Eric Drexler's report Reframing Superintelligence: Comprehensive AI Services (CAIS) as General Intelligence reshaped how a lot of people think about AI (summary 1, summary 2). I still agree with many parts of it, perhaps even the core elements of the model. However, after looking back on it more than four years later, I think the general picture it gave missed some crucial details about how AI will go.
The problem seems to be that his report neglected a discussion of foundation models, which I think have transformed how we should think about AI services and specialization.
The general vibe I got from CAIS (which may not have been Drexler's intention) was something like the following picture:
For each task in the economy, we will train a model from scratch to automate the task, using the minimum compute necessary to train an AI to do well on the task. Over time, the fraction of tasks automated will slowly expand like a wave, starting with the tasks that are cheapest to automate computationally, and ending with the most expensive tasks. At some point, automation will be so widespread that it will begin to meaningfully feed into itself, increasing AI R&D, and accelerating the rate of technological progress.
The problem with this approach to automation is that it's extremely wasteful to train models from scratch for each task. It might make sense when training budgets are tiny — as they mostly were in 2018 — but it doesn't make sense when it takes 10^25 FLOP to reach adequate performance on a given set of tasks.
The big obvious-in-hindsight idea that we've gotten over the last several years is that, instead of training from scratch for each new task, we'll train train a foundation model on some general distribution, which can then be fine-tuned using small amounts of compute to perform well on any task. In the CAIS model, "general intelligence" is just the name we can give to the collection of all AI services in the economy. In this new paradigm, "general intelligence" refers to the fact that sufficiently large foundation models can efficiently transfer their knowledge to obtain high performance on almost any downstream task, which is pretty closely analogous to what humans do to take over jobs.
The fact that generalist models can be efficiently adapted to perform well on almost any task is an incredibly important fact about our world, because it implies that a very large fraction of the costs of automation can be parallelized across almost all tasks.
Let me illustrate this fact with a hypothetical example.
Suppose we previously thought that it would take $1 trillion to automate each task in our economy, such as language translation, box stacking, and driving cars. Since the cost of automating each of these tasks is $1 trillion each, you might expect companies would slowly automate all the tasks in the economy, starting with the most profitable ones, and then finally getting around to the least profitable ones once economic growth allowed for us to spend enough money on automating not-very-profitable stuff.
But now suppose we think it costs $999 billion to create "general intelligence", which then once built, can be quickly adapted to automate any other task at a cost of $1 billion. In this world, we will go very quickly from being able to automate almost nothing to being able to automate almost anything. In other words we will get one big innovation "lump", which is the opposite of what Robin Hanson predicted. Even if we won't invent monolithic agents that take over the world by being smarter than everything else, we won't have a gradual decades-long ramp-up to full automation either.
Of course, the degree of suddenness in the foundation model paradigm is still debatable, because the idea of "general intelligence" is itself continuous. GPT-4 is more general than GPT-3, which was more general than GPT-2, and presumably this trend will smoothly continue indefinitely as a function of scale, rather than shooting up discontinuously after some critical threshold. But results in the last year or so have updated me towards thinking that the range from "barely general enough to automate a few valuable tasks" to "general enough to automate almost everything humans do" is only 5-10 OOMs of training compute. If this range turns out to be 5 OOMs, then I expect a fast AI takeoff under Paul Christiano's definition, even though I still don't think this picture looks much like the canonical version of foom.
Foundation models also change the game because they imply that AI development must be highly concentrated at the firm-level. AIs themselves might be specialized to provide various services, but the AI economy depends critically on a few non-specialized firms that deliver the best foundation models at any given time. There can only be a few firms in the market providing foundation models because the fixed capital costs required to train a SOTA foundation model are very high, and being even 2 OOMs behind the lead actor results in effectively zero market share. Although these details are consistent with CAIS, it's a major update about what the future AI ecosystem will look like.
A reasonable remaining question is why we'd ever expect AIs to be specialized in the foundation model paradigm. I think the reason is that generalist models are more costly to run at inference time compared to specialized ones. After fine-tuning, you will want to compress the model as much as possible, while maintaining acceptable performance on whatever task you're automating.
The degree of specialization will vary according to the task you want to automate. Some tasks require very general abilities to do well. For example, being a CEO plausibly benefits from being extremely general, way beyond even human-level, such that it wouldn't make sense to make them less general even if it saved inference costs. On the other hand, language translation is plausibly something that can be accomplished acceptably using far less compute than a CEO model. In that case, you want inference costs to be much lower.
It now seems clear that AIs will also descend more directly from a common ancestor than you might have naively expected in the CAIS model, since most important AIs will be a modified version of one of only a few base foundation models. That has important safety implications, since problems in the base model might carry over to problems in the downstream models, which will be spread throughout the economy. That said, the fact that foundation model development will be highly centralized, and thus controllable, is perhaps a safety bonus that loosely cancels out this consideration.
Drexler can be forgiven for not talking about foundation models in his report. His report was published at the start of 2019, just months after the idea of "fine-tuning" was popularized in the context of language models, and two months before GPT-2 came out. And many readers can no doubt point out many non-trivial predictions that Drexler got right, such as the idea that we will have millions of AIs, rather than just one huge system that acts as a unified entity. And we're still using deep learning as Drexler foresaw, rather than building general intelligence like a programmer would. Like I said at the beginning, it's not necessarily that the core elements of the CAIS model are wrong; the model just needs an update.