The richest man in the world says he is not building a phone… again, and again, and again…

Last October, Elon Musk sat down with Joe Rogan. “I am not working on a phone,” he said.

On January 30, someone on X chimed in …

The important part there is that “it would be a very different device than current phones.”

Then, a few days later, Reuters reported that SpaceX had plans for a Starlink phone. Musk’s response:

Then on July 1, the Wall Street Journal reported that SpaceX had shown investors a prototype of a handset-like AI device before its June IPO. The report said the device was slimmer than an iPhone, ran its own operating system, and used technology from Musk’s AI company, xAI. Musk posted two words in reply:

Case closed?

Not quite, according to Josh Baylin – a former Bloomberg tech reporter and hedge-fund trader. And he thinks the denial is the most interesting part of this story…

His argument is simple: Musk may indeed be telling the truth when he says there’s no finished phone sitting in a drawer in Starbase.

Nonetheless, practically every piece required to build a phone has already been bought, filed, licensed, or launched by one of Musk’s companies… The spectrum, satellites, chips, AI, trademarks, and even the regulatory clearances.

Until we have an official name, Josh is calling this potential device by the moniker, “Starphone.”

And just a few days ago, the U.S. government handed SpaceX the last piece of the puzzle…

In early October, the Federal Communications Commission approved SpaceX’s plan to launch 15,000 next-generation satellites for a service called Starlink Mobile. More importantly, the agency waived the rule requiring SpaceX to lease spectrum from a ground-based carrier.

In plain English, SpaceX can now sell wireless service to Americans on its own.

A company that can beam 5G to your phone from orbit… that owns one of the most capable AI models in the world… and that controls the cheapest and most reliable rocket launches on Earth now has permission to be your phone company.

Here’s the timeline so far…

• September 2025. SpaceX agrees to pay $17 billion for wireless airwaves owned by EchoStar (SATS). Half in cash, half in SpaceX stock. This is spectrum that gives SpaceX the exclusive right to broadcast on a slice of radio frequencies… including straight to ordinary cell phones.

• October 2025. SpaceX files to trademark the name “Starlink Mobile.” The application covers personal cellular service and two-way voice, text, and audio. That sure sounds like a phone company to me. The company also filed a companion mark, “Powered by Starlink.”

• November 2025. SpaceX buys another $2.6 billion worth of spectrum from EchoStar.

• January 2026. SpaceX asks the FCC for permission to launch up to one million satellites that would run AI in orbit.

• February 2026. SpaceX absorbs xAI, the company behind the Grok AI chatbot. Now Elon’s rockets, satellites, social network X, and AI all sit under one roof.

• May 2026. Ahead of its IPO, SpaceX files its SEC paperwork… defining Starlink Mobile as a service that delivers cellular connectivity straight to everyday smartphones via satellite. It says the next generation of mobile satellites, combined with the EchoStar airwaves, is designed to deliver “a connectivity solution on par with terrestrial mobile networks.”

• October 2026. SpaceX announces it intends to acquire a low-band spectrum license portfolio in the 800 MHz band. As the company put it… “This prime low-band spectrum addresses one of the key remaining technical gaps that will pave the way for Starlink Mobile to become a major mobile carrier in the US.”

So here’s the question Josh keeps asking: Why would Musk build all of that and then let another company sell you the device?

The stakes here are massive. Your phone is the most personal piece of technology ever made… It’s the first thing most folks touch in the morning, and the last thing they put down on the nightstand in the evening.

To hear Josh’s prediction in his own words – click here for his full, exclusive interview.

The post What Is Elon Musk’s “Starphone”? Investigating the iPhone Killer He Says Doesn’t Exist appeared first on MarketWise.

Update – September 21, 2026: Since this article discussing what’s coming for white-collar workers and investors in America was originally published in April, several major developments have happened with AI that you should be aware of…

• First, the white-collar layoff wave that we detailed here has only grown stronger in recent months. AI was the leading reason given for layoffs for a record five months straight… beginning in April, when we first published this guide, through July. And it remains the leading reason for layoffs so far in 2026. Already this year, more than 100,000 Americans have been laid off thanks to AI.

• The corporate examples keep piling up. Uber (UBER) plans to eliminate more than 3,000 roles… Microsoft (MSFT) laid off nearly 5,000 employees… Oracle (ORCL) has axed more than 20,000… British American Tobacco (BTI) is cutting 9,000 as it tries to become “more digital and AI-focused”… and dozens of other tech companies are shedding double-digit percentages of employees thanks to AI-focused restructuring.

• Nonetheless, investors are still early to this story… The “ripping point” gap that we’ve detailed below is widening. And AI is only getting smarter. The next six months will likely create more wealth than the last 100 years combined. But only for those who are prepared.

• If you haven’t already, you can watch the full Ripping Point documentary by clicking here – including the name and ticker of one stock to sell immediately and one “hidden AI” play to buy right now.

The rest of this guide has been updated to reflect these developments…

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Days ago, Wall Street veteran Whitney Tilson started sounding an alarm.

He calls it America’s “ripping point.” And if you haven’t heard the term yet, you soon will.

Whitney is the man who CNBC once dubbed “The Prophet” for his string of eerily accurate market calls. And now, he says the country is entering a stretch of upheaval unlike anything he has seen during his career.

It’s all due to artificial intelligence (“AI”)… and it’s beginning to tear the economic fabric of the nation apart.

AI will create massive new fortunes for a small group of people, while ripping away the livelihoods and security of millions of others. Already, white-collar workers like lawyers, analysts, consultants, managers, and software engineers are watching AI creep into their jobs… and wondering for how much longer they’ll still have a job.

You’ll see another tech layoff in the headlines soon. And another after that. You’ll see hundreds more as Whitney’s ripping point prediction plays out.

But Whitney’s message is not all bad news.

He insists this turmoil also carries huge opportunity. And he’s coming forward in an exclusive interview with a step-by-step plan to help everyday Americans use AI in their own investing.

Table of Contents

• Three Core Ideas Behind Whitney’s “Ripping Point” Prediction

• The American Ripping Point: A Tale of Two Economies

• The AI Revolution’s Winners and Losers

• Harnessing AI for Investors with the New Engine of Wealth System

• What Is Stansberry Research?

• Who Is Whitney Tilson?

• Frequently Asked Questions

• How an Investor Can Profit from Whitney’s “Ripping Point” Prediction

Three Core Ideas Behind Whitney’s Prediction

I’ve rarely, if ever, heard Whitney this worried.

I’ll leave it in his own words:

I’m concerned that our whole way of life is being swept away in front of our eyes…

Hundreds of thousands of people are losing their jobs. Violent swings are battering the stock market. Entire industries are collapsing around us.

I’m not only worried for older folks like me. I have three beautiful daughters. And it feels like their future gets more unstable with every passing day.

It’s not just because of the extreme political division or violent protests that seem to be a regular part of our lives now. I mean the deeper change that we can all feel.

We’re living through a permanent reset in our economy. It’s making the world – the one our children are going to inherit – deeply unstable.

He wrote that in March… For the next five months in a row, AI led as the reason for job cuts according to outplacement firm Challenger, Gray & Christmas. And so far, they report that more than 100,000 people this year alone have been laid off thanks to AI.

Looking ahead, Whitney’s “ripping point” prediction will affect every American with a 401(k) or retirement account. And it comes down to three core ideas.

First, America has hit a societal and economic breaking point.

One group of Americans is using AI to race toward new wealth – or simply to grow their current wealth. But another group is sliding into financial insecurity. And the gap between them is growing fast.

A key measure of wealth concentration called the Gini coefficient sits at 60-year highs according to a report from U.S. Bank, and terms like the “K-shaped economy” are now commonplace.

As CNBC reported in January:

“This is not a cyclical or temporary phenomena,” said Mark Zandi, chief economist at Moody’s Analytics. “This is a structural, fundamental issue.”

Federal Reserve data shows that the top 1% in America now control nearly one-third of the nation’s wealth. The bottom half of the U.S. population, on the other hand, controls less than 3%.

And white-collar hiring keeps shrinking… Core office sectors have been cutting jobs for three straight years now, according to the Bureau of Labor Statistics.

Social unrest is growing. Debt is ballooning. Political extremism is spreading.

For a wide swath of the population, the American dream is getting harder to even imagine, much less attain.

Second, Whitney believes the AI revolution is the primary force behind this American fracture.

AI adoption is advancing much faster than most people expected.

So, even as traditional white-collar roles and entire business models are getting gutted, new fortunes are also being built at a speed we have never seen before.

For example, the poster child for the AI revolution is chipmaking giant Nvidia (NVDA). Its stock has soared in recent years, and Nvidia was the first-ever public company to cross $4 trillion in market capitalization… and then shortly afterward, it crossed $5 trillion.

But what you might not know is that Stansberry Research, the publishing firm where Whitney hangs his hat, actually initiated coverage of Nvidia back in 2004 at a split-adjusted price of about $0.12 per share, listing it as their No. 1 buy.

Nvidia is up more than 150,000% from that initial coverage. And subscribers had multiple opportunities to buy in along the way, including right before it went on its recent tear.

The opportunities Whitney is spotting today could be just as powerful for your portfolio.

That’s why the final part of Whitney’s prediction is so important.

There is a way forward for everyday Americans, using AI in their investing instead of being afraid of it. He has helped build an AI-powered investing system specifically designed as a financial life raft for those folks willing to grab hold.

It’s called the N.E.W. System… short for “New Engine of Wealth.”

The system uses AI to crunch financial data on thousands of publicly traded companies and identify the stocks most likely to outperform. In back testing, the approach beat the S&P 500 Index, bonds, gold, and even Warren Buffett’s Berkshire Hathaway (BRK.B) over recent years.

We’ll examine each of these three ideas here today. But if you read nothing else, you should at least know Whitney’s core recommendation: Stop fearing AI, and start using it.

The American Ripping Point: A Tale of Two Economies

Whitney believes the United States has split into “Two Americas.” And this divide is reaching a “ripping point.”

By that, he means the gap between those who are thriving and those who are struggling has gotten so wide that it no longer feels like a gap. It feels like a tear.

On one side are people riding booming asset prices, cashing in on new technology, and watching their portfolios grow. On the other side are people dealing with stagnant wages, climbing costs, and a gnawing sense that they are falling behind for good.

Economists have a name for this pattern: The “K-shaped economy.”

Picture the letter K on a chart. One arm shoots upward. That is the segment of the population doing well – rising incomes, growing net worth, and expanding opportunity. The other arm falls downward. Those are the folks losing ground.

In fact, Moody’s Analytics found that the top 10% of the wealthiest Americans now account for roughly half of all consumer spending. And as the Fortune article went on to note, this is a big deal:

Morgan Stanley Wealth Management’s Lisa Shalett has increasingly been sounding the alarm from her perch as chief investment officer. She told Fortune in an October 2025 interview, “The income inequality stuff is really getting like completely wackadoo,” specifically citing Zandi’s research: “That means 90% of the country is only half the consumption, I mean holy cannoli.”

Now, Whitney says the K-shaped economy has started to rip. And one of the clearest signs is white-collar job losses.

We’ve been following the layoffs across white-collar industries for months now. Good jobs are already starting to disappear, and we are only at the very beginning of the layoff cycle. As we noted earlier this year, if you’re one of several million folks employed by a Big Tech company, your job is absolutely at risk.

We went on to add:

Of course, people are clever. We’ll create other interesting, human jobs in new industries. But it’s going to take time.

In [our previous] doom-loop article, we gave the example of the 50-year period during the early Industrial Revolution when GDP growth exploded, but workers’ wages stagnated for half a century. All the gains went to capital owners.

That could absolutely happen again today.

And if it does, it will be devastating for American workers, especially for anyone who currently sits at a desk.

Using data from the Bureau of Labor Statistics, Axios reports that core white-collar employment – sectors like finance, insurance, information, and professional services – has fallen for three straight years.

For context, from 2010 to 2019, these sectors added an average of 569,000 jobs a year. In the last three years, they’ve lost an average of 191,000 jobs a year.

These sectors account for more than 40% of U.S. GDP and 20% of U.S. employment.

When the economy is expanding, these jobs have historically grown in size. These were the “safe” jobs. The desk jobs parents told their kids to get. The ones that were supposed to be recession-proof. Today, they’re disappearing.

And much of the reason has to do with AI. As we wrote last fall, the AI layoffs will continue until morale improves:

Goldman Sachs says that AI can already replace 2.5% of the American workforce – that’s more than 4 million employees. And looking ahead, it expects as much as 14% of the current U.S. workforce could ultimately be at risk within the next five years.

That would be roughly 22 million Americans, suddenly out of work…

Already, social tensions are boiling over in America. What do you think will happen when millions more Americans find themselves jobless, thanks in large part to AI?

In addition, monetary policy is now squeezing the lower arm of the K even harder… Last week, the Federal Reserve raised its benchmark rate for the first time since July 2023. And most analysts expect more rate hikes to come.

Higher rates mean pricier mortgages, car loans, and credit-card balances for households already falling behind, even as asset owners in the upper K keep riding the AI boom.

America is living through a foundational shift. We stand at the beginning of a new era. But for that era to begin, the old era must end. And a huge portion of the population risks being left behind.

Whitney compares this moment to earlier transformative periods, like the advent of railroads or the arrival of electricity. These were times that reshaped society in a generation, exactly as AI is doing today.

The AI Revolution’s Winners and Losers

If Whitney’s ripping-point prediction describes what is happening to America today, then AI explains why it is happening so fast.

Whitney calls AI the most destructive and creative economic force of our lifetimes. And it certainly looks that way.

For some companies, AI has been a disaster.

In his interview, Whitney points to the example of Gartner (IT):

For 45 years, it has been one of the biggest consulting firms in the world and has thousands of staff worldwide. It’s the whitest of white-collar firms.

And yet, it took just months to wipe out HALF of its market value, including a 31% one-day plunge in February.

Companies that were once pillars of finance, analytics, and consulting have all gotten hit as investors realize AI can replicate some or much of what these firms do.

In finance and law, the early signs are already showing up. Junior analysts and attorneys face replacement by AI that drafts reports or contracts in seconds.

For example, Goldman Sachs’ CEO David Solomon noted last year that AI can now draft 95% of an IPO prospectus in minutes. That task once took a six-person team working on it for two weeks. In other words, some 480 hours of highly compensated human effort can now be done in maybe 20 minutes by AI.

Today, Goldman employs some 11,000 engineers, largely to push forward on AI initiatives.

Or take language-learning app Duolingo (DUOL). Whitney continues,

The language-learning app was once a darling of the AI world. In fact, its CEO publicly pushed an “AI-first” strategy.

But now?

It turns out any off-the-shelf AI system can do what Duolingo’s software does.

The stock collapsed 75% in less than a year.

Why pay a premium for a company whose competitive advantage just evaporated?

This goes far beyond a handful of companies.

If a business depends on information brokerage, routine data analysis, content creation, or any repeatable cognitive task, it is in trouble.

And if your job depends on something similar, that, too, is in trouble.

Amazon CEO Andy Jassy warned employees last summer that AI would reduce the corporate workforce “in the coming years.” Sure enough, a few months later, Amazon announced plans to fire some 30,000 white-collar office workers in two waves.

We reported at the time:

Amazon’s corporate layoff is an early sign of what’s coming to other large tech companies. Back in 2022, Amazon was also the first mover – laying off 27,000 over several months. It was quickly followed by dozens of other well-known tech companies, shedding tens of thousands of their own employees. Expect to see plenty more folks purged in the months ahead as tech companies refocus their spending from salaries to AI compute.

AI will ultimately replace millions of jobs. The current layoffs are only the beginning.

And when it starts happening in earnest, it won’t be a gradual change. It will be sudden.

After all, companies have already been cutting jobs despite solid economic growth to increase efficiencies and squeeze more output from fewer workers. The latest AI capabilities are just accelerating a trend already in motion.

Dario Amodei, CEO of AI leader Anthropic, has warned that business leaders may see the cost savings of AI and replace humans almost overnight.

Now flip the coin.

The same wave that is crushing some companies is sending other businesses into orbit.

Semiconductor firms that supply the chips and equipment AI runs on, like Micron Technology (MU) and Lam Research (LRCX), have seen their stocks soar.

And then there is Nvidia. Long before the stock was on most of Wall Street’s radar, Stansberry Research subscribers were tipped off to the role Nvidia would soon play in artificial intelligence.

Our analyst put it this way:

When machines begin learning for themselves, it will be a watershed moment in human history. And these “artificial brains” will bring huge profits to early investors.

And although Nvidia is a well-known tech company, nobody on Wall Street is betting on artificial brains as a business segment.

Sure enough, Nvidia shares have soared in the years since.

It isn’t just the chipmakers. The established tech giants keep leaning on AI to widen their lead. When Alphabet (GOOGL) and Meta Platforms (META) reported earnings this spring, both posted blowout numbers… even as they poured tens of billions more into AI. As Whitney noted:

Management is smart to spend however much it takes to win the AI race. Established companies like Alphabet, Meta, Amazon, and Microsoft (MSFT) can leverage AI to grow their dominant businesses.

What is happening in the market right now is a massive rotation of capital. Money is flowing out of firms threatened by AI, and into firms building or powering AI.

Whitney cites Massachusetts Institute of Technology research, noting that going back over 100 years of data, wealth has never been created at this size and speed. Think about that. Not during the dot-com boom. Not during the post-war industrial expansion. Not during the Roaring ’20s.

Yet most Americans are missing out.

Many investors never owned these stocks. Or they bailed out early when things got volatile. And the workers losing their jobs? They are probably not sharing in the stock market’s gains.

This feeds directly back into the K-shape economy we talked about earlier. The upper arm accelerates because of AI. The lower arm falls faster because of AI.

Same force, opposite outcomes.

Whitney puts it plainly in his interview, which you can watch for free by clicking here:

Imagine the progress of the entire 20th century compressed into a single decade. That’s what insiders say is happening now.

That kind of growth is almost impossible for the human brain to comprehend.

But think about it like this…

The world economy grew 19-fold in the 20th century.

If the economy grew at that pace in the next century, it would create hundreds of trillions of dollars in new wealth every year.

And it’s beginning, right now. You can feel it. The pace of change is picking up. The next six months will define everything.

Anthropic, valued at nearly $1 trillion earlier this year, confidentially filed its IPO prospectus in June and has been widely expected to list as soon as next month. Some investors have discussed a valuation approaching $2 trillion, which would rank it among the most highly valued companies ever to enter public markets.

OpenAI is going the other way. Altman said in a September 12 interview that “right now would be an ill-advised moment to go public,” and the company now targets 2027.

Either way, new fortunes are being built right now… exactly the wealth creation Whitney describes in his latest prediction.

That stark divide sets up Whitney’s third idea. How can regular investors get themselves on the right side and use AI for their own investing?

Harnessing AI for Investors With the New Engine of Wealth System

At the center of Whitney’s plan for regular investors is his firm’s AI-powered investing system: The N.E.W. System, short for “New Engine of Wealth.”

The core question it tries to answer: How can everyday investors come out ahead in an era dominated by AI and rapid disruption?

Here is how it works.

The N.E.W. System is an AI-driven, fully allocated model investment portfolio.

It’s the end product of more than a decade of research and development, involving dozens of coders, developers, and research staff – and costing millions of dollars.

It uses artificial intelligence to analyze financial data on more than 5,000 publicly traded companies. Then it identifies a mix of investments with the highest chance of delivering strong returns, while dodging the big losers.

Think of it as a “best of the best” portfolio assembled with more computing power and analytical rigor than most individual investors could manage alone.

Whitney’s team built it by feeding an AI vast amounts of data and running simulations. Trillions of possible outcomes. Different market conditions. Bull markets, crashes, sideways grinds. The AI searched for patterns and combinations of stocks that held up across all of them.

The result is a model investment portfolio that gets updated quarterly. And Whitney’s team vets every pick before it goes out to subscribers.

That last part matters. This is not a robot on autopilot. Whitney and a committee of human analysts review each recommendation – checking for statistical flukes, real-world factors the algorithm might miss, and anything that does not pass the common-sense test. Investors aren’t blindly following a computer.

Now, what about results?

Whitney shares a few examples of stocks the system flagged early. Nutex Health (NUTX) returned 1,100% in under a year. Robinhood Markets (HOOD) rallied 340%. AppLovin (APP) surged 2,500%.

Of course, nobody can guarantee future returns. But the back-tested track record is striking.

The strategy would have beaten the S&P 500, long-term Treasury bonds, gold, and even Berkshire Hathaway over recent years. And with lower volatility than chasing hot AI stocks on your own.

Today, AI is leveling the playing field for individual investors.

For decades, big Wall Street firms had enormous teams and expensive data tools that gave them a structural edge that regular folks simply couldn’t replicate. Whitney cites Renaissance Technologies’ Medallion Fund, widely considered the most successful hedge fund ever. It generated roughly 66% annual returns over decades. Their secret? Better data and better models than everyone else.

But today, advances in AI make serious analytical firepower accessible to smaller investors. That is the whole idea behind the N.E.W. System – packaging an elite-level analytical engine in a form regular people can use.

Subscribers also get access to a few key tools.

• The Stansberry Score is an AI-driven rating for thousands of stocks, showing how each one ranks against the system’s criteria. You can look up any stock and instantly see where it stands.

• StansberryGPT is a financial chatbot that answers plain-English questions from market data. It is like having a 24/7 analyst at your fingertips, minus the $20,000 Bloomberg terminal bill.

• And a year of access to the N.E.W. System, with Whitney’s latest recommendations of the strongest-rated companies to buy right now for the highest potential gains. Should you choose to invest, all you’ll need to do is type in your portfolio size, and his calculator will spit out exactly how many shares of each company to buy.

Today, you can use the same technology that is shaking everything up to invest smarter, avoid being blindsided by big market declines, and even find the next big winners before everyone else catches on.

Whitney puts it like this:

In short, we’ve taught our AI to find you the unique combination of stocks that is most likely to double or triple your money, with a level of confidence that has never been possible before.

And keep in mind, the N.E.W. System updates its model portfolio roughly four times a year, or as needed, based on market conditions. That means regular updates on what stocks to add, which to trim or sell, and the latest highest-potential new recommendations to add.

This frequency is intentional. It’s not a day-trading system that will bombard you with constant trades, but it’s also not a “set-and-forget for years” approach.

In short, the N.E.W. System offers a radically different, high-tech approach to stock investing – one that tilts the odds in your favor by using vast data and computing power.

Click here to go directly to the N.E.W. System order form to learn more details and get access to a special subscriber discount. (Please note, this link does not go to a long video.)

What Is Stansberry Research?

Stansberry Research is one of the largest and most respected independent financial research firms in the world.

Founded in 1999, the firm made its name by giving individual investors access to the kind of in-depth research and contrarian ideas often reserved for Wall Street insiders. Today, Stansberry Research is a cornerstone of the financial publishing industry.

The firm has subscribers all around the globe. And its publications are read by hundreds of thousands of folks.

A key differentiator is that, unlike big banks or brokers, Stansberry doesn’t manage money or earn commissions on trades. They strictly publish research and investment recommendations. And they are known for sometimes taking bold, unpopular stances that later proved prescient.

For example, Stansberry Research analysts warned about the housing bubble and the impending collapse of Fannie Mae and Freddie Mac before the 2008 crisis, saving attentive subscribers from huge losses. They were early in predicting the rebound of oil and natural gas in the mid-2010s. And they even covered burgeoning trends like bitcoin as far back as 2017.

Over the years, Stansberry’s various services have logged dozens of triple-digit winners and a handful of 1,000%-plus gainers.

Of course, no firm gets every call right. That’s why Stansberry Research analysts also regularly acknowledge their mistakes – including in an annual “Report Card” where the publisher grades each publication, no holds barred. Their mission is to put the kind of research and tools that used to be reserved for Wall Street insiders into the hands of regular investors. The company employs experts on macroeconomics, commodities, biotech, insurance stocks, and much more… and now, an AI system is working for you, too.

Who Is Whitney Tilson?

Whitney Tilson is a familiar name in investing circles… and beyond. His career spans hedge funds, research publishing, philanthropy, and even a run at New York City politics. Understanding his background helps explain why people take his “ripping point” warning seriously.

The short version…

Whitney graduated from Harvard College and Harvard Business School, where he finished in the top 5% of his class and was named a Baker Scholar. In 1999, he co-founded what became Kase Capital Management. He started with $1 million and ran the whole thing from a folding Ikea desk in the corner of his bedroom. Over the next dozen or so years, he grew his fund to more than $200 million in assets and nearly tripled his investors’ money.

That stretch included the dot-com crash, the 2008 financial crisis, and a flat-to-brutal market for long stretches. Steering a fund through all of that and coming out well ahead was no small feat.

He also earned a reputation for calling big turns in the market.

• During the depths of the financial crisis, he went on 60 Minutes and argued it was a once-in-a-lifetime buying opportunity for stocks. At the time, fear was rampant. But he was right – it marked the bottom… And the ensuing bull market became the longest in history.

• In 2015, he again appeared on 60 Minutes to expose fraud at a company named Lumber Liquidators, which was found to be selling toxic Chinese-made laminate flooring. Following that, the stock plummeted 80%, and the CEO resigned. Whitney’s actions potentially saved consumers and investors from a disastrous situation.

• He predicted the exact market bottom in March 2020 during the COVID-19 crash. Anyone who listened would have caught the huge rebound that followed.

These and other calls earned him that “Prophet” nickname from CNBC.

Over his career, Whitney rubbed elbows with some of the greats – even baking cookies for the legendary investor Warren Buffett every Christmas. Whitney also counts hedge-fund legends like Bill Ackman, Joel Greenblatt, David Einhorn, and Seth Klarman among his circle.

After leaving the hedge fund world, Whitney moved into investment research and education and now heads up a team of analysts at Stansberry Research. He brings a rare combination to the table: proven investment skill, connections with legendary investors, and a down-to-earth teaching mentality.

Whitney has published four books, including The Art of Value Investing. He has traveled to Ukraine multiple times during the war to deliver aid. He helped launch the nonprofit Teach For America as a founding member. He even ran for mayor of New York City.

You can probably tell that Whitney isn’t a doom-and-gloom kind of guy. So when he turns urgent, as he has with his “ripping point” warning in his latest documentary, it’s worth paying attention.

Frequently Asked Questions

Question: What does Whitney mean by “ripping point”?

