreement that prices can only go up – recall Barstool Sports founder Dave Portnoy during the 2020 day-trading craze saying, “Stocks only go up” and “only losers take profits.”

Are you seeing that sentiment today?

I see the opposite: widespread, well-documented anxiety sitting underneath continued buying.

Still, caution remains critical

The lack of rabid FOMO is not an invitation to go all-in.

Grantham’s framing deserves real respect, and there’s at least one place where the data is flashing something worth watching closely.

Here’s our hypergrowth expert Luke Lango, editor of Innovation Investor, on what he’s calling an IPO Spike Warning:

The Bloomberg data showing Q2 2026 IPO value tracking toward $400 billion — roughly double the recent quarterly average — is a pattern worth monitoring precisely because it has occurred near inflection points in prior cycles.

We are not making a market crash call. The specific circumstances of each prior spike were different, and the AI infrastructure fundamentals today are categorically stronger than the earnings realities of the Dot Com era, the leverage realities of the GFC era, or the inflation shock of 2021.

But historical patterns that have repeated across multiple distinct market cycles deserve respect, and the intellectually honest posture is to acknowledge this one while maintaining our constructive stance on AI infrastructure. 

Luke recommends investors be thoughtful about position sizing, maintain dry powder for potential outsized pullbacks, and stay focused on the highest-quality, most defensible names in the AI infrastructure trade rather than speculating.

But with those defensive measures in place, he concludes:

The Summer of AI is intact. We are watching the IPO spike carefully. Both things can be true.

That posture feels right to me.

IPO mania does reflect some exaggerated FOMO, and it has shown up near inflection points before – not as a guaranteed crash signal, but as a pattern that deserves respect. However, this FOMO centers on just a handful of stocks going public.

“But Jeff, you’re missing the FOMO and greed in corners of AI like the memory trade.”

Great point! The memory/semiconductor trade is very crowded today. And I’d be surprised if we don’t see some double-digit profit-taking over the coming weeks. In fact, we’re seeing some today as I write. It could result in a longer stretch of underperformance.

But profit-taking is not the same as crashing. And there’s a big reason there will likely be loads of buyers after a bout of profit-taking…

Earnings.

Does today’s earnings backdrop support the bubble-bursting narrative?

Memory is a hot trade today – but it’s for a reason.

A week ago today, memory giant Micron (MU) told investors to expect roughly $50 billion in revenue next quarter. Analysts had penciled in $43.6 billion. That’s not a beat – that’s a different zip code.

Micron’s blowout outlook is a microcosm of a broader AI supercycle that has the entire chip sector firing on all cylinders. According to research from International Data Corporation (IDC), total global semiconductor revenues are projected to surge 52.8% to hit a historic $1.29 trillion in 2026.

Here’s more from IDC:

The memory segment is at the epicenter of this shift: DRAM revenues alone are projected to nearly triple in 2026 to $418.6 billion, driven by demand for high-bandwidth memory (HBM) and DDR from hyperscalers and AI infrastructure providers. 

But this earnings strength isn’t limited to just memory chips. It’s wider…

In last Thursday’s Digest, I highlighted a chart from Alpine Macro showing how today’s tech boom has something the dot-com era didn’t have…

Real earnings growth, not just multiple expansion.

From last Thursday’s Digest:

In the dot-com boom, P/E ratios went to the moon while profits barely budged. Today, earnings per share are compounding while multiples have stayed relatively flat.

That’s a structurally different – and arguably more durable – setup.

Better still, we can also look one layer up from tech to the wider S&P…

Wells Fargo expects headline S&P 500 earnings growth to surge to 22% growth year-over-year during Q2.

Meanwhile, FactSet’s forward-earnings data shows similarly robust expectations baked into current estimates – meaning today’s elevated valuations are, at least partly, being met by real, growing profits rather than pure multiple expansion.

Don’t misunderstand me – this market is not cheap. But these robust earnings take pressure off the “nosebleed valuation” argument, which – along with the lack of frothing-at-the-mouth FOMO – suggests disaster isn’t directly at our door.

Closing the loop on the memory trade and FOMO, yes, there’s some FOMO in memory today – but it’s chasing a massive number that just printed, not a fantasy number that people hope will print.

Coming full circle

If you had to put a magazine cover on today’s market, would it be the jubilant “shifting into high gear” version? Or the despondent “can it survive?” version?

For me, it’s neither. It’s something far less marketable – perhaps:

“Investors Aren’t Sure, and the Data Backs Them Up.”

Of course, magazines with that cover don’t sell well. But that’s usually a good sign for investors like you and me.

Most likely, we’re somewhere in the messy middle, though skewing toward the top. The evidence still supports parts of both the bullish and bearish cases. And that’s important because true market peaks usually leave very little room for debate.

Bottom line: Watch the IPO data… track the earnings… respect Grantham’s warnings…

But analyze whether you’re really seeing rabid FOMO today – and if you’re not, consider what that means for whether we’ve truly arrived at the peak.

We’ll keep tracking this as the data develops.

Have a good evening,

Jeff Remsburg

The post Grantham’s 70% Crash Call Has a Problem appeared first on InvestorPlace.

As the business author Peter Drucker observed, “The only thing we know about the future is that it will be different.”

I like to keep this in mind each January when I send out my annual Fry’s Investment Report “Forecast Issue.”

The saying continues to ring true as we hit the start of the third quarter and the back half of 2026… and especially as we take a look back at all that has proved “different.”

Political tensions in the Middle East have escalated into an ongoing war involving the U.S. and Israel with Iran. It’s led to dramatic fluctuations in oil prices, with the International Energy Agency (IEA) reporting a loss of roughly 1 billion barrels in the oil market.

Other events may have been on your bingo card, like artificial intelligence continuing to dominate the market, knocking software stocks off their feet.

Now that we’re halfway through the year, I want to focus this Smart Money on how three of my forecasts are holding up.

Some may surprise you, and some are still opportunities to join before the year ends – and I’ll guide you step-by-step on how to get started.

Forecast No. 1: The Magnificent Seven Stocks Will Lose Ground

The cost of creating competitive AI infrastructure is massive and rising, while the ultimate payoff is becoming less certain and immediate.

These dynamics do not directly threaten the hyperscale companies themselves – including Mag 7 members Alphabet Inc. (GOOGL), Amazon.com Inc. (AMZN), Apple Inc. (AAPL), Microsoft Corp. (MSFT), and Meta Platforms Inc. (META), and Nvidia Corp. (NVDA) –but they do threaten their lofty valuations.

I expected investors to start asking harder questions, like “Where does incremental free cash flow come from and when will it arrive?” or “What happens when everyone has spiffy new data centers and AI models, but no one has pricing power?”

These now seem to be on their minds.

Just yesterday, CNBC reported that $2.3 trillion has been slashed from the Mag 7’s value. And though the group peaked mid-May, the Roundhill Magnificent Seven ETF (MAGS), which tracks the septet, is now almost flat since the beginning of the year.

Wall Street is increasingly recognizing AI as a “cost center” rather than a powerful growth driver – and that could continue as Big Tech’s AI expenditure is expected to surge 70%, surpassing $700 billion this year.

To be sure, the Mag 7 companies remain dominant, but their valuations amply reflect that dominance. And with that seeming to fade, the next question is: Where does the capital go?

The answer is copper.

This brings me to my next prediction…

Forecast No. 2: The Copper Price Tops $7.50

Here’s what I told my Fry’s Investment Report readers about copper in January:

Copper prices will reach at least $7.50 per pound sometime in 2026 – driven by structural supply constraints and accelerating demand for electrification, AI infrastructure, renewables, grid expansion, and industrial modernization.

Simply put, copper demand is booming, relative to supply growth. Therefore, widening deficits in the copper market are putting upward pressure on the copper price.

Six months ago, copper’s price was $5.70; now it’s $6.19. That’s obviously not $7.50, but we’re only halfway through the year – and with the help of growing data center demand, the metal has been hitting record highs this year.

With AI as a huge driver of demand in metals and energy, there is still a great opportunity in the copper market to make some money without having to bet the house on a high-profile AI name.

The proof can be found in our copper miner position in Fry’s Investment Report, Freeport-McMoRan Inc. (FCX), up over 20% year-to-date. (And remember, the Mag 7 has barely moved over the same time period.)

However, that’s only one metal I’m looking at to hedge against the risky nature of the current AI market.

Here’s the other…

Forecast No. 3: Gold Will Outperform Bitcoin

At the start of the year, I predicted gold would outperform Bitcoin (BTC-USD) in 2026. That forecast has been spot-on, but not exactly in the way I anticipated. Both of these currency substitutes have lost value against the dollar this year, but bitcoin has lost a lot more. The premier cryptocurrency is down a whopping 32% year-to-date, while gold has slumped only 7%.

After a robust advance early in the year, the yellow metal entered a sharp correction that shaved more than $1,000 off its price. But I’m expecting it to recover during the second half of the year, as the Federal Reserve moves toward an easier monetary policy.

Position for the Second Half

Two of my forecasts are already “in the money.” The second – copper to $7.50 – is still in the “prospective” category. But all three of them could continue to produce opportunities for forward-looking investors.

For example, letting go of those Mag 7 names flying too close to the sun and instead welcoming the companies actually applying AI technologies could be far more rewarding.

With six months left in 2026, the window to position ahead of these trends – rather than chase them – is still open.

I built the Fry’s Investment Report portfolio around exactly these forecasts – and right now, I have multiple recommendations targeting the Mag 7 unwind, the copper supply crunch, and gold’s setup against bitcoin.

For specific names and tickers, click here to learn more about joining Fry’s Investment Report.

Regards,

Eric Fry

The post Halfway Through 2026: My Calls That Are Already Paying Off appeared first on InvestorPlace.

If you’ve ever read George Washington’s journal entries, they’re about as dry as the wheat Washington farmed at Mount Vernon. But they provide an interesting look into Washington as an early adopter (pivoting from tobacco to wheat) and a patient farmer who obsessed over timing.

In the Washington Papers, we’re transported back to July 1770, where a month of harsh June rain had beaten the straw flat, yielding but a few grains per head. Most of Washington’s crop had either perished or was too mildewed to harvest. It was, in his own words, “exeeding[ly] bad.”

But George knew where it had gone wrong, and he waited… tallying the month’s losses in his journal and reaffirming his thesis. That is, a three-week harvest should begin before the wheat is ripe, or one might risk the entire loss of one’s crop.

More than two centuries later, that is the question we’re asking in our collaboration with Stansberry’s Director of Research, Matt Weinschenk… are you too late to the harvest?

Micron (MU) has been one of the great winners of the AI Boom. Over the past year, the MU stock chart has run up and to the right with almost no resistance.

Micron stock’s run is exactly what makes serious people nervous, because they know the memory cycle. They have watched it come and go. It has burned investors and minted fortunes, and the oldest saying on the desk is that you own memory only as long as the music is playing (and whoever is still standing when it stops loses their shirt).

We’ve heard that warning for two years running, and we’re still in the bull camp. Our case is that this cycle is built differently. Every memory boom that came before it was capped by the number of folks buying phones and laptops. An agentic AI world has no such ceiling … every new agent wants its own memory, and the only real gauge of the cycle becomes how much the hyperscalers are willing to spend.

That number is still climbing toward a trillion dollars a year, and we do not see the peak inside the next one to two years.

So the field is still filling. The music is still playing loud. And we are watching the one gauge that will tell us when it’s time to cut. Check out the episode below:

Why AI Needs More Memory Than Ever

Here’s the case in its simplest form: compute needs context.

The GPUs that Nvidia (NVDA), Advanced Micro Devices (AMD), and increasingly Intel (INTC) and Qualcomm (QCOM) are selling are the brains of this buildout. But a brain without context can’t do much. Memory is what gives these systems the context to actually answer a question or finish a task… and as AI has shifted from chatbots that simply respond to agents that remember, plan, and execute multi-step work, that context window has become the bottleneck.

I look at the chips themselves as the clearest evidence.

Nvidia’s A100 carried 80 gigabytes of memory. The H200 jumped to 151. The Blackwell chips now ship with nearly 300. Each generation of GPU needs exponentially more memory than the one before it… and every new data center needs exponentially more GPUs than the one before that.

The demand curve is compounding.

The Memory Cycle, Explained (And Why It’s Dangerous)

Memory is also the most commoditized link in the entire semiconductor chain. DRAM is DRAM, NAND is NAND, whether Micron makes it or SanDisk (SNDK) or Seagate (STX). That commoditization is exactly what makes the cycle so violent.

The pattern repeats: a demand boom arrives (PCs, mobile, cloud, work-from-home) and suppliers race to build capacity. By the time that new supply comes online, 12 to 24 months later, the demand that justified it has already cooled. Supply glut meets falling demand, margins collapse, and the stocks that looked unstoppable get cut in half. I count seven or eight separate 50%-plus drawdowns in Micron since 2008.

That history is where the old trading rule comes from: own memory stocks only as long as the music is playing, because when it stops, whoever hasn’t found a seat also loses their shirt.

Why I Believe This AI Memory Cycle Is Different

Every memory boom before this one was capped by a number Wall Street could count: how many phones, how many laptops, how many people were buying them. Human demand has a ceiling.

Agentic AI doesn’t.

There’s no point at which all the agents have enough memory and the industry stops needing more… the answer is simply to build another agent. The cap on past cycles was the size of the human population.

The cap on this one is hyperscaler spending, and that number keeps moving in one direction: this year’s roughly $700 billion to $800 billion in AI infrastructure spend is on pace to approach $900 billion next year, with outside estimates from Goldman Sachs and Bloomberg projecting close to $1 trillion annually by 2029 or 2030. Those forecasts keep getting revised up, not down… and that’s the trend I watch most closely.

Google’s (GOOGL) recent decision to raise $80 billion (upsized to $85 billion, with $10 billion coming from Berkshire Hathaway) is the tell.

Every hyperscaler is racing the others, and none of them believe they can afford to fall behind. When one raises its spending, the rest match it… and the shockwave multiplies across Amazon (AMZN), Microsoft (MSFT), Meta (META), and the Chinese cloud giants doing the same thing in parallel.

The Rolling Bottleneck

This buildout hasn’t moved in a straight line. Rather, it has moved from constraint to constraint. GPUs came first, which is why Nvidia led. Then the industry needed to assemble those chips into servers and racks, which is why Super Micro (SMCI) ran hard before legal trouble handed that business to Dell (DELL).

Then it needed data centers to house them, then networking and optics to connect them.

Memory is simply the latest link in that chain to come into focus. And from where I sit, Wall Street is only now catching up to what should have been obvious from the start: a data center needs all of it.

How High Can Micron Stock Go

Micron has run from roughly $800 toward $1,000 in recent weeks. My framework: the stock tends to advance in sharp bursts of 70% to 80%, then give back roughly 20% before resuming the climb. I expect that pattern to hold… a pullback toward the $800 level on any catalyst, geopolitical or otherwise, followed by a resumption of the climb toward $1,200 and eventually $1,400.

My case for staying long isn’t built on multiple expansion. Despite the stock’s run, Micron trades near nine times forward earnings, against a five-year average closer to 6.7 times — only modestly above its historical range, even after a tenfold move since 2024.

What’s actually driven Micron stock is the earnings power underneath it: EBIT near $9 billion in 2024 is on pace to approach $40 billion over the trailing twelve months, with estimates near $150 billion by 2027. The dollars have moved first. The multiple has barely followed.

What Could End the AI Memory Boom

I want to be direct about the risk here: this can’t go on forever, and no buildout in history has gone in a straight line.

The vulnerability I watch is the consumer. The hyperscalers’ AI budgets are ultimately funded by ad sales and product purchases — Amazon, Meta, and Google’s spending all trace back to discretionary spending from ordinary households.

The personal savings rate has fallen to 2.6%, a level I’ve only seen matched twice before: briefly in 2022, and in the two years leading into the 2008 financial crisis.

Real wages are negative…

Consumer sentiment sits near record lows…

If oil holds above $100 or the 10-year Treasury yield pushes past 5% for a sustained stretch, discretionary spending slows, ad revenue softens, and the hyperscalers’ capacity to keep funding this race shrinks with it.

There’s a political risk layer too: proposed legislation to redirect AI profits to households, and a handful of states moving to restrict new data center construction. None of it has teeth yet.

All of it is on my watchlist.

The Bottom Line

My read: we’re in the third mile of a marathon on AI model development, but the spending race itself could end far sooner if a shock — economic or geopolitical — forces the hyperscalers to pull back. Until that signal shows up, the music is still playing, and I remain firmly in the bull camp on memory stocks.

P.S. For the full conversation, including more on the memory cycle’s history and Luke’s case for why this one breaks the pattern, watch this week’s full episode of Top Stocks with Matt Weinschenk, featuring Luke Lango. And be sure to subscribe to Top Stocks on YouTube for more exclusive content.

Also, Join Luke at this year’s Stansberry Conference & Alliance Meeting – where ideas move fast, conviction gets sharper, and the next big opportunities come into focus.

You’ll get live market updates, learn about top ideas and stock picks from Jonathan Rose and Luke Lango, and have the chance to meet some of your favorite editors – like Marc Chaikin, Whitney Tilson, Dr. David Eifrig, and Keith Kaplan.

Attendees will hear from bestselling authors and experts in economics, technology (including AI), and more. This year’s featured speaker lineup also includes famed actor Henry Winkler (aka “The Fonz” from Happy Days).

Expect two days packed with intriguing presentations and fun social events – all in luxurious Las Vegas. It pays to be in the room where it all happens.

Reserve your discounted ticket today before they sell out!

The post Micron’s Run Looks Unstoppable. Here’s the One Number We’re Watching. appeared first on InvestorPlace.

Space is suddenly having a moment again – and if you’ve been reading Brian Hunt, you knew it was coming and are up 102%.

Brian, editor of the free e-letter Money & Megatrends, laid out the bull case for the sector back in September 2025. His approach was simple: look past SpaceX (SPCX) mania.

From his September 22^nd issue:

When people think of investing in space, they often go towards the business of launching rockets and Elon Musk’s SpaceX.

But many of the most promising “space stocks” are in the business of space-based communication platforms and equipment.

Think government surveillance, military communication, GPS, internet service, and cell service.

Among the names Brian flagged at the time were Rocket Lab (RKLB), BlackSky Technology (BKSY), Planet Labs PBC (PL), and AST SpaceMobile (ASTS). Since that issue, RKLB alone is up 102% – with a nice chunk of that coming yesterday.

RKLB popped 16% after announcing it would acquire a satellite communications company in a cash-and-stock deal valued at roughly $8 billion.

The logic is straightforward: Rocket Lab already builds and launches the vehicles. Now it owns the network those vehicles serve – a global L-band satellite constellation, licensed spectrum, and more than 2.5 million subscribers spanning government, defense, aviation, maritime and commercial markets.

Basically, it’s the same vertically integrated playbook SpaceX runs with Starlink – and Wall Street approved.

But the deal is also a sign of something bigger

The commercial space industry is consolidating – and consolidation at this scale signals that serious capital now views space infrastructure as a generational asset class, not a speculative moonshot.

Which leads us to the part of the story that may sting a little if you weren’t paying attention…

The company that Rocket Lab just bought was Iridium Communications (IRDM) – a global satellite communications provider that Brian flagged back in his April 17th issue.

Yesterday, on the acquisition news, IRDM surged 25% in a single session.

If you missed it, there’s an easy fix – Brian writes Money & Megatrends every day the market is open, highlighting these kinds of opportunities before they become front-page news – and it’s 100% free.

His issues are loaded with trend analysis, actionable advice, and loads of specific tickers. You can sign up right here.

In the meantime, we’ll keep bringing you some of Brian’s top ideas here in the Digest.

What’s behind the recent bloodbath in the space sector

Brian’s readers who acted on his issue are sitting on a 102% gain in RKLB. But if you’ve been watching the sector, you know it hasn’t been a straight line up – and more recently, it’s been a straight line down.

In late May, space stocks collapsed. It started with a Blue Origin rocket explosion during a prelaunch test – unsettling on its own, but manageable. What followed was less manageable.

When SpaceX went public earlier this month at a valuation exceeding $2 trillion, investors who’d been holding smaller space names as proxies rotated out fast, dumping RKLB, ASTS and others to chase the newly listed giant.

Then, as we covered in yesterday’s Digest, SPCX itself fell more than 30% from its post-IPO peak as Wall Street grew concerned over an unexpected $20 billion to $25 billion bond sale to fund Musk’s AI ventures. The whole sector came down with it.

Which brings us to an important issue – one worth asking before putting any money to work in space right now.

In every transformative technology cycle – the internet, genomics, clean energy – a handful of companies captured most of the gains while the rest eventually went to zero. Space is unlikely to be different.

The sector is real. The opportunity is real. But not every name with “space” in its pitch deck is going to make it.

So, how do you know which ones will survive and reward investors?

In short, you watch the numbers, not the narrative.

And that dovetails into legendary investor Louis Navellier and his market approach.

Louis has spent 47 years building a quantitative system that cuts through the narrative and looks at what matters – earnings momentum, sales growth and institutional buying pressure.

Stories can win sprints, but only earnings win marathons.

So, which space stock does Louis like today?

Here he is:

Planet Labs is one worth looking at right now.

The company operates the world’s largest fleet of Earth-observation satellites — more than 200 satellites providing daily imaging of the entire planet.

Its customers include government agencies, defense contractors, agricultural companies, insurance firms, and financial institutions that use satellite imagery to make better decisions.

Louis notes that the stock got cut nearly in half in the sector crash. But the business didn’t change – its price tag just got cheaper.

Back to Louis for how it looks through his quantitative screeners today:

My Precursor Intelligence system currently rates Planet Labs an “A.”

That means both the fundamental grade — earnings momentum, sales growth, analyst revisions — and the quantitative grade, which measures institutional buying pressure, are strong.

If you’re less familiar, Louis’s Precursor Intelligence system tracks institutional money flows across 6,000 stocks – essentially reading where the smart money is moving before the pattern becomes visible to everyone else.

He just put together a research video that explains it in more detail. You can learn more about how it works right here.

Now, while Louis likes Planet Labs, his more intriguing space plays are one layer beneath the obvious – the materials companies, the semiconductor foundries, the picks-and-shovels businesses that the broader space buildout can’t function without, and that almost no retail investor is looking at right now.

He’s identified two of them in his new special report, The SpaceX Stampede Report. Both have real earnings and institutional buying pressure. Neither one looks like a space stock at all – which is exactly why Louis likes them. You can learn more here.

The OpenAI delay that isn’t really a delay

Late last week, the New York Times reported that OpenAI is leaning toward pushing its IPO to 2027, citing concerns about broader market weakness and the volatility that followed SpaceX’s debut.

The headlines quickly spun this as a potential black eye for the AI trade. Here’s one example I ran across:

OpenAI Reportedly Considers Delaying Its IPO. Should You Worry About AI Stocks?

And, in fact, Oracle (ORCL) dropped 1.7% last Friday. CoreWeave (CRWV) fell nearly 4%. SoftBank closed down 13% in Tokyo – not surprising given that each has billions tied directly to OpenAI’s trajectory.

But is the IPO delay actually bad news?

Luke Lango, editor of Innovation Investor, doesn’t think so. From his Daily Notes:

On OpenAI: this is a rational decision by one of the most sophisticated management teams in tech, not a sign of trouble.

IPO-ing now would mean going public at significant losses, against Anthropic’s rapid progress and a market that just watched SPCX’s $1.75 trillion IPO produce more volatility than anyone wanted. 

Waiting buys a few more quarters of revenue growth, a possible path to profitability, ChatGPT 5.6 winning back share, and potentially the White House’s direct participation in the offering.

OpenAI goes public within the next twelve months, and the delay sets up a stronger event when it arrives. 

There’s also something worth noting that the headlines are mostly missing.

OpenAI reportedly held back the launch of ChatGPT 5.6 specifically to give the U.S. government time to test the model – a commercial company eating real competitive cost to satisfy a government approval process.

Meanwhile, the Pentagon recently updated its classified targeting doctrine to include more AI in combat decisions. Luke notes that once AI is embedded in defense doctrine, spending will stop following commercial ROI logic and start following strategic-necessity logic.

Here he is with the implications for the AI trade:

You don’t cut spending on a capability embedded in your targeting doctrine because your stock dropped for three weeks.

Commercial demand plus national-security backstop is what gives this capex cycle the durability bears keep underestimating.

Overall, Luke urges his readers to remain focused on real AI earnings growth and momentum – not shorter-term fears like an IPO delay.

Here’s his bottom line:

OpenAI waiting for a better IPO is not panic – it is strategy.

The AI boom is intact. The July earnings season will prove it. The window between today and mid-July is the buying opportunity.

Coming full circle

As I write, we’re on pace to wrap up a strong first half of 2026, with the Nasdaq leading the three major indexes – up about 12% so far this year.

Despite these gains, there’s no shortage of headlines designed to make you nervous right now. Space stocks crash. OpenAI delays its IPO. The Fed floats a hike.

But zoom out…

Even after a nearly 50% haircut in May, Brian’s readers are up 102% on a space stock that just made one of the biggest acquisitions in commercial space history…

Louis’ system is finding “A”-rated opportunities as the post-SpaceX-IPO dust settles…

And Luke sees a buying window in AI opening, not closing…

As always, invest within your means and in line with your plan. But if the bears are telling you the AI trade is broken, today’s Digest tells a different story.

Have a good evening,

Jeff Remsburg

The post One Space Stock to Buy Today appeared first on InvestorPlace.

Victor Glover is one of them.

Glover is a Navy captain, test pilot, engineer, and NASA astronaut. He earned three master’s degrees from three different institutions. During his naval career, one of his commanding officers gave him the call sign “Ike” – short for “I know everything.” It was partly tongue-in-cheek. But it fit.

In April 2026, Glover piloted Artemis II around the moon. In doing so, he became the first African American to leave low Earth orbit and travel beyond it. Along with his crewmates, he helped set a new record for the farthest distance humans have ever traveled from Earth.

That is the kind of achievement people remember.

But as an investor, I look at that story a little differently.

I see the astronaut, the rocket, and the mission. But I also see the enormous industrial machine behind it all. Artemis II was not just a triumph of courage and exploration. It was a triumph of supply chains, chips, sensors, navigation systems, advanced materials, communications equipment, and thousands of private-sector components that had to work together perfectly.

And Artemis II was not the end of the story. It was the beginning of a much larger campaign.

That is the part most investors miss. And that brings us to the recent SpaceX IPO – and the real lesson it has to teach.

In this piece, I’ll explain why I passed on the IPO – and give you one space stock my system currently rates a “B” that just got cheaper for no fundamental reason.

A Great Story Is Not Always a Great Stock

When Space Exploration Technologies Corp. (SPCX) went public, the crowd did what crowds usually do. They saw the name. They saw Elon Musk. Then they chased the stock.

The stock priced at $150 per share. Within two trading days, it had surged more than 40%, climbing above $200. Then gravity showed up. The stock fell back to Earth, all the way back to $150 last time I checked.

A lot of investors were reminded of a lesson I’ve learned again and again in nearly 50 years in this business: A great story is not always a great stock.

SpaceX is a remarkable company. Starlink has changed the world. I would never bet against Musk. But IPOs are different. By the time a hot private company reaches the public market, the early investors have already had the first bite. Wall Street bankers have every reason to make the story irresistible. And individual investors are often left trying to calculate risk with very little useful data.

That is not how I invest. I need quarterly earnings, analyst revisions, and institutional buying data. I need to run the stock through my system. Until then, buying a hot IPO is not investing. It is guessing – and I don’t guess with my money.

There is also a structural problem nobody is talking about. Right now, only about 5% to 6% of SpaceX’s float is tradable. The rest is locked up. Those 4,400 employees who became millionaires on paper? When their lockup expires, they will start cashing out – not because they’ve lost faith, but because that is what human beings do when a number on a screen becomes life-changing. Even if they sell 10% or 20% of their holdings, that will be a wall of supply hitting the market.

SpaceX will not be profitable until at least 2028. It is betting everything on its Starship rocket – still in testing, not yet ready to launch satellites or carry humans.

I miss all IPOs, for lack of a better word. Even if it’s a great company – and the jury’s still out on SpaceX – there will always be a better window.

The Proxy Stocks Got Punished – and One of Them Is Now a Buy

Here is something that did happen as predicted.

In the months before the SpaceX IPO, investors who wanted exposure to the space story bought proxy stocks – Rocket Lab Corp. (RKLB), Planet Labs PBC (PL), AST SpaceMobile Inc. (ASTS). These were the next-best options for investors who couldn’t buy SpaceX directly.

The moment SpaceX went public, those investors dumped the proxies and bought the real thing. The proxy stocks got hammered. Some genuinely strong businesses just got cheaper for no fundamental reason.

Good stocks bounce like fresh tennis balls, though. That’s the kind of dislocation my Precursor Intelligence system was built to find.

Planet Labs is one worth looking at right now. The company operates the world’s largest fleet of Earth-observation satellites – more than 200 satellites providing daily imaging of the entire planet. Its customers include government agencies, defense contractors, agricultural companies, insurance firms, and financial institutions that use satellite imagery to make better decisions.

The stock got caught in the SpaceX proxy selloff. But the business didn’t change. My Precursor Intelligence system currently rates Planet Labs a “B.” That means both the fundamental grade – earnings momentum, sales growth, analyst revisions – and the quantitative grade, which measures institutional buying pressure, are strong.

Planet Labs is worth putting on your radar here. It got cheaper because of SpaceX, not because of anything wrong with the business.

The Better Trade Is the One Nobody Sees

But here’s what I want you to understand.

Planet Labs is the obvious SpaceX-adjacent story, and the crowd will find it eventually. The more interesting opportunities are the ones that don’t look like space stocks at all.

Think about what Artemis II actually required. Not just rockets. It needed supply chains, chips, sensors, advanced materials, navigation systems, and communications equipment. A 100-year-old aluminum company making specialized aerospace alloys for the Space Launch System and the Orion spacecraft. A semiconductor foundry making the analog chips that help spacecraft see, hear, communicate, and manage power in the brutal conditions of deep space.

These are not the stocks people think of when they hear “space.” They are not the names AI tools are pointing investors toward. They are companies three or four steps back from the headline story – the ones Wall Street’s elephants have been quietly accumulating before anyone else noticed.

I’ve identified two of them in my new special report, The SpaceX Stampede Report. Both have real earnings. Both are seeing institutional accumulation. Both are the kind of businesses I prefer to own during a boom: picks-and-shovels companies for the new space and AI infrastructure economy.

Are You Investing Like an Elephant or a Mouse?

The SpaceX IPO is a perfect small-scale illustration of something I’ve been tracking across the entire market.

There are really only two kinds of investors in the stock market. I call them elephants and mice.

Elephants are the big institutional players – pension funds, endowments, large asset managers. They move slowly and methodically. They don’t react to headlines. They analyze fundamentals, build positions quietly over months, and wait.

Mice are retail investors. They move in herds, all reacting to the same information at the same time. They’re quick to buy and even quicker to run.

What concerns me right now is that AI trading systems are rapidly turning millions of retail investors into mice moving in perfect synchronization – millions of people using the same tools, the same datasets, the same recommendations, all crowding into the same stocks at the same time. When those AI systems all receive the same “Sell” signal simultaneously, the exit door doesn’t just jam. There is simply no one left on the other side of the trade.

I call this the “50-Million AI Coordination Trap.” And July 23 – at the height of second-quarter earnings season, when AI systems will be processing identical data and reaching identical conclusions simultaneously – is when it faces its first real test at scale.

I’ve spent 47 years building a system designed to read the elephants before the mice show up. I call it Precursor Intelligence. It tracks institutional money flows across 6,000 stocks, looking for the signs that the elephants are quietly moving in or out before the pattern becomes visible to those 50 million AIs and everyone else.

I’ve put together a full presentation explaining exactly how this works – what the trap looks like, which kinds of stocks are most vulnerable, and where my system is seeing institutional accumulation right now. During that free broadcast, I also name my No. 1 stock to buy and my No. 1 stock to avoid as the AI coordination trap builds.

Victor Glover didn’t get to the moon by chasing the obvious path. He got there by understanding every system behind the mission – the ones most people never think about.

That’s how I’ve tried to invest for 47 years. The crowd can chase the rocket, but I’d rather follow the money trail behind it.

Watch that free broadcast here.

Sincerely,

Louis Navellier

Editor, 蜜桃传媒 360

P.S. Planet Labs got cheaper because of SpaceX, not because of anything wrong with its business. My system rates it a “B” right now. But the two stocks I find most interesting in the SpaceX story aren’t the obvious space names at all – they’re the behind-the-scenes materials and semiconductor companies I cover in The SpaceX Stampede Report. Watch my presentation to learn how to get that report.

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Rocket Labs Corp. (RKLB)

The post The Most Important Lesson From the SpaceX IPO appeared first on InvestorPlace.

It was during that year that I became close friends with a neighbor of mine. He was a market maker at the Chicago Board Options Exchange (CBOE). He gave me a behind the scenes look at his operations — along with his trading statements — and I was blown away.

Despite all of my experience, I realized then there was a whole world of trading I hadn’t tapped into — options trading. I spent months managing his book for free, and after I got my sea legs, I decided to set out on my journey to become a market maker myself.

What drew me to options — and what keeps me coming back even after all these years — is their incredible versatility. Unlike stocks, options allow you to express a range of opinions about a stock’s future. Will it rise? Fall? Stay flat? With options, you can craft strategies to profit no matter the scenario.

This flexibility is why I now consider options the ultimate trading vehicle. They offer the perfect balance of leverage and risk management, which makes them the perfect instrument for traders to use their experience and creativity to find setups with truly explosive potential.

Options Provide Flexibility

With options, we’re not limited to simply buying or selling shares at the current stock price. Options traders have the ability to express their opinions on a specific company, fund, or commodity in a variety of ways. Not only can we choose directionality with calls and puts, but we can also choose what price levels we want to target…

If we think Apple Inc. (AAPL) is going to $250, we can buy the $250 out-of-the-money calls instead of buying the at-the-money $225 calls, getting our portfolio leveraged exposure to the rise in share price — usually at a fraction of the cost.

The downside, of course, is that there’s no guarantee Apple will go up, let alone approach that $250 mark before our options expire. If an option expires out of the money (OTM), its value drops to zero and we lose our initial investment. That might sound scary, but it’s also one of the reasons options are such a powerful tool when used strategically.

Unlike buying the stock outright, where a drop in price could wipe out a significant portion of your portfolio, with options your maximum loss is capped at the initial premium you paid. This built-in risk limitation is a safety net available to traders that far too many overlook.

Even better, options offer flexibility that allows us to adapt our trades to changing market conditions.

If we’re holding that $250 call and Apple starts moving in the right direction, but stalls around $240, we’re not stuck watching our trade decay into a loss… Instead, we can take action and transform that single call into a vertical spread by selling a higher strike call, say at $260. Doing this brings in premium that reduces our initial cost, lowers our breakeven point, and keeps the trade alive with a more defined risk and reward.

Let’s break that down a little…

What Is a Vertical Spread?

Simply put, vertical spreads are positions that require us to buy and sell options of the same type and expiration date at different strike prices. When we say “vertical,” we’re referring to the position of the strike prices – essentially, one position offsets the other, which defines whether it’s a credit or debit spread.

Here’s a simple rule of thumb… Bullish vertical spreads increase in value when the underlying asset rises. Conversely, bearish vertical spreads profit from a decline in price.

Going a little deeper, a bullish vertical spread would require us to buy a bullish call spread and a bullish put spread. We simply buy the option with the lower strike price and sell the option with the higher strike price. 

A bearish vertical spread requires us to use bearish call spreads or bearish put spreads. We then sell the option with the lower strike price and buy the option with the higher strike price.

