Talk of an “AI bubble” suggests a single, fragile sphere that might pop at any moment. The reality looks more like a layered stack of speculative stories, infrastructure bets and genuine productivity gains, each with its own timer quietly counting down. To understand what happens when those clocks hit zero, I need to separate the financial froth from the long‑term foundations that are actually being poured.
From one big bubble to a stack of smaller ones
When people complain about the AI bubble, they usually mean soaring market valuations rather than a belief that the technology itself will vanish. Venture investors now draw a clear line between the durability of machine learning and the more fragile pricing of AI‑branded stocks and tokens, arguing that the real question is how much of today’s capitalisation is justified by future cash flows. One prominent investor notes that when people talk about the AI bubble, they are not suggesting that one day AI will go away, they are talking about capital markets and the expectations they embed, a distinction that reframes the debate from existential risk to valuation risk and is captured in a widely shared analysis of whether we are in an AI bubble that is “yes and no both” on AI bubble dynamics.
That framing helps explain why the current cycle feels less like a single mania and more like a tower of interlocking bets. At the base are hardware and data‑center expansions, in the middle sit model providers and platforms, and on top are application companies racing to bolt generative features onto everything from email clients to tractors. Each layer has its own economics and its own potential to deflate without bringing the entire structure down, which is why some analysts describe the current phase as a “class of correction” in which investor inflows into AI do not automatically lead to a catastrophic collapse of the whole market, a point underscored in a detailed Russian‑language assessment of why investor allocations to AI are not yet triggering a classic bursting of a speculative bubble.
The Fed‑fuelled clock on cheap money
Even if AI is not a single monolith, the stack is still sitting on a macroeconomic foundation that looks increasingly unstable. Years of low interest rates and abundant liquidity encouraged investors to chase growth stories with little regard for near‑term profits, a pattern that has clearly spilled into AI infrastructure, where hyperscalers and chipmakers are recycling enormous cash flows into ever larger data‑center builds. One detailed critique argues that the AI bubble is entering “extra time” because profitable firms are walling in money and sharing it among a small group of players, a process that keeps valuations high even as the broader cost of capital rises and that is explicitly linked to a Federal Reserve‑driven cycle of cheap money in an analysis of how the clock is ticking on the Fed‑fuelled AI boom.
As rates normalise, each layer of the AI stack faces a different countdown. Hardware projects with multi‑year payback periods are most exposed to higher financing costs, while application start‑ups that depend on subsidised API access may find their unit economics deteriorating as providers push through price increases to cover their own capital spending. The risk is not that “The AI” suddenly stops working, but that the financial scaffolding around it tightens, forcing a shake‑out among companies that assumed infinite runway, a scenario that macro‑focused commentators now treat as a base case rather than a tail risk when they describe the AI bubble as a product of central bank policy rather than purely of technological hype.
Innovation bubbles versus financial bubbles
To make sense of that looming correction, I find it useful to distinguish between a financial bubble and what some founders call an innovation bubble. A financial bubble is about leverage, mispriced risk and the potential for cascading defaults when asset prices fall, the classic pattern seen in housing markets or overleveraged banks. By contrast, an innovation bubble can leave behind useful infrastructure even if many of the companies involved fail, a point made explicitly in a widely read essay that contrasts “Financial vs.” innovation cycles and argues that when we hear “bubble” we often think of crashes, but an innovation bubble leaves foundations that others can build on, a distinction that is central to the argument that AI exuberance may be destructive for some investors but constructive for the broader innovation ecosystem.
History supports that view. Every major technology boom has overpromised in the short term and underdelivered for investors, while still laying the groundwork for the next era of growth. One technology analyst notes that Every so‑called bubble leaves behind the rails of the next era, listing how the Dot‑com frenzy built the internet backbone and how the Cloud “bubble” financed hyperscale data centers that now underpin modern software, an argument that directly links past excess to present capability and that frames the current AI cycle as another round of noisy speculation with a quieter layer of infrastructure being built underneath, as described in a detailed reflection on how Every cycle leaves something durable.
What the State of AI tells us about the stack
Looking across the AI landscape, the latest State of AI assessments show a field that is both overheated and structurally deepening. Model sizes, training runs and deployment footprints have all grown rapidly, but so have the number of concrete industrial and enterprise use cases, from code generation in large software firms to predictive maintenance in logistics and energy. A comprehensive overview of the State of AI for 2025 and the forecasts that follow highlights how research breakthroughs, commercial adoption and regulatory responses are now tightly intertwined, with the report explicitly positioning AI as a general‑purpose technology whose trajectory will shape productivity, labour markets and national competitiveness over the coming decade, a framing laid out in detail in the Russian‑language review of the State of AI.
That structural view reinforces the idea of multiple overlapping bubbles rather than a single monolith. At the research layer, the clock is ticking on how far scaling laws can be pushed before costs outweigh marginal gains, a question that will determine whether current model architectures remain dominant. At the application layer, the timer is set by user patience and regulatory scrutiny, particularly in sensitive domains like healthcare and finance where hallucinations and bias can quickly trigger backlash. And at the geopolitical layer, governments are racing to secure compute, data and talent, effectively placing their own long‑term bets on AI capacity that will outlast any individual start‑up, a pattern that the State of AI overview treats as a defining feature of the current cycle.
How investors and builders should read the ticking clocks
For investors, treating AI as a stack of bubbles with different timers is more than a metaphor, it is a risk‑management strategy. Capital allocators who lump everything into a single “AI trade” risk missing the fact that some layers, such as core infrastructure, may be in a correction phase while others, such as speculative application tokens, are still inflating. Russian‑language analysis of AI investment flows describes the current environment as a “class of correction” in which money is being reallocated within the sector rather than fleeing it entirely, a pattern that suggests investors should focus on fundamentals like cash generation and pricing power rather than trying to time an all‑or‑nothing burst.
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