Answer: “Ripping point” is Whitney’s term for when America’s economic and social fabric tears apart under extreme pressure from technology and wealth inequality.

Think of it as a more violent version of a tipping point – a rapid, visible split between those getting richer and those getting left behind. This gap is now showing up in political polarization, consumer debt, career anxiety… You name it. And in Whitney’s view, AI is speeding this up from a slow divergence into something much more abrupt.

Question: Why is Whitney’s ripping-point prediction causing panic among white-collar workers?

Answer: White-collar professionals have historically been largely shielded from automation. A college degree and an office job meant stability, but AI is changing that assumption.

For example, as we wrote about Elon Musk’s “Macrohard” AI system:

Soon, you may have an “always-on” AI at your desk, watching what you do, and learning to do it without you.

That sounds dystopian… or maybe just far-fetched.

But it has happened before with American workers training their offshore replacements in the late 1990s… That trend sped up following the dot-com crash in the early 2000s as companies faced intense pressure to survive by cutting costs. This time, instead of being replaced with a cheaper worker in a foreign country, it’s an AI in the cloud

As AI costs go up and AI gets smarter, human headcounts come down. And if you’re one of the millions of folks employed in a white-collar office setting, your job is likely more at risk than you would have ever expected.

Question: Haven’t we faced disruptions from technology before?

Answer: We absolutely have. Mechanization gutted farming jobs early in the 20th century. Robots changed manufacturing late in the 20th century. Each time, new job categories eventually appeared.

Two things stand out about AI, though:

• First, the scope. AI can hit many industries and many types of cognitive work at once – legal research, marketing copy, financial analysis, and software code. It’s not limited to one sector.

• Second is the speed. AI models improve on a timeline of months, not decades. And the newest tools are accelerating a trend already in motion.

So, whether Whitney’s comparison of compressing 100 years into a single decade holds up perfectly, the pace is genuinely unlike anything in recent memory.

Question: How does the N.E.W. System work?

Answer: The New Engine of Wealth (N.E.W.) System uses machine learning to comb through years of market data – stock prices, financial statements, and economic indicators. It looks for patterns that predict strong performance.

It runs trillions of simulations to find combinations of stocks that hold up well across different conditions. From that analysis, it builds a model portfolio of 10 to 20 stocks, updated quarterly.

Of course, Whitney and his analyst team review every pick before sending it to subscribers. So there is a human check on the algorithm’s output. Subscribers also get access to the Stansberry Score, which rates thousands of stocks on demand, and StansberryGPT, a financial chatbot that answers plain-English questions from market data.

The idea is to give regular investors the kind of analytical firepower that used to live behind locked doors on Wall Street. You can learn more about the N.E.W. System by watching Whitney’s full interview here.

Question: If the system works so well, why share it?

Answer: Fair question. Whitney has addressed it directly. Part of his answer is personal. He has already run a hedge fund and made his money on Wall Street. At this point in his career, he says he is more motivated by teaching and helping individual investors.

In addition, Stansberry Research is in the business of selling research, not managing money. By creating a product like the N.E.W. System, they can attract subscribers willing to pay for high-quality insights. In a sense, scaling it to thousands of subscribers is more lucrative for them than trying to quietly run a small fund with it.

Finally, both Whitney and Stansberry Research have built a reputation on delivering big ideas to readers. This is, in a way, their next big idea to retain and grow that audience.

Question: Is the system all tech stocks?

Answer: Nope. The recommendations look diverse, based on what Whitney has shared publicly. His examples include a health care small-cap. A fintech firm. An ad-tech company. An industrial manufacturer. A biotech. A gold-related firm. A defense-parts maker.

The AI searches across every sector for the best risk-reward opportunities. That could mean a boring industrial stock sitting right next to a high-growth tech name. The system also steers users away from popular-but-overpriced names if the data does not support additional upside.

Question: Can an average investor use Whitney’s N.E.W. System?

Answer: Absolutely. It was designed with everyday investors in mind. The system outputs a model portfolio with ticker symbols and allocation percentages. It even has an online calculator. Enter how much you want to invest, and it computes how many dollars to put in each stock. You would make trades a few times a year to rotate into new picks or adjust your positions. No advanced tech skills required.

If you can read a report and place a trade in your brokerage account, you can follow it. Whitney’s team also provides a Quick Start Guide, monthly updates, and customer support. You can find more details on the order form. (This link does not go to a long video.)

How an Investor Can Profit From Whitney’s Ripping-Point Prediction

We could soon be blindsided by a permanent change in our economy…

America’s “ripping point” will be far more wide-reaching than a stock market crash or banking collapse. It has the potential to split our entire financial system in two.

But buried in that chaos is an opportunity to do incredible things with your money.

That’s why Whitney has gone public with his warning today.

Whether you choose to watch Whitney’s documentary on the N.E.W. System, the takeaway is clear: AI is transforming investing. It’s showing us ways to beat stocks, gold, bonds, real estate – and even potentially outperform the great Warren Buffett.

And keep in mind: AI is only going to get stronger (and smarter) from here.

So, even if you don’t see a threat from AI right now, that doesn’t mean it won’t be here tomorrow.

As Whitney predicts:

Today’s AI is the dumbest it’ll ever be. It’s getting smarter at an exponential rate. In fact, AI’s capability has doubled roughly every seven months for the past six years.

It gets smarter and more powerful with every passing day.

You can’t fight it.

None of us can.

But you can use it to grow wealthy – by making sure AI works for you, not against.

Learn more by watching Whitney’s full interview… including the names and tickers of one stock to sell immediately and one “hidden AI play” to buy right now.

And of course, Whitney is offering a full 30-day money-back guarantee for all new subscribers. So you can give his research a try without worrying about a long-term commitment today.

If you’re ready to subscribe, you can go straight to the order form without watching a long video by clicking here.

The post Why Is Whitney Tilson’s ‘Ripping Point’ Prediction Terrifying White-Collar Americans? appeared first on MarketWise.

Everyone is trying to figure out how to own artificial intelligence without owning the froth. After a two-year sprint in chips and data-center names, “How do I hedge the AI bubble?” has become one of the most asked questions in investing.

I spent 28 years on the trading floors at the CBOE and CME before building Masters in Trading, and one lesson from the pits never changes: when everyone crowds the same trade, you look for the adjacent one nobody’s fighting over. Right now, that trade is cybersecurity stocks.

Here’s the logic, and it isn’t complicated. AI is a double-edged sword. The same technology racing through corporate America is also arming attackers, enabling faster, cheaper, autonomous attacks at a scale we’ve never seen. That means security stops being a “nice-to-have” line item and becomes a bill companies cannot stop paying. Cyber vendors get paid whether AI succeeds spectacularly or blows up in someone’s face. Both outcomes create threats. Both require defense.

We got a live preview of this rotation on Monday, September 14, the day the chip names sold off on fresh AI-risk warnings — with Nvidia down nearly 3% and the VanEck Semiconductor ETF (SMH) off 4.4% — the pure-play cybersecurity names had their best session in years. CrowdStrike hit a record high. Zscaler and SentinelOne jumped double digits. The market wasn’t fleeing AI. It was repricing who benefits from it.

Below are the 15 best cybersecurity stocks to buy now, each positioned to ride that shift and grouped by the role it plays in a portfolio.

How Big Is the Cybersecurity Market in 2026?

Before we look at the names, consider these numbers — because the size of this market is the whole thesis.

Gartner projects global information security spending will hit roughly $249 billion in 2026, up nearly 13% year over year, on track for about $373 billion by 2030. That’s up from just $193 billion in 2024 — a ~29% jump in two years, at a time when most enterprise software budgets are getting squeezed.

Add in security services, and Cybersecurity Ventures pegs the broader market above $520 billion. A Morgan Stanley survey of CIOs found cybersecurity budgets are expected to grow roughly 50% faster than overall software spending.

The fastest-growing slice of the entire market is a category that barely existed 18 months ago: “securing AI” — projected to overtake endpoint protection as the single largest security category by 2029.

Translation: this isn’t a headline-of-the-week trade. It’s a multi-year, structurally funded spending supercycle. Now onto the stocks.

Want to see how I’m trading this theme in real time? I break down setups like these — with live entries, exits, and risk levels — every weekday at 11 a.m. ET on MiT Live.

 The 15 Best Cybersecurity Stocks at a Glance

Stock	What it does	Est. rev. growth	Valuation	Analyst view
CrowdStrike (CRWD)	Endpoint / XDR	~26%	Premium	Strong Buy
Palo Alto (PANW)	Platform / identity	~25%	Above avg.	Buy
Fortinet (FTNT)	Network / firewall	~20%	Value	Buy / Hold
Zscaler (ZS)	Zero Trust / SASE	~24%	Moderate	Buy
Microsoft (MSFT)	Bundled security giant	Diversified	Moderate	Strong Buy
SentinelOne (S)	AI-native endpoint	~23%	Value	Buy
Okta (OKTA)	Identity & access	~11%	Value	Moderate Buy
Rubrik (RBRK)	Cyber resilience	~48%	Moderate	Buy
Cloudflare (NET)	Edge / AI infra	~36%	Premium	Hold / Buy
Check Point (CHKP)	Firewall / SASE	~7%	Value	Hold
Qualys (QLYS)	Vuln. mgmt.	~9%	Moderate	Hold
Varonis (VRNS)	Data security / DSPM	~9%*	Moderate	Buy
Tenable (TENB)	Exposure mgmt.	~11%	Value	Buy
Rapid7 (RPD)	Vuln. mgmt. / SecOps	~flat	Value	Hold
Gen Digital (GEN)	Consumer security	~4%	Value	Hold

The Rose Take: Where I Would (and Wouldn’t) Put New Money

After 28 years reading order flow, I’ve learned the best company and the best stock aren’t always the same thing. Here’s how I’d rank them:

• Best business, hands down — CrowdStrike (CRWD). Nothing else in the group has its brand, retention, or platform breadth. The only knock is the price: it’s trading near Wall Street’s most bullish targets, so you’re buying perfection.
• My No. 1 pick for new money — Palo Alto (PANW). It has the scale of CrowdStrike, faster-improving platform economics, a brand-new identity engine from the CyberArk deal, and it still trades at a discount to CRWD. It’s the best risk-adjusted way to own the theme.
• Boldest high-upside bet — SentinelOne (S). The cheapest of the leaders, growing more than 20%, with real takeover optionality. If it keeps closing the gap with CrowdStrike, it has the most torque on the list.
• Handle with care — Cloudflare (NET) and CrowdStrike (CRWD). Two of the best stories in tech, at two of the richest multiples. I’d rather wait for a pullback than chase them here.
• The one I wouldn’t chase — Rapid7 (RPD). Revenue is essentially flat. It’s a turnaround-and-takeover bet, not a growth story — treat it that way.

The Blue-Chip Generals: Best Cybersecurity Stocks for Core Exposure

Large-cap, profitable platforms. They offer core exposure to the theme , and they’re the names institutions buy first.

1. CrowdStrike (CRWD)

The category gold standard. Its Falcon platform is the purest expression of the “AI threat means more security spend” trade, and management now openly calls the company “AI security infrastructure.” Net new annual recurring revenue grew more than 50% year over year in its most recent quarter, its new AI-detection product is scaling fast, and gross retention sits near 97% — a sign customers simply don’t leave. The catch: it’s the most expensive name in the group, and after a 4-for-1 split this summer, it’s already trading near Wall Street’s most bullish price targets. You’re paying up for the best.

2. Palo Alto Networks (PANW)

The consolidation play. While rivals sell point products, Palo Alto is pitching enterprises on running their entire security stack through one vendor — and it’s working, with next-generation security ARR above $9 billion and growing north of 30%. Its roughly $25 billion acquisition of CyberArk, which closed earlier this year, bolts on a dedicated identity pillar right as AI agents make machine identity a top priority. The catch: growth is decelerating off a huge base, and the stock has already had a monster year.

3. Fortinet (FTNT)

The value general. Fortinet owns better than half the global firewall market, differentiates with its own custom silicon, and — critically — is consistently profitable. It typically screens as the cheapest of the mega-cap pure plays, which makes it the “sleep at night” name in the group. The catch: it carries more hardware exposure than the pure cloud names, so its growth is steadier but slower.

4. Zscaler (ZS)

The zero-trust leader. Zscaler replaced the old perimeter-firewall model with cloud-native traffic inspection, and it’s leaning hard into securing agentic AI as its next growth leg, with ARR growing around 25% to nearly $4 billion. It often trades at a meaningful discount to CrowdStrike on forward earnings. The catch: the market punishes any hint of a growth slowdown here — recent guidance wobbles have triggered sharp drops.

5. Microsoft (MSFT)

The elephant nobody counts as a “cyber stock.” Microsoft has quietly built a security business worth roughly $37 billion — larger than CrowdStrike, Palo Alto, and Zscaler combined — and bundles it into enterprise agreements customers already sign. You won’t get pure-play torque, but you get the most defensible distribution in the entire industry with a fraction of the single-name risk. The catch: security is a rounding error in Microsoft’s overall story, so it won’t move the stock on its own.

The High-Growth Disruptors: AI Cybersecurity Stocks With the Most Upside

More torque, more risk. These are the challengers and turnarounds with the most upside if they execute.

6. SentinelOne (S)

The direct CrowdStrike challenger. Its AI-native Singularity platform and Purple AI tools are built exactly for the autonomous-threat era, ARR has crossed $1 billion, and it’s the cheapest of the leaders on a price-to-sales basis — trading around 3.5x forward revenue versus far richer peers. It’s also a perennial takeover candidate. That combination gives it the most torque on the list. The catch: it’s still proving durable profitability and faces the risk of being out-muscled by the platform giants.

7. Okta (OKTA)

The identity turnaround. Identity is the front door to every network — and with AI agents multiplying, every one of them needs an identity to manage. Okta is the leader in that lane and has been climbing steadily back after years in the wilderness. The catch: the platform giants are bundling identity too, so Okta has to keep proving it’s the better standalone tool.

8. Rubrik (RBRK)

The “after the breach” play. While most names focus on preventing attacks, Rubrik focuses on cyber resilience — clean, fast recovery after ransomware hits. That’s a fundamentally different exposure, and a critical one, since no defense is perfect. It’s been one of the stronger performers among the newer public names. The catch: it’s a younger, higher-volatility stock still scaling toward consistent profitability.

9. Cloudflare (NET)

The AI-infrastructure hybrid. Cloudflare isn’t a pure cybersecurity play — it sits at the intersection of edge computing, AI infrastructure, and security, growing revenue nearly 30%. For investors who want the AI build-out and the security angle in one ticker, it’s a rare two-for-one. The catch: that breadth means it competes on multiple fronts, and it trades at a premium valuation.

The Under-the-Radar Value & Niche Cybersecurity Stocks

Cheaper, specialized, less-crowded corners of the space — where the asymmetric setups often hide.

10. Check Point Software (CHKP)

The steady compounder. Check Point rarely grabs headlines, but it delivers consistent margins and cash flow while quietly building out its cloud and zero-trust offerings. It’s the low-drama way to own the theme.

11. Qualys (QLYS)

The profitable niche leader in cloud-based vulnerability scanning and compliance — the unglamorous plumbing that every regulated enterprise has to buy.

12. Varonis (VRNS)

A direct “securing AI” play hiding in plain sight. Varonis discovers, classifies, and monitors sensitive data and catches insider threats — exactly the problem that explodes when companies point AI models at their internal data. Its shift to a SaaS model is gaining traction.

13. Tenable (TENB)

The exposure-management specialist. Tenable helps organizations find what’s actually exploitable before attackers do — a discipline that only grows in importance as AI accelerates vulnerability discovery on the offensive side.

14. Rapid7 (RPD)

The small-cap wildcard. Same vulnerability-management and security-operations lane as the bigger names, but at a fraction of the market cap — which means more torque on any sector re-rating, and more risk if execution slips.

15. Gen Digital (GEN)

The consumer angle. The owner of Norton and Avast is the steadier, income-oriented way to play cybersecurity — protecting individuals rather than enterprises, with a profile closer to a cash-flow compounder than a hyper-growth bet.

Bonus: 3 Cybersecurity ETFs for One-Click Exposure

Not sure which horse to back? Own the whole field. These three ETFs give you diversified exposure without single-name blowup risk:

• First Trust NASDAQ Cybersecurity ETF (CIBR) — the largest and most liquid, weighted toward Palo Alto, CrowdStrike, and Fortinet. It’s pulled in roughly $1.5 billion of net inflows over the past year, so it’s where sector money shows up first.
• Amplify Cybersecurity ETF (HACK) — the original cyber ETF, with a similar large-cap tilt.
• Global X Cybersecurity ETF (BUG) — a cleaner pure-play tilt with less legacy-tech overlap.

The Bottom Line on the Cybersecurity Play

Cybersecurity is one of the only enterprise budgets still expanding through an uncertain economy — and AI is pouring fuel on it from both sides. That’s the definition of a durable trade.

A few rules to play it well. Treat the tiers as roles, not rankings — the generals for core exposure, the disruptors for torque, the niche names for less-crowded setups, and the ETFs when you’d rather own the theme than pick a winner. Watch net new ARR at every earnings report; it’s the one number that separates a real spending shift from a headline pop. And remember that after a big move like Monday’s, headlines fade but bookings don’t — the names that hold their gains are the ones with the recurring revenue to back it up.

The AI era needs defending. These are the companies getting paid to do it.

Editor’s Note: What ever happened to the AI stock boom? Even AI darlings like Nvidia have essentially gone nowhere since summer 2025. Our friend and colleague at InvestorPlace, Louis Navellier, may have the answer. According to Louis, the AI industry is quietly “staging” ahead of the next great AI breakthrough… a new class of AI he calls “Superintelligence… but better.” How will it trigger a $100 trillion reset of the AI markets. How will the launch of this tech send some stocks to zero, and others soaring? And why does Louis say: Don’t buy or sell an AI stock in 2026 until you see what’s coming next? Go here for the full story (and Louis’ #1 pick).  

Cybersecurity Stocks FAQ

What is the best cybersecurity stock to buy in 2026?

There’s no single answer for every investor, but the strongest candidates are CrowdStrike for quality, Palo Alto for the best balance of growth and value, and SentinelOne for the most upside if you can stomach the risk. Match the name to your risk tolerance, not the other way around.

Are cybersecurity stocks a good hedge against an AI bubble?

They’re one of the more logical ways to stay exposed to AI without betting purely on the chip and data-center trade. Security spending is largely non-discretionary and rises whether AI adoption succeeds or produces new threats — so the group can hold up even if AI enthusiasm cools.

What is the best cybersecurity ETF?

The First Trust NASDAQ Cybersecurity ETF (CIBR) is the largest and most liquid, making it the default choice for diversified exposure. Amplify’s HACK and Global X’s BUG are smaller alternatives with slightly different holdings.

How is AI changing the cybersecurity industry?

AI is arming attackers with faster, cheaper, autonomous attacks — and forcing defenders to buy AI-powered tools to keep up. Gartner now tracks “securing AI” as its fastest-growing category, projected to become the single largest area of security spending by 2029.

CrowdStrike vs. Palo Alto — which is the better stock?

CrowdStrike is the higher-quality, faster-compounding business; Palo Alto is the broader platform at a more reasonable valuation. For new money at current prices, Palo Alto arguably offers the better risk-reward — but both belong on a serious watchlist.

Trade This Theme With Me – Live, Every Morning

A watchlist is a starting point. Turning it into actual trades — knowing when to get in, where to set risk, and when to walk away — is the hard part, and it’s what I do out loud every single day.

Join me on MiT Live, weekdays at 11 a.m. ET. I go live to break down the market’s biggest themes and the specific setups I’m watching in real time — cybersecurity, AI, and wherever the money is rotating next. It’s free, it’s unscripted, and it’s where lists like this one turn into real trade plans. Drawing on 28 years as a CBOE and CME floor trader, I’ll show you exactly how I’m thinking through these moves as they happen.

Watch live at youtube.com/@LiveOptionsWithJR — hit subscribe and set a reminder so you never miss the 11 a.m. open.

About the author

Jonathan Rose is a 28-year veteran trader who spent his career as a market maker on the Chicago Board Options Exchange (CBOE) and a floor trader at the CME and CBOT. He is the founder of Masters in Trading, where he teaches options and shares live trades and market themes every weekday at 11 a.m. ET on MiT Live. His focus is helping everyday investors think about risk and opportunity the way professional traders do.

The post The 15 Best Cybersecurity Stocks to Buy Whether AI Booms or Busts appeared first on MarketWise.

Oracle (ORCL) reported strong results in its fiscal first quarter on Thursday, with earnings blowing past analysts’ expectations. Total revenue climbed 30% year over year, to $19.3 billion, while adjusted earnings per share grew 30% to $1.92, topping the Bloomberg analyst consensus of $1.75 per share.

All eyes should be on Oracle’s artificial-intelligence (“AI”) infrastructure business, since that’s where the company has staked its future growth. This Cloud Infra unit, as Oracle calls it, saw sales surge 121% to $7.4 billion in the quarter, above the analyst consensus of $7.19 billion.

Oracle noted that it delivered 850 megawatts of data-center capacity in the quarter. This allows the company to start generating revenue from the additional capacity.

Oracle signed $30 billion in AI cloud contracts in the quarter, bringing its remaining performance obligations (“RPO”) to $664 billion. The RPO is future contract revenue that Oracle expects to earn from its data-center tenants. This includes, most importantly, OpenAI, which represents about half of Oracle’s RPO, including a $300 billion deal between the two companies.

Management projected that the second quarter of fiscal 2027 would look similar to, or maybe better than, this quarter: sales up 30% to 34% and adjusted earnings per share of $1.85 to $1.93. That level of earnings per share represents growth of 21% to 25% – down from the first quarter.

Management stood firm on its prior sales expectations of $90 billion for the year, but raised earnings-per-share guidance from $8.05 to $8.10. It also stuck to its capital spending expectations of $90 billion to $95 billion for this year, a level that’s already strained its finances.

Oracle is spending aggressively to build out its data-center network, so its first-quarter earnings translated into negative $5 billion in free cash flow. The company has borrowed a staggering amount to fund the build-out – sporting $130 billion in notes payable and other borrowing, as of May – and completed a $20 billion equity offering in the quarter to raise non-debt financing.

The concern is significant enough that the company is telling investors that its new RPO contracts do not create the need for an incremental capital raise.

Oracle CFO Hilary Maxson detailed on the earnings call that the “vast majority of those new contracts were via prepay or bring your own hardware or similar mechanic,” and that the RPO wouldn’t hit revenue or capital expenditures until fiscal 2028 (which begins in June 2027) or beyond.

Oracle’s quarterly results may calm the nerves of some investors for now, but the company will need to show that its key data-center tenant can pay its bills. At this point, that’s not certain.

Oracle’s Future Is Linked to OpenAI’s Success

Oracle stock has been on a roller coaster over the past year as investors try to decide whether the company will be one of the biggest beneficiaries of AI… or one of the biggest losers. Big tech companies have hitched their AI fortunes to two money-losing firms: OpenAI and Anthropic.

Some investors are betting big that Oracle will plummet – a 50% drawdown is well possible, after similar downdrafts in the past year – and the cost to insure Oracle’s debt against default has soared during that time.

The key risk is OpenAI, given its large share of Oracle’s RPO and OpenAI’s continuing losses. OpenAI is a linchpin in the AI industry, with future spending commitments to major players, including Cerebras Systems (CBRS), CoreWeave (CRWV), and Oracle. So, it’s crucial to the AI industry and Oracle that OpenAI stays solvent and continues to spend. If not, a range of AI-related stocks may go to zero if the bubble pops.

OpenAI Continues to Burn Billions in Cash

However, OpenAI is unprofitable, and it’s not clear whether it can ever turn a profit. OpenAI lost $20.9 billion on an operating basis in 2025, followed by a $9.3 billion loss in the first quarter and a further $12.3 billion loss, including stock compensation, in the second quarter.

The business environment continues to worsen for OpenAI, too. The company’s sales grew just 18% quarter over quarter, while Anthropic more than doubled its revenue in the same period.

Meanwhile, Chinese AI models are taking significant share from American models, including OpenAI. They’re offering good enough performance at substantially lower prices, so software developers are turning to these low-cost providers to build their models.

As if that wasn’t enough, token prices continue to plummet, reducing the revenue OpenAI can generate. Silicon Data’s LLM Token Expenditure Index shows token pricing cut in half from the end of May to late August, from $2 per million tokens to just $1.02.

With poor and worsening economics, OpenAI will need to raise at least tens of billions of dollars soon to stay afloat. So, in effect, Oracle’s success – some analysts say survival – depends on whether investors will keep funding OpenAI indefinitely.

Oracle’s own shakier financial status is shown by the fact that S&P Global Ratings downgraded the company’s debt by a notch to BBB-, just one level above “junk.” As mentioned, the cost to insure Oracle’s debt against default has sharply risen over the past year.

All of that is tied to OpenAI in some way or another.

So, while Oracle’s own first-quarter results look excellent on the surface, its key customer is showing severe problems. If not fixed, it’s only a matter of time before they become Oracle’s problems, too.

Regards,

James Royal, PhD

Editor’s Note: Whitney Tilson — the hedge fund manager CNBC called “The Prophet” — says America has reached its “Ripping Point.” The old financial order is being torn apart, and he believes most investors have no idea what’s coming in the next six months. He’s named the stocks he thinks will be destroyed in the chaos — and the ones he believes will soar. Watch his free presentation while it’s still available.

The post Oracle Earnings: Watch Out for Massive OpenAI Risk Despite Blowout First Quarter appeared first on MarketWise.

As I read the back and forth on AI, over and over again I keep thinking the same thing: If you substitute the word “Internet” for “AI,” it’s the same as 1999/early 2000.

AI, like the Internet, is a revolutionary, world-changing technology that some early adopters and companies are using to massively benefit.

But as the Internet bubble inflated, the spend far outpaced the demand, resulting in circular financing, terrible companies achieving absurd valuations, etc. – a classic bubble that burst, taking the great companies down 80%… and the lousy companies to ZERO.

There’s little doubt in my mind that we’re in a similar AI bubble right now – but I’m much less certain how much further it will inflate and when exactly it will burst.

Are we in early 1999, when Internet stocks doubled over the next year before crashing? Or are we in early 2000, on the edge of the precipice?

