AI for science could be more impactful than chatbots. It is already helping win Nobel prizes and accelerating drug development and materials discovery. Today we published an essay about why it matters, how it’s happening and its implications. Let’s look at it like economists.

RT @GoogleDeepMind: We’re putting our most advanced AI for weather forecasting into more people’s hands. ☁️🌐 Scientists can now access @Go…

RT @adonis_singh: deepseek r1 asked for 1 truly novel insight about humans

RT @OVKovalevskiy: #AlphaFold v3.0.1 is out! Contains various efficiency and usability improvements alongside a collection of minor bug fix…

2. AI is being used to identify venom proteins that have antimicrobial properties. The haystack is the space of proteins present in venoms (40M!). Both studies validate their results in the lab, highlighting complementarities between AI & experiments.

Could much of the recent drama about DeepSeek be rooted on flawed causal thinking i.e. neglecting the counterfactual? Dario Amodei makes a compelling case here.

RT @JMateosGarcia: Employment dynamics in a rapid decarbonization of the US power sector Cool complexity economics analysis of a scenario t…

This paper is a timely reminder that tech impacts on jobs are dynamic and complex - so we need dynamic and complex models to track / anticipate / respond to them! Read it here:

RT @JMateosGarcia: "Suspense and surprise in technological innovation" Neat analysis of the link between expectations and outcomes in techn…

Who benefits from better prediction technologies? Timely review of the evidence by @mattsclancy. My reading is that, in complex settings, predictions (eg AI) complement the domain expertise required to implement and deploy them. Agents could be different!

Some limitations: top down coarse taxonomy; innovation isn't always link-creation; neglects hierarchical nature of innovation; patents offer a narrow view of expectations; expectations not evenly distributed... Still really worth reading - check it here: