This @NewYorker story is so good, and so important for everyone to understand. Through the lens of semiconductor manufacturing, it highlights the interconnectedness of today’s world and puts to shame anyone who thinks that we can go back to a world shaped only by national rivalries rather than international cooperation and trade. “The semiconductor supply chain is a planetary superorganism with its own ecology…. Globalization has become tainted in our eyes, as if it necessarily constitutes a zero-sum enterprise. And its mercenary form—which is also its most common form—has indeed inflicted harm. But the global semiconductor supply chain reflects an alternative pathway. It has united its participants in the pursuit of something profoundly difficult that could not have been achieved in the absence of intercontinental solidarity.” newyorker.com/magazine/2026/…
What game do we want to be playing? Some thoughts on how we get to the game of AI and human flourishing in my keynote at last week's AGNTcon+MCPcon on The Missing Mechanisms of the AI Economy. Despite the preview image, the link should go right to my segment. youtube.com/live/eB9I4Ixx2wc…
Tim O'Reilly retweeted
PHP’s incident is a warning about legacy software and AI-accelerated vulnerability discovery.
“If that were the end of the story, it wouldn’t be notable. But the problem goes a lot deeper.”
Mike Loukides, O’Reilly Radar: bit.ly/3UNt4Ae
Tim O'Reilly retweeted
I'm starting to sense a divide in the tech industry:
- People thriving on autonomy who are reinventing how they work and what tools they use with AI: LOTS of work, but they're enjoying it and getting very efficient
- People used to being mentored / trained / or simply told what to do and how. Frustrated with the lack of guidance from management on what to do with AI; also frustrated by seeing some of their peers thriving; increasingly bitter about AI and the state of the industry, as a whole
Neither group seems to be aware that there's another group, or have empathy for the other group
Tim O'Reilly retweeted
A gentle reminder to teams building agentic systems.
Have fun; enjoy the process of exploring novel architectures involving flashy new toys; learn new things along the way.
However
Don’t forget that you are building these things to solve a real problem. Your customers require results; they need value; they want solutions.
Tim O'Reilly retweeted
AI should help people create and flourish.
@timoreilly and Demetrios Brinkmann explore human-centered AI, and how expert context changed an agent’s hiring advice.
Watch the full conversation: bit.ly/4ij0fn3
Tim O'Reilly retweeted
My hot take from chatting to @poteto is that we should use MORE abstractions in the AI age
You can use them (combined with harsh lint rules) to reduce the design space available to the agent and constrain them only to good decisions.
Combined with the fact that high-leverage abstractions let you do more with less code - so, more token efficient.
Plus, unwinding the damage from a bad abstraction is much cheaper with agents.
This runs counter to a lot of folks thinking that agents just want to read the raw code. They can, but they're not maximally efficient that way.
Be braver! Design abstractions.
Tim O'Reilly retweeted
Dreams of AI ending aging and of AI killing everyone through a purely in-silico-designed pandemic both rely on this fallacy.
It's understated here how much we lack ground truth data about what's happening inside of cells, or sensors to get that data. A single cell contains billions of protein molecules, across at least thousands of types, and at least trillions of interactions per second.
Think we solved protein folding? We've made enormous progress predicting static protein structures. But these proteins constantly change their conformation and interact with one another, often in ways we can't observe or model accurately.
Our best sensors capture a miniscule fraction of the activity in a living cell, orders of magnitude short of what's happening.
We actually lack the ability to perceive what's going on that would be required to build such a bottoms up simulation.
Let alone to scale this up to an organism or a global population.
We know we have an expanding universe not because of breakthroughs in theory or computation - but because Edwin Hubble got new data by looking through the Hooker telescope. Possibly the greatest mind, Einstein, was wrong in his model of the universe because he lacked the right instrument to bring him the right data.
That's where we are in biology. Our sensors are still so crude. No amount of pure computation or AI can build a perfect model.
We need an instrument revolution in biology, not just a computational or intelligence revolution. AI can generate hypotheses, but without sufficient ground truth data, we can't know whether our simulations are modeling reality.
If you had an exponential amount of compute you could solve simulate biology exactly and wouldn't need to run any experiments.
Some of you will think "of course, but that's an unrealistic assumption" and others will think "no, you need experiments even with that much compute. That's just How It's Done".
