Co-CEO https://xitter.cf/t.co/4sJqik7J6q / Senior Partner at https://xitter.cf/t.co/mrxzdAKOf8 https://xitter.cf/t.co/uaAJeEiqID https://xitter.cf/t.co/BqjLuq1GW4 https://xitter.cf/t.co/UsMMAAe6cy

Berlin, Deutschland
Joined January 2014
Most of Europe has not yet absorbed what AI is about to do to us. The few who have are not saying it loudly enough. We wrote Europe 2031: a five-year scenario of the continent's slide into irrelevance, how AI is driving it, and what can still be done to change course.
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AI night at a Berlin church praying to the AI overlords alongside the Europe 2031 gang 💙. We‘re working on a second chapter, stay tuned 👀
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Huge congrats on the release of your first model @_rockt @Recursive_SI 🥳
We are releasing our first model, Flint, a best-in-class local OSS coding model that runs at 150+ tokens/s on an @nvidia DGX Spark and 125+ tokens/s on a MacBook Pro M4—developed from scratch in less than four months with help from @Recursive_SI's autoresearch system.
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Judith Dada retweeted
Un investigador de Anthropic acaba de decirle a una sala llena de doctorandos que su plan de carrera ya no existe. La charla se titula "El fin del Antropoceno" y los datos son brutales: → El cómputo para entrenar modelos se multiplicó por 10 millones en la última década → La duración de las tareas que una IA completa sola se duplica cada pocos meses → Desde hace 7 meses los modelos resuelven problemas abiertos de Erdős que los matemáticos no habían logrado cerrar → En San Francisco, tener "timelines largos" significa pensar que la AGI llega en 10 años Su proyección: el cómputo de 2030 será 10.000 veces mayor que el de 2025. Todavía estamos al principio de la exponencial. Sus 3 consejos para los estudiantes: 1. Usa la IA cada día. Si estás sobre la exponencial, nunca te quedas atrás de ella. 2. Elige proyectos que mejoren cuando el cómputo escala. El resto quedará resuelto antes de que los termines. 3. Deja de planificar tu carrera como si fuera estable. La opción segura ya no compra seguridad, ya no existe. Y una frase que resume todo: "Escribir es pensar. Si externalizas tu escritura, externalizas tu pensamiento." La charla completa aquí ⬇️
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Judith Dada retweeted
Nous sommes à la conjonction des deux phénomènes civilisationnels distincts qui mènent à la révolution: Le premier : dans une société démocratique, comme le notait Tocqueville, toute inégalité devient vite insupportable car il n’y a plus que deux moyens d’expliquer que certains réussissent mieux que soi-même : la corruption du système ou le mérite. Et psychologiquement, sans surprise, on a plutôt tendance à penser que ceux qui réussissent mieux que nous bénéficient d’un système corrompu sont plutôt qu’ils soient plus méritants. La démocratie est donc un régime qui crée structurellement des armées de mécontents, sauf quand il y a une forte croissance économique. Car alors on peut prendre son mal en patience en se disant raisonnablement que dans quelques années, on sera soi-même au même niveau que ceux que l’on envie. Cela fait 15 ans qu’il n’y a plus de croissance en France. Les jeunes générations ne croient pas que leur situation rattrapera naturellement celle des plus privilégiés. En conséquence de quoi, elles n’ont plus grand intérêt à maintenir le système en vie. D’autant plus que n’ayant souvent pas d’enfants et n’étant pas propriétaires, elles ne sont soumise à aucunes des forces conservatrices traditionnelles. Le deuxième : comme le notait Schumpeter, le capitalisme faisant exploser la productivité industrielle et agricole, il réduit à la portion congrue le nombre de métiers manuels. La société se met donc à produire des cohortes gigantesques de personnes formées aux métiers intellectuels, alors même que l’économie n’a pas besoin d’autant de personnes dans ces secteurs. On a beau démultiplier les bullshit jobs, on produit néanmoins un grand nombre d’individus éduqués et inemployables. (Ils considérèrent que les emplois manuels ne sont pas assez dignes.) Ces cohortes sont structurellement révolutionnaires puisqu’elles se vivent comme un élite éclairée en opposition au système existant. La France est une des économies les plus tertiarisees du monde et avec un des plus hauts pourcentages d’individus faisant des études supérieures. Elle n’a plus de croissance depuis 15 ans. Kaboum, donc ? Pas encore, car il existe cependant une composante de la société qui a un intérêt absolu à maintenir le système tel quel : les retraités. Et ils représentent un tiers de l’électorat. Leur rôle est ambivalent : ils empêchent la révolution destructrice tout comme toute réforme à même de viabiliser le système. Voici donc une des tâches clé du politique aujourd’hui : relancer la croissance économique et rediriger les richesses vers les actifs en réussissant à la fois à contourner les blocages des retraités et à contenir les pulsions révolutionnaires de la jeunesse. Ce n’est qu’un des nombreux chantiers urgents de la France, en cette période de crises multiples. Mais avec la crise financière qui approche, c’est la fin de l’amortisseur de l’endettement. La question fondamentale d’au service de qui et de quoi est la machine économique française ne pourra plus longtemps rester sans réponse satisfaisante.
