Toby Ord

There is an extremely important question about the near-future of AI that almost no-one is asking. We've all seen the graphs from METR showing that the length of tasks AI agents can perform has been growing exponentially over the last 7 years. While GPT-2

The current era of improving AI capabilities using Reinforcement Learning (from verifiable rewards) involves two key types of scaling: Scaling the amount of compute used for RL during training Scaling the amount of compute used for inference during deploy

The new scaling paradigm for AI reduces the amount of information a model could learn per hour of training by a factor of 1,000 to 1,000,000. I explore what this means and its implications for scaling.

Building on the recent empirical work of Kwa et al. (2025), I show that within their suite of research-engineering tasks the performance of AI agents on longer-duration tasks can be explained by an extremely simple mathematical model - a constant rate of

The shift from scaling up the pre-training compute of AI systems to scaling up their inference compute may have profound effects on AI governance. The nature of these effects depends crucially on whether this new inference compute will primarily be used d

AI capabilities have improved remarkably quickly, fuelled by the explosive scale-up of resources being used to train the leading models. But if you examine the scaling laws that inspired this rush, they actually show extremely poor returns to scale. What'

The most beautiful photographs of Earth - iconic images and unknown gems - digitally restored to their full glory.

(PDF available) I show how a standard argument for advancing progress is extremely sensitive to how humanity's story eventually ends. Whether advancing progress is ultimately good or bad depends crucially on whether it also advances the end of humanity. B

Imagine if we could somehow show the people of the eighteenth century a simple child's toy from today - say, a speaking doll. The common folk would marvel at its ability to speak its few stock phrases. The scientists and engineers would marvel even more a

What is the chance your vote changes the outcome of an election? We know it is low, but how low? In particular, how does it compare with an intuitive baseline of a 1 in $n$ chance, where $n$ is the number of voters? This baseline is an important landmark