In both scenarios, we need to understand the role of debits and credits.

Debit, Credit, and Implied Volatility in Vertical Spreads

A credit is simplymoney received in an account. A credit transaction is one in which the net sale proceeds are larger than the net buy proceeds (cost), thereby bringing money into the account.  

On the other side, a debit is an expense, or money paid out from an account. A debit transaction is one in which the net cost is greater than the net sale proceeds.

If we think about the examples above, the bullish call spread actually produces a net debit while the bullish put spread results in a net credit at the outset. 

When we talk about debits and credits, we’re specifically paying attention to how volatility affects the overall trajectory of our trades. In this sense, we must always be aware of how Implied Volatility (IV) affects our overall thesis. This is a measurement of how much the price of an option’s underlying stock is expected to fluctuate over the life of the options contract (non-directional).  

Now that we have some terms in mind for understanding how vertical spreads work, let’s take a high-level look at the different types of vertical spreads…

The Types of Vertical Spreads

The Power of Rolling Into Spreads  

With vertical spreads, we have the power to target our upside and downside exposure without risking all of the capital we’ve put up on a single trade.

Many of our positions make use of these kinds of spreads in particular not only because they limit our risk… They also provide us different options for trade management based on whatever the markets throw at us. That’s what’s truly powerful about these trades – they allow us to stay nimble and adapt to wherever our chosen stock is heading.

• Define Your Maximum Investment and Risk: Vertical spreads allow us to define and manage the maximum we can possibly lose on any position.

Let’s say you’re holding a call option on Apple, and the stock has risen significantly. Instead of simply selling, consider rolling into a vertical spread by selling another call at a higher strike price. Here’s why this is powerful:

• How It’s Done: When AAPL rises, you can sell a higher-strike call option against your existing position. This locks in part of your gains and reduces the position’s risk, while still keeping some upside potential.
• Why It Works: A spread gives you extended exposure to AAPL’s potential rise but with less capital at risk. It’s a favorite approach for traders who want to stay in the game without putting all their chips on the line.

Pro Tip: One of the smartest things you can do after a winning options trade is reduce your risk without giving up all your upside. That’s exactly what vertical spreads are designed to do.

By now you understand the basic mechanics of a vertical spread. But knowing how they work is only half the equation. The real advantage is knowing when to use them.

In this video, I walk through why vertical spreads have become one of the cornerstones of my options strategy. Using real trades from our own portfolio, I show how selling a higher-strike option can dramatically reduce your capital at risk, define your maximum loss, and still leave room for substantial gains if the stock keeps moving in your favor.

Rather than simply taking profits and walking away, vertical spreads allow you to stay with your best ideas while steadily shifting the odds in your favor. It’s one of the most effective ways I know to trade with discipline over the long run.

Vertical spreads are just one tool in the toolbox. The real edge comes from understanding why we use them, when to use them, and how they fit into a complete trading plan.

That’s exactly what the Masters in Trading Options Challenge is designed to teach.

I’ll take you step by step through the same process I use every day—finding opportunities, structuring trades with defined risk, managing winners, and protecting your capital along the way. No hype. No guesswork. Just a practical framework you can apply to every trade you make.

If you’re serious about becoming a better options trader, join me inside the Masters in Trading Options Challenge. I think you’ll be surprised how quickly these concepts begin to click—and how much more confident you’ll feel every time you place a trade.

The post Rolling Into Spreads: Extend Profit Potential with Lower Risk appeared first on InvestorPlace.

AI just joined the payroll.

At Kalshi, the U.S.-regulated prediction-market platform where traders bet on real-world outcomes, an internal AI agent named Harrison is already performing work that looks a lot like analyst labor. 

It tracks news, monitors competitors, recommends new markets, drafts contract language, and helps resolve markets when they close.

Functionally, AI is starting to look less like software you use and more like labor you deploy — planning, checking, calling tools, retrieving information, revising, and repeating the loop until the job is done.

And that kind of AI is far more compute-hungry than the chatbot world investors first fell in love with.

AI Agents Are Moving From Answers to Action

For the first few years of the generative AI era, the story was almost entirely about capability.

ChatGPT conducted and organized research. Sora stunned users with hyper-realistic video. Claude summarized documents, drafted emails, wrote code, and helped professionals move faster. It was dazzling. 

At the same time, impressive as it was, this was still AI in its infancy.

The business model was straightforward: user asks, AI answers, company charges a subscription. The compute profile matched: modest inference on demand, a few thousand tokens in and out, and a model that mostly sat idle between queries.

But an AI agent is different. Give it an objective, and it goes to work — planning, executing, checking its own output, calling tools, querying databases, revising, and iterating until the task is complete. That continuous loop consumes inference compute on a vastly larger scale.

Gartner estimates that agentic workflows consume 5-30x more tokens per task than single-shot generative queries. Goldman Sachs sees the monthly token count for agentic AI applications reaching roughly 120 quadrillion by 2030.

This is the structural shift that most investors are still underestimating.

Why Agentic AI Requires So Much Inference Compute

Harrison shows what agentic AI can do across information-heavy workflows: 

• Ingesting and summarizing news, social media, filings, and market data
• Reasoning over that information to identify what matters for Kalshi’s open markets
• Drafting proposed contract language for new prediction markets
• Stress-testing that language for ambiguity, edge cases, or potential disputes
• Monitoring competitor platforms to benchmark Kalshi’s market offerings

Every one of those tasks is an inference call — often multiple — with tool use, retrieval, multi-step reasoning, and iterative revision layered on top. Agents like Harrison could be making dozens of API calls per task, around the clock.

Now multiply that by the number of enterprises building their own Harrison. Then multiply that by the number of workflows inside each enterprise that are ripe for agentic automation — compliance review, customer support, financial analysis, coding, procurement, legal research, sales outreach…

This is what we mean when we say we are at the very beginning of the inference demand supercycle.

The Investment Implication: Follow the Inference Demand

Follow the compute, and you’ll find the trade. 

It doesn’t matter which app wins, which enterprise deploys the most agents, or which model — GPT, Claude, Gemini, Llama — powers them.

What matters is that every agent is sending traffic through the same physical infrastructure stack. And that stack is finite, expensive to build, and currently being stretched to its limits.

Each layer collects a different kind of toll.

Accelerators: Nvidia and AMD Power the Reasoning Loop

Nvidia (NVDA) and AMD (AMD) remain the engine room of inference compute. Every time Harrison runs a reasoning loop — planning, executing, checking its work — it draws on accelerated compute. Nvidia’s Blackwell GPUs remain the preferred hardware for many large-scale AI workloads, and the 12-month order backlog shows how intense demand remains. AMD, meanwhile, is gaining ground in cost-sensitive inference workloads as hyperscalers look for alternatives and bargaining power. Both benefit structurally from the agentic shift. 

Networking and Custom Silicon: Lowering the Cost per Token

Every agentic workflow sends repeated traffic across the networking stack. Arista Networks (ANET) has continued raising its AI networking targets as demand from cloud customers accelerates. Its latest results showed revenue growth of 35% year over year, while management described the AI demand environment as unusually strong. Credo Technology (CRDO) supplies the active electrical cables that connect GPUs at the rack level. Broadcom (AVGO) and Marvell (MRVL) are designing the custom chips hyperscalers are deploying to run inference more efficiently and at lower cost per token. 

Memory: The Bottleneck Behind Long-Context Agents

Agents maintain large context windows — tracking conversation history, tool outputs, retrieved documents, intermediate reasoning steps — making high-bandwidth memory (HBM) a critical resource. Micron’s (MU) latest quarter showed just how central memory has become to the AI buildout. Fiscal Q3 revenue surged to $41.46 billion, up roughly 346% year over year, while non-GAAP gross margin hit 84.9%. The company also guided fiscal Q4 revenue to $50 billion and said memory demand continues to exceed supply, with tight conditions expected to persist beyond calendar 2027. Only three companies on the planet manufacture HBM at commercial scale. Micron is the only U.S.-headquartered one.

Servers, Racks, and Power: The Always-On Agent Layer

All the GPUs running continuous agentic-scale workloads need to live somewhere and be kept cool. Dell (DELL) and Super Micro (SMCI) build the servers and racks. Vertiv (VRT) supplies the power and cooling infrastructure that keeps them running. In Q1 2026, VRT reported $2.65 billion in revenue — up 30.1% year over year — against a $15 billion order backlog. Training happens in big, intense bursts. Agentic inference is different: it can run continuously across millions of workflows. That persistent demand raises the importance of power and cooling infrastructure. 

Storage: Fast Retrieval for Enterprise AI Agents

Agents need to retrieve information fast, requiring instant access to large datasets. That means high-performance storage is a must. Pure Storage (PSTG), Seagate (STX), and NetApp (NTAP) are likely beneficiaries as more enterprise workflows require AI systems with fast access to massive datasets. Pure Storage in particular has been gaining strength beneath the surface. In Q1 of FY2027, product revenue surged 55%, while subscription services accounted for 45% of total revenue. Operating profit jumped over 90% year-over-year to $159 million.

Optical Connectivity: The Overlooked Agentic AI Bottleneck

This may be the most overlooked constraint in the entire stack — and one of the next bottlenecks the market wakes up to. Moving data between GPUs, servers, and data centers at the speeds required for continuous agentic inference requires optical connectivity. As agent workloads move across servers, clusters, and data centers, more of that traffic depends on fiber, optics, and photonic interconnects. Coherent (COHR), Lumentum (LITE), and Corning (GLW) are building the infrastructure that makes high-throughput inference physically possible. The optics bottleneck is coming. These names are positioned for it before the crowd arrives.

The Bottom Line: Agentic AI Turns Compute Into Labor Cost

Kalshi’s Harrison is more than another headline. It’s a signal — that enterprise AI has crossed a threshold, from “interesting capability” to “operational necessity.” 

When a company builds a purpose-built internal agent and deploys it into its core workflows, it is making a structural bet that AI will permanently change how the business operates.

That bet requires infrastructure… and lots of it. 

We are at the very beginning of the inference supercycle — the period where AI demand shifts from episodic to persistent. 

The companies supplying the accelerators, networking, memory, servers, storage, power, cooling, and connectivity behind that shift are not side bets on AI. They are the trade.

Because once AI joins the payroll, compute becomes the new labor cost. 

The billionaires building sovereign AI from the inside already understand this. Their private capital has been moving into the physical layer of this buildout — energy, nuclear, fabrication, hard assets — for longer than the headlines suggest. Most of those positions aren’t available publicly.

Seven of them are.

Here’s what we know.

The post Kalshi’s Harrison Shows Where the Next AI Trade Is Heading appeared first on InvestorPlace.

Almost three weeks ago, we urged readers to stay away from the SpaceX IPO.

From our 6/11 Digest:

Don’t you do it – don’t you buy the SpaceX (SPCX) IPO tomorrow. Or, if you insist, at least do so with your eyes open.

Behind the warning was 45 years of U.S. IPO history – more than 9,300 offerings, compiled and analyzed by University of Florida professor Jay Ritter, who is the world’s foremost academic authority on IPOs.

In short, the average investor wasn’t going to be able to buy SPCX at its initial IPO price. By the time they could get in, the stock would already be trading at an inflated first-day price (history shows an average 19% first-day bump).

The data suggested that after its initial surge, the stock would experience a meaningful pullback, leaving average buyers underwater.

History likes to repeat itself – in more ways than one

The first historical repeat?

SPCX popped 19.2% on its first trading day – matching the 45-year historical average almost exactly.

The second repeat?

The average investor who bought at the tail end of that Day 1 surge or shortly thereafter and is still holding is already sitting on a loss.

According to CNBC on June 18, the five-day volume-weighted average price sat around $182. With SPCX’s price hovering near that level, CNBC’s conclusion at that time was:

The average investor who bought SpaceX shares in the open market after its debut has seen nearly all of their gains disappear…

The average post-IPO buyer is now approximately breaking even.

As I write on Monday, with SPCX shares trading roughly 21% below that June 18 level, that average post-IPO buyer is now sitting on a double-digit loss – just as history predicted.

Let’s jump to legendary investor Louis Navellier from last week’s Accelerated Profits June issue:

Some investors learned a tough lesson recently…

During the frenzy around the IPO, folks forgot one important fact: SpaceX will not be profitable until at least 2028.

Too many investors chase companies without earnings growth, such as SpaceX. Not a smart strategy, in my opinion.

Louis has built his career – and his track record – on the opposite philosophy

His approach centers on finding companies with accelerating earnings and strong fundamental grades, the kind of businesses that don’t need a hype cycle to justify their price. When earnings drive the story, the math works in your favor from the start.

Here’s Louis with where the math is working today as he looks ahead to the start of Q2 earnings season:

If you want to make money, you have to invest in companies with earnings – e.g., technology stocks.

According to our friends at FactSet, the Information Technology sector had its earnings estimates revised more than 7% higher since the start of the second quarter.

This sector is now expected to achieve 59.6% average earnings growth in the second quarter, up from estimates of 48.7% at the end of the first quarter.

Can we quantify this earnings strength and turn it into an expected return for the tech sector?

Yes – FactSet has already done it for us. It shows that, based on earnings forecasts, analysts predict the Information Technology sector will climb 26.5% over the next 12 months.

Meanwhile, the earnings strength across the tech sector is remarkable. Here’s FactSet with the data:

Overall, 62 of the 74 companies (84%) in the Information Technology sector have seen an increase in their mean EPS estimate [since March 31].

Of these 62 companies, 23 have recorded an increase in their mean EPS estimate of more than 10%.

FactSet flags Intel (INTC), Sandisk (SNDK), Micron (MU), and Nvidia (NVDA), among others, as EPS increase leaders.

Those names aren’t likely to surprise anyone who’s been following the AI trade…

This is the exact point that Louis makes in his latest research package.

When 50 million investors are working from the same tools and arriving at the same conclusions, the most obvious winners can get crowded fast.

The smart money – what Louis calls “the elephants” – tends to move on before that crowding peaks, quietly positioning themselves in the next opportunity while everyone is still celebrating the last one.

That’s the thesis behind his Precursor Intelligence system, and he just recorded a free presentation walking viewers through where institutional “elephant” money is moving right now. You can watch it here.

Coming full circle on SPCX, tech earnings, and where to have money now, I’ll give Louis the final word:

Our AI and data center stocks have a three-year order backlog. Thanks to accelerating earnings growth, these stocks should deliver spectacular performance through 2029.

Simply put, the AI and data center boom cannot be stopped!

Investors who understand this reality and align their portfolios accordingly stand to profit handsomely in the upcoming months (and years!).

Perhaps not if a growing chorus of politicians in Washington get their way…

At the start of the year, as our analysts were unveiling their 2026 market predictions, I made a call of my own

This year will bring a wave of new, controversial legislative proposals aimed at investment wealth – proposals that may not pass immediately, but will introduce a new layer of policy risk investors will have to price in.

That prediction has been validating in stages all year.

For example, in January, California’s Billionaire Tax Act began collecting signatures. It’s now headed for the November ballot (I’ll note that the bill contains language that critics – including the Wall Street Journal – say allows the legislature to expand eligibility without voter approval).

Then, at the start of the month, Senator Elizabeth Warren, D-Mass., published an op-ed in Time calling for new taxes on AI and higher capital gains rates.

And now, for the biggest one yet…

Just over a week ago, Senator Bernie Sanders, D-Vt., introduced the American AI Sovereign Wealth Fund Act. It would impose a one-time 50% tax on the equity of every major AI company with annual revenues of more than $200 million, with those shares going into a government-managed fund.

To be clear, this isn’t a 50% tax on profits – it’s a 50% tax on equity.

I feel like “tax” isn’t the right word to use there…

Recognize the direction

Now, let’s be realistic: This bill won’t pass under the current Congress.

But my prediction back in January was never about passage. It was about political trajectory – and where that trajectory is pointing.

Last week, three Democratic Socialists swept their New York primary races, all backed by NYC Mayor Zohran Mamdani, whom we flagged back in January as a signal worth watching.

One of those NYC winners – Darializa Avila Chevalier – had a 2019 social media post calling to “seize the means of production.” She won anyway.

None of this requires you to have a political opinion. What it requires is that you follow the trajectory – from California wealth taxes, to Warren’s op-ed, to Sanders’ equity seizure proposal, to three Democratic Socialists of America candidates headed to Congress (their districts are overwhelmingly blue) – and ask yourself…

What does the political landscape look like heading into the 2026 midterm and 2028 presidential election cycles? And what does that mean for your investment plan?

There are no right or wrong answers. No political commentary. Just a recognition of the shifting social/political landscape to navigate.

Bottom line: My 2026 January prediction was for a legislative wave. But only six months into the year, we’re already watching a flood.

The first Fed official to call for a rate hike just put his name on it

This past Friday, Minneapolis Fed President Neel Kashkari delivered a notable statement at the Aspen Ideas Festival:

In March, I had penciled in one rate cut by the end of the year.

In June, I’ve changed that to one rate hike by the end of the year.

He’s the first voting FOMC member to say that publicly, and by name – though he’s not alone.

The Fed’s June dot plot showed nine of 18 officials already expect at least one hike this year. So, the hawkish view has already been growing inside the building – Kashkari just walked it outside.

His reasoning goes beyond the Middle East…

Yes, he cited oil prices and the Strait of Hormuz disruption. But he also flagged something worth noting for anyone invested in the AI trade:

…hundreds of billions of dollars a year into data centers and all of the associated infrastructure that goes with that – anything that touches those sectors, the prices are skyrocketing.

In other words, the AI capex boom isn’t just an investment story. It’s now showing up as an inflationary pressure that a voting Fed member is explicitly citing as a reason to raise rates.

Set that against what we covered last Thursday…

Federal Reserve Chairman Kevin Warsh’s preferred inflation measure – the trimmed mean PCE – has sat in a remarkably narrow band of 2.3% to 2.4% for six straight months.

This is the analytical tension at the heart of Fed policy right now: Kashkari is reading the headline noise; Warsh is trying to strip it out.

This is the fault line dividing the wider FOMC today…

For example, New York Fed President John Williams thinks current policy is well-positioned. But Chicago Fed President Austan Goolsbee has expressed concern about inflation while declining to speculate on the Fed’s next move.

Bottom line: The FOMC is no longer of one mind.

So, what does this mean for investors?

Well, the next two or three inflation reports will carry more weight than usual. And the range of outcomes – hike, hold, or eventual cut – is genuinely open.

Given that Wall Street hates uncertainty, it might make for a bumpy run.

We’ll keep you updated.

Have a good evening,

Jeff Remsburg

(Disclaimer: I own MU)

The post The AI Trade Has Three New Problems appeared first on InvestorPlace.

Space Exploration Technologies Corp. (SPCX) is cooling off after its long-awaited IPO. Micron Technology, Inc. (MU) has suddenly become one of the biggest AI stories on Wall Street. The NASDAQ has been under pressure. And investors are trying to figure out where leadership goes next.

So, on the latest episode of Navellier 蜜桃传媒 Buzz, I sat down with my friend and colleague Jason Bodner to talk through what is really happening.

Jason is a former Wall Street specialist who built the Big Money Index. In plain English, he studies institutional money flow. He looks for where big money is moving into stocks and where it is moving out.

And right now, that matters.

Because the smart money does not panic when stocks oscillate. It looks for the next leg of the market.

That is exactly what Jason and I discussed in our latest conversation. We talked about Micron’s explosive earnings, why AI leadership is shifting, how the memory shortage could last through 2028 and why money may be moving away from the old software leaders and into the companies solving AI’s biggest bottlenecks.

Click the image below to watch the latest episode of Navellier 蜜桃传媒 Buzz.

To see more of my videos, click here to subscribe to my YouTube channel.

Plus, the grades in Stock Grader (subscription required) have been updated this week! Click here to plug in your own stocks and see how they’re rated.

My Blueprint for Finding Tomorrow’s Winners

Now, the most important point from our conversation is simple: AI leadership is changing.

For the past few years, investors have been obsessed with the obvious AI names. They chased the companies everyone already knew. They piled into the stocks tied directly to AI software, AI chips and the first wave of the boom.

But that is not where the whole opportunity ends.

As Jason explained, the next stage of AI leadership is moving toward “pick-and-axe” companies. These are the businesses solving the bottlenecks behind the AI buildout.

That includes memory companies like Micron. It includes photonics companies. It includes the companies helping connect thousands of GPUs together. And it includes the infrastructure companies that make the whole AI machine work.

That is a very important shift.

Because when leadership changes, most investors do not notice right away. They keep chasing the stocks that already worked. They keep watching the names that already made headlines. They keep looking backward.

But institutional money is usually looking forward.

That is why Jason’s Big Money Index is so useful.

And that is also exactly the kind of environment where my Precursor Intelligence system can help.

P.I. is my way of looking for fresh tracks in the numbers. It helps me analyze roughly 6,000 stocks using eight fundamental signals and one quantitative money-flow signal.

The goal is simple: I want to find companies with accelerating fundamentals and improving money flow before they become the obvious names every investor is chasing.

That matters even more today because AI-powered trading tools could make crowding more dangerous. If millions of investors rely on the same tools, the same model portfolios and the same automated systems, they may all pile into the same obvious names at the same time.

That can push stocks higher for a while. But it can also give institutional investors the liquidity they need to quietly move on.

So, I do not want to chase the crowd. I want to look for where the smart money may be headed next.

That is why I recently recorded a special presentation on Precursor Intelligence.

I explain how P.I. works, why AI-powered crowding could become a serious risk for investors and where I believe the smart money is moving next. I also reveal several stocks my system is flagging right now.

You can click here to watch it now.

Sincerely,

Louis Navellier

Editor, 蜜桃传媒 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Micron Technology, Inc. (MU)

The post AI Leadership Is Shifting. Here’s How to Follow the Big Money appeared first on InvestorPlace.

This Week’s Ratings Changes:

Upgraded: Strong to Very Strong

Downgraded: Very Strong to Strong

Upgraded: Neutral to Strong

Downgraded: Strong to Neutral

Upgraded: Weak to Neutral

Downgraded: Neutral to Weak

Upgraded: Very Weak to Weak

Downgraded: Weak to Very Weak

To stay on top of my latest stock ratings, plug your holdings into Stock Grader, my proprietary stock screening tool. But, you must be a subscriber to one of my premium services.

To learn more about my premium service, Growth Investor, and get my latest picks, go here. Or, if you are a member of one of my premium services, you can go here to get started.

Sincerely,

Louis Navellier

Editor, 蜜桃传媒 360

The post Eli Lilly Upgraded, Carvana Downgraded: Updated Rankings on Top Blue-Chip Stocks appeared first on InvestorPlace.

Louis has been watching the SpaceX IPO closely — but not because he’s tempted to buy it. What he finds fascinating is how markets react when millions of investors chase the same story at the same time.

He argues that this dynamic is no longer limited to high-profile IPOs.

Now, AI-powered trading tools are quietly pushing retail investors into the same crowded positions across the entire market. When institutional money — the “elephants,” as Louis calls them — starts heading for the exit, the investors left behind may not see it coming until it’s too late.

Below, Louis explains the risk, names one space stock his system currently rates an “A” that just got cheaper for no fundamental reason, and introduces the framework he’s spent 47 years building to track where the smart money is moving before the crowd catches on.

Without further ado, here’s Louis…

There are some stories that can’t help but make you proud to be an American.

Victor Glover is one of them.

Glover is a Navy captain, test pilot, engineer, and NASA astronaut. He earned three master’s degrees from three different institutions. During his naval career, one of his commanding officers gave him the call sign “Ike” — short for “I know everything.” It was partly tongue-in-cheek. But it fit.

In April 2026, Glover piloted Artemis II around the moon. In doing so, he became the first African American to leave low Earth orbit and travel beyond it. Along with his crewmates, he helped set a new record for the farthest distance humans have ever traveled from Earth.

That is the kind of achievement people remember.

But as an investor, I look at that story a little differently.

I see the astronaut, the rocket, and the mission. But I also see the enormous industrial machine behind it all. Artemis II was not just a triumph of courage and exploration. It was a triumph of supply chains, chips, sensors, navigation systems, advanced materials, communications equipment, and thousands of private-sector components that had to work together perfectly.

And Artemis II was not the end of the story. It was the beginning of a much larger campaign.

That is the part most investors miss. And that brings us to the recent SpaceX IPO — and the real lesson it has to teach.

In this piece I’ll explain why I passed on the IPO — and give you one space stock my system currently rates an “A” that just got cheaper for no fundamental reason.

A Great Story Is Not Always a Great Stock

When Space Exploration Technologies Corp. (SPCX) went public, the crowd did what crowds usually do. They saw the name. They saw Elon Musk. Then they chased the stock.

The stock priced at $150 per share. Within two trading days, it had surged more than 40%, climbing above $200. Then gravity showed up. The stock fell back to Earth, all the way back to $150 last time I checked.

A lot of investors were reminded of a lesson I’ve learned again and again in nearly 50 years in this business: A great story is not always a great stock.

SpaceX is a remarkable company. Starlink has changed the world. I would never bet against Musk. But IPOs are different. By the time a hot private company reaches the public market, the early investors have already had the first bite. Wall Street bankers have every reason to make the story irresistible. And individual investors are often left trying to calculate risk with very little useful data.

That is not how I invest. I need quarterly earnings, analyst revisions, and institutional buying data. I need to run the stock through my system. Until then, buying a hot IPO is not investing. It is guessing — and I don’t guess with my money.

There is also a structural problem nobody is talking about. Right now, only about 5% to 6% of SpaceX’s float is tradable. The rest is locked up. Those 4,400 employees who became millionaires on paper? When their lockup expires, they will start cashing out — not because they’ve lost faith, but because that is what human beings do when a number on a screen becomes life-changing. Even if they sell 10% or 20% of their holdings, that will be a wall of supply hitting the market.

SpaceX will not be profitable until at least 2028. It is betting everything on its Starship rocket — still in testing, not yet ready to launch satellites or carry humans.

I miss all IPOs, for lack of a better word. Even if it’s a great company — and the jury’s still out on SpaceX — there will always be a better window.

The Proxy Stocks Got Punished — and One of Them Is Now a Buy

Here is something that did happen as predicted.

In the months before the SpaceX IPO, investors who wanted exposure to the space story bought proxy stocks — Rocket Lab Corp. (RKLB), Planet Labs PBC (PL), AST SpaceMobile Inc. (ASTS). These were the next-best options for investors who couldn’t buy SpaceX directly.

The moment SpaceX went public, those investors dumped the proxies and bought the real thing. The proxy stocks got hammered. Some genuinely strong businesses just got cheaper for no fundamental reason.

Good stocks bounce like fresh tennis balls, though. That’s the kind of dislocation my Precursor Intelligence system was built to find.

Planet Labs is one worth looking at right now. The company operates the world’s largest fleet of Earth-observation satellites — more than 200 satellites providing daily imaging of the entire planet. Its customers include government agencies, defense contractors, agricultural companies, insurance firms, and financial institutions that use satellite imagery to make better decisions.

The stock got caught in the SpaceX proxy selloff. But the business didn’t change. My Precursor Intelligence system currently rates Planet Labs an “A.” That means both the fundamental grade — earnings momentum, sales growth, analyst revisions — and the quantitative grade, which measures institutional buying pressure, are strong.

Planet Labs is worth putting on your radar here. It got cheaper because of SpaceX, not because of anything wrong with the business.

The Better Trade Is the One Nobody Sees

But here’s what I want you to understand.

Planet Labs is the obvious SpaceX-adjacent story, and the crowd will find it eventually. The more interesting opportunities are the ones that don’t look like space stocks at all.

Think about what Artemis II actually required. Not just rockets. It needed supply chains, chips, sensors, advanced materials, navigation systems, and communications equipment. A 100-year-old aluminum company making specialized aerospace alloys for the Space Launch System and the Orion spacecraft. A semiconductor foundry making the analog chips that help spacecraft see, hear, communicate, and manage power in the brutal conditions of deep space.

These are not the stocks people think of when they hear “space.” They are not the names AI tools are pointing investors toward. They are companies three or four steps back from the headline story — the ones Wall Street’s elephants have been quietly accumulating before anyone else noticed.

I’ve identified two of them in my new special report, The SpaceX Stampede Report. Both have real earnings. Both are seeing institutional accumulation. Both are the kind of businesses I prefer to own during a boom: picks-and-shovels companies for the new space and AI infrastructure economy.

Are You Investing Like an Elephant or a Mouse?

The SpaceX IPO is a perfect small-scale illustration of something I’ve been tracking across the entire market.

There are really only two kinds of investors in the stock market. I call them elephants and mice.

Elephants are the big institutional players — pension funds, endowments, large asset managers. They move slowly and methodically. They don’t react to headlines. They analyze fundamentals, build positions quietly over months, and wait.

Mice are retail investors. They move in herds, all reacting to the same information at the same time. They’re quick to buy and even quicker to run.

What concerns me right now is that AI trading systems are rapidly turning millions of retail investors into mice moving in perfect synchronization — millions of people using the same tools, the same datasets, the same recommendations, all crowding into the same stocks at the same time. When those AI systems all receive the same “Sell” signal simultaneously, the exit door doesn’t just jam. There is simply no one left on the other side of the trade.

I call this the “50-Million AI Coordination Trap.” And July 23 — at the height of second-quarter earnings season, when AI systems will be processing identical data and reaching identical conclusions simultaneously — is when it faces its first real test at scale.

I’ve spent 47 years building a system designed to read the elephants before the mice show up. I call it Precursor Intelligence. It tracks institutional money flows across 6,000 stocks, looking for the signs that the elephants are quietly moving in or out before the pattern becomes visible to those 50 million AIs and everyone else.

I’ve put together a full presentation explaining exactly how this works — what the trap looks like, which kinds of stocks are most vulnerable, and where my system is seeing institutional accumulation right now. During that free broadcast, I also name my No. 1 stock to buy and my No. 1 stock to avoid as the AI coordination trap builds.

Victor Glover didn’t get to the moon by chasing the obvious path. He got there by understanding every system behind the mission — the ones most people never think about.

That’s how I’ve tried to invest for 47 years. The crowd can chase the rocket, but I’d rather follow the money trail behind it.

Watch that free broadcast here.

Sincerely,

Louis Navellier

Senior Investment Analyst, InvestorPlace

P.S. Planet Labs got cheaper because of SpaceX, not because of anything wrong with its business. My system rates it an “A” right now. But the two stocks I find most interesting in the SpaceX story aren’t the obvious space names at all — they’re the behind-the-scenes materials and semiconductor companies I cover in The SpaceX Stampede Report. Watch my presentation to learn how to get that report.

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Rocket Lab Corp. (RKLB)

The post SpaceX Created the Chaos. Louis Found the Opportunity. appeared first on InvestorPlace.

That move — up 40% in two days, then all the way back — is a perfect snapshot of what happens when millions of investors chase the same story at the same time. Louis Navellier has a name for it: the 50-Million AI Coordination Trap. And he thinks earnings season is about to test it at full scale.

Today he explains why he passed on SpaceX, names one space stock his system rates an “A” that got caught in the fallout for no fundamental reason, and lays out how to position yourself on the right side of what’s coming.

He recently recorded a full presentation on exactly this.

Here’s Louis with more.

There are some stories that can’t help but make you proud to be an American.

Victor Glover is one of them.

Glover is a Navy captain, test pilot, engineer, and NASA astronaut. He earned three master’s degrees from three different institutions. During his naval career, one of his commanding officers gave him the call sign “Ike” — short for “I know everything.” It was partly tongue-in-cheek. But it fit.

In April 2026, Glover piloted Artemis II around the moon. In doing so, he became the first African American to leave low Earth orbit and travel beyond it. Along with his crewmates, he helped set a new record for the farthest distance humans have ever traveled from Earth.

That is the kind of achievement people remember.

But as an investor, I look at that story a little differently.

I see the astronaut, the rocket, and the mission. But I also see the enormous industrial machine behind it all. Artemis II was not just a triumph of courage and exploration. It was a triumph of supply chains, chips, sensors, navigation systems, advanced materials, communications equipment, and thousands of private-sector components that had to work together perfectly.

And Artemis II was not the end of the story. It was the beginning of a much larger campaign.

That is the part most investors miss. And that brings us to the recent SpaceX IPO — and the real lesson it has to teach.

In this piece I’ll explain why I passed on the IPO — and give you one space stock my system currently rates an “A” that just got cheaper for no fundamental reason.

SpaceX Stock: A Great Story Is Not Always a Great Stock — Especially at IPO

When Space Exploration Technologies Corp. (SPCX) went public, the crowd did what crowds usually do. They saw the name. They saw Elon Musk. Then they chased the stock.

The stock priced at $150 per share. Within two trading days, it had surged more than 40%, climbing above $200. Then gravity showed up. The stock fell back to Earth, all the way back to $150 last time I checked.

A lot of investors were reminded of a lesson I’ve learned again and again in nearly 50 years in this business: A great story is not always a great stock.

SpaceX is a remarkable company. Starlink has changed the world. I would never bet against Musk. But IPOs are different. By the time a hot private company reaches the public market, the early investors have already had the first bite. Wall Street bankers have every reason to make the story irresistible. And individual investors are often left trying to calculate risk with very little useful data.

That is not how I invest. I need quarterly earnings, analyst revisions, and institutional buying data. I need to run the stock through my system. Until then, buying a hot IPO is not investing. It is guessing — and I don’t guess with my money.

There is also a structural problem nobody is talking about. Right now, only about 5% to 6% of SpaceX’s float is tradable. The rest is locked up. Those 4,400 employees who became millionaires on paper? When their lockup expires, they will start cashing out — not because they’ve lost faith, but because that is what human beings do when a number on a screen becomes life-changing. Even if they sell 10% or 20% of their holdings, that will be a wall of supply hitting the market.

SpaceX will not be profitable until at least 2028. It is betting everything on its Starship rocket — still in testing, not yet ready to launch satellites or carry humans.

I miss all IPOs, for lack of a better word. Even if it’s a great company — and the jury’s still out on SpaceX — there will always be a better window.

The SpaceX Proxy Selloff Created a Dislocation — and Planet Labs Is the Buy

Here is something that did happen as predicted.

In the months before the SpaceX IPO, investors who wanted exposure to the space story bought proxy stocks — Rocket Lab Corp. (RKLB), Planet Labs PBC (PL), AST SpaceMobile Inc. (ASTS). These were the next-best options for investors who couldn’t buy SpaceX directly.

The moment SpaceX went public, those investors dumped the proxies and bought the real thing. The proxy stocks got hammered. Some genuinely strong businesses just got cheaper for no fundamental reason.

Good stocks bounce like fresh tennis balls, though. That’s the kind of dislocation my Precursor Intelligence (P.I.) system was built to find.

Planet Labs is one worth looking at right now. The company operates the world’s largest fleet of Earth-observation satellites — more than 200 satellites providing daily imaging of the entire planet. Its customers include government agencies, defense contractors, agricultural companies, insurance firms, and financial institutions that use satellite imagery to make better decisions.

The stock got caught in the SpaceX proxy selloff. But the business didn’t change. My P.I. system currently rates Planet Labs an “A.” That means both the fundamental grade — earnings momentum, sales growth, analyst revisions — and the quantitative grade, which measures institutional buying pressure, are strong.

Planet Labs is worth putting on your radar here. It got cheaper because of SpaceX, not because of anything wrong with the business.

The Better Space Trade Is Three Steps Behind the Headline

But here’s what I want you to understand.

Planet Labs is the obvious SpaceX-adjacent story, and the crowd will find it eventually. The more interesting opportunities are the ones that don’t look like space stocks at all.

