Right now I’m building LaunchChair. LaunchChair is a product operating system for AI-native builders. It helps founders go from a raw startup idea to market research, ICP clarity, wedge selection, positioning, MVP scope, a living product spec, build cards, agent-ready prompts, remediation, landing pages, SEO/GEO/AEO, and launch execution. The simplest way to say it is: coding agents can build, but LaunchChair tells them what to build next and keeps the product context alive the whole way.

The realization was that AI was making it easier to build software, but not easier to build the right software. I kept seeing the same pattern: someone has an idea, opens ChatGPT, Claude, Cursor, Codex, Lovable, or Bolt, and starts building immediately. They skip the customer, the wedge, the substitute behavior, the MVP boundary, the launch story. Then the product grows fast, but sideways. That felt like the real problem. Not “people need better prompts,” but “people need a product team process that AI agents can actually execute from.”

Early on, it was tempting to think the magic was in better prompts. I don’t think that anymore. Prompts matter, but they’re not the moat. The real power is the living spec, the workflow, the build board, the guardrails, and the loop that knows when an agent actually finished versus when it just said it finished. The more I’ve worked on this, the more I believe the winning layer is not a prompt library. It’s the system of record for the product: what was learned, what was chosen, what should happen next, and what the agent is allowed to change.

One moment was realizing that the strongest users didn’t just want LaunchChair to help them write a good prompt. They wanted LaunchChair to feed the entire build. That changed the product in my head. The promise became much bigger: you shouldn’t have to keep re-explaining your product to Codex or Claude Code or Cursor. LaunchChair should know the current spec, generate the next build card, compile the right prompt, catch missing SQL or weak QA, and keep the loop moving. When people understand that, they stop seeing it as “another AI tool” and start seeing it as the place the product lives.

The hardest part is making a very structured product feel fast. Founders don’t want homework. They want momentum. But the structure matters: research, ICP, wedge, MVP scope, build cards, QA, remediation, launch. If you hide too much of that, the product becomes vague. If you show too much, it can feel heavy. So the thing I’m constantly working through is: how do you give someone the leverage of a product team without making them feel like they’re filling out enterprise software?

Don’t confuse building fast with learning fast. AI can help you generate a product quickly, but speed can hide weak thinking. Before you build the whole thing, get brutally clear on the user, the pain, the current workaround, the wedge, and the smallest version that can prove something. The first version should not be a monument to your idea. It should be a test of your assumptions. Build in a way that makes the truth easier to see.