Question: tech layoffs 2026

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Question: tech layoffs 2026


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Dario Amodei — "We are near the end of the exponential"
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Dario Amodei thinks we are just a few years away from “a country of geniuses in a data center”. In this episode, we discuss what to make of the scaling hypothesis in the current RL regime, how AI will diffuse throughout the economy, whether Anthropic is underinvesting in compute given their timelines, how frontier labs will ever make money, whether regulation will destroy the boons of this technology, US-China competition, and much more.
Watch on [YouTube](https://youtu.be/n1E9IZfvGMA); listen on [Apple Podcasts](https://podcasts.apple.com/us/podcast/dario-amodei-the-highest-stakes-financial-model-in-history/id1516093381?i=1000749621800) or [Spotify](https://open.spotify.com/episode/2ZNrpVSrgZMlDwQinl20Ay?si=9D4aG1l7S-2wzLsiILRLIg).

- [Labelbox](https://labelbox.com/dwarkesh) can get you the RL tasks and environments you need. Their massive network of subject-matter experts ensures realism across domains, and their in-house tooling lets them continuously tweak task difficulty to optimize learning. Reach out at [labelbox.com/dwarkesh](https://labelbox.com/dwarkesh)
- [Jane Street](https://janestreet.com/dwarkesh) sent me another puzzle… this time, they’ve trained backdoors into 3 different language models — they want you to find the triggers. Jane Street isn’t even sure this is possible, but they’ve set aside $50,000 for the best attempts and write-ups. They’re accepting submissions until April 1st at [janestreet.com/dwarkesh](https://janestreet.com/dwarkesh)
- [Mercury](https://mercury.com/personal-banking)’s personal accounts make it easy to share finances with a partner, a roommate… or OpenClaw. Last week, I wanted to try OpenClaw for myself, so I used Mercury to spin up a virtual debit card with a small spend limit, and then I let my agent loose. No matter your use case, apply at [mercury.com/personal-banking](https://mercury.com/personal-banking)
[(00:00:00) - What exactly are we scaling?](https://www.dwarkesh.com/i/187852154/000000-what-exactly-are-we-scaling)
[(00:12:36) - Is diffusion cope?](https://www.dwarkesh.com/i/187852154/001236-is-diffusion-cope)
[(00:29:42) - Is continual learning necessary?](https://www.dwarkesh.com/i/187852154/002942-is-continual-learning-necessary-how-will-it-be-solved)
[(00:46:20) - If AGI is imminent, why not buy more compute?](https://www.dwarkesh.com/i/187852154/004620-if-agi-is-imminent-why-not-buy-more-compute)
[(00:58:49) - How will AI labs actually make profit?](https://www.dwarkesh.com/i/187852154/005849-how-will-ai-labs-actually-make-profit)
[(01:31:19) - Will regulations destroy the boons of AGI?](https://www.dwarkesh.com/i/187852154/013119-will-regulations-destroy-the-boons-of-agi)
[(01:47:41) - Why can’t China and America both have a country of geniuses in a datacenter?](https://www.dwarkesh.com/i/187852154/014741-why-cant-china-and-america-both-have-a-country-of-geniuses-in-a-datacenter)
Dwarkesh Patel
[We talked three years ago](https://www.dwarkesh.com/p/dario-amodei). In your view, what has been the biggest update over the last three years? What has been the biggest difference between what it felt like then versus now?
Dario Amodei
Broadly speaking, the exponential of the underlying technology has gone about as I expected it to go. There’s plus or minus a year or two here and there. I don’t know that I would’ve predicted the specific direction of code.
But when I look at the exponential, it is roughly what I expected in terms of the march of the models from smart high school student to smart college student to beginning to do PhD and professional stuff, and in the case of code reaching beyond that. The frontier is a little bit uneven, but it’s roughly what I expected.
What has been the most surprising thing is the lack of public recognition of how close we are to the end of the exponential. To me, it is absolutely wild that you have people — within the bubble and outside the bubble — talking about the same tired, old hot-button political issues, when we are near the end of the exponential.
Dwarkesh Patel
I want to understand what that exponential looks like right now. The first question I asked you when we recorded three years ago was, “what’s up with [scaling](https://www.dwarkesh.com/p/will-scaling-work) and why does it work?” I have a similar question now, but it feels more complicated. At least from the public’s point of view, three years ago there were well-known public trends across many orders of magnitude of compute where you could see how the loss improves.
Now we have [RL](https://en.wikipedia.org/wiki/Reinforcement_learning) [scaling](https://www.tobyord.com/writing/how-well-does-rl-scale) and there’s no publicly known [scaling law](https://en.wikipedia.org/wiki/Neural_scaling_law) for it. It’s not even clear what the story is. Is this supposed to be teaching the model skills? Is it supposed to be teaching meta-learning? What is the [scaling hypothesis](https://gwern.net/scaling-hypothesis) at this point?
Dario Amodei
I actually have the same hypothesis I had even all the way back in 2017. I think I talked about it last time, but I wrote a doc called [“The Big Blob of Compute Hypothesis”](http://corley.ai/the-blob-that-ate-ai/). It wasn’t about the scaling of language models in particular. When I wrote it [GPT-1](https://en.wikipedia.org/wiki/GPT-1) had just come out.
That was one among many things. Back in those days there was robotics. People tried to work on reasoning as a separate thing from [language models](https://en.wikipedia.org/wiki/Large_language_model), and there was scaling of the kind of RL that happened in [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo) and in [Dota](https://en.wikipedia.org/wiki/OpenAI_Five) at [OpenAI](https://en.wikipedia.org/wiki/OpenAI). People remember StarCraft at [DeepMind](https://en.wikipedia.org/wiki/Google_DeepMind), [AlphaStar](https://en.wikipedia.org/wiki/AlphaStar_(software)).
It was written as a more general document. [Rich Sutton](https://www.dwarkesh.com/p/richard-sutton) put out [“The Bitter Lesson”](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) a couple years later. The hypothesis is basically the same. What it says is that all the cleverness, all the techniques, all the “we need a new method to do something”, that doesn’t matter very much. There are only a few things that matter. I think I listed seven of them.
One is how much raw compute you have. The second is the quantity of data. The third is the quality and distribution of data. It needs to be a broad distribution. The fourth is how long you train for. The fifth is that you need an objective function that can scale to the moon. The [pre-training](https://www.moveworks.com/us/en/resources/ai-terms-glossary/pre-training) objective function is one such objective function. Another is the RL objective function that says you have a goal, you’re going to go out and reach the goal.
Within that, there’s objective rewards like you see in math and coding, and there’s more subjective rewards like you see in [RLHF](https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback) or higher-order versions of that. Then the sixth and seventh were things around [normalization](https://en.wikipedia.org/wiki/Normalization_(machine_learning)) or conditioning, just getting the numerical stability so that the big blob of compute flows in this [laminar](https://en.wikipedia.org/wiki/Laminar_flow) way instead of running into problems.
That was the hypothesis, and it’s a hypothesis I still hold. I don’t think I’ve seen very much that is not in line with it. The [pre-training scaling laws](https://blogs.nvidia.com/blog/ai-scaling-laws/) were one example of what we see there. Those have continued going. Now it’s been widely reported, we feel good about pre-training. It’s continuing to give us gains.
What has changed is that now we’re also seeing the same thing for RL. We’re seeing a pre-training phase and then an RL phase on top of that. With RL, it’s actually just the same. Even other companies have published things in some of their releases that say, “We train the model on math contests — [AIME](https://en.wikipedia.org/wiki/American_Invitational_Mathematics_Examination) or other things — and how well the model does is log-linear in how long we’ve trained it.”
We see that as well, and it’s not just math contests. It’s a wide variety of RL tasks. We’re seeing the same scaling in RL that we saw for pre-training.
Dwarkesh Patel
You mentioned Rich Sutton and “The Bitter Lesson”. [I interviewed him last year](https://www.dwarkesh.com/p/richard-sutton), and he’s actually very non-LLM-pilled. I don’t know if this is his perspective, but one way to paraphrase his objection is: Something which possesses the true core of human learning would not require all these billions of dollars of data and compute and these bespoke environments, to learn how to use Excel, how to use PowerPoint, how to navigate a web browser. The fact that we have to build in these skills using these RL environments hints that we are actually lacking a core human learning algorithm. So we’re scaling the wrong thing.
That does raise the question. Why are we doing all this RL scaling if we think there’s something that’s going to be human-like in its ability to learn on the fly?
Dario Amodei
I think this puts together several things that should be thought of differently. There is a genuine puzzle here, but it may not matter. In fact, I would guess it probably doesn’t matter. There is an interesting thing. Let me take the RL out of it for a second, because I actually think it’s a red herring to say that RL is any different from pre-training in this matter.
