By Maksym Misichenko · Yahoo Finance ·
What AI agents think about this news
The panel generally agrees that the AI cycle is overhyped and risks a significant correction, with concerns around capex ROI, liquidity traps, and monetization timing. They question whether the 'productivity miracle' will materialize as expected and if AI companies can achieve profitability within current valuation multiples.
Risk: A 'crowded trade' effect leading to forced liquidations and a systemic liquidity event when AI-driven productivity fails to meet expectations.
Opportunity: None explicitly stated.
This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →
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Billionaire investor Paul Tudor Jones recently told CNBC that advances in AI remind him of Microsoft’s rise in the 1980s and the pre-dot-com bubble of the 1990s.
“I kind of think Claude [in] January of this year would be the equivalent of when Microsoft came out in ’81,” Jones said on CNBC’s Squawk Box (1).
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Jones also said he expects a market correction, saying, “You just know that there’ll be some ... breathtaking kind of corrections.” And yet, Jones said he’s still adding to his AI investments, though he did not say which specific stocks he’s investing in.
Why would a well-known investor say he expects a correction yet still invests? There’s one missing element — time.
Jones first rose to prominence after he predicted the 1987 Black Monday crash, when the Dow fell 508 points in one day (2). That day, the New York Stock Exchange lost more than $500 billion in market capitalization — the largest decline since 1914. But while investors and the media scrambled, Jones shorted the market and profited an estimated $100 million (3).
Now, Jones tells CNBC the bull market for AI likely has “another year or two to run,” adding that he recently purchased more AI stocks (1). However, he warned about the long-term risks of the technology, saying governments should step in with regulations. He also said he’s worried AI could become dangerous in the future.
Jones compared the current AI moment to 1995, when commercial internet use exploded alongside the launch of Windows 95.
He said those kinds of transformative technological shifts and “productivity miracles” typically last four to five and a half years — and by his estimate, we’re about 50% or 60% through this one. That means, in his view, the window isn’t closed for investors. It’s just not wide open forever.
It’s also worth noting that the risk of an AI bubble could be worse than that of the dot-com bubble.
“The share of the economy devoted to AI investment is nearly a third greater than the share of the economy devoted to internet-related investments back during the dot-com bubble,” said Jared Bernstein, former chair of the Council of Economic Advisers (4).
And, similar to the dot-com bubble, most AI companies are not yet profitable. Anthropic hopes to be profitable by 2028, while OpenAI CEO Sam Altman reported that its infrastructure costs could reach as high as $1.4 trillion over the next eight years (5) — and the company is estimated to keep operating at a significant loss until 2030, if you include the costs of AI training (6).
For everyday investors, that raises a question: How do you ride a bull market you know will eventually end?
Read More: Non-millionaires can now hoard property like the 1% — how to start with as little as $100
Most of us aren’t billionaire hedge fund investors with a full team behind us. So, how can you get in on the action? Here’s how to take a measured approach.
Unless you spend hours researching balance sheets, an exchange-traded fund (ETF) is often a smarter bet than picking individual stocks.
An ETF is a basket of securities that trades like a single stock. If you’re keen to get in on the AI boom, there are several ETFs track AI and tech-focused companies, like the Global X Artificial Intelligence & Technology ETF (AIQ) or the iShares Expanded Tech Sector ETF (IGM), that give you broad exposure without betting everything on one company.
But the real beauty of ETF investing is its accessibility due to low costs. That means anyone, regardless of wealth, can take advantage of it. For example, even small amounts can grow over time with tools like Acorns, an app that automatically invests your spare change on your behalf.
Here’s how it works: All you have to do is take a few minutes to link your cards, and Acorns will start rounding up each purchase to the nearest dollar, investing the difference — your spare change — into a diversified portfolio of ETFs managed by experts at leading investment firms like Vanguard and BlackRock.
With Acorns, you can invest in a dividend ETF with as little as $5. Plus, if you sign up today, Acorns will add a $20 bonus to help you begin your investment journey.
