What it takes to win for AI startups

It’s been a while since I last wrote something - we’ve had a very busy few months but I promise I’ll get back to writing and recording podcasts.

A lot of what’s kept us busy is making investments and supporting our portfolio in a world that is moving faster than ever and where everyone is asking themselves the same question:

How do you win as an AI startup especially with the threat of the labs looming everywhere?

This post by Russell Kaplan (co-founder & president Cognition) is a great one:

# 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.

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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.

I would add a 4th point to Differentiation, Focus, and Velocity:

AMBITION

You can have the clearest differentiation, the sharpest focus, and the fastest velocity - and still lose if your endgame is too small.

The “feature company” (a product narrow enough to live as a checkbox inside someone else’s platform) worked five years ago. It doesn’t anymore - you HAVE TO at least become THE PLATFORM for your entire industry.

This means (huge) ambition has to shape every decision, not just the vision slide: the problem you pick, the team you build, the capital you raise (once you’ve proven your wedge / thesis), the markets you pursue, etc.

All of it has to be sized for an outcome an order of magnitude larger than would have been reasonable a few years ago. You can’t hedge your way into a generational company. AI is steepening the power law - the winners will be bigger and get there faster - and your ambition has to match.

Go huge or go home.