I work with software company CEOs whose product-market fit isn’t coming together - whether they built something that worked and the ground shifted, or they’re trying to get to fit for the first time. I’ve navigated five technology shifts across 30 years. GenAI is the sixth, not the first.
Engineering, product, executive - three genuinely different disciplines, each in its full depth. Chief architect. VP Product. CPO. CPTO: both at once. CEO. Four exits. Multiple turnarounds. Across companies from scrappy early-stage to acquisition-ready.
Most advisors see one slice - technical, product, or business. Three disciplines in full depth makes it possible to hold all of them simultaneously, without losing resolution when the conversation moves between them.
Sometimes that’s as a thinking partner - privately, on your side of the table, no stake in the answer. Sometimes the situation calls for stepping inside and leading product or engineering directly. The common thread is product-market fit: finding it, losing it, re-finding it.
The Problem You're About to Solve for Agents Is the One You've Been Working Around With People
The decision you’re making about agent infrastructure is more loaded than it looks. It’s not an AI investment. It’s the move the regime change has made non-optional and the companies that build this for agents will solve the same context-sharing problem your company has been working around for years.
What's the language about your customers telling you - before the metrics do?
Same language, a year later, opposite meaning. A company with product-market fit doesn’t ask its customers to bridge a gap. A company losing fit does, even if it never names it. You’ve stopped finding fit and started defending it.
Every company wants a paradigm shift. What’s getting funded is better tools.
The change everyone is asking for sits at one level - what gets valued, what counts as progress, what the organization is optimized for. These tools intervene at another - how work gets coordinated, how fast it ships, how aligned the team stays. And when that gap holds, the tools get adopted and the old model stays put. You get faster throughput of the same mediocre decisions.