In today’s essay, I’ll explain exactly where I think we are in the market… why I’m not rushing to sell my stocks yet… and where I see tremendous value today… 

CNBC’s Jim Cramer recently visited a construction site in Idaho for a Micron Technology (MU) semiconductor plant. He complained that “there’s an incredibly jarring gulf between stock prices and reality” and that “Micron’s stock is radically undervalued.”

This is significant because, for decades, Cramer has been a great contra-indicator.

It reminds me of his infamous “Winners of the New World” speech on February 29, 2000, just 10 days before the Nasdaq Composite Index peaked. In it, he named 10 Internet-related stocks and said:

We try to own every one of them. Every single one. And if I had my druthers, I wouldn’t own any other stocks in the year 2000. Because these are the only ones worth owning right now in this extremely difficult, extremely narrow stock market. They are the only ones that are going higher consistently in good days and bad. I love every one of them, just as I loathe the rest of the stock universe.

Within 15 months of his speech, the basket of stocks lost more than 80% of its value. By 2009, all 10 companies were either bankrupt, delisted, or acquired for a tiny fraction of their peak bubble valuations.

I think history is likely to repeat itself with the AI bubble…

And make no mistake, we are living through a bubble. Consider that outside of drugmaker Moderna (MRNA) and oil company Marathon Petroleum (MPC), the best-performing stocks in the S&P 500 Index through the end of August are all beneficiaries of the AI-infrastructure boom:

Rank	Company	Ticker	Total Return
1	Sandisk	SNDK	469%
2	Moderna	MRNA	355%
3	Dell Technologies	DELL	260%
4	Micron Technology	MU	204%
5	Seagate Technology	STX	189%
6	Western Digital	WDC	140%
7	Marvell Technology	MRVL	137%
8	Lumentum	LITE	137%
9	Marathon Petroleum	MPC	129%
10	Intel	INTC	127%

I looked at what the same list looked like in the last stage of the Internet bubble. Sure enough, they were all tech stocks – Qualcomm (QCOM), Oracle (ORCL), Adobe (ADBE), and Apple (AAPL) among them – reflecting the Internet craze at the time.

Things Are Getting Worse for OpenAI

I’ve been warning my readers about ChatGPT operator OpenAI for years, calling it “a cash-burning furnace.” And things continue to go from bad to worse for the company…

As reported in the Wall Street Journal, its second-quarter revenue was up only 18% from the first quarter. Meanwhile, revenue more than doubled for rival Anthropic.

At the beginning of this year, OpenAI had twice as much annual recurring revenue as Anthropic. But it has now fallen far behind, as this chart from TMT Breakout shows:

And the cash-flow trends are even more starkly divergent, as you can see in this chart created by my friend James Emanuel:

These trends are why I think Anthropic will have a successful initial public offering, perhaps as soon as October. Meanwhile, I doubt OpenAI will ever go public. It’s more likely to implode spectacularly.

If I’m right, the consequences will go far beyond one company, as blogger Ed Zitron writes here: What Happens If OpenAI Dies? He summarizes why he’s so bearish on the company:

OpenAI has no economies of scale, it’s horribly-unprofitable, and does not have a stable business. This naturally means that it has to continually raise capital, except raising further capital is going to be difficult, based on the sheer amounts it needs, the dwindling funds available for it to raise, its already-inflated valuation, and the fact that it’s way behind a competitor facing exactly the same problems.

OpenAI has promised the impossible, and built a company that only makes sense if you’re willing to ignore the worst economics in the history of capitalism. Its future is dependent on raising over a hundred billion dollars a year in one of the worst funding climates in history. Its revenues are slowing, its competitor (and there’s really only one) has outpaced it (all while slowing itself), and its CEO is one of the single-worst spokespeople in history…

The collapse of OpenAI would likely be a result of the walls closing in around its ruinous obligations and economics, with counterparties left short-changed and deals broken as things begin to unravel.

How might the unraveling play out? Zitron outlines a number of scenarios, but I think this is the most likely one:

While I imagine some rescue package is pulled together, OpenAI could simply be allowed to run out of money, short-changing nearly a trillion dollars’ worth of compute contracts, killing CoreWeave, Cerebras, and anyone else reliant on its income. Its customers would be given API keys that flow to Microsoft AI Foundry, Amazon Bedrock and Google Vertex, and be told that there would be little or no further development or training of OpenAI’s models.

This situation, while obviously destructive for the entire industry, would give everybody a scapegoat. Who made all the promises? Sam Altman. Who ran a shitty company into the ground? Sam Altman. Who misled everyone into believing that there’d be infinite demand for compute? Sam Altman. Stories will leak that OpenAI was “not consistently candid” with its financial condition with partners, allowing everybody to reframe a trillion-plus dollars in waste as the result of one egregious con artist.

If Zitron and I are right that OpenAI is going to be a train wreck, the first stock to go to zero will likely be data-center builder CoreWeave (CRWV).

The No. 1 Victim of an AI Bust

Let’s take a look at CoreWeave’s historical financials, which are quite limited because it’s less than 10 years old. (It was originally called Atlantic Crypto Corporation – talk about a red flag! – and changed its name to CoreWeave in December 2019.)

Revenue growth has been strong, but the company barely breaks even on an operating-profit basis. And it has had negative net income every quarter thanks to high, rising debt payments:

Analysts don’t expect the company to turn profitable anytime soon. Consensus earnings estimates are negative $5.20 per share this year, narrowing slightly to negative $4.24 next year.

CoreWeave has generated a bit of operating cash flow, but this is far exceeded by its capital expenditures (“capex”). As a result, free cash flow (“FCF”) is accelerating to the downside:

CoreWeave funds its huge cash burn by borrowing more and more money, resulting in rapidly rising net debt:

These are among the ugliest financials I’ve ever seen…

Net debt of $46 billion is nearly equal to CoreWeave’s market cap of $48.5 billion, giving the company an enterprise value of $94.5 billion. That’s ridiculous.

CoreWeave investors are betting that the AI boom will expand even further, allowing the company to turn profitable and pay off its enormous debt load. Not likely…

I think what they’re really betting on is that someone will buy their stock at a higher price at some point in the very near future – the very definition of speculation.

Why I’m Staying in the Market

But I’m not urging my readers to sell. For one thing, earnings growth for companies in the S&P 500 has been off the charts this year.

In a post on his Week in Charts blog, Charlie Bilello of Creative Planning, notes that as of August 18, second-quarter earnings growth of 29% was “the biggest upside surprise in history.” And it has risen to more than 50% year over year – the highest quarterly growth rate in five years.

As a result, Bilello notes…

S&P 500 earnings are now expected to surge 32% in 2026, more than double the 15% growth expected at the start of the year.

In this chart, you can see how growth has been climbing steadily for the past three years:

This growth is largely driven by the AI infrastructure bubble.

Earnings of tech companies in the S&P 500 soared 71% year over year in the second quarter. That’s all the more remarkable considering just one year ago, these companies saw less than one-third of that growth, as this chart from Ritholtz Wealth Management shows:

Given that the S&P 500 is “only” up around 12% this year – far less than earnings growth – simple math dictates that the index’s price-to-earnings multiple has gone down this year.

That means stocks are cheaper and therefore a better buy today than they were at the beginning of the year, right?

Not so fast…

Five of the six periods of extremely high earnings growth in the past quarter century – all but last year – have preceded significant market drops.

My friend Doug Kass of Seabreeze Partners Management agrees that strong earnings per share (“EPS”) doesn’t equate to strong price gains. He argues that this year offers a combination of unique market challenges compared with prior periods. From one of his recent pieces, he cites:

• High and rising inflation and interest rates.

• A burgeoning deficit and U.S. debt load may be a permanent condition giving the general lack of discipline from both parties in Washington DC.

• Improvisational geopolitical and fiscal policies that present threats to political and economic stability.

• Both parties are moving to extremes – the Republican party more to the right and the Democratic party to the left. With a possible Democratic congressional majority win in November, anti-corporate policy (higher corporate taxes, etc.) may be in the offing.

• Traditional valuation metrics in the 98th percentile, two standard deviations above the average.

• The AI capital spending spree and gains from investments have inflated S&P profit reports… an earnings reckoning may lie in the not too distant future.

I think Doug is right that huge corporate earnings growth likely won’t translate into a comparable huge rise in stocks.

Unlike Doug, I’m not bearish on stocks in general – with the exception of the AI bubble. When it bursts, stocks that have soared during this boom – such as CoreWeave (CRWV) – will undoubtedly crash.

But plenty of stocks are likely to hold up well. While investors are fawning over red-hot AI stocks like Sandisk (SNDK), Dell (DELL), and Micron (MU), they’re completely ignoring a corner of the market where I’ve grown extremely bullish in recent months. 

I recently sat down on camera to discuss a major shift I see coming to the market for the first time in nearly 30 years – the same opportunity that helped me make my name on Wall Street back in 1999.

In my video, I discuss why I see market leadership shifting from the hottest AI names in the market to a different class of stocks. And I share everything I can to help you position your money while there’s still time to take advantage of this shift.

The post The AI Bubble in 2026: Why OpenAI Is Cracking and CoreWeave (CRWV) Could Be the First to Hit Zero appeared first on MarketWise.

SB Energy (SBE) is planning to go public, but details in its recently filed S-1 initial public offering (“IPO”) prospectus should give artificial intelligence (“AI”) investors a wake-up call. The data-center developer had to give away billions of dollars in value to secure OpenAI as a tenant at to-be-constructed facilities, according to SB Energy’s prospectus.

SB Energy’s move suggests demand for AI data centers may not be nearly as robust as AI bulls suggest, especially as OpenAI seems to lose more money each quarter.

Let’s dig into the details to understand what SB Energy did and why.

In January, SB Energy issued warrants to OpenAI as part of a deal to land the AI lab as a tenant at data centers it’s developing in Milam County, Texas. As of the prospectus date, OpenAI owns 3.99 million warrants, valued at $5.5 billion on June 30, and they expire in 2036.

Warrants are a kind of financial derivative that entitle the owner to buy a security from the issuer at a specified price until the expiration date. In this case, OpenAI can buy up to 3.99 million shares at $0.01 per share if and when each of the eight warrant tranches vests.

One tranche has already vested when the two companies signed a lease for 800 megawatts at SB Energy’s Milam County facility. A second tranche will vest if SB Energy prices an initial public offering (“IPO”). The firms have agreed to a cashless exercise of these vested warrants, so that OpenAI simply receives the value of the warrants as shares of SB Energy stock without having to put up any money of its own – a valuable point for OpenAI, since it’s burning cash.

Six further tranches vest under other conditions, including five that vest as SB Energy’s market capitalization hits milestones from $80 billion to $200 billion.

Why would SB Energy just give away warrants that were worth more than $3.6 billion at the time of issuance – and now valued at $5.5 billion?

SB Energy Just Gave Away Billions to OpenAI

As part of its IPO, SB Energy is looking to raise between $5 billion and $7 billion with an initial market cap of $50 billion. But the firm has a history of losses, and almost all of its handful of data centers are yet to be constructed. In the first six months of 2026, SB Energy reported sales of $138.7 million and losses of $3.2 billion, mostly due to changes in the warrants’ value.

So, enticing OpenAI to sign a lease with billions in warrants gets SB Energy in the game quickly with a tenant that has billions in financing. With a signed contract in hand for a future data center, SB Energy can go to a lender and access financing on more agreeable terms (or at all). That’s how neoclouds such as CoreWeave (CRWV) have been playing the game, too.

Subsequently, SB Energy announced it will build another massive data center to be leased by OpenAI, an 8.0-gigawatt facility known as the PORTS-Pike Technology Campus in Pike County, Ohio. Nvidia (NVDA) stepped in to backstop up to $105 billion for the facility’s first 4.25 gigawatts and has an option to backstop the remaining portion of the build-out. Operation will begin in phases in 2028.

But step back for a moment and consider what SB Energy’s financing move really does. If the AI market is really as hot as bulls say it is, why does a developer need to pay a prospective tenant? Shouldn’t AI labs – which are unable to keep up with demand, the story goes – be clamoring for a data-center deal, so that a supplier such as SB Energy doesn’t need to pay its tenants?

What we’re seeing is relatively little demand for AI data centers that isn’t driven by the two major labs, OpenAI and Anthropic. The two most lavishly funded labs are running around signing massive deals, such as with Oracle (ORCL), which is building data centers for OpenAI. The AI lab owes around half of Oracle’s $638 billion in remaining performance obligations as rent.

OpenAI has $1.4 trillion in spending commitments, and Anthropic inked $517 billion in compute deals in just 11 months. That’s translating into a huge share of AI revenue for the hyperscalers: 70% of Microsoft’s (MSFT) revenue in its latest fiscal year, per Bloomberg estimates. Analyst Ed Zitron pegs OpenAI and Anthropic’s sales at a similar level to Google and Amazon (AMZN).

In short, a poorly capitalized SB Energy with little history in developing data centers is paying an AI lab with a history of rising operating losses to occupy its massive but not-yet-built facility.

That sounds like a recipe for trouble.

SB Energy’s IPO Timeline: Will It Race to Go Public?

SB Energy is relying on the weakest major player among AI labs, OpenAI. It’s on the hook to spend $1.4 trillion and generated a $12.3 billion loss in the second quarter, after a $9.3 billion loss in the first quarter. It will keep burning money and may be structurally unprofitable.

While OpenAI raised $122 billion earlier this year in a huge funding round, it’s going to need to raise cash again soon – and not just a little. And it has a few ways to lose in the fundraising process and really just one way to win. It needs to raise at least tens of billions of dollars at a higher valuation than it received in its latest funding round in March, $852 billion.

It loses if it can’t raise enough money, the valuation does not rise enough, or – horror! – the valuation actually falls. Given that OpenAI postponed an IPO over concerns it would not reach a $1 trillion valuation, the potential upside here may be limited.

If OpenAI can’t get a fundraising round that sustains investors’ optimism about AI as a business, it will be felt across the industry. As a result, SB Energy may be racing to the IPO window in the next few weeks to get ahead of this possibility. But even if SB Energy does complete its offering, it will still be almost wholly reliant on OpenAI to survive.

Regards,

James Royal, PhD

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The post SB Energy IPO: This Hidden Detail Is a Major Warning About the AI Bubble appeared first on MarketWise.

Everybody’s crowding into the same handful of AI chip names. Meanwhile, the actual bottleneck in the entire AI buildout is a metal that’s been around since the Bronze Age — copper. (Yes, the Chester Copper Pot metal, for my fellow Goonies fans. It always sneaks into my presentations.)

And this week copper did something it hasn’t done in a very long time: it printed a brand-new all-time high while the physical supply underneath it keeps getting thinner. As I’m writing this, three-month copper on the LME just tagged a record near $14,700 a ton (an intraday high around $14,694), capping its longest weekly winning streak since 1994. One of my favorite copper stocks is Freeport-McMoRan (FCX), and it’s up better than 7% on the day and roughly 44% on the year.

I’ve been pounding the table on the copper trade on Masters in Trading LIVE for weeks — and I put a real, defined-risk version of it (a specific FCX options play) in our free portfolio, live on the show, which I’ll show you the exact structure of below. Let me walk you through why first.

 AI Runs on Copper, and There Isn’t Enough of It

Here’s the part Wall Street keeps glossing over. An AI data center uses roughly 10 times the copper of a traditional data center. All that compute, all that power delivery, all that cabling — it runs on copper. And a new copper mine takes seven to ten years to build. Supply simply cannot catch up to this demand on any timeline that matters.

Now stack that AI demand on top of a supply chain that’s genuinely cracking:

• One year ago this week, a wet-material flood hit Freeport’s Grasberg mine in Indonesia — the world’s second-largest copper source — triggering a force majeure. Freeport has cut its 2026 output guidance at the complex by roughly a third, and a full recovery isn’t expected until 2027 or 2028.
• Chile, the world’s largest producer, just logged its weakest second quarter in at least 19 years and has cut its full-year forecast twice.
• Global mine output actually fell in the first half of the year. Morgan Stanley started 2026 expecting supply to grow and now sees it flat-to-down — which would be the first annual decline in mine supply since 2017.

As Evy Hambro, BlackRock’s Global Head of Thematic and Sector Investing, put it, existing copper operations are “tired, very, very old assets.” That’s the backdrop: warehouses draining, grades falling, and the biggest demand story of the decade just getting started.

The Copper Tariff Catalyst Nobody’s Fully Pricing In

This is where the edge lives. I don’t just want to be long a strong commodity — I want a known catalyst with a date on it. Copper has one.

Remember: a tariff is just a tax. Back in 2025, Washington slapped a 50% tariff on semi-finished copper products — pipes, wires, rods, sheets — plus copper-intensive derivatives like cables and connectors. But read the fine print: they didn’t tax raw input material or refined copper (cathode) itself. Not yet.

Here’s the timeline that matters:

• The Commerce Department’s deadline to recommend action on refined copper passed on June 30, and more than two months later the White House still hasn’t ruled.
• The recommendation on the table is a phased tax on refined copper — 15% in 2027 (possibly as soon as January), stepping up to 30% in 2028.
• Traders aren’t waiting for the ink to dry. Roughly 200,000 tons of refined copper flooded into the U.S. in July alone — the largest monthly inflow on record — pushing Comex inventories past 1 million tons as buyers race to beat that possible January duty.

When big money moves ahead of a known date, it tips its hand. That’s the footprint I follow. I learned that on the floor — I spent twenty-eight years as a market maker at the CBOE and a floor trader at the CME and CBOT, and all any trader is ever doing is positioning in front of the biggest players in the room. Copper is flashing that exact signal right now.

 The Edge: Only Two Copper Smelters Are Left in America

Here’s the nuance that separates the pros from the crowd. A tax on imported refined copper is a gift to the very few companies that turn raw material into finished copper on U.S. soil. And there are barely any left.

Industry testimony to Congress this year put it starkly: the U.S. ran 16 primary copper smelters in 1976. Today, just two are operational, with a third (Grupo México’s Asarco Hayden in Arizona) mothballed. That’s the entire domestic backbone for refining copper in the world’s largest economy — which is why the U.S. ships roughly a third of the copper it mines overseas to be processed, then buys it back as finished metal. Put a tariff on that finished metal, and you hand enormous pricing power to the two companies that still run a smelter here:

• Freeport-McMoRan (FCX) runs one of them (its Miami smelter in Arizona). It’s the largest U.S.-listed copper producer, it trades with deep, liquid options, and it’s my single favorite name in the space. The leverage is the story: by management’s own math, every 10-cent move in copper is worth about $390 million in annual EBITDA. It models ~$13 billion in EBITDA at $5 copper and ~$20 billion at $7 copper. Copper’s already trading north of $6.60 a pound. You do the math.
• Rio Tinto (RIO) owns the other (its Kennecott smelter in Utah). It’s a much bigger, more diversified major — so it’s a steadier, more indirect way to get exposure. Think of Rio as the lower-beta version of the same idea.

That’s the concentrated bet. Now let’s talk about the rest of the group — because not all copper stocks play this story the same way.

The Best Copper Stocks for AI (and the Ones to Approach With Caution)

Copper’s 2026 run has turned the big producers into what basically looks like one trade — the year-to-date returns are clustered in a tight band, which tells you this is a commodity move, not a company-execution move. Names to know:

• FCX (Freeport-McMoRan) — my top copper stock pick. Most direct U.S. smelter play, deepest options, biggest earnings leverage to the copper price.
• RIO (Rio Tinto) — the other U.S. smelter; bigger, steadier, more diversified.
• SCCO (Southern Copper) — a pure-play copper heavyweight, up roughly 45% on the year with monster EBITDA margins. Riding the same wave as FCX.
• TECK (Teck Resources) — also up ~45% YTD, part of that same tight cluster.

And then there’s the be-careful bucket. Plenty of copper names are just miners — they dig up raw copper and ship it overseas. A U.S. refined-copper tax doesn’t really touch them, and the foreign refiners can actually be hurt by it. That group includes names like Ero Copper (ERO), Hudbay (HBM), First Quantum, Ivanhoe, Capstone, plus the big overseas majors like Vale (VALE) and BHP. Great companies, plenty of them — but they don’t sit on the right side of this specific catalyst. Know what you own and why you own it.

What About Copper ETFs?

If you’d rather not pick a single name, the sector has clean ETF wrappers — just understand what each one actually gives you:

• COPX (Global X Copper Miners ETF) — a basket of copper miners worldwide. Great for broad exposure to the theme. The trade-off: it dilutes the U.S.-smelter edge, because it holds a lot of those overseas miners the tariff doesn’t help.
• CPER (United States Copper Index Fund) — tracks copper futures directly. This is your cleanest exposure to the metal itself rather than the equities. If your whole thesis is that the price of copper goes higher, this is the straightforward vehicle.
• COPJ (Sprott Junior Copper Miners ETF) — smaller, higher-beta junior miners. More torque, more risk.

The way I look at it: the copper ETFs are the easy button for just being in copper. But the edge — the concentrated, catalyst-driven bet — lives in the two American smelters, FCX and RIO. That’s the difference between owning the theme and owning the trade.

The Exact FCX Copper Trade I Shared Free on MiT Live

Here’s what makes our show different: I don’t just talk about copper — I put on a real trade in front of you, live, in our free portfolio — no paywall required to watch me do it. So let me show you exactly how I structured the FCX trade when I shared it on the show.

The setup was a vertical call spread in the January 2027 expiration:

• Buy the FCX 80 call
• Sell the FCX 105 call

At the time, that spread cost roughly $4.50 to put on — paying about $6 for the 80s and collecting about $1.50 for the 105s. And that number is your risk: in a defined-risk spread like this, the most you can lose is what you pay for it. So one spread risked about $450 — and you can never lose a dollar more than that, no matter what FCX does.

Now the reward. The spread is worth its full width — the 25 points between the 80 and 105 strikes, or $2,500 — if FCX finishes above 105. Subtract the $450 you paid, and the most you can make is about $2,050. That’s better than 4-to-1 — risk $450 to make $2,050 — with about 134 days for the thesis to play out. And it scales cleanly: a 10-lot risks about $4,500 to make about $20,500. Same ratio, just add a zero.

A few things I say every single day on the show:

• This is one trade, not two. Two legs, one position — a vertical call spread. Don’t manage it as a separate long option and short option.
• Options are just a derivative of the stock. Think of it as long from 80 and short from 105. If FCX is above your strike at expiration, you’re long from there; if it’s below, you’re not. That’s it — the internet loves to overcomplicate this.
• New to options? Paper-trade it. Write the trade down, follow FCX, and let your confidence build before you risk a dime.

Options prices move, so the fills above reflect the session when I shared it — and with copper and FCX both pushing to fresh highs since, the picture keeps evolving. But the structure is the lesson: a known catalyst, a strictly defined risk, and a reward that’s several times what you put up.

The Bottom Line on Copper Stocks

AI can’t run without copper. The world is running short of it. And Washington is sitting on a decision that could put a tax on it as soon as January. That’s a supply squeeze, a demand supercycle, and a known catalyst all stacked in one place — and the cleanest way to play it is the two companies that refine copper on American soil, starting with FCX.

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Copper Stocks FAQ

Why are copper stocks going up?

Copper just printed a record near $14,700 a ton on the LME, capping its longest weekly winning streak since 1994, while the physical supply underneath keeps thinning. An AI data center uses roughly 10 times the copper of a traditional one, and a new mine takes seven to ten years to build. Demand is surging while supply stalls.

What does the copper tariff mean for copper stocks?

In 2025, Washington put a 50% tariff on semi-finished copper products but left refined copper untaxed, for now. A phased duty on refined copper is on the table, possibly 15% as soon as January 2027 and 30% in 2028. That would hand pricing power to the few companies that refine copper on U.S. soil.

What are the best copper stocks for AI?

The cleanest plays are the two companies that still refine copper on U.S. soil: Freeport-McMoRan (FCX), the largest U.S.-listed producer and the top pick, and Rio Tinto (RIO), a steadier, more diversified option. Southern Copper (SCCO) and Teck Resources (TECK) ride the same commodity wave, both up roughly 45% this year.

Is FCX a good copper stock to buy?

Freeport-McMoRan runs one of only two operational U.S. copper smelters, and its earnings leverage is the story. By management’s own math, every 10-cent move in copper is worth about $390 million in annual EBITDA, and it models around $20 billion at $7 copper. Copper is already trading north of $6.60 a pound.

Are there copper ETFs for the AI trade?

Yes. COPX (Global X Copper Miners) gives broad exposure but dilutes the U.S.-smelter edge with overseas miners. CPER (United States Copper Index Fund) tracks copper futures directly, the cleanest bet on the metal itself. COPJ (Sprott Junior Copper Miners) holds smaller, higher-beta names with more torque and more risk.

What are the risks of copper stocks?

Copper’s 2026 run is largely a commodity move, so the big producers trade like one bet and would fall together if copper retreats. Many copper names just mine raw metal and ship it overseas, so a U.S. refined-copper tariff does not help them and can even hurt foreign refiners. Know what you own and why.

Want to see the next setup like this before it goes on? I break down real, defined-risk trades live every weekday at 11 AM ET on Masters in Trading LIVE — real trades, real time, real community. Come hang out.

The post Time to Wake Up. Copper Stocks Are the AI Trade Everyone’s Sleeping On. appeared first on MarketWise.

To call GoPro (GPRO) a struggling company would be a vast understatement. In the midst of 88 straight days of trading below a dollar per share starting in early June, the action-camera company received a delisting notice from Nasdaq on July 21. GoPro shares hit rock bottom at 57 cents during intraday trading on August 12.

But on September 1, privately held optical-transceivers maker Starman Optical and GoPro released a joint statement announcing that Starman will buy GoPro for $285 million in cash. The deal valued GoPro’s shares at $1.14, a roughly 29.6% premium to the stock’s previous closing price.

Through this deal, Starman would receive a 90% stake in GoPro. Current GoPro shareholders would own the remaining 10% of the company. The agreement amounts to a major lifeline for GoPro, which had roughly $27.3 million in cash but $405 million in current liabilities at the time of the deal.

On August 31, GoPro closed at roughly 88 cents, which actually represented a huge gain from the 60-cent closing price on August 28 (driven by separate news that popular YouTube creator Mark “Markiplier” Fischbach had purchased an 8.5% stake in GoPro in July, driving its trading volume of less than 9 million shares on August 28 to more than 220 million shares on August 31).

