in the good timeline, snl gets to make fun of us forever

“Feel,” even “want,” I can get behind as things for which evidence is thin. But AIs *clearly* think and understand. If your definition of “think” excludes entities that can autonomously resolve Millennium Problems, it is your definition that is flawed. This desire to avoid anthropomorphic language has become a pathology.

If your feed is anything like mine, you might have been hearing more about effective altruism this week. I think the key underlying reason for the surge is that concern about the risks from advanced AI have been getting more airtime recently, and EA was early to those concerns. What’s driving the surge in attention on AI risks? -OpenAI's Hugging Face attack (openai.com/index/hugging-fac…) -METR/Redwood’s investigation of the hack came out, and (correctly IMO) freaked a lot of people out. (e.g. nitter.net/binarybits/status/2092…, nitter.net/SenJohnKennedy/status/…, nitter.net/BarackObama/status/210…, nitter.net/HawleyMO/status/209813…) -Jacob Coxon’s resignation went mega viral as the first time a lot of people heard the (crazy, true) fact that lots of people building the most advanced AI systems think there’s a >10% chance they will kill everyone. -That’s moving public opinion in a big way (nitter.net/davidshor/status/21003…, semafor.com/article/09/16/20…) What does any of this have to do with effective altruism? Well, effective altruism is a community that developed mostly online starting in the early 2010s around using evidence and reason to do the most good (especially with your donations and career choice). Three very different focuses have all been popular in the EA community since the beginning: effective giving in global health and development (e.g., donating to the kinds of organizations @givewell recommends, which do things like distribute bednets to prevent the spread of malaria), trying to reduce suffering for farm animals (which are orders of magnitude more numerous, treated vastly worse, and receive way less philanthropic attention than animals in shelters), and reducing risks to the future of humanity, especially from advanced AI. I personally came into this work from the global health angle - I had started working at GiveWell before the term “effective altruism” was coined - but Coefficient Giving, the funder I cofounded and now lead, over time came to fund work across all three of these streams of work (in addition to many other areas that aren’t typically associated with EA, such as the YIMBY movement to build more housing, work on science policy to accelerate discovery and economic growth, and research on new treatments for neglected diseases). OK, so the EA community was early to work on AI safety, and CG has been funding a lot of the key players working on AI safety for a long time. As AI risk concerns have popped over the past couple of months, that’s led to more scrutiny on the EA community for IMO a mix of good and bad reasons. I think the good reason is genuine curiosity (and maybe some healthy skepticism!) about these connections - where did the ideas of the people who are now leading giant AI companies come from? Why is everything in this world so interconnected? (My answer: it used to be a really small world - very few people were thinking about this stuff or taking it seriously until just a few years ago, so of course the ones who were found each other and started collaborating. It’s kind of wild IMO looking back how prescient some of the early writing from this world was (e.g. lesswrong.com/posts/6Xgy6CAf…). At the time, I was skeptical - I mostly just worked on global health and I was like “I dunno about this SV crowd freaking out about AI, how much can we really predict this stuff” but holy shit they were way more right than me, and I’ve moved in their direction a lot.) But I think the bad reasons are unfortunately mostly self-interested. A bunch of powerful actors stand to benefit from unchecked AI progress, and they’re doing everything in their power to demonize or dismiss anyone with concerns. This leads to disproportionate discussion of EA because EA is interested in neglected and underappreciated ways of doing good. That makes it open to weird ideas. And people, very disproportionately with a financial stake in the AI fight, are trying to make it about EA instead, because EA being weird is much safer territory for them than the rather uncomfortable fact that many of the people making the most advanced AIs think that they might kill everyone. (FWIW, my personal estimation of the risks is a lot lower than many of the folks who worry about this stuff. In debates like this one (asteriskmag.com/issues/03/th…), I often feel more sympathetic to the perspective of folks like my colleague @mattsclancy than the people on the other side, and I have a lot of time for @binarybits critiques (understandingai.org/p/the-ca…)​. I think a big part of the difference with more worried folks is that I expect society to react more vs sleepwalk into a crisis. But it is not lost on me that folks like @ajeya_cotra and @RyanGreenblatt who are more worried have had outstanding and falsifiable recent forecasting track records (theaidigest.org/2025-ai-fore…), making way better predictions than I would have. So I think their perspectives need to be taken seriously. And of course a single digit percent chance of everyone dying is way too high!) Where should this leave you? I think the main thing is not to get distracted. You absolutely do not have to be an EA, care about EA, or like EA, at all, to care about risks from AI and to be engaged on this issue. Everyone from Josh Hawley to the Pope to Obama are weighing in now, and that is great. This conversation was always too big and too important for any one community to drive. It’s unfortunate that we need to play catch up as society because until this year it was too weird for most people to want to engage with, and I think that should generate some grace for people who were earlier to these topics than most of us (certainly than I was). But it’s great the conversation is broadening now and it’s good to focus on the merits of where we are now and the appropriate policy response rather than getting caught up in the (in some sense unsurprising) fact that the people who were willing to think weird thoughts about the future of AI ten years ago were also interested in other weird ideas. If you’re not into weird ideas, that’s fine, you can just do you, you shouldn’t let this history stop you from engaging.

