La vivienda no puede ser un negocio. La usura es un cáncer.

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

Five years will become perpetuity. Once investors experience faster, modern, always-on markets, there will be no going back, regardless of who is at the SEC, in the White House, or in Congress.

I'm speechless.

🔴 ÚLTIMA HORA | Jueces para la Democracia ve desproporcionada la medida del Supremo de suspender el derecho a voto de los nacionalizados con la 'ley de nietos' cadenaser.com/nacional/2026/…

🇩🇪 Friedrich Merz, canciller alemán: “Debemos desregular todos los sectores. Pido un borrón y cuenta nueva regulatoria. Las pequeñas correcciones legislativas no son suficientes. Necesitamos revisar sistemáticamente toda la legislación vigente de la UE”

Vídeo de @juanrallo en 3, 2, 1…veámoslo

I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.

The reason why Bitcoin is going up forever:

EL MERCADO SIEMPRE GANA Cuando intentan abolir el mercado, ¿qué pasa? Cuando controlan los precios, aparece la escasez. Cuando aparece la escasez, aparece el mercado negro. Cuando racionan los bienes, aparece la reventa. Cuando prohíben comerciar, aparece el contrabando. Cuando castigan las ganancias, aparecen nuevos incentivos para comerciar por fuera del sistema. Cuando prohíben un producto, aparecen sustitutos. Cuando destruyen una moneda, la gente busca otra forma de conservar su riqueza. Cuando el Estado separa artificialmente la oferta de la demanda, el mercado vuelve a conectarlas por otra vía. Cuando prohíben la ganancia, aparece el incentivo clandestino. Cuando bloquean un canal de intercambio, aparece otro. Cuando intentan eliminar los incentivos, los incentivos encuentran otra salida. Cuando intentan controlar la demanda, la demanda busca otra oferta. Cuando intentan controlar la oferta, la oferta busca otro mercado. Cuando intentan expulsar al mercado, el mercado simplemente cambia de forma. EL MERCADO SIEMPRE GANA.

La segunda residencia española de los sesenta y setenta no fue solo una operación inmobiliaria, fue un marcador de posición social. Tener una casa fuera significaba haberse subido al ascensor social. Medio siglo después, cada vez menos personas puedan permitirse una primera (y única) residencia elpais.com/estilo-de-vida/20…

Jordan Peterson explains Hayek’s “knowledge problem” argument “The proposition that central planning will work is the proposition that you can substitute one expert mind for a million distributed expert minds.” “That’s obviously not the case, because each person is going to have knowledge that pertains to their locality that isn’t accessible to everyone. So it’s much better to let everyone make the decisions and sum them.”