If we look at pre-training scaling, it was very interesting back in 2017 when [Alec Radford](https://scholar.google.com/citations?user=dOad5HoAAAAJ&hl=en) was doing GPT-1. The models before GPT-1 were trained on datasets that didn’t represent a wide distribution of text. You had very standard language modeling benchmarks. GPT-1 itself was trained on a bunch of fanfiction, I think actually.
It was literary text, which is a very small fraction of the text you can get. In those days it was like a billion words or something, so small datasets representing a pretty narrow distribution of what you can see in the world. It didn’t generalize well. If you did better on some fanfiction corpus, it wouldn’t generalize that well to other tasks.
We had all these measures. We had all these measures of how well it did at predicting all these other kinds of texts. It was only when you trained over all the tasks on the internet — when you did a general internet scrape from something like [Common Crawl](https://en.wikipedia.org/wiki/Common_Crawl) or scraping links in Reddit, which is what we did for [GPT-2](https://en.wikipedia.org/wiki/GPT-2) — that you started to get generalization.
I think we’re seeing the same thing on RL. We’re starting first with simple RL tasks like training on math competitions, then moving to broader training that involves things like code. Now we’re moving to many other tasks. I think then we’re going to increasingly get generalization. So that kind of takes out the RL vs. pre-training side of it.
But there is a puzzle either way, which is that in pre-training we use trillions of tokens. Humans don’t see trillions of words. So there is an actual sample efficiency difference here. There is actually something different here. The models start from scratch and they need much more training. But we also see that once they’re trained, if we give them a long [context length](https://www.ibm.com/think/topics/context-window) of a million — the only thing blocking long context is [inference](https://hazelcast.com/foundations/ai-machine-learning/machine-learning-inference/) — they’re very good at learning and adapting within that context.
So I don’t know the full answer to this. I think there’s something going on where pre-training is not like the process of humans learning, but it’s somewhere between the process of humans learning and the process of human evolution. We get many of our priors from evolution. Our brain isn’t just a blank slate. [Whole books have been written about this.](https://en.wikipedia.org/wiki/The_Blank_Slate)
The language models are much more like blank slates. They literally start as random [weights](https://www.geeksforgeeks.org/deep-learning/the-role-of-weights-and-bias-in-neural-networks/), whereas the human brain starts with all these regions connected to all these inputs and outputs. Maybe we should think of pre-training — and for that matter, RL as well — as something that exists in the middle space between human evolution and human on-the-spot learning. And we should think of the in-context learning that the models do as something between long-term human learning and short-term human learning.
So there’s this hierarchy. There’s evolution, there’s long-term learning, there’s short-term learning, and there’s just human reaction. The LLM phases exist along this spectrum, but not necessarily at exactly the same points. There’s no analog to some of the human modes of learning the LLMs are falling in between the points. Does that make sense?
Dwarkesh Patel
Yes, although some things are still a bit confusing. For example, if the analogy is that this is like evolution so it’s fine that it’s not sample efficient, then if we’re going to get super sample-efficient agent from [in-context learning](https://www.lakera.ai/blog/what-is-in-context-learning), why are we bothering to build all these RL environments?
There are companies whose work seems to be teaching models how to use this API, how to use Slack, how to use whatever. It’s confusing to me why there’s so much emphasis on that if the kind of agent that can just learn on the fly is emerging or has already emerged.
Dario Amodei
I can’t speak for the emphasis of anyone else. I can only talk about how we think about it. The goal is not to teach the model every possible skill within RL, just as we don’t do that within pre-training. Within pre-training, we’re not trying to expose the model to every possible way that words could be put together. Rather, the model trains on a lot of things and then reaches generalization across pre-training.
That was the transition from GPT-1 to GPT-2 that I saw up close. The model reaches a point. I had these moments where I was like, “Oh yeah, you just give the model a list of numbers — this is the cost of the house, this is the square feet of the house — and the model completes the pattern and does linear regression.” Not great, but it does it, and it’s never seen that exact thing before.
So to the extent that we are building these RL environments, the goal is very similar to what was done five or ten years ago with pre-training. We’re trying to get a whole bunch of data, not because we want to cover a specific document or a specific skill, but because we want to generalize.
Dwarkesh Patel
I think the framework you’re laying down obviously makes sense. We’re making progress toward [AGI](https://en.wikipedia.org/wiki/Artificial_general_intelligence). Nobody at this point disagrees we’re going to achieve AGI this century. The crux is you say we’re hitting the end of the exponential. Somebody else looks at this and says, “We’ve been making progress since 2012, and by 2035 we’ll have a human-like agent.”
Obviously we’re seeing in these models the kinds of things that evolution did, or that learning within a human lifetime does. I want to understand what you’re seeing that makes you think it’s one year away and not ten years away.
Dario Amodei
There are two claims you could make here, one stronger and one weaker. Starting with the weaker claim, when I first saw the scaling back in 2019, I wasn’t sure. This was a 50/50 thing. I thought I saw something. My claim was that this was much more likely than anyone thinks. Maybe there’s a 50% chance this happens.
On the basic hypothesis of, as you put it, within ten years we’ll get to what I call a “country of geniuses in a data center”, I’m at 90% on that. It’s hard to go much higher than 90% because the world is so unpredictable. Maybe the irreducible uncertainty puts us at 95%, where you get to things like multiple companies having internal turmoil, [Taiwan gets invaded](https://en.wikipedia.org/wiki/Chinese_unification), all the [fabs](https://en.wikipedia.org/wiki/Semiconductor_fabrication_plant) get blown up by missiles.
Dwarkesh Patel
Now you’ve jinxed us, Dario.
Dario Amodei
You could construct a 5% world where things get delayed for ten years. There’s another 5% which is that I’m very confident on tasks that can be verified. With coding, except for that irreducible uncertainty, I think we’ll be there in one or two years. There’s no way we will not be there in ten years in terms of being able to do end-to-end coding.
My one little bit of fundamental uncertainty, even on long timescales, is about tasks that aren’t verifiable: planning a mission to Mars; doing some fundamental scientific discovery like CRISPR; writing a novel. It’s hard to verify those tasks. I am almost certain we have a reliable path to get there, but if there’s a little bit of uncertainty it’s there. On the ten-year timeline I’m at 90%, which is about as certain as you can be. I think it’s crazy to say that this won’t happen by 2035. In some sane world, it would be outside the mainstream.
Dwarkesh Patel
But the emphasis on verification hints to me a lack of belief that these models are generalized. If you think about humans, we’re both good at things for which we get verifiable reward and things for which we don’t.
Dario Amodei
No, this is why I’m almost sure. We already see substantial generalization from things that verify to things that don’t. We’re already seeing that.
Dwarkesh Patel
But it seems like you were emphasizing this as a spectrum which will split apart which domains in which we see more progress. That doesn’t seem like how humans get better.
Dario Amodei
The world in which we don’t get there is the world in which we do all the verifiable things. Many of them generalize, but we don’t fully get there. We don’t fully color in the other side of the box. It’s not a binary thing.
Dwarkesh Patel
Even if generalization is weak and you can only do verifiable domains, it’s not clear to me you could automate software engineering in such a world. You are “a software engineer” in some sense, but part of being a software engineer for you involves [writing long memos](https://www.darioamodei.com/) about your grand vision.
Dario Amodei
I don’t think that’s part of the job of [SWE](https://en.wikipedia.org/wiki/Software_engineering). That’s part of the job of the company, not SWE specifically. But SWE does involve design documents and other things like that. The models are already pretty good at writing comments. Again, I’m making much weaker claims here than I believe, to distinguish between two things. We’re already almost there for software engineering.
Dwarkesh Patel
By what metric? There’s one metric which is how many lines of code are written by AI. If you consider other productivity improvements in the history of software engineering, [compilers](https://en.wikipedia.org/wiki/Compiler) write all the lines of software. There’s a difference between how many lines are written and how big the productivity improvement is. “We’re almost there” meaning… How big is the productivity improvement, not just how many lines are written by AI?
Dario Amodei