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Judith Dada retweeted
Harnesses + smart models = the end of the last famous hard benchmarks. Models can now work for extremely long times (bye, METR Long Tasks), they can output professional documents with correct math (so long, GDPval) and they can solve complex novel puzzles (farewell, ARC-AGI-3).
Gemini 4 Argon from @Google on ARC-AGI (Verified): - ARC-AGI-3: 97.5%, $4.8K (provider adapter harness), 49.8%, $5.8K (standard harness) - ARC-AGI-2: 91.3%, $0.32/task - ARC-AGI-1: 98.5%, $0.16/task Google DeepMind is the second frontier AI lab to score above 95% on ARC-AGI-3.
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Most people still engage in some form of forced labour.
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Some guys will never understand that a lot of what determines *taste* is embodied in a human who is - purposefully or accidentally - positioned at an angle to the world around them. That’s why they never got why many women love Leandra Cohen (her blog is called manrepeller for a reason). Much of fashion is aesthetically quite unpleasant, but social dynamics - the status games we are all playing endlessly - make certain style choices cool, vibey, or rad. The same goes for language, music, art… AI will certainly change the creative process, but it won’t change the human element of taste. In fact, I’m bullish on the human element of taste becoming 10x more important in the future.
I no longer see any compelling reason to believe that taste is some permanent boundary LLMs won’t cross. It’s already happening.
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Wenn @Westermeyer mich fragt ob ich meine „Bus-Analogie“ erzählen kann 🫡
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Von der Finanzwelt zur KI-Elite: @DadaJudith hat mich beeindruckt. Heute baut sie mit Langdock an Europas KI-Zukunft. Was für eine Story! Pod jetzt live. omr.podigee.io
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Probably the most fun I ever had on a podcast: Live recording with the one and only @Westermeyer. Talked about my early career, why I joined @langdock, what we are building, why everybody - esp. in Germany - needs to drive the damn bus now, end even my largely non-existent exercise routine 💀. Link below (German only).
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Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: felixrieseberg.com/how-to-be…
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1) Ich wünsche mir mehr Unternehmen, die in Deutschland durch KI wachsen - durch das Bauen von KI und die Adoption von KI - und das Land damit wettbewerbsfähiger machen. Wir sollten sie beschleunigen, nicht ausbremsen. 2) Wenn durch KI große Teile des Arbeitsmarktes restrukturiert werden, müssen Menschen abgesichert werden. Um uns diese Absicherung leisten zu können, brauchen wir zuallererst eine wettbewerbsfähige Wirtschaft. 3) Ich zahle gerne Steuern. Sie sind ein wichtiger Teil unserer sozialen Marktwirtschaft. Aber eine Marktwirtschaft braucht immer auch die Fähigkeit, sich global zu behaupten. Ich nehme Minister Wildberger als technologieoffen und wachstumsorientiert wahr und finde gut, dass wir gesellschaftliche und wirtschaftliche Szenarien für transformative KI thematisieren.
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The AI crowd on X still massively underappreciates how slowly AI adoption is going. Working with established enterprises on this every day gives me a profound appreciation for how long it takes people to change. FFS, it’s 2026 and I still have to fax my pediatrician. But I believe much of this debate gets a key thing wrong: it’s led by researchers and insiders who consume a lot of intelligence directly, while most people just...don’t. Instead, much of intelligence gets consumed indirectly, through derived demand for services and products. Most of us don’t have sufficiently complex digital problems for AI to solve directly. Sadly, we don’t all have a Millennium Prize problem to throw at it :(. So people use AI like a Google replacement and struggle to see the insane progress being made. But we all have complex problems that we solve through expert services: a doctor, a lawyer, or an architect. And we all rely on products that are essentially distilled intelligence: GPS that corrects for relativity or weather forecasts run on supercomputers. Most people who use the internet every single day have no clue how it works - and didn't even know they wanted something like the Internet until it was there. People don't adopt intelligence, they adopt concrete utility, just like we didn't "adopt" electricity, but bought a damn fridge. So I don’t think we need to wait for most consumers to change their habits and demand more intelligence directly. Many never will. But we’re still vastly underestimating the derived demand for indirect intelligence: far better services and products. These may arrive much faster than habits and broad demand for direct intelligence will change.
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Judith Dada retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Stating the obvious: We are very clearly going to automate jobs with AI. We are also very clearly going to grow jobs with AI. Mistrust anyone only claiming either side of this story. AI will be transformative, more than most of us have even begun to grasp. It will be humbling both in making us understand everything that is intelligence and in making us realise that intelligence is not everything. Long on humanity.
Call center jobs grew ~4% annually for 15 years Now they’re shrinking 4% annually Charts of the Week: a16z.news/p/this-week-in-cha…
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amen.
AI cracking unsolvable math problems while being kinda mid at writing is a much-needed W for humanities majors after a rough 21st century.
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Also me reading my own writing:
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Judith Dada retweeted
Open-weight models that decrease margins at the model layer are positive for AI infra, exhibit 1001.2 Since the @silicondata “token index,” which captures both price and mix, peaked on May 31: B300 prices are up 38% B200 prices are up 10% Other GPU indices are up *much* more.
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