Practical, candid writing on operating-model design, engineered operations, and how high-performing teams stay efficient at scale. No fluff — just the operational thinking we use every day with clients.
If compute keeps getting cheaper, does re-engineering your workflows stop being worth it — the way repairing electronics stopped being worth it? Sometimes, yes. But the deciding variables are volume, durability, ownership and stakes, not the token price. Cheaper inference moves the crossover point; it never removes it.
Every capability decision gets framed as build versus buy. It's the wrong question. The real variable is how much of a capability you must own versus simply access — a spectrum, not a switch — and the most valuable position is often the one the binary hides: the blend.
Automation is very good at the routine 80% of back-office work. But the routine was never where the cost, risk, or difficulty lived. As the routine disappears, everything that's left is exceptions — and the organisations that win are the ones that industrialise the exception, not the routine.
Most organisations buy tools to speed up steps, then wonder why the end-to-end result barely moves. The value doesn't leak inside the steps — it leaks in the seams between them. And because that loss compounds, the tax is far larger than any single step is slow.
For decades, the T-shaped professional was depth of execution plus breadth of knowledge. AI inverts this model: the vertical bar is now judgment, and the horizontal bar is orchestration. Execution depth is commoditised. Judgment depth is what's scarce.
Automate the easy 80% of a job and you don't get an easier job — you get a harder one. The work that remains is the work that resisted automation: judgment, exceptions, coordination. Here's why efficiency concentrates difficulty, and what it means for how you design roles and teams.
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