We’re not prepared for an AI market crash

Yes, I get you don’t just want to read about the problem but a potential solution. 

The next post in this sequence will summarise the plan by those experienced organisers.

These organisers led one of the largest grassroots movements in recent history. That took years of coalition building, and so will building a new movement. 

So they want to communicate the plan clearly, without inviting misinterpretations down the line. I myself rushed writing on new plans before (when I nuanced a press release put out by a time-pressed colleague at Stop AI). That backfired because I hadn’t addressed obvious concerns. This time, I drafted a summary that the organisers liked, but still want to refine. So they will run sessions with me and a facilitator, to map out stakeholders and their perspectives, before going public on plans.

Check back here in a month. We should have a summary ready by then.

This is a solid point that I forgot to take into account here. 

What happens to GPU clusters inside the data centers build out before the market crash? 

If user demand slips and/or various companies stop training, that means that compute prices will slump. As a result, cheap compute will be available for remaining R&D teams, for the three years at least that the GPUs last. 

I find that concerning. Because not only is compute cheap, but many of the researchers left using that compute will have reached an understanding that scaling transformer architectures on internet-available data has become a dead end. With investor and managerial pressure to release LLM-based products gone, researchers will explore their own curiosities. This is the time you’d expect the persistent researchers to invent and tinker with new architectures – that could end up being more compute and data efficient at encoding functionality. 

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I don’t want to skip over your main point. Is your argument that AI companies will be protected from a crash, since their core infrastructure is already build? 

Or more precisely: 

• that since data centers were build out before the crash, that compute prices end up converging on mostly just the cost of the energy and operations needed to run the GPU clusters inside,
• which in turn acts as a financial cushion for companies like OpenAI and Anthropic, for whom inference costs are now lower,
• where those companies can quickly scale back expensive training and R&D, while offering their existing products to remaining users at lower cost.
• as a result of which, those companies can continue to operate during the period that funding has dried up, waiting out the 'AI winter' until investors and consumers are willing to commit their money again.

That sounds right, given that compute accounts for over half of their costs. Particularly if the companies secure another large VC round ahead of a crash, then they should be able to weather the storm. E.g. the $40 billion just committed to OpenAI (assuming that by the end of this year OpenAI exploits a legal loophole to become for-profit, that their main backer SoftBank can lend enough money, etc). 

Just realised that your point seems similar to Sequoia Capital’s: 
“declining prices for GPU computing is actually good for long-term innovation and good for startups. If my forecast comes to bear, it will cause harm primarily to investors. Founders and company builders will continue to build in AI—and they will be more likely to succeed, because they will benefit both from lower costs and from learnings accrued during this period of experimentation.”

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A market crash is by itself not enough to deter these companies – from continuing to integrate increasingly automated systems into society.

I think a coordinated movement is needed; one that exerts legitimate pressure on our failing institutions. The next post will be about that. 

Thanks for your takes!  Some thoughts on your points:

• Yes, OpenAI has useful infrastructure and brands. It's hard to imagine a scenario where they wouldn't just downsize and/or be acquired by e.g. Microsoft.
• If OpenAI or Anthropic goes down like that, I'd be surprised if some other AI companies don't go down with them. This is an industry that very much relies on stories convincing people to buy into the promise of future returns, given that most companies are losing money on developing and releasing large models. When those stories fail to play out with an industry leader, the common awareness of that failure will cascade into people dropping their commitments throughout the industry.
• AI companies may fail in part because people stop using their products. For example, if a US recession happens, paid users may switch to cheaper alternatives like DeepSeek's, or stop using the tools altogether. Also, ChatGPT started as a flashy product that relied on novelty and future promises to get people excited to use it. After a while, people get bored of a product that isn't changing much anymore, and is not actually delivering on OpenAI's proclamations of how AI will rapidly improve.
• Sure, companies fund interesting research. At the same time, do you know other examples of $600 billion+ being invested yearly into interesting research without expectations of much profit?
• Other communities I'm in touch with are already outraged about the AI thing. This includes creative professionals, tech privacy advocates, families targeted by deepfakes, tech-aware environmentalists, some Christians, and so forth. More broadly, there has been growing public frustration about tech oligarchs extracting wealth while taking over the government, about a 'rot economy' that pushes failing products, about algorithmic intermediaries creating a sense of disconnection, and about a lack of stable dignified jobs. 'AI' is at the intersection of all of those problems, and therefore become a salient symbol for communities to target. An AI market crash, alongside other correlated events, can bring to surface and magnify their frustrations.

Those are my takes. Curious if this raises new thoughts.