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.

$AXP American Express CFO Christophe Le Caillec: “So the building blocks of our financial model if you want are working really well, so in Q2 we raised a little bit our guidance when it comes to revenue. We guided towards 10%. And we reaffirm our EPS range which, by the way, does not include the gain that we expect to make on the sale of our GBT shares or how we're going to use the proceeds.”

$WMT: Walmart advertising carries >70% margins versus roughly 5% for core retail “We have a great high-margin advertising business, over 70% margins compared to 5-ish percent for the core retail business. And this profitability is incremental to Walmart.”

$SIRI Sirius XM Holdings CEO: YouTube expands SiriusXM’s addressable advertising audience to 255M monthly listeners, equivalent to ~90% of Americans aged 13+ “With YouTube now, we're going to be at 255 million monthly listeners, and that's 90% of the U.S. population above the age of 13. So it's just -- it's the scale of the audience that we have, the scale of the types of content we have and the scale of the advertiser relationships that I think maybe are not fully appreciated.”

$JPM JPMorgan Chase CIB CEO Douglas Petno “We're already starting to see corporates that have product exposure, revenue exposure to the very low-end income demographics start to see some weakness, but nothing systemic that's concerning us at the moment.”

$SIRI Sirius XM Holdings CEO: YouTube expands SiriusXM’s addressable advertising audience to 255M monthly listeners, equivalent to ~90% of Americans aged 13+ “With YouTube now, we're going to be at 255 million monthly listeners, and that's 90% of the U.S. population above the age of 13. So it's just -- it's the scale of the audience that we have, the scale of the types of content we have and the scale of the advertiser relationships that I think maybe are not fully appreciated.”

Treasury Secretary Scott Bessent on CNBC: “Imagine these labs came out—or one lab in particular—and a sitting employee said there’s a 10% chance of an extinction-level event. But then the labs also said, ‘Take the liability off our hands.’ We will not do that. “I am in agreement with Daniel Huttenlocher, an MIT professor who leads the AI lab there, that it is humans who are responsible, not the AI. The Hugging Face incident is the responsibility of OpenAI’s management, not a bunch of agents. “What the president was saying is that we cannot say, ‘We absolve you of responsibility, and the government is going to take responsibility.’ These labs need to take responsibility for themselves. They can slow down any time they want to.”

$JPM JPMorgan Chase CIB CEO Douglas Petno “We're already starting to see corporates that have product exposure, revenue exposure to the very low-end income demographics start to see some weakness, but nothing systemic that's concerning us at the moment.”

we built muse from scratch, but it is definitely heavily inspired as a product by openclaw. After I used openclaw in january I bought hundreds of mac minis for the MSL team and lots of us fell in love with using openclaw (and other personal agents). @steipete is a genius and his harness was pioneering from the jump. I think a lot of people were inspired by it. our goal with muse was to build something like openclaw that we could make safe and secure and easy to use and scale to billions of people.

This will be a bigger battle than anyone anticipates. It is only a matter of time before there is an Apple and Google version of Muse and possibly TikTok, in addition to the frontier LLM agents. Maybe a commerce agent from Amazon. Every app that is a services, marketplace or commerce app will need to existentially decide to open APIs for consumer agents to interact. Smaller players have no choice. Ad revenues are more than transaction fees, either the consumer benefits or distribution aggregators will demand a higher transaction fare. I know I don't want an agent for each app. I would like my agent to be able to do tasks I require. We can already see consumers getting trained on that behavior by the frontier labs. Those with network moats - restaurants, groceries, drivers might be able to withstand for a while, over time convenience and end user experience will win and they will have to align. Content moats (protected by copyright) could decide to allow agents or chose to hold on to the consumer interaction. I suspect other than the feeling of a lack of control, it won't change their economics. Commoditized back ends will need to worry, insurance, tickets, hotels, services - if they don't adapt new players will.

AI was featured on 67% (331 of 493) of S&P 500 Q2 earnings calls, the third consecutive quarter above 65% and nearly double the five-year average of 178 calls. Companies citing AI gained 15.7% YTD versus 8.1% for those that did not. [@FactSet]