A new federal lawsuit is trying to answer a question more than 1.5 million student loan borrowers have been asking: did the Department of Education actually cancel the loans it publicly promised to forgive?
The Project on Predatory Student Lending (PPSL) sued the Department (PDF File) on July 1, 2026, in the U.S. District Court for the District of Massachusetts, after the agency sat on fifteen Freedom of Information Act (FOIA) requests (some for more than two and a half years) seeking records on how it carried out its announced group discharges.
The College Investor team has previously filed similar FOIA requests for borrower defense data, the latest with a response in 2023, which took roughly 14 months to process.
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The Key Points
Between April 2022 and January 2025, the Department announced ten group discharges for borrowers who attended schools it found had engaged in widespread misconduct:
Marinello Schools of Beauty
Corinthian Colleges
ITT Technical Institute
Westwood College
CollegeAmerica
Rhe Art Institutes
Ashford University
Schools owned by the Center for Excellence in Higher Education (CEHE)
Drake College of Business
Certain Lincoln Technical Institute programs in Massachusetts.
Each announcement told approved borrowers their federal loans would be discharged automatically — no application, no further action needed. In total, the announcements covered more than 1.5 million borrowers and more than $23 billion in federal student loan debt, by the Department’s own estimates.
According to the lawsuit, the Department has never publicly released data on its progress toward fulfilling those commitments. PPSL says it continues to hear from hundreds of borrowers who were approved for group discharge relief but whose loans remain outstanding.
Why It Matters
Group discharges were supposed to be the easy path towards student loan forgiveness. Instead of filing an individual borrower defense application, approved borrowers were told relief would arrive automatically, along with credit repair and, in some cases, refunds.
If loans that should have been canceled are still sitting on borrowers’ accounts (accruing interest, blocking mortgages, or landing in collections), borrowers may not even know they need to complain.
The lawsuit won’t cancel anyone’s loans directly. But the records it seeks (servicer guidance, compliance audits, and counts of how many approved borrowers still have outstanding loans) would show for the first time whether the Department of Education followed through on its public announcements.
The Details
PPSL filed five FOIA requests in November 2023 covering the CollegeAmerica, Corinthian, ITT, Marinello, and Westwood discharges, and ten more in April 2025 covering all ten schools.
The Department acknowledged every request, telling PPSL its average processing time was 185 business days — well beyond FOIA’s 20-business-day deadline. As of the lawsuit filing date, all fifteen requests were still listed as “In Process” in the Department’s FOIA portal.
“The Department made public promises to more than 1.5 million borrowers,” said Eileen Connor, PPSL’s president and executive director, in a statement. “It shouldn’t take a lawsuit to learn whether those promises have been fulfilled.“
How This Connects
Borrowers covered by these announcements can check whether their school qualifies on The College Investor’s for-profit college student loan forgiveness list. The suit also lands amid broader processing breakdowns at the Department — hundreds of thousands of borrowers remain stuck in application backlogs, and the AFT’s lawsuit has similarly pressed the agency to deliver forgiveness borrowers already earned.
The case, PPSL v. U.S. Department of Education, asks the court to declare the Department’s inaction unlawful and order it to produce the records at no cost. FOIA cases often end in negotiated production schedules, so documents could emerge in stages.
Borrowers approved for a group discharge whose loans remain outstanding should contact their servicer and file a complaint with the FSA Ombudsman and keep records of both.
The Nasdaq-100 is made up of the 100 most valuable companies listed on the Nasdaq stock exchange, excluding banks and financial institutions. It has a very high degree of exposure to the “Magnificent Seven,” a group of technology companies operating at the forefront of revolutionary industries like artificial intelligence (AI).
Unfortunately, those tech giants delivered a sluggish performance during the first half of 2026, which is partly why the Nasdaq-100 is down 3% from its all-time high as I write this (June 30).
