AI

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.

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

Review of the AI-powered Grand National 2026 prediction and statistical analysis

Comparison of my Claude-powered Grand National statistical analysis with BBC Sport's predictions

Evaluating Claude Sonnet 4.6 for 'reasoning redline', defensive hallucinations and structural bias.

Training a specific AI model to recognise koi and categorise them more effectively than an Anthropic off-the-shelf LLM.

Building an Anthropic-powered image recognition tool for koi breeds and individual fish

A vibe-coding horological workout for Claude, generating a fully-functional, animated Grand-Complication web application.

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.