Agi 2029 - AI Insider / Reporter as featured in Axios • The Information • NYT • Techcrunch

Joined January 2023
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Two weeks ago, I had shared that Anthropic was working on their Dots competitor. Anthropic has now unveiled Claude Duet, and it’s arriving much earlier than I expected! Duet is a proactive personal AI agent designed to work on your behalf around the clock. (Just like dots and grok bot) It connects to your email, calendar, and other applications to monitor important developments, prepare daily briefings, manage tasks, and autonomously work on longer projects. It’s currently rolling out in research preview through a waitlist. Really excited to see Anthropic entering the personal agent space hopefully they’re able to fill in the gaps missing from Dots and Grok bot!
I’m hearing preliminary rumblings of Anthropic prepping their dots / grok bot competitor. Early stages I could only imagine how many people would jump ship for an Ultra fast fable 5.5 agent. I am being wishful when I say “ultra fast” OpenAI is already prepping new fast chips. They can really pull away if they’re able to get out a 6.5 level model at Ultra fast speeds before Fable 5.5. Opus 5.5 is a phenomenal coding model.
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Wait a minute. Has nobody considered what Anthropic launching Claude Duet means for Grok Bot? Grok Bot is rolling out Claude Opus 5.5 as its underlying reasoning model. This was a great move by SpaceXai But…Anthropic just announced Duet, a first-party agent built around that same model, with native memory, skills, connectors, and its own execution environment. Why would I use Opus 5.5 through Grok Bot when I could use Anthropic’s own agent harness, which I’d expect to be better optimized for its models? Is this a “I don’t wanna play with you anymore situation”?
🤖 Made with AI
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@thsottiaux You won. She’s cooked.
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Fable 5.5 is in the wild, I repeat fable 5.5 is in the wild this is not a drill ‼️🚨
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@thsottiaux I’m going to help you dunk on the potato girl
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QUICK! We need someone to go to door to door to all the Mom and pop shops before they get completely obliterated
Effective Immediately, all organizations using PHP for their systems are advised to be prepared for an update addressing two critical vulnerabilities. PHP is widely used for web applications. These vulnerabilities often provide a direct path to the server's file system or database. If an immediate update is not feasible, it is recommend that organizations take the system offline until it can be fully mitigated. NSA will follow up shortly with more specific guidance.
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Recursive AI just released Flint, a 40B parameter MoE coding model with only 2.5B active parameters per token, reportedly achieving over 125 tokens/sec on a MacBook Pro while remaining competitive on agentic coding benchmarks. Apparently they have something called their Eureka Machine which automates the research loop of proposing hypotheses, implementing experiments, evaluating results, and iterating on improvements. It reportedly contributed to everything from model architecture and training recipes to quantization and inference kernels.
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
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Dario finally confirms Anthropic is: “"Anthropic is over 5,000 people." They’re catching up to OAI. some other quotes I enjoyed. “We likely sacrificed a significant chunk of a several-month model lead.” - speaking about mythos. "I will have more to say soon about how we are going to further live our values in this moment of rapid change." "We are nearing that point and that’s what’s driving some of our thinking about 'pacing.'"
Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic. That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it. I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits: 1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed. 2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it. 3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do. 4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this. 5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future. 6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent. 7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it. 8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company. 9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund. All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking. For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea. Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option. I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent. But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole. To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years. I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better. Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so. Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way. Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.
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This is just the beginning. Anthropic will continue pursuing biology heavily. The teams at Anthropic are fairly segregated, with each working on different fronts. The life sciences team may not know what another team is working on, but they’re all pursuing separate research goals. Every team is going to be asked how they can help Anthropic reach half a trillion dollars in revenue. There’s some low-hanging fruit: GLP-1s, peptides, disease treatments, etc. Expect to hear many more lab updates this year.
Anthropic is cooking in the lab 🧪
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SCOOP: Anthropic will be announcing another biology/process breakthrough in the next two weeks, and it will be even bigger than this one. This was not mentioned in the report, but it was specifically analyst number 3 on this project who found the discovery for the anti-venom. I know it’s not Fable 5.5, and sorry to disappoint some people, but this is an important breakthrough in biology. A group of non-experts from a financial investment research firm worked with Anthropic to direct Claude through a protein discovery campaign, that produced 17 experimentally validated protein binders targeting scorpion neurotoxins. Scorpion stings affect roughly 1.2 million people annually, and these proteins could eventually help develop new antivenoms. This is just the beginning for the discovery process, and us being able to synthesize actual medical products that can help people all around the world!