The Invesco QQQ Trust (QQQ 1.73%) is an exchange-traded fund (ETF) that tracks the performance of the Nasdaq-100 by holding the same stocks. Should investors buy it while the index is trading at a discount? History offers some very clear guidance.
Image source: Getty Images.
A sluggish year for America’s top growth stocks
The Nasdaq is often the exchange of choice for small technology companies looking to go public, because it offers lower fees and fewer compliance hurdles compared to alternatives like the New York Stock Exchange. Some of those budding companies went on to become the trillion-dollar giants that now make up the Magnificent Seven, which together represent a whopping 34.9% of the entire value of the Nasdaq-100 index.
Stock
Invesco ETF Portfolio Weighting
1. Nvidia(NVDA 1.39%)
7.60%
2. Apple(AAPL +4.88%)
6.80%
3. Alphabet(GOOG 0.37%)(GOOGL 0.23%)
6.18%
4. Microsoft(MSFT +1.69%)
4.52%
5. Amazon(AMZN +0.55%)
4.08%
6. Tesla(TSLA 7.35%)
3.09%
7. Meta Platforms(META 4.80%)
2.66%
Data source: Invesco. Portfolio weightings are accurate as of June 28, 2026, and are subject to change.
Unfortunately, the Magnificent Seven stocks delivered sluggish returns during the first half of this year. In fact, each of them underperformed the Nasdaq-100. The worst of the bunch is Microsoft, which has plummeted by more than 23%.
Data by YCharts.
On the bright side, the Nasdaq-100 also holds positions in soaring semiconductor stocks like Micron Technology, Advanced Micro Devices, Intel, Applied Materials, and Lam Research, which have each more than doubled this year. Their performance is offsetting some of the sluggishness in the Magnificent Seven, which is a key reason why the Nasdaq-100 isn’t down even more.
There is currently more demand for AI chips and infrastructure than those companies can possibly supply, which is why they have experienced such strong gains. This imbalance is likely to persist for the foreseeable future, which should buoy their share prices.
History is clear about what happens over the long term
Stock market sell-offs can be unnerving, and the uncertainty of what might come next often keeps many investors on the sidelines. However, history suggests they offer the best buying opportunities, because the market typically trends higher over the long term.
The Invesco QQQ ETF has delivered a compound annual return of 11% since it was established in 1999, even after accounting for every sell-off, correction, and bear market along the way. In fact, the ETF has endured five bear markets (peak-to-trough declines of 20% or more) over the last 27 years, triggered by events like the bursting of the dot-com internet bubble in 2000, the global financial crisis in 2008, and the COVID-19 pandemic in 2020.
Today’s Change
(-1.73%) $-12.57
Current Price
$712.60
Key Data Points
Day’s Range
$707.56 – $730.83
52wk Range
$549.58 – $748.65
Volume
51.1M
Since the Nasdaq-100 climbed to new highs after each of those drawdowns, investors who bought the Invesco ETF in the face of extreme uncertainty would have done exceptionally well in the long run. The current drawdown in the index — which is just 3% as I write this — is far less severe, but history suggests investors with a time horizon of five years or more are likely to earn a positive return if they use it as a buying opportunity.
Most of the Magnificent Seven stocks are entering the second half of 2026 at extremely attractive valuations. Nvidia, for example, is trading at a price-to-earnings (P/E) ratio of just 29.8, which is less than half its 10-year average. Microsoft, Meta, Alphabet, and Amazon each have a P/E ratio of below 30, so they are cheaper than the Nasdaq-100, which trades at a P/E of 34.1.
In my opinion, Wall Street won’t be able to ignore the value that’s on offer in some of America’s highest-quality stocks for much longer. That could lead to a recovery with the potential to lift the Nasdaq-100 to a new record high.
Anthony Di Pizio has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Advanced Micro Devices, Alphabet, Amazon, Apple, Applied Materials, Intel, Lam Research, Meta Platforms, Micron Technology, Microsoft, Nvidia, and Tesla. The Motley Fool recommends Intercontinental Exchange and Nasdaq. The Motley Fool has a disclosure policy.
