We abandoned training our own models at Defog in 2024 (despite 2M huggingface downloads) after o1 was released Our reasoning at the time - frontier models would get better and cheaper. It was just easier to use them via an API, and see gains automatically, rather than painfully handcraft our own Fully realizing the risks of that decision today. Going to spend at least a third of my time on fine-tuning frontier open-source models moving forward. Can't build a business on shifting sands, gotta own your model weights.

Frontier model performance on an open model, post-trained in under 24 hours. @trajectorylabs is showing what's possible when great open models meet the right training infrastructure. Proud to power the compute behind this work alongside @nvidia .

Everyone Operating At The Frontier Satya Nadella, Chairman & CEO, Microsoft, interviewed by @saranormous & @eladgil (No Priors) and @swyx (Latent Space) Crossover special at Microsoft Build 2026. Summary: Satya reframes Microsoft's AI strategy as an ecosystem play rather than a single model or platform, where the win is any company being able to point to AI it created and operate at the frontier with its own intelligence. Scaling laws held and intelligence still tracks the log of compute, but the value lives in deployment, where private evals become a company's biggest IP and accumulated agent traces start to look like assets on the balance sheet. Take it seriously and SaaS gets unbundled and rebundled, engineering collapses toward generalists who manage agents, and the industry has to earn community permission for the buildout by delivering benefits people can actually see. 1. Ecosystem Over Model. A platform earns its place by how much value other companies build on top of it. Satya wants any company, AI-native or traditional enterprise, to participate as a first-class participant that can point to AI it created, still using other people's models but owning a recipe of its own. He calls this the only tagline that matters for the conference: can everybody operate at the frontier with their own frontier intelligence. Without that, he says, there is no reason to hold a developer conference; you would just "worship at the altar of one model." 2. The Broken IDE. Coding agents worked so well that Microsoft now has to rebuild the IDE around them. When a developer runs a hundred agent sessions at once, the cognitive load lands back on the human and chat as the only artifact stops working, which is why the new interface needs a canvas. Even a fully agentic world still needs UI, because someone has to inspect what the agents did and decide. The lesson generalizes: every workflow handed to long-running agents will need a new surface for the human to supervise it. 3. The Harness Is The Product. The unit that matters is the harness that loops across models, data, and tools. Microsoft runs the same open GitHub harness across GitHub Copilot, security copilot, and science discovery, with progressive disclosure of tools to stay token-efficient and heavy context prep where "the magic is." The harness stays open: bring your own models, tools, and context, or swap in a Llama harness. Nadella points to M-dash finding vulnerabilities the incumbent scanner missed as proof that a multimodal harness can win in the real world. 4. Private Evals As IP. The single most valuable thing a company can own is a private eval. His acid test for control: take your private eval, run it on model A, then switch to model B; if you can still climb, you are in control, and if you cannot, you are not. Because frontier models learn from a few samples rather than mountains of data, the defensible asset is the eval you never leak. This is why Nadella reframes Microsoft's third act from operating systems to cloud to an evals-and-harness company. 5. Agents On The Balance Sheet. The traces between a company's humans and its agents become a trainable asset that belongs on the balance sheet. Human capital never made it onto the balance sheet because tacit knowledge could not be captured, but agent traces collected over time can train a "company veteran" agent that encodes how that specific enterprise creates value. As token capital and human capital both rise, the question becomes how to compound the two. Elad Gil's quip lands the point: the SEC will need accounting standards for token expertise. 6. Unbundle And Rebundle. SaaS gets taken apart and put back together, with the data model and business logic surviving the teardown. A general ledger should stay a general ledger, and a Power BI semantic model is hard-won business logic worth feeding to agents, so the work is repackaging these into new bundles and business models. Work IQ exposes what Nadella calls the most important database in a company, the M365 data that was only ever captive to email and Office apps. Now an agent can read a week of design-meeting transcripts tied to a GitHub repo and come back with a plan to change the code base, something M365 was never built to do. 7. Outcome Pricing's Catch. Per-user pricing is an artifact of buyers needing budget certainty, and it survives even as consumption pricing arrives underneath it. Subscriptions bundle some usage into per-user stacks, then consumption metering sits below, which is exactly the adjustment GitHub made after agent intensity blew past what per-seat assumed. Outcome-based pricing sounds appealing until a customer actually has an outcome and realizes they are giving away a royalty. As Nadella puts it, most people love outcomes until they have one, then they ask to go back to per-user and consumption pricing. 8. The Buy-Or-Build Test. Whether to build software or buy it reduces to a quantifiable rule: acquire it when the marginal cost of building and maintaining it yourself is higher. Maintenance is the part teams forget, because security holes that AI now finds faster also have to be fixed faster, and every fix burns tokens that someone has to own. Satya expects the current agent euphoria, where teams rebuild everything internally, to cool after one full budget cycle. The vendors that last will be the flexible ones; he sees very little tolerance ahead for any vendor that stays rigid. 9. Generalists Win. The biggest returns go to generalists whose scope just grew. LinkedIn restructured into a "full stack builder" discipline that combines design, product, and front-end while keeping each person's original edge, giving people bigger scope instead of one narrow role. Building an app now sits in the same sentence as writing a Word doc or a spreadsheet, so generalist skills suddenly carry, in Satya's words, "a higher leverage." Specialists still exist, and infrastructure science, like building the RL environment where a reward can be learned, becomes one of the hardest and most valuable roles. 10. Meta-Work. The biggest move is to make your work meta: build the agentic system that does the work instead of doing the work. Satya's example is the team running Azure's physical fiber network, who decided their job was not Azure networking but building the agentic system that does Azure networking, complete with a named agent called Miles. That team started asking for tokens instead of headcount to scale their operation. Kevin Scott's line frames why it matters: making hard things easier is one kind of progress, but true ambition is making the impossible possible, and that needs a new conceptual model of what work even is. 11. Earned Permission. The industry only gets to keep building data centers if communities feel the benefits in real ways. Satya argues the buildout has to lower energy prices through a better long-term grid, replenish water through closed-loop systems, and show up as jobs and tax base, with the burden on the industry to earn that through hard work. His read on the politics is blunt: the world will be skeptical of any tech company that says "trust us, the future will be glorious," so you have to deliver tangible benefits people can see in the next 12 to 18 months. Using a lot of energy while creating a lot of value for society has historically been a good story, and he is betting a token economy that drives productivity and broad participation lands on the right side of it. 12. A New University. The next great startup may be a new university. Satya thinks the way we educate, credential, and value those credentials has to change completely now that the means of learning and staying current have shifted so fast. Learning concepts still matters, and he points approvingly to a Stanford AI class drilling students on when to apply softmax rather than just asking a model to fix a training run. The opening he sees is for someone to build a new way of teaching that takes a person through a curriculum and out the other side into real economic opportunity, something that felt impossible for a long time.

Fable 5 is the biggest step up I’ve felt in our models since Opus 4.5 back in November. After 4.5 came out I uninstalled my IDE when I realized that I’d been doing 100% of my coding in a terminal for a few weeks. With Fable, it’s felt like Claude has stepped up from being a coding agent to a thought and design partner in building the product. Fable has judgement, taste, and dimensionality in a way that previous models didn’t, leading me to trust it more with the most complex work. I think the first time I had this realization was when I asked Fable to debug something. It is the first model I have used that was so methodical and precise, taking measurements and adding logs then verifying that it truly fixed the issue before declaring victory. There’s nothing in claude code’s prompting telling the model to do that, it’s just part of its personality. It really has this “big model smell” that I haven’t felt before.