I’ve read some of Ed Zitron’s long posts on why the AI industry is a bubble that will never be profitable (and will bring down a lot of companies and investors), and one of the recurring themes is that the AI companies are trying to capture growing market share in an industry where their marginal profits are still negative, and that any increase in revenue necessarily increases their costs of providing their services.
But some of the comments in various HackerNews threads are dismissive, saying that each new generation of models makes the cost of inference lower, so that with sufficient customer volume, the companies running the models can make enough profit on inference to make up for the staggering up-front capital expenditures it took to build out the data centers, train their models, etc.
It’s all pretty confusing to me. So for those of you who are familiar with the industry, I have several questions:
- Is the cost of running any given pretrained model going down, for specific models? Are there hardware and software improvements that make it cheaper to run those models, despite the model itself not changing?
- Is the cost of performing a particular task at a particular quality level going down, through releases of newer models of similar performance (i.e., a smaller model of the current generation performing similarly to a bigger model of the previous generation, such that the cost is now cheaper)?
- Is the cost of running the largest flagship frontier models going down for any given task? Or does running the cutting edge show-off tasks keep increasing in cost, but where the companies argue that the improvement in performance is worth the cost increase?
I suspect that the reason why the discussion around this is so muddled online is because the answers are different depending on which of the 3 questions is meant by “is running an AI model getting cheaper over time?” And the data isn’t easy to synthesize because each model has different token prices and different number of tokens per query.
But I wanted to hear from people who are knowledgeable about these topics.
On some issues, absolutely.
He flagged the issue with flat rate subscriptions not making any sense for the underlying token pricing and usage by users, and predicted that a lot of the AI startups that act as some kind of subscription middleman would feel the squeeze and eventually impose rate limits/quotas, degrade the quality of their offerings (i.e., push users towards cheaper models), or fail. I think that’s a pretty good summary of what has been happening at the user/pricing level with Perplexity, Lovable, and Cursor. Microsoft’s Copilot plans are also seeing a lot of changes to pricing and rate limits, as well as model choice, in ways that user complaints have gotten louder in the past month or two.
He was a skeptic on Stargate right out of the gate, and I think that external visibility into how that loose collection of projects under that banner has been going over the past year shows that something inside is fundamentally wrong. That isn’t necessarily an indictment of the broader AI ecosystem as a whole, but Zitron’s most pointed financial criticism has been directed at OpenAI and Oracle, and the costs of data center construction. Those criticisms have looked especially prescient this calendar year (and generally fits into my preconceived notions that building physical stuff is slow and expensive and that we Americans aren’t very good at keeping megaprojects on schedule and under budget).
I’m a money guy. I don’t have any special expertise in industry trends and how money will be spent in the future on industries where I’m not an insider (i.e., AI), but I find Zitron’s accounting of how money is being spent in the present to largely seem accurate. So that’s why I’m in this thread asking people about how they see the present and the future of spending/pricing/volume, to see if those projections of revenue needed are actually feasible.