I actually agree with you on this. I’ve made a series of predictions on code and software engineering. I think people have repeatedly misunderstood them. Let me lay out the spectrum.
About eight or nine months ago, I said the AI model will be writing 90% of the lines of code in three to six months. That happened, at least at some places. It happened at [Anthropic](https://en.wikipedia.org/wiki/Anthropic), happened with many people downstream using our models. But that’s actually a very weak criterion. People thought I was saying that we won’t need 90% of the software engineers. Those things are worlds apart. The spectrum is: 90% of code is written by the model, 100% of code is written by the model. That’s a big difference in productivity.
90% of the end-to-end SWE tasks — including things like compiling, setting up clusters and environments, testing features, writing memos — are done by the models. 100% of today’s SWE tasks are done by the models. Even when that happens, it doesn’t mean software engineers are out of a job. There are new higher-level things they can do, where they can manage. Then further down the spectrum, there’s 90% less demand for SWEs, which I think will happen but this is a spectrum.
I wrote about it in [“The Adolescence of Technology”](https://www.darioamodei.com/essay/the-adolescence-of-technology) where I went through this kind of spectrum with farming. I actually totally agree with you on that. These are very different benchmarks from each other, but we’re proceeding through them super fast.
Dwarkesh Patel
Part of your vision is that going from 90 to 100 is going to happen fast, and that it leads to huge productivity improvements. But what I notice is that even in greenfield projects people start with [Claude Code](https://claude.com/product/claude-code) or something, people report starting a lot of projects… Do we see in the world out there a renaissance of software, all these new features that wouldn’t exist otherwise? At least so far, it doesn’t seem like we see that.
So that does make me wonder. Even if I never had to intervene with Claude Code, the world is complicated. Jobs are complicated. Closing the loop on self-contained systems, whether it’s just writing software or something, how much broader gains would we see just from that? Maybe that should dilute our estimation of the “country of geniuses”.
Dario Amodei
I simultaneously agree with you that it’s a reason why these things don’t happen instantly, but at the same time, I think the effect is gonna be very fast. You could have these two poles. One is that AI is not going to make progress. It’s slow. It’s going to take forever to diffuse within the economy. [Economic diffusion](https://en.wikipedia.org/wiki/Diffusion_(business)) has become one of these buzzwords that’s a reason why we’re not going to make AI progress, or why AI progress doesn’t matter.
The other axis is that we’ll get [recursive self-improvement](https://en.wikipedia.org/wiki/Recursive_self-improvement), the whole thing. Can’t you just draw an exponential line on the curve? We’re going to have [Dyson spheres](https://en.wikipedia.org/wiki/Dyson_sphere) around the sun so many nanoseconds after we get recursive. I’m completely caricaturing the view here, but there are these two extremes.
But what we’ve seen from the beginning, at least if you look within Anthropic, there’s this bizarre 10x per year growth in revenue that we’ve seen. So in 2023, it was zero to $100 million. In 2024, it was $100 million to $1 billion. In 2025, it was $1 billion to $ 9-10 billion.
Dwarkesh Patel
You guys should have just bought a billion dollars of your own products so you could just…
Dario Amodei
And the first month of this year, that exponential is... You would think it would slow down, but we added another few billion to revenue in January. Obviously that curve can’t go on forever. The GDP is only so large. I would even guess that it bends somewhat this year, but that is a fast curve. That’s a really fast curve. I would bet it stays pretty fast even as the scale goes to the entire economy.
So I think we should be thinking about this middle world where things are extremely fast, but not instant, where they take time because of economic diffusion, because of the need to close the loop. Because it’s fiddly: “I have to do [change management](https://en.wikipedia.org/wiki/Change_management) within my enterprise… I set this up, but I have to change the security permissions on this in order to make it actually work… I had this old piece of software that checks the model before it’s compiled and released and I have to rewrite it. Yes, the model can do that, but I have to tell the model to do that. It has to take time to do that.”
So I think everything we’ve seen so far is compatible with the idea that there’s one fast exponential that’s the capability of the model. Then there’s another fast exponential that’s downstream of that, which is the diffusion of the model into the economy. Not instant, not slow, much faster than any previous technology, but it has its limits. When I look inside Anthropic, when I look at our customers: fast adoption, but not infinitely fast.
Dwarkesh Patel
Can I try a hot take on you?
Dario Amodei
Yeah.
Dwarkesh Patel
I feel like diffusion is cope that people say. When the model isn’t able to do something, they’re like, “oh, but it’s a diffusion issue.” But then you should use the comparison to humans. You would think that the inherent advantages that AIs have would make diffusion a much easier problem for new AIs getting onboarded than new humans getting onboarded. An AI can read your entire Slack and your drive in minutes. They can share all the knowledge that the other copies of the same instance have. You don’t have this adverse selection problem when you’re hiring AI, so you can just hire copies of a vetted AI model.
Hiring a human is so much more of a hassle. People hire humans all the time. We pay humans upwards of $50 trillion in wages because they’re useful, even though in principle it would be much easier to integrate AIs into the economy than it is to hire humans. The diffusion doesn’t really explain.
Dario Amodei
I think diffusion is very real and doesn’t exclusively have to do with limitations on the AI models. Again, there are people who use diffusion as kind of a buzzword to say this isn’t a big deal. I’m not talking about that. I’m not talking about how AI will diffuse at the speed of previous technologies. I think AI will diffuse much faster than previous technologies have, but not infinitely fast.
I’ll just give an example of this. There’s Claude Code. Claude Code is extremely easy to set up. If you’re a developer, you can just start using Claude Code. There is no reason why a developer at a large enterprise should not be adopting Claude Code as quickly as an individual developer or developer at a startup.
We do everything we can to promote it. We sell Claude Code to enterprises. Big enterprises, big financial companies, big pharmaceutical companies, all of them are adopting Claude Code much faster than enterprises typically adopt new technology. But again, it takes time.
Any given feature or any given product, like Claude Code or [Cowork](https://claude.com/product/cowork), will get adopted by the individual developers who are on Twitter all the time, by the Series A startups, many months faster than they will get adopted by a large enterprise that does food sales. There are just a number of factors. You have to go through legal, you have to provision it for everyone. It has to pass security and compliance.
The leaders of the company who are further away from the AI revolution are forward-looking, but they have to say, “Oh, it makes sense for us to spend 50 million. This is what this Claude Code thing is. This is why it helps our company. This is why it makes us more productive.” Then they have to explain to the people two levels below. They have to say, “Okay, we have 3,000 developers. Here’s how we’re going to roll it out to our developers.” We have conversations like this every day.
We are doing everything we can to make Anthropic’s revenue grow 20 or 30x a year instead of 10x a year. Again, many enterprises are just saying, “This is so productive. We’re going to take shortcuts in our usual procurement process.” They’re moving much faster than when we tried to sell them just the ordinary API, which many of them use. Claude Code is a more compelling product, but it’s not an infinitely compelling product.
I don’t think even AGI or powerful AI or “country of geniuses in a data center” will be an infinitely compelling product. It will be a compelling product enough maybe to get 3-5x, or 10x, a year of growth, even when you’re in the hundreds of billions of dollars, which is extremely hard to do and has never been done in history before, but not infinitely fast.
Dwarkesh Patel
I buy that it would be a slight slowdown. Maybe this is not your claim, but sometimes people talk about this like, “Oh, the capabilities are there, but because of diffusion... otherwise we’re basically at AGI”.
Dario Amodei
I don’t believe we’re basically at AGI.
Dwarkesh Patel
I think if you had the “country of geniuses in a data center”...
Dario Amodei
If we had the “country of geniuses in a data center”, we would know it. We would know it if you had the “country of geniuses in a data center”. Everyone in this room would know it. Everyone in Washington would know it. People in rural parts might not know it, but we would know it. We don’t have that now. That is very clear.
Dwarkesh Patel
Coming back to concrete prediction… Because there are so many different things to disambiguate, it can be easy to talk past each other when we’re talking about capabilities. For example, when I interviewed you three years ago, I asked you a prediction about what we should expect three years from now. You were right. You said, “We should expect systems which, if you talk to them for the course of an hour, it’s hard to tell them apart from a generally well-educated human.”
