Notice how Cursor isn’t saying “for developers”. Just “Useful AI”. Cursor will likely become a direct competitor to Codex and Claude Desktop. - Their in app browser is great. - Their composer model is good and fast for most tasks They just need to render documents like cowork and codex, and I’d consider using it for most of my work (as a non-dev)

SpaceX just bought a $60 billion company without spending a dollar. The deal for Cursor, the AI coding tool, is all stock. No cash. SpaceX prints new shares, hands them over, done. Now connect it to what happened last week. SpaceX went public by floating just 4% of itself. 556 million shares against 13 billion. The tiniest free float a mega-cap has ever listed with. Index funds were forced to buy it. Retail piled in. Tiny supply, enormous demand, and the stock rocketed past $200. Here's the part that should make you sit up. The price SpaceX pays for Cursor is set by its own share price in the seven days before closing. The higher the stock, the fewer shares it has to print to cover $60 billion. So the engineered scarcity that pumped the stock now makes the acquisition cheaper. The squeeze pays for the shopping spree. A company losing $4 billion a quarter is now buying AI startups with paper it manufactured out of a 4% float. This isn't aerospace. It isn't even AI. It's the finest financial engineering of the century, and it's only getting started.

The Cursor deal is symbolically quite significant. It was effectively the first mega success in the applied layer of AI. They firmly proved out the value proposition of having a deep domain focus, the role you play as a model router, when to lean into frontier models vs. when to train your own, and the role of applied AI GTM and distribution to make sure you’re actually taking advantage of the market opportunity. Every aspect of their business was tuned to carve out ground and keep doubling down in a highly competitive space. This is really the first at scale template for how to execute this playbook.

$60Billion. This is the first, but not the last, big exit at the application layer of AI. As product value accrues and accelerates upwards, the focus over the next few years will be firmly on the “control plane”: What gives organizations who want to go all in on AI the governance, control, auditability and business continuity across models and across time that they will need to firmly make the leap. This is the next big phase of AI value creation that the SpaceX/Cursor merger is highlighting.

⚡️Satya is describing the new balance sheet of the firm. The old firm owned people, processes, software, customer relationships, brand, data, and IP. The new firm will own a compounding cognition loop. Every workflow becomes a training surface. Every decision becomes a trace. Every expert judgment becomes reusable signal. Every internal correction becomes model improvement. Every model run becomes a chance to turn human judgment into institutional intelligence. That is what “token capital” really means. It is accumulated machine-operable cognition. A company’s expertise becomes executable, queryable, evaluable, improvable, and portable across models. That is a massive shift. The most important line is the one about switching out the generalist model without losing the company veteran expertise. That is the entire enterprise AI war. Model providers want the firm’s knowledge to flow into the model layer. Enterprises need that knowledge to stay inside their own loop. Whoever owns the loop owns the future economic rent. Satya is laying out Microsoft’s answer to the frontier-model monopoly problem. If all company knowledge flows upward into a few foundation models, the foundation model labs become landlords of the entire economy. They absorb everyone’s expertise, commoditize every workflow, and capture the value created by every firm’s learning process. That equilibrium will trigger political backlash, customer resistance, regulatory pressure, and corporate revolt. So Microsoft’s doctrine is: every company should build its own AI learning system on top of frontier models, while Microsoft owns the infrastructure where that happens. That is elegant and self-serving. Microsoft does not need to own the single best frontier model forever. It needs to own the enterprise control plane: identity, security, permissions, data, workflow, evals, agents, memory, developer tools, cloud, compliance, and model routing. If the model becomes swappable, the platform underneath the firm’s learning loop becomes the durable asset. Satya is quietly saying the frontier model alone is unstable. A world of a few models eating every company’s expertise breaks the political economy. A world where every company builds firm-specific AI capital on top of models is more stable, more defensible, and much better for Microsoft. The “human capital gets more valuable” line is partly true and partly corporate diplomacy. High-agency humans become more valuable. People with taste, judgment, relationships, domain intuition, ambition, and the ability to direct agentic systems become much more valuable. Routine cognitive labor loses bargaining power. The future firm does not need every human equally. It needs humans who can generate high-quality signal for the loop. The human becomes a trainer, judge, strategist, relationship node, taste layer, and goal-setter. The work that cannot feed the loop or direct the loop gets compressed. This also connects directly to the Anthropic crisis. If frontier model access can be restricted, pulled, nationality-gated, or subordinated to state power, then enterprises cannot allow their intelligence layer to live entirely inside one external model. They need portability. They need private evals. They need internal memory. They need their own traces. They need model-agnostic learning systems. The model can change. The firm’s cognition loop has to survive. That is the new sovereignty test. A company that only buys AI access is a renter. A company that turns its workflows, judgments, corrections, and outcomes into a private learning loop is building capital. The deeper implication: the future economy splits between firms that compound cognition and firms that leak cognition. Firms that compound cognition will get stronger every time they operate.