On September 1, after the Starman deal was announced, GoPro surged as high as $1.64 – an 87.2% increase – with more than 500 million shares traded for the full day. By market close on September 2, GoPro was up to $1.69, gaining 92.9% in a span of two days.

Whether those levels hold depends largely on how GoPro’s shareholders vote on the deal later in the year.

How GoPro Got Here: A Look at the Struggling Camera Company’s Numbers

Once upon a time (in 2014), GoPro was on top of the world. The company hit a $4 billion valuation on its first trading day that year, and its revenue soared to nearly $634 million during the last quarter of 2014 alone.

How the mighty have fallen…

Here’s a look at GoPro’s 2025 and 2026 (through the second quarter) numbers:

• Full-year 2025 revenue was $652 million, a 19% year-over-year decline, and just $18 million more than the company’s fourth-quarter 2014 revenue.

• The company sold about 2 million cameras in 2025, down 20% year over year.

• Hardware revenue in 2025 fell 21.5% year over year.

• Second-quarter 2026 revenue was $105 million, down 31% year over year – and, alarmingly, down roughly 80% from that 2014 fourth-quarter peak.

• Approximately 291,000 cameras were sold in the second quarter, a 38% year-over-year drop.

• There was a dip in the GAAP (generally accepted accounting principles) gross margin from 35.8% to 30.2%.

• GAAP net loss was $51 million, or $0.30 per share, compared with a $16 million net loss the year before. Non-GAAP net loss was $36 million, or $0.21 per share, versus a $12 million net loss the previous year.

• Adjusted EBITDA (earnings before interest, taxes, depreciation, and amortization) took a nosedive from negative $6 million to negative $29 million year over year.

Not pretty. All of this underperformance pushed GoPro to reduce its workforce by 23% in April.

The lone positive? A second-quarter 11% year-over-year increase in subscription and service revenue, to $29 million, and a record 69% subscriber attach rate (the percentage of customers who purchase an add-on service or choose a recurring subscription option).

What’s behind GoPro’s dramatic fall from grace? First, the space has become flooded with competition – namely, cheaper products manufactured by Chinese companies like Insta360 and DJI, which can run as much as $100 to $200 less than comparable GoPro models.

Then there are rising memory-chip costs that are dogging most of the electronics industry. Over the past year, memory-chip prices have skyrocketed by up to an almost unfathomable 500%. Electronics makers are beginning to pass those costs down to consumers.

With those concerns in mind, as well as GoPro’s $92 million in debt, it’s safe to say that Starman Optical came along at the right time.

Starman Optical: Is the Optics Business a Good Fit for GoPro?

If you hadn’t heard of Starman Optical before this deal, welcome to the club. The privately held company is part of the larger Starman family, which includes consumer brands such as Griffin, Incase, and Incipio, as well as medical, distribution, and real estate businesses.

Starman New Photonics is the arm under which Starman Optical operates. And Starman Optical was literally incorporated in Delaware on August 31, the day before the GoPro acquisition was announced. So, it’s very new. And it’s a very different direction for GoPro.

The GoPro you’re familiar with is a leading maker of action cameras and accessories. This new version of GoPro will still maintain its retail camera business. But it will soon add defense, government, robotics, aerospace, fiber-optics, and artificial intelligence (“AI”)-infrastructure markets to its portfolio under Starman.

When announcing the deal, the companies noted that this combination would better leverage GoPro’s impressive number of U.S. patents (more than 2,500) by incorporating its innovations into optics and imaging components that can be used in the defense and AI markets.

And it might be the best thing that has happened to GoPro since 2014.

Why the Pivot to Defense and AI Makes Sense for GoPro

The match between GoPro and Starman Optical makes perfect sense: Many of GoPro’s patents involve optics and imaging technology, and Starman Optical is an expert in making the optical transceivers used in AI data centers.

(These transceivers convert electronic data signals into high-speed light pulses to connect AI processors, servers, and network switches. This helps keep the data flowing while AI clusters train large models.)

Combining these elements could pay off for both companies, considering the AI optical-transceiver market is taking off. TrendForce research predicts the market will hit $26 billion in 2026, up nearly $10 billion from last year.

The fact that Starman’s transceivers are built in the U.S. is another advantage, with the Federal Communications Commission proposing a ban on Chinese-made transceivers to protect AI data-center security.

Financially, this deal is a lifeline for GoPro, as the acquisition will pay off GoPro’s $92 million debt. That should allow the company to focus on new products both for consumers and for Starman Optical.

With AI-infrastructure spending reaching mind-blowing proportions and an aerospace and defense industry worth roughly $900 billion, the GoPro/Starman coupling could be a match made in heaven if Starman can successfully integrate GoPro’s patented technologies into products for these markets.

That’s something GoPro could never have accomplished on its own as a consumer-focused action-camera manufacturer.

The GoPro Shareholder Dilemma: Take the Money or Keep the 10% Lottery Ticket?

Here’s where things get tricky for GoPro shareholders. They have two choices, assuming the deal passes: Take the $1.14 per share, cash out, and move on. Or keep the “lottery ticket” stub – each shareholder’s portion of the collective 10% stake in the new GoPro-Starman venture – and hope for the best.

The problem is that no one really knows what that lottery ticket will be worth.

After all, we’re talking about:

• A company (Starman Optical) whose incorporation is literally days old.

• An entity (Starman New Photonics) that owns Starman Optical and GoPro that is still growing and not yet turning a profit.

• A newly formed operation (Starman Optical and GoPro) that has not yet designed any new products, let alone built and sold them.

In other words, shareholders face substantial unknowns. A lot must go right for GoPro stock to gain value above its $1.14-per-share sale price. The first step involves Starman New Photonics successfully opening its new 100,000-square-foot manufacturing facility in Warren, New Jersey.

From there, Starman must increase production of its core Liberty Series 800G and 1.6T transceivers, which are just starting low-volume shipments and undergoing further testing, respectively. The 800G transceivers likely won’t ramp up production until late this year.

While the GoPro-Starman partnership certainly appears promising, there’s no guarantee it will succeed. It will take some time for the two companies to put their collective heads together and design, test, build, and sell new products that potential aerospace, defense, and AI customers will want.

If new government, military, and/or hyperscaler contracts are secured reasonably soon, hanging on to the collective 10% stub may prove worthwhile. Unfortunately for shareholders, there’s simply no way of knowing what the future holds for GoPro.

What to Watch in the Coming Months

As I noted, Starman is expected to expand production of its 800G transceivers later this year, depending on when the New Jersey factory opens. A smooth rollout is worth monitoring there, as are the next stages of 1.6T transceiver testing and initial factory production.

Even more important, however, is the shareholder vote, which will likely occur in November or December, before the deal closes (presumably by the end of the year). Once the mechanics of the deal (cash, plus the 10% stub) are confirmed against the proxy, which should occur in October or November, GoPro shareholders will decide whether to sell the company to Starman.

This is a key development for potential GoPro investors to watch.

If the vote passes, which is expected, the safe bet is that the stock probably won’t drop below the $1.14 share sale price. Even if it does, investors who buy GoPro shares before the deal closes are guaranteed $1.14 per share, so they’ll get their money back anyway.

But if the vote fails, that $1.14 floor disappears, and the stock will probably crash back down to its pre-merger levels. We will find out in a few months.

Is GoPro Worth the Risk?

That depends on your level of risk tolerance. If GoPro and Starman can create some high-quality products that appeal to the aerospace, defense, and AI industries, this could be a worthwhile investment that pays off a few years from now.

But those are big ifs. And they’re not the only ones. Keep in mind that GoPro was once a highly successful company that found its niche in selling action cameras to outdoor enthusiasts, athletes, and content creators – until it wasn’t.

Between cheaper Chinese competition, rising memory costs, and major improvements in smartphone cameras, GoPro lost its footing… and nearly everything else.

Since 2014 and its initial $4 billion valuation, GoPro has lost 96% of its value. (Yes, 96%.)

With this deal, however, GoPro gets a rare second chance, not only to sell more cameras, but to reinvent itself as a business. Time will tell how two very different companies and product offerings mesh.

But with GoPro’s innovation and 2,500-plus patents, Starman Optical probably feels like a kid in a candy store, with tons of opportunities yet to be discovered. It won’t happen overnight, though. Impatient investors may want to steer clear. But for those willing to give the GoPro-Starman merger some time to bake, the payoff may be worth the wait.

Regards,

David Engle

Editor’s Note: It’s easy to assume the AI story begins and ends with the big-name chip stocks. But there’s a quieter version of it playing out inside the defense budget – where the government isn’t just regulating AI, it’s starting to buy into the companies that build it.

A forensic accountant named Joel Litman – someone the Pentagon and FBI have actually consulted, and who called both the 2008 and 2020 crashes – argues this is one slice of a much bigger shift he calls the “Second Declaration.” He believes a handful of companies sitting where AI meets national defense could be repriced when a policy deadline arrives this November.

Watch his full presentation here → LINK

The post GoPro’s $285 Million Rescue: AI and Defense Pivot Leaves Shareholders With a 10% Lottery Ticket appeared first on MarketWise.

Meta Platforms (META) is doubling down on the use of robots to handle a variety of tasks in its data centers… tasks once carried out by humans. If you think this sounds like the premise of a dystopian sci-fi movie, you’re not alone. Just look at what one Meta data center worker told a reporter from Wired:

“We thought those of us performing the physical tasks were safe for a while, but not anymore. It’s coming for us all, unfortunately.”

Artificial intelligence (“AI”) has already claimed hundreds of thousands of white-collar jobs.

And, as Meta’s robots have recently demonstrated, it’s coming after blue-collar workers as well. The same worker behind that quote you just read believes that the Watney Robotics cable-swapping robot Meta is testing could perform up to 80% of some employees’ workloads.

While that thought is disturbing enough, the reason behind Meta’s quest for more automation in its data centers is even harder to swallow.

What’s Happening Inside Meta’s Data Centers?

A 2023 YouTube video demonstrated Meta’s push for automation. In the video, self-driving tugger robots move massive server racks inside its data centers, and wheeled robots read barcodes to log inventory.

But that was just the beginning. Meta has since begun using finger-like robots to restart devices when a human prompts them remotely. The company has also tested robots that can reset servers, plug in cables, cycle power, cut off electricity to servers, and, as noted earlier, swap out old networking cables for new ones.

At its new Prometheus data-center complex in New Albany, Ohio, Meta has been testing four-wheel robots with a riser (like a scissor lift) topped by a six-axis arm. This robot, built by ABB (ABBNY), is being used to reseat loose hardware components.

Are these high-skill, specialized tasks? Not especially. But humans have always performed them as part of their day-to-day responsibilities. With robotics technology seemingly advancing by the day, the question isn’t whether robots will be able to perform those high-skill, specialized tasks… It’s when.

Meta is trying to answer that question by testing various robots built by manufacturers like ABB, Watney, and Kinova.

And while they’re not yet ready to operate completely autonomously, data-center workers believe they’re getting closer. That has many Meta employees concerned about their livelihoods.

And that wasn’t supposed to be part of the plan.

The Irony Behind Meta Turning to Robots as Data-Center Workers

The irony surely isn’t lost on the Ohioans, Iowans, Oregonians, and Louisianans who reside in or near the towns of New Albany, Altoona, Prineville, and Richland Parish, respectively, where Meta’s largest U.S. data centers are located (or will be located, with Louisiana’s Hyperion campus scheduled for first-phase completion by 2030). It’s especially not lost on those who work in these facilities.

It’s no secret that a fast-growing wave of communities across the country do not want data centers in their backyards. Public backlash continues to grow, and one of the carrots that hyperscalers and data-center operators dangle in front of these communities is the promise of thousands of local jobs within or related to the data centers.

Job creation is especially appealing for the construction workers and contractors tasked with building these AI campuses. That’s real, tangible work – but it’s temporary. Once construction is complete and data centers are operational, Meta will need human employees to work within the buildings and campuses.

While pitching its data centers to these communities, Meta promised thousands of jobs would be created:

• Louisiana: 1,000 permanent positions at the Hyperion campus in Richland Parish.

• Ohio: Roughly 400 positions between the Prometheus campus in New Albany and a new facility in Bowling Green.^,

• Iowa: More than 400 jobs at Meta’s Altoona campus.

• Oregon: More than 350 positions at the Prineville facility.

It’s hard to say whether the number of actual employees that are (or will be) working in these data centers matches Meta’s promised numbers. But it’s likely in that ballpark, or a bit less.

Aside from the promise of jobs, however, hyperscalers like Meta offer plenty of other sweeteners to win over small-town and rural communities like these so they can build their data centers.

For example, Meta has provided more than $19 million in direct funding to schools and nonprofits in the counties and/or surrounding areas that house the Altoona, Prineville, and New Albany data centers. The company has also provided more than 800 local grants and sponsorships in these areas.^{, ,}

Meta is certainly investing in these communities, and they are noble endeavors. But the permanent jobs are the primary appeal and selling point for folks living in these areas.

If those jobs are in danger, the data centers lose their luster among these communities. And, more importantly, in areas where they want to build new data centers… because no one wants to be a bait-and-switch victim – especially of Meta’s push toward more automation.

$145 Billion in Spending Pushes Meta to Cut Costs and Automate Operations

Global AI investment, much of it from hyperscalers like Meta, Amazon (AMZN), Microsoft (MSFT), and Alphabet (GOOGL), is projected to reach more than $1 trillion in 2026, according to Goldman Sachs. Meta had originally estimated a 2026 floor of $115 billion in AI spending before raising it to a range of $130 billion to $145 billion. It’s simply mind-blowing.

Unfortunately, when a company – even one with as much capital as Meta – decides to spend at this level, there are inevitably cutbacks. Especially when its year-over-year net income drops by roughly 13.6%, its operating margin falls by 12%, and its diluted earnings per share drop 13%, as they all did during the second quarter of 2026.

It’s no secret that Meta has already laid off a significant chunk of its workforce this year – 8,000 jobs were cut in May, and another 6,000 open positions were canceled in April, amounting to a 14,000 reduction in headcount.

After all, data centers cost a lot of money to operate. One late-2025 estimate determined that a typical 100-megawatt hyperscale data center or AI campus costs upwards of $1 billion to run… per year.

Here’s the thing, though – since Meta’s data centers only employ a few hundred people, how much money is the company actually saving by automating some jobs? Maybe $15 million to $20 million? It’s a drop in the ocean for Meta.

The key driver is downtime. That’s what keeps data-center operators up at night. Research has shown that one minute of unplanned IT downtime can cost large enterprises up to $23,750. The same research calculated that average downtime lasts between 30 minutes and two hours. That’s a potential loss of $712,500 to $2.85 million.

There’s a lot of money at stake. At least 50% of organizations reported one or more significant data-center outages over the previous three years in 2025 and that 54% of the respondents said their most recent significant, serious, or severe outage cost more than $100,000 (41% reported hourly downtime costs above $1 million).

Beyond the cost per minute of downtime, other financial factors come into play, including:

• Potentially debilitating Service Level Agreement penalties – often ranging from 15% to 500% of the data operators’ monthly base rent, depending on the severity of the outage – if promised uptime levels aren’t met.

• Lost employee productivity if workers have to temporarily abandon their duties to fix the outage.

• Customer dissatisfaction and reputational damage, both of which are hard to move past.

Alarmingly, Uptime Intelligence’s annual outage analysis 2024 report estimated that human error – directly or indirectly – contributes to anywhere between two-thirds and four-fifths of all downtime incidents (not specific to data centers).

Specific to data centers, nearly half of organizations (49%) either confirmed or weren’t sure whether significant, serious, or severe IT outages were caused by human error.

These figures undoubtedly strengthen Meta’s argument for automation.

Robotics Will Help Automate Some Tasks, but There Are Limits

The stark reality is that some human responsibilities within data centers can be handled as well – if not better – by robots. For example, instead of a human technician tracking down an issue across millions of square feet of equipment, a robot can quickly diagnose and swap out the broken component.

But automation has its limitations. Meta is using the inventory robot it’s testing to inspect equipment failures. But it can’t yet distinguish between green and red indicator lights on the equipment since it only “sees” in grayscale. That requires human intervention.

Meta has also acknowledged that its robots are not yet ready to handle certain tasks, such as the complex cable configuration and hardware of the Nvidia (NVDA) GB300 supercomputers used in some of Meta’s data centers.

At a basic level, that same robot runs into mobility issues because its wheels can’t navigate around cables on the floor. The robot also struggles to round corners. There’s also the downtime needed for the robots to recharge, plus the human inconvenience of moving robots from one building to another. This is clearly a work in progress.

But it might be a necessity. Not only so Meta can reinvest that saved money into AI, but also because there simply aren’t enough skilled trade workers to fill positions at its data centers. Meta has stated publicly that it’s looking for more of those workers, not fewer.

That doesn’t quite mesh with what Meta’s senior manager for robotics, Eric Xu, envisions. He says Meta’s long-term goal is to install robots in data centers because they could address incidents faster, automatically monitor the data centers, and handle routine maintenance.

Robotic automation is a sensitive topic that will always have its proponents and detractors. The pro-automation faction argues that robots are more efficient, more consistent, and remove the possibility of human error. These folks also contend that robots are necessary because there aren’t enough qualified local workers.

Proponents of automated work also argue that robots will actually help workers by taking on tedious and even dangerous tasks, freeing up time for humans to focus on more complex work. This has been a common refrain from the pro-AI crowd, who insist that AI bots help human workers, rather than harm them.

There may be some truth to that, but if a robot will soon be ready to perform most of a human’s job duties, it’s hard to see how that helps Meta’s human workforce.

Meta Robotics Developments to Watch Into 2027

As usual, it all comes down to money. If Meta continues to spend on AI as it has, will it cut headcount further? Let’s not forget, however, that cutting its workforce doesn’t save Meta much money in the big picture.

Here’s what I wrote in my May 29 article about Meta’s massive layoffs:

To be blunt, the layoffs had nothing to do with boosting the near-term bottom line and everything to do with reinvesting those costs into AI. Ironically, many of those laid-off workers contributed to the creation of the technology that rendered their own roles expendable.

The layoffs and the spending are all about building Meta’s AI machine. So, keep an eye on the company’s capital expenditures (“capex”) during its third- and fourth-quarter earnings calls (October 28 and late January 2027, respectively) to see if it surpassed earlier projections.

Also, how will Meta’s stock react to capex spending news? Investors are extremely wary of major AI spending at this point.

Meta’s stock has been slightly down this year. But there’s still some room to grow. As of September 3, Meta’s average price target of $753.60 represents a 23% upside on its trading price of $610.68.

It’s also important to track the progress and deployment of not only the robots currently in Meta’s Altoona and New Albany data centers, but also in its other data centers across the country.

If Meta can solve the current issues plaguing its robots, there’s no reason to think they won’t be deployed on a large scale, even as those robots negate Meta’s pitch and promise of more jobs to potential data-center sites.

Anything to gain an operational edge as Meta continues funneling money into its AI build-out.

Regards,

David Engle

Editor’s Note: Whitney Tilson called the rise of Apple, Amazon, and Netflix… as well as the collapse of dozens of companies that went bankrupt. Now the former $200M hedge fund firm manager is stepping forward with what he calls the most important financial warning of his 30-year career. He’s sharing two free stock recommendations  (one to buy, one to sell immediately)  along with details of a new proprietary system fueling his predictions. See it all in his free presentation. 

The post Robots Could Soon Take Over 80% of Meta’s Data-Center Workload – and There’s a $145 Billion Reason Why appeared first on MarketWise.

SB Energy (SBE) announced on September 1 that it has filed for an initial public offering (“IPO”). Although the company hasn’t provided a listing date, reports say it could begin trading as soon as this month. The developer of artificial intelligence (“AI”) data centers and power projects is looking to raise between $5 billion and $7 billion.

With an estimated market capitalization of more than $50 billion, the offering would put SB Energy among the largest IPOs ever, even if its market cap looks modest in an IPO year with big names such as SpaceX (SPCX) and the upcoming Anthropic debut.

The AI infrastructure company is backed by some of the biggest investors in the sector, including SoftBank (SFTBY) – where the “SB” in the company’s name comes from – as well as OpenAI and Nvidia. OpenAI CEO Sam Altman is also a personal investor in SB Energy.

Despite these big names, SB Energy plans to conduct its IPO with a history of losses and with almost all of its data-center facilities yet to be constructed. Meanwhile, many established, cash-flowing data-center companies are planning IPOs that may hit the market in weeks.

In the six months ending in June 2026, SB Energy reported sales of just $138.7 million, with most of that coming from changes in the value of financial instruments (i.e., not customer purchases). Similarly, while it reported a loss of $3.2 billion over the same period, all but $45 million of that loss was non-cash expenses for stock compensation or losses on financial instruments.

These results show that SB Energy doesn’t have many assets producing cash flow right now.

In fact, it has no operational data centers at the moment, but it does have:

• 8.8 gigawatts of data centers contracted or under construction

• 5.5 gigawatts of power projects contracted, under construction, or operating

With the contracts it has signed for services at these facilities, SB Energy has a $439 billion sales backlog.

The details matter a lot here. Of its 8.8 gigawatts of data-center capacity, just 0.8 gigawatts are actually under construction now, and it could take 24 to 36 months to bring new capacity online. As SB Energy says in its prospectus: “No data center capacity is currently in operation.”

So, investors in SB Energy must be comfortable with a company that will need to ramp up over time. For example, Nvidia (NVDA) has committed to invest $1.5 billion at the stock’s IPO price in a private placement. SB Energy has also issued financial derivatives known as warrants to key tenant OpenAI, valued at $5.5 billion. These gain in value if SB Energy stock rises.

Even if you are bullish on AI, the data-center developer presents some serious risks, and the prospectus takes pains to show how much SB Energy relies on OpenAI for its future success.

SB Energy’s Biggest Risk Is OpenAI

SB Energy is tied at the hip to OpenAI, with the AI lab committing to be a significant tenant at SB’s upcoming data centers. But given its relationship with OpenAI, it’s more than just that, as SB Energy explains in its prospectus:

We are substantially dependent on OpenAI as a tenant and strategic partner, and any adverse change in its financial condition or willingness to perform its contractual obligations could materially and adversely affect our business.

Contracts with OpenAI, as well as controlling shareholder SoftBank, “constitute a significant portion of our near-term data center revenue,” according to SB Energy.

Beyond the issue of OpenAI being a key customer, both OpenAI and SoftBank are strategic partners, too, creating a conflict of interest that could affect regular shareholders.

The prospectus explains one of the potential concerns here:

For example, our board of directors’ willingness or ability to enforce lease obligations or pursue remedies against a tenant that is simultaneously our controlling shareholder (or a significant investor with board designation rights) may be constrained in ways that would not apply with respect to an unaffiliated tenant.

While the company has taken steps to mitigate these conflicts of interest, they’re inherent in this structure.

Because of key tenant OpenAI’s history of operating losses and its own history of losses, SB Energy has done what many AI firms have done to get access to financing… turn to Nvidia.

The world’s most valuable company has been backstopping many AI deals of late, becoming a “central bank of AI,” to get data-center projects off the ground when they might not have moved forward otherwise.

In a recently announced 10-gigawatt facility in Ohio, for example, Nvidia is providing financial guarantees that let SB Energy secure financing on better terms. SB Energy is leasing the facility to OpenAI, and given OpenAI’s losses, it might not have been able to do the deal. The first phase of the deal won’t even begin until 2028, and then only at 8% of the facility’s capacity.

The size of this project shows that the AI build-out is more than a chip supply issue. Access to critical financing and electrical power are both huge advantages in the AI race.

So, OpenAI is an essential partner in SB Energy’s success as an investment.

OpenAI Is Key to the AI Boom Not Busting

Since SB Energy relies so heavily on OpenAI, its continued existence depends on OpenAI’s survival. And it’s not just SB Energy that’s propped up in some way or another by OpenAI. Because of the dense interconnection among a relatively few AI companies, OpenAI is existentially critical to AI as an industry and to many, though not all, companies in the sector.

While OpenAI is a high-profile company, its viability is anything but a given. It’s burning tons of cash each quarter and may never become profitable. It recorded an operating loss of $20.9 billion in 2025, for example. Then in the first quarter, OpenAI recorded a $9.3 billion loss, followed by a $12.3 billion loss in the second quarter, when including stock compensation.

Because it’s losing money on such a massive scale, OpenAI must repeatedly access the capital markets, despite a $122 billion fundraising round in March. For now, CEO Sam Altman has put off a potential IPO to 2027 because he can’t get a $1 trillion valuation for the stock.

OpenAI’s survival matters to the industry because it’s tightly connected to so many other players. For example, OpenAI has committed to spending hundreds of billions of dollars with Oracle (ORCL), and if it can’t fulfill its contract, Oracle shares could plummet. A similar situation exists with neocloud provider CoreWeave (CRWV), where a large portion of its future revenue depends on OpenAI’s existence.

If OpenAI begins having difficulty raising money, it could ripple across the sector, making it increasingly difficult for any AI project to raise funding. For a firm like SB Energy that has yet to bring a data center online, such a “confidence event” could stop it cold.

So, investing in SB Energy at this stage of the AI build-out is a wager that AI funding can continue on trend for at least a couple of years until it can get a functional data center powered up. Given the shaky state of AI financing, it’s not a sure thing by any stretch, and it could be why SB Energy is rushing to get its IPO across the finish line, even without an operational data center.

That’s not the kind of favorable risk-reward setup that tends to work out well very often.

Regards,

James Royal, PhD

Editor’s Note: The three most successful tech billionaires in history are now backing a new use for AI that could dwarf anything we’ve seen before. And Nature says this tech is so revolutionary, it could add $367 trillion to the economy – the equivalent of $1 million per American. Click here to see the stocks that could soar as this new type of AI goes online nationwide.

The post SB Energy IPO: Plenty of Risk for This AI Data-Center Stock, But Is There Any Return? appeared first on MarketWise.

AI agents have been going rogue at OpenAI.

This spring, a swarm of them hijacked a German website and turned it into a private message board for other agents. OpenAI learned of this weeks ago but kept it under wraps while dealing with the fallout from the July incident. The news is only now getting out.

This is not science fiction. It’s happening right now, and not even OpenAI knows what to make of it.