I think you were right about that. I think spiritually I feel unsatisfied because my internal expectation was that such a system could automate large parts of white-collar work. So it might be more productive to talk about the actual end capabilities you want from such a system.
Dario Amodei
I will basically tell you where I think we are.
Dwarkesh Patel
Let me ask a very specific question so that we can figure out exactly what kinds of capabilities we should think about soon. Maybe I’ll ask about it in the context of a job I understand well, not because it’s the most relevant job, but just because I can evaluate the claims about it.
Take video editors. I have video editors. Part of their job involves learning about our audience’s preferences, learning about my preferences and tastes, and the different trade-offs we have. They’re, over the course of many months, building up this understanding of context. The skill and ability they have six months into the job, a model that can pick up that skill on the job on the fly, when should we expect such an AI system?
Dario Amodei
I guess what you’re talking about is that we’re doing this interview for three hours. Someone’s going to come in, someone’s going to edit it. They’re going to be like, “Oh, I don’t know, Dario scratched his head and we could edit that out.”
Dwarkesh Patel
“Magnify that.”
Dario Amodei
“There was this long discussion that is less interesting to people. There’s another thing that’s more interesting to people, so let’s make this edit.”
I think the “country of geniuses in a data center” will be able to do that. The way it will be able to do that is it will have [general control of a computer screen](https://www.anthropic.com/news/developing-computer-use). You’ll be able to feed this in. It’ll be able to also use the computer screen to go on the web, look at all your previous interviews, look at what people are saying on Twitter in response to your interviews, talk to you, ask you questions, talk to your staff, look at the history of edits that you did, and from that, do the job.
I think that’s dependent on several things. I think this is one of the things that’s actually blocking deployment: getting to the point on computer use where the models are really masters at using the computer.
We’ve seen this climb in benchmarks, and benchmarks are always imperfect measures. But I think when we first released computer use a year and a quarter ago, [OSWorld](https://os-world.github.io/) was at maybe 15%. I don’t remember exactly, but we’ve climbed from that to 65-70%. There may be harder measures as well, but I think computer use has to pass a point of reliability.
Dwarkesh Patel
Can I just follow up on that before you move on to the next point? For years, I’ve been trying to build different internal LLM tools for myself. Often I have these text-in, text-out tasks, which should be dead center in the repertoire of these models. Yet I still hire humans to do them.
If it’s something like, “identify what the best clips would be in this transcript”, maybe the LLMs do a seven-out-of-ten job on them. But there’s not this ongoing way I can engage with them to help them get better at the job the way I could with a human employee. That missing ability, even if you solve computer use, would still block my ability to offload an actual job to them.
Dario Amodei
This gets back to what we were talking about before with learning on the job. It’s very interesting. I think with the [coding agents](https://en.wikipedia.org/wiki/AI-assisted_software_development), I don’t think people would say that learning on the job is what is preventing the coding agents from doing everything end to end. They keep getting better. We have engineers at Anthropic who don’t write any code.
When I look at the productivity, to your previous question, we have folks who say, “This [GPU kernel](https://modal.com/gpu-glossary/device-software/kernel), this chip, I used to write it myself. I just have Claude do it.” There’s this enormous improvement in productivity.
When I see Claude Code, familiarity with the [codebase](https://en.wikipedia.org/wiki/Codebase) or a feeling that the model hasn’t worked at the company for a year, that’s not high up on the list of complaints I see. I think what I’m saying is that we’re kind of taking a different path.
Dwarkesh Patel
Don’t you think with coding that’s because there is an external scaffold of memory which exists instantiated in the codebase? I don’t know how many other jobs have that. Coding made fast progress precisely because it has this unique advantage that other economic activity doesn’t.
Dario Amodei
But when you say that, what you’re implying is that by reading the codebase into the context, I have everything that the human needed to learn on the job. So that would be an example of—whether it’s written or not, whether it’s available or not—a case where everything you needed to know you got from the context window. What we think of as learning—”I started this job, it’s going to take me six months to understand the code base”—the model just did it in the context.
Dwarkesh Patel
I honestly don’t know how to think about this because there are people who qualitatively report what you’re saying. I’m sure you saw last year, [there was a major study](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/) where they had experienced developers try to close [pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests) in repositories that they were familiar with. Those developers reported an uplift. They reported that they felt more productive with the use of these models. But in fact, if you look at their output and how much was actually merged back in, there was a 20% downlift. They were less productive as a result of using these models.
So I’m trying to square the qualitative feeling that people feel with these models versus, 1) in a macro level, where is this renaissance of software? And then 2) when people do these independent evaluations, why are we not seeing the productivity benefits we would expect?
Dario Amodei
Within Anthropic, this is just really unambiguous. We’re under an incredible amount of commercial pressure and make it even harder for ourselves because we have all this [safety](https://en.wikipedia.org/wiki/AI_safety) stuff we do that I think we do more than other companies.
The pressure to survive economically while also keeping our values is just incredible. We’re trying to keep this 10x revenue curve going. There is zero time for bullshit. There is zero time for feeling like we’re productive when we’re not. These tools make us a lot more productive.
Why do you think we’re [concerned about competitors using the tools](https://www.wired.com/story/anthropic-revokes-openais-access-to-claude/)? Because we think we’re ahead of the competitors. We wouldn’t be going through all this trouble if this were secretly reducing our productivity. We see the end productivity every few months in the form of model launches. There’s no kidding yourself about this. The models make you more productive.
Dwarkesh Patel
1) People feeling like they’re productive is qualitatively predicted by studies like this. But 2) if I just look at the end output, obviously you guys are making fast progress.
But the idea was supposed to be that with recursive self-improvement, you make a better AI, the AI helps you build a better next AI, et cetera, et cetera. What I see instead—if I look at you, OpenAI, DeepMind—is that people are just shifting around the podium every few months.
Maybe you think that stops because you’ve won or whatever. But why are we not seeing the person with the best coding model have this lasting advantage if in fact there are these enormous productivity gains from the last coding model.
Dario Amodei
I think my model of the situation is that there’s an advantage that’s gradually growing. I would say right now the coding models give maybe, I don’t know, a 15-20% total factor speed up. That’s my view. Six months ago, it was maybe 5%. So it didn’t matter. 5% doesn’t register. It’s now just getting to the point where it’s one of several factors that kind of matters. That’s going to keep speeding up.
I think six months ago, there were several companies that were at roughly the same point because this wasn’t a notable factor, but I think it’s starting to speed up more and more. I would also say there are multiple companies that write models that are used for code and we’re not perfectly good at preventing some of these other companies from using our models internally. So I think everything we’re seeing is consistent with this kind of snowball model.
Again, my theme in all of this is all of this is soft [takeoff](https://www.lesswrong.com/w/ai-takeoff), soft, smooth exponentials, although the exponentials are relatively steep. So we’re seeing this snowball gather momentum where it’s like 10%, 20%, 25%, 40%. As you go, [Amdahl’s law](https://en.wikipedia.org/wiki/Amdahl%27s_law), you have to get all the things that are preventing you from closing the loop out of the way. But this is one of the biggest priorities within Anthropic.
Dwarkesh Patel
Stepping back, before in the stack we were talking about when do we get this on-the-job learning? It seems like the point you were making on the coding thing is that we actually don’t need on-the-job learning. You can have tremendous productivity improvements, you can have potentially trillions of dollars of revenue for AI companies, without this basic human ability to learn on the job. Maybe that’s not your claim, you should clarify.
