🚨🚨HISTORIC🚨🚨 #49ERS# QB BROCK PURDY HAS THE HIGHEST CAREER PASSER RATING OF ANY QUARTERBACK WITH AT LEAST 1,000 PASS ATTEMPTS. Purdy’s 104.9 rating is ahead of Lamar Jackson, Aaron Rodgers, Patrick Mahomes, and every Hall of Famer. INSANE STAT. 🤯🤯🤯

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.

With @elonmusk & @sama both agreeing with @DarioAmodei 's recommendations, all eyes now turn to @Google ! @sundarpichai are you also supportive? Can we make progress on this critical issue at this critical time together as an industry?

With @elonmusk & @sama both agreeing with @DarioAmodei 's recommendations, all eyes now turn to @Google ! @sundarpichai are you also supportive? Can we make progress on this critical issue at this critical time together as an industry?

I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we've had at OpenAI in recent weeks. Committing to having independent evaluators with employee-like access is a great idea, and we will do the same. We'll have more to share soon.

When I was at SendGrid, the company needed me to be a different leader each year. I sat down with @MaxJunestrand to discuss how his leadership changes at @WeAreLegora, but in his case—it’s 𝗾𝘂𝗮𝗿𝘁𝗲𝗿𝗹𝘆. From Max’s pre-YC days drumming up momentum to going all in on hiring at scale, we cover it all.

Pause our national security and economic growth so that China can lead?! Even if it made sense to freeze innovation (which it doesn’t!) it’s ludicrous to think this could ever be implemented. We’d be committing an “own goal” of epic proportions to unilaterally attempt it.

“openai should pay its taxes” is a coherent position “openai should pay its taxes, then personally build our housing and run our schools because city hall didn’t” is not but that’s the argument being by the sf gate in this article the funniest part is that if openai actually did it, the backlash would be immediate build 1000 apartments in the mission? gentrification start a school? billionaires shouldn’t control education fund a hospital? keep tech out of healthcare we don’t even have to speculate. we’ve run this experiment already zuck gave $100m to newark schools and got attacked for billionaire influence over public education he and priscilla chan gave $75m to sf general, the largest private gift in its history. a few years later, people were protesting to get his name off the hospital ray dalio put $100m toward helping connecticut’s poorest students. the public-private partnership collapsed amid fights over transparency and private influence so what exactly is the acceptable option here? pay taxes? apparently not enough directly solve the problem? unacceptable corporate influence give the government even more money? that’s just taxes again if sf takes hundreds of millions from openai and still can’t deliver housing or education, the obvious question is what happened to the hundreds of millions not why openai hasn’t started running schools and building apartments itself

Well written and you're absolutely right. These two-tier deal structures are getting out of hand! I've seen several more this week pitched into our existing portfolio and on new deals. The optics were initially to trick journalists and media into only covering the higher number (two tier optics). Now they're extending to try to obfuscate things for employees and other co-investors (two tier economics). This is PE style games. Venture is all about being simple and aligned. The more straightforward and clean the deal in the early days, the better for everyone. If you have to build a model to understand different return scenarios in a positive outcome for different shareholders (employees or investors), that's generally a bad sign. This is going to end badly.