Willingness to spend: Cotra assumes that the willingness to spend on Machine Learning training runs should be capped at 1% the GDP of the largest country, referencing previous case studies with megaprojects (e.g. the Manhattan Project), and should follow a doubling time of 2 years after 2025.
This seems like a critical decision term.
1. Nvidia charges $33k per H100. Yet the die is a similar size as the 4090 GPU, meaning nvidia could likely still break even were they to charge $1500 per H100.
Just imagine a counterfactual: A large government (China, USA etc) decides they don’t enjoy losing and invests 1% of GDP. They pressure Nvidia, or in China’s case, get domestic industry to develop a comparable (for AI training only which reduces the complexity) product and sell it for closer to cost.
That’s a factor of 22 in cost, or 9 years overnight. Also I’m having trouble teasing out the ‘willingness to spend’ baseline. Is this what industry is spending now, spread across many separate efforts and not all in to train one AGI? It looks like 1% is the ceiling. Meaning if a government decided in 2023 that training AGI was a worthy endeavor, and the software framework were in place that would scale to AGI (it isn’t):
Would 1% of US GDP give them enough compute at 2023 prices, if the compute were purchased at near cost, to train an AGI?
As a side note, 1% of US GDP is only 230 billion dollars. Google’s 2021 revenue was 281 billion. Iff a tech company could make the case to investors that training AGI would pay off it sounds like a private investment funding the project is possible.
Willingness to spend: Cotra assumes that the willingness to spend on Machine Learning training runs should be capped at 1% the GDP of the largest country, referencing previous case studies with megaprojects (e.g. the Manhattan Project), and should follow a doubling time of 2 years after 2025.
This seems like a critical decision term.
1. Nvidia charges $33k per H100. Yet the die is a similar size as the 4090 GPU, meaning nvidia could likely still break even were they to charge $1500 per H100.
Just imagine a counterfactual: A large government (China, USA etc) decides they don’t enjoy losing and invests 1% of GDP. They pressure Nvidia, or in China’s case, get domestic industry to develop a comparable (for AI training only which reduces the complexity) product and sell it for closer to cost.
That’s a factor of 22 in cost, or 9 years overnight. Also I’m having trouble teasing out the ‘willingness to spend’ baseline. Is this what industry is spending now, spread across many separate efforts and not all in to train one AGI? It looks like 1% is the ceiling. Meaning if a government decided in 2023 that training AGI was a worthy endeavor, and the software framework were in place that would scale to AGI (it isn’t):
Would 1% of US GDP give them enough compute at 2023 prices, if the compute were purchased at near cost, to train an AGI?
As a side note, 1% of US GDP is only 230 billion dollars. Google’s 2021 revenue was 281 billion. Iff a tech company could make the case to investors that training AGI would pay off it sounds like a private investment funding the project is possible.