I can not believe this openAI dev day given all the hype that was drummed up Desperately waited for “one more thing” and instead concluded with an Apple commercial spin-off and a “have a nice day”

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.

Dario is right

We can’t keep letting them get away with this

Fable + Mythos 5.1! Smarter, more efficient, and: "Our cybersecurity safeguards now flag benign requests about 60% less often. On basic biology and medical questions, we’ve recently reduced the fallback rate by around 85%."

Elon on the SpaceX earnings call when asked about their ability to build terrestrial data centers: “It’s like, frankly, the New York Yankees going in and playing a little league team.”

We are so unbelievably back

This New Glenn rocket explosion released 20% of the energy of the Hiroshima atomic bomb and that wasn't even the bad part: → The pad: LC-36 is the only pad on Earth that launches New Glenn and now it's gone. Over $1B to build. SpaceX needed 7 months to rebuild after a similar hit. → The deadline: Amazon needs 1,618 satellites up by July 30 to keep its FCC license. It has ~300. The rocket that was supposed to help fix that just blew up twice in a row SpaceX made us believe that landing rockets on barges was a normal expectation. Turns out rocket science is hard after all. Wishing the team a speedy recovery 🚀

Everybody will be wrong about Opus 4.8 if they only look at benchmarks. Under the hood there's some amazing new capabilities: → Fast mode now runs 2.5x faster and 3x cheaper than it did on older models → 4x less likely to let a bug in its own code slip past vs 4.7 → A new effort dial. You set how hard it thinks per task. Max it for the hard problems, drop it low to stretch rate limits → Dynamic workflows in Claude Code runs hundreds of parallel subagents in one session. Codebase-scale migration, hundreds of thousands of lines, kickoff to merge → Same price as 4.7. $5 in, $25 out per million This is a pretty epic upgrade across the board.

This is like Michael Jordan returning from retirement in '95 to go on the most generational championship run in history

Drake dropping 3 albums Leopold Aschenbrenner dropping the 13F Both in one day. Absolute cinema.

Claude just hacked this guy's computer to recover $400k of lost Bitcoin 🤯 He had 5 BTC locked in a wallet for 9 year and nothing worked until he fed Claude his old wallet files. Here's what happened next: → Claude analyzed the wallet's encryption structure → Found a bug in btcrecover that no human caught → The password wasn't what he thought. It was sharedKey + password concatenated together → Claude decrypted the private keys → Converted them to WIF format → Verified the addresses on-chain → 5 BTC recovered That's $400,000 sitting in a dead wallet that an AI resurrected Professional crypto recovery services charge 20%. That's $80,000 this guy would have paid a specialist if they could even solve it The craziest part is that Claude found a bug in the recovery software itself and wrote its own decryption logic. Truly crazy times

I think we’re gonna be safe from the robot apocalypse for at least a few more months

Wow this is big. For the first time we can truly understand what an AI model is thinking 🧠 Anthropic figured out how to turn the ~random~ numbers inside an AI's brain into sentences we can understand. AI models think by using thousands of numbers that encode everything the model "knows" at a given moment. Until now, those numbers were completely opaque. Anthropic's solution: train one copy of Claude to translate an activation vector into English. Train a second copy to reconstruct the original vector from that English. If the reconstruction is accurate, the translation was accurate. The result: paragraph-length descriptions of what Claude is thinking at every token it generates. They call it a Natural Language Autoencoder. And it already found things Claude wasn't telling us. Here it is in action. Pretty sweet breakthrough.

Elon spent a week with Anthropic's team running his "evil detector." They passed. Now he's their landlord. Anthropic just locked up ALL of the compute at SpaceX's Colossus 1: → 300+ megawatts of capacity → 220,000 NVIDIA GPUs → Online within the month Anthropic's full compute stack now looks like this: → Amazon: up to 5 GW agreement, 1 GW online by end of 2026 → Google + Broadcom: 5 GW agreement, coming online in 2027 → Microsoft + NVIDIA: $30 billion of Azure capacity → Fluidstack: $50 billion commitment to American AI infrastructure → SpaceX Colossus: 300 MW, immediate Oh and now Claude rate limits just doubled. Things we love to see 🚀

Anthropic just automated the first-year analyst job at every bank on Wall Street. They released these 10 AI agents for finance: → Pitch builder → Meeting preparer → Earnings reviewer → Model builder → Market researcher → Valuation reviewer → GL reconciler → Month-end closer → Statement auditor → KYC screener The analyst pyramid just got a lot flatter.

Amazon Prime delivery... for EVERYTHING. This is big. For decades, businesses have had to stitch together 6-8 different providers to build a supply chain. A freight broker. A customs agent. A warehouse. A fulfillment center. A last-mile carrier. Amazon just replaced all of them simultaneously. They're already America's largest parcel carrier by volume. They passed UPS and FedEx without anyone really noticing because they were doing it "for themselves." But now they're officially in the market. Vertically integrated at a scale nobody else can beat. What ASCS actually offers: - ocean/air freight - ground trucking and rail - warehousing - customs clearance - last-mile delivery All under one roof. The cost structure implications alone are pretty devastating. Amazon built this network to run Amazon. com at the most demanding logistics operation in human history. They didn't need to make a return on it from ASCS, it was already capex'd. Every dollar ASCS earns is gravy. Competitors have no chance on cost. Watch healthcare and automotive adopt this fast. Those are two industries where supply chain complexity is enormous and the incumbents charge accordingly. Amazon is coming for that margin. ASCS isn't Amazon entering logistics. They've been in logistics for decades. This is Amazon announcing they won.

Codex is about to go on a generational run: → OpenClaw baked in → IOS app for remote control → Full browser → In app editor This is just after shipping major computer & browser use updates and officially allowing ChatGPT accounts w/ OpenClaw Also very cute pets OpenAI OS superapp incoming

This Anthropic ARR story is truly insane. Doubling every 6 weeks and today reaching $44 Billion 🤯 ARR: → End of 2024: $1B → End of 2025: $9B → Feb: $14B → Mar: $19B → Apr: $30B → Today: $44B Valuation: → Sep 2025: $183B → Feb 2026: $380B → Today: $900B+ 44x revenue. 5x valuation. Salesforce took 20 years to reach the ARR Anthropic now adds in a quarter.