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Sometimes people give a short description of their work. Sometimes they give a long one.
I have an imaginary friend named James whose work I’m excited about. I recently overheard James introduce and motivate his work to a crowd of young safety researchers, and I took notes. Here’s my best reconstruction of what he’s up to:
"I work on median-case out-with-a-whimper scenarios and automation forecasting, with special attention to the possibility of mass-disempowerment due to wealth disparity and/or centralization of labor power. I identify existing legal and technological bottlenecks to this hypothetical automation wave, including a list of relevant laws in industries likely to be affected and a suite of evals designed to detect exactly which kinds of tasks are likely to be automated and when.
"My guess is that there are economically valuable AI systems between us and AGI/TAI/ASI, and that executing on safety and alignment plans in the midst of a rapid automation wave is dizzyingly challenging. Thinking through those waves in advance feels like a natural extension of placing any weight at all on the structure of the organization that happens to develop the first Real Scary AI. If we think that the organizational structure and local incentives of a scaling lab matter, shouldn’t we also think that the societal conditions and broader class of incentives matter? Might they matter more? The state of the world just before The Thing comes on line, or as The Team that makes The Thing is considering making The Thing, has consequences for the nature of the socio-technical solutions that work in context.
"At minimum, my work aims to buy us some time and orienting-power as the stakes raise. I’d imagine my maximal impact is something like “develop automation timelines and rollout plans that you can peg AI development to, such that the state of the world and the state of the art AI technology advance in step, minimizing the collateral damage and chaos of any great economic shift.”
"When I’ve brought these concerns up to folks at labs, they’ve said that these matters get discussed internally, and that there’s at least some agreement that my direction is important, but that they can’t possibly be expected to do everything to make the world ready for their tech. I, perhaps somewhat cynically, think they’re doing narrow work here on the most economically valuable parts, but that they’re disinterested in broader coordination with public and private entities, since it would be economically disadvantageous to them.
"When I’ve brought these concerns up to folks in policy, they’ve said that some work like this is happening, but that it’s typically done in secret, to avoid amorphous negative externalities. Indeed, the more important this work is, the less likely someone is to publish it. There’s some concern that a robust and publicly available framework of this type could become a roadmap for scaling labs that helps them focus their efforts for near-term investor returns, possibly creating more fluid investment feedback loops and lowering the odds that disappointed investors back out, indirectly accelerating progress.
"Publicly available work on the topic is ~abysmal, painting the best case scenario as the most economically explosive one (most work of this type is written for investors and other powerful people), rather than pricing in the heightened x-risk embedded in this kind of destabilization. There's actually an IMF paper modeling automation from AI systems using math from the industrial revolution. Surely there's a better way here, and I hope to find it."