The AI boom has created extraordinary wealth — and mounting unease. In August, Jerry Kaplan, a Silicon Valley veteran who has experienced four tech bubbles, remarked, “We’re in the middle of an AI bubble. There is no need for dozens of companies to have foundation models.”
Similarly, other optimistic insiders now acknowledge that the market is overheated. The Bank of England recently warned that soaring valuations of tech giants like Nvidia and Microsoft are reminiscent of the concentration levels seen at the peak of the dot-com bubble.
When this bubble does burst, it will unlikely be due to a single dramatic event, but rather to multiple intersecting factors, making it hard to speculate on its timing.
Some not-so-known risk factors
While risks like the 95% AI pilot failure rate highlighted in an MIT study and tech giants’ “circular investing” have been widely noted, several other crucial factors haven’t received their due in mainstream commentary.
Factor #1: If AI breakthroughs drastically reduce compute needs, billions in infrastructure investments could be rendered obsolete overnight. Take DeepSeek. In early 2025, the lab claimed to have trained GPT-4-level models for just USD5.6 million — versus USD78 million for GPT-4 — and to offer inference at a fraction of OpenAI’s cost. By September, DeepSeek reported another 50%–75% reduction with its latest model.
Even if some claims are exaggerated, the direction is clear: algorithmic and tech innovation gains can collapse costs. This echoes the early-2000s telecom bust, when fibre-optic advances sent infrastructure costs plummeting.
Factors #2: The second major pressure comes from buyers. Enterprises are realising they don’t need — or can’t afford — massive, general-purpose models for many of their use cases. Gartner predicts that by 2027, companies will use small, task-specific models (SLMs) three times more than general-purpose LLMs (large language models). SLMs offer lower costs (up to 80% reduction), faster inference, and better data control. As adoption of SLMs increases, it undermines the revenue base that currently supports capex in large, generalised AI models.
Factors #3: AI infrastructure builders are spending heavily with little immediate revenue. In early October, Harris Kupperman estimated, based on 2025 capex projections, the industry would need USD320 billion–USD480 billion in annual revenue to break even. Currently, AI revenues are in the tens of billions.
This capex-to-revenue gap is much larger than in previous infrastructure booms, like railroads (2×) or late-1990s telecom (4×). Once large-cap tech companies begin downgrading growth expectations, valuations could fall fast, exacerbating market instability.
Who wins when the bubble pops
The collapse of the AI bubble won’t destroy all the value created — it will redistribute it, shaping winners and losers. In the aftermath, the groups best positioned to benefit are:
The pragmatic AI builders – the SLMs and domain-specific model providers. These firms, building efficient, task-optimised models and the infrastructure to manage them, are best placed to capitalise on enterprise pragmatism. Providers with lower cost bases that focus on ROI — not brute-force scale — will align with the direction most businesses are heading.
The real-world AI integrators – application and service innovators. As foundation models commoditise, value shifts to companies integrating AI into industry-specific workflows and business processes. Historically, this layer captures durable margins after infrastructure bubbles. To find winners, focus on companies behind the 5% of successful AI pilots in the MIT study, and solutions that boost the productivity of blue‑ and gray‑collar workers and SMBs.
The resilient infrastructure titans – hyper-scalers with staying Power. After the bubble, hyper-scalers like Amazon Web Services, Microsoft Azure, and Google will consolidate overbuilt capacity. They may take short-term losses but will remain essential infrastructure providers in the AI economy — much like telecom carriers post-2001.
The road ahead for India
An AI bubble burst is no more the end of AI than the dot-com burst was the end of the Internet.
For India, the impact is likely to be less severe. Most AI investments are application-focused rather than in costly foundation models, meaning fewer losers and limited capital destruction. The investment in data centres (nearly 150 built already) will remain valuable, repurposed to meet domestic needs and support enterprise adoption across vital sectors like fintech, healthcare, logistics, etc. The most exposed will be conventional IT services firms and AI upstarts planning to drive revenue via sales to US businesses, which will slow down while overseas markets regroup post-bubble.
Turbulence is coming, and likely some of the biggest in living memory, and as individuals, companies, and nations, we would be well-advised to prepare for it.
(Will Poole, a former senior Microsoft executive based in Seattle, is a veteran venture capitalist investing in India since 2012. Views expressed are personal)
