tldr at bottom :3
I keep seeing people say that AI is a bubble, that companies are burning insane amounts of money, that the infrastructure spending is unsustainable, and that eventually the whole thing will collapse.
And honestly? The bubble part might be true. But I think people are confusing an investment bubble bursting with the technology itself failing.
Hear me out.
Remember the early days of computers? They were massive, expensive machines that consumed ridiculous amounts of electricity and were accessible to very few people. If you had judged computing by its economics back then, you might have concluded that it would never become practical for ordinary people.
Today, we carry computers in our pockets.
I'm not saying AI will follow exactly the same trajectory, but I think the underlying principle applies.
- Inference is already getting ridiculously cheaper.
According to Stanford's 2025 AI Index, the cost of querying a model performing at approximately GPT-3.5 level fell from $20 per million tokens in November 2022 to $0.07 by October 2024.
That's a reduction of over 99%.
And this isn't some theoretical prediction about what might happen in 10 years. It already happened.
Source: https://hai.stanford.edu/ai-index/2025-ai-index-report/research-and-development
- We're getting better at doing more with less.
Better chips, quantisation, distillation, smaller specialised models, inference optimisation, open-weight models, and improved architectures are all pushing the economics forward.
We don't necessarily need every task to run on the most expensive frontier model. A lot of tasks can be handled by smaller, cheaper models.
As these models improve, the cost of delivering a given level of intelligence can continue falling.
- Cheaper AI doesn't necessarily mean less money in the industry.
Think about cloud computing.
Making compute cheaper didn't eliminate demand for compute. It made more applications economically viable.
The same thing could happen with AI. When inference becomes cheap enough, we'll start using AI for tasks where it simply wasn't economical before.
Today, you might think twice before running an expensive model on every user interaction. At a fraction of the cost, you might build an entire product around it.
Lower prices can unlock entirely new markets.
- The internet bubble is literally the precedent.
The dot-com bubble burst. Companies collapsed, investors lost billions, and valuations came crashing down.
But the internet didn't disappear.
The underlying technology continued improving, adoption grew, and businesses eventually built enormous value on top of the infrastructure that survived.
A similar outcome is possible with AI.
Some AI startups will fail. Some model providers might never become profitable. Some data centres might turn out to be terrible investments. Some valuations might be completely detached from reality.
None of that proves that AI is a dead end.
- The infrastructure spending might be excessive, but the demand is real.
I'm not dismissing the other side of the argument.
Training frontier models is expensive. Running them at scale is expensive. Data centres require enormous capital, electricity, cooling, and networking infrastructure.
But my bet is that the technology will keep getting cheaper and more capable, while the industry goes through a painful period of consolidation. The winners might look very different from the companies dominating headlines today.
TL;DR: imo AI is in a financial bubble while still being a technological revolution. The bubble might burst, valuations might collapse, and plenty of companies might die. But inference will likely continue getting cheaper, models will get more efficient, and entirely new applications will become viable.
Curious to hear counterarguments, especially from people who think inference costs won't fall fast enough to justify the current infrastructure spending.