Last week, I teased a significant healthcare discovery at Anthropic, specifically mentioning Mythos 5 and its role in the scientific discovery process. Well, Anthropic just published the research! Working alongside Citrini Research, an internal Claude model autonomously generated over 318,000 protein designs in just 64 hours. Of the 211 candidates selected for laboratory testing, 17 successfully bound to four dangerous scorpion neurotoxins. Keep in mind, this project was initiated by a financial investment research firm, not a biotech company. Non-experts were able to direct an AI system through a genuine molecular discovery campaign and produce experimentally validated results. The implications here are enormous. We are quite literally democratizing access to biological research in ways that could accelerate the discovery of treatments for rare and neglected diseases, potentially helping millions of people who have historically been underserved by traditional drug development.
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There is actually no date for the “much much bigger” breakthrough - a lot of interest in GLP 1s, & peptides.
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“That’s it?” Anthropic generated $16.8 billion in Q3 alone, exceeding the entire first half of 2026. So, - 45% quarter-over-quarter revenue growth. But zero hedge and others will completely ignore the staggering revenue growth we’ve seen for the second half of the year.
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*ANTHROPIC HAD $16.3B IN REVENUE IN FIRST HALF OF 2026: WSJ that's it?
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Post idea inspired by @ChaseBrowe32432
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Been hearing some pretty interesting things about a small AI research team experimenting with post-training Qwen 3.8 27B. Apparently their post-hoc GQA → MLA conversion, which reportedly reduces KV-cache memory requirements by up to 8.4x without additional training
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Don’t mean to disappoint chubby ! I was specifically referring to the healthcare breakthrough. I’ve been pretty excited about for the last week !
Looks like today is the day: Fable 5.5 coming this tuesday
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I think specialized inference hardware will become increasingly important as AI agents continue to scale. When agents are chaining hundreds of model calls together, memory bandwidth, latency, and cost per token become increasingly important bottlenecks. Good to see General Compute partnering with Positron to expand its heterogeneous cloud infrastructure. Looking forward to seeing more diversity in inference hardware over the next few years.
🤝 Paid partnership
Our heterogeneous cloud just got better. We're announcing a cluster scale deployment of @positron_ai as our next partner. We believe demand for fast inference will outstrip supply for the next decade. Our bet on Positron is another step towards getting fast inference for opensource models into builders' hands. Hardware goes live Q1 2027. Thanks to @mitesh711, @trsohmers and the whole Positron team! We're excited for this partnership. LFG!
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This robot makes me happy, not necessarily because it’s useful or some major technological breakthrough, but because it genuinely looks like something I’d expect to see in Star Wars or another sci-fi movie. I love seeing companies experiment with unique robotic form factors, and I’m especially happy to see another East Coast robotics company entering the space. Tuesday Labs is targeting a price of around $20,000, which honestly isn’t that bad. They’ve even brought in former Pixar animators to help design its personality and movements, with plans to ship in 2028. Always happy to see more robots in the world.
Introducing Voxel, a droid butler made by Tuesday Labs.
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Last week, I teased a significant healthcare discovery at Anthropic, specifically mentioning Mythos 5 and its role in the scientific discovery process. Well, Anthropic just published the research! Working alongside Citrini Research, an internal Claude model autonomously generated over 318,000 protein designs in just 64 hours. Of the 211 candidates selected for laboratory testing, 17 successfully bound to four dangerous scorpion neurotoxins. Keep in mind, this project was initiated by a financial investment research firm, not a biotech company. Non-experts were able to direct an AI system through a genuine molecular discovery campaign and produce experimentally validated results. The implications here are enormous. We are quite literally democratizing access to biological research in ways that could accelerate the discovery of treatments for rare and neglected diseases, potentially helping millions of people who have historically been underserved by traditional drug development.
New on our Science Blog: designing protein binders against scorpion neurotoxins. Scorpions sting about 1.2m people every year, and antivenoms are often costly or in short supply. Claude has made an early step toward finding new treatments. anthropic.com/research/claud…
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