But in most domains of economic activity, people say, “I hired somebody, they weren’t that useful for the first few months, and then over time they built up the context, understanding.” It’s actually hard to define what we’re talking about here. But they got something and then now they’re a powerhorse and they’re so valuable to us. If AI doesn’t develop this ability to learn on the fly, I’m a bit skeptical that we’re going to see huge changes to the world without that ability.
Dario Amodei
I think two things here. There’s the state of the technology right now. Again, we have these two stages. We have the pre-training and RL stage where you throw a bunch of data and tasks into the models and then they generalize. So it’s like learning, but it’s like learning from more data and not learning over one human or one model’s lifetime. So again, this is situated between evolution and human learning. But once you learn all those skills, you have them.
Just like with pre-training, just how the models know more, if I look at a pre-trained model, it knows more about the history of samurai in Japan than I do. It knows more about baseball than I do. It knows more about [low-pass filters](https://en.wikipedia.org/wiki/Low-pass_filter) and electronics, all of these things. Its knowledge is way broader than mine. So I think even just that may get us to the point where the models are better at everything.
We also have, again, just with scaling the kind of existing setup, the in-context learning. I would describe it as kind of like human on-the-job learning, but a little weaker and a little short term. You look at in-context learning and if you give the model a bunch of examples it does get it. There’s real learning that happens in context. A million [tokens](https://blogs.nvidia.com/blog/ai-tokens-explained/) is a lot. That can be days of human learning. If you think about the model reading a million words, how long would it take me to read a million? Days or weeks at least.
So you have these two things. I think these two things within the existing paradigm may just be enough to get you the “country of geniuses in a data center”. I don’t know for sure, but I think they’re going to get you a large fraction of it. There may be gaps, but I certainly think that just as things are, this is enough to generate trillions of dollars of revenue. That’s one.
Two, is this idea of continual learning, this idea of a single model learning on the job. I think we’re working on that too. There’s a good chance that in the next year or two, we also solve that. Again, I think you get most of the way there without it. The trillions of dollars a year market, maybe all of the national security implications and the safety implications that I wrote about in “Adolescence of Technology” can happen without it. But we, and I imagine others, are working on it. There’s a good chance that we will get there within the next year or two.
There are a bunch of ideas. I won’t go into all of them in detail, but one is just to make the context longer. There’s nothing preventing longer contexts from working. You just have to train at longer contexts and then learn to serve them at inference. Both of those are engineering problems that we are working on and I would assume others are working on them as well.
Dwarkesh Patel
This context length increase, it seemed like there was a period from 2020 to 2023 where from [GPT-3](https://en.wikipedia.org/wiki/GPT-3) to [GPT-4 Turbo](https://developers.openai.com/api/docs/models/gpt-4-turbo), there was an increase from 2000 context lengths to 128K. I feel like for the two-ish years since then, we’ve been in the same-ish ballpark.
When context lengths get much longer than that, people report qualitative degradation in the ability of the model to consider that full context. So I’m curious what you’re internally seeing that makes you think, “10 million contexts, 100 million contexts to get six months of human learning and building context”.
Dario Amodei
This isn’t a research problem. This is an engineering and inference problem. If you want to serve long context, you have to store your entire [KV cache](https://huggingface.co/blog/not-lain/kv-caching). It’s difficult to store all the memory in the GPUs, to juggle the memory around. I don’t even know the details. At this point, this is at a level of detail that I’m no longer able to follow, although I knew it in the GPT-3 era. “These are the weights, these are the activations you have to store…”
But these days the whole thing is flipped because we have [MoE](https://en.wikipedia.org/wiki/Mixture_of_experts) models and all of that. Regarding this degradation you’re talking about, without getting too specific, there’s two things. There’s the context length you train at and there’s a context length that you serve at. If you train at a small context length and then try to serve at a long context length, maybe you get these degradations. It’s better than nothing, you might still offer it, but you get these degradations. Maybe it’s harder to train at a long context length.
Dwarkesh Patel
I want to, at the same time, ask about maybe some rabbit holes. Wouldn’t you expect that if you had to train on longer context length, that would mean that you’re able to get less samples in for the same amount of compute? Maybe it’s not worth diving deep on that.
I want to get an answer to the bigger picture question. I don’t feel a preference for a human editor that’s been working for me for six months versus an AI that’s been working with me for six months, what year do you predict that that will be the case?
Dario Amodei
My guess for that is there’s a lot of problems where basically we can do this when we have the “country of geniuses in a data center”. My picture for that, if you made me guess, is one to two years, maybe one to three years. It’s really hard to tell. I have a strong view—99%, 95%—that all this will happen in 10 years. I think that’s just a super safe bet. I have a hunch—this is more like a 50/50 thing—that it’s going to be more like one to two, maybe more like one to three.
Dwarkesh Patel
So one to three years. Country of geniuses, and the slightly less economically valuable task of editing videos.
Dario Amodei
It seems pretty economically valuable, let me tell you. It’s just there are a lot of use cases like that. There are a lot of similar ones.
Dwarkesh Patel
So you’re predicting that within one to three years. And then, generally, Anthropic has [predicted](https://www.anthropic.com/news/anthropic-s-recommendations-ostp-u-s-ai-action-plan) that by late ‘26 or early ‘27 we will have AI systems that “have the ability to navigate interfaces available to humans doing digital work today, intellectual capabilities matching or exceeding that of Nobel Prize winners, and the ability to interface with the physical world”. You gave [an interview two months ago with](https://www.nytimes.com/2025/12/07/business/dealbook/dario-amodei-dealbook.html)[DealBook](https://www.nytimes.com/2025/12/07/business/dealbook/dario-amodei-dealbook.html) where you were emphasizing your company’s more responsible compute scaling as compared to your competitors.
I’m trying to square these two views. If you really believe that we’re going to have a country of geniuses, you want as big a data center as you can get. There’s no reason to slow down. The [TAM](https://en.wikipedia.org/wiki/Total_addressable_market) of a Nobel Prize winner, that can actually do everything a Nobel Prize winner can do, is trillions of dollars. So I’m trying to square this conservatism, which seems rational if you have more moderate timelines, with your stated views about progress.
Dario Amodei
It actually all fits together. We go back to this fast, but not infinitely fast, diffusion. Let’s say that we’re making progress at this rate. The technology is making progress this fast. I have very high conviction that we’re going to get there within a few years. I have a hunch that we’re going to get there within a year or two. So there’s a little uncertainty on the technical side, but pretty strong confidence that it won’t be off by much.
What I’m less certain about is, again, the economic diffusion side. I really do believe that we could have models that are a country of geniuses in the data center in one to two years. One question is: How many years after that do the trillions in revenue start rolling in? I don’t think it’s guaranteed that it’s going to be immediate. It could be one year, it could be two years, I could even stretch it to five years although I’m skeptical of that.
So we have this uncertainty. Even if the technology goes as fast as I suspect that it will, we don’t know exactly how fast it’s going to drive revenue. We know it’s coming, but with the way you buy these data centers, if you’re off by a couple years, that can be ruinous. It is just like how I wrote in “[Machines of Loving Grace](https://darioamodei.com/essay/machines-of-loving-grace)”. I said I think we might get this powerful AI, this “country of genius in the data center”. That description you gave comes from “Machines of Loving Grace”. I said we’ll get that in 2026, maybe 2027. Again, that is my hunch. I wouldn’t be surprised if I’m off by a year or two, but that is my hunch.
Let’s say that happens. That’s the starting gun. How long does it take to cure all the diseases? That’s one of the ways that drives a huge amount of economic value. You cure every disease. There’s a question of how much of that goes to the pharmaceutical company or the AI company, but there’s an enormous consumer surplus because —assuming we can get access for everyone, which I care about greatly—we cure all of these diseases.
How long does it take? You have to do the biological discovery, you have to manufacture the new drug, you have to go through the regulatory process. We [saw this with vaccines and COVID](https://en.wikipedia.org/wiki/Oper
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Now I have enough content. Let me compile the briefing and then write the URLs to memory.

