Why we should think a little harder about what it takes to build a Dyson Sphere

Inferring Chinese AI labs’ strategies from their job descriptions

Proposing a new way to track AI research automation

Compiling all the public evidence on Mythos Preview’s cyber abilities

We look at reference classes, factory buildout timelines, and upstream component supply to estimate plausible production rates for humanoids, quadrupeds, robotic arms, wheeled robots, and drones.

We investigate progress trends on four capability metrics to determine whether AI capabilities have recently accelerated. Three of four metrics show strong evidence of acceleration, driven by reasoning models.

A fast increase in go-to-market roles, and hints about upcoming products

New evidence following the MiniMax and Z.ai IPOs

We release a database of over 1,100 biological AI models across nine categories. We analyze their safeguards, accessibility, training data sources, and the foundation models they build on.

Toby Ord argues that RL scaling primarily increases inference costs, creating a persistent economic burden. While the framing is useful, the cost to reach a given capability level falls fast, and the RL scaling data is thin.