We're finally shedding the .so (thank you Somalia!), and using the .com for @NotionHQ. And for this beautiful moment, I want to share a fun story: Back in 2018, I had just joined Notion, and one of the first things @ivan asked me to do was figure out how we could own notion.com. I had never done a big domain purchase before, so I reached out to a few domain brokers to understand the landscape. We tried different brokers, kept things anonymous, and attempted to surface a price the seller might consider. A year went by… nothing. Meanwhile, it was pretty clear this was only going to get more expensive as we grew. We needed a different approach. A fellow founder connected me to a broker who took a very different tack. Less transactional, more long-term relationship builder. He spent months getting to know the domain owner. Turns out owner was a fellow entrepreneur in the west coast… and a huge Grateful Dead fan. So we figured, why not get creative? Something beyond just price. So I called up our investor Ronny Conway and asked if there was any way he could help set up a private meeting between the domain owner and the Grateful Dead. Ronny is one of those people who somehow makes impossible things possible. A week later he calls me back: “New York City. Halloween. 15 minutes after the concert. Done.” The broker went back to the owner with an offer: some cash, some equity, and a private meeting with the Grateful Dead. That got his attention. He didn’t take the band meeting in the end, but he did lean into the equity (great call, in hindsight). We shook hands, and a few weeks later, the deal was done. I’ve been waiting years for the day we move our product to notion.com. Looks like 2026 is finally the year. Safe to say I’m unreasonably excited about this update!

Your startup's growth rate is also its learning rate. Setting an ambitious growth goal and trying to hit it is the fastest way to find out what's broken and how to fix it. Not just the obvious stuff. Wrong customer, wrong problem, product nobody wants. It also stresses the founding team. You find out fast whether you can handle hard problems under pressure. Bottlenecks only show up when you push. If you're not having uncomfortable discoveries early, you're not pushing hard enough.

My advice to founders in 2026: spend tokens, not headcount. Record everything. Make your company queryable. Build self-improving loops. Before long, AI won’t just help you operate your company. It will make it self improving. Don't think AI adoption, think AI transformation. This is the biggest shift in how startups get built since cloud computing.