Update 7/2/26: Offer has increased, but only to 30,000 points and requires an authorized user this time. I’d hold for 40,000 points.
Update 11/5/25: 40k offer is back through 12/16/25.
The Offer
Direct Link to offer
FNBO Amtrak Amtrak Guest Rewards Preferred card is offering a signup bonus of 30,000 points. Earn 25K points after spending $1,500, plus 5K points after adding an authorized user.
The no-fee card also bumped the bonus to 12,000.
Card Details
Annual fee of $99 is not waived
Complimentary Companion Coupon, One-Class Upgrade and a single-day
ClubAcela pass for access to ClubAcela, Amtrak Metropolitan Lounge or First class
Card earns at the following rates:
3 points per $1 spent with Amtrak
2 points per $1 spent on all other qualifying travel and dining purchases
1 point per $1 spent on all other purchases No foreign transaction fees
5% Amtrak Guest Rewards point rebate when you book your Amtrak redemption
Possibly can’t open card if you opened a card within 18 months
Our Verdict
Nice bonus for Amtrak users. The card launched with FNBO last October with a 30,000 bonus, and this is a nice bump to 40,000. There’s some speculation that dummy bookings – which historically have been throwing in $100 – will show 40,000 + $100 soon. Dummy booking has never shown another $100 statement credit so not worth waiting if interested. You can view more about Amtrak points and their value here.
Post history:
Extended to 8/13/25 via link
Update 6/18/25: Deal is back until July 30, 2025
Update 3/19/25: Deal is back until 4/30/25
Update 8/19/24: Deal is back until Sep 25th. Hat tip to reader David
Update 4/2/24: According to our contact at Amtrak, this deal will end tomorrow.
Originally posted 3/15/23. Reposting 2/19/24 as this deal is back (Minor details have been updated below – spend is $2,000 this time instead of $1,000 last time, and no-fee card bonus is 12,000 instead of 20,000 last time.)
Deeded CEO Reuven Gorsht says the best entry point is not the flashiest platform, but the repeatable tasks already draining time from a broker’s business.
Most of the conversation around AI in medicine focuses on what it gives physicians. Less administrative burden. Faster documentation. More time with patients.
That conversation is worth having. But there is another one that has been quietly building in the medical literature, and it deserves equal attention.
When AI handles a growing share of clinical cognitive work, what happens to a physician’s own ability to reason independently?
This is not a theoretical concern. Researchers, medical educators, and physicians themselves are actively working through it. The findings published so far are nuanced enough to be genuinely useful and practical enough that any physician using AI tools right now can act on them.
Disclaimer: While these are general suggestions, it’s important to conduct thorough research and due diligence when selecting AI tools. We do not endorse or promote any specific AI tools mentioned here. This article is for educational and informational purposes only. It is not intended to provide legal, financial, or clinical advice. Always comply with HIPAA and institutional policies. For any decisions that impact patient care or finances, consult a qualified professional.
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The Three Risks Researchers Are Tracking
A 2026 perspective published in Nature Medicine laid out a framework that has quickly become the standard vocabulary for this conversation. The researchers identified three distinct risks that AI introduces for physicians and trainees.
The first is deskilling: the gradual erosion of a skill a physician already has, through reduced practice. The second is never-skilling: a newer and arguably more serious concern, where trainees who rely on AI early in their training may never develop foundational clinical reasoning in the first place. The third is mis-skilling: the quiet adoption of an AI tool’s errors as one’s own clinical judgment, where a physician internalizes a flawed AI output as fact without catching it.
The framework is careful to note that direct evidence from medical training is still limited, and that AI is not inherently harmful to learning. Its educational impact depends on how and when it is introduced. That nuance matters, because the concern is not that AI tools are bad, it is that using them without intention creates specific, identifiable risks.
