The AI-pilled compounding startup

Over the last month I've been doing office visits with AI-native companies in San Francisco and we’ve had the chance to actually see how they work today and we saw a lot that surprised us. Here’s a short summary of what an AI-native company might mean for the future: The Product Manager is disappearing On a single day where we visited five companies, we found only one full-time PM across all of them (even though one company had as many as 40 employees). Engineers talk to customers daily and own product decisions end-to-end. The PM isn't being "augmented." With many startups we saw, the role is being absorbed into engineering and design. The most dangerous side effect: the feature factory When you can build anything a customer asks for in a day, the temptation to build everything is overwhelming. Multiple companies told us this is their biggest strategic risk right now. The ones winning this fight have hard constraints. One company's agents can only configure existing features through JSON — they literally cannot create new application code. Another uses squad-level North Star metrics to kill ideas before they ship. Several emphasized that the founder has to decide where the product has opinions and where it's flexible. When execution is nearly free, taste becomes the moat but how a company organizes to make taste evident is still being decided. The stack is converging Almost every company we visited runs the same core: Slack, Claude Code, GitHub. Codex for code review and Linear. Linear has not only survived the SaaSpocalypse, they are creating a roadmap for how to thrive. Slack has also become a central orchestration layer for agents. Emoji reactions auto-create tickets. Bots report diagnostics and triage customer issues. Agents get tagged in threads and start working on a fix. Six months ago, Cursor came up in every conversation. Today it gets mentioned sporadically. Today engineers are living in Claude Code. One researcher told us he'd run Cursor alongside Claude and kept asking himself why he even needed the second window. Troubling for all of these coding platforms: engineers don't seem terribly loyal or attached to any particular tool, which raises the question of how a coding platform maintains value over time unless they get the benefit of the data being generated by engineers and train the model - advantage Anthropic especially with news of Mythos. The people across the org are empowered to build real things An enterprise account manager had been asking her product team for months to automate account uploads. Nobody prioritized it. Then she asked an AI agent in Slack. It was done in an hour. An accounting team is writing database queries and using MCP to interrogate their own business data. A Chief of Staff is producing direct mail and marketing materials in under 30 minutes. The most underestimated shift isn't what AI does for engineers. It's what it does for everyone else. The cost of experimentation has collapsed creating compounding impact A researcher tests 10 interface designs, runs each for a day, and throws 9 away. A designer generates multiple competing iterations in separate tabs in under 6 minutes. A growth PM with zero coding experience built a full Meta Ads pipeline (strategy briefs, AI-generated video ads, automated posting to Meta) in two days. Companies are using AI to simulate customers before real ones ever touch the product. One team built AI agents that play different user personas to stress-test their product without waiting for real feedback. Another runs hundreds of research interviews in a week instead of 50 in a quarter. One company built customer personas with full negotiation histories, communication preferences, and decision-making patterns and uses them to prepare for sales calls. These companies are iterating 3-5x faster and that speed shows up in two ways. For some it means getting through a single experiment faster so they can run more experiments over the same period. For others it means running multiple experiments in parallel. In either case, the surface area of unknowns gets covered faster. Both the build and learn steps are compressing across the organization. Knowledge compounds. But this is fundamentally a different way of operating. It reminded us of how warfare has shifted from fighter jets to swarms of drones and the impact on strategy in warfare. Something similar is happening in how companies operate. What comes next We're continuing to visit companies and will publish deeper case studies with more specific examples in the future. But the pattern is already clear: the gap between companies that have internalized these practices and those still debating "AI strategy" is enormous — and it's widening every week. If you're a founder building this way, we'd love to hear from you.