Organisational

Lots of rhetoric aimed at leadership teams positions the question about AI and software development as primarily whether it will replace engineers. It's the wrong question. Asking it means missing something more useful. The better question is what a single engineer can build in an hour, starting from scratch, and what that tells you about how technical work is changing.

Engineering, at its best, is repeatable, testable, transferable between people because it's written down as a rule. That's exactly what the razor unit's makers had. What they didn't have was design judgement - the thing rooted in observation rather than specification, that knows when the rule has stopped serving the purpose it was built for. They applied technical rationality to a problem that needed reflection instead, and the result was an object with exact tolerances and n

"Systems thinking" has become the kind of phrase people add to their profile the way a small business adds "artisan" to a jar of jam. It's been emptied of meaning by people using it as a shield. So it's worth asking what it would actually mean, precisely, if the word still had edges.

Somewhere this quarter, in a talent review, a manager will describe an engineer as T-shaped. Heads will nod and a box will be ticked. The engineer, who is not in the room, will spend the next year receiving work shaped like the letter rather than shaped like their ability. Nobody will check whether the label was accurate, because it does not feel like a claim. It feels like an observation, but it’s neither.

Rubens designed the major works, established the compositional framework, and reviewed his apprentices' execution. Van Dyck eventually forked the codebase, established his own studio, and surpassed Rubens in the field he chose. Git, the version control system underpinning most of the world's software, formalises exactly this: a master repository, branches, independent lines of work, the ability to merge or to diverge permanently.

Just about every senior leader has already decided to adopt AI. The all understand the risks of not doing, and a large proportion of them understand the risks of doing so. The question some aren't asking is what happens when one of the risks becomes real, and whether they can recover from it.

Developing concepts with Claude means understanding how Gen AI works and working with it's limitations. During an exploratory workflow today I ask it several questions as starting points to develop the nucleus of a concept. It came back with systems logic: structure, audience, constraints, examples. I told it the thinking would emerge from exploration instead. The AI pushed back. We went sideways anyway.

Call this the rising floor. Every useful machine absorbs the layer of work it can do better than people. The human contribution does not vanish. It moves upward, into judgement, selection, taste, and ethics, questions a machine cannot answer for you because they are not technical questions at all. That is where AI is heading now, into code, legal drafting, HR policy, marketing copy, the layer of execution that used to define a competent professional.