I am not specced in economics or finance but that looks like a sustainable business, and it’s obviously self amplifying
“Make a giant LLM and deploy it” is a self-sustaining business, yes, and if all major AI Labs died tomorrow a plethora of companies filling the niche of “make a giant LLM and deploy it” would spring up, yes.
“Re-invest revenue into making an even larger LLM” is a sensible company policy, as well.
But is “have a roadmap to AGI and invest into research that brings your models closer to it, even if that doesn’t immediately translate into revenue” a self-sustaining business model? I’m much less confident on that. It’s possible that the ideas of “AGI = money” have already propagated enough that profit-oriented non-imaginative business people would decide to spend their revenue on that. But that’s not obvious to me.
I expect the non-ideologically-motivated replacements for the current major AI labs to just have no idea what “racing to AGI” even means, in terms of “what research directions to pursue” as opposed to “what utterances to emit”. The current AI industry as a whole is pretty bad at it as-is, but the major AI labs explicitly have some vision of what it physically means. I don’t expect the replacements for them that the market would generate on its own to have even that much of a sense of direction.
Again, it’s possible that it’s no longer true, that the ideas propagated enough that some of the “native” replacement companies would competently race as well. But it’s not an open-and-shut case, I think.
So your model is that people can make big llms, and the innovation from openAI and from open source will eventually all be in one large model. Aka “gpt 4.1”. But that each llm shop, while free of encumbrances and free to seek maximum profit, would not have the necessary concentration of money and talent in one place to develop AGI.
Instead they would simply keep making smaller delta’s to their product, something a less talented and GPU poorer crew could do, and capabilities would be stuck in a local minimum.
So you believe that either this would push back AGI several years (eventually the staff at these smaller shops would skill up from experience and as compute gets cheaper they would eventually have what 100B of compute will buy in 2024) or possibly longer if there is no smooth path of small incremental steps from gpt-4.1 to AGI.
I will add one comment to this : it’s not actually a threshold of “gpt4.1 to AGI”. Assuming you believe RSI will work, you need “a good enough seed model plus sufficient compute to train and benchmark thousands of automatically generated AGI candidates”.
Gpt4.1 plus a reinforcement learning element might be enough for the “seed AI”.
Giant LLMs are as useful as they are agentic (with ability to remain aware of a specific large body of data and keep usefully chipping away at a task), which doesn’t seem particularly different from AGI as a direction (at least while it hasn’t yet been walked far enough to tell the difference). The distinction is in AGI being a particular crucial threshold of capability that local pursuit of better agentic LLMs will ignore until it’s crossed.
“Make a giant LLM and deploy it” is a self-sustaining business, yes, and if all major AI Labs died tomorrow a plethora of companies filling the niche of “make a giant LLM and deploy it” would spring up, yes.
“Re-invest revenue into making an even larger LLM” is a sensible company policy, as well.
But is “have a roadmap to AGI and invest into research that brings your models closer to it, even if that doesn’t immediately translate into revenue” a self-sustaining business model? I’m much less confident on that. It’s possible that the ideas of “AGI = money” have already propagated enough that profit-oriented non-imaginative business people would decide to spend their revenue on that. But that’s not obvious to me.
I expect the non-ideologically-motivated replacements for the current major AI labs to just have no idea what “racing to AGI” even means, in terms of “what research directions to pursue” as opposed to “what utterances to emit”. The current AI industry as a whole is pretty bad at it as-is, but the major AI labs explicitly have some vision of what it physically means. I don’t expect the replacements for them that the market would generate on its own to have even that much of a sense of direction.
Again, it’s possible that it’s no longer true, that the ideas propagated enough that some of the “native” replacement companies would competently race as well. But it’s not an open-and-shut case, I think.
So your model is that people can make big llms, and the innovation from openAI and from open source will eventually all be in one large model. Aka “gpt 4.1”. But that each llm shop, while free of encumbrances and free to seek maximum profit, would not have the necessary concentration of money and talent in one place to develop AGI.
Instead they would simply keep making smaller delta’s to their product, something a less talented and GPU poorer crew could do, and capabilities would be stuck in a local minimum.
So you believe that either this would push back AGI several years (eventually the staff at these smaller shops would skill up from experience and as compute gets cheaper they would eventually have what 100B of compute will buy in 2024) or possibly longer if there is no smooth path of small incremental steps from gpt-4.1 to AGI.
I will add one comment to this : it’s not actually a threshold of “gpt4.1 to AGI”. Assuming you believe RSI will work, you need “a good enough seed model plus sufficient compute to train and benchmark thousands of automatically generated AGI candidates”.
Gpt4.1 plus a reinforcement learning element might be enough for the “seed AI”.
That summary sounds right, yep!
Except that. It might, but I don’t think that’s particularly likely.
Giant LLMs are as useful as they are agentic (with ability to remain aware of a specific large body of data and keep usefully chipping away at a task), which doesn’t seem particularly different from AGI as a direction (at least while it hasn’t yet been walked far enough to tell the difference). The distinction is in AGI being a particular crucial threshold of capability that local pursuit of better agentic LLMs will ignore until it’s crossed.