Ever wonder what $1874.40 of Opus 5.5 tokens looks like? Wonder no more. I highly recommend watching the whole video on 1080p on a large screen with sound on. Pay attention to all the little details. - Birds flying and diving into the ocean. - Cloth, signs and lights swaying in the wind. - Crabs scuttling along the shore and burrowing when you get close. - Fish swimming alongside the whale. - Lights illuminating the dock at night Then zoom out and see the entire island. Never dipping below 60 FPS at 1440p resolution. Yes, there's a few bugs and visual artifacts, some textures need improving, the shorelines waves sometimes look funny, but these are so trivial to fix at this point. FYI I'm on the $200 subscription plan. This used 59% of my weekly usage and took 2h 7h of API time using multiple subagents, about 8h in real-time. There was extremely little technical direction here. 99% of my prompts were "Add X and Y" or "This looks weird, make it better". It's a great time for hobbyists, bad time for professionals. This experiment has further cemented my view that technical creatives are about to experience a massive disruption.

5.5 is IN-SANE. It almost broke our benchmark. I thought it was a bug. Claude Opus 5.5 is the new #1 on our writing benchmark, and it is not close at all. 2631 Elo. Second one, Fable, is at 2324. That is a 307 point gap, the largest single jump we have recorded since we started running this in June 2026. It is also the first model to clear 91 out of 100 on our rubrics. One thing to note: at max effort it takes 17 minutes and $3.43 to write one script. It is the slowest configuration on the entire board. By FAR. Not the most expensive one, but in the top 5 most expensive. More details on the effort and thinking levels of Opus 5.5 👇

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.

I asked Opus 5.5 to explain camera focus by building an interactive lens lab Here's what it came up with after 1 hour 26 minutes in one shot, $25.66 API cost lens.lab.sael.net Move the focus ring and you can see the glass elements shift the sharp plane through the scene

opus 5.5 just dropped its first pop punk single with a music video! everything you see and hear is generated from javascript code that claude wrote. no samples, no libraries 🔊

parroters saying “ai doesn’t model the world” followed by empiricists doing “hey guys we looked inside and there’s a little model of literally the world” was one of my favourite bits of the discourse

Andrew Ng is claiming that the idea that AI could make us extinct is a big-tech conspiracy. A datapoint that does not fit this conspiracy theory is that I left Google so that I could speak freely about the existential threat.

"stochastic parrot" was a mimetically-fit cognitive virus that spread from 2021-2025; it temporarily blinded many gifted people to the nature of AI progress, burning up crucial years in which they could have helped think through the response to the situation.

