Monday AI Brief #9
We start this week’s brief with two pieces about the impact of AI on the economy and in particular employment. My money is on very major AI-related unemployment, fairly soon—I don’t think that’s certain, but the alternatives look increasingly unlikely.
In related news, prinz discusses an AI takeoff, we assess how well the AI forecasters are doing, and Nathan Lambert shares some guidance on how to pick the right model for the job. To finish up on a gruesome note, Dean Ball shares some of the worst ideas for AI legislation currently under consideration in various states.
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The economics of transformative AI
This is a lightly edited transcript of a recent lecture where [Anton Korinek] lays out what economics actually predicts about transformative AI — in our view it's the best introductory resource on the topic, and basically anyone discussing post-labour economics should be familiar with this. […]
The uncomfortable conclusion is the economy doesn't need us. It can run perfectly well "of the machines, by the machines, and for the machines." Whether that's what we want is a different question.
This is a great piece from a very serious mainstream economist who understands the implications of where AI is headed.
Lynette Bye: AI might or might not take all the jobs
Lynette Bye at Transformer reviews the basic arguments on both sides.
The gentle singularity; the fast takeoff
This feels increasingly like the early stages of an AI takeoff. Prinz looks at how we got here and where we’re headed.
Rating the AI forecasters
This is the way. The AI Digest Survey is a survey of predictions about AI. Each year, last year’s entries get graded and a new survey begins. Epoch just released the 2025 survey results, and a few points stand out to me:
- Predictions are hard, but forecasters did quite well (especially big name participants like Ajeya Cotra, Peter Wildeford, and the AI Futures Project team).
- Forecasters were better at predicting technical capabilities than societal impacts.
- Median timeline for “high-level machine intelligence” was 2030 and median p(doom) was 26%.
Use multiple models
Nathan Lambert has a nice overview of which models to use when. Everyone’s a bit different—I use:
- Claude Code + Opus 4.5 for coding
- Opus 4.5 for most things
- ChatGPT 5.2 Pro for a second opinion on anything major
- Nano Banana Pro for images
The AI patchwork emerges
It’s the beginning of legislative season, and Dean Ball reports on some of the madness being proposed in various state legislatures. As AI becomes a more salient political issue, expect to see a lot more of this.