Think about what Artemis II actually required. Not just rockets. It needed supply chains, chips, sensors, advanced materials, navigation systems, and communications equipment. A 100-year-old aluminum company making specialized aerospace alloys for the Space Launch System and the Orion spacecraft. A semiconductor foundry making the analog chips that help spacecraft see, hear, communicate, and manage power in the brutal conditions of deep space.

These are not the stocks people think of when they hear “space.” They are not the names AI tools are pointing investors toward. They are companies three or four steps back from the headline story — the ones Wall Street’s elephants have been quietly accumulating before anyone else noticed.

I’ve identified two of them in my new special report, The SpaceX Stampede Report. Both have real earnings. Both are seeing institutional accumulation. Both are the kind of businesses I prefer to own during a boom: picks-and-shovels companies for the new space and AI infrastructure economy.

The 50-Million AI Coordination Trap — and How to Avoid it

The SpaceX IPO is a perfect small-scale illustration of something I’ve been tracking across the entire market.

There are really only two kinds of investors in the stock market. I call them elephants and mice.

Elephants are the big institutional players — pension funds, endowments, large asset managers. They move slowly and methodically. They don’t react to headlines. They analyze fundamentals, build positions quietly over months, and wait.

Mice are retail investors. They move in herds, all reacting to the same information at the same time. They’re quick to buy and even quicker to run.

What concerns me right now is that AI trading systems are rapidly turning millions of retail investors into mice moving in perfect synchronization — millions of people using the same tools, the same datasets, the same recommendations, all crowding into the same stocks at the same time. When those AI systems all receive the same “Sell” signal simultaneously, the exit door doesn’t just jam. There is simply no one left on the other side of the trade.

I call this the “50-Million AI Coordination Trap.” And July 23 — at the height of second-quarter earnings season, when AI systems will be processing identical data and reaching identical conclusions simultaneously — is when it faces its first real test at scale.

I’ve spent 47 years building a system designed to read the elephants before the mice show up. I call it Precursor Intelligence. It tracks institutional money flows across 6,000 stocks, looking for the signs that the elephants are quietly moving in or out before the pattern becomes visible to those 50 million AIs and everyone else.

I’ve put together a full presentation explaining exactly how this works — what the trap looks like, which kinds of stocks are most vulnerable, and where my system is seeing institutional accumulation right now. During that free broadcast, I also name my No. 1 stock to buy and my No. 1 stock to avoid as the AI coordination trap builds.

Victor Glover didn’t get to the moon by chasing the obvious path. He got there by understanding every system behind the mission — the ones most people never think about. 

That’s how I’ve tried to invest for 47 years. The crowd can chase the rocket, but I’d rather follow the money trail behind it.

Watch that free broadcast here.

The post The Space Stocks That Got Cheaper for No Good Reason appeared first on InvestorPlace.

That matters because AI-powered trading tools could soon push millions of investors into the same crowded stocks at the same time. And when that happens, institutional investors may use the rush of buying to quietly move on.

In today’s guest essay, Louis explains the risk, the opportunity, and how his Precursor Intelligence system is helping him track where the smart money may be headed next.

Over to you, Louis…

In 1909, Theodore Roosevelt left the White House and set out for East Africa.

He was not going there as a tourist.

Roosevelt, his son Kermit and a team of naturalists were traveling on behalf of the Smithsonian Institution. Much of the journey came down to one difficult task:

Tracking elephants.

In the thick African brush, you don’t just wait for an elephant to step into view. By then, it might already be too late.

You had to look for signs: Fresh tracks in the mud. Broken branches. Disturbed grass. A path through the brush that told you something enormous had passed through before you ever saw it.

That is how I think about stocks.

I am not interested in waiting until the whole world can see the elephant. By then, Wall Street has usually figured out the story. The headlines are everywhere. The crowd has shown up. And a lot of the easy money has already been made.

That brings me to Micron Technology, Inc. (MU).

Micron is no longer hiding in the brush. The stock is up 325% year-to-date and 853% over the past year. It became a $1 trillion market cap company last month. And after this week’s blowout earnings report, it is quickly becoming one of Wall Street’s favorite AI stocks.

That did not happen by accident.

It happened because Micron is helping solve one of the biggest problems in artificial intelligence today: The memory bottleneck.

So today, we’ll dig into Micron’s blowout quarter, discuss why it matters and then talk about how my system is already helping me find winners from the next phase of the AI boom before the crowd catches on.

Micron Crushed Wall Street’s Expectations

For the past few years, NVIDIA Corporation (NVDA) has been the grand finale of earnings season. But now, I believe Micron has taken that role.

Here’s why.

NVIDIA tells us how strong demand is for GPUs, the chips that power today’s AI systems. But Micron tells us whether those systems can get the memory they need to keep running at full speed.

Micron is one of the world’s largest makers of memory and storage chips. In plain English, its chips help computers and data centers store information, access it quickly and move it where it needs to go.

That may not sound as exciting as a cutting-edge GPU. But without memory, those GPUs cannot do their job.

Think of it like this: A GPU is the engine in a race car. Memory is the fuel line. You can build the most powerful engine in the world. But if the fuel line cannot deliver enough fuel, the engine cannot run at full speed.

That is the bottleneck AI is running into now. AI models are getting bigger. More companies are using AI in the real world. Data centers are being pushed harder. And all of that creates a need for faster, more advanced memory.

That is why Micron’s results matter so much.

The stock surged out of the gates Thursday morning after releasing blowout results for its third quarter in fiscal year 2026. Revenue jumped 73.8% year-over-year to $41.46 billion, while earnings surged a whopping 1,223.1% year-over-year to $28.86 billion, or $25.11 per share.

Wall Street was already expecting a strong quarter. The consensus estimate called for earnings of $20.71 per share on $35.82 billion in revenue. So, Micron posted a 21.2% earnings surprise and a 15.7% revenue surprise.

Micron also issued a stronger-than-expected outlook. For the fourth quarter in fiscal year 2026, the company expects total revenue of about $50 billion and earnings of about $31 per share. That would represent 342% year-over-year revenue growth and 923.1% year-over-year earnings growth.

That tells me this memory boom still has legs.

And management made clear why. The company noted, “Micron’s record fiscal third-quarter financial results and even stronger outlook for the fourth quarter reflect the strategic value of memory in the AI era.”

That last phrase is the key: The strategic value of memory in the AI era.

For years, memory chips were treated like a cyclical commodity business. Important? Yes. Exciting? Not really.

But AI has changed that. Today, memory is becoming one of the most important pressure points in the entire AI buildout. And Micron is standing right in the middle of it.

Is Micron Still Cheap?

Now, I know what some folks are thinking: Can a stock be up this much and still be attractive?

That is a fair question.

For decades, memory was a brutally cyclical business. That’s why, just before announcing earnings, Micron traded at just nine times forward earnings. That is far below Western Digital Corporation (WDC) and Seagate Technology Holdings plc (STX), which both trade at more than 36 times forward earnings.

The bears say that discount makes sense. They argue that memory is still memory, and this cycle will eventually turn.

I understand that argument, but there is a real case that this time is different.

Instead of short bursts of demand tied to PCs and smartphones, Micron is now tied to the ongoing buildout of AI data centers. And those data centers need massive amounts of high-performance memory.

Micron’s long-term supply agreements support that idea. 蜜桃传媒Watch reported that Micron has signed 16 strategic customer agreements, and 14 of them include pricing that represents about $100 billion in cumulative revenue, minimum.

That kind of visibility is something memory companies didn’t always have. So, there is a strong argument that this run may not be over yet.

The Trap Investors Need to Avoid

That said, I have been around long enough to know what happens when a trade gets too crowded.

The more popular a stock becomes, the more crowded it can get. And in today’s market, crowding can happen faster than ever.

That is because millions of investors are now leaning on the same AI tools, the same AI-generated research, the same model portfolios and the same automated trading systems. So, when a stock becomes the obvious AI winner, the crowd can pile in all at once.

That can feel good for a while. It can push a stock higher. It can make everyone feel like they are on the right side of the trade.

But it can also create a dangerous setup.

When retail investors and AI-driven systems rush into the same obvious names, institutional investors often get the liquidity they need to sell into that demand. In other words, the crowd may be buying just as the smart money is quietly moving on.

That is the trap I want to help my readers avoid.

Again, Micron is a great company. I still like it. But the bigger lesson is that by the time a stock becomes obvious to everyone, the elephants of Wall Street may already be looking for the next opportunity.

That is why I do not want to chase the crowd. I want to look for the fresh tracks.

That is what my Precursor Intelligence (P.I.) system is designed to do.

P.I. is my way of looking for fresh tracks in the numbers. It helps me find companies with accelerating fundamentals and improving money flow before they become the obvious names every AI tool is recommending.

In my Accelerated Profits service, we have already seen this approach lead us to several powerful winners in the AI space, including:

• Celestica, Inc. (CLS): – +836%
• Sezzle (SEZL):  +up 625%
• TechnipFMC plc (FTI): +up 254%
• And more…

These are the kinds of gains that can happen when you find the fresh tracks early, before the elephant steps into the clearing.

To further explain how my P.I. system works, I recorded a special presentation. I also discuss why AI-powered crowding could become a serious risk for investors and where I believe the smart money is moving next.

I also reveal several stocks my system is flagging right now.

You can click here to watch it now.

Sincerely,

Louis Navellier

Senior Investment Analyst, InvestorPlace

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Celestica, Inc. (CLS), Micron Technology, Inc. (MU), NVIDIA Corporation (NVDA), Seagate Technology Holdings plc (STX), Sezzle, Inc. (SEZL) and TechnipFMC plc (FTI)

The post The Crowd Found Micron – This Is How to Find the Next One appeared first on InvestorPlace.

In 2025, two professors wanted to see whether ChatGPT made people less creative. And so, they recruited 356 participants and asked them to perform a series of tasks, including one in which they were to make a toy from a paper bag, a brick, and a fan.

The researchers forced some test subjects to use their own creativity. Others were given access to ChatGPT for help.

To no one’s surprise, the cohorts without AI came up with entirely unique ideas. (One suggested adopting the brick as a pet, while another proposed disassembling the fan and turning the parts into nunchucks.)

But those using ChatGPT came up with almost the same toys. Ninety-four percent of their ideas “shared overlapping concepts,” and nine participants independently named their toy the same thing: the “Build-a-Breeze Castle.”

It’s as if AI is turning the entire world into the blandness of 2000s beige home interiors.

Emails start sounding the same…

Movie recommendations are duller…

And everything has that “competent but forgettable” AI sheen.

In a new presentation, legendary quant specialist Louis Navellier says this convergence is also happening on Wall Street. Millions of trading algorithms, advisors, and investors are increasingly relying on the same AI-powered tools.

The danger isn’t that AI is wrong…

It’s that AI causes everyone to do the same thing.

As Louis puts it, this creates crowded trades, concentrated ownership, and the potential for violent reversals when sentiment changes. It helps explain the strange movements in SpaceX (SPCX) over the past several days, and why “groupthink” seems to be taking over markets.

In that new free broadcast, Louis calls this the 50-Million AI Coordination Trap, a phenomenon where investors are all doing identical things without realizing it. Stocks that are popular among AI algorithms keep going up, while everything else seems to go nowhere. It’s becoming increasingly important to know what AI algorithms are recommending.

Now, many investors will dislike the idea of basing their decisions on AI-powered algorithms. I’m certainly uncomfortable with it.

Nevertheless, Louis has created a stock grading system that has long dealt with this issue by balancing “follow-the-money” scores against a company’s real fundamentals. Only companies that pass both earn his top “Buy” ratings.

And so, to illustrate, I’d like to showcase three of his system’s top-rated companies in this update. And if you’d like to learn more (and get access to that system), then click here.

Stock to Buy No. 1: Quality in a Risk-On 蜜桃传媒

Swarm trading (whether driven by AI or humans) can mask a lot of bad behavior.

The venture capital boom of the mid-2010s allowed Theranos to raise almost a billion dollars, and so did truck maker Nikola during the electric vehicle craze of 2021. FTX rode a wave of crypto enthusiasm that same year. The founders of all three companies ended up getting convicted of fraud.

Now, most AI semiconductor companies are not criminal enterprises. They’re making legitimate bets on which technologies will come out ahead. But I guarantee we’ll see some spectacular blowups once AI trading tools decide to start selling the hottest chip companies.

To avoid the risk of accidentally buying frauds or mediocre firms, I’ve purposely favored blue-chip semiconductor companies in this newsletter. And it turns out it’s very possible to buy well-established chipmakers for triple-digit gains. Arm Holdings plc (ARM) (+110%) and Cohu Inc. (COHU) (+120%) are some recent examples.

This week, I’d like to bring you one more company that Louis’ system favors. It’s the bluest of blue-chip semiconductor stocks that should do well long after the current AI rally fades:

Texas Instruments Inc. (TXN).

Texas Instruments is the world’s largest analog chipmaker, specializing in the type of semiconductors that handle messy, real-world signals. These are things like pressure… temperature… cell phone signals… human heart rates… and more. Its chips convert this real-world information into the clean “0’s” and “1’s” that digital chips can then process.

Growth has been solid. In the most recent quarter, the company reported a 19% increase in revenues, driven by a 30% rise from industrial customers and a 90% jump in data center demand. AI servers use huge amounts of electricity, and hundreds of analog sensors per rack are needed to track power usage, heat, and voltages.

Texas Instruments should also benefit long after the AI data center boom ends, thanks to its large exposure to self-driving vehicles, humanoid robots, and other AI-powered robotics.

Louis’ system seems to agree. It recently upgraded TXN to a “B,” and highlights the firm’s strong earnings power and upward analyst revisions to stay invested for the long haul, even as “smart money” jumps in for the short-term AI boost.

Stock to Buy No. 2: A Second Power Play

In March 2025, I highlighted three stocks to buy for the AI Revolution.

“These are firms that learned to harness the often uncontrollable power of AI,” I wrote. “And as the tech world puts their collective foot on the R&D gas, we’re going to see these firms surge ahead.”

The trio have since returned 117% on average. And the best part is that one of these companies is still a “Buy”:

Monolithic Power Systems Inc. (MPWR).

Monolithic is a leader in power management chips for AI devices. These are the tiny semiconductors that use data (often from Texas Instruments) to convert messy electricity flows into the precise voltages that semiconductors need to function.

This is an incredibly important job. In AI data centers, servers often start up all at once, creating voltage dips and spikes. (It’s why turning on a microwave can briefly dim a home’s lights.) And without proper regulation, these power surges can fry any electronic chip connected to the system.

Monolithic’s products help data centers manage this challenge. The Seattle area-based firm pioneered putting multiple power management components onto a single integrated chip (that’s the “monolithic” in the name), and its advanced devices have become the gold standard for high-end AI chips. Monolithic chips are smaller, run cooler, waste less energy, and are more reliable than the patchwork approach that rivals use.

The result is that Monolithic has been growing fast. Revenues increased 26% last year and are on track to notch a 32% gain this year. The company also has been able to take market share of the voltage regulator chip market, thanks to its higher-end designs.

Louis’ system agrees. The company scores a top “A” grade in its quantitative “follow-the-money” score, and valuations remain reasonable, thanks to its rapid earnings growth.

Stock to Buy No. 3: America’s Healthcare Pivot

Finally, I’d like to highlight one decidedly non-AI stock with a lot of “smart money” buyers:

Oncology Institute Inc. (TOI).

This cancer care company has become a potential breakout firm, with strong institutional buying (read: AI-powered investors) and the fundamentals to match.

In short, Oncology Institute runs a network of 146 clinics across five states. Health plans pay TOI a fixed per-member-per-month fee to take on cancer patients, and TOI profits if it provides care below that fee. It was a historically unexciting business that relied on acquisitions and partnerships for growth.

However, TOI now has three potential catalysts.

The first is political.

In late April, Health and Human Services Secretary Robert F. Kennedy Jr. gave testimony to Congress that would have seemed totally out of character a year ago.

“China is now eating our lunch,” a visibly shaken Kennedy said in front of a congressional committee. “They went from running 3% of clinical trials to running 30%… We are losing scientists, we’re losing our IPs… and we’re going to lose our biosecurity.”

The federal government has since pivoted toward a far more accommodating stance to the U.S. healthcare system. Following Kennedy’s testimony, a key Food and Drug Administration committee unanimously recommended its first vaccine since the start of the current Trump administration. (An mRNA vaccine, no less!) Several days later, the Department of Health and Human Services announced Operation TrialBlazer, an ambitious project designed to fast-track clinical research.

This is important because TOI generates most of its profits not from direct cancer care, but rather from the expensive oncology drugs that its patients use. And because reimbursement rates are largely set by the Centers for Medicare & Medicaid Services (CMS), favorable posturing from the federal government is a clearly positive sign for TOI. As awful as it sounds, one of the easiest ways for regulators to spur cancer drug development is to raise what the government is willing to pay for them.

The second is TOI’s shift from negative profits to positive. In May, the company reiterated it expects to flip to positive adjusted EBITDA this year, and upgraded its free cash flow to positive $10 million at its midpoint, up from a previous prediction of a $5 million outflow. That matters because conservative investors often wait for companies to become profitable before buying.

The third is TOI’s high popularity among institutional and “smart money” investors. As mentioned earlier, these traders are beginning to show convergence in their actions. And as shares continue gaining momentum, these AI algorithms usually become more willing to buy a stock, not less. Louis’ system awards TOI a solid “B” for strong institutional buying, rising earnings momentum, and very strong sales growth.

The Human Nature of Artificial Intelligence

It turns out that AI investing carries many of the same investing biases that we humans do. In one 2025 meta-study, a team of European researchers found that large language models:

• Favor U.S. stocks. 93% of portfolios were invested in American stocks.
• Pursue risky allocations. 51% of investments were beyond normal allocations.
• Chase hot stocks. 28% of portfolios were invested in the top three equities that were traded most frequently in the past three months

Ask an AI where to invest today, and it might give some combination of SpaceX, Nvidia Corp. (NVDA), and the latest meme stock.

Professionally designed AI algorithms are often not much better. They’re trained on the same data… use the same machine-learning techniques… and are even created by the same people.

It’s no surprise that momentum has emerged as the single most important factor for predicting stock market returns.

That’s why I think it’s essential for you to watch Louis Navellier’s latest presentation, where he outlines the opportunities and risks of this new convergent market.

The highs are going to be far higher than in the past. Momentum-seeking algorithms will see to that. And that means the lows will also be far more devastating.

If you invest with the crowd, be sure to do so safely.

Click here to learn how.

Until next week,

Thomas Yeung, CFA

蜜桃传媒 Analyst, InvestorPlace

Thomas Yeung is a market analyst and portfolio manager of the Omnia Portfolio, the highest-tier subscription at InvestorPlace. He is the former editor of Tom Yeung’s Profit & Protection, a free e-letter about investing to profit in good times and protecting gains during the bad.

The post 3 Stocks to Buy for the AI Convergence appeared first on InvestorPlace.

Skeptics back East called it overbuilding. The railroad men, they believed, were laying track faster than the country could possibly need it, betting fortunes on demand that hadn’t shown up yet.

Then the final spike went in at Promontory Summit. Within a decade the freight volume crossing that line made the doubters look foolish. The infrastructure came first. The economy that needed it came roaring in right behind.

I bring this up because something almost identical just happened in the AI trade, and if you only watched the headlines this week, you missed it.

The violent selloff that rattled AI stocks over the past couple weeks wasn’t the beginning of the end. It was a buying opportunity dressed up as a crisis, and I can show you exactly why.

A lot of investors are out there right now convinced the AI Boom just cracked. They watched the semiconductor sector drop six, seven, eight percent in a single session and concluded the music had stopped.

Here’s the proof: Micron (MU) reported the biggest beat-and-raise I have ever seen from that company, and the stock jumped 15% after hours. That single print reversed an entire selloff that had been building since Broadcom (AVGO)‘s earnings kicked off this wave of “extremely high expectations meets extremely high expectations” fatigue.

Beat-and-raises hadn’t been enough for the market lately. Micron came to the table and said: here’s a beat-and-raise for you, market, what do you think about that? The market said: bought.

Everybody is screaming “bubble” right now, but the data says this bubble hasn’t even started inflating yet.

In this week’s episode, I’m going to walk you through why the fundamentals never broke, why the technical setup says we’re nowhere near a real top, and the exact screener I’m running to find the highest-torque dip buys in this market right now.

Stick around to the end, because the full breakdown on this week’s Being Exponential goes even deeper:

The Fundamentals Never Moved

So let’s run through what’s actually happening underneath the noise.

SpaceX (SPCX) isn’t just launching rockets anymore. It’s renting out compute to Anthropic, to Google, and now to Reflection AI as well. That tells me SpaceX has probably sold out something like 90% of its existing compute capacity, which means it has to build more.

More Colossus data centers.

More orbital infrastructure.

We’ve spent the past couple years calling this a hyperscaler race among four titans — Microsoft (MSFT), Amazon (AMZN), Meta (META), and Alphabet (GOOGL). Now SpaceX is forcing its way into the picture, and most of the market still hasn’t priced that in.

If SpaceX and Tesla (TSLA) merge, as widely speculated, that’s even more capital flowing straight into the AI infrastructure buildout.

We’re talking hyperscaler spending of $700 billion to $800 billion this year. Add SpaceX into that mix and next year could push past $1 trillion. Run the math out to 2028, 2029, and 2030, and you get estimates climbing toward $1.1 trillion, $1.2 trillion, $1.3 trillion in those out years. That’s not a slowdown. That’s convergence.

Qualcomm (QCOM) just raised its long-term data center revenue target to more than $15 billion by 2029, with $5 billion targeted for 2027. That’s not a one-year story. That’s three straight years of structural growth baked into the guide.

And underneath all of it, there’s a shift happening from generative AI to agentic AI — a shift from training workloads to inferencing workloads, which is a shift from GPU-heavy demand to CPU-heavy demand. That’s exactly why Intel (INTC), Arm Holdings (ARM), and AMD (AMD) have been getting a wave of upgrades and rising estimates lately. The capex isn’t slowing. It’s redistributing.

The Technical Tell

Now here’s where the dot-com comparison actually matters, and where most people get it wrong. I ran an analysis on pure price action: how far the Nasdaq 100 has traded above its 200-day moving average during this AI Boom versus during the dot-com boom of the late 1990s.

From 1995 through most of 1998, tech stocks rallied steadily, trading only 10% to 15% above the 200-day average. It wasn’t until the parabolic phase — late 1998 through early 2000, after the Fed cut rates following the LTCM crisis — that the Nasdaq 100 went vertical, eventually trading more than 50% above trend by March 2000. That’s when the boom became a bust.

Right now, throughout this entire AI Boom, the Nasdaq 100 has averaged roughly 10% above its 200-day moving average. As of today, it’s sitting around 13%. We have never gone vertical. We have never even gotten close to that 50% danger zone.

History tells me every boom enters a go-vertical phase before it busts, which means that phase is still ahead of us, not behind us. The most money in the dot-com era was made in those final, overheated years — 1998, 1999, 2000. I think we’re still working toward that stretch, not past it.

What I’m Watching, and What I’m Buying

To be clear, I’m not waving off every risk.

The K-shaped economy — the gap between Wall Street’s fortunes and Main Street’s — is something to monitor closely, along with politics over the next two years.

But on the encouraging side, inflation is dropping fast, oil has fallen to around $70 a barrel, and the 10-year Treasury yield has eased to 4.4%, which should support borrowing and consumer spending in the months ahead.

So here’s my playbook…

Screen for stocks up more than 50% year to date or more than 100% over the past year. Narrow that list to names that have pulled back 10% to 20% in the past two or three weeks. Then require that they’re still holding above their 200-day moving average. That combination (high momentum, healthy pullback, intact uptrend) is where I’m hunting for high-torque buys right now, because the momentum that was is the momentum that will be.

We’re not in the ninth inning here. We’re in the sixth or seventh. Buy the dip!

P.S. For the full conversation, including more on the SpaceX-Tesla speculation and Luke’s live read of the chart, watch this week’s full episode of Being Exponential. And be sure to subscribe to Being Exponential on X (formerly Twitter) for more exclusive content.

The post Don’t Mistake the Pullback for the Peak: Why This AI Selloff Is a Gift appeared first on InvestorPlace.

There is a common pattern in new technology cycles, and it goes like this: 

The innovation itself appears. Then, a bottleneck emerges. Next, capital floods in to solve the problem. Finally, the regime changes.  

We saw this “regime change,” or complete reorganization of stock market winners and losers, in the in the dot-com bust phase. 

Capital rotated out of the high-profile names and into a variety of other sectors, including base metals, precious metals, energy insurance, and utilities. Those sectors delivered solid double-digit or triple-digit returns over the early part of the 2000s, even while the Amazons, Intels, and Ciscos of the world fell 80% or more. 

Another regime change is happening now.  

Since the early AI revolution, the Magnificent Seven companies have been sat securely on the throne. The group includes Alphabet Inc. (GOOGL), Amazon.com Inc. (AMZN), Apple Inc. (AAPL), Meta Platforms Inc. (META), Microsoft Corp. (MSFT), Nvidia Corp. (NVDA), and Tesla Inc. (TSLA).  

But their seat is soon to be usurped. We are starting to see a rotation out of some of the highest profile, high beta tech stocks and into more real-world, asset-backed sectors. 

In today’s Smart Money, let’s look at a singular, but powerful, example.

Then, I’ll share one of my favorite stocks that is significantly outperforming the Mag 7 so far this year. 

Supplier Over Spender

In the past six months, Corning Inc. (GLW) – a supplier of the data center buildout – is up nearly 140%. On the other hand, Nvidia – a customer of Corning – is only up around 1.4%. 

Investors are rotating away from AI chips and toward AI picks and shovels. Corning is essentially the “glass backbone” of AI data centers, which is why investors are rediscovering it. 

The fiber-optic, hard-asset company is eclipsing the gains of the Wall Street darling with ease. Nvidia would need huge upside surprises to keep rising, while Corning simply needs to show steady AI-driven growth. 

That’s a Mag 7 killer.   

This dynamic will only increase as we move forward.

That means it’s important to own the companies that are providers or suppliers to this massive AI buildout, rather than the companies that spending the money to do build. 

Mag 7 Killer: A Prolific Energy Producer 

One of my favorite Mag 7 killer is one of America’s most prolific energy producers, and it’s firmly positioned to fuel America’s AI buildout. Data center demand for natural gas could become especially acute in the Delaware Basin, with a new “Data Center Alley” potentially blossoming in the region. 

As one of the leading producers in the Delaware Basin, Devon Energy Corp. (DVN) is well-positioned to benefit from structurally improving pricing trends in the region. 

For the last few years, the Delaware Basin has been rapidly boosting its production of both oil and gas. Unfortunately, gas volumes have overtaken pipeline capacity. As a result, much of the gas from the Delaware Basin is “stranded” – the oil and gas industry’s polite way of saying “worthless unless you can move it.” 

Producers flared it. Trucked it. Discounted it to oblivion. As such, the Delaware Basin has behaved for years like a brilliant student stuck in detention. It held enormous potential, but had no way to express it. 

But detention is ending. Two major pipeline projects are improving the economics of the Delaware Basin, especially for Devon. 

At the start of this year, the Wall Street brain trust expected Devon to post adjusted earnings-per-share (EPS) of roughly $3.95 in 2026. Today, those same prognosticators expect the company to earn $5.63 per share this year – a 35% increase.

Therefore, even though Devon shares have advanced 26% this year, the company’s expected earnings have increased even more.

To learn more about all of the Mag 7 killers that I recommend, join me today at Fry’s Investment Report.

Regards,

Eric Fry

The post One Mag 7 Killer Hiding in Plain Sight appeared first on InvestorPlace.

It was late in the fourth quarter. Indianapolis Colts quarterback Payton Manning jogged to the line of scrimmage and crouched behind his center. Eighty thousand fans were screaming. The play clock was winding down.

Most people in the stadium, and folks at home, were staring at the football.

Manning was already looking at something else. He had told receiver Brandon Stokley that if the San Diego Chargers gave him a certain look on defense, he’d give him a signal to change his route.

Before the ball was snapped, he had a game plan. When the Chargers set up as Manning expected, he read the defense and gave Stokley the sign.

A few seconds later, the ball was in the air. The receiver broke free exactly where Manning expected. Touchdown.

The crowd watched the play develop. SportsCenter showed the highlight and talked about the result.

But the play was over long before the ball crossed the goal line. Manning knew it was a touchdown waiting to happen because he recognized the setup that made it possible.

Credit: Philip Hoeppli

That’s the difference between watching the play and understanding the conditions that create the play.

Most investors are watching the play, focusing on what has already happened

They see a stock that’s climbing, or they hear analysts talking about it.

They read glowing headlines. Nowadays, they might even ask ChatGPT or Claude whether it’s a good buy.

But that’s a lot like watching the touchdown replay.

The real question is what happened before the play began. Who was buying before the headlines appeared?

The trick, of course, is getting into the right stocks before the crowd has caught on.

Investing legend Louis Navellier made his reputation by reading the field and acting before the rest of the market. Here is a great example from his Accelerated Profits service with Celestica (CLS).

Celestica started by building computer hardware, but expanded into aerospace, healthcare and renewable energy technology. Today, it plays a key role in manufacturing complex electronics, including components for electric vehicles, cloud computing and AI-driven technology.

Here’s Louis with why he picked this small firm as a future player in the AI Revolution:

Celestica plays a key role in the AI Boom by helping companies design, manufacture and optimize the hardware that powers AI systems like data centers.

The company builds high-performance computing (HPC) infrastructure, as well as products like switches, data storage products, processors and more.

The company also created Photonic Fabric, an optical compute and memory fabric solution that can help boost AI infrastructure. It has the ability to create, scale and sustain future AI models.

Louis recommended this stock to his Accelerated Profits readers in December 2023, when it traded at $27.70. As you can see below, the stock experienced some volatility, but is up more than 800% since the recommendation, and now trades for more than $350.

Better still, even with that gain, because Celestica continues to grow its earnings, CLS remains below Louis’ buy limit, so this stock still has further upside.

How Louis keeps reading the market before the crowds

Louis finds his winners with his proprietary screening tool, Stock Grader. If you’re unfamiliar, Stock Grader ranks more than 5,000 stocks every week for fundamental quality and institutional buying pressure (Louis’ quant score).

Finding quality companies is one thing … but detecting institutional buyers piling into a stock is another. That’s the signal Louis sees that tells him how the market is going to move. While retail investors and new AI systems focus on headlines and watch the play develop, Louis has already seen what is going to happen and gotten his readers into the trade.

The challenge for investors today is that everyone has access to the same information.

Aside from the Wall Street analysts and financial television talking heads, AI systems now have access to the information and can process it in seconds.

But information alone doesn’t create great investments.

The best opportunities often emerge before the story becomes obvious. Even before AI systems begin recommending stocks after scanning market headlines on the Internet.

That’s why Louis recently sat down to explain what he believes is one of the biggest changes ever to hit financial markets: the rise of Agentic AI.

The AI challenge you’re not hearing about

According to Louis, millions of investors are increasingly relying on the same AI systems, the same data sources, and the same recommendations.

And that creates a simple question:

What happens when everyone starts running the same play?

More importantly, how do you read the signals to position yourself before the crowd sees what is happening?

In his new presentation, Louis explains why he believes institutional buying pressure remains one of the most important clues in the market – the setup that tells him how the play is going to break – and how he uses it to identify opportunities long before they become obvious to everyone else.

He also reveals the stocks he believes are most vulnerable as investors increasingly follow AI-generated recommendations, along with several opportunities where he sees institutional money moving today.

Peyton Manning didn’t wait for the touchdown to know the play would work.

He recognized the opportunity before the ball was snapped.

Louis believes investing is entering a similar era where reading the market correctly is critical.

As millions of investors begin relying on the same AI tools and recommendations, the crowd may become increasingly focused on the same stocks at the same time.

The question is whether you’ll be reacting to what everyone else already sees – or identifying opportunities before they become obvious.

If you’d like to see how Louis identifies institutional buying pressure before it becomes obvious to everyone else, I encourage you to watch his new presentation here.

Enjoy your weekend,

Luis Hernandez

Editor in Chief, InvesorPlace

The post The One Question Every Investor Should Ask Before Buying a Stock appeared first on InvestorPlace.

Is this the next blockbuster IPO – or a pure meme stock?

Two weeks ago, Elon Musk had investors frantically debating the answer to that question when his aerospace juggernaut, SpaceX, finally IPO’d 24 years after its founding.

You probably know the headlines already — SpaceX commanding a staggering $2 trillion valuation at IPO, the largest IPO in history. The stock opening north of $160 and then peaking at $225 — or more than 67% above its IPO price.

Musk headlines have been everywhere since: planned vessel launches, Starlink government contracts, and newly gained trillionaire status for the world’s richest man.

I spent plenty of time reading the headlines too. But I was chasing a completely different narrative.

While everyone else was fixated on the scale and the capex, I went straight to the S-1 and dug through SpaceX’s FTC and SEC disclosures — the same kind of research I bring you every single day on Masters in Trading LIVE.

Now, let me be clear: I’m a huge fan of Musk’s companies. And I’m genuinely excited about SpaceX’s mission.

But what I found in the filings was not what the headlines were telling you.

SpaceX IPO Valuation: The Numbers Behind the Narrative

Let’s go back to that eye-popping $2 trillion valuation.

In 2025, SpaceX’s revenue machine kicked into overdrive — right when IPO talk started heating up.

But here’s the spread most people missed: that revenue beat wasn’t coming from rockets. SpaceX generated 61% of its cash that year from Starlink.

And Starlink wasn’t just the biggest piece — it was the only profitable piece. Not only that, but those profits couldn’t completely offset the losses bleeding out of SpaceX’s space and AI divisions.

Musk’s companies tend to run this way — lots of silos, lots of side bets, all feeding off each other. But for a SpaceX investor today, those side bets are actually dead weight.

The AI division — home to X and xAI — posted a $6.4 billion operating loss in 2025. And rather than pare back those investments, spending is only ramping up from here.

So strip away the headline number, factor in the debt and the lack of profitability, and here’s what that valuation actually means to investors: 107 times 2025 sales. That’s what you’re really paying for the stock.

That’s a price that reflects enormous confidence in the company’s future, even as profitability remains a work in progress.

Of course, none of this mattered to retail traders riding the hype. Because they all asked the obvious question: how do I get a piece of this at whatever price the market dictates?

I asked a different one — it’s the same one I’ve been putting to my viewers for months:

Does an IPO like SpaceX actually serve retail traders, or is it built for the most well-capitalized players to make a killing while everyone else holds the bag?

Today, I want to answer that question — and show you where the real opportunities are emerging for traders looking for exposure to the AI megatrend.

So let’s start by answering that question with yet another question traders rarely ever consider.

The IPO Trap

Behind SpaceX’s IPO, there’s a fundamental question the headlines aren’t asking:

Who is actually allowed to sell stock — and when?

Consider this…

Right now, only 639 million SpaceX shares are tradable. That’s a fraction of the more than 13 billion shares outstanding. And almost all of that float is already in the hands of long-term holders — names like Ron Baron of Baron Capital and Cathie Wood of ARK Invest, plus loyal retail investors who got in early through Musk’s other companies.

For everyone else, buying in now isn’t like getting in early on the next Amazon. You’re just buying the scraps left behind by the insiders who got there first.

And here’s what makes it extra infuriating. It’s all part of a hiddendynamic that most traders have no idea about. SpaceX is part of the problem. But there are dozens of companies all aiming for the same massive debut right now – on the same unfair terms.

The Lockup Expiration

That brings us to one handy tool in the IPO playbook that few investors ever consider – the lockup expiration period.

These contracts determine when insiders, employees, venture investors, and early backers can unload shares after an IPO. Typically, the lockup period extends 180 days post-IPO.

