And other important voices have also spoken publicly about it, such as Ilya Sutskever
RESEARCH
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Anthropic’s AI Safety Stance Receives Support from Sam Altman
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Sobre la postura de Anthropic incluso Sam Altman, con sus diferencias, ha mostrado su apoyohttps://t.co/0i2SV9ADPz
— Carlos Santana (@DotCSV) 27 février 2026Sobre la postura de Anthropic incluso Sam Altman, con sus diferencias, ha mostrado su apoyo
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LLM De-anonymization: Why Routing Anonymity Alone Won’t Protect Privacy
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Much talk about increasing privacy by creating a layer of anonymity routing between user & AI model provider. But what I truly don't get is this: commodity LLMs are already exceptionally good at identifying patterns & de-anonymizing. Why should we think they aren't capable of quickly relinking us? I'd wager that 1-shot re-identification by a model is easily possible across single prompts from multiple accounts, even if they are 'anonymously' routed to the API. Almost certainly especially true when users are working on the same bit of code, project, or from the same environment. Upshot: sort of like using Tor browser thinking you're anonymous from websites, but keeping cookies across sessions. The amount of muckery you'd have to do to context to be truly hardened against relinking by large models seems truly substantial to the point of massive inefficency. John Scott-Railton (@jsrailton) A lot of privacy has turned out to be just friction… …thats quickly being automated away. E.g. random anons could rest easy knowing manually de-anonymizing them wasn't worth the cost to anybody. Not true anymore. Now it's fast & scalable, especially for governments. — https://nitter.net/jsrailton/status/2027196811736485982#m
→ View original post on X — @mer__edith, 2026-02-27 21:39 UTC
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Neil Lawrence discusses humanity and AI at Pi Campus event
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Thank you, @lawrennd, for spending the evening with us at Pi Campus. Discussing with you what makes us really human with you has been amazing. Conversations like this remind us that #AI is fascinating, but #humans are still deeper and harder to model. #pinetworking
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METR’s Exponential AI Capability Evals and Emerging Threat Models
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METR’s @joel_bkr on exponential Time Horizon Evals, Threat Models, and the Limits of AI Productivity https://
latent.space/p/metr Everyone is going beserk over the @METR_Evals plots going exponential. Yet we -do- think Something Big Is Happening and it kicked off with Opus 4.5 in -
World Models Accelerate Robot Policy Development via Simulation
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Testing robot policies on hardware is slow, expensive and hard to scale. World models offer a promising path to accelerating robot policy development.
— Runway (@runwayml) 27 février 2026
We're sharing new research from the Runway Robotics team, in which we simulated 8 robot policies inside our General World Model… pic.twitter.com/169xGHIbFITesting robot policies on hardware is slow, expensive and hard to scale. World models offer a promising path to accelerating robot policy development. We're sharing new research from the Runway Robotics team, in which we simulated 8 robot policies inside our General World Model
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AGI Timeline and Implications of Fast Takeoff Scenarios
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AGI timeline and a fast takeoffpic.twitter.com/SNBTWgAG5w
— The Rundown AI (@TheRundownAI) 27 février 2026AGI timeline and a fast takeoff
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WordPress Categories for AI-Related Content
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Several people have asked me for a link, so here it is:
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Baseball Player Builds ML Pitching Analytics Platform with AI
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I’m a baseball player. I’ve never written a single line of code in my life. But over the last few months, I used AI to build a pitching analytics platform from scratch. 8.9M pitches. Custom ML models. Total transparency. Here is a look at what happens when you give an
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LLMs’ Biological Limitations in Therapy Development
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“LLMs are ‘not particularly good’ at the kind of biology necessary to develop therapies” See also my pinned tweet.