For twenty years, enterprise SaaS vendors have optimized for human operators. They addressed pain points acute enough to convince their customers' finance teams to underwrite the expense. Once entrenched, they optimized UIs, simplified onboarding flows, and counted seats. The model worked because humans were comfortable clicking through interfaces, forgiving enough to interpret ambiguous API documentation, and paying on a per-user basis made sense. Now the target user is evol

Three words on the minds of AI builders over the last twelve months are Model Context Protocol, abbreviated as MCP. Heralded as the “HTTP of AI”, MCP is a public and open set of protocols that can be used to dictate how an AI model interacts with other digital actors — websites, applications, databases, and other AI models. The cool kids are all busy building MCP servers. These act as gateways and traffic cops for AI use, ensuring that an AI agent or application can only acce

“ Technology roadmap” is a commonly misunderstood term. For the most part, CEOs, boards, and investors see a roadmap as a plan for a business to grow. However, a technology roadmap is different. Because of the complexities of building great technology products and infrastructure, a technology roadmap must be more carefully designed and implemented than a broader company roadmap. It must be both high-level enough that non-technical leadership can understand it and sufficient

Some firms outperform for reasons that have nothing to do with luck, capital, or timing. It is quieter than that. Every company carries a story about why things worked: Timing, talent, market fit, a bold founder, a patient investor, etc. If you listen long enough, the list begins to feel predictable, almost rehearsed. But when you sit inside enough companies, from diligence to post-close to the messy middle of delivery, a different story starts to reveal itself. The companies

Ninety percent of the chronic “technology problems” I see in organizations are actually people problems. That might sound glib, but it’s a pattern that’s emerged over decades of building, scaling, fixing, and now coaching technology organizations. Sure, code can be broken, architecture can be faulty, and tooling can be bad, but all of that is acute and fixable. Even tech debt can be maintained and eliminated with the right program. But when I walk into a company that’s “plag