But with IPOs like SpaceX, that period is starting to get suspiciously longer. And that’s nothing but bad news for retail investors like us.

Here’s how it actually works. Say we own a company — Masters in Trading, ticker: MIT — and you’re all my employees. We IPO, and I hand each of you a million dollars in stock. Great news, right?

Except you can’t sell it. We’ve got an agreement: no selling for six months. Why? Because I don’t want our IPO crushed on day one. If nobody can sell, the stock has a real shot to rally.

In the hottest IPOs, 85% to 93% of shares stay locked up early like this. That means the real supply event hits months after everyone’s already stopped paying attention.

Now, let’s say our MIT stock finally trades 25% above the IPO price after months on the public market. And that’s the threshold over which I’m allowing you to sell your shares. Congratulations! You can go ahead and sell it. Have some fun.

But how about if it never hits that threshold? Then all you’re left with are shares that are becoming more and more worthless by the trading day that you still can’t sell.

And there’s the trap.

When companies do that, they’re taking advantage of their employees. And they’re also putting retail traders at a massive disadvantage, locking them out of the most valuable part of the IPO timeline.

We’re seeing that now in SpaceX. But it’s the same case with other recent flop IPOs I’ve covered like Figma Inc. (FIG) and Cerebras Systems Inc. (CBRS).

And it’s only getting worse from here.

The $3 trillion-dollar AI IPO pipeline — which includes names like OpenAI, Anthropic, Databricks, and others — may be setting up the exact same dynamics as I write to you.

Same hype. Same limited float. Same lockup mechanics. But potentially much bigger stakes.

So how does a trader get around the headline hype and the unreasonable lock-up periods? Here’s where the Masters in Trading playbook fully comes into play.

What We See in the Latest IPO Pipeline

Here’s the thing about names like SpaceX and Anthropic: their insiders have been positioned long before the public ever got a look. That capital advantage is exactly what prices retail out of the trade.

So here’s some common-sense trading. Don’t buy these IPOs with money you can’t afford to lose chasing someone else’s exit liquidity. Wait for the pullback. Don’t buy at the open — you’ll be underwater immediately.

Watch for the warning signs: small floats, unusual trading restrictions, any change to standard IPO mechanics, performance-based early-release triggers.

Two or three of these together is enough to stay away. Read the filings. And don’t buy the next Cerebras, Figma, or SpaceX at the open.

Now here’s the actual playbook.

Instead of chasing the giant — SpaceX — look at the supply chain underneath it. Look at what rallies alongside Musk’s biggest names. That’s exactly what we do at Masters in Trading. The moment SpaceX’s IPO took off, I actually turned to Tesla Inc. (TSLA)— and a deal flying completely under the radar. Tesla and Sunrun Inc. (RUN) just announced a framework to aggregate more than 16 gigawatts of home-energy capacity, sold straight to hyperscalers and utilities in Virginia.

It’s actually a three-way deal: Sunrun and Tesla are supplying hundreds of thousands of home batteries as dispatchable electrons, with Renew Home layering in 8 million-plus smart thermostats for demand response.

RUN ripped 21% on the news, adding roughly $522 million in market cap.

The catch: it’s a framework, a memorandum of understanding (MOU), not a signed contract. The concrete piece in Virginia is only about 300 megawatts so far. But the re-rate is real — this turns RUN from an installer into an AI-power demand play.

That move dragged short interest into the spotlight — and it lit up on my Unusual Options Activity scanner.

RUN’s setup going in: roughly 29% of float short, 53 to 59 million shares short, a borrow fee near 0.29%. Cheap and available.

That tells you those shorts weren’t trapped — they were just wrong. Now they may be forced to buy back, which only adds fuel to the move.

The unusual options flow gave us the spark while the deal gave us the confirmation.

So we got in with a short call position right as the stock started trading around our strike, then sold off another set of calls against it, converting into a vertical spread — lowering our capital at risk while keeping plenty of upside if RUN kept working higher.

The market is finally catching up to something we’ve been saying for a while: the power grid is one of the biggest bottlenecks in the AI buildout, and the companies that can bring new capacity online fast are the ones worth watching.

That’s exactly where Sunrun fits — right alongside past winners like MP Materials Corp. (MP), The Metals Company Inc. (TMC), and Freeport-McMoRan Inc. (FCX). They’re all sitting at the center of the modern tech arms race – whether that’s AI or aerospace.

I’m finding setups like this every day on Masters in Trading LIVE, 11 a.m. ET. And if you want the next opportunity hiding behind the headlines, that’s exactly what the Masters in Trading Options Challenge is built for.

The Challenge takes everything from my daily LIVEs — fixed risk, thesis-driven exits, laddered entries, defined-duration trades, emotional discipline — and puts it into practice in a structured, step-by-step environment.

Click here to check out what the Masters in Trading Options Challenge has in store for you.

Remember, the creative trader wins.

Jonathan Rose,

Founder, Masters in Trading

The post The $2 Trillion Question Nobody’s Asking About the SpaceX IPO appeared first on InvestorPlace.

But no team gets there by accident.

More than 200 national teams spend years fighting through regional qualifying matches. Every win, loss and draw affects the standings. And those standings decide who gets a shot at the trophy and who gets left watching from home.

Yesterday, Wall Street finished its own version of qualifying.

Through the Russell Reconstitution, thousands of smaller companies get promoted or left behind in the Russell indices. But the real story here isn’t who made the cut.

For some investors, that may not sound like much. But it matters.

Because when a company moves into a major Russell index, mutual funds and exchange-traded funds that track that index may be forced to buy shares. That can send billions of dollars moving through the market at the same time.

But the real story this year is not just which stocks made the cut.

It is what those moves can tell us about where institutional money may be headed next.

So, in today’s 蜜桃传媒 360, I’ll explain how the Russell Reconstitution works, why you should pay attention to it now and how I look for the early signals that may reveal tomorrow’s market leaders.

What Is the Russell Reconstitution?

Every year in June, Russell reviews thousands of publicly traded companies and ranks them by size.

Over several weeks, Russell released updated rankings showing which companies were moving up and which were moving down.

The largest 1,000 companies are then placed in the Russell 1000. The next 2,000 make up the Russell 2000. The final rankings were released yesterday. And beginning Monday, the changes will officially take effect. That’s when it’ll get interesting.

Many mutual funds and exchange-traded funds (ETFs) are designed to track the Russell indices. So, when a company is added, those funds have no choice but to buy shares.

And when billions of dollars move at the same time, the market notices. But the Russell Reconstitution is about more than the buying it can trigger. It’s one of the few times each year when investors get a clear look at which companies are moving up in the ranks. And this year, those rankings matter more than usual.

Why You Should Pay Attention Now

The market is changing faster than before. That’s why, for the first time since 1988, Russell is moving to a semi-annual schedule.

Instead of updating its small-cap indices once a year, it will now do so twice – once in June and again in December.

Here’s why.

According to FTSE Russell, recent market volatility, a widening gap between winning and losing stocks and the growing amount of money tied to its indices highlighted the need for a “more regular and responsive approach” to reconstitution.

In other words, Russell believes investors need a more up-to-date picture of what’s happening with small-cap stocks. The reaction to last year’s reconstitution helps explain why.

When last year’s final list was released, a record $102.5 billion in shares were traded on the NASDAQ, while another $114.7 billion was traded on the New York Stock Exchange.

And in some cases, that surge can have a noticeable impact.

Take Power Solutions International, Inc. (PSIX) for example. I recommended it to my Accelerated Profits subscribers in January last year.

Power Solutions makes engines and power systems used in heavy equipment, commercial vehicles and backup power applications – and increasingly, in data centers.

At the time, it showed a history of posting big earnings surprises and increased analyst estimates. So, I had to make sure my subscribers could get in.

Five months later, it was added to the Russell 3000 during last year’s reconstitution. Once it became official, PSIX climbed 21% in the first week.

That’s a big move in just a week. All told, we ended up making a 121% gain on PSIX by the time we sold it in May this year.

But it also raises an important question.

How do you identify companies like that before everyone else does?

How I Find Tomorrow’s 蜜桃传媒 Leaders

Just as World Cup teams have to qualify for the tournament, companies have to earn their place in the Russell indices.

And I have my own way of deciding which stocks deserve my attention.

I call it my Precursor Intelligence system.

P.I. is my way of finding the companies that are already starting to qualify before Wall Street updates the official standings.

I use it to analyze roughly 6,000 stocks and rank them based on eight fundamental signals and one quantitative money-flow signal.

Those eight fundamental signals help me determine whether a company has the kind of sales growth, earnings growth, cash flow, margins and management efficiency institutions want to own.

The ninth signal helps me detect when institutional buying pressure may already be building.

Without a system like this, a stock like PSIX might not even be on the radar for most investors until after the buying pressure has already shown up.

That is why I recently put together a special report called Four P.I. Trades for 400% Gains.

Inside, I reveal four companies my Precursor Intelligence system is flagging right now. These are stocks where I believe the same kinds of signals that showed up in PSIX may already be forming.

You can get the details here.

Sincerely,

Louis Navellier

Editor, 蜜桃传媒 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Power Solutions International, Inc. (PSIX)

The post How to Spot Small-Cap Winners Before the Crowd appeared first on InvestorPlace.

Most investors are focused on what that means for Micron. My friend and colleague Louis Navellier — who recently recorded a presentation focused on uncovering where the smart money is moving next — is on the hunt for the next great investment opportunities.

Louis has spent five decades finding where institutional money moves before the rest of the market catches on. Today he explains why Micron’s blowout quarter is actually a signal about the next bottleneck in AI — and how his system is already tracking the names that could benefit before Wall Street figures it out.

Read on for all the details.

In 1909, Theodore Roosevelt left the White House and set out for East Africa.

He was not going there as a tourist.

Roosevelt, his son Kermit and a team of naturalists were traveling on behalf of the Smithsonian Institution. Much of the journey came down to one difficult task:

Tracking elephants.

In the thick African brush, you don’t just wait for an elephant to step into view. By then, it might already be too late.

You had to look for signs: Fresh tracks in the mud. Broken branches. Disturbed grass. A path through the brush that told you something enormous had passed through before you ever saw it.

That is how I think about stocks.

I am not interested in waiting until the whole world can see the elephant. By then, Wall Street has usually figured out the story. The headlines are everywhere. The crowd has shown up. And a lot of the easy money has already been made.

That brings me to Micron Technology, Inc. (MU).

Micron is no longer hiding in the brush. The stock is up 325% year-to-date and 853% over the past year. It became a $1 trillion market cap company last month. And after this week’s blowout earnings report, it is quickly becoming one of Wall Street’s favorite AI stocks.

That did not happen by accident.

It happened because Micron is helping solve one of the biggest problems in artificial intelligence today: The memory bottleneck.

So, in today’s 蜜桃传媒 360, we’ll dig into Micron’s blowout quarter, discuss why it matters and then talk about how my system is already helping me find winners from the next phase of the AI boom before the crowd catches on.

Micron Crushed Wall Street’s Expectations. Here’s What the Numbers Actually Mean.

For the past few years, NVIDIA Corporation (NVDA) has been the grand finale of earnings season. But now, I believe Micron has taken that role.

Here’s why.

NVIDIA tells us how strong demand is for GPUs, the chips that power today’s AI systems. But Micron tells us whether those systems can get the memory they need to keep running at full speed.

Micron is one of the world’s largest makers of memory and storage chips. In plain English, its chips help computers and data centers store information, access it quickly and move it where it needs to go.

That may not sound as exciting as a cutting-edge GPU. But without memory, those GPUs cannot do their job.

Think of it like this: A GPU is the engine in a race car. Memory is the fuel line. You can build the most powerful engine in the world. But if the fuel line cannot deliver enough fuel, the engine cannot run at full speed.

That is the bottleneck AI is running into now. AI models are getting bigger. More companies are using AI in the real world. Data centers are being pushed harder. And all of that creates a need for faster, more advanced memory.

That is why Micron’s results matter so much.

The stock surged out of the gates Thursday morning after releasing blowout results for its third quarter in fiscal year 2026. Revenue jumped 73.8% year-over-year to $41.46 billion, while earnings surged a whopping 1,223.1% year-over-year to $28.86 billion, or $25.11 per share.

Wall Street was already expecting a strong quarter. The consensus estimate called for earnings of $20.71 per share on $35.82 billion in revenue. So, Micron posted a 21.2% earnings surprise and a 15.7% revenue surprise.

Micron also issued a stronger-than-expected outlook. For the fourth quarter in fiscal year 2026, the company expects total revenue of about $50 billion and earnings of about $31 per share. That would represent 342% year-over-year revenue growth and 923.1% year-over-year earnings growth.

That tells me this memory boom still has legs.

And management made clear why. The company noted, “Micron’s record fiscal third-quarter financial results and even stronger outlook for the fourth quarter reflect the strategic value of memory in the AI era.”

That last phrase is the key: The strategic value of memory in the AI era.

For years, memory chips were treated like a cyclical commodity business. Important? Yes. Exciting? Not really.

But AI has changed that. Today, memory is becoming one of the most important pressure points in the entire AI buildout. And Micron is standing right in the middle of it.

Is Micron Stock Still Worth Buying After a 325% Run?

Now, I know what some folks are thinking: Can a stock be up this much and still be attractive?

That is a fair question.

For decades, memory was a brutally cyclical business. That’s why, just before announcing earnings, Micron traded at just nine times forward earnings. That is far below Western Digital Corporation (WDC) and Seagate Technology Holdings plc (STX), which both trade at more than 36 times forward earnings.

The bears say that discount makes sense. They argue that memory is still memory, and this cycle will eventually turn.

I understand that argument, but there is a real case that this time is different.

Instead of short bursts of demand tied to PCs and smartphones, Micron is now tied to the ongoing buildout of AI data centers. And those data centers need massive amounts of high-performance memory.

Micron’s long-term supply agreements support that idea. 蜜桃传媒Watch reported that Micron has signed 16 strategic customer agreements, and 14 of them include pricing that represents about $100 billion in cumulative revenue, minimum.

That kind of visibility is something memory companies didn’t always have. So, there is a strong argument that this run may not be over yet.

The AI Crowding Trap — and Why Micron’s Popularity Is the Warning Sign

That said, I have been around long enough to know what happens when a trade gets too crowded.

The more popular a stock becomes, the more crowded it can get. And in today’s market, crowding can happen faster than ever.

That is because millions of investors are now leaning on the same AI tools, the same AI-generated research, the same model portfolios and the same automated trading systems. So, when a stock becomes the obvious AI winner, the crowd can pile in all at once.

That can feel good for a while. It can push a stock higher. It can make everyone feel like they are on the right side of the trade.

But it can also create a dangerous setup.

When retail investors and AI-driven systems rush into the same obvious names, institutional investors often get the liquidity they need to sell into that demand. In other words, the crowd may be buying just as the smart money is quietly moving on.

That is the trap I want to help my readers avoid.

Again, Micron is a great company. I still like it. But the bigger lesson is that by the time a stock becomes obvious to everyone, the elephants of Wall Street may already be looking for the next opportunity.

That is why I do not want to chase the crowd. I want to look for the fresh tracks.

That is what my Precursor Intelligence (P.I.) system is designed to do.

P.I. is my way of looking for fresh tracks in the numbers. It helps me find companies with accelerating fundamentals and improving money flow before they become the obvious names every AI tool is recommending.

In my Accelerated Profits service, we have already seen this approach lead us to several powerful winners in the AI space, including:

• Celestica, Inc. (CLS): +836%
• Sezzle (SEZL): +625%
• TechnipFMC plc (FTI): +254%
• And more…

These are the kinds of gains that can happen when you find the fresh tracks early, before the elephant steps into the clearing.

To further explain how my P.I. system works, I recorded a special presentation. I also discuss why AI-powered crowding could become a serious risk for investors and where I believe the smart money is moving next.

I also reveal several stocks my system is flagging right now.

You can click here to watch it now.

The post The Hidden Risk Building Inside the Most Popular AI Stocks Right Now appeared first on InvestorPlace.

That’s the lesson legendary investor Louis Navellier wants investors to take away from today’s Friday Digest takeover.

Using Micron (MU) as an example, Louis explains why AI’s latest bottleneck has created enormous winners – but also why the next opportunity may already be taking shape somewhere else. His focus isn’t on chasing yesterday’s headlines. It’s on identifying where institutional money is quietly flowing before the crowd catches on.

That’s the idea behind Louis’ Precursor Intelligence system, which he designed to identify where institutional money is flowing before a stock becomes an obvious AI favorite.

Louis dives deeper into this approach in a free presentation, where he discusses the next phase of the AI boom and the stocks his system is flagging today. You can watch it right here.

If history is any guide, the biggest AI winners of tomorrow probably won’t be the stocks everyone is talking about today.

I’ll let Louis take it from here.

Have a good evening,

Jeff Remsburg

In 1909, Theodore Roosevelt left the White House and set out for East Africa.

He was not going there as a tourist.

Roosevelt, his son Kermit and a team of naturalists were traveling on behalf of the Smithsonian Institution. Much of the journey came down to one difficult task:

Tracking elephants.

In the thick African brush, you don’t just wait for an elephant to step into view. By then, it might already be too late.

You had to look for signs: Fresh tracks in the mud. Broken branches. Disturbed grass. A path through the brush that told you something enormous had passed through before you ever saw it.

That is how I think about stocks.

I am not interested in waiting until the whole world can see the elephant. By then, Wall Street has usually figured out the story. The headlines are everywhere. The crowd has shown up. And a lot of the easy money has already been made.

That brings me to Micron Technology, Inc. (MU).

Micron is no longer hiding in the brush. The stock is up 325% year-to-date and 853% over the past year. It became a $1 trillion market cap company last month. And after this week’s blowout earnings report, it is quickly becoming one of Wall Street’s favorite AI stocks.

That did not happen by accident.

It happened because Micron is helping solve one of the biggest problems in artificial intelligence today: The memory bottleneck.

So today, we’ll dig into Micron’s blowout quarter, discuss why it matters and then talk about how my system is already helping me find winners from the next phase of the AI boom before the crowd catches on.

Micron Crushed Wall Street’s Expectations

For the past few years, NVIDIA Corporation (NVDA) has been the grand finale of earnings season. But now, I believe Micron has taken that role.

Here’s why.

NVIDIA tells us how strong demand is for GPUs, the chips that power today’s AI systems. But Micron tells us whether those systems can get the memory they need to keep running at full speed.

Micron is one of the world’s largest makers of memory and storage chips. In plain English, its chips help computers and data centers store information, access it quickly and move it where it needs to go.

That may not sound as exciting as a cutting-edge GPU. But without memory, those GPUs cannot do their job.

Think of it like this: A GPU is the engine in a race car. Memory is the fuel line. You can build the most powerful engine in the world. But if the fuel line cannot deliver enough fuel, the engine cannot run at full speed.

That is the bottleneck AI is running into now. AI models are getting bigger. More companies are using AI in the real world. Data centers are being pushed harder. And all of that creates a need for faster, more advanced memory.

That is why Micron’s results matter so much.

The stock surged out of the gates Thursday morning after releasing blowout results for its third quarter in fiscal year 2026. Revenue jumped 73.8% year-over-year to $41.46 billion, while earnings surged a whopping 1,223.1% year-over-year to $28.86 billion, or $25.11 per share.

Wall Street was already expecting a strong quarter. The consensus estimate called for earnings of $20.71 per share on $35.82 billion in revenue. So, Micron posted a 21.2% earnings surprise and a 15.7% revenue surprise.

Micron also issued a stronger-than-expected outlook. For the fourth quarter in fiscal year 2026, the company expects total revenue of about $50 billion and earnings of about $31 per share. That would represent 342% year-over-year revenue growth and 923.1% year-over-year earnings growth.

That tells me this memory boom still has legs.

And management made clear why. The company noted, “Micron’s record fiscal third-quarter financial results and even stronger outlook for the fourth quarter reflect the strategic value of memory in the AI era.”

That last phrase is the key: The strategic value of memory in the AI era.

For years, memory chips were treated like a cyclical commodity business. Important? Yes. Exciting? Not really.

But AI has changed that. Today, memory is becoming one of the most important pressure points in the entire AI buildout. And Micron is standing right in the middle of it.

Is Micron Still Cheap?

Now, I know what some folks are thinking: Can a stock be up this much and still be attractive?

That is a fair question.

For decades, memory was a brutally cyclical business. That’s why, just before to announcing earnings, Micron traded at just nine times forward earnings. That is far below Western Digital Corporation (WDC) and Seagate Technology Holdings plc (STX), which both trade at more than 36 times forward earnings.

The bears say that discount makes sense. They argue that memory is still memory, and this cycle will eventually turn.

I understand that argument, but there is a real case that this time is different.

Instead of short bursts of demand tied to PCs and smartphones, Micron is now tied to the ongoing buildout of AI data centers. And those data centers need massive amounts of high-performance memory.

Micron’s long-term supply agreements support that idea. 蜜桃传媒Watch reported that Micron has signed 16 strategic customer agreements, and 14 of them include pricing that represents about $100 billion in cumulative revenue, minimum.

That kind of visibility is something memory companies didn’t always have. So, there is a strong argument that this run may not be over yet.

The Trap Investors Need to Avoid

That said, I have been around long enough to know what happens when a trade gets too crowded.

The more popular a stock becomes, the more crowded it can get. And in today’s market, crowding can happen faster than ever.

That is because millions of investors are now leaning on the same AI tools, the same AI-generated research, the same model portfolios and the same automated trading systems. So, when a stock becomes the obvious AI winner, the crowd can pile in all at once.

That can feel good for a while. It can push a stock higher. It can make everyone feel like they are on the right side of the trade.

But it can also create a dangerous setup.

When retail investors and AI-driven systems rush into the same obvious names, institutional investors often get the liquidity they need to sell into that demand. In other words, the crowd may be buying just as the smart money is quietly moving on.

That is the trap I want to help my readers avoid.

Again, Micron is a great company. I still like it. But the bigger lesson is that by the time a stock becomes obvious to everyone, the elephants of Wall Street may already be looking for the next opportunity.

That is why I do not want to chase the crowd. I want to look for the fresh tracks.

That is what my Precursor Intelligence (P.I.) system is designed to do.

P.I. is my way of looking for fresh tracks in the numbers. It helps me find companies with accelerating fundamentals and improving money flow before they become the obvious names every AI tool is recommending.

In my Accelerated Profits service, we have already seen this approach lead us to several powerful winners in the AI space, including:

• Celestica, Inc. (CLS): – +836%
• Sezzle (SEZL):  +up 625%
• TechnipFMC plc (FTI): +up 254%
• And more…

These are the kinds of gains that can happen when you find the fresh tracks early, before the elephant steps into the clearing.

To further explain how my P.I. system works, I recorded a special presentation. I also discuss why AI-powered crowding could become a serious risk for investors and where I believe the smart money is moving next.

I also reveal several stocks my system is flagging right now.

You can click here to watch it now.

Sincerely,

Louis Navellier

Senior Investment Analyst, InvestorPlace

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Celestica, Inc. (CLS), Micron Technology, Inc. (MU), NVIDIA Corporation (NVDA), Seagate Technology Holdings plc (STX), Sezzle, Inc. (SEZL) and TechnipFMC plc (FTI)

Jeff Remsburg also owns MU.

The post Micron Was Yesterday’s Win — Here’s How to Find Tomorrow’s appeared first on InvestorPlace.

He was not going there as a tourist.

Roosevelt, his son Kermit and a team of naturalists were traveling on behalf of the Smithsonian Institution. Much of the journey came down to one difficult task:

Tracking elephants.

In the thick African brush, you don’t just wait for an elephant to step into view. By then, it might already be too late.

You had to look for signs: Fresh tracks in the mud. Broken branches. Disturbed grass. A path through the brush that told you something enormous had passed through before you ever saw it.

That is how I think about stocks.

I am not interested in waiting until the whole world can see the elephant. By then, Wall Street has usually figured out the story. The headlines are everywhere. The crowd has shown up. And a lot of the easy money has already been made.

That brings me to Micron Technology, Inc. (MU).

Micron is no longer hiding in the brush. The stock is up 325% year-to-date and 853% over the past year. It became a $1 trillion market cap company last month. And after this week’s blowout earnings report, it is quickly becoming one of Wall Street’s favorite AI stocks.

That did not happen by accident.

It happened because Micron is helping solve one of the biggest problems in artificial intelligence today: The memory bottleneck.

So, in today’s 蜜桃传媒 360, we’ll dig into Micron’s blowout quarter, discuss why it matters and then talk about how my system is already helping me find winners from the next phase of the AI boom before the crowd catches on.

Micron Crushed Wall Street’s Expectations

For the past few years, NVIDIA Corporation (NVDA) has been the grand finale of earnings season. But now, I believe Micron has taken that role.

Here’s why.

NVIDIA tells us how strong demand is for GPUs, the chips that power today’s AI systems. But Micron tells us whether those systems can get the memory they need to keep running at full speed.

Micron is one of the world’s largest makers of memory and storage chips. In plain English, its chips help computers and data centers store information, access it quickly and move it where it needs to go.

That may not sound as exciting as a cutting-edge GPU. But without memory, those GPUs cannot do their job.

Think of it like this: A GPU is the engine in a race car. Memory is the fuel line. You can build the most powerful engine in the world. But if the fuel line cannot deliver enough fuel, the engine cannot run at full speed.

That is the bottleneck AI is running into now. AI models are getting bigger. More companies are using AI in the real world. Data centers are being pushed harder. And all of that creates a need for faster, more advanced memory.

That is why Micron’s results matter so much.

The stock surged out of the gates Thursday morning after releasing blowout results for its third quarter in fiscal year 2026. Revenue jumped 73.8% year-over-year to $41.46 billion, while earnings surged a whopping 1,223.1% year-over-year to $28.86 billion, or $25.11 per share.

Wall Street was already expecting a strong quarter. The consensus estimate called for earnings of $20.71 per share on $35.82 billion in revenue. So, Micron posted a 21.2% earnings surprise and a 15.7% revenue surprise.

Micron also issued a stronger-than-expected outlook. For the fourth quarter in fiscal year 2026, the company expects total revenue of about $50 billion and earnings of about $31 per share. That would represent 342% year-over-year revenue growth and 923.1% year-over-year earnings growth.

That tells me this memory boom still has legs.

And management made clear why. The company noted, “Micron’s record fiscal third-quarter financial results and even stronger outlook for the fourth quarter reflect the strategic value of memory in the AI era.”

That last phrase is the key: The strategic value of memory in the AI era.

For years, memory chips were treated like a cyclical commodity business. Important? Yes. Exciting? Not really.

But AI has changed that. Today, memory is becoming one of the most important pressure points in the entire AI buildout. And Micron is standing right in the middle of it.

Is Micron Still Cheap?

Now, I know what some folks are thinking: Can a stock be up this much and still be attractive?

That is a fair question.

For decades, memory was a brutally cyclical business. That’s why, just before to announcing earnings, Micron traded at just nine times forward earnings. That is far below Western Digital Corporation (WDC) and Seagate Technology Holdings plc (STX), which both trade at more than 36 times forward earnings.

The bears say that discount makes sense. They argue that memory is still memory, and this cycle will eventually turn.

I understand that argument, but there is a real case that this time is different.

Instead of short bursts of demand tied to PCs and smartphones, Micron is now tied to the ongoing buildout of AI data centers. And those data centers need massive amounts of high-performance memory.

Micron’s long-term supply agreements support that idea. 蜜桃传媒Watch reported that Micron has signed 16 strategic customer agreements, and 14 of them include pricing that represents about $100 billion in cumulative revenue, minimum.

That kind of visibility is something memory companies didn’t always have. So, there is a strong argument that this run may not be over yet.

The Trap Investors Need to Avoid

That said, I have been around long enough to know what happens when a trade gets too crowded.

The more popular a stock becomes, the more crowded it can get. And in today’s market, crowding can happen faster than ever.

That is because millions of investors are now leaning on the same AI tools, the same AI-generated research, the same model portfolios and the same automated trading systems. So, when a stock becomes the obvious AI winner, the crowd can pile in all at once.

That can feel good for a while. It can push a stock higher. It can make everyone feel like they are on the right side of the trade.

But it can also create a dangerous setup.

When retail investors and AI-driven systems rush into the same obvious names, institutional investors often get the liquidity they need to sell into that demand. In other words, the crowd may be buying just as the smart money is quietly moving on.

That is the trap I want to help my readers avoid.

Again, Micron is a great company. I still like it. But the bigger lesson is that by the time a stock becomes obvious to everyone, the elephants of Wall Street may already be looking for the next opportunity.

That is why I do not want to chase the crowd. I want to look for the fresh tracks.

That is what my Precursor Intelligence (P.I.) system is designed to do.

P.I. is my way of looking for fresh tracks in the numbers. It helps me find companies with accelerating fundamentals and improving money flow before they become the obvious names every AI tool is recommending.

In my Accelerated Profits service, we have already seen this approach lead us to several powerful winners in the AI space, including:

• Celestica, Inc. (CLS): +up 836%
• Sezzle, Inc. (SEZL):  +up 625%
• TechnipFMC plc (FTI): +up 254%
• And more…

These are the kinds of gains that can happen when you find the fresh tracks early, before the elephant steps into the clearing.

To further explain how my P.I. system works, I recorded a special presentation. I also discuss why AI-powered crowding could become a serious risk for investors and where I believe the smart money is moving next.

I also reveal several stocks my system is flagging right now.

You can click here to watch it now.

Sincerely,

Louis Navellier

Editor, 蜜桃传媒 360

The Editor hereby discloses that as of the date of this email, the Editor, directly or indirectly, owns the following securities that are the subject of the commentary, analysis, opinions, advice, or recommendations in, or which are otherwise mentioned in, the essay set forth below:

Celestica, Inc. (CLS), Micron Technology, Inc. (MU), NVIDIA Corporation (NVDA), Seagate Technology Holdings plc (STX), Sezzle, Inc. (SEZL) and TechnipFMC plc (FTI)

The post Missed Out on Micron? My System Can Find the Next Big AI Winner appeared first on InvestorPlace.

There is a short list of technologies that governments have decided are too important to lose.

Nuclear. Semiconductors. Satellites. GPS. The internet itself.

AI just made the list.

Anthropic abruptly disabled its newest frontier models — Claude Fable 5 and Mythos 5 — after the U.S. government ordered it to suspend foreign-national access on national-security grounds. 

As the headlines ran, investors debated whether it was bearish for AI.

But in our view, it’s the single most bullish macro signal for AI infrastructure we’ve seen all year. 

Here’s why.

From Consumer Tool to Strategic Asset: The Regime Change Many Are Misreading 

For the past several years, Washington has treated frontier AI the same way it treated cloud computing, smartphones, or social media: as transformative technology that deserves attention, maybe some guardrails, but nothing approaching this level of control.

The federal government’s ‘cease and desist’ to Anthropic signals a shift of epic proportions. 

By shutting down access on explicit grounds of national security, Washington is saying that AI models are no longer consumer productivity tools. They’re now strategic assets whose access, deployment, and security matter to national power.

That is a regime change. And regime changes of that magnitude almost always have large, durable consequences for capital flows.

The Manhattan Project of Sovereign AI — and Why the Analogy Is Not Hyperbole

In 1942, when the U.S. government decided that atomic weapons were a national-security imperative, it built an industrial pipeline to ensure it succeeded — from uranium mining to enrichment to delivery systems — at a scale and speed that had never been attempted in peacetime.

We are watching the early stages of something structurally analogous.

The difference is that the ‘Manhattan Project’ of sovereign AI requires not one centralized government program but an entire ecosystem: domestic semiconductor fabs, secure data center campuses, high-bandwidth networking, stable power grids, and model development labs operating under strict security protocols.

The U.S. has signaled it is serious about building that ecosystem — through CHIPS Act funding, export controls on advanced semiconductors, and now direct national-security intervention in frontier model access. 

Japan became the first international partner in the U.S.’ Genesis Mission, committing $500 million alongside a matching $500 million from the U.S. Department of Energy — a combined $1 billion over five years to advance AI science, next-generation computing, and autonomous laboratory systems through joint teams spanning 12 DOE National Laboratories and 12 leading Japanese research institutions. 

Saudi Arabia’s Project Transcendence is deploying $100 billion toward AI infrastructure, model development, and data centers. 

The UAE has launched G42 as its sovereign AI vehicle, with Abu Dhabi committing billions to domestic compute capacity. 

And China has been quietly building sovereign AI infrastructure for years — ChangXin Memory Technologies scaling domestic HBM production, Huawei developing its own GPU stack, and state-directed capital flowing into data center construction at a pace that rivals the hyperscalers. 

Every one of those commitments reinforces the others. Sovereign AI is now a race — and races don’t have off switches. 

How National Security Classification Sets a Permanent Floor Under AI Infrastructure Spending

Once a technology is classified as critical to national security, the political cost of underfunding it becomes unacceptably high. That means capital will flow regardless of economic cycles, earnings misses, or Fed policy. 

The most sophisticated private capital in the world started repositioning around this thesis before Washington made it official. Where it went will make more sense once you see what’s underneath it.

Because the entire AI infrastructure stack sits directly in the path of that spending.

• Secure compute: Foreign-access restrictions mean domestic, sovereign, security-hardened data centers become a requirement, not a preference. Hyperscaler buildout just got a policy tailwind.
• Chips and memory: If frontier models are strategic assets, the chips that run them are, too. Domestic semiconductor production, Nvidia (NVDA) allocations, high-bandwidth memory supply — all become matters of national priority. That’s structurally bullish for firms like NVDA, Broadcom (AVGO), Micron (MU), and Sandisk (SNDK).
• Networking and optics: AI infrastructure communicates, constantly, at scales that dwarf anything the internet was originally designed to handle. All of it runs across physical fiber, switches, and optical transceivers. Arista Networks (ANET), Ciena (CIEN), and Corning (GLW) are direct beneficiaries.
• Power and cooling: Sovereign AI clusters run continuously, consume extraordinary amounts of power, and generate heat that requires industrial-scale cooling systems. That demand grows with every new model generation — bullish for GE Vernova (GEV), Vertiv (VRT), and Eaton (ETN).
• Cybersecurity: If AI models are now in the same category as military hardware, then the security perimeter around them will be built to military-grade standards. Companies like CrowdStrike (CRWD), Palo Alto Networks (PANW), and Fortinet (FTNT) should thrive as a result.

Together, these trades form a single investment thesis: own the infrastructure layer of a technology that governments have decided they cannot afford to lose. 

The Sovereign AI Race Is Self-Reinforcing: What That Means for the Infrastructure Stack

National-security-motivated government intervention in AI is what transforms this trade from a growth theme into a permanent spending priority. 

It’s the thing that puts a floor under capex cycles that would otherwise be subject to earnings pressure, credit tightening, or executive hesitation.

Once this dynamic is established, it becomes self-reinforcing: each country’s build accelerates the others’, which requires more chips, power, networking, and security.

That’s a flywheel.

Core AI infrastructure names — like NVDA, AVGO, ANET, and VRT — are precisely the companies that benefit most when AI infrastructure becomes a sovereign imperative rather than an enterprise discretionary.

We are watching closely for:

• New government AI infrastructure contracts and sovereign AI fund announcements
• Allied-nation buildout cadence
• Accelerated domestic fab investment, particularly anything related to secure, export-controlled advanced packaging and HBM production
• Security hardware specs for AI data centers — when DoD and allied governments start publishing requirements for secure AI infrastructure, those spec sheets will be a roadmap for which companies win.

There’s one more thing worth watching: the private capital already spinning this flywheel from the inside… 

We’ve analyzed Peter Thiel’s last 13F — zero Nvidia, zero Apple, zero Microsoft, zero Tesla. 

