Every few decades, technology goes through a cycle of euphoria. Capital floods in, valuations soar, and expectations stretch far beyond reality. The question everyone is asking today is simple: Are we in the era of the AI bubble?
The answer is less simple. What’s happening in AI has more depth than the headlines suggest. Different parts of the ecosystem are moving at different speeds, with very different economics. Treating Artificial Intelligence as a single story misses that nuance. To understand whether we’re truly in an AI bubble, it helps to look at the four main groups of stakeholders and AI companies shaping this market.
The four layers of the AI economy
AI isn't one single market. It’s an ecosystem with very different dynamics depending on where you sit. To make sense of whether there’s an AI bubble, it helps to break it down into the following four layers: infrastructure, data infrastructure operators, platforms, and applications.
1. The infrastructure to power artificial intelligence
This is the hardware layer, with tech companies like Nvidia. They design and sell the GPUs (chips) and servers that power today’s AI boom. Right now, demand is insatiable: every model needs more compute, every platform is scaling capacity, and every startup is buying credits that ultimately trace back to this hardware. As a result, Nvidia and its peers are generating enormous revenues and profits in the short term.
But here’s the dynamic:
- They invest heavily to expand production. Building new fabs, securing supply chains, and scaling output all require billions in upfront capital.
- Their stock price reflects extreme expectations. Investors are pricing in continuous growth, assuming demand will keep accelerating.
- If expectations fall short, the market correction is sharp. If demand slows or efficiency gains reduce the need for hardware, these companies could be left with overcapacity. The sunk investment doesn’t go away, but the market value does, and that’s when the share price "hits the wall."
So today, infrastructure players are the ones bringing home the cold, hard cash. But they could also be quite exposed if the AI bubble burst comes from inflated expectations not matching reality.
2. Data infrastructure operators & data centers
The second group in the AI industry is the hyperscalers, such as Microsoft, Amazon, Google, and Oracle. They buy hardware from Nvidia and then lease that compute capacity to platforms and enterprises.
- What they do: Act as intermediaries, turning raw hardware into scalable cloud services.
- Why it works now: Platforms and startups don’t want to own servers; they want flexible access. Hyperscalers provide that, monetizing infrastructure through recurring contracts.
- Where the risk lies: They are capital‑intensive businesses too, with margins dependent on utilization. If platforms or enterprises pull back, hyperscalers absorb the hit.
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3. AI companies & platforms
On top of that sit the platforms: OpenAI and others that sell access to models.
- What they do: They take compute capacity from data infrastructure operators and package it into usable AI models and APIs.
- Why it works now: Demand is exploding. Every company experimenting with AI needs access to these models, and platforms have positioned themselves as the gatekeepers.
- Where the risk lies: The economics are stretched. Platforms commit enormous capital to secure infrastructure, while revenues are still catching up. Margins remain thin or negative, and the business model depends on adoption scaling fast enough to close the gap.
It’s also important to note that platforms are deeply intertwined with data infrastructure operators. Microsoft is both a key investor and strategic partner of OpenAI, blurring the line between platform and infrastructure.
4. Applications and startups
Finally, there are the startups building products on top of the platforms.
- What they do: They train AI models and buy credits from platforms like OpenAI and turn them into consumer or enterprise applications, such as chatbots, copilots, productivity tools, niche vertical solutions.
- Why it works now: Growth in ARR is overall spectacular for many of these companies. They are scaling users quickly, riding the wave of AI enthusiasm, and attracting investor attention.
- Where the risk lies: The economics are arguably fragile.
- Gross margins are often 20–30%, far below traditional software, and sometimes negative.
- Some companies inflate their numbers by excluding the cost of serving free users.
- Many apps risk obsolescence, since a significant amount of these products can be displaced overnight as platforms evolve or incumbents integrate features directly.
This layer is where volatility will be most visible. Many of these companies will not survive if the AI bubble bursts in 2025 or if this scenario plays out in 2026.
AI bubble or not?
So, is this an AI bubble? The honest answer is: it depends on whether expectations are met.
There is real demand for AI infrastructure and applications, no one doubts that. However, expectations could be 100x reality. Investors might already be showing signs of unease. As TechCrunch reported, Meta’s heavy AI spending has raised questions about ROI with investors seeing the costs, but not yet the returns.
On the other hand, as the Wall Street Journal highlighted in its recent analysis of AI circular deals, while some of the capital flows in AI could look eerily familiar to past bubbles, where companies bought from each other to inflate activity, the fundamentals are also stronger than in the 2000s dot-com bubble. That said, sustainability depends on execution.
Take OpenAI as an example: as mentioned earlier, it has committed $1 trillion in infrastructure investments while generating about $12 billion in revenue (just 1.2% of that commitment). On its own, those numbers look daunting. However, revenues have tripled in six months. If that pace continues, payback could arrive within a couple of years. That’s the tension at the heart of the bubble debate: whether reality can keep up with expectations.
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What founders should focus on in the midst of the AI boom
Whether this moment turns out to be an AI bubble or not will depend entirely on whether today’s lofty expectations are met. Infrastructure demand is real, adoption is growing, and the technology is advancing quickly, but valuations and business models are priced for perfection. If reality keeps pace, this won’t look like a bubble at all. If it doesn’t, the correction will be sharp.
For founders, that uncertainty is the point. You can’t control the cycle, but you can control how resilient your company is when expectations shift. That means staying anchored in fundamentals:
- Building defensible IP.
- Protecting sustainable margins.
- Creating products customers can’t easily churn from.
Growth alone won’t be enough. The fact that we’re even debating this probably signals how investors and acquirers are starting to look past the hype and into economics.
In the end, whether AI is remembered as a bubble or as the next great platform shift, the companies that endure will be the ones built on fundamentals.