You would of course need evaluations once you have a solution, but you don't in principle need experiments to find it: Experiments are a way to save compute, dating from a time when it was much more expensive.
And I think the presentation is exponential but real systems aren't irreducibly exponential. AlphaFold worked, after all.
The best, most comprehensive take on AI I’ve read. After a year of confusion and fear about the future, I finished it feeling like a weight had physically lifted.
It’s long. Make the time.
@vgr is undefeated.
contraptions.venkateshrao.co…
I’ve been working to teach Claude how to write more like a human, so it can do more routine reporting like this for me, and it’s finally gotten good enough that we can give it a byline! Let me know what you think. oreillyradar.substack.com/p/…
Tim O'Reilly retweeted
I think many of us working in tech tend to forget that the reason software engineering vs other engineering fields pays (a lot!) more, is a lot more flexible (WFH/remote), and has better perks:
Because this field changes much faster than other engineering fields. The premium is because fewer people are able/willing to pick up new technologies, methodologies so quickly.
But the field had ~10-20 years of stability, and so lots of folks forgot this is why a premium exists. And that AI is just another tech wave: software engineers who pick it up and master it will generally fare better; those not will struggle a lot more.
Tim O'Reilly retweeted
I was ashamed to realize that I did not notice at the time when one of humanity's greatest accomplishments was announced: photos of Pluto, taken from the edge of our solar system looking back. Blue Haze is up there with Earthrise and Pale Blue Dot as stunningly beautiful works of art, testimonies to human ingenuity and daring, and opportunities to reflect on our place in the cosmos.
Tim O'Reilly retweeted
Anthropic has made a lot of costly sacrifices out of conviction. At the same time, I have found the cynical view of Anthropic outperforming the optimistic view, in many ways, over the last year, when I try to predict what the company will do.
You would do poorly to model Anthropic as only caring about its stock price. That's clearly wrong! But the costly sacrifices are mostly for things - China, biosecurity - that Dario is personally highly motivated by, and it's much less clear which costly sacrifices have been made on behalf of "humanity not losing control to AI" or "crucial decisions about the fate of humankind being made by everybody, even at the cost of them being made less well-according-to-leadership".
Takeover in particular is something Dario seems less worried about than many of his employees. But the employees who are more worried than him about losing control have great confidence he'll make strategic sacrifices for this goal, on the strength of the sacrifices he's made for goals he seems much more convinced are a real problem.
As for crucial decisions being made by everybody - Anthropic has made some efforts at this. But because making this happen would be insanely difficult, for Anthropic to actually achieve it they'd have to make truly heroic efforts at it, instead of distracted and cross-pressured ones.
When Anthropic was founded, its creators took for granted that they should not be trusted alone with - that no one should be trusted alone with - the power that building a superintelligence would imply. But everyone has a plan until you get punched in the face. Their rapid growth and especially this year’s events seem to have shaped Anthropic in a more defensive, less pluralistic, less liberal direction, which scares me given that it also has brought them a lot closer to AGI.
The recent safety bill was really reflective of this. It does not suggest a company that is trying to get policymakers to make the decisions Anthropic's technical staff think are coming up. It kind of suggests a company that thinks the best thing that can be hoped for from the executive is that it enforce the export controls and not get in the way. A lot of people don't seem to even want Congress to pass a bill! And like, frequently that is the best thing that can be hoped for from Congress, yet -
I think that the typical person in Anthropic still regards themselves as having a lot of duties to all of humanity, to liberal and pluralistic institutions, etc. But almost every effort that Anthropic originally made to set hard constraints to which they are subject (rather than being subject mostly to their best judgment), they have subsequently come to regret as ill-conceived, and while they could have replaced them with better-conceived hard constraints, they haven't. They've largely replaced them with something that rests on their judgment more. (I think the embedded evaluators are a counterexample and I am glad of them.) The gap between the capabilities of models used internally and publicly seems to me to be growing (I think it should ideally be as close as possible to zero). Anthropic does not, broadly, seem to me to have good institutional governance - guarantees that allow its auditors independence, strong contractual protections for its workers (I'd like them to be able to go on strike if they thought it was a good idea, honestly). Incident reporting is happening on a best-judgment basis.