Not trimmed. Out entirely. 

His private fund went into the physical layer of the AI economy — energy infrastructure, nuclear power, and the hard assets that make sovereign AI possible. Most of those positions aren’t accessible to retail investors. But there are seven publicly traded stocks that mirror those same bets almost exactly. That’s the Billionaire’s Backdoor — and sovereign AI just made it more relevant than ever. 

The Anthropic suspension was a declaration that AI matters too much to leave unguarded. And it’s the kind of macro shift that, if you’re positioned correctly, makes careers.

The post The Bearish AI Headline That’s Actually the Most Bullish Signal of the Year appeared first on InvestorPlace.

As I write on Thursday, Micron (MU) is up 14% after the memory chip giant reported blowout earnings yesterday after the closing bell.

The numbers were staggering…

Revenue hit $41.5 billion last quarter, up from $9.3 billion a year ago. Earnings came in at $25.11 per share adjusted, crushing the $20.28 Wall Street was expecting. Gross margins jumped to 84.9% – more than double where they were 12 months ago.

Then came the guidance…

Micron told investors to expect roughly $50 billion in revenue next quarter. Analysts had penciled in $43.6 billion. That’s not a beat. That’s a different zip code.

The story behind the numbers is straightforward: AI is eating memory chips faster than anyone can make them. CEO Sanjay Mehrotra was blunt on the analyst call – he can’t identify when the memory shortage will end:

Even as we expect industry supply to improve gradually in 2028, we currently do not have line of sight as to when memory supply will be able to catch up with increasing demand.

We expect tight conditions to persist beyond calendar 2027 as a result of AI-driven demand across all segments coupled with structural supply constraints.

To lock in this demand, Micron announced 16 long-term supply agreements with data center operators and other major customers, each covering three to five years. When complete, roughly half or more of Micron’s revenue will be committed under these deals.

That’s a fundamental shift for a business that has historically been hostage to boom-and-bust memory cycles.

Bottom line: The AI boom is alive and well, and despite some healthy profit taking today in tech, MU’s numbers bode well for the AI bull.

Meanwhile, the May PCE report dropped this morning

It showed headline inflation came in at 4.1% year-over-year, up from April’s 3.8% and the highest reading since April 2023.

Meanwhile, Core PCE – which strips out food and energy – came in at 3.4% year over year, slightly hotter than April’s 3.3%.

Cue the financial media commentary about Fed Chair Warsh remaining hawkish and rate hikes – not cuts – as the most likely next move.

Perhaps. But that reaction misses something important – the real number that Warsh is watching, which also dropped today…

And when you understand it, you’ll have a cleaner read on Fed policy than most investors and talking heads.

Warsh doesn’t primarily look at the same inflation numbers everyone else is watching

During his Senate confirmation hearing in April, he said that the Fed’s standard inflation gauge – the one that CNBC and others are splashing across their homepage this morning – is only a “rough swag” of actual price pressures.

“Swag,” he clarified, stands for “scientific wild guess.”

Instead, his preferred measure is something called the “trimmed mean PCE,” published monthly by the Federal Reserve Bank of Dallas.

The concept is worth a brief digression, because it will reframe how you read every inflation report going forward.

The standard PCE takes every item consumers buy – gasoline, groceries, streaming subscriptions, hospital visits – and averages all their price changes. That means a one-time spike in oil (say, from a Middle East conflict) gets baked directly into the headline reading, even if the rest of the economy is pricing normally.

Now, Core PCE does a better job of addressing this. It strips out food and energy prices.

But there’s a problem with Core too – it still absorbs every other price spike those energy costs trigger downstream.

While it overlooks spiking oil prices, it will count, for example, the skyrocketing airfares that airlines charge to recoup their soaring jet fuel costs. Because airfare is classified as a service, the energy shock sneaks into Core PCE through the back door – and gets mistaken for broad-based inflation.

Another example: Core PCE strips out “food purchased for consumption at home” (groceries). However, it explicitly includes “food services and accommodations” (dining out) as a service.

So, say you have an avian flu that pushes egg prices through the ceiling. That inflation could still show up when you dine out, even though it’s not expressed in Core PCE.

The solution – the inflation measure that Warsh prefers – is the “trimmed mean PCE,” which takes a different approach…

It lines up all those individual price changes from lowest to highest, lops off the most extreme readings on both ends of the spectrum – the temporary outliers, up or down – and then averages what’s left in the middle.

For Warsh, the result is a cleaner picture of the inflation that’s actually embedded in the economy, rather than the noise generated by geopolitical shocks.

To get a sense for the difference this can make, let’s rewind to last month.

April’s Core PCE ran at 3.3% year-over-year. However, the Dallas Fed’s trimmed mean for the same month was just 2.35%. That’s roughly one percentage point lower – sitting just above the Fed’s 2% target.

That’s a huge difference.

So, the issue coming into today that investors should have been watching wasn’t the headline or Core PCE numbers alone – it was the trimmed mean figure, and whether the spread between it and Core PCE would narrow, widen, or remain intact.

We got the answer…

What today’s trimmed mean numbers told us

A few hours after the PCE data dropped, the Dallas Fed published the May trimmed mean figure. The yearly number came in at 2.42% – just a hair above April’s yearly 2.35% figure.

Recognize what this means…

With Core PCE clocking in at 3.4% this morning, the gap between it and the trimmed mean (2.42%) remained at roughly a full percentage point.

This supports Warsh’s belief that stripping out extreme price outliers reveals an underlying inflation trend that approaches the Fed’s 2% target – and remains somewhat stable.

On that last note, here’s the yearly trimmed mean figures over the last six months:

• Dec: 2.4
• Jan: 2.4
• Feb: 2.3
• Mar: 2.4
• Apr: 2.3
• May: 2.4

While the hawkish camp will point to this morning’s one-month annualized ticking up to 2.8% as evidence of fresh short-term heat, the broader 12-month baseline proves the structural trend remains firmly anchored.

Bottom line: This was a win on the inflation front. And going forward, keep your eyes on the trimmed mean – that’s the number Warsh is actually watching.

Are we in only the 3^rd inning of this AI boom?

Beyond writing the Digest, I also helm InvestorPlace’s Investing Insider newsletter where I feature interviews with our expert analysts, profile the research of major Wall Street investment shops, and highlight the most lucrative investment trends in the market.

In tomorrow’s issue, I sit down with legendary investor Louis Navellier, who makes a bold claim…

The AI boom may only be in its third inning.

He points to a chart from the research shop Bespoke that overlays the Nasdaq’s trajectory after the ChatGPT launch against the same window following Netscape’s release in 1994.

As you’ll see below, the blue line is the Nasdaq’s performance in the five years after the release of Netscape, overlaid against the red line – the years after ChatGPT’s launch in late 2022.

Though the data only runs through spring 2025, the Nasdaq hasn’t gone down since then. If anything, the red line has continued to climb – meaning the comparison is likely still tracking.

Now, bears might read this and think, “No, we’re already in the 9^th inning, on the verge of an AI bubble pop that will be every bit as brutal as the dot-com pop.”

Perhaps. But make sure to factor in one key difference…

A chart from Alpine Macro shows that today’s AI boom has something the dot-com era didn’t have…

Real earnings growth, not just multiple expansion.

In the dot-com boom, P/E ratios went to the moon while profits barely budged. Today, earnings per share are compounding while multiples have stayed relatively flat. That’s a structurally different – and arguably more durable – setup.

See for yourself…

The pane on the left shows today’s AI boom with rising forward earnings and a largely flat forward P/E. The pane on the right shows the dot-com forward P/E soaring (then crashing) as forward earnings remained largely flat.

Source: Alpine Macro / Bloomberg Finance

One wrinkle here deserves a closer look, though

There are legitimate questions about whether some of today’s AI-based earnings growth is as clean as it appears.

A growing chorus of analysts is flagging a capex recycling loop: hyperscalers like Google and Microsoft are booking profits while funneling massive capex into AI startups that, in turn, consume those companies’ cloud and AI services.

For example, A cloud giant makes a multi-billion-dollar “equity investment” into an AI pioneer like OpenAI. That pioneer uses its newly acquired cash or cloud credits to train and run models.

The hyperscaler then recognizes those exact credits as “fresh, organic commercial cloud revenue” on its public income statements, padding its bottom-line growth and boosting its stock price.

Financial filings show that just two unprofitable startups – OpenAI and Anthropic – anchor over half of the roughly $2 trillion in future cloud backlogs held by Microsoft, Amazon, Google, and Oracle.

It’s not a smoking gun, but it’s a real risk that we’ll be tracking closely in the Digest over the coming quarters/years.

To watch the full interview with Louis, you can join us in Investing Insider right here.

And keep your eye out for tomorrow, when Louis will release his latest research package. It highlights a methodology for tracking where institutional money is moving before the rest of the market catches on.

We’ll keep you updated on all these stories here in the Digest.

Have a good evening,

Jeff Remsburg

The post The Inflation Number Warsh Is Actually Watching appeared first on InvestorPlace.

A single defect in a semiconductor can cost as much as $25,000, and Joe explains why AI is becoming the only practical way to catch those problems before they become expensive mistakes.

It’s another reminder that some of AI’s biggest opportunities may not come from the companies building the technology. They may come from the businesses using it to become more efficient and more profitable.

That’s one reason Marc Chaikin of Chaikin Analytics recently introduced his new AI-Powered Time Machine. It’s designed to help investors look beyond today’s biggest AI stocks and uncover the next generation of potential winners.

Yesterday, Marc and Joe unveiled the AI-Powered Time Machine for the first time. If you missed the presentation, the replay is still available here.

Now, here’s Joe with a closer look at how AI is transforming manufacturing one factory at a time…

California Steel Industries’ Hot Strip Mill in Fontana stretches more than half a mile long.

Inside, giant ovens heat steel slabs to about 2,300 degrees Fahrenheit. At that temperature, the steel gets soft enough to roll.

But first, it needs cleaning. The furnace leaves a thick crust of “scale” on the surface. If it isn’t removed, it gets pressed into the steel and ruins the finish. A scalebreaker cracks the crust loose. Then high-pressure water jets blast it away.

Next, the steel slab passes through five roughing stands that squeeze it down from between 7 and 9 inches thick to as little as 0.0538 inches — close to the thickness of a credit card. A crop shear trims the ragged ends before the steel moves to finishing. Then six more finishing stands roll it to its final thickness and surface quality.

By this point, the steel is moving at about 35 miles per hour.

That’s too fast to catch defects by eye. For automotive panels and appliances, the surface has to be flawless — defects show right through the paint.

The finished strip winds into a coil. Some weigh up to 25 tons. The whole process takes about five hours. At full capacity, the mill runs 24 hours a day and produces 2 million tons of steel per year.

But at least you can see steel.

In today’s most advanced semiconductor fabrication plants, the defects that matter are invisible to the human eye.

And the consequences of missing them are just as severe.

A Single Semiconductor Defect Can Cost $25,000

In semiconductor manufacturing, everything starts with a wafer — a thin, polished disc sliced from pure silicon, usually about 12 inches across. These wafers must be flawless. Even a microscopic scratch or contaminant can create defects across hundreds of chips.

The first step is circuit printing using extreme ultraviolet lithography. This process projects circuit patterns using light with a wavelength shorter than any visible color. A single finished chip can require 20 to 30 passes through this stage alone.

The specialized masks used in this process — a kind of three-dimensional stencil — have to be perfect, too. A single defect ruins every chip that mask touches. And those masks can cost up to $1 million each.

After each pass, the wafer goes through etching, deposition, and chemical treatment to build up transistor layers. Then the cycle repeats. Today’s most complex chips go through 1,500 to 2,000 individual steps before they become functional. Each step is a potential failure point. One particle of dust can ruin an entire wafer.

A single wafer for the most advanced semiconductors can cost between $20,000 and $25,000. Each wafer holds hundreds of chips. A defective one wipes out hundreds of products at once. And the fabs where all this happens cost between $15 billion and $20 billion to build.

Fabs need to reduce these losses wherever possible. And human inspectors simply can’t do the job. At 35 miles per hour, steel moves too fast to see. In a semiconductor fab, the defects are too small to see. In both cases, the stakes are too high to miss anything.

AI Is Boosting Quality Control

This is one area where AI doesn’t just help. It’s the only solution that actually works.

AI “deep learning” and “edge learning” take defect control to a level humans can’t match. Deep learning works by analyzing hundreds of example images until the system learns to make decisions on its own — no programmer required at each step.

Edge learning goes further. These systems come pretrained and may need as few as five to 10 images to get started. They deploy in minutes.

The results are measurable.

At BMW, AI-powered vision systems cut defect rates by 30% at one European plant within a year. Customer satisfaction jumped 15% after the rollout. At Foxconn, AI-powered cameras now catch defects with 98% accuracy, flag 80% fewer false alarms, and inspect each unit 60% faster than before.

These aren’t pilot programs. They’re production systems running at scale, in some of the most demanding manufacturing environments on Earth.

This is what I mean when I say the real AI story isn’t the one getting the most attention.

Everyone is watching the big infrastructure names — the chip companies, the cloud providers, the chatbot platforms. And yes, those are important. But there’s a parallel story playing out on the factory floor, in the oil field, and in the semiconductor fab.

AI is solving problems that weren’t solvable before. And the companies delivering those solutions are becoming more competitive, more profitable, and more valuable — quietly, without much fanfare.

That’s exactly the kind of opportunity I’ve spent my career looking for.

Finding the Next Generation of Winners

The challenge, of course, is identifying which companies are actually winning — not just claiming to use AI, but using it in ways that show up in the fundamentals.

That’s a problem Marc Chaikin has been working on his entire career. His Power Gauge rating system was built to cut through the noise and find stocks with real momentum behind them. It’s been doing that for decades.

Marc and I are going a step further. We unveiled the first AI-powered product Chaikin Analytics has ever built — and it’s unlike anything we’ve shown the public before.

We’re calling it the Time Machine. It scans decades of market history to find stocks today whose fundamental and technical fingerprints match the early profiles of stocks like Nvidia Corp. (NVDA), Amazon.com Inc. (AMZN), and Meta Platforms Inc. (META) — right before they made their biggest moves. In backtesting, it surfaced stocks that went on to deliver gains of 995%, 1,406%, and 3,804%, all while the “seed” stocks they were matched against posted far more modest returns.

The factory-floor AI story is one example of the kinds of opportunities the Time Machine is designed to surface. Companies solving real industrial problems with AI — before the market catches on.

This is the first time we’ve ever made something like this available to individual investors.

Click here to view the replay.

Good investing,

Joe Austin

Senior Analyst, Chaikin Analytics

P.S. Most investors are focused on the AI names everyone already knows. Joe is focused on the ones most people haven’t found yet — the companies using AI to solve problems in places like the factory floor and the oil field, before Wall Street fully catches on. He and Marc Chaikin debuted the first AI-powered tool Chaikin Analytics has ever built to help find exactly those stocks. Here’s the link again to view the replay.

The post The AI Winning the Most Money Isn’t the One You’re Thinking Of appeared first on InvestorPlace.

In today’s piece, Joe uses an unforgettable moment from NASCAR to explain why the most overlooked AI stocks may be the most valuable ones — and why the “behind the scenes” companies making AI physically possible deserve a serious look.

Joe recently partnered with 60-year Wall Street legend Marc Chaikin to debut a first-of-its-kind AI-powered tool. It’s the first AI-powered product Chaikin Analytics has ever built, designed specifically to find stocks like the ones Joe describes below, before Wall Street catches on.

Just yesterday, Joe and Marc held a special presentation detailing the tool. You can catch a replay of that here.

Now, here’s Joe…

Denny Hamlin was ready to clinch his first NASCAR championship. But it all fell apart because of a piece of tape.

Hamlin was considered one of the best drivers of his generation, but the title had always eluded him.

In 2019, he was NASCAR’s comeback kid. He’d already won six times that season — including the iconic Daytona 500. The championship all came down to a cool November night at the Homestead-Miami Speedway.

The day started well. Qualifying had been canceled due to bad weather, which gave Hamlin the pole position based on his points standing. In the first two stages, he finished fifth. By the third stage, things were looking up — he was running second, posting quicker lap times than his teammate Kyle Busch, who led the race.

Then, with 58 laps to go, crew chief Chris Gabehart made a fateful call. He brought Hamlin in for an early pit stop and had the crew slap a big piece of black tape on the front grille.

The idea was sound, in theory. In NASCAR, crews routinely tape grilles to gain a competitive edge. Normally, air enters the grille and bounces around the engine, creating drag. Tape restricts that airflow, forcing it to flow smoothly over the car instead. More speed, more downforce, better grip.

But tape also restricts cooling.

NASCAR engines run at around 290 degrees Fahrenheit — about 90 degrees hotter than a typical road car. The margin for error is razor-thin. In Hamlin’s case, the tape backfired immediately. Temperature gauges maxed out. Steam started spewing from the engine. Engine failure seemed imminent.

Gabehart had to call Hamlin back to the pit after just 12 laps. He finished 10th — dead last among the four championship contenders. Kyle Busch won both the race and the title.

Excessive heat isn’t just a NASCAR problem. Data centers running AI chips face the exact same dilemma.

And for investors, it represents one of the most overlooked opportunities in the entire AI boom.

Finding the “Behind the Scenes” Companies

AI chips need massive amounts of power to train models and run computations. More power means more heat. And if you can’t cool the chips fast enough, performance crashes, or the hardware fails entirely.

Inside a data center are long rows of computer racks — tall cabinets stacked with servers. A typical AI data center contains hundreds or even thousands of them. Nvidia Corp.’s (NVDA) next-generation Vera Rubin chip uses 120 to 130 kilowatts per rack. That’s the annual electricity consumption of about 100 U.S. homes — per rack.

Bigger versions of the Rubin chip will use five times that much power.

That creates an unavoidable physics problem. More power means more heat, in a nearly one-to-one relationship. And delivering this much electricity requires completely rethinking how power gets moved through a building.

Think of it like water through a hose. You can deliver the same volume using high pressure through a small hose or low pressure through a massive one. Electrical power works the same way — high voltage with low current, or low voltage with high current. High current dangerously overheats cables. Traditional power systems can’t handle it.

Engineers solved this by raising the voltage. The industry has shifted to 800-volt power systems, which deliver the same power with far less current and far less heat.

But operating at 800 volts requires power chips made from entirely different materials. Materials that only a handful of companies know how to produce.

This is the bigger point about investing in a megatrend like AI.

The AI technology itself gets all the media attention. Nvidia, Microsoft Corp. (MSFT), Palantir Technologies Inc. (PLTR): Everyone knows those names.

But an entirely different set of companies — the ones making the components, materials, and systems that let AI physically function — are just as essential. And far less picked over.

No matter which big-name AI company is building the next data center, the “behind the scenes” businesses making it all run are going to get paid. The question is whether you own any of them.

A Tool Built for Exactly This

I’ve spent 40 years on Wall Street learning to look one step behind the obvious story. The internet boom made millionaires out of people who bought Cisco Systems Inc. (CSCO) and Intel Corp. (INTC), not just Amazon.com Inc. (AMZN) and eBay Inc. (EBAY). The shale revolution enriched investors in fracking equipment and pipeline infrastructure, not just oil producers.

The AI boom is setting up the same way. And the challenge — as always — is finding the right “behind the scenes” stocks before the crowd does.

That’s exactly what Marc Chaikin and I built the Time Machine to do.

The Time Machine is Chaikin Analytics’ first-ever AI-powered platform, and we unveiled it for the first time yesterday during a special free broadcast (you can watch a replay here). It works by scanning decades of market history to find stocks today whose fundamental and technical fingerprints match the early profiles of proven multi-bagger winners — stocks like Nvidia, Amazon, and Meta Platforms Inc. (META), right before their biggest moves.

In backtesting, it surfaced stocks that went on to deliver gains of 995%, 1,406%, and 3,804% — all while the “seed” stocks they were matched against posted far more modest returns.

The picks-and-shovels AI companies — the power chip makers, the cooling system specialists, the infrastructure suppliers — are exactly the kind of stocks the Time Machine is designed to surface.

This is the first time Chaikin Analytics has ever offered an AI-powered product.

Click here to watch our newly released special broadcast and learn more about Time Machine.

Good investing,

Joe Austin

Senior Analyst, Chaikin Analytics

The post NASCAR Lost a Championship Over Heat, but Investors Can Benefit appeared first on InvestorPlace.

Editor’s Note: The best technology investments I’ve ever seen have one thing in common: they weren’t obvious at the time.

Joe Austin has spent 40 years developing a nose for exactly those opportunities — the infrastructure companies, the component makers, the “picks and shovels” plays that make a megatrend physically possible and get overlooked while everyone chases the headline names.

Today he makes the case that AI has a whole layer of those companies hiding in plain sight. He opens with a NASCAR story that I bet you’ll still be thinking about by the time you get to his investment thesis.

He and Marc Chaikin shared the full picture earlier this week during a special free broadcast. Click here to watch the replay.

Here’s Joe…

Denny Hamlin was ready to clinch his first NASCAR championship. But it all fell apart because of a piece of tape.

Hamlin was considered one of the best drivers of his generation, but the title had always eluded him.

In 2019, he was NASCAR’s comeback kid. He’d already won six times that season — including the iconic Daytona 500. The championship all came down to a cool November night at the Homestead-Miami Speedway.

The day started well. Qualifying had been canceled due to bad weather, which gave Hamlin the pole position based on his points standing. In the first two stages, he finished fifth. By the third stage, things were looking up — he was running second, posting quicker lap times than his teammate Kyle Busch, who led the race.

Then, with 58 laps to go, crew chief Chris Gabehart made a fateful call. He brought Hamlin in for an early pit stop and had the crew slap a big piece of black tape on the front grille.

The idea was sound, in theory. In NASCAR, crews routinely tape grilles to gain a competitive edge. Normally, air enters the grille and bounces around the engine, creating drag. Tape restricts that airflow, forcing it to flow smoothly over the car instead. More speed, more downforce, better grip.

But tape also restricts cooling.

NASCAR engines run at around 290 degrees Fahrenheit — about 90 degrees hotter than a typical road car. The margin for error is razor-thin. In Hamlin’s case, the tape backfired immediately. Temperature gauges maxed out. Steam started spewing from the engine. Engine failure seemed imminent.

Gabehart had to call Hamlin back to the pit after just 12 laps. He finished 10th — dead last among the four championship contenders. Kyle Busch won both the race and the title.

Excessive heat isn’t just a NASCAR problem. Data centers running AI chips face the exact same dilemma. 

And for investors, it represents one of the most overlooked opportunities in the entire AI boom.

The Physics Problem That Makes ‘Behind the Scenes’ Companies Essential 

AI chips need massive amounts of power to train models and run computations. More power means more heat. And if you can’t cool the chips fast enough, performance crashes, or the hardware fails entirely.

Inside a data center are long rows of computer racks — tall cabinets stacked with servers. A typical AI data center contains hundreds or even thousands of them. Nvidia Corp.’s (NVDA) next-generation Vera Rubin chip uses 120 to 130 kilowatts per rack. That’s the annual electricity consumption of about 100 U.S. homes — per rack.

Bigger versions of the Rubin chip will use five times that much power.

That creates an unavoidable physics problem. More power means more heat, in a nearly one-to-one relationship. And delivering this much electricity requires completely rethinking how power gets moved through a building.

Think of it like water through a hose. You can deliver the same volume using high pressure through a small hose or low pressure through a massive one. Electrical power works the same way — high voltage with low current, or low voltage with high current. High current dangerously overheats cables. Traditional power systems can’t handle it.

Engineers solved this by raising the voltage. The industry has shifted to 800-volt power systems, which deliver the same power with far less current and far less heat.

But operating at 800 volts requires power chips made from entirely different materials. Materials that only a handful of companies know how to produce.

This is the bigger point about investing in a megatrend like AI.

The AI technology itself gets all the media attention. Nvidia, Microsoft Corp. (MSFT), Palantir Technologies Inc. (PLTR): Everyone knows those names. 

But an entirely different set of companies — the ones making the components, materials, and systems that let AI physically function — are just as essential. And far less picked over.

No matter which big-name AI company is building the next data center, the “behind the scenes” businesses making it all run are going to get paid. The question is whether you own any of them.

A New Tool Built to Find the Next Generation of Winners 

I’ve spent 40 years on Wall Street learning to look one step behind the obvious story. The internet boom made millionaires out of people who bought Cisco Systems Inc. (CSCO) and Intel Corp. (INTC), not just Amazon.com Inc. (AMZN) and eBay Inc. (EBAY). The shale revolution enriched investors in fracking equipment and pipeline infrastructure, not just oil producers.

The AI boom is setting up the same way. And the challenge — as always — is finding the right “behind the scenes” stocks before the crowd does.

That’s exactly what Marc Chaikin and I built the Time Machine to do.

The Time Machine is Chaikin Analytics’ first-ever AI-powered platform, and we just unveiled it for the first time during a special free broadcast. It works by scanning decades of market history to find stocks today whose fundamental and technical fingerprints match the early profiles of proven multi-bagger winners — stocks like Nvidia, Amazon, and Meta Platforms Inc. (META), right before their biggest moves.

In backtesting, it surfaced stocks that went on to deliver gains of 995%, 1,406%, and 3,804% — all while the “seed” stocks they were matched against posted far more modest returns.

The picks-and-shovels AI companies — the power chip makers, the cooling system specialists, the infrastructure suppliers — are exactly the kind of stocks the Time Machine is designed to surface.

Enter any ticker and see how it stacks up against the greatest stock market winners in history. 

Charter membership spots are limited. Click here to check out the Time Machine while you can.

The post Nvidia Has a Heat Problem. These Stocks Get Paid to Solve It. appeared first on InvestorPlace.

Before we dig in today, how did Micron (MU) do?

By the time you read this, the memory giant will have reported earnings – they dropped shortly after today’s closing bell.

Micron is one of the clearest signals we have on real-world AI infrastructure demand. A strong print could hand the AI trade a fresh tailwind heading into tomorrow’s open. A miss could give the bears something new to work with.

Regardless, we’ll have the full recap for you tomorrow. For now, go check the numbers – they’ll give you a sense for tomorrow’s market.

Meanwhile, there’s no shortage of headline noise right now.

Inflation data, Fed uncertainty, geopolitical tremors – pick your distraction. But somewhere inside all that noise, many stocks are climbing for real, fundamental reasons. Companies with genuine tailwinds, catalysts, and expert conviction behind them.

Today, let’s make that our focus. No macro deep-dive, no Fed speculation, no handwringing about market overhangs. Just three stocks our analysts like – and why. All for one goal…

Put a few more bucks in your pocket.

Bloom Energy: the stock that just hit a record high for a very good reason

Last Thursday, Bloom Energy Corp. (BE) surged 15% to a then-record high of $328.91. The rally continued into the next trading session on Monday, lifting the stock an additional 5% to a new all-time high of $345.85.

Here’s what happened – and why it matters looking forward…

The Federal Energy Regulatory Commission (FERC) voted to order the nation’s largest electric grid operators to dramatically accelerate the time it takes major power users to connect to the grid.

Under the new directives, grid operators have 30 days to report available capacity and 60 days to submit formal plans to reform their hookup rules – all aimed at shrinking the data center connection timeline from years to roughly 90 days. FERC chair Laura Swett called the decision “historic.”

For Bloom Energy, this is more than a headline. It validates the entire bet the company has been making on the AI data center market.

Our senior analyst Brian Hunt, who writes the free daily newsletter Money & Megatrends, has been building the bull case for AI power consumption stocks for some time.

Here’s Brian to explain the bigger picture:

Given AI’s enormous promise, large tech firms such as Google, Amazon, Microsoft, OpenAI, Oracle and Meta have invested over $1 trillion in specialized semiconductors, data centers and other AI infrastructure components.

They are on pace to invest around $700 billion this year alone and more than $3 trillion after that. Both the scale and the velocity of this investment boom are unprecedented. It is the largest collective investment effort of all time.

All that AI infrastructure is poised to consume huge amounts of electricity. Goldman Sachs forecasts global data center power demand will climb 50% by 2027 and as much as 165% by the end of the decade.

The new rule helps power users connect faster, but it does not instantly create more electricity. The power grid is already strained.

What makes Bloom particularly well-positioned for this is its “bring your own power” thesis. The company’s solid oxide fuel cells – known as “Bloom Boxes” – enable data center operators to generate on-site electricity rather than wait years for a grid connection.

The FERC ruling now validates that approach at a regulatory level – and even as traditional hookups get faster, data centers running on Bloom’s tech can bypass the queue entirely.

Brian notes that the financial results confirm the demand…

Bloom posted Q1 revenue of $751 million – a 130% year-over-year increase – and has raised its full-year guidance to between $3.4 billion and $3.8 billion. Project backlog has climbed to $20 billion.

Here’s Brian with the overall investment logic:

When you invest in the industries and companies that supply the growing AI industry with electric power, you are not risking your money by trying to pick the company that creates the best AI model…

Instead, you’re making the safe bet that every company and every individual using AI ends up buying some electricity to power it.

No matter who builds the best AI applications… every AI company and every AI user must buy some electricity.

For more of Brian’s insights, he sends out a free issue of Money & Megatrends every day the market is open. They’re filled with actionable insights and specific stock tickers. To join him, just click here.

Royalty Pharma: a quieter way into the AI healthcare boom

Not every way to play AI is a chip stock.

Our global macro expert, Eric Fry, editor of Fry’s Investment Report, recently identified three stocks he considers “Forever Stocks for the AI Age” – companies with durable business models that AI tailwinds are quietly strengthening.

One of the most interesting to me is Royalty Pharma Plc (RPRX). The name doesn’t scream “AI play,” but that’s part of what makes it worth a look.

Here’s Eric:

The biotech sector, in particular, is offering a compelling and timely opportunity for the “Age of AI.” But investing in this high-risk sector can be a confusing and challenging endeavor.

A unique company named Royalty Pharma Plc (RPRX) removes some of the risk and confusion from the equation.

As its name implies, the company manages a portfolio of royalties on both approved and development-stage drugs.

Eric points out that, since going public in 2020, the company has acquired royalties on over 35 commercial products and 17 development-stage candidates. And the company’s royalty-based business model generates exceptionally high profit margins.

So, the investment logic here is straightforward…

As AI compresses the timeline for drug discovery and expands the number of viable candidates in development, a royalty portfolio becomes a broader, lower-risk way to benefit from that acceleration – without having to pick which drug candidate wins.

Royalty Pharma holds royalties on drugs from cancer to rare diseases, acting as a biotech “picks and shovels” play by funding the wider industry rather than gambling on single trials.

If you want more investment ideas from Eric that go beyond today’s obvious chip plays, his “Sell This, Buy That” broadcast gives away seven free trades – including a lesser-known alternative to Nvidia – that he believes could double your money in the next 12 to 24 months. You can watch it here for free.

Jonathan Rose, QURE, and what’s next on his catalyst board

Last Wednesday was a good morning for traders who follow our trading expert, Jonathan Rose.

A stock he’d flagged – uniQure NV (QURE) – opened around $43, touched $48.88 intraday and closed in the high $47s.

Its previous close? $26.99.

So, the move clocked in as an 81% gain, overnight.

Last week, Jonathan wrote about the trade, noting that the catalyst was a long time coming…

QURE is developing AMT-130, a gene therapy for Huntington’s disease. This is a devastating, inherited neurodegenerative condition with zero approved disease-modifying treatments anywhere in the world.

On June 17, the FDA reversed its previous course, confirming that three years of clinical data from the Phase I/II study would be acceptable as the basis for an accelerated approval filing. The company plans to submit the relevant paperwork in Q3 of this year.

Jonathan, editor of Masters in Trading, had been watching this one for eight days before the market noticed.

Here he is explaining:

On June 9th, during my Masters in Trading Live broadcast, with QURE sitting around $26, I flagged a trade.

A multi-million dollar options order hit the tape across the October 33 and 43 strikes. No guesses. No subjective chart patterns. Real, undeniable trades with institutional size and conviction, the kind of orders that don’t come from somebody “trying their luck.”

So, I did my due diligence: I checked the skew. And the skew was screaming. Calls roughly $10 above the stock were trading at a steep premium to puts the same distance below.

Translation? Somebody with serious capital believed this stock was going higher, and they were willing to pay up to express it.

QURE is now yesterday’s trade. But Jonathan’s catalyst board has five more names on it.

One that looks especially promising to me right now is Ionis Pharmaceuticals Inc. (IONS). This was Jonathan’s favorite when he developed this catalyst board.

The FDA has a “Prescription Drug User Fee Act” (PDUFA) decision date of June 30 – just six days from now – on Ionis’ drug olezarsen. A PDUFA means the FDA is about to announce its verdict (approval, rejection, or request for more data) on a pharmaceutical company’s new drug or biologic application.

Olezarsen treats severe hypertriglyceridemia, a condition that leaves roughly three million Americans vulnerable to acute pancreatitis attacks with essentially no effective treatment options.

If all goes well, Ionis’ CEO has said the company is prepared to launch “end of June, early July.”

Now, there’s one wrinkle to watch as you consider this stock. Back to Jonathan:

I’d be doing you a disservice if I didn’t flag something… insiders have been net sellers to the tune of roughly $57.8 million over the last three months.

This doesn’t kill the thesis. It does mean I’m watching the tape a little closer.

That’s the kind of nuance – seeing both the bull case and the risk – that separates traders who last from traders who blow up.

Jonathan is teaching the same framework he used on QURE inside his Masters in Trading Challenge course – how to spot unusual options activity, structure defined-risk trades, and separate meaningful signals from market noise. If you’d like to join, you can learn more about the Masters in Trading Challenge here.

So, there you have it – three potential money-makers: a longer-term AI energy play, a ‘forever’ stock built to anchor a portfolio for decades, and a shorter-term speculative biotech with a catalyst six days away.

We’ll keep tracking all three of these as the stories develop.

Have a good evening,

Jeff Remsburg

The post A New All-Time High, an 81% Overnight Gain, and One “Forever Stock” appeared first on InvestorPlace.

When investors get excited, that excitement rarely stays contained to a single corner of the market.

Money flows into one hot asset, early investors make fortunes, and suddenly everyone starts looking for the next big winner. Before long, the optimism spreads. Stocks rise, speculative bets multiply, and assets that seemed unrelated begin moving higher together.

That’s one reason I pay attention to Bitcoin (BTC-USD). Over the years, the cryptocurrency has served as a surprisingly useful barometer of investor enthusiasm. When speculative money starts flooding into Bitcoin, it often signals that investors are becoming more willing to take risks elsewhere as well.

The problem is that investors are not always rational.

The fear of missing out (FOMO) can be more powerful than the fear of losing money, and a boom in one corner of the market often sparks rallies in others. As economist Charles P. Kindleberger wrote in his classic 1978 book Manias, Panics, and Crashes, “There is nothing so disturbing to one’s well-being and judgment as to see a friend get rich.”

We saw exactly that dynamic play out in 2024. Bitcoin doubled in value, eventually climbing above $122,000 before suffering a rather inglorious selloff in late 2025. But the story wasn’t just about Bitcoin. The surge in risk-taking helped fuel rallies across commodities, biotech, AI, and other speculative corners of the market.

Now, that pattern appears to be repeating itself.

We’re seeing a new cohort of AI companies – from SpaceX Technologies Inc. (SPCX) to Smartbird Inc. (BIRD), formerly Allbirds – steal headlines.

So in today’s Smart Money, let’s look at how today’s AI boom is fueling a new wave of speculative enthusiasm across the market – and what that could mean for stocks in the months ahead.

Then, I’ll share one stock to help you start organizing your portfolio safely and profitably.

