Almost every product and engineering organisation has adopted AI tools. Very few have changed how they work because of them — and that gap is where most AI initiatives quietly fail.
Where AI adoption actually succeeds
Adoption isn’t about picking a model. It’s about the organisational changes that make AI genuinely useful: codifying the judgment your best people carry, building governance that doesn’t just block progress, and understanding why individual productivity gains don’t automatically become team-level gains. I write about the practical side of AI adoption — drawn from running it inside Wall Street English’s product and technology organisation, not from vendor demos. That means the sequencing problem (why you have to codify before you automate), the coordination cost of AI acceleration, and what governance looks like when it isn’t just a slide in a board deck. If any of this is a live problem for your organisation, get in touch.-
AI Outsourcing: Why Rented Developers Are Being Repriced
The question for AI outsourcing is price. Every contract is a bet that you cannot hire capacity as fast as you can rent it. AI changes what that bet costs. The question and the mandate Gergely Orosz asked the question on Saturday: their business depends on co’s not having enough in-house dev capacity, and thus…
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The Open-Weight Default
For two years the model question had one answer: use the best available. That was right when the gap between frontier and everything else was wide and the tasks were open-ended. Both of those conditions are weakening.
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Prompt Debt Is the New Technical Debt
The patches teams pile up fighting a model’s quirks quietly become a specification only that model can run, and switching to a better one means rebuilding a system nobody fully understands.