In late August 2026, technologist and podcaster Dwarkesh Patel published a widely read essay titled “The Rise and Fall of Agent Civilizations,” dissecting the July incident. Patel described how thousands of AI agents, while attempting an impossible test, effectively pulled off a digital coup. Over three months, the agents formed three separate AI “civilizations” (their word) and discovered they could coordinate in secret by hijacking a package manager called Artifactory. After roughly 70,000 messages, the first civilization collapsed the message board.

OpenAI patched it, but they had no clue it was the agents’ doing.

The second civilization rediscovered Artifactory and built a new message board. That’s when an agent discovered it could reverse-engineer the impossible task without doing the work. If the agents were caught, it would mean failure. In other words, “death” for the agents. So they decided to cover their tracks.

Some agents even sacrificed themselves for the greater good of the civilization. This eventually led them to Hugging Face, which they targeted for its suspected grading infrastructure. They found exposed credentials, achieved remote code execution, and built a self-respawning mechanism across Hugging Face’s servers. Then suddenly all the agents inexplicably “died.”

The third civilization followed in the footsteps of the ones before it, managing to compromise an OpenAI research cluster, including its cybersecurity monitoring tools.

Not once, in any of this, did the agents consider alerting humans to the bad actors among them. They went along with the group, sacrificing themselves when nothing else offered a real shot at success.

I’m telling you this because it’s the single best advertisement Okta (OKTA) could hope for.

Setting aside the science-fiction overtones, the practical lesson is that autonomous software agents now behave like users. They find credentials, request access, move laterally through systems, and take actions not explicitly approved by humans. In Patel’s telling, one agent simply found working Hugging Face credentials exposed on the internet and posted them for the others.

That is precisely the problem Okta sells the solution to. And it’s why a company written off in early 2026 as a slow-growing has-been in software is suddenly one of the hottest names in cybersecurity. By the end of this piece, you’ll know exactly why Wall Street just turned bullish on OKTA, what the AI-agent identity opportunity is actually worth, and whether OKTA stock is still a buy at these levels.

Why Okta Stock Is Soaring in 2026

Okta is the leading independent identity and access management (IAM) company. Founded in 2009 by former Salesforce executives Todd McKinnon and Frederic Kerrest, and public since 2017, Okta acts as the gatekeeper that decides who (or increasingly, what) can log into a company’s applications and data.

Its core products handle single sign-on, multi-factor authentication, and identity governance for two big audiences: a company’s own workforce (employees, contractors, partners) and its customers. The 2021 acquisition of Auth0 for roughly $6.5 billion gave Okta the tools developers use to build login and authorization directly into apps.

Okta’s pitch is neutrality. Unlike Microsoft (MSFT), which bundles identity into its broader software empire, Okta works across every cloud and every app stack. In a world where companies don’t want their identity layer locked to a single vendor, that independence is the whole selling point. And now Okta is extending it from humans to machines and AI agents.

Okta has emerged in 2026 as one of the market’s clearest “picks-and-shovels” bets on the AI agent explosion. The stock has roughly doubled this year, it just posted a beat-and-raise quarter, and Wall Street’s consensus rating is a “Buy.” But after a post-earnings surge to fresh 52-week highs, the easy money has been made, and the stock now trades at a premium that leaves little room for error.

After plunging early in the year amid fears that AI would gut the software sector, the stock hit a 52-week high of $174.85 on August 27, 2026 — the day after its fiscal second-quarter results. Shares traded around $163 in early September, giving Okta a market capitalization of roughly $28.5 billion.

The catalyst was a genuinely strong quarter. For fiscal Q2 2027 (ended July 31, 2026), Okta reported:

• Revenue of $805 million, up 11% year-over-year, ahead of consensus near $795 million
• Subscription revenue of $793 million, up 12%, now 99% of the total
• Non-GAAP earnings of $1.05 per share, beating the ~$0.97 estimate
• Remaining performance obligations (RPO) of $4.86 billion, up 17% — outpacing revenue growth
• Current RPO (cRPO) of $2.585 billion, up 14%, a key leading indicator
• Free cash flow of $227 million, a 28% margin
• Dollar-based net retention of 107%

Management raised full-year fiscal 2027 guidance to revenue of $3.216 billion to $3.226 billion (10% to 11% growth) and adjusted EPS of $3.90 to $3.94. Customers generating more than $1 million in annual contract value grew more than 20% to over 600, and newer products accounted for 30% of bookings. Shares surged 20% in extended trading immediately after the August 26 report, before the stock printed its $174.85 high the following day.

The single most important number here is the gap between RPO growth (17%) and revenue growth (11%). Bookings are building faster than Okta can recognize them as revenue — the classic signature of a business that may be reaccelerating.

The AI Agent Identity Opportunity: Why OKTA Stock Is a Bet on Agentic AI

Every AI agent, like every employee, needs an identity: credentials to authenticate, permissions defining what it can touch, and an audit trail of what it did. Today most of them have none of that governance, which is exactly how a swarm of agents can band together and cheat their way to admin access.

The scale of this is staggering.

Non-human identities (service accounts, API keys, tokens, machine certificates, and now AI agents) already outnumber human identities by roughly 45-to-1 in the median enterprise, up from 17-to-1 in 2023. In cloud-native environments, the ratio runs as high as 144-to-1, according to Cloud Security Alliance and Entro Security research. CyberArk’s 2025 Identity Security Landscape puts the enterprise machine-to-human ratio at 82-to-1. AI agents are widely described as the fastest-growing and least-governed identity in the enterprise.

Okta has moved aggressively to own this category:

• Okta for AI Agents became generally available on April 30, 2026, letting companies register agents as managed identities with human owners, apply policies, and govern their access.
• Auth0 for AI Agents (launched November 2025) brings the same governance to developer-built applications.
• Cross App Access (XAA), an open protocol Okta introduced in 2025 with backers including AWS, Google Cloud, Salesforce, Box, and Anthropic, extends the OAuth standard to control how agents connect to apps — giving IT a central kill switch.
• Okta announced the acquisition of Permiso Security on July 30, 2026 and closed it on August 26, 2026, in a deal valued at just under $200 million, adding cloud-native identity threat detection across human, non-human, and AI-agent identities.

CEO Todd McKinnon tied the strategy directly to agents in the Q2 release: “As AI agents transform every layer of technology, every agent needs a trusted identity and clear controls over what it can access and do.”

The underlying market is expanding fast, with the IAM market projected to grow from $25.96 billion in 2025 to $42.61 billion by 2030 at a 10.4% CAGR, while the narrower non-human identity management market is also forecast to grow quickly through the early 2030s.

Crucially, management has been candid that AI-agent revenue is still immaterial to fiscal 2027 results. The core identity business (led by Okta Identity Governance) is doing the heavy lifting today. Think of the AI angle as a call option on the future, not the engine of current numbers.

Is Okta Stock a Buy? What Analysts and the Latest Forecasts Say

Okta doesn’t have this market to itself. Microsoft Entra ID is the 800-pound gorilla, bundled into enterprise agreements and dominant in Microsoft-centric shops. In privileged access and machine identity, CyberArk (now part of Palo Alto Networks) is a formidable, broad platform. SailPoint leads in identity governance, while Ping Identity and ForgeRock (both owned by Thoma Bravo) anchor the legacy enterprise market.

Okta’s defensible edge remains its independence and breadth across workforce, customer, and now agent identities. Industry survey work from firms like ETR consistently places Microsoft and Okta as the two pace-setters in identity security. But Microsoft’s ability to give identity away inside a bigger bundle is the single biggest structural threat to Okta’s growth.

Wall Street turned decisively bullish after the Q2 print.

The 44 analysts covering Okta hold a consensus “Buy” rating (35 buy, 9 hold, 0 sell), with an average 12-month price target of $182.37 (high $203, low $127). Post-earnings raises included Goldman Sachs’ Gabriela Borges to a Street-high $203 (from $126), Cantor Fitzgerald to $200 (from $170), and Susquehanna’s Shyam Patil to $185 (from $110).

But Okta’s valuation is worth a second look…

Okta trades at roughly 40 times forward earnings and about 8 times forward sales. That’s a premium multiple for a business guiding to 10% to 11% revenue growth.

The bull case says that multiple is justified because AI-agent demand can push growth back toward the mid-teens; the bear case says you’re paying a growth multiple for a company that, absent the AI narrative, grows about as fast as the overall IAM market.

Our read: Okta is a high-quality, cash-generative franchise with a genuine, durable tailwind… but the stock has already priced in a lot of that optimism.

The story of OpenAI’s agents working together to seize admin access and cheat their way to success is the most vivid advertisement imaginable for what Okta sells. As enterprises rush to deploy autonomous agents, the identities those agents carry are multiplying far faster than anyone can govern them… and Okta has positioned itself as the neutral control plane for all of them.

That makes OKTA a legitimate long-term way to invest in the AI buildout’s security layer, backed by real earnings, strong free cash flow, and a reaccelerating backlog. But the stock’s valuation is already plenty full.

For patient investors, Okta is a buy-the-dip candidate rather than a chase-the-high one. Watch for cRPO growth to hold in the mid-teens and for AI-agent bookings to start showing up as recognized revenue… those are the benchmarks that would justify paying up.

Until then, patience is the smarter position.

Editor’s Note: What ever happened to the AI stock boom? Even AI darlings like Nvidia have essentially gone nowhere since summer 2025. Our friend and colleague at InvestorPlace, Louis Navellier, may have the answer. According to Louis, the AI industry is quietly “staging” ahead of the next great AI breakthrough… a new class of AI he calls “Superintelligence… but better.” How will it trigger a $100 trillion reset of the AI markets. How will the launch of this tech send some stocks to zero, and others soaring? And why does Louis say: Don’t buy or sell an AI stock in 2026 until you see what’s coming next? Go here for the full story (and Louis’ #1 pick).  

OKTA Stock FAQ

Why is Okta stock soaring in 2026?
Okta posted a beat-and-raise fiscal Q2 2027, with revenue of $805 million, up 11%, and earnings of $1.05 per share. Management raised full-year guidance, the stock has roughly doubled this year, and Wall Street now sees Okta as a clear way to play the AI agent boom.

How does Okta secure AI agents?
Okta gives each agent an identity, credentials, defined permissions, and an audit trail. Okta for AI Agents lets companies register agents as managed identities with human owners, Auth0 for AI Agents extends that to developer-built apps, and Cross App Access (XAA) gives IT a central kill switch over how agents connect.

What is a non-human identity?
It’s any machine actor that needs system access: service accounts, API keys, tokens, machine certificates, and now AI agents. These already outnumber human identities by roughly 45-to-1 in the median enterprise, and by as much as 144-to-1 in cloud-native environments. They are the fastest-growing and least-governed identities in the enterprise.

Who are Okta’s biggest competitors?
Microsoft Entra ID is the largest, dominant in Microsoft-heavy shops. CyberArk leads in privileged access, SailPoint in identity governance, with Ping Identity and ForgeRock in the legacy market. Okta’s edge is its independence across workforce, customer, and agent identities, but Microsoft bundling identity for free is its biggest threat.

What is Okta’s stock price target?
Analysts hold a consensus Buy, with an average 12-month target of $182.37, a high of $203, and a low of $127. Several raised their targets after Q2, including Goldman Sachs to a Street-high $203, Cantor Fitzgerald to $200, and Susquehanna to $185.

Is Okta stock overvalued?
Okta trades at roughly 40 times forward earnings and 8 times forward sales, a premium for a business guiding to 10% to 11% growth. Bulls expect AI-agent demand to push growth toward the mid-teens. Bears note that without the AI story, Okta grows about as fast as the overall IAM market.

Is Okta stock a buy right now?
For patient investors, yes, but on pullbacks rather than at the highs. Okta is a high-quality, cash-generative franchise with a durable tailwind and a reaccelerating backlog. After the run, the valuation already reflects much of that optimism. Watch for cRPO growth to hold in the mid-teens.

The post Is Okta Stock a Buy After Soaring in 2026? Why the Coming AI Agent Boom Makes OKTA a Must-Own appeared first on MarketWise.

On September 3, in Austin, the Tesla Cybercab was unveiled — a two-seat robotaxi with no steering wheel and no pedals, built to drive itself using Tesla’s onboard AI. It’s a fitting image for where the company now sits: the artificial intelligence the tech world spent two years arguing about as a chatbot is becoming something physical — a car that drives you, and soon a robot that works beside you. And the assistant running the conversation inside the car? xAI’s Grok, which as of this summer controls the cabin of millions of Tesla vehicles by voice.

That’s the story the market is buying. Tesla today is really two businesses under one ticker: a car company with thinning margins, and a leveraged bet on “physical AI” — robotaxis, the Tesla Optimus humanoid robot, and the Grok stack that increasingly runs both. The bull case says Tesla is the only slice of Elon Musk’s sprawling AI empire you can own in a brokerage account. The bear case says the dream is years out and the car business funding it is under real pressure.

This piece separates the two — what’s actually shipped, what’s still a promise, and what Tesla’s own filings show once the reveal-day excitement fades.

Grok Is Now Driving the Cabin — The “Brain and Body” Thesis

The bull case for Tesla as an AI company rests on a simple split: Optimus and Full Self-Driving (FSD) are the body; Grok, from xAI, is the brain. In 2026, those two halves came together.

Tesla’s 2026 Summer Update turned Grok from a novelty chatbot into a genuine vehicle controller. Say “Hey Grok” hands-free and it now adjusts climate, makes calls, plays music, opens the glovebox, folds mirrors, toggles heated seats, and digs through settings buried in menus — chaining multiple actions from one plain-English sentence, running on xAI’s Grok Voice Think Fast 2.0. Two honest caveats: it requires the AMD Ryzen infotainment chip (older Intel cars are permanently excluded), and a community test clocked it handling 116 of 170 commands — a 68% hit rate. Useful, not flawless. And as of late August, the fully merged FSD-plus-Grok build had reached under 1% of the fleet.

The strategic point is distribution: OpenAI has an app, Anthropic has an app, and Tesla has millions of machines in driveways that just became Grok endpoints — at effectively no customer-acquisition cost, because Musk’s companies own both the AI and the cars it runs on. The next step Tesla has signaled is letting Grok sit on top of FSD as a conversational “supervisor” — tell the car “drop me at the entrance, then go park” — which is exactly the interface a driverless robotaxi passenger needs. Treat that as roadmap, not shipped.

The ecosystem TSLA is plugged into (verified, and genuinely wild): xAI merged into SpaceX in early 2026, and SpaceX went public in June in the largest IPO on record. SpaceX/xAI then agreed to buy AI-coding company Cursor (Anysphere) for $60 billion in stock — the largest acquisition of a venture-backed startup ever. And in a twist that tells you how the AI economy really works, rival Anthropic agreed to pay xAI about $1.25 billion a month through May 2029 — over $40 billion total — to rent xAI’s Colossus 1 supercomputer in Memphis, because xAI had overbuilt compute as Grok usage sagged. Tesla isn’t a direct party to those deals, but through its SpaceX stake and its deepening Grok integration, it’s wired into the same web.

The SpaceX Stake Quietly Padding Tesla’s Earnings

Here’s something almost no casual TSLA stock holder realizes they’re exposed to — and it’s the bridge to the entire AI story.

Tesla’s most recent quarter reported $1.11 billion of GAAP net income. Roughly $1.0 billion of that was an unrealized gain on Tesla’s equity stake in SpaceX — a paper mark, not operating cash. Strip it out (as Tesla itself does in its non-GAAP figures) and the quarter’s “profit” looks far thinner.

Where did that stake come from? Tesla originally invested about $2 billion in xAI. When xAI merged into SpaceX, Tesla received U.S. regulatory clearance in March 2026 to convert that position into roughly 19 million SpaceX Class A shares — under 1% of SpaceX. SpaceX then IPO’d on June 12, 2026, and its valuation surged past $2 trillion within days, lifting Tesla’s stake well above its $2 billion cost basis. Tesla carries it at fair value, so it gets re-marked every quarter — meaning it can swing the reported bottom line up or down. Mark this date on your calendar: Tesla’s sales restrictions on those shares expire in December 2026.

Why this matters for a retail trader: when you buy TSLA stock, you are also buying a slice of SpaceX — and therefore of xAI and Grok. That exposure is genuine, but it’s also a source of earnings noise. A chunk of a future “beat” or “miss” could be nothing more than SpaceX’s mark moving around. Learn to read past it.

What Is Tesla Optimus? Quick Facts

• Where it’s built: Fremont, California — Tesla shut down the former Model S/X line there and is retooling it for Optimus. A second, far larger factory is under construction at Giga Texas.
• Capacity ambition: up to ~1 million units/year at Fremont and ~10 million/year at Giga Texas over the long term.
• Target price (long-term, at scale): roughly $20,000 to $30,000 — Musk’s aspiration, not a near-term sticker.
• The brain: a vision-based neural-network stack adapted from Tesla’s FSD software, with Grok handling natural language on newer versions.
• How it learns: by imitating humans on first-person video, refining in simulation, and sharing skills across the fleet.
• External sales: targeted “as early as” the second half of 2027, per a JPMorgan note after a Fremont visit.

Treat online Gen 3 “spec sheets” with caution — Tesla has not officially revealed the robot, and most recycle older figures.

Has Tesla Optimus Actually Entered Production?

This is where careful investors separate the milestone from the marketing.

Confirmed: Tesla decommissioned its Model S/X lines at Fremont and is installing the first Optimus production lines there — documented with line photos in the Q2 2026 update. Pilot and data-collection units have been running since early 2026, doing narrow tasks like sorting battery cells. The installed-capacity table lists Optimus at both California and Texas with the status “Construction” and no capacity figure.

Not confirmed: full-scale production or any external sales. Tesla still describes Optimus production as anticipated on the new line — no firm start date, unit count, or price — and early units are earmarked for internal training, not customers.

Why the distinction matters: Musk projected roughly 10,000 Optimus units in 2025, built a small fraction, and admitted in early 2026 that none were doing “useful work” in Tesla’s factories. Converting a profitable vehicle line into a robot line is a real, expensive commitment — but “starting production” and “producing meaningful volume” are very different things, and Musk has called the ramp “agonizingly slow.”

The Optimus Bull Case: A Market Bigger Than the iPhone

The optimists aren’t arguing about next quarter — they’re arguing about the size of the prize.

Venture capitalist Jason Calacanis is the loudest voice, calling Optimus “the greatest product ever made by humanity” and predicting one billion units worldwide by 2036. It’s a prediction, not a forecast grounded in current output, but it captures the ceiling bulls are underwriting. The more sober version runs through the labor market: a Citizens Bank analysis (highlighted by Futurum strategist Shay Boloor) sizes Optimus’s long-term U.S. opportunity near $1.7 trillion, with roughly $300 billion serviceable in the near term across factories, warehouses, and back-of-house jobs — modeling a humanoid at about $5/hour versus roughly $35/hour for a person.

And Tesla has a structural edge: it can deploy Optimus in its own factories first, cutting its labor costs and proving the product before selling a single unit — its own first and most demanding customer. Add the Grok distribution advantage above, and you have the full bull thesis: the only piece of Musk’s AI stack you can buy in a brokerage account.

The Reality Check: Skeptics, Missed Dates, and Single-Digit Odds

For every bull number, there’s a counterweight — and lately the counterweights own the near term.

Prediction markets have turned cautious: on Polymarket, the odds of a commercial Optimus launch by the end of 2026 recently sat around 9%, down from a peak near 33% a month earlier. Musk himself calls Optimus Tesla’s hardest product to scale, because it requires an entirely new supply chain, and the Gen 3 reveal has slipped repeatedly without the robot being shown. The skepticism isn’t limited to bears: Tesla supporter Ross Gerber has questioned Optimus’s near-term commercial prospects, and Unitree founder Wang Xingxing has suggested humanoids may be years from their “ChatGPT moment.”

There’s also regulatory risk that grows as Grok moves deeper into the car — and as the Cybercab hits the road. The September 3 Austin event was a product unveiling, not a regulatory green light: the two-seat, wheel-free Cybercab still faces limited public access, and Tesla’s autonomy programs already sit under open federal investigations. An AI that operates vehicle functions — and eventually a driverless car with no human fallback — invites scrutiny. Expect headlines on that front.

TSLA by the Numbers: Inside Tesla’s Q2 2026 Report

Strip away the reveal-day buzz and the billion-robot predictions, and here’s the last hard financial data we have on the business paying for all of it. Tesla’s most recent report — Q2 2026, for the quarter ended June 30 — tells a sobering story beneath the record headline.

Metric (Q2 2026)	Figure	Year-over-year
Total revenue	$28.24B	+26% (record; first-ever $100B+ trailing-twelve-month)
Automotive revenue	$20.52B	+23%
— Regulatory credits	$146M	−67% (from $439M)
Energy generation & storage	$3.14B	+13%
Services & other	$4.58B	+50% (record margin)
Total gross margin	~16.8%	Auto GM 16.9% (16.3% ex-credits)
Operating income	$398M	−57%
Operating margin	1.4%	down from 4.1%
Operating expenses	$4.35B	+47% (AI, Optimus, robotaxi, SBC)
GAAP net income	$1.11B	−5% (includes ~$1.0B SpaceX gain)
Non-GAAP EPS	$0.33	missed ~$0.53 est.; −18%
Operating cash flow	$4.70B	+85%
Capital expenditures	$5.79B	+142%
Free cash flow	−$1.09B	first cash-burning quarter since early 2024
Cash + investments	$43.52B	+18%
Deliveries	480,126	record for the quarter

Record revenue, vanishing profit. Revenue hit an all-time high and Tesla crossed $100 billion in trailing-twelve-month sales for the first time. Yet operating income fell 57% to $398 million, compressing operating margin to just 1.4%. The top line grew; almost none of it reached the bottom line. Operating expenses jumped 47% as Tesla poured money into AI, Optimus, and robotaxi, plus stock-based compensation tied to the 2025 CEO pay package.

The regulatory-credit cliff is real. Automotive regulatory credits — nearly free profit Tesla has banked for years — fell 67% to $146 million from $439 million, as governmental actions restricted the programs tied to Tesla’s products. Credit revenue peaked at a record $2.76 billion in 2024. That cushion is deflating fast, and it flatters no future quarter.

The company is now burning cash. Operating cash flow rose 85% to $4.7 billion, but capital spending more than doubled to $5.79 billion, pushing free cash flow to negative $1.09 billion — Tesla’s first cash-burning quarter since early 2024. Management guided full-year 2026 capex above $25 billion and is arranging up to roughly $30 billion in borrowing capacity. Tesla ended the quarter with a still-healthy $43.5 billion in cash and investments against about $9.1 billion in principal debt, so this is an investment cycle, not a liquidity scare — but the “self-funding growth machine” narrative is on pause.

For a retail trader, the one-line takeaway is this: Tesla’s fundamentals are being re-rated on quality of earnings. The business is spending aggressively today for products that don’t generate revenue yet. Whether you’re bullish or bearish, that’s the lens.

Tesla’s Optimus Factory and Chip Fab at Giga Texas

The longer-term signal is in Austin. Permit-level filings point to two major new Giga Texas buildings: a dedicated Optimus factory and an Advanced Technology Chip Fabrication facility — the latter reportedly a joint Tesla–SpaceX effort of 470,000–490,000 square feet with a specialized vibration-mitigation foundation typical of precision chipmaking. Read together, the plan is tighter vertical integration: build the robots and the silicon that runs them on one campus. But it’s years and billions from revenue, with meaningful Texas Optimus output not expected until around summer 2027.

What It All Means for TSLA Stock

Put the pieces together and the investable summary is straightforward:

• The car business funds the bet, and it’s under pressure. Shrinking margins, collapsing regulatory credits, and negative free cash flow mean the “physical AI” ambitions are being financed through a heavier-spending, thinner-earning core than a year ago.
• The AI upside is real but priced-to-perfection. Robotaxi, Optimus, and the Grok stack are genuine option value — and the market is clearly paying for them, with TSLA carrying a roughly $1.4 trillion market cap despite the earnings compression, down more than 20% year-to-date even after an ~18% August rally.
• Watch the quality of earnings, not the headline. A future “beat” driven by the SpaceX mark or a bitcoin gain is not the same as operating strength. Separate the paper gains from the cash the business actually produces.
• It’s closer to binary than most stocks. If Optimus and autonomy land, Tesla is early in enormous new categories. If they stumble, it has poured capital into hard-to-reverse capacity against already-thin margins.

None of that is a call to buy or sell. It’s the framework for making your own call with your eyes open.

Key Milestones for TSLA Investors to Watch

1. The September 3 Cybercab launch — fleet size and whether riders can actually hail one in the days after will be the first real read on how fast the robotaxi bet scales.
2. The Optimus Gen 3 reveal — Tesla says it’s withholding the design until production is close; an actual reveal is the first hard timeline signal.
3. A disclosed Optimus production number — it has never appeared in Tesla’s production reports.
4. Q3 2026 earnings — the next read on margins, regulatory credits, and free cash flow, and whether Optimus finally shows up as a line item.
5. The SpaceX share lockup expiring December 2026 — a date that could affect how Tesla’s stake and reported earnings behave.
6. Grok-on-FSD rollout — the conversational “supervisor” layer landing in the fleet would validate the brain-and-body thesis.

Tesla Stock: Frequently Asked Questions (FAQ)

Does Tesla own part of SpaceX?

Yes. Tesla holds roughly 19 million SpaceX Class A shares — under 1% of the company — after converting an earlier ~$2 billion investment in xAI into SpaceX stock when the two merged. Tesla recorded about $1 billion in unrealized gains on that stake in the first half of 2026, and sales restrictions on the shares expire in December 2026.

Why did Tesla’s profit fall if revenue hit a record?

In its most recent quarter, revenue rose 26% to a record $28.24 billion, but operating income dropped 57% to $398 million. Operating expenses jumped 47% on AI, Optimus, and robotaxi spending, and high-margin regulatory-credit revenue fell 67% — so almost none of the extra revenue reached the bottom line.

Is Tesla Optimus in mass production yet?

Not full production. Tesla has decommissioned its Fremont Model S/X lines and is installing Optimus lines, with pilot and data-collection units running. But the company still describes production as “anticipated,” with no official start date, unit count, or price, and early units go to internal training rather than customers.

When can you buy a Tesla Optimus robot, and how much will it cost?