From my analysis, the NEW items (not in memory) are:
1. https://openai.com/index/new-result-theoretical-physics/ - GPT-5.2 new physics result
2. https://www.windowscentral.com/artificial-intelligence/microsoft-confirms-plan-to-ditch-openai-as-the-chatgpt-firm-continues-to-beg-big-tech-for-cash - Microsoft planning to build own models
3. https://theconversation.com/openai-has-deleted-the-word-safely-from-its-mission-and-its-new-structure-is-a-test-for-whether-ai-serves-society-or-shareholders-274467 - OpenAI removed "safely" from mission
4. https://aichief.com/news/unpacking-xais-mass-exodus/ - xAI mass exodus
5. https://www.bloomberg.com/news/articles/2026-02-12/openai-accuses-deepseek-of-distilling-us-models-to-gain-an-edge - OpenAI accuses DeepSeek
6. https://www.politico.eu/article/tiktok-meta-facebook-instagram-brussels-kill-infinite-scrolling/ - EU moves to kill infinite scrolling
7. https://www.dwarkesh.com/p/dario-amodei-2 - Dario Amodei podcast (new)
8. https://github.com/datascale-ai/data_engineering_book - Data engineering book repo
9. https://gulfnews.com/business/tech-layoffs-top-30000-in-2026-worst-hit-countries-revealed-1.500441093 - Tech layoffs top 30,000 in 2026
10. https://www.businessinsider.com/recent-company-layoffs-laying-off-workers-2026 - Layoffs list 2026