And you can be like, that's because this is all insane and happening really fast and we're doing our best! And yeah, the thing that needs to happen is very difficult. But it does still need to happen and it doesn't look like Anthropic's on track to do it. And it's also not fully clear that Anthropic leadership agrees with me that there's a real issue here. No external criticism is every very convincing, right, when you have secret information and a set of factual beliefs that everyone else thinks are insane. I have more context on Anthropic than almost anyone and yet I cannot phrase my worries about Anthropic in a way that rings true to the people who work there - perhaps because I am wrong, or perhaps because they are very busy and very tired and do very little other than work, which creates conditions where it is hard for people to communicate, or learn, or change their minds, or reconstruct this flawed and mangled criticism into a form that would resonate like this one does not.
But man, I think that the admirable sacrifices Anthropic has made in a few areas it really believes in (bio, China) do not reassure me it'll do the extremely hard work to get things right in a few other areas that it seems to have a lot more internal or leadership disagreement about (alignment, takeover, disempowerment).
And so I don't expect things to go well.
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.
Tim O'Reilly retweeted
This is perhaps one of the great ironies of our time: China is increasingly shaping the future of AI, yet very few people in the West have the faintest idea how its key thinkers see it - its philosophers, sociologists or ethicists.
As is too often the case, we - much like the frog in the well - assume our own AI debate covers every possible view, when much of it springs from the same cultural soil, built on assumptions about what defines us that we don’t even know we’re making.
Take for instance the assumption that intelligence is a key part of what makes us human. It’s so intuitively obvious to us that we literally named our species after it - Homo sapiens (Latin for "wise man").
If you think about it, just this assumption explains a large share of the anxiety: if intelligence is what makes us human, then artificial intelligence - or super intelligence - threatens the very thing that makes us us: we’re about to compete with artificial or super “us”. No wonder this feels existential.
But China has a tradition of thought as old as ours, if not older, and every bit as rich and sophisticated, which never placed intelligence at the core of what makes us human, and so meets the thinking machine from a completely different starting point.
Have you, for instance, ever heard of Zhao Tingyang? Probably not, and yet he may be the most original thinker on AI you’ve never read. He’s one of China’s most prominent philosophers, at the Chinese Academy of Social Sciences. While Silicon Valley spends billions trying to make AI share our values, he for one argues that doing so “may very well be a suicidal mistake”.
Sounds pretty important to hear him out, don’t you think?
Or take Liu Yongmou, a philosopher of technology at Renmin University in Beijing. His thinking is almost the opposite of ours. Where we fear machines that become more and more like us, he says that “humanity’s biggest problem is the machinization of humans, not the humanization of machines.”
You could even go back to antiquity, when, in many ways, we started asking questions about AI.
Did you know, for instance, that Aristotle, in the Politics, imagined a world where “the shuttle would weave and the plectrum touch the lyre without a hand to guide them”, and wondered about the social order consequences of this (mostly whether slaves would still be needed)?
There are Chinese equivalents to this. For instance, in the Zhuangzi, a foundational Taoist classic, you’ll find a similar thought experiment, with a completely different worry attached. An old gardener is watering his vegetables by hand, hauling water out of a well jar by jar. A visitor shows him a labor-saving device, and the gardener turns it down for the reason that whoever uses machines ends up with 机心 (jīxīn), a “machine-heart” - a mind that schemes, calculates and loses its simplicity. “It is not that I do not know [the machine],” he says, “but I should be ashamed to use it.”
Two labor-saving machines, two very different questions: where Aristotle wondered what automation would do to the social order, the Zhuangzi wondered what it would do to us, on the inside. Which, you’ll notice, is exactly Liu Yongmou’s worry, more than two thousand years earlier.
In fact, as we’ll see, 心 (xīn), the heart, is a motif that runs deep through the Chinese AI debate. It appears both in the fear, from Zhuangzi to Liu Yongmou, that machines could give us a “machine-heart” and its mirror image: the ambition to give machines a heart of their own. Zhu Songchun, who heads the Beijing Institute for General Artificial Intelligence, wants “to set a heart for machines” (为机器立心, wèi jīqì lì xīn).
“Heart” in the Chinese sense, that is, which differs totally from the Valentine's Day fluff we’ve reduced it to. 心 (xīn) means heart and mind at once: it’s where we feel but also where we think, tell right from wrong and care for others.