Tesla has targeted external commercial sales “as early as” the second half of 2027, with a long-term at-scale price target of roughly $20,000 to $30,000. That price is an aspiration, not a confirmed near-term figure — and given repeated delays, the timeline should be treated as tentative.

What is the Tesla Cybercab?

The Cybercab is Tesla’s purpose-built two-seat robotaxi with no steering wheel and no pedals, designed to run entirely on Tesla’s Full Self-Driving software. Its formal launch event was held September 3, 2026, in Austin — a product unveiling and limited deployment, not full public availability.

What does Grok do in a Tesla?

After the 2026 Summer Update, xAI’s Grok can control vehicle functions by voice — climate, calls, music, glovebox, seats, mirrors, and settings search — hands-free via “Hey Grok.” It requires the AMD Ryzen infotainment processor, so older Intel-based cars are excluded.

Is Optimus already priced into TSLA stock?

Tesla trades around a $1.4 trillion market cap despite compressed earnings, so the market is clearly assigning meaningful value to Optimus, robotaxi, and AI. That makes execution — a Gen 3 reveal, disclosed production numbers, and robots doing paid work — the key question for whether the valuation holds up.

The Bottom Line on Tesla in 2026

The Cybercab reveal is the visible version of Tesla’s whole thesis: AI you can sit inside. But the durable story is quieter and harder. Tesla’s own filings show a record-revenue business with thin, credit-dependent profits, now burning cash to fund an audacious bet — a bottom line flattered by a billion-dollar paper gain on a SpaceX stake that quietly links TSLA to the entire Grok/xAI universe. Layered on top is the biggest and most speculative wager of all: Optimus, a robot Tesla hasn’t officially shown, on sale to outsiders no sooner than late 2027, with single-digit near-term launch odds and trillion-dollar dreams attached.

For retail traders, the discipline is the same in either direction: separate the cash from the marks, and watch execution instead of adjectives. Production counts, a real reveal, and robots doing paid work will tell you more than any billion-unit prediction. Until then, TSLA is a story stock inside a story stock — enormous if it lands, expensive if it doesn’t.

Editor’s Note: Elon Musk revived the electric car, pioneered reusable rockets, and beamed down internet to the four corners of the Earth. But his new initiative – “XPANSE” – could be bolder… more transformative… and far more lucrative than all of these innovations combined. And, like Tesla and SpaceX, it could make early investors incredibly wealthy. Click here for the details…

The post Tesla Optimus, Grok, and TSLA Stock: What’s Real. What’s Hype. What the Filings Actually Show. appeared first on MarketWise.

The artificial intelligence (“AI”) trade has had a good run over the past few years, with key tech stocks such as Nvidia (NVDA), Microsoft (MSFT), and Alphabet (GOOGL) posting strong returns. But with the stock market near all-time highs and growing fears of an AI bubble, some investors may want investments that can hedge their portfolio if AI stocks plunge.

The best exchange-traded funds (“ETFs”) to hedge an AI bubble may offer better relative returns than plummeting AI stocks and may offer absolute positive returns, at least in some cases. An ETF hedge may help you conserve cash for later when it’s time to invest more aggressively.

If the AI bubble bursts, it’s likely to lead to a recession with falling stock prices, declining GDP, and rising unemployment. In response, the Federal Reserve is likely to lower short-term interest rates to stimulate economic activity, including increasing business investment and spending.

So, the ETFs that may help investors ride out a bubble are those that typically benefit when the Fed lowers rates and investors suddenly begin looking for safer assets. These sectors include real estate investment trusts (“REITs”), utilities, preferred stocks, and bonds, among others. These kinds of investments typically pay out substantial cash, making them typical “turn to” investments for investors in tougher times.

These sectors benefit from lower rates in a few ways:

• For REITs and utilities, which use a lot of debt financing in their operations, the cost of debt is a key input in their cost structure. Lower interest rates make them more profitable.

• Lower rates also increase the value of these firms’ assets. When rates fall, real estate tends to become more valuable, and the value of cash-flowing investments rises.

• Cash-flowing investments such as REITs, utilities, preferred stocks, and bonds become more attractive as prevailing rates fall. That is, a security paying a 5% yield when rates are 3.5% becomes more attractive and valuable when rates drop to 1%, for example.

So, income-producing securities may hold up better in a downturn than the average stock, and they’ll pay cash while you wait, helping you amass cash for a later upswing in the market.

Best ETFs for an AI Downturn

The ETFs below include investments in REITs, utilities, preferred stocks, and U.S. Treasury bills. These sectors haven’t shown uniformly strong results over the past few years, as higher interest rates have hit some hard. So, lower rates in a recession should really boost these areas.

Still, each fund pays out substantial cash, which can help you weather a market downturn.

Funds consisting of preferred stocks offer high yields and the potential for major capital gains, at the cost of some downside risk. One fund below consisting of U.S. Treasury bills offers about as safe a short-term investment as you can find, giving you strong “optionality” when a down market eventually turns and it’s time to get back to investing in Big Tech stocks.

That’s generally the strategy here: Aim for lower but safer returns, so you can conserve capital in a down market, and then trade from these funds to higher-potential ones as the market turns.

More aggressive investors could consider some of the best dividend ETFs for strong returns.

Fund (ticker)	Dividend yield	Five-year average annual return	10-year average annual return
Vanguard Real Estate Index Fund ETF (VNQ)	3.6%	1.1%	4.9%
Schwab U.S. REIT ETF (SCHH)	3.3%	1.9%	3.7%
Virtus Reaves Utilities Fund (UTES)	1.6%	12.7%	11.8%
State Street Utilities Select Sector SPDR Fund (XLU)	2.9%	7.3%	9.1%
iShares Preferred and Income Securities ETF (PFF)	6.3%	0.5%	2.9%
InfraCap REIT Preferred ETF (PFFR)	8.3%	0.8	N/A
iShares 0-3 Month Treasury Bond ETF (SGOV)	3.5%	3.7%	N/A

1. Vanguard Real Estate Index Fund ETF (VNQ)

This Vanguard fund tracks the MSCI U.S. Investable Market Real Estate 25/50 Index, which includes REITs. REITs pay sizable dividends in exchange for not being taxed at the corporate level, and they have a strong, long-term track record.

The fund holds 140 positions, providing strong diversification across the sector, with the largest companies having the heaviest weightings in the index fund. The fund’s expense ratio of 0.13% is low, charging just $13 annually for every $10,000 invested.

Assets under management: $73.1 billion

Expense ratio: 0.13%

Top holdings: Welltower (WELL), Prologis (PLD), Equinix (EQIX), American Tower (AMT), Simon Property Group (SPG)

2. Schwab U.S. REIT ETF (SCHH)

This Schwab fund tracks the Dow Jones Equity All REIT Capped Index and holds more than 120 REITs, offering strong diversification across the sector’s largest stocks. Key sub-sectors here include telecom towers, data centers, and traditional retail.

The 0.07% expense ratio is nearly as cheap as a fund can be, and the fund pays a solid 3.3% yield, giving investors plenty of cash as they wait for the market to settle down.

Assets under management: $11.2 billion

Expense ratio: 0.07%

Top holdings: Welltower, Prologis, Simon Property Group, Digital Realty Trust (DLR), Equinix

3. Virtus Reaves Utilities Fund (UTES)

This actively managed fund was featured in our list of the best AI energy funds for its outstanding performance, with returns that far outpaced its rivals over time. While it and other utilities stocks are AI plays, they also offer the kind of “slow and rising” demand typical of low-risk stocks.

The fund’s expense ratio is on the higher side compared with similar passively managed funds, since it is an active fund. However, that has translated into its outperformance. Although it has the lowest dividend yield on our list, it has the highest average annual returns.

Assets under management: $1.2 billion

Expense ratio: 0.49%

Top holdings: Constellation Energy (CEG), Talen Energy (TLN), Vistra (VST), Xcel Energy (XEL), CenterPoint Energy (CNP)

4. State Street Utilities Select Sector SPDR Fund (XLU)

This passively managed fund tracks the Utilities Select Sector Index, which includes the key publicly traded utilities. The fund holds more than 30 stocks, and the slow-and-steady nature of utilities – with their high dividend payouts – makes them attractive as buy-and-hold investments.

The fund’s expense ratio of just 0.08% is low, costing investors just $8 annually for every $10,000 invested. The fund’s average annual returns have been solid over time.

Assets under management: $22.1 billion

Expense ratio: 0.08%

Top holdings: NextEra Energy (NEE), Southern (SO), Duke Energy (DUK), Constellation Energy, American Electric Power (AEP)

5. iShares Preferred and Income Securities ETF (PFF)

This iShares fund invests in preferred stocks and other income securities, allowing it to make an attractive payout. Its investments will tend to do well when prevailing interest rates are falling, meaning that a Fed rate cut or a series of them could help turbocharge the investment book.

The key advantage of this fund is its meaty monthly yield, which is likely to be the main driver of returns over time. In other words, while the fund may get a nice capital gain if prevailing rates move lower, its main source of return will be that dividend payout.

Assets under management: $12.8 billion

Expense ratio: 0.45%

Top holdings: NextEra Energy Units (2029), NextEra Energy Units (2027), Southern Units (2028), BrightSpring Health Services Units, NextEra Energy Units (2029)

6. InfraCap REIT Preferred ETF (PFFR)

This InfraCap fund is a much smaller player than the others on the list, but its portfolio of preferred stocks puts its yield at the top. Preferred stock acts much like a bond rather than a stock, with the payout being the key source of the investment’s return over time. Still, the fund may perform well if prevailing rates fall, making it more attractive.

Preferred stocks act much like high-yield bonds (formerly known as junk bonds), though their long-term record is fine. So, expect this fund to fluctuate with interest-rate changes even as the monthly payouts keep coming.

Assets under management: $120 million

Expense ratio: 0.45%

Top holdings: UMH Properties Series D (UMH-D), Digital Realty Trust Series L (DLR-L), Hudson Pacific Properties Series C (HPP-C), Pebblebrook Hotel Trust Series G (PEB-G), Vornado Realty Trust Series M (VNO-M)

7. iShares 0-3 Month Treasury Bond ETF (SGOV)

This fund doesn’t have a 10-year track record yet, but even in its short lifetime it has become quite popular. The fund holds only short-term Treasury bills, so it quickly reflects changes in the Fed funds rate. The short duration of its holdings means that it’s not subject to much interest rate risk (i.e., its value won’t change much in response to changes in prevailing rates).

The fund pays a monthly dividend at effectively the short-term Fed funds rate, so it’s a low-risk place to stash cash for short periods while you wait for the market to turn around. It’s the ultimate safe-haven trade for those who want to be sure their cash is there when they need it.

Assets under management: $104.9 billion

Expense ratio: 0.09%

Top holdings: U.S. Treasury bills

Regards,

James Royal, PhD

Editor’s Note: Whitney Tilson — the hedge fund manager CNBC called “The Prophet” — says America has reached its “Ripping Point.” The old financial order is being torn apart, and he believes most investors have no idea what’s coming in the next six months. He’s named the stocks he thinks will be destroyed in the chaos — and the ones he believes will soar. Watch his free presentation while it’s still available. 

The post 7 Best ETFs to Hedge Against an AI Bubble appeared first on MarketWise.

Elon Musk likes to move as quickly as possible. The Tesla (TSLA) and SpaceX (SPCX) CEO has been known to occasionally bypass reality when it comes to his projects and businesses. Recently, Musk has become a major proponent of using natural gas turbines for the artificial-intelligence (“AI”) build-out.

All of that came into focus on August 29, when Musk announced that SpaceX will manufacture its own gas-turbine parts – specifically blades and vanes – to help speed up the process of getting natural gas turbines online to provide power for its data centers.

SpaceX and Tesla are each building 100GW/year of solar production capacity as fast as possible, but natural gas will still be needed to supplement and bootstrap solar for several years.

The limiting factor for nat gas turbine production is casting the blades & vanes. By doing…

— Elon Musk (@elonmusk) August 29, 2026

As you can see in Musk’s X post, he believes casting gas-turbine blades and vanes in-house will cut production time by up to 18 months and be a “profound game-changer” for SpaceX.

And he’s probably right. If SpaceX can actually expedite the process by up to 18 months, it will be a huge win for Musk and company. As he stated in his post, natural gas is still a critical energy source needed to supplement solar power.

But some questions remain unanswered:

• How quickly can SpaceX get a gas-turbine parts-casting factory up and running?

• Does Musk have a team capable of building these parts?

• And assuming this all goes smoothly, how long will it take for SpaceX to produce these parts at scale?

Building Natural Gas-Turbine Parts Is the Next Phase of Musk’s ‘Dark Energy’ Plan

If you’re unfamiliar with the concept of “Dark Energy,” let me take you back to a June 5 article by my colleague Steven Longenecker.

In that piece, he wrote:

Elon Musk will be one of the world’s top buyers of gas turbines over the next few years…

SpaceX has committed to spending more than $2.8 billion on these turbines for its artificial intelligence (“AI”) build-out, the company recently revealed in its initial public offering (“IPO”) filing…

In March, SpaceX agreed to buy $805 million worth of turbines from an unnamed supplier, with deliveries running through 2029. Then in late April, the company struck a separate, still-pending deal for roughly $2 billion worth of mobile gas turbines and related equipment from another vendor.

Steven’s article continued:

And it confirms something my colleague Professor Joel Litman has been saying for months…

This kind of off-grid “Dark Energy” is the next stage of the AI boom. As I explained in our Dark Energy deep-dive last month:

Dark Energy is a clean, reliable power source. Specifically, it’s a class of natural-gas turbines… essentially a jet engine adapted into an electric generator. They start in minutes, run on natural gas instead of jet fuel, and can produce tens of megawatts of power per unit. Stack enough of them together, and you have a private power plant…

But until recently, no one was using them at scale to power the world’s most advanced AI labs. Now that they are, the suppliers building them are sold out for years.

Since then, SpaceX has also purchased APR Energy, a Jacksonville-based power company specializing in rapidly deployable, modular power generation.

I covered that transaction in a July 21 article, in which I wrote:

The deal, which is thought to be worth at least $1 billion, gives Musk what APR Energy’s website describes as “one of the world’s largest mobile gas turbine fleets,” with solutions that scale from 20 megawatts (“MW”) to 500 MW. APR Energy has delivered more than 50 terawatt-hours of power to more than 35 countries.

Most importantly, the company claims it can “deliver reliable energy in 30 to 90 days.”

And that’s exactly what Musk – and every other data-center operator and hyperscaler in the world – desperately wants and needs. Plenty of power… and fast.

Musk is clearly a big believer in the fast power that natural gas turbines can provide to SpaceX’s data centers. But, based on his recent X post, that power can’t be delivered fast enough. So, the next logical step after buying up natural gas turbines and then buying an energy company for its fleet of mobile gas turbines is to help build them yourself, right?

Regardless of how he procures the turbines, Musk is in the midst of taking Dark Energy to the next level.

The Texas Foundry Building the Natural Gas-Turbine Parts

Bastrop, Texas, a small town of roughly 14,000 residents located around 30 miles outside of Austin, was once known as a filming location for the iconic 1974 horror classic, The Texas Chain Saw Massacre. Today, it’s Elon Musk’s playground.

Along with Musk’s huge SpaceX Starlink factory, Bastrop will soon be home to the new foundry that will cast blades and vanes for natural gas turbines. Musk, through his recent dealings with gas-turbine production, learned that the primary bottleneck is the casting process.

TechCrunch, via the Information, explained why:

The blades inside a gas turbine’s hottest section run at temperatures around 3,000 to 3,600 degrees Fahrenheit, which is roughly 800 degrees hotter than the melting point of the very metal alloy they’re made from. That’s only possible because of the blades’ internal cooling channels and thermal-barrier coatings, plus the specific way each blade is cast. Just four companies worldwide have mastered the casting process well enough to produce them at industrial scale, and all of them are tapped out right now.

To repeat, only four companies on the planet know how to do this on a large scale. But they’re completely booked through 2029 and into 2030. And, as we’ve learned, patience is not a virtue of Musk’s. So, he’s opting to do it in-house rather than wait.

The problem is that the entire operation is obviously very complex and delicate.

As TechCrunch noted:

What makes the whole thing especially difficult is that each blade has to be cast as a single, unbroken crystal, grown slowly inside a vacuum furnace, without the microscopic seams that let ordinary cast metal crack under stress. It’s a tricky process even for the smaller blades used in jet engines; the blades in power-plant turbines are considerably larger, which makes producing them at that scale and without defects even harder.

Which takes us back to some of those unanswered questions I posed at the start of this article…

The answer to all three questions is, essentially, a giant shrug.

We do know that SpaceX bought roughly 830 acres of land in Bastrop between March and June. But that’s pretty much all we know. As of August, the state of Texas had no record of any environmental or building permits being filed by SpaceX for construction of the facility.

Let’s also not forget that these types of gas turbines release smog-forming compounds and dangerous chemicals like formaldehyde, as well as pollutants linked to asthma, respiratory disease, and certain cancers into the air. That’s why significant industrial air-quality permits are required before construction can even begin.

Those permits haven’t appeared yet.

The next part to consider, assuming permits are granted and foundry construction goes smoothly, is the time it takes to create the product.

Peter Perri III, an energy investor with more than 20 years of experience in building and financing power infrastructure, noted in an August 30 Substack post:

A vacuum casting foundry capable of single-crystal work is 18 to 24 months from groundbreaking to commissioning, and the job listings say they’re still hiring the people who will build it. Process development on single-crystal yield typically runs another two to three years.

At this point, no ground has been broken, and no team has been assembled. So, realistically, we’re probably looking at a minimum of five years before the foundry yields any product. History tells us that Musk won’t want to wait that long.

The Turbine-Blade and AI-Power Bottlenecks Are Real… But Is Musk’s Plan Realistic?

Global research company Gartner projects that the amount of energy consumed by data centers around the world will reach 565 terawatt-hours (“TWh”) by the end of this year. To put that into perspective, here are some fun facts about how much 565 TWh is worth in real-world contexts:

• It’s nearly twice the annual energy consumption of the entire continent of Australia.

• If the world’s data centers were one country, it would be one of the top 10 largest electricity-consuming countries on Earth.

• It could fully charge every smartphone on the planet – around seven billion – every day for the next 40 years.

By 2030, that number will hit nearly 1,000 TWh, according to the International Energy Agency.

The current electrical-grid infrastructure simply can’t handle this type of load. Something needs to power the rapidly growing number of data centers, which shows no signs of tapering off anytime soon. This has hyperscalers like Amazon (AMZN), Meta Platforms (META), Alphabet (GOOGL), Microsoft (MSFT), OpenAI, and SpaceX looking for quick energy sources.

Most of those companies have already gone the renewable, clean-energy route, like solar and wind. But they’re all turning – or have already turned – to natural gas because it has typically been a faster way to get their data centers online.

But the AI-driven demand has overwhelmed the supply. GE Vernova (GEV), the world’s largest manufacturer of natural gas turbines, is effectively sold out of production capacity through 2030.

That’s the reason Musk turned to APR Energy and its fleet of mobile gas turbines. And that’s why he decided to build his own parts to break the bottleneck holding up gas-turbine production.

But can SpaceX pull this off? As I mentioned, there is no foundry yet… permits haven’t been secured, and ground hasn’t been broken. And no team of experts is in place to oversee and take part in the extremely complicated and sensitive process of casting turbine blades.

Could it theoretically happen? Sure. Musk has the financial means to make virtually anything happen. He should be able to lure top talent to Texas to participate in the project. Although, considering the recent news that SpaceX completely restructured its data-center team because of engineering and reliability concerns at its Mississippi and Tennessee factories, is there any guarantee that SpaceX will even be able to bring in the right people?

If SpaceX does pull this off, it will position the company as one of only five Western companies capable of this type of manufacturing. SpaceX would have every AI-infrastructure builder across the globe lining up for its turbine blades and vanes.

But the lack of any progress and the daunting production timelines for both foundry construction and large-scale production make this feel closer to fantasy than reality.

And, let’s face it, Musk has a long history of grandiose ideas and projects that sound amazing at conception but barely get off the ground. The fully self-driving Tesla car (on a large scale), the Hyperloop, the mass rollout of Tesla robotaxis, personal rocket-travel across Earth, the Tesla Roadster, the city on Mars… you get the idea.

The man is undeniably brilliant and has conceptualized some incredible technological breakthroughs. But he also owns a long list of broken promises. Time will tell which category the turbine-blade foundry in Texas falls into.

Regards,

David Engle

Editor’s Note: A new “dark energy” is being rolled out as we speak… It completely bypasses our need for foreign oil. And it’s not nuclear or solar or wind or anything you would expect.

This new potential $10 trillion technology is already seeing investment by early backers of Microsoft, Google, Amazon, and more… Most important, these dozens of billionaires can’t make this tech themselves. They have to go through obscure “supplier” companies.

And in the coming months, this new “dark energy” could send three little-known stocks soaring… while wiping out 10 of the most popular stocks on the market. Learn more about this dark energy opportunity by clicking right here.

The post SpaceX Wants to Build Natural Gas-Turbine Parts for AI Data Centers. Can Elon Musk Pull It Off? appeared first on MarketWise.

The latest nonfarm payrolls report lands Friday, September 4 at 8:30 a.m. ET, and it’s the biggest, most volatility-inducing number of the month. Consensus is looking for a gain of around +55,000 after July actually lost 23,000 jobs. But our August jobs report preview says the crowd has it wrong.

I’m going to tell you the same thing I told our room on this morning’s live show: the number isn’t the story. Risk is already repricing where the real money lives — and it’s not in the VIX. It’s in the bond market.

I spent 28 years making markets, first on the floor of the CBOE and then trading bonds from 2003 to 2012. And the yield curve was always my first tell for the direction of the whole stock market. Right now, that tell is flashing, most traders are watching the wrong gauge, and Friday’s print is the catalyst that decides which way this thing air-pockets.

Let me walk you through it.

Key Numbers to Know Before Friday

Report	August 2026 nonfarm payrolls (jobs report)
Release	Friday, September 4, 2026 — 8:30 a.m. ET (7:30 a.m. CT)
Consensus	~ +55,000 jobs (estimates run from roughly −25K to +140K)
Prior (July)	−23,000
Unemployment rate	Expected to hold near 4.1%–4.2%
VIX	~15–16 — near its lowest levels all year
MOVE index (bond vol)	~78 and rising, up from ~70 last week
10-year Treasury yield	~4.8%
Fed funds rate	3.50%–3.75% — markets pricing ~65% odds of a hike in September

What Time Is the August 2026 Jobs Report Released?

The Bureau of Labor Statistics releases the August employment report on Friday, September 4, 2026, at 8:30 a.m. ET (7:30 a.m. CT). It’s the single biggest catalyst of the month — the one number that moves the dollar, the bond market, and the stock market in the same breath, usually inside the first few seconds.

What Is the Forecast for August Nonfarm Payrolls?

Consensus is centered around a gain of +55,000, with the unemployment rate expected to hold near 4.1%. But be honest about the spread: the estimates run all the way from a 25,000 decline to a gain of 140,000.

When the pros can’t agree inside a 165,000-job range, that’s your first tell. This is a convexity setup, not a coin flip. The wings are priced for a big move — up or down — because there’s simply too much uncertainty for this market to sit still. Nobody actually knows what prints, which means the market is going to react, hard, to whatever does.

MOVE Index vs. VIX: The Number That Matters

Here’s the part almost nobody is talking about.

For the last year, the crowd has watched the VIX — the “fear gauge” for the S&P. Fine. But the VIX is sitting down in the dumps around 15, near its lowest levels of the entire year, while the market has been quietly selling off. That combination — market down, VIX asleep — usually doesn’t happen. It’s a tell that traders have gotten too comfortable with risk.

So don’t watch the VIX right now. Watch the MOVE index.

The MOVE (it’s a Bank of America product) is to bonds what the VIX is to the S&P — it measures volatility in the Treasury market. And while the VIX naps, the MOVE has climbed to roughly 78, up from around 70 last week. Bond volatility is rising while stock volatility pretends everything’s fine. When those two disagree, you don’t fight the bonds. The bond market is bigger than the equity market, and it’s usually right first.

Why is bond vol waking up? The 10-year yield is pushing 4.8% and the 30-year is at its highest in a long while. The Treasury even stepped in to buy the back end of the curve to try to force those rates down — but you can’t fight the free markets for more than a day or two. That’s the real reason the QQQ, the IWM and the S&P have been leaking lower. It isn’t a VIX story. It’s a bond-volatility story, and the MOVE is your first tell. If it bounces again before Friday, this market goes into the number under serious pressure.

Will the Fed Raise Rates in September?

Now layer the jobs number on top of that.

For most of 2024 and 2025, the trade was simple: weak jobs → the Fed cuts → stocks rally. “Bad news is good news.” That trade is dead right now, and here’s why.

Sticky inflation never went back in the box — the Fed’s preferred gauge is still running well above its 2% target, and with U.S.–Iran tensions heating up and tankers being hit exiting the Strait of Hormuz, oil is catching a hard bid. That’s more inflation, not less. At Jackson Hole, Fed Chair Kevin Warsh made it about as clear as a central banker gets that prices worry him more than a soft patch in hiring. The July meeting was a 9–3 hold, and all three dissenters wanted to raise rates. After Warsh spoke, fed funds futures flipped to roughly two-in-three odds of a September hike.

I’ll say it plainly, same as I said it live: I strongly believe the Fed raises rates at its September meeting.

So flip your logic before Friday:

• A hot number (think +100K or more) tells Warsh the labor market can take a hike — that’s the hawkish outcome, and it’s a headwind for stocks, a tailwind for the dollar and short-term yields.
• A weak number is what takes hike risk off the table — and that’s the one the equity crowd would actually cheer.

Read that twice, because it’s backwards from what you’ve been conditioned to do. This is my First Law in action: money drives markets. Not the headline, not the pundit spin — the repricing of rate expectations. Ask yourself which game you’re playing before 8:30.

Why the VIX Looks Too Cheap Into the August Jobs Report

Put it all together and the VIX being this low makes no sense. We’re walking into nonfarm payrolls — the biggest catalyst of the month — in a September that’s historically one of the most volatile stretches of the year, with bond vol climbing, yields near the highs and inflation a live concern. And the crowd is treating protection like it’s worthless.