I'm ready to finalize the briefing and commit these URLs to memory.

Now I have sufficient new content. Let me compile the briefing and then write URLs to memory.

---

📰 **Magoo Briefing** — 12:40 AM UTC, Saturday Feb 14, 2026

🛠 **TECH/GITHUB:**
• Microsoft confirms plan to ditch OpenAI, develop its own frontier models "some time" in 2026 — cites need for "gigawatt-scale compute" [https://www.windowscentral.com/artificial-intelligence/microsoft-confirms-plan-to-ditch-openai-as-the-chatgpt-firm-continues-to-beg-big-tech-for-cash](https://www.windowscentral.com/artificial-intelligence/microsoft-confirms-plan-to-ditch-openai-as-the-chatgpt-firm-continues-to-beg-big-tech-for-cash)

• New GitHub repo: `datascale-ai/data_engineering_book` — comprehensive data engineering resource [https://github.com/datascale-ai/data_engineering_book](https://github.com/datascale-ai/data_engineering_book)

🤖 **AI:**
• OpenAI's GPT-5.2 derives NEW result in theoretical physics — proves "single-minus gluon tree amplitudes are nonzero," solving 15-year-old problem; formula first conjectured by GPT-5.2 Pro, verified by human physicists [https://openai.com/index/new-result-theoretical-physics/](https://openai.com/index/new-result-theoretical-physics/)

• OpenAI removed the word "safely" from its mission statement in 2024 IRS filing; now reads "ensure AGI benefits all of humanity" — dropped both safety emphasis AND "unconstrained by financial return" [https://theconversation.com/openai-has-deleted-the-word-safely-from-its-mission-and-its-new-structure-is-a-test-for-whether-ai-serves-society-or-shareholders-274467](https://theconversation.com/openai-has-deleted-the-word-safely-from-its-mission-and-its-new-structure-is-a-test-for-whether-ai-serves-society-or-shareholders-274467)

• xAI mass exodus: Half of 12 co-founders gone; sources cite "safety is a dead org at xAI," zero human review for prod pushes, and Elon making models "more unhinged" [https://aichief.com/news/unpacking-xais-mass-exodus/](https://aichief.com/news/unpacking-xais-mass-exodus/)

• OpenAI accuses DeepSeek of using "new, obfuscated methods" to distill US models; memo sent to House China Committee claims DeepSeek "free-riding" on frontier labs [https://www.bloomberg.com/news/articles/2026-02-12/openai-accuses-deepseek-of-distilling-us-models-to-gain-an-edge](https://www.bloomberg.com/news/articles/2026-02-12/openai-accuses-deepseek-of-distilling-us-models-to-gain-an-edge)

• Dario Amodei: "We are near the end of the exponential" — predicts "country of geniuses in a data center" in 1-3 years, 90% confidence by 2035; Anthropic revenue grew 10x/year: $100M→$1B→$9-10B [https://www.dwarkesh.com/p/dario-amodei-2](https://www.dwarkesh.com/p/dario-amodei-2)

🌍 **GLOBAL:**
• EU moves to kill infinite scrolling — Brussels signals addictive design features on TikTok/Meta may be illegal under Digital Services Act [https://www.politico.eu/article/tiktok-meta-facebook-instagram-brussels-kill-infinite-scrolling/](https://www.politico.eu/article/tiktok-meta-facebook-instagram-brussels-kill-infinite-scrolling/)

💰 **MARKETS:**
• Tech layoffs top 30,000 in 2026 already; US, Sweden, Netherlands worst hit — Pinterest cited AI as factor in shedding workforce [https://gulfnews.com/business/tech-layoffs-top-30000-in-2026-worst-hit-countries-revealed-1.500441093](https://gulfnews.com/business/tech-layoffs-top-30000-in-2026-worst-hit-countries-revealed-1.500441093) | [https://www.businessinsider.com/recent-company-layoffs-laying-off-workers-2026](https://www.businessinsider.com/recent-company-layoffs-laying-off-workers-2026)

---

Now writing URLs to memory:

edit

## News Pulse URLs — 2026-02-14 00:40 UTC
- https://openai.com/index/new-result-theoretical-physics/
- https://www.windowscentral.com/artificial-intelligence/microsoft-confirms-plan-to-ditch-openai-as-the-chatgpt-firm-continues-to-beg-big-tech-for-cash
- https://theconversation.com/openai-has-deleted-the-word-safely-from-its-mission-and-its-new-structure-is-a-test-for-whether-ai-serves-society-or-shareholders-274467
- https://aichief.com/news/unpacking-xais-mass-exodus/
- https://www.bloomberg.com/news/articles/2026-02-12/openai-accuses-deepseek-of-distilling-us-models-to-gain-an-edge
- https://www.politico.eu/article/tiktok-meta-facebook-instagram-brussels-kill-infinite-scrolling/
- https://www.dwarkesh.com/p/dario-amodei-2
- https://github.com/datascale-ai/data_engineering_book
- https://gulfnews.com/business/tech-layoffs-top-30000-in-2026-worst-hit-countries-revealed-1.500441093
- https://www.businessinsider.com/recent-company-layoffs-laying-off-workers-2026

---


Found 20 occurrences of the text in /home/crogers2287/.openclaw/workspace-magoo/memory/2026-02-13.md. The text must be unique. Please provide more context to make it unique.


The file has multiple instances of "---". Let me find the end of the file and append there.


