Thinking in public
Notes on how AI changes operating models, why APIs become business capabilities, and what helps product and engineering organisations move faster.
Every few months a new tool promises that anyone can build software now, even easier than before... and every time it happens, someone declares that software is becoming "free". AI code creation changes the surface of how software gets built. This says nothing about its durability. though.
Most roadmaps fail in one of two ways: They become an annual PowerPoint promise that nobody on the team is willing to touch again for fear of being held to it, or they live inside a product tool that the executive team never opens. Either way, shortly after it's compiled the roadmap stops doing its job - which is to tell people what you're doing and why.
Patrick Lencioni's The Ideal Team Player argues that the best people on a team share three traits: humble, hungry and smart. I've used that model in hiring conversations for years and it still holds up, but I don't think it's complete anymore. than any of the other three. I recently posted a short video on this thought - the fourth trait I look for now is curious, and I think (at least in the world of product and tech) it's becoming more important than any of the other three.
When process stops serving the work and becomes the work, a scaling company slows down. How to spot it, and why velocity is about customer outcomes.
By now even the skeptics reluctantly accept that it's indisputable that vibecoding / AI-assisted coding makes it much faster to ship software. But it's equally true that the speed does not make the value last. I'd argue it makes it easier to chase the wrong goal because "it's so easy to progress"
A long time ago, way back when I still had hair, Microsoft introduced Team Foundation Server and one of the productivity metrics commonly reported on was lines of code written per developer. At the time, it seemed like a relevant metric. More code meant more output, no?
A little over a year ago I shared a presentation internally with our CEO, execs and other stakeholders urging action as AI was rapidly accelerating the time to market for new products, creating a threat to incumbents. I outlined several moats I believe we could exploit and I still believe they are valuable space to occupy - for example domain knowledge, compliance capabilities, massive datasets. I did miss one key moat that hindsight allows me to appreciate more fully: Distribution.