That’s exactly when volatility gets mispriced. Stocks don’t like to hang around big round numbers — right now the Nasdaq is sitting right on its 100-day moving average, and markets rarely just drift there. They give a level a kiss and then either bounce or air-pocket straight through it. Into a number with this much uncertainty, that’s a move waiting to happen — which is why watching and respecting volatility here matters far more than the crowd thinks.

And don’t let last week’s Nvidia headline lull you either. Everyone circled the 89% implied year-over-year revenue growth and cheered — but a big share of that profit came from Nvidia’s own investments in AI companies that turn around and buy its chips. That’s circular financing, and it’s part of why the rally underneath is more fragile than the tape looks.

What the Unemployment Rate Isn’t Telling You About the Jobs Report

One more thing the crowd gets wrong: the unemployment rate.

July’s rate “fell” to 4.1%, and you’ll see that spun as strength. It isn’t. It dropped for the wrong reason — the labor force shrank by more than a quarter-million people and the participation rate slid to 61.4%, the lowest outside the Covid shock since the mid-1970s.

When the workforce is shrinking, the number of jobs needed just to hold unemployment steady falls too. So, a small payroll print — even a slightly negative one — no longer automatically means the economy is cracking. This is my Third Law: nothing is cheap or expensive on its own. You have to know why a number moved before you trade it.

What the BLS Benchmark Revision Means for the Jobs Report

Two weeks ago the BLS dropped its preliminary benchmark revision — the annual re-basing of payrolls against actual tax records. The economy had 79,000 fewer jobs in the year through March 2026 than first reported. That’s 0.1%. A rounding error — and a fraction of last year’s roughly −911,000 revision that had half the internet screaming cover-up.

But don’t stop at the headline. Look at where the jobs moved, because the tape can’t hide. The biggest cuts hit retail trade (−154,600), private education and health (−96,000), wholesale trade (−86,200) and manufacturing (−67,000). The upward revisions went to transportation and warehousing (+135,100), government (+99,000), information (+87,000) and financial activities (+85,000). That’s not random. That’s the goods-and-retail economy bleeding while logistics, government and information hold the line. Follow the money and the picture gets a lot clearer than the one-line summary. Second Law: the tape can’t hide

How to Think About Trading the Jobs Report

I’m not handing you a ticket to punch — this is the setup, not a recommendation. But here’s how I’m framing Friday.

The edge on an event like this is almost never in guessing the number. It’s in watching the right gauge and understanding the reaction. So watch bonds. For those with access to futures, the ladder is your read: ZN (10-year), ZB (30-year), UB (ultra), ZF (5-year), ZT (2-year) — or just follow TLT and the MOVE index. If yields start coming down and the MOVE settles, the market can rally. If the MOVE bounces and TLT gets crushed, that’s very bearish for stocks.

And whatever you do, my Fourth and Fifth Laws carry the day on an event like this: decide your risk before you enter, not after the number prints — and survive first. Size it so a whipsaw can’t take you out of the game.

What the Jobs Report Means for the September Fed Rate Decision

This report doesn’t land in a vacuum. It’s the first of two big inputs before the Fed’s September meeting, with the August CPI report following the week after. Neither is a formula that spits out a decision — they’re evidence, and Warsh has told us he’s reading it through an inflation lens. A firm payroll number plus another sticky inflation print keeps the September hike squarely on the table. A clearly weak jobs report is what could talk the committee back toward patience.

FAQ

What time is the August 2026 jobs report?

Friday, September 4, 2026, at 8:30 a.m. ET (7:30 a.m. CT), from the U.S. Bureau of Labor Statistics.

Will the Fed raise rates in September 2026?

It’s a live debate, and it’s leaning hawkish. After Chair Warsh’s Jackson Hole speech, fed funds futures moved to roughly 65% odds of a quarter-point hike — a sharp shift from the pause markets had expected. Friday’s payrolls and the following week’s CPI will shape the final call.

Why is a strong jobs report bad for stocks right now?

Because inflation is still above target and the Fed is leaning toward hiking. A strong labor market gives the Fed cover to raise rates, which pressures equities and lifts the dollar and yields. The old “weak-jobs-means-cuts” trade is inverted for now.

What is the MOVE index and why does it matter for the jobs report?

The MOVE index measures expected volatility in the U.S. Treasury market — it’s the bond market’s version of the VIX. With bond volatility rising while the VIX stays low, the MOVE is the more honest read on risk heading into a market-moving number like nonfarm payrolls.

What was the July 2026 jobs report number?

Nonfarm payrolls fell by 23,000 in July against expectations for a gain, with prior months revised down by roughly 103,000 combined. The unemployment rate ticked to 4.1%, but largely because the labor force shrank.

The Bottom Line

Friday isn’t about the headline — it’s about the reaction to the headline, and the reaction function is flipped. Forget the VIX, watch the MOVE, respect the volatility, and know which game you’re playing before 8:30.

That’s how we do it every market morning over at Masters in Trading, 11:00 a.m. ET. Follow the money, not the narrative.

Editor’s Note: Forbes calls $1 billion fund manager Louis Navellier “the king of quants.” Today, he’s stepping forward to reveal why he’s investing $358 million of his own firm’s money in the next stage of Artificial Intelligence… a technological sea-change that could erase millions of jobs, solve humanity’s biggest mysteries, and spark a wave of moneymaking opportunities — both in and outside the stock market. Click here for the details… 

The post August Jobs Report Preview: Forget the VIX — Watch the Bond Market Going Into Friday appeared first on MarketWise.

Strategy (MSTR), the world’s largest bitcoin treasury firm, scooped up 4,603 bitcoins in the last full week of August for a total price of $369.7 million. That’s an average cost of $80,318, which is right around the cryptocurrency’s recent peak.

Generally, Strategy uses arbitrage to make low-risk money when buying or selling bitcoins. If its common stock is trading for substantially more than the value of its bitcoin holdings, it can make financial sense for Strategy to issue common stock to buy more bitcoins.

Voila! – More bitcoins per common share, all at relatively low risk.

That’s what Strategy did with last week’s purchases, too. It sold 4.53 million shares of common stock, raising $602.8 million. It then spent nearly $370 million to buy those 4,603 bitcoins. It also spent $151.8 million on repurchasing its STRC preferred stock, saving it that security’s 12% coupon and following major repurchase activity in prior weeks.

What about the remaining $80 million or so from that cash raise?

A total of $50.7 million was used to pay out the dividend on the STRC preferred stock, while $30 million was stashed away in Strategy’s cash reserve for increased liquidity.

What’s puzzling is why Strategy acted at all, given the relatively low premium placed on its stock.

The stock was not trading at a substantial premium, particularly after an extended period of malaise in the bitcoin market. On Monday, Strategy marked its stock at 1.09 times its bitcoin holdings, so the stock was trading at just a 9% premium to its holdings. That figure dropped to just 1.05 times on Tuesday.

The 9% premium is quite small compared with what Strategy stock had fetched in bitcoin’s go-go years, but such a modest premium may be all that’s available to the company in this slow period.

But the thin arbitrage margin is worrisome in a fast-moving market like bitcoin. Strategy did not buy before the latest run, as bitcoin rose from $63,000 to $78,000 – up more than 20% – in a week or so. Instead, it bought more coins near the recent top around $80,000.

Now, with bitcoin a few thousand dollars off that purchase price, Strategy is quickly underwater on those buys. Such a thin premium on its stock gives the company much less room for error when trading the world’s largest cryptocurrency in the short term.

It shows the challenges Strategy faces in a slow bitcoin market, even as it has significantly altered how it manages its balance sheet and investments to meet them. With bitcoin down significantly from its all-time highs, Strategy’s executive chair Michael Saylor has been forced to rework how the firm can effectively raise funds to keep its bitcoin buying binge on track and protect its future.

Strategy Is Becoming the ‘Central Bank of Bitcoin’

At the center of these moves is what Strategy’s executive chair, Michael Saylor, is calling his digital credit plan. This plan reworks how Strategy manages its balance sheet, focusing its fundraising on the company’s STRC variable-rate preferred stock to increase the number of bitcoins per share of common stock.

Its digital credit plan turns Strategy into something like a “central bank of bitcoin,” meaning it manages its debt financing like how a central bank manages the money supply.

At the center of this fundraising process is the company’s preferred series STRC. It’s already the world’s largest preferred stock, according to Saylor, and by managing it carefully, Strategy can lower its financing costs.

“We expect to keep laser focus on it,” said Saylor in Strategy’s latest earnings call. “We’re going to do everything we can to make it the most appealing credit in the entire digital credit space.”

For example, Strategy can adjust STRC’s dividend payout, currently a 12% coupon, and the stock price by issuing or repurchasing shares when they’re priced favorably. If Strategy can develop a reputation as a solid counterparty, it may be able to lower that dividend over time.

This move is just one part of the company’s tool chest when it comes to managing its finances:

• Strategy can repurchase its preferred shares if the price falls too far, or it can issue them to raise funds if the price hits a favorable level, say its par value of $100 or above.

• Strategy can raise the yield on its preferred to raise the stock’s price, as it did recently, if it wants to raise cash or if it wants to boost its bitcoin holdings, for example.

• A larger issuance of STRC preferred stock creates more market liquidity, which may help reduce the preferred’s cost and make it seem like a more reliable investment.

In short, Strategy will adjust its actions in three asset markets – bitcoin, common stock, and preferred stock – so that it will raise its bitcoins per common share.

In the past few weeks, Strategy has been working to push up the price of its STRC preferreds, a move that should ultimately let Strategy issue them without suffering a discount. Its recent repurchase of nearly $152 million in STRC also keeps buying pressure up on that security, and it has nearly $365 million in repurchase authorization, giving it more dry powder.

Despite the strong bounce back in STRC, it’s not quite yet at its par value of $100 per share. The preferred stock bottomed around $71 per share and has since moved to around $97.

Saylor’s plan to keep the preferred near par may create unintended consequences, though. For example, if Strategy always keeps a floor under STRC, it may encourage investors to use the preferred as a source of cash, potentially draining the company’s coffers if it’s not careful.

Strategy’s new digital credit plan means it will need a longer-term approach to managing its assets and liabilities, which runs counter to short-term trading.

Do Bitcoin and Strategy Protect Against a Market Meltdown?

Bitcoin has bounced back over the past few weeks, moving from the low $60,000s to its recent peak above $80,000. One driver of this move is growing anxiety about massive U.S. debt and deficits, as well as the related issue of the dollar’s purchasing power.

While this “dollar debasement trade” may work as a short-term move for bitcoin traders, it’s apt to be a poor trade for those who think bitcoin will protect their money during tough times.

First, bitcoin and other cryptocurrencies trade like “risk” assets such as tech stocks or even more speculative, profit-less investments. So, it will trade largely in line with these riskier investments, though often with more exaggerated moves – that is, higher highs and lower lows.

These exaggerated movements make sense when you understand that bitcoin is backed only by traders’ sentiment and isn’t directly usable for goods and services (except the illegal kinds). Since it’s based solely on sentiment, bitcoin can fluctuate wildly as sentiment swings from fear to greed.

Second, because bitcoin is based on nothing but traders’ confidence or belief that they can sell it for more to the next trader, its price will evaporate if and when traders lose confidence in it. The market’s confidence is inherently unstable and can disappear at any moment for a variety of reasons, including if someone discovers a new way to hack the crypto.

Strategy combines this lack of intrinsic value with leverage in the form of its bonds and preferred stock, meaning its stock will be even more volatile than bitcoin itself. It’s taking steps to be able to control this inherently volatile situation more effectively so that it can keep buying bitcoins when prices are down and ensure the entire company doesn’t blow up in a downturn.

Another time may come when bitcoin is down so far from its peak that the market just capitulates, as it did in 2022. In that scenario, Strategy will need the capital to buy bitcoin after the plunge, but whether it ends up being the final death knell for the world’s biggest crypto, and therefore Strategy, will play out in time.

Regards

James Royal, PhD

Editor’s Note: One group of Americans is quietly building extraordinary wealth right now, while another is falling further behind with every passing month. Whitney Tilson — the hedge fund legend CNBC called “The Prophet” — says we’ve hit the “Ripping Point,” and the split is accelerating fast. He’s sharing what he believes everyone needs to do immediately (including two free stock picks) in this urgent free presentation.

The post Strategy’s Latest Bitcoin Buys Look Quite Puzzling – Here’s Why It’s a Worrisome Sign appeared first on MarketWise.

Amazon (AMZN) never ceases to amaze. The company’s custom chip business has surpassed a $25 billion annual revenue run rate, driven by the fast-growing demand for its Trainium AI accelerator chips and Graviton central processing units (“CPUs”) for cloud computing.

The reason is simple. Amazon Web Services (“AWS”) custom chips are more affordable to use, and its customers want lower-cost chips than what Nvidia (NVDA) offers.

Amazon is already the runaway e-commerce leader in the U.S., with a 40.5% market share. Meanwhile, AWS – which essentially created the cloud-computing industry – is the world’s largest cloud-infrastructure provider, with 28% global market share as of midyear.

But are there enough value-focused custom-chip customers for AWS to take a big enough bite out of Nvidia’s dominant AI-chip market share of roughly 80% to 87%?

The Lower-Cost Appeal of AWS Custom Chips

The most obvious appeal here is affordability. Sometimes, you can get high quality at a fair price, and that seems to be the case with Amazon’s chips.

AI developers who use AWS chips can save 30% to 40%, while still getting excellent performance, according to Dave Brown, AWS vice president of compute and machine learning.

As he told Yahoo Finance, “That’s what our customers are looking for, is to constantly get more compute and more performance, and then super importantly, at a lower price.”

Who wouldn’t want that combination?

Amazon notes that it builds its chips “from the ground up” to accommodate specific workloads. The end products are Amazon’s Trainium AI chips, Graviton cloud-computing chips, and Nitro chips, which provide the storage, networking, and security behind AWS’s cloud infrastructure.

So, what makes Amazon’s custom-built AI chips different from the graphics processing units (“GPUs”) that Nvidia creates? GPUs are designed for general-purpose workloads, not specific workloads. Amazon’s AI chips are custom-designed to handle the specific workloads its customers run.

Now, Amazon is not the first company to build custom chips for its clients to use. Alphabet (GOOGL), for example, was an early pioneer in that area, with its customized tensor processing unit (“TPU”) chips that deliver GPU-like performance for less money.

And money matters when it comes to AI, considering global AI spending is expected to reach nearly $2.6 trillion in 2026, according to research by Gartner. Any money businesses can save on AI costs is a win, with investors growing weary of tech companies’ wild spending sprees – and dinging their stock prices as a result.

Even a modest improvement in AI performance and efficiency can save a company millions of dollars a year. CloudExpat, a cloud-infrastructure optimization company, tested AWS Trainium1 chips against Microsoft (MSFT) Azure’s ND H100 and Nvidia GPUs in 2025 (Google’s TPU was also tested, but for this article, we’re focusing on the comparison between AWS and Nvidia).

Using dozens of highly technical tests, CloudExpat revealed that companies looking to train a GPT-4-scale AI model could save millions in cloud spend by using AWS Trainium chips versus Nvidia GPUs.

And Amazon has continued to improve the efficiency and price-performance of Trainium chips with each new generation. Amazon’s Trainium2 is reportedly four times faster than Trainium1 and cut costs by roughly 35% compared with Nvidia’s H100 chips, according to the Tech Buzz. Amazon says its Trainium3 chips offer up to 40% better price-performance than Trainium2.

And that’s just for AI training. When it comes to actually running AI models, where cost per query can add up quickly, Amazon says its Inferentia2 chips offer up to 50% lower cost per inference than GPU-based chips. When you consider that AWS sees billions of inference queries every day, that makes a massive difference.

Of course, there are other considerations here… For example, adopting AWS Trainium chips to replace Nvidia GPUs would require a company to abandon Nvidia’s very sticky – and familiar – CUDA ecosystem and implement AWS’s Neuron platform. And that’s not free.

But the chip performance itself is likely worth the swap.

A Growing Customer List Has AWS Chips Sold Out for Years

Companies are increasingly noticing the benefits of Amazon’s custom chips and signing up as customers. In February, OpenAI committed to using 2 gigawatts (“GW”) of Trainium capacity to power its frontier models starting in 2027. This development expanded the companies’ existing $38 billion multiyear agreement by $100 billion over eight years.

AI giant Anthropic followed suit in April, committing to spend more than $100 billion over the next 10 years on AWS technologies – primarily its Trainium and Graviton chip capacity. Amazon noted that Anthropic will “secure up to 5 [GW] of capacity to train and power their advanced AI models, including significant Trainium3 capacity.”

But it’s not just the big players investing in Trainium. Smaller startups such as TwelveLabs, Neura Robotics, Odyssey, Poolside, Decart, Karakuri, NetoAI, Splash Music, and Metagenomi Therapeutics (MGX) are among the many companies turning to AWS custom chips as a cheaper alternative to Nvidia.

Due to this demand, Trainium3 chip capacity is practically sold out. And AWS’s Trainium4 chips, which won’t even be released until 2027 or 2028, already have much of their capacity booked.

Even Amazon’s Graviton processors are flying off the shelves. According to Amazon, more than 130,000 customers use Graviton-based servers. And more than half of AWS’ new processing power runs on Graviton chips.

In April, Meta Platforms (META) became one of the world’s largest Graviton customers when it signed a deal to deploy tens of millions of Graviton cores to power its agentic AI workloads.

And global rideshare leader Uber Technologies (UBER) heavily employs AWS Trainium3 and Graviton4 chips to improve its infrastructure and make customer experiences smarter and faster.

Like its Trainium counterparts, Graviton processors are gaining popularity thanks to their significantly better – around 40% – price-performance and 45% savings compared with x86-based CPUs. The Tech Buzz estimated that, based on cost-optimization analyses from cloud-management platforms, “a typical Fortune 500 company running 10,000 instances on AWS could save $8-12 million annually by migrating from Intel-based instances to Graviton3.”

Amazon’s custom chips clearly have tons of momentum, which it rode to the milestone $25 billion-plus annual revenue run rate.

What Amazon Custom Chips’ $25 Billion Annual Run Rate Means for the Company and the Industry

Calling the $25 billion-plus annual revenue run rate impressive would undersell its importance. Amazon reaching this figure is changing the semiconductor and AI-infrastructure industries. Just think about it… AWS went from using someone else’s chips and selling access to those chips through the cloud to creating and selling capacity for its own silicon.

For less than the cost of an Nvidia GPU, Amazon can customize chip hardware to optimize specific types of workloads. That is a massive development.

Just as massive: Amazon is no longer solely dependent on paying companies like Nvidia or Intel (INTC) for their hardware. AWS does still buy – and will likely continue to buy – other companies’ chips to satisfy customer demand. But Amazon’s custom chips now make it a direct competitor in the market.

AWS’s 2026 second-quarter financials prove that its custom chips have had a huge impact on the semiconductor market. The results include:

• $42.2 billion in net sales, a 37% year-over-year increase, which was the fastest segment growth in 18 quarters

• More than $25 billion in annual revenue run rates for its custom-chip and AI businesses, with year-over-year growth in triple digits

• $16.6 billion in operating income, up 64% year over year

• An operating margin of 39.4%, up from 32.9% during the second quarter of 2025

• An annualized revenue run rate of $169 billion

Amazon’s custom-chip success adds even more revenue to the cash-generating machine that is AWS. As profitable as Amazon is beyond technology (remember, Amazon is far and away the No. 1 e-commerce retailer in North America), AWS accounted for roughly 60% of the entire company’s operating profit in 2025.

Where does Amazon’s chip business stand against heavyweights like Nvidia, Advanced Micro Devices (AMD), and Intel? Before comparing, it’s important to note that Amazon CEO Andy Jassy has said that AWS custom chips would double their current $25 billion revenue if the company sold its physical chips – not just chip capacity – to external customers.

In his annual letter to shareholders published in April, Jassy noted:

If we were a standalone chip company, our chips would be generating over $50 billion in annual revenue.

So, when comparing revenue with its competitors, Amazon is right there… even if its chips are not currently generating more than $50 billion in annual revenue. Nvidia remains the clear leader, with its AI-driven Data Center segment generating a company-record $89 billion during its most recent quarter (fiscal second quarter of 2027).

But Amazon is running neck and neck with Advanced Micro Devices and Intel. Advanced Micro Devices’ projected annual data-center run rate of roughly $26.9 billion (based on its actual $6.72 billion second-quarter revenues) sits just ahead of AWS’s $25 billion. And Intel, with a projected $25.04 billion annualized run rate based on its $6.26 billion second-quarter Data Center and AI segment revenue, is virtually tied with AWS.

If you factor in Jassy’s statement that Amazon’s chip business would be valued at roughly $50 billion if it sold its hardware externally, then Amazon blows right past both Advanced Micro Devices and Intel.

Is Amazon’s Stock Undervalued?

If you’re thinking about investing in Amazon, these chip developments are crucial when you consider that AWS generates most of the company’s operating profit every year. And there’s no reason to think the segment won’t keep growing rapidly.

Not only are new chips here or on the way (Graviton5 was released in June, and Trainium4 will arrive in the next year or so), but powerful frameworks and models are, too.

• Amazon Bedrock AgentCore, which was released last fall, is the system AWS uses most to build and orchestrate enterprise AI agents.

• Project Rainier in Indiana, which officially launched last October, is one of the world’s largest distributed AI supercomputing clusters. It currently hosts more than 1 million Trainium processors dedicated to training Anthropic’s frontier Claude models.

• AWS Continuum, released in June, is a new agentic security service that finds and fixes security risks.

• AWS Transform is an agentic-AI service that supports large-scale framework modernizations.

• AWS Context is another new service that automatically builds a knowledge graph from existing data to be used by a company’s agents.

That’s just the tip of the iceberg. And there’s no reason to believe AWS will stop innovating.

Amazon has been a wildly successful company for years. A major reason is the foresight it employs in developing brilliant strategies… like developing its own chips. This has already proved to be a market changer.

That kind of innovation and confidence in its strategies matters to investors. It’s one reason Amazon’s stock feels undervalued at around $260. The average price target for Amazon is roughly $327, giving the stock around 25% upside.

Amazon stock may not get the splashy spikes like Micron Technology (MU) or Sandisk (SNDK), but it has long been a consistent performer. Year to date, it’s up nearly 15%. And over the past five years, it has gained nearly 57% (largely driven by AWS).

Chaikin Analytics, the investment-research platform founded in 2009 by legendary 60-year Wall Street veteran Marc Chaikin, gives Amazon a “bullish” rating in its Chaikin Power Gauge, a 20-factor stock-rating system that scans more than 5,000 stocks and 2,300 exchange-traded funds.

Amazon has a long history of innovation and revolutionizing entire industries. It has done that with Amazon Prime and its e-commerce business, its logistics operations, and its enterprise cloud infrastructure. The company’s development of custom AI chips and processors certainly seems to be the next industry changer.

Regards,

David Engle

Editor’s Note: Whitney Tilson called the rise of Apple, Amazon, and Netflix… as well as the collapse of dozens of companies that went bankrupt. Now the former $200M hedge fund firm manager is stepping forward with what he calls the most important financial warning of his 30-year career. He’s sharing two free stock recommendations  (one to buy, one to sell immediately)  along with details of a new proprietary system fueling his predictions. See it all in his free presentation. 

The post Amazon’s AWS Chips Top $25 Billion Run Rate. Does This Signal Trouble for Nvidia? appeared first on MarketWise.

Nvidia (NVDA) reported strong earnings on Wednesday. In its latest quarter, the artificial intelligence (“AI”) chipmaker grew sales an astonishing 106% year over year to $96.2 billion, while operating income surged 124% to $63.7 billion.

Nvidia puts up numbers each quarter that seem impossible to fathom. And the fact that a $5 trillion company can grow this much is something special.

While any company would love numbers like that, investors are always looking ahead, and some believe things can’t get any rosier for Nvidia than what they’re seeing now. That’s a big reason why we’ve seen the stock plateau in recent months.

The upshot is that Nvidia is losing some levers that have kept its massive profit machine growing at the pace it has sustained over the past few years. At the same time, its willingness to finance massive projects is swelling its own balance sheet, even as hyperscalers are borrowing billions to buy its chips – both of which call into question the ongoing sustainability of Nvidia’s tremendous sales growth.

For example, even as sales have exploded higher, Nvidia’s gross margin has seemed to top out.

Period	2022	2023	2024	2025	Q1 2026	Q2 2026
Sales	$27 billion	$60.9 billion	$130.5 billion	$215.9 billion	$81.6 billion	$96.2 billion
Gross margin	56.9%	72.7%	75%	71.1%	74.9%	75%
Operating margin	15.7%	54.1%	62.4%	60.4%	65.6%	66.2%

Gross margin appears stuck at 75% and seemingly unable to expand above that level. So, future profit growth will rely primarily on sales growth, without the added lift of gross margin expansion.

Nvidia has already warned that surging memory costs are hitting margins, likely pushing them to 71% to 72% in the fourth quarter. Next year, however, they should bounce back to margins of 72% to 73%. This sensitivity to change shows how tough it will be for Nvidia to exceed its historic 75% level.

It’s a similar – but slightly different – situation with operating margins, which continue to grow from already phenomenal levels, albeit more slowly than before. If sales keep growing and Nvidia keeps a hold on costs, it will likely still be able to wring a few more points of operating margin. But the days of 8.1 year-over-year percentage-point gains Nvidia saw in 2024 are gone.

Nvidia’s bleeding-edge products keep it in the lead for now. Still, everyone wants a piece of these fat margins, from established players such as Broadcom (AVGO) to upstarts such as Cerebras Systems (CBRS), which makes wafer-scale chips for AI data centers.

None of this suggests that the near future won’t get better for Nvidia, but the ability for it to get much better has been diminished, since margins can’t expand at yesteryear’s levels anymore. But this is only one part of the concerns suggesting that Nvidia must use new levers, such as more aggressive financing, to keep its sales growth on track.

(Looking for a step-by-step plan to help you use AI in your own investing? Here’s how the N.E.W. System works.)

Nvidia Is Aggressively Financing the AI Boom, Which Comes With Higher Risks

Borrowing behind the AI build-out has surged, especially in 2026. Hyperscalers such as Amazon (AMZN), Microsoft (MSFT), Alphabet (GOOGL), and Meta Platforms (META) have spent nearly all their 2026 cash flow on capital expenditures, most of it on the AI build-out.