And, crucially, it’s in the heart that the quality Confucianism sees as the very definition of a human being takes root: 仁 (rén). We need two words to translate it, “benevolence” and “humaneness”, but in Chinese they’re one and the same: to be benevolent is to be human. It’s not about intelligence.
This is the entire point of my new article: to step out of our well for a moment and peer over the edge of China's.
What you’ll find there will, I think, surprise you, with questions you’d never thought to ask: from Zhao Tingyang warning that AI’s bad consequences can at least be fought, but that “humanity may die of all the good things AI creates”, to whether the fear that AI will make us “useless” rests on the false premise that a person is worth what they’re useful for.
A premise that a 2,300-year-old Taoist tree, which survived precisely because it was useless, demolished long ago. Could our predicted uselessness turn out, much like that tree’s, to be our saving grace?
Chinese thinkers’ answers to these questions are, I think, among the most interesting anyone has given to the defining question of our time. Whether or not you agree with them, they’re the intellectual backdrop of a country shaping much of AI, and with it our future: you might as well know what they think.
Enjoy the read here: arnaudbertrand.substack.com/…
Imagine talking to the creators of ChatGPT about the Attention is All You Need paper a few months before its release, and you'll be able to imagine what it's like to talk to Alex Komoroske with me on Live with Tim O'Reilly today at 8 am Pacific/11 Eastern. Alex wrote something called the Resonant Computing Manifesto last year, and he's now building something based on it, which he calls Common Fabric. It's a completely revolutionary approach to making a human-centered version of the agentic future. I've seen the prototypes, and it's something I've dreamed about! We'll be talking the vision, not showing it off yet, but even that will change how you see the future. learning.oreilly.com/live-ev…
Tim O'Reilly retweeted
Given the acceleration Anthropic is reporting in coding, it is amazing that you still cannot change your email address in Claude and the official advice is that if you need to change your email the only way to do that is delete your account.
Tim O'Reilly retweeted
Some observations about the "total" automation of software engineering:
1. There are more software engineers now than in 2025, and demand is growing.
2. Software engineers have never worked harder than they do now.
3. They largely don't write code anymore, agents write ~100% of the code.
4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption.
This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
Tim O'Reilly retweeted
The biggest misread of anyone predicting the end of software engineering was imagining that the world only needed a certain amount of software, and bringing automation to the field would reduce the need for labor.
Exactly the opposite is happening where essentially most things are actually turning into software. Because agents can use tools, work with computers, write software on the fly, and process data like humans (but far faster), we can now throw “software” at problems that never could reasonably be digitized before.
What that leads to is the role of engineering taking on greater importance in the economy, because we can now engineer far more systems and processes than were possible before.
If you’re a bank trying to do new forms of trading, engineers are often your first bet. If you are in life sciences, you expect more and more research and lab work to be engineering centric. If you’re in manufacturing, you’re trying to automate more of the supply chain, all that is engineering. More and more work is shaped like software problems.
Yes we’re not going to be coding manually in the future. But almost everything will be code, which means those that understand it are in a fantastic position.
Some observations about the "total" automation of software engineering:
1. There are more software engineers now than in 2025, and demand is growing.
2. Software engineers have never worked harder than they do now.
3. They largely don't write code anymore, agents write ~100% of the code.
4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption.
This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
Tim O'Reilly retweeted
Disgusting.
This White House isn’t just corrupt, they are enablers of evil Putin
“The order to limit a satellite system used by Ukraine for battlefield communications and drone operations came from inside the White House, according to two US officials and a Western official briefed on the matter.
The officials said leaders at the Defense Department received a message that the SpaceX Starshield service for the Ukrainian military needed to be slowed.
The officials, who spoke on the condition of anonymity, did not specify who in the White House made the decision or who was responsible for implementing the order. The Pentagon is the biggest customer of Starshield, which is the military-grade version of the Starlink service.”
@ErinBanco @G_Slattery
reuters.com/world/europe/ord…
Tim O'Reilly retweeted
New context failure mode: Context Inertia 🚂
During long-horizon tasks, persistent models can hyper-focus. The solve? Throw out your context. dbreunig.com/2026/10/14/cont…






















