Relatedly, I’m also interested in the simple method of extrapolating AI revenue growth trends until AI revenue is most of the world economy. That seems like a decent proxy for when AGI will be achieved. I trust this method less than our model for obvious reasons, but I still put some weight on it. What does it say? Well, it says “Early 2030s.” OK.
I’m curious why you trust revenue extrapolation less than the model. Intuitively revenue seems like a better thing to extrapolate to me than benchmarks or flops or whatever because it’s much less gameable and there’s a much more clear threshold for AGI (revenue is similar size to GDP).
I think revenue extrapolations seem like a useful exercise. But I think they provide much less evidence than our model.
Which revenues would you extrapolate? You get different results for e.g. doing OpenAI vs. Nvidia.
Also (most importantly) are you saying we should assume that log(revenue) is a straight line?
If so, that seems like a really bad assumption given that usually startup revenue growth rates slow down a lot as revenue increases, so that should be the baseline assumption.
If not, how else do we predict how the revenue trend will change without thinking about AI capabilities? We could look at base rates for startups that have this level of revenue growth early on, but then obviously none of those revenue trends have ever grown until world GDP, so that would say AGI never.
Also I disagree with this, I think time horizon is about as good as revenue on this dimension, maybe a bit better. Both are hugely uncertain though of course.
Most successful startups slow down a lot after a brief hypergrowth phase. We should be looking for signs that AI companies like OpenAI and Anthropic* are experiencing unusually long and persistent hypergrowth: surprisingly little slowdown in growth, or maintaining >2x growth/year at surprisingly high revenue levels like 100B. They are both already growing very surprisingly fast for companies with multiple billions in revenue, to be clear, but whether that continues is valuable evidence.
This could be a sign that present-day models have a higher economic ceiling than we realize (closer to TAI than they might look), or that companies are making real progress towards transformative AI. Most companies don’t dramatically improve their product lineup over and over again after they find initial product-market-fit, so sustained rapid growth means that AI development is leading to a new batch of successful products on a regular basis, i.e. escalating economic usefulness.
*I think companies that serve AI to end-users are the most useful indicators
I basically agree with Eli, though I’ll say that I don’t think the gap between extrapolating METR specifically and AI revenue is huge. I think ideally I’d do some sort of weighted mix of both, which is sorta what I’m doing in my ATC.
I’m curious why you trust revenue extrapolation less than the model. Intuitively revenue seems like a better thing to extrapolate to me than benchmarks or flops or whatever because it’s much less gameable and there’s a much more clear threshold for AGI (revenue is similar size to GDP).
I think revenue extrapolations seem like a useful exercise. But I think they provide much less evidence than our model.
Which revenues would you extrapolate? You get different results for e.g. doing OpenAI vs. Nvidia.
Also (most importantly) are you saying we should assume that log(revenue) is a straight line?
If so, that seems like a really bad assumption given that usually startup revenue growth rates slow down a lot as revenue increases, so that should be the baseline assumption.
If not, how else do we predict how the revenue trend will change without thinking about AI capabilities? We could look at base rates for startups that have this level of revenue growth early on, but then obviously none of those revenue trends have ever grown until world GDP, so that would say AGI never.
edited to add: relevant graph from https://epoch.ai/gradient-updates/openai-is-projecting-unprecedented-revenue-growth:
Also I disagree with this, I think time horizon is about as good as revenue on this dimension, maybe a bit better. Both are hugely uncertain though of course.
Most successful startups slow down a lot after a brief hypergrowth phase. We should be looking for signs that AI companies like OpenAI and Anthropic* are experiencing unusually long and persistent hypergrowth: surprisingly little slowdown in growth, or maintaining >2x growth/year at surprisingly high revenue levels like 100B. They are both already growing very surprisingly fast for companies with multiple billions in revenue, to be clear, but whether that continues is valuable evidence.
This could be a sign that present-day models have a higher economic ceiling than we realize (closer to TAI than they might look), or that companies are making real progress towards transformative AI. Most companies don’t dramatically improve their product lineup over and over again after they find initial product-market-fit, so sustained rapid growth means that AI development is leading to a new batch of successful products on a regular basis, i.e. escalating economic usefulness.
*I think companies that serve AI to end-users are the most useful indicators
I basically agree with Eli, though I’ll say that I don’t think the gap between extrapolating METR specifically and AI revenue is huge. I think ideally I’d do some sort of weighted mix of both, which is sorta what I’m doing in my ATC.