Plotting ARC-AGI model scores reveals a strong relationship across all three benchmarks. For example, the power curve v2 = v1^4.53 explains about 98% of the variation in ARC-AGI-2 scores. Models tied on ARC-AGI-1 can still differ by over 20 percentage points on ARC-AGI-2.

Workers in six occupations do 79 tasks the government's official occupational taxonomy has yet to add. That work is worth about $140B a year in wages. That taxonomy is called O*NET, the U.S. Department of Labor's database of the tasks each occupation involves. Workers use it to help with job transitions, and economists use it to estimate which jobs AI will change. If the lists are incomplete, so are the estimates. Today we're releasing TaskNet, an open-source task catalog built on O*NET and updated for how AI is changing work. Built with researchers from Stanford, the O*NET Resource Center, and NBER. Thank you to @robinsonaisaac @erikbryn @diyi_yang @jennjwang @andreas_h0wpt @Digeconlab @stanfordnlp @stanfordhai @stanfordAIlab for contributing to this work. Read the blog:

A common sign that you are in an insane millenarian death cult is that you axiomatically convinced that the Apocalypse will happen within a few months, you are constantly on the lookout for signs that it may have started, you feel both fearful and *excited* about it, and you're constantly talking about it with your friends, who happen to *all* have the exact same set of beliefs.

Introducing SKY-001, the first Engineering World Model. On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested: -> 51-125x faster - > 150-210x cheaper -> Higher IOU than all frontier models SKY-001 lets you dream a physical object before you build it. 18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch). The first ‘AI General Engineer’ will be the backbone for Autonomous Manufacturing.

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.

I truly believe conversational AI will drive the biggest educational leap of our generation. 1-on-1 tutoring is the gold standard for education, but it doesn't scale. AI can change that with truly active conversational learning. Annie and team set out to solve this with @gakkuai, and they've done a great job. Gakku is an AI mentoring app for K-12 students. Rather than doing the work for them, the voice chat coach guides students through 10 min essay writing "missions" that cover brainstorming, drafting, and revising. It actively trains kids to generate their own ideas, structure logical arguments, and articulate themselves clearly. If you have school-aged kids, I highly recommend checking it out:

Everyone wants to have solved a hard problem, but almost nobody wants to live inside the grind of actually solving it. The breakthroughs belong to the few who are obsessed with the process, not just the idea of the outcome

You can't solve real-world physical tasks through autoregressive sampling alone. The physical world is entirely unforgiving to hallucination or approximate pattern matching. If we want actual autonomous robotics we have to engineer causality and discrete reasoning back into the loop. Biwei and the team at Aether AI is doing exactly the kind of foundational, non-consensus work the field actually needs right now.

String theory is what happens when an intelligent system is starved of sensory input and starts hallucinating. Intelligence needs grounding. For this reason, intelligence is practically never the bottleneck to solving impactful problems. The bottleneck is iterated contact with reality.

The ideal outcome for science isn't a couple of labs using proprietary models and millions of dollars to crack trophy problems, it's to have millions of AI-aided human scientists who follow their curiosity and become way more ambitious

DeepSeek V4.1 Flash from @deepseek_ai on ARC-AGI (Verified): - ARC-AGI-3: 3.3%, $878 (provider adapter harness), 0.9%, $1.2k (standard harness) DeepSeek V4.1 Flash is the first model we've evaluated on ARC-AGI-3 to score above 3% for under $1k in total evaluation cost.

If you explained agriculture to folks in SF, you'd blow so many minds. It's... self-replicating, recursively self-improving nanotech for the physical world. And it's all open-source! One instance can turn into hundreds of seeds, that turn into hundreds of instances. Next thing you know you have trillions of them. It's super-exponential

How AI shifted from a transductive paradigm (token-by-token answer completion) to an inductive paradigm (on-the-fly reasoning chain synthesis), in one chart. This is the big story of 2025 and 2026. This is what made coding agents work.

The real world abounds with recursively self-improving systems, but one in particular deserves attention: Science, modeled as a system (perhaps even as an agent, with goals and resources). If you want to really understand AI RSI, science should be your reference point. Science is an intelligent system, and it is obviously recursively self-improving: 1. Scientific discoveries unlock new technology that helps build better experimental tools. This is a top driver of progress in nearly all fields. 2. They unlock new conceptual advances (ideas, theories) that help solve more problems. 3. They increase society's economic output, leading to more resources flowing into science. 4. They unlock better faster tooling (e.g. more compute via better chip & networking technology). As a result, many measures of scientific *input* grow exponentially: 1. Headcount (doubles every ~15 years) 2. Global R&D spending (doubles a bit faster, every ~13 years) 3. Papers and patents (technically this is a measure of headcount) 4. Compute dedicated to science (doubles every ~2 years) But is scientific progress exponential? Historically, the rate of scientific impact over time has remained roughly constant since the start of the industrial revolution (i.e. scientific progress is *linear*). 1850-1900 was about as dramatic as 1900-1950 or 1950-2000. 1850–1900: Evolution, germ theory & antiseptic surgery, thermodynamics, electromagnetic field equations, the periodic table, pharmaceuticals, electricity, telegraph and telephone, internal combustion engine, skyscrapers, mechanized agriculture... 1900–1950: special and general relativity, quantum mechanics, nuclear fission & atomic energy, antibiotics, genetic theory, electronic computers, information theory, synthetic polymers and plastics, the transistor, aviation... 1950–2000: DNA, genetic engineering, integrated circuits, microprocessors & personal computing, the Internet, crewed spaceflight, moon landing, satellite communications & GPS, standard model of particle physics... In real terms, like life expectancy, which has increased in a remarkably linear fashion of roughly 3 months per year since 1840, progress is a straight line. This is especially apparent for fields where impact is easy to measure, like biology, medicine, and agriculture. I first wrote about this phenomenon and its causes in 2012, and a steady stream of research has confirmed it in the years since. Examples include the 2018 paper by Nielsen and Collison, "Science Is Getting Less Bang for Its Buck," and the 2020 economic paper, "Are Ideas Getting Harder to Find?" (In fact, I believe the Nielsen paper stemmed from a conversation I had with him about this exact idea six months earlier) In short, the primary cause is that research solves the highest-impact, easiest problems first, and every subsequent problem is either harder or lower-impact. Exponentially so. The paper that presented information theory wasn't very hard to write (single author!) but you'd have a hard time ever writing a CS paper that beats it in impact. This is why science as a system requires exponential resources (input) to produce linear impact (output). It gets exponentially harder over time. Worth thinking about if you're pondering RSI for AI. I fully believe AI RSI is already happening and will accelerate in the future. But I do not believe this leads to an "intelligence explosion" -- that would fly in the face of everything I know about intelligence and everything I know about recursively self-improving systems.