Research Notes · This week Trend (published around 07-20)
From the Jul 22, 2026 daily brief
Raia Hadsell, VP of Research at Google DeepMind, argued in a talk that the recipe that built large language models — big models, big data, self-supervision — applies equally to world simulation, robotics, biology, and weather, and that the next decade's progress runs through those systems rather than through ever-bigger chatbots (Air Street, relaying, 07-20). The same week, scientists at AI drug-discovery company Xaira threw cold water from the front line: training their "virtual cell" models, they found performance plateauing early — the ceiling wasn't model size but the fact that observational data describes without predicting. Only causal data from gene-perturbation experiments unlocks further progress. If that holds, the binding constraint in scientific foundation models is wet-lab throughput rather than GPU count (Latent Space, 07-20). Both are single-source arguments for the speaker's own book (DeepMind pitching its roadmap, Xaira its bet); this brief holds them as a tension pair: the recipe may generalize, but each new domain's entry fee may be whether you can produce causal data.
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