Carl Shulman: belief

Transcript context

…Just to emphasize for the audience the initial point about revenue made. If it costs OpenAI 100 million dollars to train GPT-4 and it generates 500 million dollars in revenue, you pay back your expenses with 100 million and you have 400 million for your next training run. Then you train your GPT 4.5, you get let's say four billion dollars in revenue out of that. That's where the feedback group of revenue comes from. Where you're automating tasks and therefore you're making money you can use that money to automate more tasks. On the ability to redirect the fab production towards AI chips, fabs take a decade or so to build. Given the ones we have now and the ones that are going to come online in the next decade, is there enough to sustain a hundred billion dollars of GPU compute if you wanted to spend that on a training run? Yes, you definitely make the hundred billion one. As you go up to a trillion dollar run and larger, it's going to involve more fab construction and yeah, fabs can take a long a long time to build. On the other hand, if in fact you're getting very high revenue from the AI systems and you're actually bottlenecked on the construction of these fabs then their price could skyrocket and that could lead to measures we've never seen before to expand and accelerate fab production. If you consider, at the limit you're getting models that approach human-like capability, imagine things that are getting close to brain-like efficiencies plus AI advantages. We were talking before a cluster of GPU supporting AIs that do things, data parallelism. If that can work four times as much as a highly skilled motivated focused human with levels of education that have never been seen in the human population, and if a typical software engineer can earn hundreds of thousands of dollars, the world's best software engineers can earn millions of dollars today and maybe more in a world where there's so much demand for AI. And then times four for working all the time. If you can generate close to 10 million dollars a year out of the future version H100 and it cost tens of thousands of dollars with a huge profit margin now. And profit margin could be reduced with large production. That is a big difference that that chip pays for itself almost instantly and you could support paying 10 times as much to have these fabs constructed more rapidly. If AI is starting to be able to contribute more of the skilled technical work that makes it hard for NVIDIA to suddenly find thousands upon thousands of top quality engineering hires. If AI hasn't reached that level of performance then this is how you can have things stall out. A world where AI progress stalls out is one where you go to the 100 billion and then over succeeding years software progress turns out to stall. You lose the gains that you are getting from moving researchers from other fields. Lots of physicists and people from other areas of computer science have been going to AI but you tap out those resources as AI becomes a larger proportion of the research field. And okay, you've put in all of these inputs, but they just haven't yielded AGI yet. I think that set of inputs probably would yield the kind of AI capabilities needed for intelligence explosion but if it doesn't, after we've exhausted this current scale up of increasing the share of our economy that is trying to make AI. If that's not enough then after that you have to wait for the slow grind of things like general economic growth, population growth and such and so things slow. That results in my credences and this kind of advanced AI happening to be relatively concentrated, over the next 10 years compared to the rest of the century because we can't keep going with this rapid redirection of resources into AI. That's a one-time thing. If the current scale up works we're going to get to AGI really fast, like within the next 10 years or something. If the current scale up doesn't work, all we're left with is just like the economy growing 2% a year, we have 2% a year more resources to spend on AI and at that scale you're talking about decades before just through sheer brute force you can train the 10 trillion dollar model or something. Let's talk about why you have your thesis that the current scale up would work. What is the evidence from AI itself or maybe from primate evolution and the evolution of other animals? Just give me the whole confluence of reasons that make you think that.…

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