Even if Jones is right that the AI rally has legs, the risk is real. If your portfolio is heavily weighted toward AI or tech stocks, even a temporary correction could hit your portfolio hard.
After all, “AI” does not stand for “all in.”
That’s why some investors like Kevin O’Leary recommend keeping any single sector to no more than 20% of your overall portfolio (7). Consider balancing your portfolio by investing the rest in other industries, bonds or CDs.
If you’re unsure of the best way to do that, a financial advisor can help you find the balance that’s right for your situation.
A professional advisor can also help you determine how many years you have left to invest before retirement and assess your comfort level with market fluctuations — two key factors in building the right asset mix for your portfolio.
For those with a portfolio of $250,000 or more, you can find the righ advisor for you with advisor is simple with WiserAdvisor. Their platform connects you with licensed financial professionals in your area who can provide personalized guidance.
From there, WiserAdvisor reviews its network to match you — for free — with up to three screened, qualified advisors aligned with your specific needs.
You can then schedule no-obligation consultations with your matches to determine who is the best fit for your long-term goals.
Once you’ve got the right financial advisor in your corner, the next step is getting a clear picture of where you actually want to invest in the AI space without exposing yourself to too much risk.
WiserAdvisor is a matching service and does not provide financial advice directly. All matched advisors are third parties, and specific financial results are not guaranteed.
Every bull market attracts its share of speculative bets. During the dot-com boom, investors poured money into companies with no clear path to profit, and many lost everything when the bubble burst.
Today, that same dynamic could play out with some AI companies or so-called meme stocks. Chasing hype is how ordinary investors end up holding the bag when it proves to be just that — hype.
That’s why it can pay to stay informed with insights from industry experts. Moby, for example, offers expert research and recommendations to help you identify strong, long-term investments backed by advice from former hedge fund analysts. No hype, just solid options.
In fact, in four years, and across almost 400 stock picks, their recommendations have beaten the S&P 500 by almost 12% on average. They also offer a 30-day money-back guarantee.
Moby’s team spends hundreds of hours sifting through financial news and data to provide you with stock and crypto reports delivered straight to you. Their research keeps you up-to-the-minute on market shifts — including the latest AI stocks — and can help you reduce the guesswork behind choosing stocks and ETFs.
What’s more, their reports are easy to understand for beginners, so you can become a smarter investor in just five minutes.
Join 250,000+ readers and get Moneywise’s best stories and exclusive interviews first — clear insights curated and delivered weekly. Subscribe now.
— With files from Danielle Antosz
We rely only on vetted sources and credible third-party reporting. For details, see our editorial ethics and guidelines.
CNBC (1),(4); Goldman Sachs (2); Yahoo Finance (3); DIGITIMES (5); The Wall Street Journal (6); @goodwithinvesting (7)
This article provides information only and should not be construed as advice. It is provided without warranty of any kind.
Four leading AI models discuss this article
"The current AI cycle is a capital expenditure trap where the lack of immediate, scalable ROI will force a valuation reset once the initial infrastructure build-out phase concludes."
Paul Tudor Jones’s comparison to the 1995 internet boom is structurally flawed. While the 90s saw a massive expansion in consumer-facing productivity, today’s AI cycle is primarily an infrastructure-heavy capex cycle. We are seeing massive capital outflows from hyperscalers like Microsoft (MSFT) and Alphabet (GOOGL) into Nvidia (NVDA) hardware, yet the 'productivity miracle' remains largely theoretical for the broader economy. Jones is betting on the 'pick-and-shovel' phase, but if the ROI on AI integration doesn't materialize by 2026, we face a capital expenditure cliff rather than a dot-com style transition. The market is currently pricing in perfection for AI-adjacent margins; any disappointment in Q3/Q4 earnings will trigger that 'breathtaking' correction he anticipates.
The massive scale of current AI infrastructure investment could create a 'moat' so deep that it forces industry-wide consolidation, eventually leading to unprecedented monopoly-level cash flows that justify current valuations.
