It’s an open secret that essentially all major AI companies are burning cash and running at massive losses.
If progress is slow enough such that it requires X years of continued funding to achieve AI capabilities at least useful enough to produce a net ROI, at what value of X will the economics collapse, resulting in a major downscaling or total collapse of these companies?
I don’t think significant capability gains are being priced in, so that’s not a crux. Burning cash is normal/healthy for startups, the unusual thing with AI startups is their size and growth rate. It’s plausible that OpenAI and Anthropic will grow into revenues of about $100bn with adoption alone, even at a level of capabilities reasonably close to what’s already available. A 1 GW AI datacenter campus costs $40-50bn in capex (buildings, infrastructure, and compute) or $10-15bn per year to use (with a long term commitment). So they might be able to afford about 5 GW of AI compute (each, in total across that company’s datacenters). This scale is still within means of tech giants to backstop, so Meta and Google could also persist at this level.
The conservative estimate does imply a slowdown in frontier training AI compute, since the current trend would ask for 5 GW individual training systems per AI company in 2028 (rather than in total, inference and all). It would take significant capability gains to sustain the trend (even ignoring the logistical difficulties), and at that point there will be more debt involved in an essential way. So the consequences of a slowdown that happens after such capability gains will be more serious than if it starts in the near future.
There’s no hard delimiter on financially induced sector collapse, and it’s often not directly attributable to the sector that collapses. The dot com crash was tied to federal reserve interest rate increases that resulted in a sell off as investors moved towards less speculative investments.
AI is in a fairly safe position right now through sheer variety of vested interests. Government, construction, infrastructure, computing hardware, software, and early corporate adopters of AI are all doing everything they can to keep the ball rolling. They’ve crossed a line where sunk costs have an outsized role in future decisions. There’s also the wildcard of AI being deemed a strategic defense asset.
The present state of spending will continue until there’s some catastrophic event that scares people off or public pressure forces a reduction in scale.
My money is on public pressure leading to change. Data centers are being publicly subsidized and the general public is beginning to push back. One or two public service commissions refusing to comply with energy and water subsidies will lead to wide scale changes that will put the brakes on. Things will move out of startup mode and into the realm of actual business.
[Question] How long do AI companies have to achieve significant capability gains before funding collapses?
It’s an open secret that essentially all major AI companies are burning cash and running at massive losses.
If progress is slow enough such that it requires X years of continued funding to achieve AI capabilities at least useful enough to produce a net ROI, at what value of X will the economics collapse, resulting in a major downscaling or total collapse of these companies?
I don’t think significant capability gains are being priced in, so that’s not a crux. Burning cash is normal/healthy for startups, the unusual thing with AI startups is their size and growth rate. It’s plausible that OpenAI and Anthropic will grow into revenues of about $100bn with adoption alone, even at a level of capabilities reasonably close to what’s already available. A 1 GW AI datacenter campus costs $40-50bn in capex (buildings, infrastructure, and compute) or $10-15bn per year to use (with a long term commitment). So they might be able to afford about 5 GW of AI compute (each, in total across that company’s datacenters). This scale is still within means of tech giants to backstop, so Meta and Google could also persist at this level.
The conservative estimate does imply a slowdown in frontier training AI compute, since the current trend would ask for 5 GW individual training systems per AI company in 2028 (rather than in total, inference and all). It would take significant capability gains to sustain the trend (even ignoring the logistical difficulties), and at that point there will be more debt involved in an essential way. So the consequences of a slowdown that happens after such capability gains will be more serious than if it starts in the near future.
There’s no hard delimiter on financially induced sector collapse, and it’s often not directly attributable to the sector that collapses. The dot com crash was tied to federal reserve interest rate increases that resulted in a sell off as investors moved towards less speculative investments.
AI is in a fairly safe position right now through sheer variety of vested interests. Government, construction, infrastructure, computing hardware, software, and early corporate adopters of AI are all doing everything they can to keep the ball rolling. They’ve crossed a line where sunk costs have an outsized role in future decisions. There’s also the wildcard of AI being deemed a strategic defense asset.
The present state of spending will continue until there’s some catastrophic event that scares people off or public pressure forces a reduction in scale.
My money is on public pressure leading to change. Data centers are being publicly subsidized and the general public is beginning to push back. One or two public service commissions refusing to comply with energy and water subsidies will lead to wide scale changes that will put the brakes on. Things will move out of startup mode and into the realm of actual business.