In 1958 Ian Donald published what is now the foundational paper on medical ultrasound for obstetrics. He was so widely ridiculed by his colleagues at the time that they nicknamed him Mad Donald [1], and one said ultrasound would be useful only to "a gynecologist who was blind and had lost the use of both hands" [2]. Another noted that he'd invented a £10,000 device to undertake a task that could be accomplished with a £0.02 rubber glove. [3] Last week, my wife and I welcomed our first child into the world. She had a rare pregnancy complication that until recently would have meant only a 28% intact survival rate for our newborn. But in 2013 US guidance was updated to add regular preventative screening for her condition at the 20-week ultrasound, and with early detection the survival rate is ~99%. (In the UK, preventative screening is still not recommended, for reasons like "it is not known how accurate screening tests are" [4].) The entire history of radiology is people expressing skepticism about the work done by innovators. I for one am grateful for folks like @DavidSHolz building new classes of devices that can help us see things in new ways, and I'll be rooting for their success. Hand in hand with my wife and our healthy baby boy.

Having to manually prompt your coding agent to do work will soon feel like a UX bug. The future is setting up automations so that your agent already does the work by the time you log on. We've shipped a ton of functionality around this over the past few months. Excited to release Auto-Triage publicly today!

# The Path Forward for AI Startups A lot of founders are messaging each other after the SpaceXAI <> Cursor “IPO-deferred acquisition”. Common discussion topic: what is the future for independent startups? Must ~everyone ultimately be acquired by a frontier lab or go extinct? The data from our direct experience @cognition suggests the opposite. The more startups in a category that defect from independent competition by selling to a lab, the stronger the remaining ones become. We experienced this firsthand last year with Windsurf. When the founders went to Google and we acquired the remaining company, it dramatically accelerated our product roadmap and GTM. Now, cloud agents are ready for prime time, and our usage has exploded. (We’re in the fastest rate of usage growth in Cognition’s history - almost 50% month-over-month growth in Devin enterprise.) We already see the next round of acceleration with yesterday’s news, from prospects and customers to candidate inbound. In just about every category, there’s a clear market for a winning independent offering that’s not tied to models from any one lab. Especially in a space as dynamic as software engineering, where customers value model flexibility as the rankings from different providers are constantly changing. For startups to seize that independence opportunity, here are the lessons we’ve learned so far: 1. DIFFERENTIATION You need to have extremely clear differentiation vs. what’s already offered by the labs. Cursor had stiff competition from Claude Code in self-serve, in part because one tool was substitutable for the other, which presented a challenge. Our approach has been to differentiate heavily for enterprises, which is the largest market for software engineering. Specifically: 1. We invest as much in forward deployed engineering and AI enablement as we do in core R&D. Our customers treat us as a change management partner, not just an AI software engineering platform. We run 1000-person workshops all around the world to help train developers inside companies on frontier AI adoption. We target specific use cases and outcomes in addition to providing developer tooling. 2. We focus on accelerating the *entire software development lifecycle* at large company scale, not just the writing of code. Devins now spin up automatically for everything from ticket scoping to DeepWiki codebase indexing to security vulnerability remediation and application monitoring alert response. 3. We eat the pain of deployment complexity to work well in the largest and most complex environments imaginable. Cognition can run inside a customer’s virtual private cloud, has a permissioning and team collaboration model that can scale to 100,000+ developers inside one company, runs as well for COBOL mainframes as it does for modern Python. From day 1 each Devin ran in a microVM on its own machine, vs running locally as a CLI tool, which allows arbitrary horizontal scaling and is a better fit for event-driven automation. Of course, one element of startup differentiation will always be model independence. This is particularly powerful in large enterprises, who value supplier continuity and the ability to centralize tooling without taking on the business risk that they committed to the wrong foundation model. And useful for individual developers, who always want to try the latest models. (If you haven’t yet tried the Windsurf 2.0 release which came out last week, it’s a good day to give it a shot!) I expect the labs will catch up on some of these fronts at some point. But at that point, we’ll have already made the next leap in differentiation, because… 2. FOCUS You won’t outcompete the labs in everything, but you can outcompete the labs in *your* thing. Every application domain has fractal complexity at the edges. Lean in to what makes your domain special and offer things no one else can. Does it make sense for a lab to devote training resources to a specialized code review model? Probably not - they’re working on AGI. But for the 3-6 month window where the latest frontier models don’t solve that use case at acceptable performance, cost, or latency, do it yourself and build a better product experience than would otherwise be possible. Rinse and repeat as the frontier of what’s possible via specialization continues to evolve. 3. VELOCITY One of our values at Cognition is: “Every second counts.” Maniacal urgency helps in every startup, but it counts extra in today’s accelerated AI times where advantages compound faster than before. With sufficient focus, you can out-accelerate the AI labs on any one specific feature or workflow. Do this consistently to stretch the overhang of what’s enabled by each new generations of models, and you can maintain your edge on a differentiated product experience. - In many ways the SpaceXAI <> Cursor news is a win for everyone. SpaceX gets a new research team and the chance to become competitive in coding. Cursor gets a meaningful exit and the opportunity to accelerate their research roadmap with much more compute. And the whole ecosystem benefits from increased competitiveness among the foundation model labs. Congrats to the teams on the outcome.