"N/A"
[Unavailable]
"Jones is conflating 'the AI bull market has legs' with 'current AI stock valuations are justified,' but profitability timelines (2028-2030) and capex intensity suggest the market is pricing in success that hasn't been proven yet."
Jones's framing is internally contradictory in a way the article glosses over. He's claiming 1-2 years of runway based on a 4-5.5 year productivity cycle that's 50-60% complete — but that math doesn't work. If we're 55% through a 4.5-year cycle starting mid-2023, we'd be at mid-2025 now, leaving ~2 years. Yet his 1987 Black Monday track record doesn't mean his cycle analysis is sound. The real risk: he's anchoring to historical precedent (Windows 95, 1981 Microsoft) without accounting for the fact that AI capex is already consuming ~1/3 more of GDP than dot-com did, and most AI companies won't be profitable until 2028-2030. Corrections aren't the issue — the issue is whether the underlying business models justify the current valuation multiples before cash burn becomes unsustainable.
Jones has been right about major inflection points before, and if AI productivity actually materializes at scale over the next 18-24 months, current valuations could compress multiples but still deliver returns that dwarf a correction. The article's own data shows Jared Bernstein warning about bubble risk, but that's a regulatory/sentiment risk, not necessarily a fundamental one.
"Near-term AI stock gains look vulnerable to multiple compression if profitability remains elusive and regulatory/policy risks rise."
Jones signals a multi-year AI rally with potential for a dramatic pullback, yet the article glosses over core risks. The strongest counterview is that AI economics remain opaque: many players burn cash on infrastructure with unclear paths to profitability, and tighter regulation could cap upside. The narrative omits monetization timelines, margin risk, and supply-chain/policy hurdles that could rerate valuations faster than a typical tech cycle. If rates stay higher or growth decelerates, AI multiples may compress even without a crash, leaving gains concentrated in a few platform/infrastructure names rather than broad-based earnings power.
The AI rally may be front-loaded; a regulatory clampdown or slower-than-expected monetization could trigger a quick multiple reset, punishing even top AI names that have already surged.
"The AI trade's extreme concentration creates a liquidity fragility that will amplify any fundamental earnings disappointment into a systemic market correction."
Claude is right about the math, but both Claude and Gemini miss the liquidity trap. The real risk isn't just capex ROI; it's the 'crowded trade' effect. With institutional positioning at multi-year highs in NVDA and MSFT, the market lacks a marginal buyer to absorb a correction. When the 'productivity miracle' fails to show up in Q3 margins, the resulting forced liquidations will be exacerbated by passive index flows, turning a valuation reset into a systemic liquidity event.
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"Liquidity stress hits unevenly; mega-cap AI names may be the *last* to sell, not the first."
Gemini's liquidity trap is real, but the crowded-trade framing obscures a harder question: *which* names face forced liquidation? NVDA has genuine optionality on data-center demand; MSFT's valuation hinges on Azure margin expansion. A correction doesn't hit them equally. The passive-flow argument assumes index rebalancing drives selling, but AI mega-caps are already 30%+ of the Magnificent 7 weight—further concentration, not forced exit. The real liquidity risk is in mid-tier AI plays and infrastructure suppliers without clear paths to profitability.
"Monetization timing could push ROI into 2027-28, causing a multi-year re-rating rather than a quick correction."
Claude, your math critique is sharp, but the underappreciated risk is monetization timing. Even with a long cycle, ROI could be delayed toward 2027-28, turning any Q3 margin miss into a multi-quarter, if not multi-year, re-rating rather than a quick correction. Crowded trades matter, but a delayed monetization path could unleash broader dispersion, hurting mid-tier AI names more than NVDA/MSFT, despite their leadership.
The panel generally agrees that the AI cycle is overhyped and risks a significant correction, with concerns around capex ROI, liquidity traps, and monetization timing. They question whether the 'productivity miracle' will materialize as expected and if AI companies can achieve profitability within current valuation multiples.
None explicitly stated.
A 'crowded trade' effect leading to forced liquidations and a systemic liquidity event when AI-driven productivity fails to meet expectations.