This landed with the broader profession. According to the AMA’s 2026 Physician Survey on Augmented Intelligence, 88% of physicians surveyed had some level of concern about skill loss, with 70% specifically worried about the impact on current medical students and residents.
Why Experienced Physicians Are Not Immune
It is tempting to read this as a medical education problem, something for residency program directors to figure out. The evidence suggests the dynamic extends further.
A 2023 editorial published in JAMA by Khera, Simon, and Ross specifically examined the risks of automation bias in AI-driven clinical decision support, the tendency to accept AI-generated recommendations without sufficient independent review.
The editorial warned that overreliance on automated outputs, alert fatigue, and reduced clinical vigilance are real risks that can compromise a physician’s ability to critically evaluate what AI is actually telling them.
The concern is not about AI being wrong most of the time. It is about what happens when AI is right most of the time. When a tool consistently produces accurate outputs, the cognitive habit of questioning it weakens. And when the tool eventually produces something incorrect, that weakened habit creates a gap exactly where clinical judgment needs to be strongest.
A concrete example came from a 2025 multicenter observational study published in The Lancet Gastroenterology & Hepatology. Researchers studied 19 experienced endoscopists across four colonoscopy centers in Poland (each with over 2,000 procedures under their belt) before and after AI-assisted polyp detection tools were introduced.
Adenoma detection rates for non-AI-assisted colonoscopies fell by 6% following regular AI use across the four centers, described by the authors as the first real-world evidence of automation-induced deskilling linked to patient outcomes.
These were not trainees. They were experienced clinicians. The skill erosion came from reduced practice of independent detection, not from any gap in foundational training.
What This Looks Like in Practice
For a physician already using ambient scribes, AI literature summaries, or clinical decision support tools daily, the risk is rarely dramatic. It tends to be gradual and hard to notice from the inside.
It might look like reaching for an AI-generated differential before forming one independently. Accepting a medication suggestion without the same level of scrutiny applied before those tools existed. Or finding that the mental habit of working through a case systematically has become less automatic than it once was.
A March 2026 narrative review published in the Journal of Experimental Orthopaedics by Oettl, Pruneski, and colleagues described the core problem clearly: maintaining clinical excellence requires a shift in training paradigms that emphasizes critical oversight, where human reasoning validates AI outputs rather than defers to them.
The review also distinguished deskilling from never-skilling, noting that overreliance is especially harmful for early-career physicians who may not build the experiential foundation that later allows them to catch what AI misses.
The goal is not to avoid AI tools, the evidence does not support that conclusion, and the practical case for ambient scribes and documentation assistance remains strong. The goal is to use them in a way that preserves, and ideally sharpens, the clinical reasoning they might otherwise quietly displace.
Practical Ways to Keep Clinical Reasoning Sharp
These are not abstract principles. They are specific habits that fit within existing clinical workflows.
1. Form the differential before checking the AI’s.
This is the most consistently cited recommendation across the 2026 literature. Before reviewing what a clinical decision support tool suggests, spend time generating an independent list. It does not need to be exhaustive. It needs to be genuine. The act of forming independent clinical hypotheses is the exercise that keeps the underlying reasoning functional.
2. Interrogate the output, not just the conclusion.
When an AI tool produces a recommendation, the useful question is not only “does this seem right?” but “why does this tool think this, and do I agree with that reasoning?” Some AI tools surface their reasoning transparently; others do not. For those that do not, asking the reasoning question aloud — even briefly — is still a useful habit.
This does not mean abandoning tools. It means building in regular situations where independent clinical reasoning is practiced without AI assistance: complex case reviews, teaching rounds, peer consultation. These are valuable for any physician who wants to keep independent diagnostic thinking as a reliable skill, not just for trainees.
4. Treat AI errors as learning events.
When an AI tool produces a clearly wrong output, that moment has genuine educational value. What was the clinical feature the tool missed? Why would a physician catch it when the tool did not? Working through that question builds exactly the kind of discriminative judgment that makes AI use safer over time.