black mirror

My biggest takeaways from @danshipper: 1. The future of work will happen inside Codex or Claude Code. Instead of putting AI into your SaaS tool, you’ll use your SaaS tools inside your favorite AI agents' in-app browser. Dan spends all his time in Codex now—writing documents, managing email, doing research, everything. He's using Google Docs, PostHog, and everything he needs within the agent's in-app browser. The agent can see what he’s doing, and has all of his context, so he and his agent collaborate quickly and super effectively. 2. Automation is a lie—every automation needs a human. Dan's company doubled in size this year despite being incredibly AI-forward. Why? Because in order to make automation work well, you need humans making sure everything keeps working. This is why benchmarks are misleading—they measure AI on problems we’ve already framed and can score, but there’s always a higher frame. 3. PMs will win the AI era. Marcus, a former PM who previously ran Axios’s writing product, joined Every after getting super AI-pilled. Now he runs their product Spiral, and ships faster than anyone on the team. He pairs technical knowledge with spiky product sense, deep user empathy, and an eye for what matters. Dan thinks any PM who gets really AI-native will be incredibly dangerous because the building is done for you—what matters is figuring out what to build and if it’s great. 4. Full-stack designers are becoming superheroes. Designers used to make beautiful interactions that engineers didn’t want to build or couldn’t execute properly. Now designers don’t need to hand things off; they can build it themselves. Designers are naturally creative people, and AI is the perfect tool for them because it lets them bring their vision to life without the traditional bottlenecks. 5. SaaS is not dead. In fact, Dan is bullish on SaaS stocks. When users bring their own AI (via Codex or Claude Code) to use SaaS products, the user—not the SaaS company—pays for tokens. This saves SaaS company’s margins. Since the agents need their own seats, Dan predicts that agents will create massive new demand for SaaS because there will be tons of agents using these products at high volume. 6. Every company will have one “super-agent” inside their Slack that every employee will use. Dan initially thought every employee would have their personal work agent, like a shadow AI org chart, but he’s completely flipped his view. He realized agents need humans who care about them. When someone gets tired of maintaining their personal agent, it becomes useless. The winning model is one forward-deployed engineer or AI-savvy person who maintains a company-wide agent (like Shopify’s River or Viktor), and then it trickles down to more specialized team agents as models improve and become less fiddly. 7. The AI job apocalypse is not happening, but you do need to evolve to stay relevant. Models make yesterday’s human competence cheap. But because everyone uses the same models, it all looks the same if you use it the default way; it becomes commoditized slop. Humans then take that frozen competence and use it to make something new and interesting for their specific situation. The key: “ride the models”—use them for everything you do, try new models when they drop, keep turning over rocks. 8. We will read way more AI-generated writing, and we will like it. Human writing is incredibly important for things that matter, but for internal docs, planning, and email, AI-generated is often better because most people are bad at writing strategy documents. 9. Build software for humans and agents to use together. The current model is building a CLI that an agent uses independently. Instead, you and your agent should be using the app together. This creates new design challenges—agents can make a billion requests in three seconds, so you need approval flows, inboxes that summarize what happened, logs, and easy rollback. 10. Forward-deployed engineers are the new most essential role. The big model companies have teams of people managing their internal agents, and those teams aren’t going away. It’s different from traditional software building, and certain engineers love it. As models get better, this role will evolve—you’ll be managing more agents doing more things.

A brilliant one from @jaminball DC delays are about to explode so this category is about scarcity and intellectual property

Delivery Hero confirms takeover offer from Uber reut.rs/4dF2WwJ reut.rs/4dF2WwJ

Co-founder and CEO of Stripe’s advice to ambitious people:

You will know that the AI labs believe in ASI when they disband their newly formed consulting (sorry “forward deployed engineering”) groups. As long as people are required to figure out how AI is useful & do organizational change & systems integration, jobs seem to be pretty safe

It's an unimpressive-sounding word, but one of the most powerful motivations is the motivation of the hobbyist. That's what keeps successful founders working on their companies long past the point when they've made enough to quit. It's their beloved project.

"Your lack of urgency is wasting your potential."

There’s a tension in fintech that most companies avoid addressing directly, but @scott_shannon, CPO of @Airwallex, leaned into it. If you are building infrastructure and applications at the same time, aren’t you inevitably competing with your own customers? That assumption only holds if you believe the value sits in the product layer, but what became clear in this conversation is that the real leverage is shifting underneath, into the infrastructure itself. If your infrastructure is truly differentiated, then restricting access to protect your own applications becomes a limiting strategy, because no single company can build the full range of products that an open ecosystem can create on top of the same foundation. This is the AWS analogy playing out in fintech. Amazon did not weaken itself by opening AWS; it expanded the market by turning internal capabilities into external infrastructure, allowing thousands of companies to build on top of it while still continuing to build its own products. The same logic applies here. Airwallex offering expense management does not eliminate partners like Brex; it establishes a baseline while enabling others to go further, faster, and in more specialized directions. The real shift is from vertical control to horizontal enablement, where the goal is no longer to own every use case, but to become the layer others rely on to build theirs. And that changes the surface area of fintech entirely. Because once financial infrastructure becomes as accessible as cloud infrastructure, every software company can embed financial services without becoming one, which is where the next wave of expansion actually begins. That’s why this conversation matters 👇 youtu.be/g19KZzvmPWk?si=36TP…

Love this approach from @tobi. A system that compounds learning and spreads it through the organization. Transparency and openness is key.