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When Your Partner Ships Your Product
Partnerships across the stack were essential to us — and one of them shipped something that overlapped our product with no warning. That is not betrayal. It is how platform economics work, and a founder should design for it from the first conversation. Read ⇢
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Services Before Product-Market Fit
Before we found product-market fit, consulting and implementation work paid the bills — and taught us the problem in enough detail to eventually build the right platform. The AI wave has rediscovered this and given it a new name. Read ⇢
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Being Early Is Not an Advantage
I believed arriving early to a technology wave was the whole game. It isn’t. Early means you pay the education tax — funding the market’s learning so that whoever arrives after the dust settles can harvest the clarity you paid for. Read ⇢
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Start With the Problem, Not the Wave
In the Big Data era, organisations bought the infrastructure before writing down the question. Most of the value, when it finally arrived, came from the data they already had. The AI wave is running the same play — and the discipline of naming the problem first is the cheapest advantage… Read ⇢
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I’ve Seen This Wave Before — What a Big Data Startup Taught Me About the AI Era
A decade ago I founded a startup in the Big Data and Cloud wave. Watching the AI wave from the inside for the past two years, the sequence is uncannily familiar — the same buying patterns, the same founder traps, the same shakeout. Four lessons that carried over. Read ⇢
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Does a Smarter Model Change Any of This?
The obvious objection to any agent-architecture doctrine is that the models will fix it — a smarter, self-correcting model won’t need the boundaries and the harness. It’s worth answering directly: model progress moves what sits above the line, not the line itself. Read ⇢
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The Model Is the Least Defensible Part of Your Agent
Anyone can call the same model you do. What can’t be copied is everything the model helped you build once — the format libraries, the entity graph, the confirmed-rule library, the evidence chain. Four things not to build, and the reason the moat was never the weights. Read ⇢
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The Model Upgrade That Quietly Breaks You
The scariest failure in an agent system is the one where nothing breaks — a model upgrade just makes the agent slightly more agreeable, and every number drifts a little worse. You catch it with a three-tier eval harness and four metrics, or you don’t catch it at all. Read ⇢
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An Agent Is a State Machine, Not a Loop
A production agent is a declared workflow, not a model in a loop with tools and an instruction to be helpful. Four properties make the difference — capability-scoped tools, runs as rows, hard budgets, and replay — and a while-loop can give you none of them. Read ⇢
Digital Leadership, Distilled.
Scaling transformation through Data, AI, and human–agent teaming — 23 years of hard-won lessons, distilled into strategic deep dives. Don’t miss the next shift.
