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The AI boom has created extraordinary wealth — and mounting unease. Even the most optimistic insiders now acknowledge that parts of the market look overheated. If and when the bubble bursts, it will likely do so through forces less discussed than simple investor mania. Just as importantly, the post-burst winners may be unexpected. I’ll explore both areas below.

A Wary Chorus

Leading figures across technology are increasing in their frequency of warnings about overvaluation:

• Sam Altman: “When bubbles happen, smart people get overexcited about a kernel of truth.” Naturally, reasonable people question Sam’s veracity, especially when he’s called the bubble multiple times while raising $ billions.
• Jeff Bezos: At the 2025 Italian Tech Week in Turin last week, Bezos described artificial intelligence as being in an ‘industrial bubble’ where valuations will be reshaped, but lasting technological progress will emerge. 
• Silicon Valley four-bubble veteran Jerry Kaplan noted on August 25th “We’re in the middle of an AI bubble. There is no need for dozens of companies to have foundation models.”

Macro institutions have voiced concern about escalating concentration risk tied to AI-driven gains. The Bank of England warned on October 8th that soaring valuations in a small set of tech names — notably NVIDIA, Microsoft and Meta — mirror dot-com peak concentration levels. The IMF’s 2024 Global Financial Stability Report highlights how AI adoption may intensify market correlations, herding behaviour, and dependency on key model providers.  Meanwhile, abundant commentary reports that the major AI-led firms dominate recent S&P 500 gains in both earnings and capital expenditure metrics.  Estimates of $1.5 trillion in AI spending by 2025 circulate widely, though mainstream industry forecasts (IDC, Gartner) are far lower. Whether or not that figure proves correct, the imbalance between capital spent and realized revenue is hard to ignore.

My take on the above: we are bound to hit a creative destruction cycle, but the timing is entirely unclear. When it happened, valuations will be reshaped, but lasting technological progress will emerge, just as it did from the telecom and dot-com crash.

Three Under-Appreciated Bubble Triggers

The burst timing is not something I will call, and I think it’s unlikely to stem from one dramatic failure. Instead, it will come from 2 or more intersecting pressures. Many risks have been broadly discussed, including the MIT study reporting 95% failure in enterprise pilots, the recent “circular investing” between the tech giants, the effects of US tariff wars, and more. But the following triggers are not equally visible in mainstream commentary — and the order below reflects where the under-appreciated risks lie.

Trigger A: Radical Algorithmic Efficiency 

The most destabilizing risk isn’t demand drying up — it’s supply-side disruption. If algorithmic breakthroughs drastically reduce compute needs, billions in CapEx could be rendered obsolete overnight. The case study is DeepSeek. In early 2025, the lab claimed to have trained GPT-4-level models at a fraction of the cost — roughly $5.6 million versus ~$78 million for GPT-4 — and to deliver inference at $2.19 per million tokens, compared with $60 for top-tier Western labs. On September 29th, DeepSeek announced further 50-75% cost reductions with the DeepSeek-V3.2-Exp model.

Even if some claims are exaggerated, the direction of travel is clear. Algorithmic gains can collapse cost structures — as fiber-optic breakthroughs did in the early 2000s telecom bust. If training or inference becomes 5×–10× cheaper, the “moat” of compute scarcity disappears.

Trigger B: Enterprise Pivot to Small & Medium Models (SLMs)

The second pressure comes from buyers, not builders. Enterprises are realizing they don’t need – or can’t afford – massive general-purpose models for many use cases. Gartner predicts that by 2027, companies will use small, task-specific models three times more than general-purpose LLMs. SLMs offer lower costs (up to 80 % reduction, and growing), faster inference, and better data control. As adoption grows, it erodes the revenue base underpinning the CapEx build-out.

Trigger C: CapEx / Revenue Disconnect –> Analyst Downgrades

AI infrastructure builders are spending heavily with little immediate revenue. Hedgy Harris Kupperman estimated on Oct 5th that with 2025 CapEx projections, the industry would need $320 – 480 billion in annual revenue just to break even. Current AI revenues are in the tens of billions. This CapEx-to-revenue gap is far larger than in previous infrastructure manias like railroads (2×) or late-1990s telecom (4×). Once large-cap tech downgrades begin, the valuation floor could fall fast.

Who Wins When the Bubble Pops

The collapse won’t destroy all of the newly created AI value — it will redistribute most of it. Three groups are best positioned to benefit.

Winner 1: SLM and Domain-Specific / Vertical Model Providers

Firms building efficient, task-optimized models and the infrastructure to manage them are best placed to capitalize on enterprise pragmatism. Their lower cost base and focus on ROI — not brute-force scale — aligns with the direction most businesses are heading. These players will effectively mediate how enterprises interact with AI. 

Winner 2: Application & Service Builders

As foundation models commoditize, value shifts to companies integrating AI into vertical workflows, solutions with and without UX, and business process optimizers. This layer historically captures durable margins after infrastructure bubbles. One way to look for these is to find the ones supplying the 5% of successful pilots in the MIT study and to understand what they are doing right. Another domain for successful apps and services will be those catering to and uplifting the productivity of blue and gray collar workers and SMBs that have much more to gain than lose from widespread deployment of AI.

Winner 3: Infrastructure Players with Staying Power

Hyperscalers like Amazon Web Services, Microsoft Azure, and Google could consolidate overbuilt capacity. They may take short-term write-downs but will remain essential utilities in the AI economy — much like telecom carriers post-2001. While enterprises will do proofs of concept with AI native startups and their associated infra providers, they will likely retreat to established enterprise vendors post-bubble.

The Reordering Ahead

An AI bubble burst is no more the end of AI than the dot-com burst was the end of the Internet. But it will reorder who profits. When bubbles pop, value doesn’t vanish; it migrates. Algorithmic breakthroughs could be the spark, financial downgrades the accelerant, and enterprise behavior the structural shift.

Those building efficient models and practical applications are likely to benefit most, while hyperscalers will endure. The biggest losers will be the over-leveraged builders betting everything on ever-growing compute demand and adjacent areas. And those paying arguably insane valuations for anything with AI.

What the Bubble Means to Capria: It will not surprise you to learn that Capria has been active in this space for the past 3 years,  investing in applied AI and verticalized solution providers. We learned our lesson in 2021/22 and have been cognizant of lofty valuations; we make sure we do not overpay. 

As growth in developed economies slows (which a bubble burst will accelerate), we continue to back opportunities in the Global South. We believe that investments in startups shaping industries serving the domestic needs of fast-growing emerging markets are a sound source of outsized returns. 

Our new fund for India is open. Early portfolio companies are in place. We recently closed 2 deals in Vertical AI.