X：Peter McCrory（Anthropic 首席经济学家）

What happens to jobs, productivity, inequality, and economic policy as AI transforms the way we work? Anthropic Chief Economist @PeterMcCrory and economist @JasonFurman took to the JFK Jr. Forum stage to explore these questions and more. https://ken.sc/forum0923-live

A few (personal) thoughts on reading empirical AI papers on the economy. Economists have gotten used to reading papers with super clean identification, arguing about the validity of an instrument, making sure parallel trend assumptions are satisfied. This is what gets you into a top journal, and it is *very* important research (no question here). But it also takes years and sometimes decades to get these types of papers right---people often don't find a good instrument to answer a specific causal question decades after the natural experiment. We will eventually have this type of research for AI as well, and it is absolutely necessary. But right we also need signals *right now*, even if they are noisier than what we are used to. We need papers where we can trust that researchers did their best methodologically, while at the same time acknowledging that the space is moving way too fast to wait for perfect identification. This will allow us to accumulate enough signals, coming at the same question using different angles, for example, to say "yes, X is likely happening in the economy". The AI exposure and early career hiring papers are a good example of this. There is no silver bullet paper with super clean identification. But at this point we have several independent teams reaching the same general conclusion, enough where we can say "there seems to be a slow down in AI-exposed, early career hiring."

I've had the most wonderful time working on this project for the last few months. This was (equally) co-led w/ @JMateosGarcia , @alexolegimas and a fantastic team.

We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: https://darioamodei.com/post/we-must-pace-the-frontier

Anthropic's economic scenario analysis is interesting. But this is not something you can ignore, this is the most important consideration! "the model cannot generate the negative feedback in which disruption depresses demand and amplifies its own labor-market consequences"

New from us: Anthropic just published scenarios for AI’s possible economic impacts, which range from minimal, to explosive GDP growth of 15% by 2030 as knowledge-worker unemployment hits 18%. I sat down with their co-founder Jack Clark to pick his brains on how they’re thinking about all of this.