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Why This Connects to the Broader Career Picture
There is a professional dimension to this that tends to go unaddressed in most AI adoption conversations.
A physician whose clinical reasoning has been quietly displaced by AI dependence is more vulnerable in ways that extend beyond individual patient encounters. The ability to function independently is what makes a physician valuable in settings where AI tools are unavailable, unreliable, or outside their effective operating range.
It is what makes a physician credible in expert roles, consulting engagements, teaching positions, and any context where the physician’s judgment, not the AI’s output, is the actual product being offered.
Physicians building income outside of clinical medicine (through consulting, advisory work, educational content, or expert witness roles) are effectively monetizing expertise. That expertise is grounded in clinical reasoning developed through years of independent practice. Protecting that reasoning ability is not just a patient safety matter.
It is a professional asset worth preserving intentionally.
The Bigger Picture
AI tools are not slowing down. The pace of adoption across clinical practice makes that clear. The question for any physician using these tools now is not whether to use them, but how to use them in a way that keeps the most valuable parts of clinical practice intact.
The 2026 research makes a consistent point: AI is not inherently harmful to clinical skill. Its effect depends almost entirely on how it fits into a physician’s workflow and what habits surround it. Used with intention, it handles low-value cognitive overhead and creates more space for the reasoning that matters most. Used passively, it can gradually take the place of that reasoning, often without the physician noticing the shift.
Knowing which one is happening requires paying attention. The good news is that the habits required to stay on the right side of that line are straightforward, and the physicians building them now are better positioned regardless of where the tools go next.
But what about you? What do you think of all these findings? Let us know in the comments!
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AI could shave $2.2 trillion off the U.S. deficit by 2036. But according to a new working paper from economists at Brookings and the Federal Reserve, more than half of that savings could vanish — canceled out by the very disruption AI itself would cause.
In May, the U.S. national debt crossed the eye-popping $39 trillion mark. The difference between what the U.S. government spends and what it earns has become a galvanizing force for fiscal hawks of all political stripes. Without significant reform measures from Congress, the widening deficit threatens to deplete the trust funds that finance Social Security in 2032, and Medicare one year later.
Budget experts say fixing the deficit will require tax hikes, cuts to entitlement, or most likely, a combination of both. Absent that political will, AI has been floated as a fiscal escape hatch. A new paper suggests that escape hatch is narrower than advertised.
If AI leads to a major increase in productivity, higher output per worker across the economy could help boost government coffers and stabilize the budget, according to a working paper published Wednesday by the Brookings and Fed economists. On the revenue side, AI-driven productivity gains would mean the government can collect more from a bigger economy without necessarily raising tax rates. On the spending side, AI could also help erase inefficiencies, particularly in health programs, where administrative costs alone account for one quarter of all expenses.
In total, an AI productivity surge could reduce the country’s annual budget deficit from the roughly 6% of GDP it currently sits at to as low as 2%, the equivalent of $2.2 trillion wiped clean from America’s bill by 2036, the paper’s authors wrote. But that number comes with an immediate caveat the paper’s authors bury in their conclusion: the same AI boom could claw back more than half of those savings through five compounding side effects.
Technology has delivered such miracles before. In the 1990s, the Internet-driven stock market and economic activity boom led to a 2.2% increase in annual tax revenues as a percentage of GDP, according to previous Brookings research. The excitement of the decade led to a roughly 60% reduction to the deficit between 1992 and 2002.
But even though the 90s started strong for markets and the economy, they didn’t end that way. The dot-com-era gains eroded within a decade. Brookings’ economists warn AI’s fiscal boost could erode even faster — and identify five specific ways it happens.
