The presumption that the Fed raising short-term rates reduces inflation is predicated on the belief that higher rates reduce demand and investment. But what if higher rates don’t reduce demand and investment because the demand for intelligence and energy is unaffected by higher rates because winning the race for super intelligence has a near infinite ROI and the demand for compute will remain incalculable. Why won’t higher rates at this unique moment in history therefore lead to more inflation as interest costs are embedded in everything? And the problem is compounded as the more the Fed raises rates, the more inflation we will have and the more the Fed will need to raise rates further and so on. But what if the old models don’t apply to the current paradigm and the Fed is wrong? I think the Fed might have just made a mistake. Am I right or am I wrong?

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.

25 years ago today, Jagged Edge and Nelly's 'Where the Party At' was the No.1 R&B single in America.

Today, we’re launching an ambitious new school called The Horowitz Andreessen Academy. Based in San Francisco, The Academy serves the most promising young high school graduates. We think this can be an elite institution that attracts top tier talent. One that prepares students for the future rather than remaining stuck in the past. The #1 goal is to help students learn to build, which is the most important skill in the AI era. They'll learn primarily by pursuing their own projects, either individually or in groups. There are classes and guest lectures, too, from some truly amazing people who have built modern-day Silicon Valley. The Academy is designed as a network, since that’s the reason students go to school in the first place. Core to that network are our 10 Founding Partners: Anduril, Anthropic, Coinbase, Google, Meta, NVIDIA, OpenAI, Palantir, Replit, and Stripe. The network includes over 50 hiring partners and over 200 speakers and mentors. To join as a hiring partner or faculty member, you can apply on our website. We raised $42M in funding led by @a16z. I'll be CEO and @pmarca and @eriktorenberg will join me on the board. Applications are open for our Founding Class Fellowship, which will be one year and tuition-free. Eventually, pending regulatory approval, we plan to offer a two-year program that charges tuition, similar in cost to an elite private university. We're looking for the most unusually ambitious young builders on the planet. Come join us in San Francisco: theacademysf.com/

instinct made up my middle name, and now I can’t board the flight

Q: China is one of the most repressive countries for journalists in the world-- TRUMP: Well, I don't know about that. I think we're repressive also

Today in @Nature, we report a new approach to studying human brain development and modeling disease in vivo. Human neural #assembloids and #organoids have opened new ways to study development in vitro, but they also have important limitations. Transplantation can provide a more physiological environment, yet human cortical cells then develop within a rodent brain that matures much faster, constraining their growth and integration. To address some of these limitations and obtain more advanced functional readouts relevant to disease biology, we generated #apallial mice, in which ~98% of the cortex and hippocampus fail to form, creating space for transplanted human cortical organoids to grow and integrate extensively. We call these #xenocortical mice (XCX). Nearly 7 years in the making. Congratulations to an extraordinary team and wonderful collaborators! Link to the article below 👇

One of the greatest viruses of the modern man is looking at Instagram Stories. You will melt your brain looking at a barrage of outcomes—like vacations, parties, cars—with no visibility of the process that got them there.