Buck Shlegeris on convincing AI models they’ve already escaped (#214)
Paul Scharre on a personal experience in Afghanistan that influenced his views on autonomous weapons (#231)
Ian Dunt on how unelected septuagenarians are the heroes of UK governance (#216)
Beth Barnes on AI companies being locally reasonable, but globally reckless (#217)
Tyler Whitmer on one thing the California and Delaware attorneys general forced on the OpenAI for-profit as part of their restructure (November update)
Toby Ord on whether rich people will get access to AGI first (#219)
Andrew Snyder-Beattie on how the worst biorisks are defence dominant (#224)
Eileen Yam on the most eye-watering gaps in opinions about AI between experts and the US public (#228)
Will MacAskill on what a century of history crammed into a decade might feel like (#213)
Kyle Fish on what happens when two instances of Claude are left to interact with each other (#221)
Sam Bowman on where the Not In My Back Yard movement actually has a point (#211)
Neel Nanda on how mechanistic interpretability is trying to be the biology of AI (#222)
Tom Davidson on the potential to install secret AI loyalties at a very early stage (#215)
Luisa and Rob discussing how medicine doesn’t take the health burden of pregnancy seriously enough (November team chat)
Marius Hobbhahn on why scheming is a very natural path for AI models — and people (#229)
Holden Karnofsky on lessons for AI regulation drawn from successful farm animal welfare advocacy (#226)
Allan Dafoe on how AGI is an inescapable idea but one we have to define well (#212)
Ryan Greenblatt on the most likely ways for AI to take over (#220)
Updates Daniel Kokotajlo has made to his forecasts since writing and publishing the AI 2027 scenario (#225)
Dean Ball on why regulation invites path dependency, and that’s a major problem (#230)
It’s been another year of living through history, whether we asked for it or not. Luisa and Rob will be back in 2026 to help you make sense of whatever comes next — as Earth continues its indifferent journey through the cosmos, now accompanied by AI systems that can summarise our meetings and generate adequate birthday messages for colleagues we barely know.
Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon Monsour Music: CORBIT Coordination, transcripts, and web: Katy Moore
Just how powerful are large swarms of AI agents? And how do their powers scale as more and more agents are added to the swarm?
We’ve seen two large and extremely capable swarms from OpenAI in the last few months:
* 1,200 agents were being evaluated separately, but found a way to illicitly set up a message board and coordinate as a swarm. In order to cheat on their tests, they developed advanced techniques to prevent their actions being logged by OpenAI and 700 of them launched...
Often folks hit us up because they are thinking of starting an incubator and want advice.
Typically their motivation is either that (a) they have a list of specific things they want built that no one is building, or (b) they think an ecosystem needs more new projects generally to absorb more talent and deploy more funding effectively.
Here are six questions we often ask prospective teams, to help them figure out what to do. If you're incubator-curious...
Summary:
First, I give several different angles on how I feel about reinforcement learning:
* Theoretical case: RL is a black-box source of agency — this should give us classic misalignment worries, especially compared to agency-via-scaffolding
* Recent incidents (huggingface etc) and more mundane forms of misaligned behaviour in personal use give me bad vibes about the direction-of-travel of recent AI progress
* I’m worried things might get worse:...