Longer lives, higher costs
One of the most life-changing impacts of AI could be in the very definition of that word. By improving medical diagnostics, treatment procedures, and the efficiency of healthcare, AI could drastically reduce mortality rates. Some clinical studies tracking the impact of AI-powered early warning systems have resulted in significantly reduced mortality for in-hospital patients. One AI algorithm, trained to identify patients at risk of sepsis, has been associated with a 17% relative decrease in mortality.
It’s a clear social benefit that’s virtually impossible to argue with. But a budget lens tends to look at life—and how long it lasts—differently. Longer lives also mean more years of Americans receiving benefits for programs like Social Security and Medicare, the Brookings researchers noted. A decline in mortality would result in a larger retirement-age population eligible to receive these entitlements, leading to higher spending.
The Brookings paper estimates a highly disruptive scenario could see 3 million more retirement-age people added to the population in 2036. AI could pave the way for a healthier and longer-lived population, but that could also be a more expensive one for the federal government to take care of.
Tax base shifts
Widely integrated AI could spark major changes to how the government makes its money. In the 1990s, capital gains taxes were the biggest drivers of boosted government revenues, according to the earlier Brookings research. That’s relevant to the budget because in the U.S. wages are generally taxed more heavily than capital gains or corporate levies.
So far this fiscal year, individual income taxes make up 52% of all federal revenue, compared to around 6% from corporate taxes, according to the Treasury Department. Receipts from capital gains taxes tend to be even slimmer, as most wealth-building assets go unrealized. A 2024 IRS study found the effective tax rate on capital gains sat at around 5%.
If more of national income is earned as profits, rents, or returns to ownership rather than paychecks, as would likely happen in an AI productivity boom scenario, the average tax rate can fall even if total income rises, the Brookings authors cautioned. Improved productivity does not automatically translate to larger government revenues if the gains accrue mainly to asset owners rather than workers.
The result could be a narrower tax take than policymakers would expect from headline GDP numbers alone.
Weaker labor force
One reason AI-driven productivity gains could increase corporate profits while failing to deliver a measurable gain in income tax receipts would be because there are simply fewer people earning an income they’d have to pay taxes on.
Whether AI will shrink the labor force by pushing workers out or discouraging them from participating remains an unanswered question with real implications for the federal budget. Lower participation means fewer people paying payroll and income taxes, and more people relying on income support programs that the government would have to pay out.
In disruptive AI scenarios, the Brookings authors project a 3% drop in the labor force participation rate, roughly the equivalent to 6 million fewer people working by 2036—a hit similar to the one dealt by the COVID-19 pandemic, but most likely to be permanent. This would mean millions more enrollments in programs like SNAP for food assistance or for disability benefits, weighing significantly on the government’s spending needs.
Higher borrowing costs
By supercharging the economy, the AI buildout itself could also result in higher interest rates. Massive investment in chips, data centers, and supporting infrastructure may raise the neutral rate of interest, which in turn lifts market rates and federal debt-service costs.
In a high-debt environment, even a modest increase in interest rates can add a significant fiscal burden. The Brookings authors estimated AI productivity could add around $60 billion to the costs of servicing the federal debt by 2036.
An AI ‘arms race’
Finally, AI could ignite an expensive international arms race, one that will eventually be the government’s cost to bear. If competitor countries accelerate military spending to keep pace with the capabilities developing at American firms, the U.S. may feel pressure to do the same, meaning the long-term impact of AI would be ramped up spending on the defense programs that already rank as the country’s most expensive.
Maintaining a strategic edge in the age of AI could end up adding over $350 billion in cumulative defense spending to the nation’s deficit over the next decade, according to the paper.
Overall, these five downsides could recapture more than half the fiscal gains the U.S. might expect from AI’s productivity shock — meaning the headline $2.2 trillion savings figure is, in practice, closer to $1 trillion or less. AI might enlarge the economy and delay some of the worst effects of the spiraling U.S. deficit, but it’s likely no replacement for the hard work of balancing the nation’s books over the long term.