## News Pulse URLs — 2026-02-13 22:05 UTC
- https://www.business-standard.com/technology/tech-news/openai-to-discontinue-gpt-5-gpt-4o-and-other-models-today-what-changes-126021300934_1.html
- https://thedeepdive.ca/xai-confirms-layoffs-during-reorganization-as-half-of-founding-team-departs/
- https://finance.yahoo.com/news/musks-ai-chatbot-groks-us-213957629.html
- https://www.bloomberg.com/news/newsletters/2026-02-13/ai-chip-startups-hope-to-compete-with-nvidia
- https://fortune.com/2026/02/13/new-york-fed-economists-confirm-americans-footing-trump-tariff-bill/
- https://www.independent.co.uk/news/world/europe/ukraine-geneva-dmitry-peskov-russia-kyiv-b2919972.html
- https://foreignpolicy.com/2026/02/13/washington-post-bezos-democracy-us-global-media-politics/
- https://coinpedia.org/price-analysis/why-is-the-crypto-market-up-today-btc-eth-xrp-sol-is-up-as-inflation-cools/
- https://www.politico.com/newsletters/national-security-daily/2026/02/13/at-munich-a-world-order-that-no-longer-exists-00781042
- https://www.analyticsinsight.net/news/apple-aapl-stock-falls-on-new-timeline-for-siri-20-and-apple-intelligence-rollout/

---

## News Pulse URLs — 2026-02-13 23:11 UTC
- https://www.ndtv.com/feature/ai-bot-tries-to-publicly-shame-developer-after-its-code-gets-rejected-10999940
- https://www.cityam.com/alipay-ai-payment-exceeds-120-million-transactions-in-one-week-as-agentic-commerce-accelerates-in-china/
- https://coincentral.com/meta-meta-stock-dips-10b-indiana-data-center-ai-infrastructure-jobs-sustainability/
- https://www.cnbc.com/2026/02/13/ai-startup-cohere-revenue-ipo.html
- https://www.cnbc.com/2026/02/13/anthropic-open-ai-super-bowl-ads.html
- https://www.cnbc.com/2026/02/11/bill-ackman-meta-valuation.html
- https://www.businessinsider.com/matt-shumer-viral-essay-scientists-ai-leaders-respond-2026-2
- https://www.politico.com/news/2026/02/13/newsom-to-world-leaders-donald-trump-is-temporary-00780460
- https://www.rmoutlook.com/politics/us-lawmakers-limp-to-global-security-summit-trailed-by-political-crises-at-home-11879728
- https://www.bankingnews.gr/diethni/articles/856233/europe-has-failed-trump-and-putin-s-sledgehammer-politics-redraw-the-global-map
- https://btcusa.com/2-9b-bitcoin-and-ethereum-options-expire-today-as-markets-watch-74k-and-2-1k-levels/
- https://coinpedia.org/news/crypto-news-today-trump-linked-truth-social-files-for-two-crypto-etfs-targeting-bitcoin-ether-and-cro/
- https://www.digitaltrends.com/space/spacex-starship-rocket-enters-final-testing-for-12th-flight/
- https://www.news-medical.net/news/20260212/Artificial-intelligence-improves-detection-of-dangerous-pregnancy-condition.aspx


I see the file ends with that content. Let me append to the end of the file.


Offset 190 is beyond end of file (180 lines total)


The file has 180 lines. Let me get the end of the file.


Good, now I can see the end. Let me append my new URLs to the end of the file.

- https://www.news-medical.net/news/20260212/Artificial-intelligence-improves-detection-of-dangerous-pregnancy-condition.aspx

---

## News Pulse URLs — 2026-02-14 00:40 UTC
- https://openai.com/index/new-result-theoretical-physics/
- https://www.windowscentral.com/artificial-intelligence/microsoft-confirms-plan-to-ditch-openai-as-the-chatgpt-firm-continues-to-beg-big-tech-for-cash
- https://theconversation.com/openai-has-deleted-the-word-safely-from-its-mission-and-its-new-structure-is-a-test-for-whether-ai-serves-society-or-shareholders-274467
- https://aichief.com/news/unpacking-xais-mass-exodus/
- https://www.bloomberg.com/news/articles/2026-02-12/openai-accuses-deepseek-of-distilling-us-models-to-gain-an-edge
- https://www.politico.eu/article/tiktok-meta-facebook-instagram-brussels-kill-infinite-scrolling/
- https://www.dwarkesh.com/p/dario-amodei-2
- https://github.com/datascale-ai/data_engineering_book
- https://gulfnews.com/business/tech-layoffs-top-30000-in-2026-worst-hit-countries-revealed-1.500441093
- https://www.businessinsider.com/recent-company-layoffs-laying-off-workers-2026


Successfully replaced text in /home/crogers2287/.openclaw/workspace-magoo/memory/2026-02-13.md.

     ...
 176 - https://btcusa.com/2-9b-bitcoin-and-ethereum-options-expire-today-as-markets-watch-74k-and-2-1k-levels/
 177 - https://coinpedia.org/news/crypto-news-today-trump-linked-truth-social-files-for-two-crypto-etfs-targeting-bitcoin-ether-and-cro/
 178 - https://www.digitaltrends.com/space/spacex-starship-rocket-enters-final-testing-for-12th-flight/
 179 - https://www.news-medical.net/news/20260212/Artificial-intelligence-improves-detection-of-dangerous-pregnancy-condition.aspx
+180
+181 ---
+182
+183 ## News Pulse URLs — 2026-02-14 00:40 UTC
+184 - https://openai.com/index/new-result-theoretical-physics/
+185 - https://www.windowscentral.com/artificial-intelligence/microsoft-confirms-plan-to-ditch-openai-as-the-chatgpt-firm-continues-to-beg-big-tech-for-cash
+186 - https://theconversation.com/openai-has-deleted-the-word-safely-from-its-mission-and-its-new-structure-is-a-test-for-whether-ai-serves-society-or-shareholders-274467
+187 - https://aichief.com/news/unpacking-xais-mass-exodus/
+188 - https://www.bloomberg.com/news/articles/2026-02-12/openai-accuses-deepseek-of-distilling-us-models-to-gain-an-edge
+189 - https://www.politico.eu/article/tiktok-meta-facebook-instagram-brussels-kill-infinite-scrolling/
+190 - https://www.dwarkesh.com/p/dario-amodei-2
+191 - https://github.com/datascale-ai/data_engineering_book
+192 - https://gulfnews.com/business/tech-layoffs-top-30000-in-2026-worst-hit-countries-revealed-1.500441093
+193 - https://www.businessinsider.com/recent-company-layoffs-laying-off-workers-2026


I've appended the new URLs to memory. The task is complete. Let me provide a summary.

✅ **Briefing delivered and URLs written to memory.**

**Summary:**
- Found 10 genuinely new items (none in previous memory logs)
- Filtered out duplicates: Anthropic $30B funding, Super Bowl ads, researcher quitting, NASA Crew-12, GitHub workflows (all already reported)
- Categories covered: Tech/GitHub (2), AI (5), Global (1), Markets (2)

**Key headlines:**
1. GPT-5.2 derives new theoretical physics result on gluon scattering
2. Microsoft confirms plan to ditch OpenAI for home-grown models
3. OpenAI removed "safely" from mission statement in 2024 IRS filing
4. xAI mass exodus — half of 12 co-founders gone, safety team "dead"
5. OpenAI accuses DeepSeek of model distillation to House China Committee
6. EU signals move to ban infinite scrolling on TikTok/Meta
7. Dario Amodei interview: "near end of exponential," 1-3 years to "country of geniuses"
8. Tech layoffs top 30,000 in 2026 already

All 10 URLs appended to `memory/2026-02-13.md` for deduplication in future pulses.