The context problem in AI is much deeper than it looks. Feeding an LLM more documents doesn't solve it. Context isn't just information. It's understanding built from experience over time. The only real fix is continual learning: AI that keeps updating from the environment it operates in, rather than freezing at training time. At Skyfall, this has shaped our research roadmap from the start. First, we explored world models as planners (worth reading Dr. Fei-Fei Li's recent breakdown on this). Our work on SCOPE showed that a small, specialized world model could outperform frontier LLMs on sequential decision-making by being 55x faster than GPT-3.5, 160,000x smaller than GPT-4o, and more accurate on planning tasks. Now we're focused on world models as simulators. A simulator isn't just a rendering of the world. It's a physically and dynamically faithful environment that agents can actually train in. And for a simulator to stay useful over time, it needs continual learning at its core. Agents that train in a static simulation will eventually hit a ceiling. Agents that train in a world that evolves with them won't. Context is a learning problem. That's what we're building toward, and we're launching something soon. Stay tuned. ✨

We're proud to welcome Arjun Mohan (@arjunmhn) as Chief of Staff at Skyfall AI, based in our San Francisco office! Arjun brings deep operational and financial expertise from four years at Barclays in private markets and private wealth management, where he built financial systems, raised capital, and advised family offices on their private market investments. He's a @Wharton school MBA (Mayer Fellow) with an Economics degree from @uniofwarwick . During his time at Wharton, he interned at Superscript, a Series A healthcare startup, where he worked across product deployment, fundraising and strategic finance. Welcome aboard Arjun!

Language models gave AI the ability to talk about the world. World models will give AI the ability to understand it. But “world model” is an overloaded term. What does it mean? HAI Founding Director @drfeifei offers the taxonomy that matters now. brnw.ch/21x36eY

Why LLMs are a dead end for human-level intelligence, and especially for Physical AI / Robotics. The next leap isn’t bigger language models. It’s World Models. I just dropped a full 1-hour presentation from Shanghai: “World Models: the ChatGPT moment for robotics?” → Why LLMs hit a wall → Why action-conditioned world models planning in latent space are the real path → Live World Forge demo with LeWorldModel Hugging Face LeRobot Watch here. The future of intelligence is embodied, not just chatty.

Dr. Fei-Fei Li outlines three functions of a world model in her recent blog: renderer, simulator, planner. It's a useful taxonomy and one that maps closely to our research roadmap at Skyfall. Our work on SCOPE earlier this year addressed the planner: can a small, specialized world model outperform frontier LLMs on sequential decision-making tasks? On TextCraft, it did. - 55x faster than GPT-3.5 (3s vs 164s) - 160,000x smaller than GPT-4o (11M vs 1.8T parameters) - Higher planning accuracy (56%) than frontier models The planner was step one but the harder problem is the simulator. That's what we've been cooking. More soon. 👀

This is the single best read on World Models and one of the most important reads in AI. $10B has flowed into "world models" in the last 18mos, from Yann LeCun to FeiFei Li. The promise is, like LLMs, world models will provide the data it takes to scale robotics foundation models, and solve robotics. ..but the word has been abused to mean one of many things. This post unpacks: – What 5 traits makes a world model? – How do the different approaches stack up? – What is it used for within and beyond robotics? – Where is the opportunity? – Citations to research, news and blog posts Companies / products in the space include: – BigCo products: Google Genie, Tesla Optimus, Nvidia DreamDojo, DreamZero, Microsoft Muse – Pure world model: AMI Labs, World Labs, Runway, Rhoda, Decart, Spaitial, Odyssey, Embo, Dream Labs, OneWorld – Robot foundation model cos: Skild, Physical Intelligence, Figure, Mind Very likely one of the seminal technologies of the next decade.

microsoft MAI tech report is a gold mine, one of the most transparent for a model at this scale. this model uses zero synthetic data or distillation from previous models. this means reasoning, agentic behavior, tool use are all learned fully during post-training with no cold start. bold choice that makes it harder and requires more iterations to reach sota, but you get FULL control over your model series and it proves they are serious about being a frontier lab. the tech report is insanely detailed and precise about numbers. to give an example, they give the exact MFU across all the iterations of the model, with the exact changes etc. they also share the full scaling ladder recipe, to my knowledge this is the first time i've seen this in a tech report at this scale let's look at all of this in this likely very long thread 🧵

There will be no AI jobpocalypse. The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it. I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines. Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%. Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable! Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more. Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus. To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market. Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades. Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have). Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future! [Original text in The Batch newsletter.]

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Sam Altman reveals that OpenAI’s top “token leader” uses 100B tokens every month, and still falls short of the world’s highest user. Source: axios --- axios. com/2026/06/02/altman-openai-top-token-user

LLMs learn by predicting tokens. World models (JEPA, data2vec) learn by predicting their own abstractions. Which needs more data? For data with hidden hierarchy, we prove the gap is exponential. arxiv.org/pdf/2605.27734

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