This is superb, and I think it’ll have a substantial impact on debate going work. Great work!
Short-term willingness to spend is something I’ve been thinking a lot about recently. My beliefs about expansion rates are strangely bimodal:
If AI services are easy to turn into monopolies—if they have strong moats—then the growth rate should be extraordinary as legacy labour is displaced and the revenues are re-invested into improving the AI. In this case, blowing through $1bn/run seems plausible.
If AI services are easy to commodify—weak or no moats—then the growth rate should stall pretty badly. We’ll end up with many, many small AI systems with lots of replicated effort, rather than one big one. In this case, investment growth could stall out in the very near future. The first $100m run that fails to turn a profit could be the end of the road.
One outside view is that AI services are just one more mundane technology, and we should see a growth curve much like the tech industry’s so far.
A slightly-more-inside-view is that they’re just one more mundane cloud technology, and we should see a growth curve that looks like AWS’s.
A key piece of evidence will be how much profit OpenAI turns on GPT. If Google and Facebook come out with substitute products in short order and language modelling gets commodified down to zero profits, that’ll sway me to the latter scenario. I’m not sure how to interpret the surprisingly high price of the OpenAI API in this context.
Another thing which has been bugging me—but I haven’t put much thought into yet—is how to handle the inevitable transition from ‘training models from scratch’ to ‘training as an ongoing effort’. I’m not sure how this changes the investment dynamics.
Worth noting that the “evidence from the nascent AI industry” link has bits of evidence pointing in both directions. For example:
Training a single AI model can cost hundreds of thousands of dollars (or more) in compute resources. While it’s tempting to treat this as a one-time cost, retraining is increasingly recognized as an ongoing cost, since the data that feeds AI models tends to change over time (a phenomenon known as “data drift”).
Doesn’t this kind of cost make AI services harder to commodify? And also:
We’ve seen a massive difference in COGS between startups that train a unique model per customer versus those that are able to share a single model (or set of models) among all customers....
That sounds rather monopoly-ish doesn’t it? Although the blogger’s takeaway is
Machine learning startups generally have no moat or meaningful special sauce
I’ll be somewhat surprised if language modeling gets commodified down to 0 profits even if Google and Facebook release competing models. I’d expect it to look more like cloud infrastructure industry, “designed to extract maximum blood” as the author of your blog post puts it. See e.g. https://www.investopedia.com/terms/o/oligopoly.asp
I haven’t thought too much about short term spending scaleup; thanks for the links, My current intuition is that our subjective distribution should not be highly bimodal the way you describe—it seems like the industry could land somewhere along a broad spectrum from perfect competition to monopoly (with oligopoly seeming most plausible) and somewhere along a broad spectrum of possible profit margins.
This is superb, and I think it’ll have a substantial impact on debate going work. Great work!
Short-term willingness to spend is something I’ve been thinking a lot about recently. My beliefs about expansion rates are strangely bimodal:
If AI services are easy to turn into monopolies—if they have strong moats—then the growth rate should be extraordinary as legacy labour is displaced and the revenues are re-invested into improving the AI. In this case, blowing through $1bn/run seems plausible.
If AI services are easy to commodify—weak or no moats—then the growth rate should stall pretty badly. We’ll end up with many, many small AI systems with lots of replicated effort, rather than one big one. In this case, investment growth could stall out in the very near future. The first $100m run that fails to turn a profit could be the end of the road.
I used to be heavily biased towards the former scenario, but recent evidence from the nascent AI industry has started to sway me.
One outside view is that AI services are just one more mundane technology, and we should see a growth curve much like the tech industry’s so far.
A slightly-more-inside-view is that they’re just one more mundane cloud technology, and we should see a growth curve that looks like AWS’s.
A key piece of evidence will be how much profit OpenAI turns on GPT. If Google and Facebook come out with substitute products in short order and language modelling gets commodified down to zero profits, that’ll sway me to the latter scenario. I’m not sure how to interpret the surprisingly high price of the OpenAI API in this context.
Another thing which has been bugging me—but I haven’t put much thought into yet—is how to handle the inevitable transition from ‘training models from scratch’ to ‘training as an ongoing effort’. I’m not sure how this changes the investment dynamics.
Worth noting that the “evidence from the nascent AI industry” link has bits of evidence pointing in both directions. For example:
Doesn’t this kind of cost make AI services harder to commodify? And also:
That sounds rather monopoly-ish doesn’t it? Although the blogger’s takeaway is
I’ll be somewhat surprised if language modeling gets commodified down to 0 profits even if Google and Facebook release competing models. I’d expect it to look more like cloud infrastructure industry, “designed to extract maximum blood” as the author of your blog post puts it. See e.g. https://www.investopedia.com/terms/o/oligopoly.asp
Thanks so much, glad you’re finding it helpful!
I haven’t thought too much about short term spending scaleup; thanks for the links, My current intuition is that our subjective distribution should not be highly bimodal the way you describe—it seems like the industry could land somewhere along a broad spectrum from perfect competition to monopoly (with oligopoly seeming most plausible) and somewhere along a broad spectrum of possible profit margins.