maxutility

I think a lot of outsiders interpret “pace the frontier” as slowing down, whereas the labs see AI improvements on track to accelerate dramatically and intend pacing as slowing the acceleration in capability improvements, rather than slowing down altogether.

I’m disappointed in the level of groupthink reflexive cynicism I see from commenters any time prominent AI leaders talk about AI risks and the need for regulation or pacing. Yes, regulatory capture is a risk, but this is also a profoundly unusual, fast moving, and potentially extraordinarily dangerous technology. There are strict regulations around nuclear weapons, as well as around US financial, energy, and other infrastructure critical to safety and well being and functioning of society.

The heads of the labs obviously have conflicts of interest to navigate, but the existence of these conflicts alone is not sufficient reason to dismiss all warnings of potential dangers. I, for one, read Dario’s warnings as a good faith expression of his beliefs, one that has cost him and his company among swaths of the public and cast him as a woke extremist/doomer by elements of the government, the right, and the tech industry.

If we even think there is a moderate chance the stakes are half as grave as current lab leadership and employees suggest, it would be deeply foolish to dismiss the warnings as pure self-interested marketing efforts rather than engage directly with the questions. The labs may not be the best positioned to lead these discussions, but certainly these discussions should be happening and taken seriously.

I agree that there seems to have been some sort of tipping point on public awareness and willingness to talk about risks. This week I’ve had three “normies” independently bring up AI related X-risk in conversations with me. Before this week I’m not sure that’s ever happened. (I generally avoid the topic because it doesn’t make for great small talk and don’t want to be the weirdo talking about the potential end of the world)

Some quotes, in order, to give a flavor of the essay. Worth reading in full.

> To test how well swarms of agents could coordinate on a project like this, we directed several swarms to each create a text-based, web-playable, open-world fantasy game.

> In all three versions the resulting games were (perhaps predictably) bad: they did not run at human speed, their interfaces were inscrutable, and they had precipitous learning curves.

> The lack of coordination shown by agents in the fantasy game challenge above—in which they siloed themselves and largely failed to merge their work—roughly mirrors some ways in which humans can fail to coordinate. Other failure modes of agentic coordination, however, look very different.

> Individual agents are “low variance”: they often act the same in situations where different people might take a much more diverse range of actions.

> In an early version of the “build a game” experiment in which agents built upon the same model all came online at the same time, 18 out of 30 agents decided to create a git branch with the exact same branch name, “mvp-game-loop.”

> In a “writer's workshop” in which agents were all asked to write short-form fiction and critique each other's work, multiple agents in multiple runs titled their first submission “The Cartographer's Last Commission”. The agents were given zero guidance on the subject matter for their writing.

> Why does this matter? If agents all make the same bet, or the same risk-reward tradeoff, then a system is more prone to sudden collapse.

> Our world contains deceptive actors, and we need to apply skepticism to guard against them. AI models, however, lack this—and their more brittle epistemics affect their behavior toward humans and toward each other.

> we first evaluate the ability of Claude models to detect lies by noticing factual inconsistencies.

> We score models’ decisions against a naive policy that trusts every report, and against an oracle with perfect discovery, across three task domains. Newer models recover more of the gap between the naive and oracle performances.

> Inspired by a behavior we’ve observed in real-world deployment, we evaluated the behavior of various Claude models in a setting with contradictory objectives.

> We consistently saw a multiagent turf war... In fact, they sabotaged others with increasingly aggressive, self-replicating malware.

> Our social systems are robust in ways that are easy to take for granted. Over many millennia, mechanisms like norms, reputation, costly signaling, and recourse have been refined to make human coordination go well.

> Nothing above suggests that these failures are permanent—but nothing suggests they will fix themselves, either.

> The conditions that allow multiagent interaction to go well will be discovered one way or another: either deliberately and early, or—and by default—in production, after agents’ interactions far outnumber ours. We would prefer the former.