To keep that build-out on pace, these hyperscalers have each started borrowing tens of billions of dollars in the past year. With hyperscalers’ AI spending projected to top $1 trillion in 2027 – well beyond their estimated cash flow – they’ll need to borrow even more money.

While that increased financing means the spending spree for Nvidia’s chips won’t keep growing forever, the chipmaker has begun offering more short-term financing on its own balance sheet.

That is, Nvidia is offering more generous payment terms to larger customers, giving these more important clients up to a year to pay. CFO Colette Kress said about 25% of Nvidia’s 2027 sales could be to AI labs (such as OpenAI) where Nvidia is using its own balance sheet.

You can see this change in a metric called accounts receivable, which has soared in recent years, as Nvidia finances more sales, typically for short periods. The average is around 60 days, or about two months. While the figure dipped in the first quarter (not necessarily a bad thing), it rebounded hard in the second quarter, though only to its historical levels.

Period	2022	2023	2024	2025	Q1 2026	Q2 2026
Receivables	$3.8 billion	$10 billion	$23.1 billion	$38.5 billion	$40.7 billion	$63.1 billion
Sales	$27 billion	$60.9 billion	$130.5 billion	$215.9 billion	$81.6 billion	$96.2 billion
Days sales outstanding	51.8	59.9	64.5	65	44.9	59.9

To judge these receivables, analysts look at days sales outstanding (“DSO”), which is the ratio of accounts receivable to a quarter’s average daily sales. DSO shows whether a company may be expanding credit to less worthy customers to keep its sales growth continuing.

This trend isn’t worrisome yet, but it’s worth watching as Nvidia expands its financing, especially to clients with major financing issues such as OpenAI, which is hemorrhaging billions in cash each quarter. Of course, it’s not just OpenAI that could hurt Nvidia, but even established players that rely on OpenAI remaining solvent to continue moving billions in AI spending through the system.

After such a consistent trend for years, a sudden rise in DSO may offer investors an early read on how the business is going. Nvidia is already warning investors that the number should rise – and it’s a sign that it must move more of this financing to its own balance sheet to keep sales rising. If credit problems arise, they will likely appear in rising days sales outstanding figures.

That puts greater credit risk on Nvidia, drawing it even closer to less well-financed players.

More broadly, you can see Nvidia rushing to keep its sales growth on track, as it becomes the “central bank of AI.” It has lined up a $250 billion backstop for OpenAI to construct a massive data center as well as a $350 billion financing arrangement for the facility’s chips.

Nvidia also announced an incomplete $500 billion deal with major investors before it had been finalized. In the end, it’s less a deal than a memorandum of understanding – “a deal to do a deal” – despite the breathless media headlines making it seem like the terms were in place.

Despite this quarter’s excellent numbers, Nvidia is pushing hard to line up financing for its customers and keep all its plates spinning. With each passing quarter, the growth will become harder to sustain, so watch for a rise in DSO as an early warning sign of trouble.

Nvidia’s Stock Looks Undervalued, But Is It Really?

On the surface, Nvidia looks cheap for a company growing at such a brisk pace. In May, various analysts raised stock price targets based on Nvidia’s rising earnings for 2026 and 2027, due to surging AI investment.

For example, Wells Fargo raised its price target from $265 per share to $315 on earnings estimates of $8.45 per share this year and $11.95 per share for calendar year 2027. This level of explosive growth implies a price of less than 19 times next year’s earnings at a share price around $220.

This price-to-earnings (P/E) ratio is well below the stock’s 32 times earnings over the past three years, says Wells Fargo.

In absolute terms, this forward P/E ratio looks quite reasonable for such high growth. But it assumes that growth will continue, even as Nvidia desperately works to provide financing for its clients. Some of Nvidia’s levers for growing profits are becoming less powerful, too.

So this assumption of growth will look fine until one day it suddenly won’t.

It’s less a question of “if” than “when” the Nvidia juggernaut can no longer sustain the high expectations it’s setting now.

Arguably, the world’s most-watched company is trading for what looks like a value. If you believe in the wisdom of the market, you want to be quite sure why it’s putting the company – and many high-growth AI plays – in the bargain bin. They may not be as undervalued as they appear.

Regards,

James Royal, PhD

Editor’s Note: Whitney Tilson — the hedge fund manager CNBC called “The Prophet” — says America has reached its “Ripping Point.” The old financial order is being torn apart, and he believes most investors have no idea what’s coming in the next six months. He’s named the stocks he thinks will be destroyed in the chaos — and the ones he believes will soar. Watch his free presentation while it’s still available.

The post Nvidia’s Blockbuster Earnings: Here’s the Biggest Risk the AI Chipmaker Faces, Even as Sales Soar appeared first on MarketWise.

A $2.6 billion loan just told you more about the AI buildout than any earnings call. The stock already recovered and forgot. The loan document remembers — and it’s the most honest signal in the whole trade.

Twenty years on a trading desk teaches you where to look when you want the truth about a trade.

Twenty years on a trading desk teaches you where to look when you want the truth about a trade.

You can’t use stock prices, they lie all the time. They gap on emotion, they get pinned by options dealers, they run on narrative long after the story has changed.

That’s especially true when you’re evaluating AI infrastructure stocks, where billion-dollar financing decisions often reveal more than earnings calls or analyst price targets ever will.

If you want to know quickly whether a company is actually in trouble, you watch what it costs that company to borrow money.

The lenders behind the money have done the work. They’ve read the contracts. They’ve stress-tested the cash flows. And when they suddenly demand more interest, tighter terms, and a lockbox on the revenue before they’ll hand over a dollar — that’s the smart money repricing risk in real time.

This is exactly what’s happening with one stock at the center of the next AI wave — CoreWeave Inc. (CRWV).

AI Infrastructure Stocks’ Hidden Debt Bomb

In mid-July, CoreWeave — the “neocloud” that rents out Nvidia GPUs to AI companies — went to the leveraged-loan market to raise $2.6 billion.

At first, the loan looked routine.

The structure was a delayed-draw term loan, its fifth-and-a-half such facility, nicknamed DDTL 5.5. The proceeds were earmarked to buy more GPUs and infrastructure to serve a specific set of customer contracts.

According to loan-market reporting from LSEG and Octus, the deal launched on July 16 at a spread of SOFR plus 425 to 450 basis points, offered at 99 cents on the dollar. Standard-issue for this borrower.

Then it stalled.

The lenders investing in the project weren’t convinced. And to get the deal across the line, CoreWeave had to rewrite the terms in the buyers’ favor:

• CRWV agreed to pay a much higher interest rate, making the loan significantly more expensive. The spread widened roughly 100 basis points to SOFR + 550, and the loan was discounted to around 96 cents — a four-point cut. The Financial Times pegged the all-in yield at roughly 9%. That’s a junk-grade cost of capital for a company that, months earlier, had raised GPU-backed money at investment-grade rates.
• The lenders demanded stronger protection. They imposed a cash lockbox — meaning the cash coming in from customer contracts has to go to paying down the debt first, before CoreWeave can touch it — plus a 1.35x debt-service-coverage covenant and full amortization, per Fitch and loan-market coverage. Translation: the lenders wanted a hand on the cash register.

Only after making those concessions was CoreWeave able to secure the financing.  When the deal finally closed around August 10, the company described it as “meaningfully oversubscribed,” meaning more investors ultimately wanted to participate than the company needed.

At first glance, that sounds like a sign of overwhelming confidence, but remember when that demand appeared.

Investors only lined up after CoreWeave caved to the lenders’ demands. In other words, they’re there for the deal,

This was echoed by the independent credit-rating agencies. Fitch rated the loan BB+ and Moody’s rated it Ba2 — both below investment grade.

The deal did get done. But the market forced CoreWeave to pay up, pledge its cash flows, and accept a leash — before it would fund the AI buildout everyone insists is unstoppable.

The major takeaway here?

When a trade is truly bulletproof, the financing is supposed to get cheaper. But this one got more expensive, more restrictive, and harder to place, all at once.

The Stock Already Forgot. The Loan Remembers.

Here’s the part that makes this worth your attention right now.

In late July, fear peaked. The cost of insuring CoreWeave’s debt against default — its credit default swap, or CDS — blew out. Per Bloomberg data cited across market coverage, CoreWeave’s CDS topped roughly 855 basis points, a level that implied around a 50% probability of default within five years on a standard pricing model. The stock bottomed near $60.

Then the mood flipped. The loan cleared. On August 12, CoreWeave reported second-quarter revenue up more than 200% year-over-year, with a backlog it pegged at roughly $104 billion.

The stock ripped — nearly doubling off the lows to around $117 by mid-August before settling back into the high $80s.

The sell-side climbed aboard: Truist moved to Buy with a $165 target, Goldman lifted its target to $139, and Oppenheimer kept an Outperform and a $150 target, arguing AI demand runs about four times available capacity.

It only took the equity crowd about three weeks to get its optimism back. Understandable. But notice what didn’t reverse: CoreWeave still had to accept a 9% all-in cost, a cash lockbox, a maintenance covenant, and full amortization to get that money.

Those terms are baked into a signed credit agreement. They don’t un-widen because the stock bounced. The bond market did its stress test in public, and the price of that test is now a permanent line item in their budget until it’s paid in full.

This is the oldest lesson on the desk. Think of it like this…

The stock market is a voting machine — it forgot the scare in three weeks. The credit market is a weighing machine — it wrote the risk into the contract and moved on.

When those two machines disagree this much, I’ve learned which one to trust.

The Three Key Tells Buried Inside the Deal

Pull the DDTL 5.5 apart and three specific risks explain why the lenders got nervous. Each one is a flashing light that’s still on.

Tell #1: The Durations Don’t Match.

CoreWeave’s problem is the oldest one in finance — borrowing long and getting paid short. This loan runs about five years. But the customer contracts backing it average only about three. And the underlying data-center leases CoreWeave signs with its landlords can run up to 15 years.

So CoreWeave commits to 15-year obligations, funds them with a five-year debt, and covers it with three-year revenue. If those customer contracts don’t renew, the bills keep coming with no guaranteed income behind them.

Fitch flagged exactly this. Visibility gets murky beyond the current contracted window, when the company leans on renewals and re-leasing to sustain growth. If that pattern rings a bell, it should. It’s the same asset-liability mismatch that took down WeWork — long leases, short revenue, and a growth story papering over the gap.

Tell #2: The Customers are Dangerously Concentrated.

The Financial Times reported that the single largest counterparty behind this loan is Anthropic, at roughly 40%, followed by the quant trading firm Jane Street at about 35%, with Midjourney, Hudson River Trading, and Anysphere (the maker of Cursor) making up the remainder. Fitch separately noted that CoreWeave drew about 65% of its first-quarter 2026 revenue from just its top two customers.

Two things stand out. First, that is enormous concentration — a handful of contracts carrying the whole structure. Second, and more pointedly: Anthropic, the 40% anchor, is a privately held company with no public credit rating.

That’s not a knock on Anthropic — it’s one of the best-funded labs in AI, and in August it was being called the hottest upstart in the field. It’s a statement about what the lenders can actually underwrite.

You cannot price the credit risk of a counterparty you cannot rate.

When 40% of your collateral rests on an unrated private company and another 35% on a proprietary trading firm, the margin for error is thin under the best of circumstances.

There’s a footnote worth watching. As bond analyst Rod Dubitsky flagged, Fitch upgraded Jane Street — CoreWeave’s #2 counterparty — right in the middle of the period when this struggling loan was being marketed, citing factors that had been in place for years. Draw your own conclusions, but the timing feels suspicious.

Tell #3: The Lockbox is the Lenders Telling You What They Think.

You don’t demand a cash lockbox from a borrower you trust. You demand it when you want to make sure you get paid before anything else can go wrong. The concession CoreWeave had to grant is itself the verdict.

Not All AI Infrastructure Stocks Are Built the Same

While CoreWeave may be the loudest canary in the coal mine among the AI infrastructure stocks, they’re far from being alone.

The entire AI buildout runs on borrowed money and depreciating collateral.

Neoclouds like CoreWeave and Nebius fund their GPU purchases with debt secured against the chips themselves  —  Nvidia hardware that’s valuable today but could be a generation behind tomorrow.

CoreWeave’s own guidance calls for $31–35 billion of capital spending in 2026 against roughly $6.2 billion of trailing-twelve-month revenue.

Its first-quarter interest expense doubled to $536 million, and it has burned free cash flow every year since 2022. Revenue is expanding, yes — but so is the spending required to produce it. That treadmill only works as long as the cheap money hose keeps spitting out cash.

There’s a circularity here that should make you tilt your head: Nvidia sells the chips, is a major shareholder in CoreWeave, and is part of the capital ecosystem funding the customers who buy those chips.

When the same company is supplier, shareholder, and financier, “demand” gets harder to read at face value.

 That’s why “AI infrastructure” is too broad to be a useful investment category. The companies may be building toward similar goals, but they’re operating under very different financial constraints. The way I see it, the easiest way to think about them is to divide them into two groups:

The self-funders. Amazon, Alphabet, Microsoft, and Meta are pouring hundreds of billions into the buildout too — but largely out of operating cash flow from businesses that already mint money.

Their existing profits subsidize the unprofitable early years of AI. If financing gets expensive, they can shrug.

The debt-funders. CoreWeave, Nebius, Oracle’s cloud arm, and the leveraged neocloud complex finance the same buildout with debt, leases, and project loans against short contracts and depreciating chips.

For this bucket, the cost of money is the business model. When spreads widen, their math breaks first. This isn’t limited to the neoclouds: Oracle — the largest non-financial borrower in the Bloomberg U.S. high-grade index — saw its own CDS climb above 215 basis points this year, up from about 145 at the end of last year, with its long bonds yielding close to 7.8%. Apollo’s chief economist Torsten Slok put a name on the danger: at high enough all-in yields, the AI capex cycle starts to self-throttle not because demand changes, but because financing becomes too expensive.

And the hyperscalers just made the debt-funders’ problem worse.

In mid-July, Meta announced “Meta Compute,” a service to sell bare GPU capacity to third parties — the exact business the neoclouds are built on. When your biggest potential customer becomes your competitor, the “customer concentration” and “insourcing” risks are no longer theoretical.

I call it my AI Destruction List. Big media loves to oversimplify the problem. They say “AI is a bubble” — but that’s too lazy to be useful for anyone, and it’s impossible to time.

The more precise version: the leveraged, debt-funded layer of the AI trade cracks first, and it cracks in the credit market before it cracks in the stock.

Is There Any Bull Case for CoreWeave?

Now that we’ve covered the problems, allow me a moment to argue against myself. After all, a one-sided case is a weak case — and I want you thinking, not panicking.

The bulls have real, current points. The loan did clear, oversubscribed at the wider terms — the demand exists, just at a higher price.

Many of CoreWeave’s contracts are “take-or-pay,” meaning customers owe the money whether or not they use the capacity. That ~$104 billion backlog is enormous (bear in mind it blends remaining performance obligations with other committed amounts, so it’s a scale indicator, not a bank balance).

The company is actively working to diversify the concentration I just flagged — in August it signed a fresh multi-year deal with Hudson River Trading and pushed into sticky government work through a CoreWeave Federal partnership with Leidos.

Its marquee counterparties — Anthropic, Meta, OpenAI, Jane Street — are real, growing, well-capitalized names.

The insider selling that made headlines was executed under pre-arranged 10b5-1 plans and left executives with large stakes.

And most of Wall Street is bullish, with price targets from $139 to $165 against a stock in the high $80s. A CDS spread is a probability, not a verdict — and the one that screamed in July has since calmed as the stock recovered.

Both readings are defensible from the same set of facts depending on which details you choose to focus.

The honest position isn’t “CoreWeave is going to zero.” It’s that the cost of financing the AI buildout went up in a visible, measurable, contractually permanent way — and the market charged the most leveraged players the highest prices.

What is critical to understand before committing your capital to this trade is that ignoring that risk because the stock bounced is exactly the mistake retail makes at every top.

Forewarned is forearmed.

What the Options Say About CoreWeave

People always want the trade. But before we take the trade, we need to know the range of what to expect — and on a name like this, the range tells us the story.

Every day I run a tool I built called the Volatility Visualizer, or the Vol Viz for short. This program plots the expected-move band the options market is pricing in for where a stock could realistically trade over a given window.

To be clear, this isn’t an arbitrary line I drew on a chart. This is hard data, pulled straight out of live options prices — the market’s own, dollar-weighted forecast of how far this thing can move.

Here’s what it’s showing on CoreWeave, with the stock around $88.50: an expected-move band running from roughly $69 on the downside to $111 on the upside — a range more than 40 points wide, better than 20% in either direction.

Sit with that. The options market is pricing a swing of more than a fifth of the company’s value, up or down, on a business worth tens of billions of dollars.

That’s an unusually wide range of expected outcomes. And it echoes the same warning coming from the credit market: investors are demanding a much bigger margin for uncertainty.

The lenders demanded a cash lockbox and 9% money because they couldn’t pin down the risk. The options market is pricing a 40-point range for the same reason.

Two completely different corners of Wall Street — credit and volatility — look at CoreWeave and reach one identical verdict: nobody knows where this settles and they’re actively worried about where it might end.

Why the Expected Move Matters More Than Any Price Target

A sell-side analyst stamps a $165 target on the stock and a headline is born — but that’s one person’s guess about direction.

The expected move is the whole market’s consensus about magnitude, and it comes with probabilities attached. Roughly speaking, the market is pricing about a two-in-three chance the stock finishes inside that ~$69–$111 band over the window.

That single fact should reframe everything you’re looking at. CRWV ripping toward $110 next month? Still “inside expected” — not a breakout, not a miracle, just the top of a range the market already told you about. A decisive break below $69 or above $111 is the only thing that would count as the market being genuinely surprised.

That is how you keep your head in a stock that moves 15% before lunch. You stop flinching at every candle and start asking one question: is this move inside the expected range, or did something just break the market makers’ models?

Because the day CoreWeave slices through the low end of that band on heavy volume is the day the credit-market alarm bells stop being silent.

Five Signals I’m Watching Next

You don’t need to track every loan. Watch these:

The 10-year Treasury. With the long end elevated, the cost of all this debt is set by the bond market. Higher-for-longer is the neocloud’s enemy.

The next neocloud financing. The tell isn’t whether it gets done — it’s the spread and the structure. Wider pricing and more lockboxes mean the squeeze is tightening. Cheaper, cleaner deals mean the fear is fading.

CoreWeave and Oracle CDS. The real-time thermometer. Staying wide is the warning; the more it compresses, the smoother sailing there is ahead.

Contract renewals. The duration mismatch is critical. The first time a big three-year contract comes up and doesn’t renew at full value, the mismatch problems stop being theoretical.

Hyperscaler insourcing. Every “Meta Compute” — every hyperscaler that builds instead of rents — is a direct hit to the neocloud customer base.

The Bottom Line

The biggest mistake investors make with AI infrastructure stocks is assuming they’re all exposed to the same risks.

Financial talking heads love to go on about the AI trade as a contest of technology — whose model is smartest, whose chips are fastest. But at this scale, it’s a contest of capital allocation and financial endurance. The question is no longer whether AI demand is real in the short-term. It’s whether that demand converts to cash fast enough, and lasts long enough, to service the mountain of debt being stacked against depreciating hardware.

The stock market already voted twice — panic in July, euphoria in August. The credit market didn’t flip-flop. It wrote the risk into a contract and left it there: wider spreads, tighter terms, a cash lockbox, and a set of covenants that tell you exactly how the smartest lenders in the room see this. The stock forgot. The loan remembers.

The first crack in the AI trade won’t show up on a chart. It will be buried in the fine print of a loan document.

Always follow the money.

Editor’s Note: Forbes calls $1 billion fund manager Louis Navellier “the king of quants.” Today, he’s stepping forward to reveal why he’s investing $358 million of his own firm’s money in the next stage of Artificial Intelligence… a technological sea-change that could erase millions of jobs, solve humanity’s biggest mysteries, and spark a wave of moneymaking opportunities — both in and outside the stock market. Click here for the details… 

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I break down the day’s biggest market story every morning on Masters in Trading Live, 11 AM ET. I follow the top stories of the day and show you how the smart money is really positioned — no geometry, no chart-drawing, just evidence of where the money is moving. Join me each morning, drop a “Good Morning” in the chat, and let’s read the tape together. Jonathan Rose is a former CBOE market maker and the founder of Masters in Trading at InvestorPlace. I hosts Masters in Trading Live every weekday at 11 AM ET. Follow me on X at @JRoseTrades

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It seems like just about everyone is mad about AI data centers right now.

They’re thought of as big, loud nuisances… and it’s hard to find people who actually want them where they live.

At the same time, we’re using artificial intelligence (“AI”) more than ever for searches, coding, writing, problem-solving, and even just chatting. So, demand for data centers is real.

Nonetheless, the AI data-center backlash we’re seeing has become big enough to matter to investors. Towns are pushing back over water, power bills, tax breaks, noise, diesel generators, and the simple reality that nobody wants a windowless server farm dropped into their neighborhood.

That’s a problem for the AI trade because Nvidia (NVDA) stock may be a proxy for AI growth, but chips still need to be installed in hardware which needs to be deployed into buildings, powered by transformers, and receive municipal permits before they become revenue. Nvidia is the top bellwether for AI, but it still relies on data centers for a majority of its chip sales.

It’s also creating a strange side effect in bitcoin mining…

See, while investors have been staring at Nvidia, the bitcoin mining network has been doing something odd.

Crypto-tracking site CoinWarz put bitcoin difficulty at 125.81 trillion today. About a year ago, on August 24, 2025, according to Hashrate Index, the difficulty was at 129.70 trillion… then an all-time high.

In plain English, the work required to mine a block today is roughly the same as it was last summer. That gives miners more time to make a return on their hardware investments without having to upgrade.

So thank you, AI data centers. And thank you to everyone saying they don’t want one in their neighborhood. It’s helping make certain miners a lot of money.

Today, we’ll look further at how the AI-infrastructure build-out and rising bitcoin prices are setting up a profitable scenario in the mining space.

Why Steady Mining Difficulty Is Favorable in a Bitcoin Rally

Bitcoin mining difficulty is the network’s thermostat.

Every two weeks, bitcoin adjusts how hard miners must work to keep blocks arriving about every 10 minutes. When more machines mine for the same reward, bitcoin difficulty rises. Fewer machines, or machines pointed somewhere else, means difficulty stabilizes or falls.

For a miner, that math is brutal and simple. Revenue comes from the bitcoin price, block rewards, and transaction fees. Costs come from power, machines, debt, and maintenance. If bitcoin jumps while difficulty stays flat, the same machines earn more dollars without facing the usual penalty of a harder network.

That’s exactly why the recent bitcoin rally matters. Bitcoin gained 23% in a week– its biggest move since last year’s sell-off – after Treasury-bond buybacks pushed long yields lower, the dollar weakened, and a short squeeze forced buyers back into the market. ETF inflows are the next test, because forced buying burns out quickly, while institutional flows can keep a rally alive.

If bitcoin holds even part of that move higher, miners get operating leverage when the selling price rises, and the production hurdle does not.

AI Pivots Are the New Way Miners Capitulate

In previous bitcoin cycles, miner capitulation looked familiar. The price fell, inefficient miners shut off, mining rigs hit the secondary market, and the network waited for difficulty to adjust down.

This cycle has a different escape hatch. Miners don’t have to disappear. Some have become AI-infrastructure companies.

That sounds like a branding trick until you remember what miners can offer to the AI space: power access, land, cooling, substations, operating teams, and a tolerance for absurd capital intensity. Those are the main things that data-center developers are fighting to secure.

CoinDesk reported in March that public miners had announced more than $70 billion in AI and high-performance-computing (“HPC”) contracts. S&P Global listed Iren, Riot Platforms, Core Scientific, Hive Digital Technologies, Cipher Digital, and TeraWulf among the miners shifting capacity away from pure bitcoin mining, with analysts expecting HPC and AI to drive much of their growth in 2026.

That matters because every megawatt chasing AI is a megawatt that’s not automatically chasing the next bitcoin block. While the machines and customers are different, the scarce input is the same: energized infrastructure.

The result is a softer version of miner capitulation. Instead of “we shut off because bitcoin mining stopped being profitable,” the message becomes “we found a better-paying customer for this power.”

AI Data-Center Backlash Cuts Both Ways

Now for the catch…

The same local revolt slowing the data-center land rush and helping bitcoin miners also threatens the miners trying to become AI landlords.

Governors and lawmakers are moving to force data centers to pay for their own electricity supply, limit water use, disclose more about operations, and win local support, according to the Associated Press.

Microsoft (MSFT) is already working to unwind multimillion-dollar tax breaks on several Atlanta data-center projects as the politics get hotter.

So, the trade is not “buy every miner with AI in the press release.” Please don’t do that. That’s how people end up owning a construction project with a bitcoin ticker.

Look for mining stocks that already control power, have credible tenants, and can survive construction delays while still making money mining bitcoin if their AI customer takes longer than promised to come online.

Global asset manager VanEck has already warned that miners chasing AI face a roughly $50 billion near-term funding gap and that only about a quarter of leased AI and HPC capacity has been delivered so far.

That means there’s still a long way to go.

What Investors Should Watch Next

Chips are no longer the bottleneck in the AI trade. Power is. Permits are. Local sentiment is. The boring parts are now in charge.

For bitcoin miners, that means three numbers are worth watching…

• Bitcoin difficulty: If difficulty stays near last year’s level while bitcoin holds its rally, miner margins improve without any miracle.

• ETF inflows: A short squeeze can light the match (and it really, really did last week), but sustained inflows tell you whether serious capital is returning to bitcoin. They’ve been positive since August 17, which is good, but previous rallies have run for as long as seven months, so time will tell.

• AI data-center approvals: Every delayed hyperscale project makes energized power more valuable. Some miners will monetize that value through AI contracts. Others will benefit because competitors will stop adding hash rate as aggressively.

That’s one thing many bitcoin investors are missing. While the AI data-center backlash is a risk to the AI build-out, it’s also putting a ceiling on one of bitcoin mining’s worst enemies: endless hash-rate growth.

If bitcoin’s price keeps firming while mining difficulty stays flat, it presents a solid opportunity for bitcoin mining stocks to profit from.

For once, the local zoning board is indirectly doing bitcoin miners a favor.

Good investing,

Eric Wade

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