deepmind

Tweet by Ted Underwood commenting skeptically on skill atrophy from AI forecasting tools ('the more you use a tool like this, the more you lose your own ability to forecast cyclones'), quoting Matt Kane's 'Spicy autocomplete' post which links a DeepMind WeatherNext announcement about an AI model breakthrough in cyclone forecasting, illustrated with a map of the Gulf Coast showing spiral storm-track graphics.

Tweet from Joshua Achiam commenting on personnel moves involving Demis Hassabis and Jeff Dean, arguing these should not be read as bearish for Google DeepMind but as early AI leaders repositioning for what they see as the next frontier now that AGI/ASI feels overdetermined.

A tweet from Dean W. Ball, sardonic commentary on AI risk discourse asymmetrically focused on frontier labs.

Zvi Mowshowitz quote-tweets gfodor's post about a Google DeepMind position paper ('LLMs can't jump' by Tom Zahavy, dated Jan 27th 2026) arguing LLMs lack the abductive capacity for Einstein-style conceptual leaps despite mastering induction and deduction. Zvi predicts new forms of 'cope' if AI eventually replicates such leaps.

X thread: Cheng Lou recalls Jeff Dean saying Google had an internal chatbot before ChatGPT but didn't think it beat googling; Tibo (@thsottiaux), reposted by Machine Learning Street Talk, says he was on that team — the bot ('LMChat' and another codename) was basically ChatGPT a year early, but Google was too nervous to release it and DeepMind was blocked from shipping products that could disrupt Google's core business. Jeffrey Emanuel replies asking why Google still seems unable to ship competitive products, suggesting it's a case study in corporate dysfunction.

Announcement tweet with a selfie in front of a Google DeepMind office sign, plus a reply promoting an AI safety coworking space with a linked website card.

Repost of the famous AlphaGo Zero paper plot showing pure self-play RL surpassing supervised learning from human data and eventually the AlphaGo Lee benchmark. Classic reference image for RL-vs-imitation-learning discussions, relevant to Nathan's ML/RL interests.

Zvi Mowshowitz criticizing Google DeepMind for releasing Gemini 3 Deep Think — a model with dramatic capability jumps across ARC-AGI-2, IMO, IPhO, IChO, and Codeforces — without publishing a system card or safety explanation, on the grounds that it was merely a "runtime improvement." Directly relevant to Nathan's AI governance/safety interests: a documented case of a lab treating major capability gains as exempt from safety disclosure norms. Follow-up to the previous tweet — Google walked back its earlier claim that no safety evals were needed for Gemini 3 Deep Think, saying evals were in fact run and would be shared, blaming a "communication issue." Part of Nathan's tracked thread on lab transparency practices around capability jumps.

NOT-ARCHIVE-MATERIAL (mostly): a DeepMind researcher (Petar Veličković, known for geometric deep learning / GNN theory) sharing a theoretical ML paper on "filter equivariant functions" for length-general extrapolation, co-authored with researchers at Goodfire AI (an interpretability company Nathan tracks — GoodFire SAE feature findings are in Nathan's memory notes) and Google DeepMind. Mostly abstract math/ML theory, low direct relevance beyond the Goodfire AI co-authorship link.

A tweet about a stealth-mode AI startup founded by former DeepMind researchers raising $130M to pursue superintelligence via autonomous coding agents. Relevant to Nathan's tracking of frontier AI capability/funding trends and the race dynamics around AGI-focused startups.

An AI-safety-meme account (AISafetyMemes) calls out apparent hypocrisy: AI lab leaders (Schmidt, Altman) have publicly warned recursive self-improvement (RSI) is dangerous, while DeepMind is openly hiring for automated-AI-research roles. Directly relevant to Nathan's AI safety/governance interests — RSI is a core concern in his research area, and this documents a real-world gap between stated caution and hiring practice.