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.

Most organisations now have an AI strategy. Very few have asked what it is doing to the planet. That is not an abstract concern. It is a capital allocation question with a measurable environmental consequence attached to it. Boards are declining to ask it. The omission will not age well.

For decades the businesses of expertise, craftsmanship and luxury have run on a simple exchange: you paid for authority because authority was grounded in the living presence of a specific person. A marketer. A strategist. A lawyer. Someone who could sit across from you, who had walked through the problem themselves, whose judgement was embodied in a living mind that you could hire and pay. Presence became the marker of truth. Not the accuracy of what they said, but the fact t

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.

The board asked for an AI strategy. What they got was a vendor presentation dressed in strategy's clothes. The vocabulary is being set by people with a commercial interest in your confusion. AI is not complicated. The industry around it is. What follows is the version of this conversation that the hyperbolic presentations skip. Here's the AI landscape for executives.

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.

A review of research paper 'The AI Layoff Trap' by Falk and Tsoukalas

After creating a few AI agents, orchestration is the obvious next step; you get those agents talking with each other and working collaboratively, you go from having an assistant to having a team at your disposal. This requires an orchestration framework to connect the agents - I opted for LangGraph for this project due to it working on a state-machine basis, allowing cycles and loops where the agents could get into critique and refine loops with each other.

This project explores Claude's data analysis capabilities, with the upcoming Grand National as it's subject.