I've occasionally heard people suggest that at some point AI companies are going to run out of money, the cost of using AI will shoot up, demand will collapse, and the AI bubble will be over.
At first glance this risk seems real. OpenAI spent $25 billion in the first half of 2025, on revenue of just $4 billion. Whilst data is sorely lacking for other top AI labs, our best guess is that they're burning through cash at similar rates. Scaling laws imply that we need exponentially more compute to achieve linear AI performance improvements, so we should only expect this situation to worsen in the future. A few more doublings, and OpenAI could be spending hundreds of billions on training runs - something likely unsustainable even for the largest tech companies.
However most of these expenses are infrastructure expenses, building out the data centres needed for further training runs and serving future customers. If we look at the actual cost of serving, AI labs are already profitable, and have been for a long time.
In other words the marginal cost to respond to an AI API call is significantly lower than the price of that call. We can see that by comparing open source models, which are often served at prices significantly lower than that of leading labs by infra providers who are unlikely to be running loss leaders.
For example DeepSeek-V4-Pro is a MOE model with 1.6 trillion parameters, and usually costs around 1.74 per 1 million input tokens and $3.48 per 1 million output tokens with neutral vendors. In benchmarks it performs worse than the leading labs models, but not by much, and is a newer model. However leading labs charge significantly more for their flagship models, ranging from $2-5 per million input tokens, to $12-25 per million output tokens. Given the minor differences in quality, either the leading labs are bad at inference optimisation, or they're raking it in.
This means that even if all funding for AI dried up tomorrow, AI would still be a profitable business, and existing models would continue to be served.
Some people have responded that AI companies can't just serve the existing models and make a steady profit - they have to train new models, and models just don't stay at the forefront long enough to recover high up-front training costs. If they don't other AI companies will eat their lunch. It takes all the running they can do just to stay in the same place.
This is definitely a concern for individual AI companies, but it can't possibly be for the industry as a whole. If investment is insufficient for any company to train the next gen models, current gen models will continue to be served indefinitely (and eventually make enough profit to incrementally improve the state of the art, at a sedate enough pace that the newer models can recoup their initial investment).
The question of how long current AI scaling rates can be kept up is an important one for predicting the future of AI (and humanity) but it's irrelevant to whether AI will collapse. The genie is out of the bottle and cannot be put back.
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