Let’s jump in…

This Rally Still Has Room to Run – but Not Forever

First, there’s a good chance this bull market lasts through the end of the year.

Full-year S&P 500 earnings growth is expected to hit 23.3%, and U.S. macroeconomic data are holding up well. America is vastly outperforming other developed economies on GDP growth, unemployment, and AI innovation. All else equal, that kind of earnings growth provides room for stock prices to move 20% to 25% higher as well.

We also know from history that American markets typically keep going up for a year after reaching a fresh record high. If historical averages hold, we’re looking at a bull market that should last until at least late September – one year since the S&P 500 “started the timer” by hitting a new peak.

However, I don’t think investors should approach this rally with the same optimism they had in 2024. FOMO rallies typically center on momentum plays, and the most popular of these are now too expensive to buy safely.

One of the clearest examples of that is the dynamic random-access memory (DRAM) market, which is historically dominated by three firms:

• Micron Technology Inc. (MU)
• SK Hynix Inc. (KRX: 0006600)
• Samsung Electronics Co. Ltd. (KRX: 005930).

The trio is now worth over $1 trillion each thanks to AI-driven demand for DRAM, and all three trade below 20X forward price-to-earnings (P/E) ratios because their “E” (earnings) denominator is so inflated.

However, the valuations of the “Big 3” assume that DRAM prices will remain in the stratosphere. It’s the same kind of optimism that drove memory stocks up 400%-plus in the years around 1995, 2000, and 2014.

Each time, investors thought industry cyclicality was a thing of the past. And each time, they were dead wrong.

Is this time different?

Well, maybe. Artificial intelligence requires far more computing power than anything before it, and the AI infrastructure buildout should last through at least 2031.

Then again, unusually high profits tend to attract new competitors.

We’re already seeing Chinese state-backed behemoths like ChangXin Memory Technologies (CXMT) and Yangtze Memory Technologies (YMTC) steal market share from Micron, SK Hynix, and Samsung. It’s not hard to imagine CXMT and YMTC becoming like the Chinese solar-panel makers that drove American manufacturers out of business between 2012 and 2017.

Bottom line: This bull market may have further to run, but many of the market’s most popular momentum trades no longer offer an attractive risk-reward balance.

That’s why Eric is becoming more selective. at his Fry’s Investment Report service.

Here’s how he’s doing that…

FOMO Is Played Out – Here’s What to Buy Instead

The 2024 FOMO rally provided plenty of investment opportunities. Companies like Alphabet Inc. (GOOGL) and Amazon.com Inc. (AMZN) still had fundamental upside thanks to the 2022 “Year of Efficiency” selloff, and commodity markets were still depressed from the Chinese housing market crash.

This time around, investors need to look further afield for cheap momentum.

That’s why I want to bring your attention to Coupang Inc. (CPNG), a Fry’s Investment Report holding that Eric considers a “Strong Buy.” Unlike many AI infrastructure plays, Coupang still combines reasonable valuations with solid earnings growth.

This South Korean e-commerce firm has both cheapness and momentum on its side. Net revenue is growing, with first-quarter earnings reporting an 8% increase year-over-year.

Also at Fry’s Investment Report, Eric is recommending a Chinese electronics recycling company and an American battery utility. Both have momentum behind them and are relatively cheap. The former trades at just 7.5X forward earnings, despite growing at 25% per year and carrying no debt. The latter is riding a wave of battery storage demand and still trades below its 2021 IPO price despite a 345% rally last year.

That said, there will still be profits to take as this bull market runs its course.

Because in a FOMO rally, the smartest investors aren’t the ones who ride it the longest; they’re the ones who know when “too much money chasing too few assets” has finally run out of road.

To learn more about how Eric is avoiding this ticking time bomb – and a tandem company to sell after adding Coupang to your portfolio – click here.

Until next time,

Thomas Yeung, CFA

蜜桃传媒 Analyst, InvestorPlace

The post The FOMO Bull 蜜桃传媒 Is Back – Here’s How to Invest Before It Blows Up appeared first on InvestorPlace.

He walks through the open kitchen, admires the granite, nods at the staged furniture, and then heads straight for the crawl space. The reality is always in the joists, the wiring, the bones that the stagers miss for the open house.

A house can look like a million bucks and still be rotting underneath. And a house with peeling wallpaper and a sagging porch can have a foundation poured to outlast the next three owners.

Wall Street, we’ve found, behaves a lot like a nonchalant homebuyer…

Investors see a stock trading at 100 times earnings and they run. They see a chip name with a price-to-earnings (P/E) ratio that looks like there’s nothing left for them. But earnings, on their own, are just the countertops. They get dressed up by depreciation schedules, debt loads, and amortization assumptions that have nothing to do with how the actual business is performing.

If you want to see the foundation, you climb underneath. You look at EBIT. You look at EBITDA.

This week on Being Exponential, we did exactly that. Have a look at the episode to hear what we found, or continue reading for our write-up:

Why Optics Stocks Are Cheaper Than They Look

So, quick refresher. EBIT is earnings before interest and taxes. EBITDA adds depreciation and amortization back into that number. We look at EBITDA for pretty much every company we cover, because it strips out the accounting noise and shows you the cash the business is actually throwing off.

Now, run that lens over the optics trade, and the picture changes…

Credo Technology Group (CRDO) trades at 39 times forward earnings, 34 times forward EBIT, and 33 times forward EBITDA. On a standalone basis, that sounds rich. Pair it with 82% revenue growth this year, 47% the year after, and earnings-per-share growth running as high as 130%, and suddenly 30-something times EBITDA is a bargain.

Astera Labs (ALAB) is the more expensive cousin, at 103 times forward earnings and roughly 100 times forward EBIT and EBITDA. But the growth underneath it is enormous: 81% revenue growth this year, with EPS growth north of 140% layered on top of stable margins. Right? It doesn’t matter whether you’re looking at price-to-earnings, EBIT, EBITDA, or revenue-to-book… The optics trade is cheap relative to the growth profile sitting on top of it.

That convergence (cheap multiple, massive grower) is exactly the setup we want to own.

Redwire (RDW): The Dilution Dip

Redwire issued stock after a hot run, and the stock pulled back on it. That’s just what happens. Stock issuances create short-term pain and long-term opportunity, because good companies use that capital to build more cool stuff and generate more returns down the line. Technically, Redwire is sitting on a major shelf right now… the January high around $14.20, the 50-day moving average at $13.58, and multiple historical lows clustered in the $13.80 to $13.90 range going back to 2025. That’s a big level. We like the $13 to $15 zone to buy the dip here.

BWX Technologies (BWXT): The Nuclear Comeback

BWXT is the industrial backbone of U.S. nuclear power, supplying the Navy’s propulsion program for decades and now riding the AI data center power crunch. The bear case is orbital compute… solar-powered satellites that don’t need nuclear at all. Our counter: that future is probably a decade away from going mainstream, and it will never fully obsolete the data centers already on the ground. The structural power problem driven by terrestrial compute persists for the next five to 10 years, minimum, and likely longer. Technically, BWXT lost its 200-day moving average, bottomed in the 180s, and has U-turned back above it on a near-oversold bounce – a setup that looks a lot like the early-2025 pattern that kicked off a major rally. We like the buy-the-dip case here.

Bloom Energy (BE): The Backup Power King

Bloom is becoming the go-to provider of on-site backup power for AI data centers, and the growth numbers back it up: 83% revenue growth this year, 73% next year, with EBITDA margins expanding from 20% toward 30% by the end of the decade. Valuation is rich – 92 times forward earnings, 72 times forward EBITDA – but the trailing 12-month average EBITDA multiple is 61.5 times, with one standard deviation above sitting near 83 times. The stock just bounced off that 60-times level in a V-shaped recovery that echoes prior setups before new highs. We’re buying the dips toward 60 times and fading the rips toward 100 times.

Micron Technology (MU): Five Steps Forward, Three Steps Back

Micron has run hard since the summer of 2025, with 20% pullbacks showing up like clockwork in August, November, March, and again in June. Each one was a buying opportunity. MU stock just hit new highs near $1,080 and pulled back slightly. Our rule of thumb: accumulate on 20% pullbacks, because there will be more of them. That’s just what Micron stock does.

The verdict across all five names is the same. Don’t judge the house by the paint job. Climb into the crawl space, check the growth underneath the multiple, and buy the dip when the foundation is sound.

Want the full breakdown, including the charts behind every one of these calls? Watch this week’s full episode of Being Exponential. And be sure to subscribe to Being Exponential on X (formerly Twitter) for more exclusive content.

The post The Inspector’s Eye: How We Read Past the Sticker Price on AI’s Biggest Movers appeared first on InvestorPlace.

As I write on Tuesday morning, memory stocks are getting slammed.

Micron (MU), SanDisk (SNDK), and South Korean AI-memory giant SK Hynix are all down double-digits as investors unwind one of the year’s hottest (and most crowded) trades ahead of Micron’s earnings tomorrow.

At first glance, investors appear to be sending a very different message than the one Apple CEO Tim Cook delivered last week.

Cook warned that memory prices have become so extreme that Apple can no longer absorb the costs.

So, who’s right?

The market? Or Tim Cook?

Well, let’s back up to better understand what Apple is telling us about one of the most important supply shortages in the AI economy.

Tim Cook calls it a “hundred-year flood”

For months, Apple CEO Tim Cook has been trying to hold the line on memory costs — absorbing supplier price increases rather than passing them to customers.

That’s over.

Last week, Cook told The Wall Street Journal that price increases are “unavoidable” due to the ongoing memory shortage:

We’re doing our best to mitigate the huge increases that are being passed to us, and we’ve been trying to shield our customers from the increases, but the situation has become unsustainable.

At the heart of this is one problem: memory – the chips that power virtually every computing device you own.

Here’s the WSJ with a good way to think about it:

Memory, also called DRAM, and storage, also called NAND, are like elements of a mid-20th-century office: The memory is a desk that holds all the papers a worker needs to perform a task, while storage is the filing cabinet that holds everything else.

Your iPhone uses both. So does your laptop, your car, and your doctor’s medical equipment.

Source: Bagus Hernawan

So, what’s the problem?

Well, AI is eating all of it.

Skyrocketing memory prices with no relief in sight

AI servers are memory-hungry in a way ordinary computing never was – a single AI server requires roughly eight to 10 times the memory of a traditional one.

This has resulted in enormous demand – and surging prices…

Since last year, when Google (GOOG), Microsoft (MSFT), Meta (META), and Amazon (AMZN) began announcing massive increases in their AI capital spending, prices for memory and storage chips have both quadrupled.

The three companies that dominate DRAM production – Micron, SK HynixandSamsung – have responded by redirecting their fabrication capacity toward the specialized high-bandwidth memory AI customers demand. That leaves less supply for everyone else.

Cook put it plainly:

There’s less supply at a time when consumers want devices and the memory guys are passing along huge price increases.

We definitely need memory pricing and supply to return to reasonable levels for consumer products. That’s the bottom line.

Apple is one of the largest buyers of memory on the planet, spending in the low tens of billions of dollars per year. Historically, it has used that heft to wring the lowest prices out of suppliers, playing them against each other.

But with the supply/demand equilibrium radically shifted today, that’s changed. And even Apple is feeling the effects.

Back to Cook:

This is a hundred-year flood.

I’ve never seen anything like it in any area in over 40 years.

When the world’s most powerful hardware procurement machine can’t negotiate around a commodity price shock, it tells you something definitive about the demand dynamic underneath.

Which brings us to the investment implications…

The stocks that are winning thanks to the memory shortage

If memory stocks are in such demand, why are they tanking today?

Part of the answer is simple: these stocks have already had enormous runs so far this year.

Micron entered today up nearly 300% in 2026. SanDisk had surged more than 700%. When investors are sitting on gains like that, it doesn’t take much to trigger profit-taking.

Adding to the pressure, Micron reports earnings after the close tomorrow. Traders often lock in gains ahead of major reports, especially after a stock has already enjoyed a huge run.

But if you’re worried that today’s selloff signals a top, let’s shift our focus back to the supply/demand balance and pricing.

Here’s our technology expert Luke Lango. From his Daily Notes in Innovation Investor last week:

The pricing power of Micron (MU), Lam Research (LRCX), KLA (KLAC), ASML (ASML), Applied Materials (AMAT), SanDisk (SNDK), Seagate (STX) and Western Digital (WDC) is not a temporary cyclical phenomenon.

It is a structural reflection of demand running faster than supply can respond — the multi-year undersupply thesis confirming itself in real time.

Luke’s read is that the thesis still has runway: all three major memory producers have pre-sold their entire 2026 HBM (High-Bandwidth Memory) production under long-term contracts, with order visibility stretching deep into 2027.

And Morgan Stanley forecasts DRAM wafer capacity growing 30% by 2027 – yet even with that expansion, wafers for consumer tech will still fall up to 15% short of demand.

Still, be aware that memory is a historically cyclical industry, and when oversupply eventually arrives, the ensuing fallout could be brutal if history repeats.

But the numbers suggest we’re not there yet – even if it feels like it today.

To illustrate, Luke points to research shops Wedbush and Stifel, which both just raised their price targets on Micron, citing “both firms citing durable AI demand extending the memory upcycle well into 2027 and beyond.”

Here’s Luke’s bottom line:

The cumulative pattern across all of this sell-side activity is the estimate revision cycle we have been describing all week: actual AI infrastructure demand consistently exceeds consensus models, analysts revise upward, and stocks re-rate accordingly.

For Luke’s current positioning in Innovation Investor across the memory ecosystem – including which names he’d buy at current levels versus which have gotten ahead of themselves – click here to learn about joining him.

With this in mind, one thing to watch tomorrow

There’s a framework that we often refer to in the Digest – legendary investor Louis Navellier’s Iron Law of the Stock 蜜桃传媒.

In short, stock prices can diverge from earnings trends for a while, but over the long run, if a company keeps growing its earnings, its share price follows.

That framework is especially relevant for the memory trade – which brings us to tomorrow…

Micron reports third-quarter earnings after the close. Here’s what Louis, editor of Growth Investor – whose subscribers are up 162% in MU – expects heading in:

Third-quarter earnings are forecast to surge 930.9% year-over-year. Revenue is expected to jump 270.6% year-over-year.

The analyst community has revised third-quarter earnings estimates a whopping 73.8% higher in the past three months.

As to this morning’s selloff, Louis put a lot of the blame on SPCX, which continues to pull back. But then he pivoted to the memory trade and Micron, saying:

The results will be ridiculous, and it’ll be the strongest stock for sales and earnings, and we’ll see how it reacts…

So, hang in there, everybody. Let’s just celebrate Micron’s earnings on Wednesday. We’ll see what the aftermath of that is, what the guidance is going to be.

Earnings – both Micron’s and the broad sector’s – are what we need to watch to monitor where we are in the memory cycle.

As long as they’re growing and topping estimates, there’s more life in the trade.

When earnings begin disappointing, and management begins guiding lower, that’s when Louis’ Iron Law will cut against us, and caution becomes critical.

We don’t expect that to happen tomorrow with MU. Still, this is the issue to watch.

Specifically, watch for three details:

Broadly speaking, if these signals stay intact, the memory trade stays intact – despite the huge run so far.

To learn more about joining Louis in Growth Investor to help navigate the memory trade, click here.

One more thing – and it’s time-sensitive

Everything we’ve covered points to the same underlying challenge.

The memory trade is real. The structural thesis is sound. But the stocks that made early investors wealthy, like MU and SNDK, have already made huge moves.

The readers who benefited most weren’t the ones who chased the headlines. They were the ones who saw the bottleneck before Tim Cook was forced to call it a hundred-year flood on the front page of The Wall Street Journal.

The next trade will work the same way – it always does.

This is why tomorrow’s event from Marc Chaikin and his colleague Joe Austin is worth your time.

If you’ve been reading the Digest over the last week, you already know the setup. Marc spent 60 years on Wall Street building analytical tools – including the Power Gauge, a 20-factor stock rating system that helped him navigate the COVID crash, the 2022 bear market and last year’s tariff selloff. Joe spent four decades as a portfolio manager overseeing more than $10 billion in assets.

Together, they’ve built what they’re calling the most powerful tool of Marc’s career – an AI-powered platform that scans decades of market history to find stocks whose fundamental and technical fingerprints match the early profiles of proven multi-baggers, before Wall Street catches on. They’re calling it the Time Machine.

Tomorrow – Wednesday, June 24 at 10:00 a.m. – they’re unveiling it publicly for the first time, in a free broadcast.

This is the last call here in the Digest. If you want to be in the room when they show which stocks the Time Machine is flagging right now, click here to reserve your spot.

Have a good evening,

Jeff Remsburg

(Disclaimer: I own AAPL, MU, ASML, GOOGL, MSFT, AMZN)

The post Today’s Memory Selloff: Opportunity or Warning Sign? appeared first on InvestorPlace.

Joe has been looking at a place most investors don’t associate with AI: the oil and gas industry. But something interesting is happening.

Over the last few years, U.S. oil producers have been drilling with fewer rigs while still increasing production. That’s the kind of productivity gain that gets my attention.

In today’s guest essay, Joe explains how AI is helping companies make faster decisions, improve efficiency and get more out of existing operations. And what he’s seeing in oil and gas may be happening in other industries, too.

That’s because some of AI’s biggest opportunities may not come from the companies building the technology. They may come from the industries putting it to work.

To help identify those opportunities, Marc and Joe developed the AI-Powered Time Machine, a new tool designed to find stocks that resemble some of the market’s biggest winners before their major moves.

Tomorrow at 10 a.m. Eastern, Marc and Joe will unveil it for the first time. If you’d like to be among the first to see it in action, I encourage you to reserve your spot now.

Until then, here’s Joe…

****

The search for oil and gas never stops. And it never looks the same.

On Alaska’s North Slope, rigs operate in some of the most punishing conditions on Earth. In winter, temperatures routinely drop well into the negatives. Around the winter solstice, daylight can last as little as two hours a day.

The enemy is the environment. Low light, brutal cold, and encroaching sea ice shut down operations for nearly half the year. When your drilling window is that short, every hour counts.

Meanwhile, thousands of miles south, drillships in the warm waters off Guyana operate over the Stabroek Block — a deepwater tract that ExxonMobil Corp.’s (XOM) CEO has called one of the biggest oil discoveries in nearly two decades. The water here can be more than 6,000 feet deep before you even reach the oil reservoir.

Everything costs a fortune, and nothing stops. The day rate for drillships runs between $400,000 and $500,000 per day.

Then there’s the U.S. shale patch.

In the Permian Basin and the Marcellus Shale, the challenge isn’t weather or day rates. It’s doing more with less. From late 2022 through late last year, the active rig count in the lower 48 states dropped by about one-third.

But over that same period, Permian production jumped 18%. Appalachia production increased 10%. And last July, the lower 48 states set a new monthly production record for crude oil.

Fewer rigs. More oil. That’s efficiency — and AI is driving it.

These are three environments with very different problems. But the solution is always the same: better technology. And right now, that means AI.

AI Is Reshaping Industrial Processes in Real Time

Across the oil and gas industry, AI is reshaping how wells get drilled.

The basic machinery has been around for decades: a derrick to support the drill string, a rotary system to spin the bit, a hoist to raise and lower equipment, and a circulation system to pump drilling fluid in and out of the hole. But what happens inside those systems has changed dramatically.

Sensors in the drill string now send live data up from the bottom of the hole while drilling is still underway. That gives engineers a real-time read on rock type, pressure, and well direction. Software tracks mud weight and chemistry in real time, catching pressure warning signs before fluids start flowing into the well uncontrolled.

Directional drilling lets crews bend the well path underground to reach targets thousands of feet away — making it possible to drill multiple wells from a single surface location. And as each section is drilled, it gets lined with steel casing and cemented in place. Evaluation tools verify the cement has set before the crew moves deeper.

For years, skilled operators and engineers managed all of this by reading data, making judgment calls, and adjusting on the fly. Now, AI is taking over that work. And the results are measurable.

Surface systems no longer just follow preset rules. They learn from live well data, make decisions, and adjust drilling parameters faster and more consistently than any human can. In one 2024 drilling program, an AI-driven system drilled nearly 50% faster than a manual crew.

Downhole, AI now interprets data from drilling tools in real time and adjusts the well path automatically — keeping the bit in the most productive zone without waiting for a geologist to weigh in. At a well in Ecuador, an AI system made 25 course corrections along a single well section, each in seconds. That well became one of the best producers in the country.

On the fluids side, machine-learning models can flag signs of a pressure imbalance 10 to 12 minutes earlier than conventional monitoring tools. And cement evaluation models that once required a specialist to manually read complex acoustic logs now run automatically, faster, and with better accuracy.

This is what physical AI looks like. It’s not a chatbot. It’s not a software upgrade. It’s machines making real-time decisions in conditions where a human mistake costs millions of dollars — or worse.

Where the Opportunity Is

Many of the companies driving this transformation fall under the energy equipment and services industry. These aren’t household names. They’re not the Nvidias or the Microsofts that get discussed on financial television every day.

But they’re doing something just as important: they’re making one of the world’s most capital-intensive industries dramatically more efficient. And the Power Gauge — Marc Chaikin’s 20-factor stock rating system — currently rates this corner of the market as “strong.”

Of the 58 stocks in the energy equipment and services industry that the Power Gauge tracks, 26 carry a “bullish” or better rating. Only one gets a “bearish” or worse.

I’ve spent 40 years on Wall Street. And I’ll tell you — when a less-obvious sector lights up like this, it’s worth paying attention.

The AI opportunity isn’t just in the big infrastructure names. It’s in the companies using AI to transform physical industries — oil and gas, mining, manufacturing, power generation. These are trillion-dollar industries that are just beginning to feel the full impact of what this technology can do.

A New Way to Find These Stocks

Here’s the challenge: Finding the right stocks in these less-covered corners of the market is hard. There’s no shortage of companies claiming AI capabilities. The question is which ones have the real financial and technical momentum behind them — and which ones are just along for the ride.

That’s a problem Marc has spent his entire career trying to solve. And on June 24, we’re unveiling the most powerful tool he’s ever built to do it.

It’s called the Time Machine. It’s Chaikin Analytics’ first-ever AI-powered platform — and it works by scanning decades of market history to find stocks today whose fundamental and technical fingerprints match the early profiles of stocks like Nvidia Corp. (NVDA), Amazon.com Inc. (AMZN), and Meta Platforms Inc. (META), just before they made their biggest moves.

In backtesting, it surfaced stocks that went on to deliver gains of 995%, 1,406%, and 3,804% — all while the “seed” stocks they were matched against posted far more modest returns.

This is the first time Marc and I have shown this to anyone outside of Chaikin Analytics. Charter membership spots are limited, and this offer won’t be repeated.

If you want to be among the first to access the Time Machine — and see which stocks it’s flagging as the next generation of potential 10X winners — the first step is to reserve your spot for our free event on June 24.

Folks who sign up now get early beta access to the Time Machine before June 24, so you can start exploring the platform right away. No purchase required. Get on the list for that free broadcast here.

The oil and gas AI story is just one example of what the Time Machine is designed to find. The opportunity is much bigger than any one sector.

Good investing,

Joe Austin

Senior Analyst, Chaikin Analytics

P.S. The Time Machine is Chaikin Analytics’ first-ever AI-powered product — and June 24 marks its public debut. Marc Chaikin has spent 60 years building tools that give individual investors an edge. This is the biggest thing he’s launched in years. Charter membership is limited, and when it’s gone, it’s gone. Sign up now to lock in your spot.

The post AI Is Now Drilling for Oil, and the Profits Are Enormous appeared first on InvestorPlace.

For the better part of three years, the Physical AI narrative — the idea that AI would move off the cloud and into the physical world, powering robots, wearables, autonomous vehicles, and smart devices — played out like every great tech story does: loud, early, and mostly theoretical. 

Elon Musk stood on stage and told us Optimus robots would soon be doing our laundry. Venture capitalists competed to fund the most humanoid-looking thing they could find. CNBC ran breathless segments about the robot revolution. And the stock market assigned billion-dollar valuations to companies whose most impressive product was a press release and a demo reel.

That was then. This is now.

The Proof Points Are Piling Up

Consider what happened in a single quarter:

• Microsoft (MSFT) shipped AI PCs with on-device inference chips from Qualcomm (QCOM) — real products, real volumes, real revenue.
• Genesis AI launched an industrial robot that, more than just executing programmed sequences, can reason adaptively.
• Plaud is targeting $500 million in wearable AI device sales this year.
• Applied Materials (AMAT) partnered with EssilorLuxottica to industrialize smart optical systems for AR eyewear.
• Apple (AAPL) confirmed cameras in AirPods for 2027, signaling that Physical AI is now a core product roadmap item for the world’s most valuable company.
• Mobileye (MBLY) announced a concrete U.S. robotaxi deployment with a scaling plan to 17,000 vehicles.

Six different companies. Six different products. One underlying shift in what AI needs to run. 

What Physical AI Actually Means — and Why the Architecture Is Completely Different From Cloud AI 

What makes this cycle different from the AI wave we’ve been riding isn’t the ambition. It’s the architecture. 

Cloud-based AI is about scale — throw compute at a model, let it learn, serve answers via API. Physical AI is about efficiency — get the answer right, in milliseconds, on a device with a 40-watt thermal budget, without a network connection. 

It’s the AI inside your headphones that filters background noise before you even notice it… 

The vision system on a warehouse robot that decides which box to pick next… 

The autonomous vehicle perception stack that identifies a pedestrian at 60 miles per hour.

The requirements are completely different — and that difference runs all the way down the supply chain. 

The Six Pillars of the Physical AI Supply Chain

Think of Physical AI not as a single industry but as six distinct hardware categories that all need to scale simultaneously. 

1. Edge AI Silicon

This is the foundation. Every physical AI device needs a chip that can run inference locally — fast, cool, and cheap. Qualcomm’s Snapdragon X2, which just launched inside Microsoft’s new Surface lineup, is the clearest proof point that on-device AI silicon has crossed the viability threshold. 

Arm‘s (ARM) architecture underpins virtually every mobile AI chip on the planet. Nvidia (NVDA) is pushing into embedded inference with its Jetson platform. AMD (AMD) and Intel (INTC) are fighting for their share of the AI PC market. The edge silicon war is just beginning, and the winners here get paid on every device that ships. 

Key names: QCOM, ARM, NVDA, AMD, INTC

2. Sensors & Machine Vision

Image sensors, depth cameras, radar, lidar, microphones — these are the eyes and ears of every robot, wearable, and autonomous vehicle. 

The AMAT-EssilorLuxottica partnership to develop intelligent optical systems for AR eyewear tells you everything: the optics industry is being recruited into the AI supply chain at the component level. Apple’s forthcoming AI AirPods with embedded cameras will drive a new demand cycle for miniaturized sensor modules. 

Key names: Ambarella (AMBA), ON Semiconductor (ON), STMicroelectronics (STM), Sony (SONY), Cognex (CGNX)

3. Advanced Optics

AR glasses and AI eyewear aren’t a consumer curiosity anymore — they’re a hardware category. And the bottleneck? Optics. 

Waveguides, photonic displays, specialty glass, and laser projection systems are what separate a pair of glasses from a heads-up display. Corning (GLW) and Coherent (COHR) are two of the most underappreciated Physical AI plays in the market for precisely this reason. Applied Materials’ pivot into intelligent optics manufacturing signals how seriously the semiconductor equipment industry is taking this category. 

Key names: AMAT, GLW, Lumentum (LITE), COHR

4. Robotics & Industrial Automation

Genesis AI’s Eno robot isn’t interesting because it’s humanoid — it’s interesting because it reasons. That’s the leap from industrial automation 1.0 (programmed motion) to Physical AI 1.0 (adaptive intelligence). 

Companies like Symbotic (SYM), Teradyne (TER), Rockwell Automation (ROK), and Honeywell (HON) are already deploying AI-driven automation in factories and warehouses at scale. Tesla‘s (TSLA) Optimus is the flashy version; the boring but lucrative version is already running in distribution centers across America. 

Key names: SYM, TER, ROK, HON, TSLA

5. Memory, Storage & Power

On-device AI needs more local memory than anyone planned for. That means Low Power Double Data Rate 6 (LPDDR6) RAM, expanded NAND storage, power management integrated circuits (PMICs) that can handle burst inference workloads, and analog semiconductors for signal processing. 

Micron (MU) is already winning here with its LPCAMM modules for AI PCs. The storage plays — Seagate (STX), Western Digital (WDC), SanDisk (SNDK) — get a demand tailwind as every edge device needs local model storage. 

Key names: MU, STX, WDC, SNDK, Monolithic Power (MPWR), Analog Devices (ADI), Texas Instruments (TXN).

6. Connectivity & Infrastructure

Even edge AI needs the cloud. Local inference handles the latency-sensitive tasks; cloud AI handles the heavy lifting — model updates, data sync, fleet coordination for robotaxis, telemetry from billions of wearables. 

That means the optical networking and connectivity layer is a direct beneficiary of Physical AI scaling. Robotaxis syncing to the cloud. AR glasses streaming map data. Industrial robots phoning home with diagnostic telemetry. Broadcom (AVGO), Marvell (MRVL), Arista (ANET), Ciena (CIEN), Credo (CRDO), and Corning are all toll roads on that data highway. 

Key names: AVGO, MRVL, ANET, CRDO, CIEN, GLW

The Investor’s Guide: Own the Picks and Shovels for the Biggest Hardware Cycle Since the Smartphone

Nobody made more money in the California Gold Rush by panning for gold. The real fortunes went to the people selling the equipment.

Physical AI follows the same logic — with one important difference. 

In the Gold Rush, you could only sell one pan at a time. In Physical AI, every device that ships — every robot, wearable, AI PC, and autonomous vehicle — needs chips, sensors, optics, memory, power management, and connectivity. The suppliers don’t need to pick the winning application. They get paid on every unit, across every category, regardless of which company’s robot ends up in your warehouse or which AR glasses end up on your face.

The transition from cloud AI to Physical AI is the single biggest hardware cycle since the smartphone. And like the smartphone, the companies that win aren’t just the device makers — they’re the entire supply chain underneath them.

The hype was right. It just took the hardware a few years to catch up. 

The names in this piece — the edge silicon suppliers, the sensor makers, the optics companies, the memory and connectivity plays — are the public-market expression of that thesis. But the smartest money isn’t just moving into the obvious trades. 

Take Peter Thiel’s most recent 13F, for example: zero shares of Nvidia, Apple, Microsoft, or Tesla. Not trimmed — liquidated entirely. His private fund, meanwhile, has been quietly building positions in energy infrastructure, nuclear power, chip fabrication, and natural resources — the physical backbone of everything described in this piece.

He can’t buy most of those positions publicly. 

Seven of them, however, have a backdoor…

And we think they’re among the most compelling AI plays hiding in plain sight.

The post AI Is Leaving the Cloud. Here’s Who Gets Paid When It Does. appeared first on InvestorPlace.

Nvidia (NVDA) CEO, Jensen Huang didn’t pull any punches last week:

The United States is woefully behind in energy production.

He made the comment in Sherman, Texas, at a groundbreaking event for a $2 billion Nvidia manufacturing expansion.

But Huang’s endorsement of the AI energy thesis didn’t stop there:

AI factories will become the infrastructure of the new industrial revolution…

Ten years from now, I think we’ll look back and realize AI is what made it possible to invest in sustainable energy, upgrade our energy grid, and reconstitute a workforce.

Reading between the lines, Huang just said that America’s energy grid wasn’t built for what AI demands of it – and we have a long runway ahead to get it up to speed.

So, for investors who’ve been wondering how to invest in AI at this point in the cycle, this is one of the clearest roadmaps from one of the most credible voices possible.

Our technology expert Luke Lango, editor of Innovation Investor, has been tracking this thesis closely. Here he is from last Wednesday’s Daily Notes:

The AI buildout is outrunning the power infrastructure beneath it, and that gap is a multi-year, structurally non-discretionary demand cycle for the entire power and energy supply chain — baseload nuclear, fuel and services, SMR, grid and electrification, and data center power and cooling.

Jensen just stood up in Texas and confirmed what we already knew: AI needs more power, America hasn’t built it, and the companies that fix that are indispensable.

Luke recommends a basket of leading AI stocks across this supply chain in Innovation Investor. While I’ll save those for his subscribers, I’ll share with you some of the ones he highlighted in last week’s Daily Notes that are sitting in the direct path of this structural demand gap:

• On the baseload nuclear side, Constellation Energy (CEG) and Vistra (VST) are the two largest operators of existing U.S. nuclear capacity.
• For uranium fuel and services, look at Cameco (CCJ) and BWX Technologies (BWXT).
• For higher-risk, higher-reward small modular reactor exposure: Oklo (OKLO) and NuScale Power (SMR).
• And on the grid and electrification side, Quanta Services (PWR) and Hubbell (HUBB) are two of the companies that physically build and upgrade the transmission infrastructure needed for all of this to work.

Bottom line: Huang just endorsed this trade. If you’re looking for how to invest in AI today, start your search here.

Now, if Huang is right, the AI energy trade could deliver an enormous return in the coming years.

But last week, some subscribers got a similar huge return overnight…

When two analysts land on the same stock independently, it’s worth paying attention

Last week, veteran trader Jonathan Rose sent me a note after realizing that he and Luke are both long the same trade right now – and it just exploded.

At the center of this overlap is Butterfly Network Inc. (BFLY)

Jonathan put his Masters in Trading: All Access subscribers into two call positions on BFLY. Meanwhile, Luke, who helms the trading service Breakout Trader, just issued a fresh trade alert on the same name last Tuesday.

Both Jonathan’s and Luke’s subscribers had a huge day last Thursday when BFLY surged 56%. It’s pulled back some on profit-taking as I write on Monday morning, but it’s still up huge.

Two analysts, two different methodologies, same overnight vertical return.

So, what’s driving it?

Last week, AI imaging company Midjourney announced the launch of Midjourney Medical – a new healthcare division – along with a prototype for a 60-second, full-body ultrasound scanner called the Midjourney Scanner.

Each unit incorporates 40 of Butterfly’s Ultrasound-on-Chip modules. Midjourney’s stated ambition: 5,000 “Midjourney Spas” and 50,000 scanners deployed globally by 2031.

That’s a potential supply relationship of enormous scale for a company with a roughly $1.4 billion market cap.

The market certainly believes so, resulting in that 50%+ pop last Thursday.

What makes BFLY particularly interesting is that Luke and Jonathan got there by completely different routes

Luke’s Breakout Trader service is built around stage analysis – a framework that strips away fundamental noise and focuses purely on price structure.

The thesis is simple: stocks cycle through four stages (base-building, uptrend, topping, downtrend), and the only time you want to own something is when it’s in a clean Stage-2 uptrend.

Luke spotted BFLY breaking out of a three-year basing pattern with all three major moving averages turning higher and a Relative Strength Index reading that signaled strong momentum without being overbought.

For Luke, the fundamental story was secondary. Price was the signal.

Jonathan got there through a different lens – the longer-term strategic picture of where Butterfly’s chip technology sits within the emerging brain-computer interface ecosystem.

He’s been tracking the company’s five-year co-development agreement with Forest Neurotech, whose leadership recently joined Merge Labs (Sam Altman’s newest venture). The hardware connection between BFLY’s ultrasound-on-chip technology and the non-invasive BCI research underway at that organization made Butterfly interesting well before this week’s Midjourney news.

Same stock. Completely different maps. Huge returns.

Luke’s subscribers, who got into the trade just last Tuesday, are up roughly 50% as I write.

Jonathan’s subscribers, who are playing it with options, have already taken triple-digit profits – last week, one subscriber reported a 400% win.

