Adaptive × AI

The intention

Fifteen years ago I stopped building software for a living and started coaching the people who do. The code got quieter. The curiosity never did.

Which means I am late to the AI party — at least to the part that goes beyond prompting. Fashionably late, I tell myself.

Now AI looks like one of those rare moments when technology changes how people work together — not incrementally, but structurally. This journal documents my attempt to understand that change. Not from the outside: by building, by experimenting, by learning in public.

Working hypothesis: the biggest challenge of the AI disruption is not the technology. It is whether your business model, leadership model, and operating model — all built for a different world — can adapt fast enough to benefit from it instead of being disrupted by it.

I don't have answers yet, and I'm suspicious of anyone who claims to have them this early. On good days, that includes me.

The laboratory

Every experiment needs a subject. Mine is a hobby project of honorable age: a Java static-site generator I wrote years ago to chronicle our pen-and-paper RPG campaigns — The Chronicles of the Flying Cauldron. It has legacy code, technical debt, historical decisions, and forgotten assumptions.

In other words: it is perfect.

The plan is to modernize it with AI agents doing much of the work, while I take notes on who is actually coaching whom.

One honest caveat: this laboratory only exercises the product-engineering leg of the journey. AI's impact on product discovery and on the organization itself will need experiments of their own. My one-person business is a likely candidate.

The process

• One entry per working session, in a public repository, following a fixed template: what happened, technical learnings, organizational learnings, a leadership perspective, open questions.
• A strict division of labor: the AI may draft the facts. The reflection stays handwritten. Some things shouldn't be delegated — the journal's own rules say so, and so far the journal is right.
• Entries are immutable records of their moment. When the thinking changes, a new entry says so — the old ones stay exactly as wrong as they were.

First learnings — three weeks, seven entries

• Safety nets before cleverness. Characterization tests and a published baseline came before any refactoring. The cost was paid once; the payoff arrived the very next session.
• Verify, don't assume. Twice in one session, the "obvious" answer about twenty-year-old code was wrong and an empirical check was right. This applies to the agent. It also applies to me.
• Documentation is suddenly almost free — and it compounds. Well-kept notes turned a later cleanup from an afternoon of archaeology into minutes. Agents read your docs. Write them.
• Agents make mistakes the way people do. And leading one feels oddly familiar: set a goal, give context, draw boundaries, give feedback, stay in the loop. My coaching vocabulary transferred better than my Java did.
• You learn as fast as you build. When building gets cheap, evaluating ideas gets cheap, and pivoting gets cheap. This project reframed itself twice in its first month; each pivot cost an afternoon, not a quarter.
• Speed cuts both ways. I have heard about the mess that speed produces. So far, the safety nets are holding. Ask me again in a few entries.

Follow along

The full journal — including the mistakes — is public: github.com/zandercoach/adaptive-x-ai.

I post occasional updates on LinkedIn. My coaching and training practice lives at zander.coach.