Welcome to the latest issue of your guide to AI, an editorialized newsletter covering key developments in AI research, industry, geopolitics and startups during February 2023. We wrote an op-ed for Sifted on how generative AI will change the software landscape and commented for TIME's cover story on ChatGPT. On the politics side, we reviewed and recommended spinout policy reform in Tony Blair Institute for Global Change's paper A New National Purpose and were included in Politico's 20 people who matter in UK technology. Air Street was featured in Insider's list of top AI investors See some of you at London.AI on Thurs 9 March w/DeepMind, Adept, Palantir and Basecamp Research. Register for our one-day RAAIS conference on research and applied AI 23 June 2023 in London. We'll be hosting speakers from Meta AI, Cruise, Intercom, Genentech, Northvolt and more to come! FYI, you might have to read this issue in full online vs. in your inbox. As usual, we love hearing what you're up to and what's on your mind, just hit reply or forward to your friends:-) Building large-scale AI models requires enormous computing power, which has emerged as the soft power of our time.

De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds1,2, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence–structure relationships. Here we describe a deep-learning-based ‘family-wide hallucination’ approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (kcat/Km = 106 M−1 s−1) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes. A deep-learning-based strategy is used to design artificial luciferases that catalyse the oxidative chemiluminescence of diphenylterazine with high substrate specificity and catalytic efficiency.