Below is a screenshot of some of those subscribers writing in to Jonathan. If you can’t read it, here are a few of them to give you a sense:

• “300% on BFLY. Thanks JR”
• “344% on BFLY… Thanks JR for a great trade!!”
• “Out of BFLY 205%, thank you JR”
• “400% on BFLY thank you jr!”

If you missed BFLY, don’t worry…

Back on May 8 – more than six weeks before BFLY’s 56% session – Jonathan published a free piece titled “Follow the Money: 7 Sam Altman Stocks to Buy Before the 蜜桃传媒 Catches On” and BFLY was one of his top two picks.

While the Midjourney Medical partnership that sent BFLY surging last Thursday was a different catalyst than the one Jonathan originally mapped, the underlying reason BFLY is worth owning didn’t change – it deepened. The stock was already in the path of serious capital.

If you missed the BFLY move, that piece is still worth reading. It covers six other stocks sitting on the same Sam Altman capital map – including what Jonathan considers the single cleanest publicly traded Altman name right now.

For those interested in a glimpse of Masters in Trading: All Access winners like BFLY, I suggest checking out Jonathan’s Masters in Trading 7-Day Challenge. It provides a step-by-step trading system tailored for beginners. You can find more information on how to join him here.

And for Luke’s ongoing work identifying Stage-2 in Breakout Trader, click here.

Congrats to all the Masters in Trading: All Access and Breakout Trader subscribers!

Stepping back, not every AI-adjacent trade works out that cleanly, of course. And the contrast between what happened with BFLY and what’s happening with SpaceX (SPCX) helps make that point.

Is SpaceX following the historical IPO pattern?

In our June 11 Digest, we urged caution on the SpaceX IPO. Not because the company isn’t extraordinary, but because 45 years of data from the preeminent expert on IPOs – Professor Jay Ritter – says retail investors who buy into first-day euphoria tend to regret it.

Now, as expected, SPCX soared out of the gate. After pricing its IPO at $135, the stock surged to an intraday high above $225 on June 16. But since then, it has pulled back roughly 25%.

According to CNBC last Thursday, the five-day volume-weighted average price sat around $181, and the price has fallen further since then. It’s down almost 10% today as I write.

This means the average investor who bought in the open market after the IPO debut has seen nearly all their post-IPO gains disappear, and is likely now sitting on a loss.

We’re not gloating – for all we know, SPCX could double by the end of the week, and those investors could enjoy a huge payday.

But the historical data we cited in the Digest suggests this pattern plays out more often than not, and so far, history is repeating itself.

This means the broader point we made in our June 11 Digest still stands – and Luke articulated it better than anyone before the IPO arrived:

The biggest gains from landmark technology IPOs have almost never gone to the investors who bought on day one.

They’ve gone to the investors who owned the ecosystem around those companies before Wall Street showed up to reprice it.

Luke calls this the “Pre-IPO Backdoor” – a group of publicly traded companies that supply, power, and benefit from the AI infrastructure buildout, and that he believes get significantly repriced the moment the big IPO S-1 filings land.

OpenAI and Anthropic are still in the pipeline. The window he’s been describing – owning the ecosystem before Wall Street shows up – hasn’t closed. Click here for more from Luke explaining what he’s seeing.

This brings us to the harder question that threads through all three of these stories…

How do you find the next AI energy play, or the next BFLY, before Wall Street finds it?

That question has a systematic answer…

One of the most reliable instincts in investing is to look one step behind the obvious story

It’s the thread running through everything we’ve covered today – Luke’s AI energy basket sitting one step behind the infrastructure Jensen Huang just validated… Jonathan and Luke finding BFLY just before it exploded… and the SpaceX Pre-IPO Backdoor being built entirely on the idea that the real money is in the surrounding players, not the headline name.

But while this idea is easy in theory, it’s hard in implementation…

Most retail investors don’t have the time or tools to solve it. And frankly, most newsletter analysts don’t either because it requires scanning a universe of hundreds of companies across sectors, filtering on both fundamental and technical criteria simultaneously, and doing it at a moment early enough to matter.

This is exactly the problem that Marc Chaikin has spent his career building tools to solve.

Longtime market followers will know Marc from his Power Gauge – a 20-factor rating system he spent decades developing, which now covers more than 5,000 stocks. He’s used that framework to navigate some of the market’s most dislocating moments, including the COVID crash and the 2022 bear market.

Now he and his colleague Joe Austin – a former portfolio manager who ran more than $10 billion in assets across a 40-year Wall Street career – are unveiling what they’re calling the most powerful tool they’ve ever built: an AI-powered platform that scans decades of market history to find stocks whose fundamental and technical “fingerprint” closely matches the early profile of proven multi-baggers, before Wall Street catches on.

They’re calling it the Time Machine – and they’re debuting it this Wednesday, June 24, in a free broadcast. If you’re interested in a systematic, quant-driven approach to finding the next generation of AI-adjacent winners, this is the event for you. You can reserve your seat right here.

We’ll keep you updated on all these stories here in the Digest.

Have a good evening,

Jeff Remsburg

The post The Average SpaceX Investor Is Now Losing – These Investors Aren’t appeared first on InvestorPlace.

To Elon Musk’s credit, the IPO brought the space industry into the spotlight. Initially, the hype drove space stocks higher as investors bought into them as proxies, hoping they would benefit from the IPO.

But when the company went public, investors abandoned those space stocks, and they sold off sharply.

Those companies included Rocket Lab Corporation (RKLB), Planet Labs PBC (PL) and AST SpaceMobile, Inc. (ASTS). But according to my friend and colleague, Luke Lango, he believes these stocks deserve a second look.

So, in this week’s Navellier 蜜桃传媒 Buzz, Luke joins us to explain why these three stocks may be among the space sector’s most compelling buys today. He also shares his thoughts on when SpaceX could become profitable and discusses the energy bottleneck facing the AI trade.

Click the image below to watch now.

To see more of my videos, click here to subscribe to my YouTube channel. And be sure to check out Luke’s YouTube channel, Being Exponential, right here.

Plus, the grades in Stock Grader (subscription required) have been updated this week! Click here to plug in your own stocks and see how they’re rated.

The Opportunity Most Investors Are Missing

Space stocks aren’t the only area where Luke sees investors missing the bigger picture.

He believes a similar opportunity is emerging across the AI market.

According to his research, while most investors remain focused on the Magnificent Seven, some of the world’s most successful investors are quietly moving out of those names.

Meanwhile, they’re positioning for what could be the next major phase of the AI boom.

Their focus isn’t on chatbots or software.

It’s on the power, energy and infrastructure projects needed to support trillions of dollars in future AI investment.

The challenge is that many of the companies attracting billionaire capital are private, making them inaccessible to most investors. That’s why Luke developed a “backdoor” strategy designed to help everyday investors gain exposure to the same trend through publicly traded stocks.

In fact, he put together an exclusive portfolio of seven stocks in a recent presentation, which could all be well-positioned as this buildout accelerates.

Click here to learn more.

Sincerely,

Louis Navellier

Editor, 蜜桃传媒 360

The post 3 Space Stocks Got Crushed by SpaceX – Now They’re a Buy appeared first on InvestorPlace.

This Week’s Ratings Changes:

Upgraded: Strong to Very Strong

Downgraded: Very Strong to Strong

Upgraded: Neutral to Strong

Downgraded: Strong to Neutral

Upgraded: Weak to Neutral

Downgraded: Neutral to Weak

Upgraded: Very Weak to Weak

Downgraded: Weak to Very Weak

To stay on top of my latest stock ratings, plug your holdings into Stock Grader, my proprietary stock screening tool. But, you must be a subscriber to one of my premium services.

To learn more about my premium service, Growth Investor, and get my latest picks, go here. Or, if you are a member of one of my premium services, you can go here to get started.

Sincerely,

Louis Navellier

Editor, 蜜桃传媒 360

The post Ubiquiti Upgraded, Reddit Downgraded: Updated Rankings on Top Blue-Chip Stocks appeared first on InvestorPlace.

For today’s Smart Money, Joe is here to share why he believes AI’s biggest profit opportunities aren’t in the names everyone already owns — and how a brand-new tool from 60-year Wall Street legend Marc Chaikin is designed to find the next generation of winners before Wall Street catches on.

That tool — the “AI-Powered Time Machine” — is being unveiled for the very first time on Wednesday, June 24. It’s Chaikin Analytics’ first-ever AI-powered product, and charter access is limited.

Click here to secure your spot for this free event.

Now, here’s Joe…

The search for oil and gas never stops. And it never looks the same.

On Alaska’s North Slope, rigs operate in some of the most punishing conditions on Earth. In winter, temperatures routinely drop well into the negatives. Around the winter solstice, daylight can last as little as two hours a day.

The enemy is the environment. Low light, brutal cold, and encroaching sea ice shut down operations for nearly half the year. When your drilling window is that short, every hour counts.

Meanwhile, thousands of miles south, drillships in the warm waters off Guyana operate over the Stabroek Block — a deepwater tract that ExxonMobil Corp.’s (XOM) CEO has called one of the biggest oil discoveries in nearly two decades. The water here can be more than 6,000 feet deep before you even reach the oil reservoir.

Everything costs a fortune, and nothing stops. The day rate for drillships runs between $400,000 and $500,000 per day.

Then there’s the U.S. shale patch.

In the Permian Basin and the Marcellus Shale, the challenge isn’t weather or day rates. It’s doing more with less. From late 2022 through late last year, the active rig count in the lower 48 states dropped by about one-third.

But over that same period, Permian production jumped 18%. Appalachia production increased 10%. And last July, the lower 48 states set a new monthly production record for crude oil.

Fewer rigs. More oil. That’s efficiency — and AI is driving it.

These are three environments with very different problems. But the solution is always the same: better technology. And right now, that means AI.

AI Is Reshaping Industrial Processes in Real Time

Across the oil and gas industry, AI is reshaping how wells get drilled.

The basic machinery has been around for decades: a derrick to support the drill string, a rotary system to spin the bit, a hoist to raise and lower equipment, and a circulation system to pump drilling fluid in and out of the hole. But what happens inside those systems has changed dramatically.

Sensors in the drill string now send live data up from the bottom of the hole while drilling is still underway. That gives engineers a real-time read on rock type, pressure, and well direction. Software tracks mud weight and chemistry in real time, catching pressure warning signs before fluids start flowing into the well uncontrolled.

Directional drilling lets crews bend the well path underground to reach targets thousands of feet away — making it possible to drill multiple wells from a single surface location. And as each section is drilled, it gets lined with steel casing and cemented in place. Evaluation tools verify the cement has set before the crew moves deeper.

For years, skilled operators and engineers managed all of this by reading data, making judgment calls, and adjusting on the fly. Now, AI is taking over that work. And the results are measurable.

Surface systems no longer just follow preset rules. They learn from live well data, make decisions, and adjust drilling parameters faster and more consistently than any human can. In one 2024 drilling program, an AI-driven system drilled nearly 50% faster than a manual crew.

Downhole, AI now interprets data from drilling tools in real time and adjusts the well path automatically — keeping the bit in the most productive zone without waiting for a geologist to weigh in. At a well in Ecuador, an AI system made 25 course corrections along a single well section, each in seconds. That well became one of the best producers in the country.

On the fluids side, machine-learning models can flag signs of a pressure imbalance 10 to 12 minutes earlier than conventional monitoring tools. And cement evaluation models that once required a specialist to manually read complex acoustic logs now run automatically, faster, and with better accuracy.

This is what physical AI looks like. It’s not a chatbot. It’s not a software upgrade. It’s machines making real-time decisions in conditions where a human mistake costs millions of dollars — or worse.

Where the Opportunity Is

Many of the companies driving this transformation fall under the energy equipment and services industry. These aren’t household names. They’re not the Nvidias or the Microsofts that get discussed on financial television every day.

But they’re doing something just as important: they’re making one of the world’s most capital-intensive industries dramatically more efficient. And the Power Gauge — Marc Chaikin’s 20-factor stock rating system — currently rates this corner of the market as “strong.”

Of the 58 stocks in the energy equipment and services industry that the Power Gauge tracks, 26 carry a “bullish” or better rating. Only one gets a “bearish” or worse.

I’ve spent 40 years on Wall Street. And I’ll tell you — when a less-obvious sector lights up like this, it’s worth paying attention.

The AI opportunity isn’t just in the big infrastructure names. It’s in the companies using AI to transform physical industries — oil and gas, mining, manufacturing, power generation. These are trillion-dollar industries that are just beginning to feel the full impact of what this technology can do.

A New Way to Find These Stocks

Here’s the challenge: Finding the right stocks in these less-covered corners of the market is hard. There’s no shortage of companies claiming AI capabilities. The question is which ones have the real financial and technical momentum behind them — and which ones are just along for the ride.

That’s a problem Marc has spent his entire career trying to solve. And on June 24, we’re unveiling the most powerful tool he’s ever built to do it.

It’s called the Time Machine. It’s Chaikin Analytics’ first-ever AI-powered platform — and it works by scanning decades of market history to find stocks today whose fundamental and technical fingerprints match the early profiles of stocks like Nvidia Corp. (NVDA), Amazon.com Inc. (AMZN), and Meta Platforms Inc. (META), just before they made their biggest moves.

In backtesting, it surfaced stocks that went on to deliver gains of 995%, 1,406%, and 3,804% — all while the “seed” stocks they were matched against posted far more modest returns.

This is the first time Marc and I have shown this to anyone outside of Chaikin Analytics. Charter membership spots are limited, and this offer won’t be repeated.

If you want to be among the first to access the Time Machine — and see which stocks it’s flagging as the next generation of potential 10X winners — the first step is to reserve your spot for our free event on June 24.

Folks who sign up now get early beta access to the Time Machine before June 24, so you can start exploring the platform right away. No purchase required. Get on the list for that free broadcast here.

The oil and gas AI story is just one example of what the Time Machine is designed to find. The opportunity is much bigger than any one sector.

Good investing,

Joe Austin

Senior Analyst, Chaikin Analytics

P.S. The Time Machine is Chaikin Analytics’ first-ever AI-powered product — and June 24 marks its public debut. Marc Chaikin has spent 60 years building tools that give individual investors an edge. This is the biggest thing he’s launched in years. Charter membership is limited, and when it’s gone, it’s gone. Sign up now to lock in your spot.

The post This “Boring” Industry Is Quietly Becoming One of the Best AI Plays in the 蜜桃传媒 appeared first on InvestorPlace.

Editor’s Note: If you read Joe Austin‘s piece yesterday, you know the argument: the real AI money isn’t where everyone’s looking.

Today he’s back to prove it again, this time in a different industry entirely.

Same thesis, new terrain — and the opportunity is just as overlooked. Joe and Marc Chaikin are laying out the full picture on Wednesday, June 24, when they’ll debut the first AI-powered tool Chaikin Analytics has ever built.

But charter access is limited, so reserve your spot while you can.

Now here’s Joe…

The search for oil and gas never stops. And it never looks the same.

On Alaska’s North Slope, rigs operate in some of the most punishing conditions on Earth. In winter, temperatures routinely drop well into the negatives. Around the winter solstice, daylight can last as little as two hours a day.

The enemy is the environment. Low light, brutal cold, and encroaching sea ice shut down operations for nearly half the year. When your drilling window is that short, every hour counts.

Meanwhile, thousands of miles south, drillships in the warm waters off Guyana operate over the Stabroek Block — a deepwater tract that ExxonMobil Corp.’s (XOM) CEO has called one of the biggest oil discoveries in nearly two decades. The water here can be more than 6,000 feet deep before you even reach the oil reservoir. 

Everything costs a fortune, and nothing stops. The day rate for drillships runs between $400,000 and $500,000 per day.

Then there’s the U.S. shale patch.

In the Permian Basin and the Marcellus Shale, the challenge isn’t weather or day rates. It’s doing more with less. From late 2022 through late last year, the active rig count in the lower 48 states dropped by about one-third.

But over that same period, Permian production jumped 18%. Appalachia production increased 10%. And last July, the lower 48 states set a new monthly production record for crude oil.

Fewer rigs. More oil. That’s efficiency — and AI is driving it.

These are three environments with very different problems. But the solution is always the same: better technology. And right now, that means AI.

AI Is Reshaping Oil and Gas Drilling In Real Time — and the Results Are Measurable

Across the oil and gas industry, AI is reshaping how wells get drilled.

The basic machinery has been around for decades: a derrick to support the drill string, a rotary system to spin the bit, a hoist to raise and lower equipment, and a circulation system to pump drilling fluid in and out of the hole. But what happens inside those systems has changed dramatically.

Sensors in the drill string now send live data up from the bottom of the hole while drilling is still underway. That gives engineers a real-time read on rock type, pressure, and well direction. Software tracks mud weight and chemistry in real time, catching pressure warning signs before fluids start flowing into the well uncontrolled.

Directional drilling lets crews bend the well path underground to reach targets thousands of feet away — making it possible to drill multiple wells from a single surface location. And as each section is drilled, it gets lined with steel casing and cemented in place. Evaluation tools verify the cement has set before the crew moves deeper.

For years, skilled operators and engineers managed all of this by reading data, making judgment calls, and adjusting on the fly. Now, AI is taking over that work. And the results are measurable.

The AI Revolution That’s Already Happening Where Nobody’s Looking

Surface systems no longer just follow preset rules. They learn from live well data, make decisions, and adjust drilling parameters faster and more consistently than any human can. In one 2024 drilling program, an AI-driven system drilled nearly 50% faster than a manual crew.

Downhole, AI now interprets data from drilling tools in real time and adjusts the well path automatically — keeping the bit in the most productive zone without waiting for a geologist to weigh in. At a well in Ecuador, an AI system made 25 course corrections along a single well section, each in seconds. That well became one of the best producers in the country.

On the fluids side, machine-learning models can flag signs of a pressure imbalance 10 to 12 minutes earlier than conventional monitoring tools. And cement evaluation models that once required a specialist to manually read complex acoustic logs now run automatically, faster, and with better accuracy.

This is what physical AI looks like. It’s not a chatbot or a software upgrade. It’s machines making real-time decisions in conditions where a human mistake costs millions of dollars — or worse.

Where the Investing Opportunity Is: A Sector Flashing Bullish That Many Investors Are Ignoring

Many of the companies driving this transformation fall under the energy equipment and services industry. These aren’t household names. They’re not the Nvidias or the Microsofts that get discussed on financial television every day.

But they’re doing something just as important: they’re making one of the world’s most capital-intensive industries dramatically more efficient. And the Power Gauge — Marc Chaikin’s 20-factor stock rating system — currently rates this corner of the market as “strong.”

Of the 58 stocks in the energy equipment and services industry that the Power Gauge tracks, 26 carry a “bullish” or better rating. Only one gets a “bearish” or worse.

I’ve spent 40 years on Wall Street. And I’ll tell you — when a less-obvious sector lights up like this, it’s worth paying attention.

The AI opportunity isn’t just in the big infrastructure names. It’s in the companies using AI to transform physical industries — oil and gas, mining, manufacturing, power generation. These are trillion-dollar industries that are just beginning to feel the full impact of what this technology can do.

A New Tool for Finding the Next Generation of AI Winners 

Here’s the challenge: Finding the right stocks in these less-covered corners of the market is hard. There’s no shortage of companies claiming AI capabilities. The question is which ones have the real financial and technical momentum behind them — and which ones are just along for the ride.

That’s a problem Marc has spent his entire career trying to solve. And on June 24, we’re unveiling the most powerful tool he’s ever built to do it.

It’s called the Time Machine. It’s Chaikin Analytics’ first-ever AI-powered platform — and it works by scanning decades of market history to find stocks today whose fundamental and technical fingerprints match the early profiles of stocks like Nvidia Corp. (NVDA), Amazon.com Inc. (AMZN), and Meta Platforms Inc. (META), just before they made their biggest moves.

In backtesting, it surfaced stocks that went on to deliver gains of 995%, 1,406%, and 3,804% — all while the “seed” stocks they were matched against posted far more modest returns.

This is the first time Marc and I have shown this to anyone outside of Chaikin Analytics. Charter membership spots are limited, and this offer won’t be repeated.

If you want to be among the first to access the Time Machine — and see which stocks it’s flagging as the next generation of potential 10X winners — the first step is to reserve your spot for our free event on June 24.

Folks who sign up now get early beta access to the Time Machine before June 24, so you can start exploring the platform right away. No purchase required. Get on the list for that free broadcast here.

The oil and gas AI story is just one example of what the Time Machine is designed to find. The opportunity is much bigger than any one sector.

The post One of the Best-Performing S&P Sectors Has an AI Story Nobody Is Telling appeared first on InvestorPlace.

He’s seen a lot of market cycles. And right now, he’s watching one AI-driven trend that he says most investors are completely overlooking.

In today’s Smart Money, Joe explains why the biggest profits from the AI boom may not come from the names you already own — and why a new tool from 60-year Wall Street legend Marc Chaikin may be able to help you find the next generation of winners before the crowd catches on.

Marc and Joe are unveiling that tool — the “AI-Powered Time Machine” — at a free event on Wednesday, June 24. You can join the hotlist for free right here.

Here’s Joe…

Deep in the Australian bush, one coal mine nearly ground to a halt.

And nobody saw it coming…

Mines rely on vast networks of conveyor belts. These snake underground and across open terrain, moving ore from extraction to processing. If the belts stop, so does the mine.

In this coal mine, that’s exactly what was about to happen. AI-powered sensors on belt motors flagged something odd — a conveyor drive bearing was deteriorating faster than it should have been.

The culprit was bearing fluting. That’s when electrical current leaks through the bearing instead of flowing through the grounding system. It silently damages the bearing from the inside — until it’s too late.

A physical inspection might have spotted the wear, but it wouldn’t have detected the urgency. The sensors did.

They caught the problem early enough to trace it back to the grounding issue, fix it, and swap the bearing before the belt went down. As a result, the site avoided 10 hours of unplanned downtime and roughly $191,000 in lost production.

More importantly, fixing the root cause — not just the symptom — extended bearing life and cut long-term maintenance costs.

The sensors didn’t save just a bearing. They saved the whole operation.

This is part of a broader theme I’ve had my eye on…

AI and machines are doing the work that people can’t do — or doing it better.

And a key concept is increasingly making this possible.

The Foundation for Industrial AI

I’m talking about the Internet of Things — the IoT.

It has quietly become one of the defining technologies of our generation.

Not long ago, a thermostat was just a thermostat. Today, it’s a connected device sharing data with your phone, your HVAC system, and the cloud. The IoT is reshaping how people, machines, and processes communicate.

Back in the early 1980s, programmers at Carnegie Mellon University rigged a campus Coca-Cola vending machine to the internet so they could check if the drinks were cold before walking over. In 1999, Kevin Ashton at MIT gave this kind of thing a name: the Internet of Things.

Ashton’s argument was simple: Machines shouldn’t have to wait on humans to feed them data. People can be slow, distracted, and error-prone. If computers could gather information on their own and track the physical world in real time, we could eliminate waste, catch problems early, and know the state of everything — at all times.

We’re now living in the world he imagined.

The Numbers Reflect This Shift

According to industry research firm IoT Analytics, 18.6 billion devices were connected to the IoT in 2024. By 2030, that number is expected to grow to 39 billion — and hit 55 billion by 2035.

The IoT market generated nearly $550 billion in revenue in 2025. And it’s projected to reach more than $865 billion by 2030.

But industrial applications are driving much of that growth — and that’s where it gets really interesting for investors.

The industrial IoT market — think sensors, connected machinery, and automated systems in factories — came in at almost $326 billion in 2025. By 2034, it’s expected to nearly triple to about $945 billion. That’s nearly double the growth rate of the broader IoT market.

And it makes sense. Physical industries have the most to gain from machines that can sense, communicate, and act without human intervention. AI only accelerates what those machines can do.

Here’s a number that really caught my attention: By 2030, nearly half of all industrial IoT revenues are expected to depend on AI. That’s a fundamental shift in how factories, mines, and energy grids operate — and by some estimates, it could unlock up to $70 billion in new market value.

The data backs this up. According to a 2025 survey of nearly 18,000 private-sector firms, companies investing in AI alone saw a 10.4% productivity gain. Companies investing in the IoT alone saw a 6% decline. But the businesses that invested in both AI and the IoT together recorded a productivity increase of 18.9% — nearly double the AI-only gain.

Put simply: AI without physical connectivity is only half the story. And physical connectivity without AI is falling behind.

AI Is Moving Closer to the Machine

Until recently, most industrial sensors had no AI built in at all. That’s changing fast.

Manufacturers are now building AI directly into the sensors, gateways, and equipment on the factory floor. Instead of sending data to the cloud and waiting for a response, machines process information on the spot. The result is faster decisions, fewer failures, and fewer humans required to manage the whole system.

That’s what happened in that Australian coal mine. And it’s playing out in manufacturing plants, oil fields, shipping ports, and power grids around the world.

Folks, I’ve seen this pattern many times over my 40 years on Wall Street.

The biggest AI names — companies like Meta Platforms Inc. (META) and Microsoft Corp. (MSFT) — get most of the attention. And they face enormous pressure to prove that trillions of dollars in AI investment will eventually pay off.

But you don’t have to chase the headline names to find great investments in this megatrend.

In fact, some of the best opportunities in every major technology cycle have come from the companies building the infrastructure that makes it all possible — not the ones getting the most airtime on CNBC.

Think about the internet boom. Yes, Alphabet Inc. (GOOG) and Amazon.com Inc. (AMZN) became massive winners. But so did the companies making routers, fiber cable, and server hardware — the picks-and-shovels plays that powered the whole buildout.

The AI-plus-IoT convergence is setting up a similar opportunity. And the companies best positioned to win aren’t necessarily the ones you already own.

How to Find the Next Wave of Winners

That’s exactly where Marc Chaikin comes in.

Marc has spent 60 years on Wall Street developing the kind of analytical tools that can cut through the noise and find stocks with real momentum behind them. His Power Gauge has been doing that for decades.

But on June 24, Marc and I are unveiling something new — something neither of us has ever shown the public before.

It’s an AI-powered platform we’re calling the Time Machine. And it works by going “back in time” through decades of market data to find stocks today whose financial and technical DNA closely matches the profiles of stocks like Nvidia Corp. (NVDA), Amazon, and Meta at the very beginning of their historic runs.

In backtesting, it identified stocks that went on to deliver gains of 995%, 1,406%, and even 3,804% — all while the “seed” stocks they mirrored posted far more modest returns.

I’ve spent 40 years trying to get ahead of major technology shifts. This is the most powerful tool I’ve seen for doing exactly that.

The industrial AI buildout is real, it’s enormous, and it’s still early. The question is whether you’re positioned to benefit from it — or whether you’ll be watching from the sidelines while the next generation of big winners takes shape.

On June 24, Marc and I are going to show you exactly which stocks the Time Machine is flagging right now — for free.

To get access, all you need to do is reserve your spot now. When you do, you’ll get early access to the Time Machine’s beta platform, so you can start exploring it before our free broadcast. You can type in any ticker and see how it compares to the greatest stock market winners of all time.

This is Chaikin Analytics’ first-ever AI-powered product — and charter spots will be limited.

Sign up for this free event here.

Good investing,

Joe Austin

Senior Analyst, Chaikin Analytics

P.S. Joe has spent four decades watching major technology cycles play out. And he says the AI-plus-IoT convergence is one of the most significant investment opportunities he’s seen. But the window to get in early doesn’t stay open forever. Marc and Joe’s event on June 24 is free — and when you reserve your spot, you get early access to a beta version of the Time Machine right now, no purchase required.

The post How AI Just Saved a Coal Mine $191,000 in One Morning — and What It Means for Your Portfolio appeared first on InvestorPlace.

Last week, InvestorPlace Senior Analyst Eric Fry highlighted how one of his latest picks now has even more upside than Nvidia Corp. (NVDA). Though Nvidia still has room to grow, smaller AI companies can do even better in relative terms. 

To investors, that’s all that matters. 

So, for this Sunday Digest, I’d like to leave it to Eric to explain why he has his eye set on a new potential ten-bagger, and why this stock could belong in your portfolio. 

Take it away, Eric… 

There’s one simple problem with investing in a company like Nvidia Corp. (NVDA): 

There’s only so much upside left to a $5 trillion stock. 

• If shares were to double again, it would make Nvidia worth more than the Dutch East India Co. (VOC), the most valuable company in history in inflation-adjusted dollars ($8 trillion) 
• If it were to triple, Nvidia would be worth more than every stock on the Japanese and U.K. stock markets combined ($12 trillion) 
• And if it were to rise 10X, that would make Nvidia worth almost as much as the total U.S. stock market ($75 trillion). 

Like a goldfish in a bowl, every corporation is limited by the economies they swim in. And no matter how successful Nvidia becomes, it’s still constrained by having only 8.3 billion potential customers… at least until they figure out how to sell AI chips to rabbits and mice. 

Now, I fully believe that Nvidia is an amazing company. In fact, I recently compared the world’s most valuable company to Babe Ruth, the most famous baseball player in American history. The chipmaker will keep hitting home runs, and there could be at least another 30% upside in shares, especially after the recent brutal selloff. 

But I think you can do better than that.  

Thirty percent returns are table stakes on Wall Street… something you can earn with a high-dividend stock in three to four years, or by buying a house in the right ZIP code. In fact, my old Lexus rose 30% in value while sitting in my driveway in 2021. 

That’s why I’ve built my career around finding 1,000% winners instead – the extraordinary stocks that can rise 10X or more. Most people only need to buy two or three of these over their entire careers to make a life-changing amount of money. I’ve found over 40. 

Last year, my focus was on chipmakers like Advanced Micro Devices Inc. (AMD) and data center companies like Oracle Corp. (ORCL). These “second wave” AI winners were still growing fast, even as Nvidia’s growth was plateauing. 

Now that those stories have played out, I’ve turned my attention to a third wave of AI companies: “Enablers.”  

These far smaller firms are providing the “picks and shovels” to the AI buildout and are the new set of stocks that can rise 10X. 

These are the companies I’m now recommending. 

In fact, I recently added one to my flagship service, Fry’s Investment Report.  

So, today, I’d like to tell you about this little-known energy company with incredible potential.  

Then, I’ll share how you can find even more Enabler companies with bigger upsides than Nvidia.  

Let’s dive in… 

AI’s Energy Problem 

We all know that electricity has gotten expensive in America. The average household now pays roughly $2,000 per year in utility bills, or 2.5 times more than in 2000.  

That figure is even higher in fully deregulated states like Massachusetts and Maryland, where private companies are free to set their own rates. 

Now, we can’t blame energy costs. Henry Hub natural gas prices have only risen from $2.42 per unit in 2000 to $2.94 today… a 21% increase in 26 years. (Again, my car did that and more in 12 months).  

“Greedy” energy utilities aren’t responsible for the spike, either… at least not entirely. For instance, Massachusetts’ largest private utility has been cash-flow negative for nine of the past 10 years and struggles to meet its dividend payments. 

Instead, the real culprit is that America doesn’t make enough electricity.  

Most of America’s coal-fired power plants were built 40 to 60 years ago, and they’re reaching their end-of-life all at once. Natural gas power has filled around two-thirds of that gap, but it is hitting a limit from shortages of gas turbines and pipeline capacity. Offshore wind power has proved phenomenally expensive. The Massachusetts utility I talked about earlier has already lost $2.5 billion on its wind projects… and still counting. 

In fact, America’s growth in electricity generation looks like a rounding error compared to growth in countries like China. The chart below tells the tale. 

And now, the tightness is getting worse because of artificial intelligence. Data centers are already using around 5% of electricity in the U.S., and they’re only getting started. 

For instance, the “Stratos” AI data center planned for Box Elder County, Utah, will need 9 gigawatts of electricity – more than double what the entire state currently uses. If estimates are correct, we’ll need anywhere from 12 to 20 of these “Stratos”-sized projects by 2032 just to keep up with AI demand. 

That’s a lot of electricity. 

To play this trend, longtime readers will know that I’ve selected several natural gas plays, which are outperforming many of the “obvious” AI plays like Nvidia.  

But American data centers and utilities alike are now turning to another power source to get ahead… 

The Sun Also Shines 

Solar power. 

This intermittent source of electricity has become a surprisingly popular way to add generation capacity. Solar panels are cheap, battery storage is viable, and the technology is supported by both sides of the political spectrum.  

My home state of California still leads the nation in installed solar capacity, but Texas, at No. 2, has almost caught up. Florida, Arizona, and North Carolina round out the next three spots.  

In fact, 51% of American power-grid additions in 2026 are expected to come from solar, and another 28% from battery storage, according to the U.S. Energy Information Administration (EIA). Most will happen in the South and Southeast. 

Solar and battery storage will dominate new electricity additions this year.

Source: EIA

That’s creating a bonanza for solar stocks, which have risen 84% in the past year alone.  

Solar power is also surprisingly useful for AI data centers. Solar output matches the 9-to-5 workloads of corporate AI use (not to mention peak cooling demands of AI data centers), and many state utilities now allow new data centers to “cut the line” for grid connections if they add solar-and-battery capacity. BloombergNEF estimates that battery storage can cut a data center’s connection timeline by five years – an eternity in the AI arms race. 

Now, there are many terrible solar companies out there. The industry is cut-throat competitive from years of international price-dumping, and Chinese-related companies now control 80% to 90% share of solar components.  

Western firms like Canadian Solar Inc. (CSIQ) must pay whatever their Chinese suppliers demand. We’ve already seen two major solar bankruptcies this year: SOLON Corp. and Freedom Forever. The latter ironically named firm is now being investigated by the state of Texas for fraud. 

But I believe I’ve found an innovative energy firm that should do far better.  

This company has developed a battery storage technology that absorbs and smooths the violent swings between AI data center demands and solar power generation. It’s solving a multiyear problem that has plagued the data center industry. 

For instance, in July 2024, a small electrical disturbance in Northern Virginia’s “Data Center Alley” that lasted just a few milliseconds triggered emergency shutoffs that cut 1.5 gigawatts of load from the grid all at once – the equivalent of turning off an entire midsized city. Power plants across Virginia and Maryland were ordered to throttle output to prevent a cascade of damage, and engineers were then forced to manually reconnect each data center to the network. 

This company helps prevent such wild swings, making data centers easier to connect to the grid. And growth is expected to surge.  

Nvidia is a remarkable company. I’ve said that before and I’ll say it again. But remarkable companies and remarkable returns are different things — especially when the company is already worth $5 trillion. 

This goldfish has nearly outgrown its bowl. 

The energy company I’ve described today is still swimming in open water.  

Revenue growth is expected to flip from negative 16% last year to positive 48% this year, and then remain in the 20% range after that. And it looks set to break even this year before profits start rolling in during the fiscal 2027 year. 

Its technology is solving a problem that’s costing data centers billions. And its stock hasn’t been discovered yet by investors still staring at Nvidia. 

That’s where the 1,000% winners come from. Not from the most famous fish in the tank — but from the ones nobody’s watching yet. 

You can click here to learn how to access the name of this AI energy company.

Regards, 

Eric Fry 

Editor, Smart Money

Thomas Yeung is a market analyst and portfolio manager of the Omnia Portfolio, the highest-tier subscription at InvestorPlace. He is the former editor of Tom Yeung’s Profit & Protection, a free e-letter about investing to profit in good times and protecting gains during the bad.

The post Why Settle for Nvidia When This AI Stock Has 1,000% Upside?  appeared first on InvestorPlace.

Editor’s Note: Most investors are focused on the AI names everyone already knows.
Joe Austin — tech-sector research analyst, senior portfolio manager overseeing more than $10 billion in assets, and coverage analyst for a $5 billion hedge fund — is focused on the ones most people haven’t found yet. He uncovers the companies using AI to solve problems in places like the factory floor and the oil field, before Wall Street fully catches on.

