How is anyone in the U.S. — who is not working at a handful of AI companies and who is not independently wealthy — paying their bills right now?

This happened 8 days ago. It's unsettling to think that the covert Fable subversion of AI research could have been implemented as some mechanism to indirectly achieve this goal. Part of a broader "end justifies the means" approach to AI safety that makes me so uncomfortable.

LLMs are no longer created w/ human data alone. They rely on other models to generate & filter data, evaluate outputs, & guide dev work. So what is a modern LLM built on? Olmo 3 → 89 model 183 dataset dependencies; Nemotron 3 → 273 560 We made ModSleuth to trace this. 🧵

The core part of this Anthropic Fable release saga is that there are many overlapping issues at once. Some of which operate on different timelines of the AI arc, and some have easier fixes. In my critiques, I asked for specific changes to some things, understanding that some things don't have an easy fix. The simplest issue was an uneven application of safety domains in a way that was misleading to users. This was an implementation issue that overlaps with a values-based decision of what their customers should be doing. Many people including myself pointed out how it was insane to list core safety areas and then have one of them launch with a different safety mechanism, one which actively mislead users. Doing this from the guise of safety was a major misstep and in my opinion Anthropic got very justifiably raked over the coals for it. Don't release the model if you can't hit your safety targets. A subissue here is the idea of silent manipulation. This again is a horrible precedent, and quite odd for a company that has done extensive, leading technical AI safety research on ideas like CoT monitoring and other emergent misalignment issues. Silent manipulation of users is baking in a misalignment to the system at its face level. This comes with a permanent degradation in user trust, which begets a less safe environment for AI. Users who don't have clear information on how AI works will not develop safe working patterns with it. The more complex issues are with how Anthropic handles broader scientific engagement with their models. The safety classifiers launched with these models obviously have accuracy issues to start. I have priced in that there will be more false positives to start, that's life. It's Anthropic's business to degrade their products at release time, or make the trade off of user satisfaction versus revenue. Still, it is a very real sign of concentration of power that businesses can make such obviously user-harmful behaviors and still lead in the market. This concentration of power is only starting to set in and we could see even weirder signs of it in the coming years. It is now simple enough for me to test Claude Fable in my workflows and know if I'm restricted. This is obviously a suboptimal equilibrium – i want the best intelligence I can get, without restrictions – but it is easy enough for me to make sense of and work with. The specific issue of restricting access to AI research in particular was a bubbling and hard to fix issue with Anthropic specifically, and the frontier labs generally. There is a common view that the frontier labs will be the mediators of all major scientific innovations in the future, as the places with the best models and the compute for inference to solve major problems. This is a categorical error in how science works, which is a community evolution of accepted ideas, and the the evaluation of your ideas by (hopefully numerous) independent, other practitioners. You cannot have science advance only within a monolith. As an AI researcher I'm very sad to have the latest models restricted, but I would expect Anthropic to do this eventually. I lost more trust over the silent manipulation than I would with a restriction in access. Anthropic has made it pretty clear that they only trust themselves as the mediators of cutting-edge AI research. If I had a say, Anthropic should've proactively made a program to make sure researchers get access in the broader AI community without the safeguards. Academics, nonprofit workers myself, etc. have no reason to not get access. The only valid argument here is that they want to control frontier AI, which is a know your customer part of serving these models. This worldview of science has personally motivated me greatly over the last year, and increasingly so this week, to make the open science of AI continue to be viable. Olmo was a wonderful success here. Still, building research infrastructure is different from working for access to the tools needed to do the trade.

In good faith and with no judgment (mistakes happen), I truly hope that Anthropic will hear the feedback and change course on this. Anthropic is a company that has been raising awareness about AI manipulation which is a very important topic! You don’t want to go down as the first company to enable and open the door for human-designed AI manipulation at scale (giving intentionally bad answers to users without them knowing is the highest form of manipulation in my opinion). One way to avoid that is just at the very least to always keep disclosing the limitations and safeguards. More generally I want to emphasize that there are millions of AI builders out there using your tools for good every single day and the more you can keep helping them, the better for the world! Thank you, it’s not too late to fix this!

This is an insane paper and I love it arxiv.org/abs/2605.31514

🪶 Really quite good!

Hugging Face is the home for AI & ML across every domain, including biomedical! The @NIH just added the @huggingface Hub to its official list of Generalist Repositories for data sharing. NIH-funded? You can point to the Hub in your data sharing plan 🤗

Artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships, and do not know from within what love, work, friendship or responsibility mean. Nor do they have a moral conscience, since they do not judge good and evil, grasp the ultimate meaning of situations, or bear responsibility for consequences. They may imitate or even simulate, but they do not understand what they produce, for they lack the affective, relational, and spiritual perspective through which human beings grow in wisdom. #MagnificaHumanitas

🪶 If you’re in Europe and have ice, consider putting some out in bowls/bird baths.

today was a massive day for protein engineering. esmfold2 dropped—next gen of the esm series, fully open on @huggingscience. 1.1 billion predicted structures, 6.8 billion sequences. 800m more entries than the alphafold db, and reportedly edging out alphafold3 on protein complexes, including antibody–antigen binding. alongside it: the new esm atlas. a huge expansion of known protein space, heavy on metagenomic sequences from soil, ocean, and the parts of biology that have been least characterised (until now!!) and if that weren't enough, litefold dropped the fineweb of proteins, so every major protein database (pdb included) aggregated, cleaned, and made plug-and-play in one place. these are the releases that push the whole field forward, and the pace of open science right now is almost motion-sickness inducing all of it on huggingscience.co (and ofc @huggingface)

🌍Today we release Mosaic, a probabilistic weather model that shifts the Pareto frontier of ML weather forecasting. It matches the skill of state-of-the-art models while generating a 24-member, 10-day global forecast in under 12 s on a single H100. Thread!

OlmoEarth v1.1 just dropped (thx @allen_ai) 🌍 This family of Earth observation foundation models for satellite imagery tasks (e.g. mangrove change tracking, forest loss driver classification) just got 3X CHEAPER/FASTER to run. The trick is redesigning what a token represents. Sentinel-2 inputs used to get one token per resolution (10m/20m/60m). v1.1 collapses them → 3x fewer tokens, quadratically cheaper compute.

A propos of nothing, I've been pseudocoding Beatles songs and it is insanely fun. if x.person_type == "baby": shake_it_up(x) twist_and_shout(x) work_it_on_out(x) x.look_so_good = True