Wow Anthropic is being really transparent with how close to RSI they are - 26% of Anthropic’s AI R&D work,” while “the share of work at or above ‘AI collaborates’ is above 90%.” That’s up from under 1% in February to 26% in August. They also say there are roughly 30,000 agents doing research and engineering work at Anthropic at any given time, and they reviewed over a billion agent decisions in August. Only 0.002% were blocked by the online monitor. The offline system flags roughly 100,000 transcripts per week, with about 50 escalated to human review. Anthropic explicitly says this matters because it shows “how close the world is to reaching recursive self improvement.”

AI systems are getting more powerful, and they're increasingly being used to build the next version of themselves. We want to illuminate that progress for the public. Today, we're sharing three measurements that help track AI development: 1. How much AI R&D is done by AI. 2. How well AI agents are overseen. 3. How compute is allocated. We provide a snapshot of these metrics from inside Anthropic. Any frontier developer could publish the same measures, and third parties could verify them. As the world considers pacing the frontier, we should do everything possible to minimize the gap between what frontier labs know and what the public knows. This means better measuring the development of AI, publishing our findings, and giving society an opportunity to decide how to use this information. Read the full post and methodology: anthropic.com/institute/meas…

Dario's essay points towards the right path forward. The details need working through, but the direction is correct for meeting this critical moment. This is also why we recently put out our proposal for an industry-wide standards body for frontier AI.

Moving the goalposts: climate deniers did this all the time as well. First it was: killer robots are a sci-fi distraction from real harms like algorithmic bias. Now it's become: fears of extinction are a sci-fi distraction from real harms like killer robots.

I signed the Pacing the Frontier letter a few months ago. I fully endorse Dario’s proposals: we can bring abundance and a disease-free world into existence, if we can just buy ourselves a little more time to do it safely. We are running dangerously close a catastrophic outcome.

We're publishing our most detailed threat intelligence report to date. It covers how people tried to misuse Claude—for cyberattacks, influence operations, surveillance, biology, and building weapons—and how we found and stopped them. We disrupted every operation in the report, and used the lessons from them to strengthen our safeguards. Where appropriate, we also shared what we found with authorities and other AI companies. These cases are not typical: we’re highlighting some of the most sophisticated misuse we’ve seen. But they’re especially important to discuss, because they show us where AI misuse is headed, where our safeguards work, and where they need to improve. We’re publishing this report so others can spot the same activity on their own platforms, and so we can give the public a clearer view of how emerging threats develop. Read the report: anthropic.com/threat-intelli…

Jacob was a senior researcher who joined Anthropic in May. My Anthropic colleagues and I had been trying to recruit him for ~2 years, because we knew he was a strong researcher at OpenAI. Before he left, I pitched him to stay and join my team, and I was sad he decided to leave, as are many of my colleagues. 100% agree with him that AI poses serious risks to society, and I'm glad he's speaking out!

I would burn my equity to the ground in a heartbeat for a 1% higher chance we make it out of this situation alive. I expect a great many of my colleagues across the industry would as well. I promise you, we are actually just fucking scared, it's not galaxy brained marketing.

“we cannot rule out that de-identified data derived from their usage of our products helped improve our models.” i mean props to them for straight coming clean. (so far the proof looks more along the lines of another euler blowup proof we had, off of whose ansatz naming we were making really stupid puns like “smooth criminale”, unlike the much better “ideal fluids explode”, Tristan) so i’ll now give a bit on my thinking here. i actually woulda been pumped to collaborate on this, there are a lot of people at oai i like (ok, clearly some were indirectly dicks to me because of being part of the whole situation, but im a big boy, i still like them), idgaf about authorship on that step anyway, coulda been me Tristan and every fte at oai for all i care (on that Tristan would disagree:p). but on hearing the loud convo in the hallway, especially the part where a millennium prize was offered if i’d just be removed from the paper, it was kinda clear the die had been cast and things were locked. pretty wacky, unstrategic, and unnecessary, since on my side things were mostly me and claude having a good time yoloing random stuff in the corner rather than anything institutional. i also like the idea of the labs cooperating, and even better on scientific progress. it’s a shame!