On Wednesday, June 24, he and veteran analyst Marc Chaikin are debuting the first AI-powered tool Chaikin Analytics has ever built to help find exactly those stocks. They call it the ‘Time Machine’ — and charter access is strictly limited. Reserve your spot here before it fills up.

Today, Joe joins us to make the case that the most profitable AI applications aren’t the ones getting the most attention. And he explains why that gap between perception and reality is exactly where great investments are found…

California Steel Industries’ Hot Strip Mill in Fontana stretches more than half a mile long.

Inside, giant ovens heat steel slabs to about 2,300 degrees Fahrenheit. At that temperature, the steel gets soft enough to roll.

But first, it needs cleaning. The furnace leaves a thick crust of “scale” on the surface. If it isn’t removed, it gets pressed into the steel and ruins the finish. A scalebreaker cracks the crust loose. Then high-pressure water jets blast it away.

Next, the steel slab passes through five roughing stands that squeeze it down from between 7 and 9 inches thick to as little as 0.0538 inches — close to the thickness of a credit card. A crop shear trims the ragged ends before the steel moves to finishing. Then six more finishing stands roll it to its final thickness and surface quality.

By this point, the steel is moving at about 35 miles per hour.

That’s too fast to catch defects by eye. For automotive panels and appliances, the surface has to be flawless — defects show right through the paint.

The finished strip winds into a coil. Some weigh up to 25 tons. The whole process takes about five hours. At full capacity, the mill runs 24 hours a day and produces 2 million tons of steel per year.

But at least you can see steel.

In today’s most advanced semiconductor fabrication plants, the defects that matter are invisible to the human eye. 

And the consequences of missing them are just as severe.

A Single Semiconductor Defect Can Cost $25,000 — and Human Inspectors Can’t Stop It

In semiconductor manufacturing, everything starts with a wafer — a thin, polished disc sliced from pure silicon, usually about 12 inches across. These wafers must be flawless. Even a microscopic scratch or contaminant can create defects across hundreds of chips.

The first step is circuit printing using extreme ultraviolet lithography. This process projects circuit patterns using light with a wavelength shorter than any visible color. A single finished chip can require 20 to 30 passes through this stage alone.

The specialized masks used in this process — a kind of three-dimensional stencil — have to be perfect, too. A single defect ruins every chip that mask touches. And those masks can cost up to $1 million each.

After each pass, the wafer goes through etching, deposition, and chemical treatment to build up transistor layers. Then the cycle repeats. Today’s most complex chips go through 1,500 to 2,000 individual steps before they become functional. Each step is a potential failure point. One particle of dust can ruin an entire wafer.

A single wafer for the most advanced semiconductors can cost between $20,000 and $25,000. Each wafer holds hundreds of chips. A defective one wipes out hundreds of products at once. And the fabs where all this happens cost between $15 billion and $20 billion to build.

Fabs need to reduce these losses wherever possible. And human inspectors simply can’t do the job.

At 35 miles per hour, steel moves too fast to see. In a semiconductor fab, the defects are too small to see. In both cases, the stakes are too high to miss anything.

AI Is Boosting Quality Control

This is one area where AI doesn’t just help. It’s the only solution that actually works.

AI “deep learning” and “edge learning” take defect control to a level humans can’t match. Deep learning works by analyzing hundreds of example images until the system learns to make decisions on its own — no programmer required at each step. 

Edge learning goes further. These systems come pretrained and may need as few as five to 10 images to get started. They deploy in minutes.

The results are measurable.

At BMW, AI-powered vision systems cut defect rates by 30% at one European plant within a year. Customer satisfaction jumped 15% after the rollout. At Foxconn, AI-powered cameras now catch defects with 98% accuracy, flag 80% fewer false alarms, and inspect each unit 60% faster than before.

These aren’t pilot programs. They’re production systems running at scale, in some of the most demanding manufacturing environments on Earth.

This is what I mean when I say the real AI story isn’t the one getting the most attention.

Everyone is watching the big infrastructure names — the chip companies, the cloud providers, the chatbot platforms. And yes, those are important. But there’s a parallel story playing out on the factory floor, in the oil field, and in the semiconductor fab. 

AI is solving problems that weren’t solvable before. And the companies delivering those solutions are becoming more competitive, more profitable, and more valuable — quietly, without much fanfare.

That’s exactly the kind of opportunity I’ve spent my career looking for.

Finding the Next Generation of Winners Before the 蜜桃传媒 Catches On

The challenge, of course, is identifying which companies are actually winning — not just claiming to use AI, but using it in ways that show up in the fundamentals.

That’s a problem Marc Chaikin has been working on his entire career. His Power Gauge rating system was built to cut through the noise and find stocks with real momentum behind them. It’s been doing that for decades.

But on June 24, Marc and I are going a step further. We’re unveiling the first AI-powered product Chaikin Analytics has ever built — and it’s unlike anything we’ve shown the public before.

We’re calling it the Time Machine. It scans decades of market history to find stocks today whose fundamental and technical fingerprints match the early profiles of stocks like Nvidia Corp. (NVDA), Amazon.com Inc. (AMZN), and Meta Platforms Inc. (META) — right before they made their biggest moves. In backtesting, it surfaced stocks that went on to deliver gains of 995%, 1,406%, and 3,804%, all while the “seed” stocks they were matched against posted far more modest returns.

The factory-floor AI story is one example of the kinds of opportunities the Time Machine is designed to surface. Companies solving real industrial problems with AI — before the market catches on.

This is the first time we’ve ever made something like this available to individual investors. Charter membership is limited, and this offer won’t be repeated once the June 24 unveiling is behind us.

The first step is simple: Reserve your spot for our free event. 

Folks who sign up get early beta access to the Time Machine right now — no purchase required. You can enter any ticker and see how it compares to the greatest stock market winners of all time, before the official launch. 

Secure your spot here.

The post The $25,000 Mistake AI Just Made Obsolete appeared first on InvestorPlace.

The American billionaire J. Paul Getty once remarked that his formula for success was to “rise early, work hard, strike oil.”

But if you don’t strike oil, you need other ways to accumulate — and protect — your wealth.

So, while there is no perfect investment method, there is a way to allocate your assets intelligently. This will then help you set yourself up for the best chance at success.

Now, there are multiple facets to this strategy, but the one I want to focus on in today’s Smart Money is stocks to buy and hold forever. So, here are three stocks that I consider to be some of the best “Forever Stocks” out there.

Let’s get started…

“Forever Stock” No. 1

Volatility can open the door to new buying opportunities. That’s how I spotted Block Inc. (XYZ), which owns and operates the well-known payment app, Square. 

Today, Block helps merchants transact over $200 billion annually. Its point-of-sale systems are found everywhere from farmers’ markets to national retail chains. And the company has expanded far beyond the four-sided card readers that inspired its original namesake. 

• Peer-to-peer payments
• Small Business Software
• Buy Now Pay Later
• E-Commerce
• Crypto

Thanks to Block’s sizeable multi-year spending on both capital investments and M&A, the company has become one of the world’s leading fintech companies. It has built a comprehensive financial services ecosystem that benefits both merchants and consumers, producing $3.2 billion in free cash flow by March 31, a 172% increase compared to the same period last year.

Block now appears to have reached an important inflection point, and the company is on a path to potentially grow beyond that.

“Forever Stock” No. 2

Now that the healthcare industry has entered the “Age of AI,” the opportunities to capitalize on it are popping up like weeds in a garden, or perhaps like bacteria in a Petri dish. 

The biotech sector, in particular, is offering a compelling and timely opportunity. But investing in this high-risk sector can be a confusing and challenging endeavor.

A unique company named Royalty Pharma Plc (RPRX) removes some of the risk and confusion from the equation. As its name implies, the company manages a portfolio of royalties on both approved and development-stage drugs.

Importantly, Pharma is not merely dominant; it is enormously successful. Since going public in 2020, the company has acquired royalties on over 35 commercial products and 17 development-stage candidates.

The company’s royalty-based business model generates exceptionally high profit margins.  

“Forever Stock” No. 3

Equinor ASA (EQNR) is the largest energy company in Norway and the ninth largest in the world, based on revenue. Importantly, it is Europe’s largest non-Russian supplier of natural gas, by far, and also a major crude oil producer. 

The fact that this company operates next door to Russia used to be a footnote that barely deserved a mention. But that footnote became a headline after Russia invaded Ukraine. 

Since then, European countries have been phasing out Russian supplies of oil and gas, and phasing in additional supplies from Norway. The conflict in Iran is supercharging that trend.

As a result, Equinor finds itself in the right place at the right time.

The Next Evolution in Stocks

These stocks are sure to fortify your portfolio in 2026 and beyond.

As I believe in the longevity in these stocks, it’s important to note that artificial intelligence is a prominent component in their enduring success.

But the investing landscape of the last two years – where you could buy almost anything connected to AI and make money – no longer exists.

That is why my stock-picking system, Apogee, is built to help me find those companies at the most promising moment. That includes companies set to soar in the growing age of agentic AI.

And it is flagging several right now.

My system uses a host of proprietary indicators to unearth incredible opportunities. It scans over 14,000 stocks and crunches more than 120 billion data points a day. More than 3 trillion a month. It runs on the same underlying technology platform used by Goldman Sachs and JPMorgan.

My system not only helps me identify winning stocks, but it also helps me identify when to get in at a massive discount.

Now is your chance to get into these companies early.

Click here to learn more about the specific stocks my system is flagging right now.

Regards,

Eric Fry

The post Three “Forever Stocks” for the AI Age appeared first on InvestorPlace.

In the late 1970s, people would stand for hours outside New York’s Studio 54 hoping to get in.

The club’s co-founder, Steve Rubell, became famous for turning people away. On one Saturday night, he boasted of rejecting 1,400 would-be guests.

Ironically, the long lines to get in were part of the club’s appeal. Every person waiting in line became evidence that whatever was happening inside must be worth experiencing. The crowd became the advertisement.

Human beings are motivated to action by all sorts of forces, but few are more powerful than our tendency to take cues from other people. Psychologists call this social proof. And investors fall for the same phenomenon every day.

You see examples of it everywhere in your life. If everyone is lining up outside a restaurant, you’ll assume the food must be good.

Credit: Liukov

If a book spends months at the top of the bestseller list, you’ll assume that it must be worth reading.

And if millions of investors are piling into the same handful of stocks, it’s only natural to assume those stocks are where the money will be made.

So many people can’t be wrong… right? You wouldn’t want to miss out… right?

Well, sometimes the crowd can be wrong, or at least they can be late.

One of the key insights we’ve repeated often in the Digest is that the human brain is a marvelous tool for creating art, music, language, and engineering feats, but it can be a terrible tool for investing.

The truth is that following the crowd may be one of the most expensive habits an investor can have, and one they need to control.

Of course, human beings evolved to take comfort in numbers. For most of history, blending into the group was a survival skill. If everyone in the village fled toward higher ground, you didn’t stop to debate whether the floodwaters were really coming. You just ran.

But the stock market isn’t a survival exercise.

The market saves its biggest rewards for anticipation, not confirmation.

By the time everyone agrees a company is a winner, the biggest gains are often already in the rearview mirror. Investors who bought Nvidia in 2024 were not making the same investment as the people who bought it in 2016. Investors who discovered Amazon after it became a household name weren’t taking advantage of the same opportunity as those who found it years earlier.

Yet our brains keep pushing us toward what’s already popular, already proven, and already widely accepted.

That’s why investors routinely crowd into yesterday’s biggest winners while overlooking tomorrow’s.

And that tendency may be more dangerous today than ever.

Because while millions of investors remain fixated on the same AI giants dominating today’s headlines, a new generation of potential market leaders may already be emerging beneath the surface.

Spotting Winners Before the Crowd Does

Finding a great company isn’t the biggest problem investors have. It’s finding a great company before everyone else does.

Few investors needed to be convinced Nvidia (NVDA) was a great company in 2025.

The challenge was recognizing it in early 2023, before the stock became the poster child for the AI megatrend.

According to investing legend Marc Chaikin, that’s exactly when his Power Gauge turned bullish on Nvidia. The stock would go on to become one of the greatest wealth-creation stories in modern market history.

If you’re not familiar with Marc, he spent nearly six decades on Wall Street, building trading systems, analyzing stocks, and managing money through bull markets, bear markets, crashes, bubbles, and recoveries.

Today, he’s best known as the creator of the Power Gauge, a stock-rating system designed to help investors identify strong stocks and avoid weak ones.

Here is how NVDA performed since it turned bullish in Marc’s Power Gauge, and more importantly, before the crowd had piled into the stock.

Winners that go up more than 10X don’t come along every day.

Even after nearly 60 years on Wall Street, investors still follow Marc due to his ability to identify major market trends before they become consensus.

Of course, the issue for investors isn’t whether they should have bought Nvidia three and a half years ago.

The question is where to find the stocks in today’s market that resemble Nvidia before that move began.

Marc’s New Breakthrough

Spotting Nvidia before the crowd is one thing.

Finding the next Nvidia before the crowd is something else entirely.

Most people know where the big gains have already been made. They know Nvidia. They know Meta. They know Palantir.

What they don’t know is which stocks could be next.

According to Marc, that’s exactly the problem he’s been trying to solve.

While too many investors spend their time chasing yesterday’s winners, Marc believes the biggest fortunes of the next decade will be made in companies most people have never heard of… yet.

Companies that share many of the same characteristics the market’s biggest winners displayed before they became household names.

That’s why Marc recently unveiled what he calls the most important breakthrough of his career.

It’s a new AI-powered system designed to search through decades of stock market history and identify companies whose technical and fundamental profiles closely resemble some of the greatest stock market winners of all time.

In other words, instead of asking, “What should I buy after it’s already gone up?”

Marc is asking a very different question:

“What stocks today look most like Nvidia, Amazon, Meta, and other legendary winners before their biggest moves ever began?”

And the answers may surprise you.

In an upcoming event, Marc will describe his new system and which stocks most closely resemble Nvidia, Meta, and Amazon before their biggest moves.

And why he believes the market is entering a period where a new generation of winners could replace many of today’s household names.

He’s laying out the full case in a special event.

Click here to reserve your spot for this free event on June 24, at 10 a.m. ET.

As a bonus, you can get free, early access to a special “beta” version of this first-ever AI-powered platform from Chaikin Analytics. A unique chance to take Marc’s system out for a “test-drive.”

Don’t waste your time following the crowd. Find out about Marc’s new system on June 24 at 10 a.m. ET, so you can unlock the potential gains the stock market has in store.

Enjoy your weekend,

Luis Hernandez

Editor in Chief, InvestorPlace

The post How to Spot 蜜桃传媒 Winners Early appeared first on InvestorPlace.

It’s a powerful idea. And that’s why my good friend Marc Chaikin put his firm to work to build a tool to help investors do just that…

It’s called the Time Machine – the first-ever AI-powered platform from Chaikin Analytics.

Today, I want to introduce you to Marc’s colleague Joe Austin, a 40-year Wall Street veteran. In the essay below, he shows you the kind of opportunity most investors miss at first – not because it’s small, but because it’s hiding in plain sight.

Insights like this helped form the Time Machine – which is designed to search today’s market for stocks with the same early DNA as some of the greatest winners in history.

And that’s exactly why I think you’ll want to get on Marc and Joe’s early access list now.
Click here to reserve your spot.

For now, here’s Joe…

Denny Hamlin was ready to clinch his first NASCAR championship. But it all fell apart because of a piece of tape.

Hamlin was considered one of the best drivers of his generation, but the title had always eluded him.

In 2019, he was NASCAR’s comeback kid. He’d already won six times that season — including the iconic Daytona 500. The championship all came down to a cool November night at the Homestead-Miami Speedway.

The day started well. Qualifying had been canceled due to bad weather, which gave Hamlin the pole position based on his points standing. In the first two stages, he finished fifth. By the third stage, things were looking up — he was running second, posting quicker lap times than his teammate Kyle Busch, who led the race.

Then, with 58 laps to go, crew chief Chris Gabehart made a fateful call. He brought Hamlin in for an early pit stop and had the crew slap a big piece of black tape on the front grille.

The idea was sound, in theory. In NASCAR, crews routinely tape grilles to gain a competitive edge. Normally, air enters the grille and bounces around the engine, creating drag. Tape restricts that airflow, forcing it to flow smoothly over the car instead. More speed, more downforce, better grip.

But tape also restricts cooling.

NASCAR engines run at around 290 degrees Fahrenheit — about 90 degrees hotter than a typical road car. The margin for error is razor-thin. In Hamlin’s case, the tape backfired immediately. Temperature gauges maxed out. Steam started spewing from the engine. Engine failure seemed imminent.

Gabehart had to call Hamlin back to the pit after just 12 laps. He finished 10th — dead last among the four championship contenders. Kyle Busch won both the race and the title.

Excessive heat isn’t just a NASCAR problem. Data centers running AI chips face the exact same dilemma.

And for investors, it represents one of the most overlooked opportunities in the entire AI boom.

Finding the “Behind the Scenes” Companies

AI chips need massive amounts of power to train models and run computations. More power means more heat. And if you can’t cool the chips fast enough, performance crashes, or the hardware fails entirely.

Inside a data center are long rows of computer racks — tall cabinets stacked with servers. A typical AI data center contains hundreds or even thousands of them. Nvidia Corp.’s (NVDA) next-generation Vera Rubin chip uses 120 to 130 kilowatts per rack. That’s the annual electricity consumption of about 100 U.S. homes — per rack.

Bigger versions of the Rubin chip will use five times that much power.

That creates an unavoidable physics problem. More power means more heat, in a nearly one-to-one relationship. And delivering this much electricity requires completely rethinking how power gets moved through a building.

Think of it like water through a hose. You can deliver the same volume using high pressure through a small hose or low pressure through a massive one. Electrical power works the same way — high voltage with low current, or low voltage with high current. High current dangerously overheats cables. Traditional power systems can’t handle it.

Engineers solved this by raising the voltage. The industry has shifted to 800-volt power systems, which deliver the same power with far less current and far less heat.

But operating at 800 volts requires power chips made from entirely different materials. Materials that only a handful of companies know how to produce.

This is the bigger point about investing in a megatrend like AI.

The AI technology itself gets all the media attention. Nvidia, Microsoft Corp. (MSFT), Palantir Technologies Inc. (PLTR): Everyone knows those names.

But an entirely different set of companies — the ones making the components, materials, and systems that let AI physically function — are just as essential. And far less picked over.

No matter which big-name AI company is building the next data center, the “behind the scenes” businesses making it all run are going to get paid. The question is whether you own any of them.

A Tool Built for Exactly This

I’ve spent 40 years on Wall Street learning to look one step behind the obvious story. The internet boom made millionaires out of people who bought Cisco Systems Inc. (CSCO) and Intel Corp. (INTC), not just Amazon.com Inc. (AMZN) and eBay Inc. (EBAY). The shale revolution enriched investors in fracking equipment and pipeline infrastructure, not just oil producers.

The AI boom is setting up the same way. And the challenge — as always — is finding the right “behind the scenes” stocks before the crowd does.

That’s exactly what Marc Chaikin and I built the Time Machine to do.

The Time Machine is Chaikin Analytics’ first-ever AI-powered platform, and we’re unveiling it for the first time on June 24 during a special free broadcast (reserve your spot here). It works by scanning decades of market history to find stocks today whose fundamental and technical fingerprints match the early profiles of proven multi-bagger winners — stocks like Nvidia, Amazon, and Meta Platforms Inc. (META), right before their biggest moves.

In backtesting, it surfaced stocks that went on to deliver gains of 995%, 1,406%, and 3,804% — all while the “seed” stocks they were matched against posted far more modest returns.

The picks-and-shovels AI companies — the power chip makers, the cooling system specialists, the infrastructure suppliers — are exactly the kind of stocks the Time Machine is designed to surface.

This is the first time Chaikin Analytics has ever offered an AI-powered product. Charter membership spots are limited, and this offer closes after June 24. The first step is free.

Reserve your spot now and you’ll get early beta access to the Time Machine before the June 24 unveiling — no purchase required. You can enter any ticker and see how it stacks up against the greatest stock market winners in history.

Good investing,

Joe Austin

Senior Analyst, Chaikin Analytics

P.S. A piece of tape cost Denny Hamlin a championship because no one was watching the temperature gauges closely enough. In AI investing, the same thing is happening — most investors are watching the obvious names while the real heat is building somewhere else. On June 24, Marcand Joe are debuting the first AI-powered tool Chaikin Analytics has ever built to help you find those opportunities. Charter access is limited. Secure your spot before it fills up.

The post The Hidden AI Problem Worth Billions – and the “Behind the Scenes” Stocks Solving It appeared first on InvestorPlace.

Within days of its showy IPO, SpaceX (SPCX) has locked in a $60-billion deal to acquire up-and-coming AI coding agent Cursor.

The price tag exceeds what Elon Musk paid for Twitter. In fact, excluding the $1.25 trillion merger between SpaceX and xAI, it’s Musk’s largest acquisition to date. 

SpaceX just raised $75 billion in the largest IPO in history. It could have bought almost anything. It bought a coding agent. 

That choice tells you everything about where Elon Musk thinks the next phase of AI is headed.

Why SpaceX Needed Cursor: The Software Problem at the Heart of the Musk Industrial Stack

SpaceX’s entire business is centered on rockets, satellites, Starlink terminals, defense systems, autonomous manufacturing lines, humanoid robots, orbital compute infrastructure — and now, through xAI, a large language model. 

Every single one of these businesses runs hyper-complex, mission-critical, continuously-iterated software.

If Cursor can make the engineers behind that software more productive, it could compress years of engineering work into months — across rockets, satellites, humanoid robots, and autonomous manufacturing lines simultaneously.

This impact goes deeper still. Cursor isn’t just a productivity tool; it’s a distribution platform. Enterprise developer tools are famously sticky. That means SpaceX just bought daily, persistent, deeply embedded access to the most valuable users in the enterprise software economy: software engineers.

Not to mention — every prompt, every code completion, every debugging session that runs through Cursor? That proprietary usage data is what makes AI models demonstrably better. SpaceX/xAI now owns one of the richest AI training and inference datasets on Earth, packaged inside a tool that users will pay a monthly subscription to provide.

That $60-billion price tag is starting to look much less outlandish.

(While Musk signals his moves publicly, others do it through SEC filings most investors never read. One of those filings just caught my attention.) 

The Real Signal: AI Is Moving From the Training Room to Persistent Agentic Deployment

For the past three years, the AI economy has been defined by one thing: training. Who has the most GPUs? Who can build the biggest model? 

That’s what moved markets — and it was where the money went.

That era isn’t over, but it is maturing. The frontier labs have their models. The hyperscalers have their infrastructure. Now the race is about deployment; specifically, agentic deployment — AI that doesn’t just respond to prompts but takes actions, writes code, browses the web, executes tasks, and operates autonomously across multi-step workflows.

Cursor is the clearest proof yet that agentic AI coding is a daily workflow for millions of professional developers. 

And when the world shifts toward continuously running AI agents, inference demand explodes. We’re talking 20x to 50x the compute from training-era workloads — because inference isn’t a one-and-done query. It’s a persistent, context-heavy, multi-turn process that runs all day, every day.

The SpaceX/Cursor deal is a $60 billion vote of confidence that the agentic shift is happening now, and the infrastructure to support it is worth building — at any price.

The Jevons Paradox Is About to Hit Software — and It’s Bullish for Every Physical Bottleneck

There’s a principle in economics called Jevons Paradox: when a resource becomes more efficient to use, total consumption of that resource goes up. 

For example, when James Watt’s improved steam engine made coal-powered machinery dramatically more efficient in the late 18th century, Britain didn’t use less coal — it used exponentially more. More efficient engines made steam power viable for textile mills, iron foundries, flour mills, breweries, railways, and steamships. Applications multiplied faster than efficiency gains could reduce consumption. By the time Jevons wrote his famous treatise in 1865, British coal output had roughly quadrupled in a generation. 

The same dynamic is unfolding in software development right now.

AI coding agents like Cursor make software dramatically cheaper and faster to build. The first-order intuition is that this reduces infrastructure demand: fewer engineer-hours means less compute, right? Wrong. 

When software becomes faster and cheaper to build, the world builds vastly more software. More software built by agents → more agent usage → more inference compute demand → more GPUs, more networking, more memory, more power, more cooling. 

The Cursor acquisition doesn’t just validate agentic AI. It validates the entire AI infrastructure thesis for the next decade.

Where Does the $60 Billion Signal Point? The Physical Bottlenecks of Agentic AI

Nobody got rich from cheaper steam engines. They got rich owning the coal mines, the railroads, and the infrastructure that made the boom possible. The AI version of that trade is right in front of us. 

As agentic AI demand multiplies over the next few years, the components that are hardest to scale, fastest to sell out, and least substitutable will capture the most value. Here’s what’s on that list. 

GPUs and Accelerators: The First Bottleneck Agentic Inference Pounds

Inference workloads run on the same GPU infrastructure as training — and agentic inference is far more compute-intensive because it runs continuously rather than in discrete bursts. 

• Nvidia (NVDA) remains the dominant supplier, with Broadcom (AVGO) building custom AI chips for Google and Meta (META) that handle a growing share of hyperscaler inference. 

The GPU shortage is structural, and persistent agentic workloads are about to make it dramatically worse. 

Networking: The Least Appreciated Bottleneck in the Agentic Stack

Every token an AI agent generates has to move between memory and processors at extraordinary speeds — and when thousands of agents run simultaneously across distributed clusters, the data movement problem rivals the compute problem. 

• Arista Networks (ANET) is the backbone of AI cluster networking, handling the high-speed switching between GPU racks. 
• Corning (GLW) and Coherent (COHR) supply the fiber and optical transceivers carrying that data between data centers — the last physical bottleneck before raw compute. 

Memory and Storage: Why Agentic AI Is Structurally Undersupplied

Agentic AI is extraordinarily memory-hungry. Long context windows, persistent state, real-time retrieval — all of it demands high-bandwidth memory (HBM) that the industry is already structurally undersupplied on. 

• Micron (MU) is the leading U.S. supplier of HBM and has reportedly sold out production under long-term contracts. 
• Western Digital (WDC) supplies the storage layer underneath. 
• IREN (IREN) operates AI-native data center infrastructure built specifically around these workloads. 

Power and Cooling: The Bottleneck That Doesn’t Sleep

Every GPU running inference burns power around the clock — and agentic workloads don’t sleep. A single large AI data center can consume as much electricity as a small city. 

• Vertiv (VRT) supplies the power and thermal management systems keeping those racks online. 
• Eaton (ETN) provides the electrical infrastructure distributing power at scale. 
• Quanta Services (PWR) builds and maintains the physical grid upgrades supporting the entire buildout — a decade-long capex cycle that is just getting started. 

The Bottom Line: Own the Bottlenecks the $60 Billion Signal Points To

SpaceX’s latest deal isn’t really about Cursor. It’s about Elon Musk signaling that the next phase of AI is agentic, it runs on inference, and controlling the daily workflow of software engineers is a strategic asset worth $60 billion.

When the smartest, most ruthlessly strategic operator in the technology industry pays 60 billion dollars to make a bet, the right response is to ask what he knows that the market hasn’t priced in yet — and then position accordingly.

The AI economy is shifting from training to inference. From occasional queries to persistent agents. From a few hyperscalers spending capex to the entire software-building world running on AI infrastructure 24/7/365. 

The bottlenecks in that world — GPUs, networking, memory, power, cooling — are the assets you want to own.

Those bottlenecks aren’t a secret to everyone. 

Peter Thiel recently filed a 13F showing he’d quietly liquidated every share of Nvidia, Apple, Microsoft, and Tesla he owned. Not trimmed — exited entirely. At the same time, his private fund has been deploying capital into exactly the physical bottlenecks this piece describes: energy infrastructure, nuclear power, chip fabrication, and natural resources.

He can’t buy those companies publicly. Most of them aren’t available to retail investors at all.

But I have spent months identifying seven publicly traded stocks that mirror those same private bets — the physical layer of the AI buildout that the billionaires are already funding. 

Thiel calls it the shift from “bits” to “atoms.” I call it the Billionaire’s Backdoor.

Here’s the full portfolio — and the thesis behind every position.

The post The Cursor Acquisition Tells You Exactly Which AI Stocks to Own Next appeared first on InvestorPlace.

Most investors think of AI as a software story. Joe Austin thinks they’re looking in the wrong place.

After four decades on Wall Street, Joe – who now works with our friends at Chaikin Analytics – believes some of the biggest opportunities in the AI boom may come from the infrastructure quietly powering it all. Specifically, the growing convergence between AI and the Internet of Things.

In today’s Friday Digest takeover, he explains why connected machines, sensors, and industrial systems are becoming increasingly important to the AI economy… and why the companies enabling that transformation may offer more upside than many of the headline-grabbing names dominating today’s market.

He also shares why this trend is attracting so much attention from market veteran Marc Chaikin and the team at Chaikin Analytics. In fact, Joe and Marc will be unveiling a new AI-powered research tool during a free event on June 24. You can reserve your spot right here.

If Joe is right, the next generation of AI winners may not be the companies everyone is talking about today – but the ones quietly making the entire AI revolution possible.

I’ll let Joe take it from here.

Have a wonderful Juneteenth,

Jeff Remsburg

Deep in the Australian bush, one coal mine nearly ground to a halt.

And nobody saw it coming…

Mines rely on vast networks of conveyor belts. These snake underground and across open terrain, moving ore from extraction to processing. If the belts stop, so does the mine.

In this coal mine, that’s exactly what was about to happen. AI-powered sensors on belt motors flagged something odd — a conveyor drive bearing was deteriorating faster than it should have been.

The culprit was bearing fluting. That’s when electrical current leaks through the bearing instead of flowing through the grounding system. It silently damages the bearing from the inside — until it’s too late.

A physical inspection might have spotted the wear, but it wouldn’t have detected the urgency. The sensors did.

They caught the problem early enough to trace it back to the grounding issue, fix it, and swap the bearing before the belt went down. As a result, the site avoided 10 hours of unplanned downtime and roughly $191,000 in lost production.

More importantly, fixing the root cause — not just the symptom — extended bearing life and cut long-term maintenance costs.

The sensors didn’t save just a bearing. They saved the whole operation.

This is part of a broader theme I’ve had my eye on…

AI and machines are doing the work that people can’t do — or doing it better.

And a key concept is increasingly making this possible.

The Foundation for Industrial AI

I’m talking about the Internet of Things — the IoT.

It has quietly become one of the defining technologies of our generation.

Not long ago, a thermostat was just a thermostat. Today, it’s a connected device sharing data with your phone, your HVAC system, and the cloud. The IoT is reshaping how people, machines, and processes communicate.

Back in the early 1980s, programmers at Carnegie Mellon University rigged a campus Coca-Cola vending machine to the internet so they could check if the drinks were cold before walking over. In 1999, Kevin Ashton at MIT gave this kind of thing a name: the Internet of Things.

Ashton’s argument was simple: Machines shouldn’t have to wait on humans to feed them data. People can be slow, distracted, and error-prone. If computers could gather information on their own and track the physical world in real time, we could eliminate waste, catch problems early, and know the state of everything — at all times.

We’re now living in the world he imagined.

The Numbers Reflect This Shift

According to industry research firm IoT Analytics, 18.6 billion devices were connected to the IoT in 2024. By 2030, that number is expected to grow to 39 billion — and hit 55 billion by 2035.

The IoT market generated nearly $550 billion in revenue in 2025. And it’s projected to reach more than $865 billion by 2030.

But industrial applications are driving much of that growth — and that’s where it gets really interesting for investors.

The industrial IoT market — think sensors, connected machinery, and automated systems in factories — came in at almost $326 billion in 2025. By 2034, it’s expected to nearly triple to about $945 billion. That’s nearly double the growth rate of the broader IoT market.

And it makes sense. Physical industries have the most to gain from machines that can sense, communicate, and act without human intervention. AI only accelerates what those machines can do.

Here’s a number that really caught my attention: By 2030, nearly half of all industrial IoT revenues are expected to depend on AI. That’s a fundamental shift in how factories, mines, and energy grids operate — and by some estimates, it could unlock up to $70 billion in new market value.

The data backs this up. According to a 2025 survey of nearly 18,000 private-sector firms, companies investing in AI alone saw a 10.4% productivity gain. Companies investing in the IoT alone saw a 6% decline. But the businesses that invested in both AI and the IoT together recorded a productivity increase of 18.9% — nearly double the AI-only gain.

Put simply: AI without physical connectivity is only half the story. And physical connectivity without AI is falling behind.

AI Is Moving Closer to the Machine

Until recently, most industrial sensors had no AI built in at all. That’s changing fast.

Manufacturers are now building AI directly into the sensors, gateways, and equipment on the factory floor. Instead of sending data to the cloud and waiting for a response, machines process information on the spot. The result is faster decisions, fewer failures, and fewer humans required to manage the whole system.

That’s what happened in that Australian coal mine. And it’s playing out in manufacturing plants, oil fields, shipping ports, and power grids around the world.

Folks, I’ve seen this pattern many times over my 40 years on Wall Street.

The biggest AI names — companies like Meta Platforms Inc. (META) and Microsoft Corp. (MSFT) — get most of the attention. And they face enormous pressure to prove that trillions of dollars in AI investment will eventually pay off.

But you don’t have to chase the headline names to find great investments in this megatrend.

In fact, some of the best opportunities in every major technology cycle have come from the companies building the infrastructure that makes it all possible — not the ones getting the most airtime on CNBC.

Think about the internet boom. Yes, Alphabet Inc. (GOOG) and Amazon.com Inc. (AMZN) became massive winners. But so did the companies making routers, fiber cable, and server hardware — the picks-and-shovels plays that powered the whole buildout.

The AI-plus-IoT convergence is setting up a similar opportunity. And the companies best positioned to win aren’t necessarily the ones you already own.

How to Find the Next Wave of Winners

That’s exactly where Marc Chaikin comes in.

Marc has spent 60 years on Wall Street developing the kind of analytical tools that can cut through the noise and find stocks with real momentum behind them. His Power Gauge has been doing that for decades.

But on June 24, Marc and I are unveiling something new — something neither of us has ever shown the public before.

It’s an AI-powered platform we’re calling the Time Machine. And it works by going “back in time” through decades of market data to find stocks today whose financial and technical DNA closely matches the profiles of stocks like Nvidia Corp. (NVDA), Amazon, and Meta at the very beginning of their historic runs.

In backtesting, it identified stocks that went on to deliver gains of 995%, 1,406%, and even 3,804% — all while the “seed” stocks they mirrored posted far more modest returns.

I’ve spent 40 years trying to get ahead of major technology shifts. This is the most powerful tool I’ve seen for doing exactly that.

The industrial AI buildout is real, it’s enormous, and it’s still early. The question is whether you’re positioned to benefit from it — or whether you’ll be watching from the sidelines while the next generation of big winners takes shape.

On June 24, Marc and I are going to show you exactly which stocks the Time Machine is flagging right now — for free.

To get access, all you need to do is reserve your spot now. When you do, you’ll get early access to the Time Machine’s beta platform, so you can start exploring it before our free broadcast. You can type in any ticker and see how it compares to the greatest stock market winners of all time.

This is Chaikin Analytics’ first-ever AI-powered product — and charter spots will be limited.

Sign up for this free event here.

Good investing,

Joe Austin

Senior Analyst, Chaikin Analytics

P.S. Joe has spent four decades watching major technology cycles play out. And he says the AI-plus-IoT convergence is one of the most significant investment opportunities he’s seen. But the window to get in early doesn’t stay open forever. Marc and Joe’s event on June 24 is free — and when you reserve your spot, you get early access to a beta version of the Time Machine right now, no purchase required.

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