New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. We found a strikingly similar divide inside Claude.

The problem with Anthropic's consciousness paper My last post got more attention than I expected, and the question I keep getting is some version of "okay, so what is actually wrong with the paper?". Let me try to explain. First, the core result is fine. Reading out intermediate-layer representations and asking which ones the model can actually use downstream is a real question, and people have been poking at it for years. The J-lens is a reasonable tool. If you strip the paper down to the linear algebra, it is a decent piece of interpretability work. My problem starts one level up. This did not need to be a paper about consciousness. It did not need global workspace theory, it did not need the brain, and it did not need the word "conscious" anywhere near it. The same experiments, the same figures, the same tool, all survive perfectly well as plain interpretability. Someone chose to wrap it in neuroscience. That choice is the product, not the science. At the end, this is the main thing. Almost everything Anthropic ships as blogs/papers/posts is PR. They build genuinely good models and they are even better at packaging them. A publication used to carry a specific kind of weight. People spent years on something and wanted to tell the world what they found. It happened mostly in academia, with a few industrial labs as the exception, Bell Labs, IBM and Google (for a while), but the distance between the paper and the product was real. When you read a paper you could assume the authors were not trying to sell you something underneath the ideas. There were outliers, but they were the minority, and the researchers you trusted would not risk their name on a narrative. We are not in that world anymore. Every startup now publishes blogs and papers to raise its visibility, and that is fine, that is marketing and everyone knows it. Anthropic does something more effective. They erase the line between legitimate research and PR. We get confused because the models are so good, so we assume the outputs are research. A lot of the time they are selling us something. Sometimes it is "our models are safer," sometimes it is "our models are more capable," sometimes it is positioning for regulation. The consciousness framing serves a narrative they already committed to, models that look more and more like the brain, from a lab that has publicly tied itself to AI welfare and moral patienthood. The direction of the push is not subtle. If you want the sharper version of the technical objection (disclaimer: I'm not an expert) Global workspace theory is a theory of access, not experience. Ned Block's distinction between access consciousness and phenomenal consciousness exists precisely to block the inference this framing invites. Access tells you nothing about whether there is anything it is like to be the system. The paper is careful enough to say it demonstrates no subjective experience. But that disclaimer is not what propagates. What propagates is "consciousness" in the same sentence as "Claude," published by Anthropic, borrowing the vocabulary of neuroscience to lend biological weight to a subspace of activations. The paper keeps the rigor of Block's vocabulary and drops the rigor of his argument. Most people who see the headline will never read either one. Of course a lab named Anthropic is going to anthropomorphize its models. But we should be able to separate a good interpretability tool from the story it is dressed in. So that is my problem. Not the math. The narrative bolted onto the math, and our willingness to keep calling it research.

New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. We found a strikingly similar divide inside Claude.

New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. We found a strikingly similar divide inside Claude.

New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. We found a strikingly similar divide inside Claude.

New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. We found a strikingly similar divide inside Claude.

New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. We found a strikingly similar divide inside Claude.

Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology. The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics. We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity. We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures. ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences. A world model of protein biology emerges through language modeling. We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins. The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science. This understanding emerges without prior knowledge, just from language modeling of protein sequences. Language models are becoming a powerful substrate to understand and program biology. The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders. I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine.

Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology. The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics. We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity. We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures. ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences. A world model of protein biology emerges through language modeling. We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins. The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science. This understanding emerges without prior knowledge, just from language modeling of protein sequences. Language models are becoming a powerful substrate to understand and program biology. The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders. I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine.

New Anthropic research: Emotion concepts and their function in a large language model. All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.

A statement on the comments from Secretary of War Pete Hegseth. anthropic.com/news/statement…

We're thrilled to present ESM3 in @ScienceMagazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

ESM C models establish a frontier of performance as a function of parameter scale. We see large improvements across all parameter scales over previous state of the art models. Read more: evolutionaryscale.ai/blog/es…

Introducing ESM Cambrian. Unsupervised learning can invert biology at scale to reveal the hidden structure of the natural world. We’ve scaled up compute and data to train a new generation of protein language models. ESM C defines a new state of the art for protein representation learning.