The Nonlinear Library EA - The market expects AI software to create trillions of dollars of value by 2027 (but not more) by Benjamin Todd
May 6, 2024
02:16
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The market expects AI software to create trillions of dollars of value by 2027 (but not more), published by Benjamin Todd on May 6, 2024 on The Effective Altruism Forum.
We can use Nvidia's stock price to estimate plausible market expectations for the size of the AI chip market, and we can use that to back-out expectations about AI software revenues and value creation.
Doing this helps to understand how much AI growth is expected by society, and how EA expectations compare. It's similar to an earlier post that uses interest rates in a similar way, except I'd argue using the prices of AI companies is more useful right now, since it's more targeted at the figures we most care about.
The exercise requires making some assumptions which I think are plausible (but not guaranteed to hold).
The full analysis is here, but here are some key points:
Nvidia's current market cap implies the future AI chip market reaches over ~$180bn/year (at current margins), then grows at average rates after that (so around $200bn by 2027). If margins or market share decline, revenues need to be even higher.
For a data centre to actually use these chips in servers costs another ~80% for other hardware and electricity, then the AI software company that rents the chips will typically have at least another 40% in labour costs.
This means with $200bn/year spent on AI chips, AI software revenues need reach $500bn/year for these groups to avoid losses, or $800bn/year to make normal profit margins. That would likely require consumers to be willing to pay up to several trillion for these services.
The typical lifetime of a GPU implies that revenues would need to reach these levels before 2028.
This isn't just about Nvidia - other estimates (e.g. the price of Microsoft) seem consistent with these figures.
These revenues seem high in that they require a big scale up from today; but low if you think AI could start to automate a large fraction of jobs before 2030.
If market expectations are correct, then by 2027 the amount of money generated by AI will make it easy to fund $10bn+ training runs.
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org
We can use Nvidia's stock price to estimate plausible market expectations for the size of the AI chip market, and we can use that to back-out expectations about AI software revenues and value creation.
Doing this helps to understand how much AI growth is expected by society, and how EA expectations compare. It's similar to an earlier post that uses interest rates in a similar way, except I'd argue using the prices of AI companies is more useful right now, since it's more targeted at the figures we most care about.
The exercise requires making some assumptions which I think are plausible (but not guaranteed to hold).
The full analysis is here, but here are some key points:
Nvidia's current market cap implies the future AI chip market reaches over ~$180bn/year (at current margins), then grows at average rates after that (so around $200bn by 2027). If margins or market share decline, revenues need to be even higher.
For a data centre to actually use these chips in servers costs another ~80% for other hardware and electricity, then the AI software company that rents the chips will typically have at least another 40% in labour costs.
This means with $200bn/year spent on AI chips, AI software revenues need reach $500bn/year for these groups to avoid losses, or $800bn/year to make normal profit margins. That would likely require consumers to be willing to pay up to several trillion for these services.
The typical lifetime of a GPU implies that revenues would need to reach these levels before 2028.
This isn't just about Nvidia - other estimates (e.g. the price of Microsoft) seem consistent with these figures.
These revenues seem high in that they require a big scale up from today; but low if you think AI could start to automate a large fraction of jobs before 2030.
If market expectations are correct, then by 2027 the amount of money generated by AI will make it easy to fund $10bn+ training runs.
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org