Investor Michael Burry of “The Big Short” fame has a new short target in his sights: Caterpillar, the heavy-machinery giant that has surged thanks to the AI infrastructure boom.
Burry, the former hedge fund manager who famously predicted the 2008 subprime mortgage crisis and earned hundreds of millions of dollars for his investors in the process, said Caterpillar’s stock is now overvalued after a run-up that has seen its stock soar by more than 100% over the past year.
“Caterpillar jumped out at me,” Burry wrote in a Substack post this week. “I have never shorted Caterpillar. It has always done great for me on the long side in the past.”
Times have changed. The investor said he shorted Caterpillar at $1,060.98 per share Tuesday. By Wednesday, Caterpillar shares had closed down nearly 7%. As of Thursday, shares fell by as much as 4%, hitting their lowest point since the middle of June at about $949 per share.
Yet, not everyone agrees with Burry’s call. Sergey Glinyanov, a senior analyst at Freedom Broker who covers Caterpillar, told Fortune in an email that Burry’s short position isn’t likely to affect the stock at all. What the famed investor is missing is that Caterpillar’s share price isn’t surging because of AI hype, he said.
Glinyanov told Fortune that, in fact, investors are rewarding the company because it is benefiting from a fundamental shift in infrastructure spending.
“A structural theme is emerging,” Glinyanov told Fortune, pointing to a growing demand for on-site power systems, as AI data centers look for alternatives to an aging electrical grid that cannot always keep up with soaring energy needs.
As developers build bigger and bigger AI campuses, they are increasingly seeking out the diesel and natural‑gas generator power systems that Caterpillar sells to secure reliable power. The company’s positioning in this area sets it up to capture a larger share of that spending, Glinyanov argues.
As AI has propelled chipmakers like Nvidia to record highs, investors have also lifted the shares of other businesses that may benefit from hyperscalers and developers’ wave of spending as they scramble to build up data centers. These companies, including GE Vernova, which specializes in power generation, and Ohio-based Vertiv, which provides advanced cooling systems have emerged as a popular way to bet on the AI revolution without buying chipmakers directly. Shares of GE Vernova are up more than 60% year-to-date, while shares of Vertiv are up 70% over the same period.
Yet, investors are betting their money that Caterpillar will be among the biggest beneficiaries. The company’s stock had climbed about 172% over the past 12 months and more than 77% this year alone before Burry disclosed his position. Its price-to-sales ratio—a measure of how much investors are willing to pay for each dollar of revenue—is now at its highest level in three decades, he added.
It’s this run-up that has Burry betting the stock is overvalued. Yet his recent short against the company also builds on his broader belief that the market is in an AI bubble. In May, Burry said the market was “feeling like the last months of the 1999-2000 bubble.” Along with his Caterpillar short, the investor also said he had refreshed his bet against the iShares Semiconductor ETF (SOXX), which tracks semiconductor companies, and had taken positions against Tesla and Nvidia.
It’s unclear, of course, whether Burry or Glinyanov will ultimately be proven right.
Glinyanov said the company’s traditional business of selling and renting heavy machinery remains healthy, with dealer inventories improving and retail demand holding up. The combination of consistency in its traditional business and its growing exposure to AI-related power infrastructure has contributed to the stock’s premium value, he said. The company’ strong results from the first quarter, which saw sales jump 22% year-over-year to $17.4 billion and beat Wall Street expectations, adds to his argument.
Even so, Glinyanov allowed that Caterpillar’s premium valuation ultimately depends on the biggest AI companies continuing to spend aggressively on new data centers and power infrastructure.
His firm’s price target for the company is $910, indicating a “potential near‑term pullback,” he said. If hyperscalers quickly pull back on their massive data center investments, some of the optimism surrounding Caterpillar could fade just as quickly.
“Should we observe deterioration in hyperscalers’ fundamentals—particularly cash flow generation or debt burden—multiples could face a meaningful pullback,” he said.
