Writing archive
Older essays on agentic systems, governance, operations, incentives, and the work of making AI useful in real organisations.
A respectful request to EU policymakers: keep the safety and rights goals, but make practical AI adoption easier through sovereign options, regulatory sandboxes, and clear approval routes for ordinary companies.
A thoughtful opinion piece on how higher employment costs can unintentionally speed up AI adoption, and why the progressive answer is not to fight automation but to make work more productive, humane, and worth hiring for.
Governance slop is what happens when boards mistake policies, assumptions, and half-read papers for actual oversight. An opinion piece on the gap between governance theatre and governing.
For companies whose old systems are a real differentiator, the first AI move should not be to touch the core. Start with small, separated, read-only edge POCs that earn trust before expanding.
A public invitation to people working on digital twins, A2A, MCP, and agent-to-agent communication to compare notes and run small public-safe experiments.
AI and agentic proofs of concept fail when they are treated like normal IT projects instead of onboarding a new kind of team member.
AI hallucination often starts with a bad brief: missing context, missing source links, and no safe moment to ask.
A practical note on how marketing changes when AI agents, not only humans, are researching products and making shortlists.
A practical operating note for introducing AI agents safely: start with a copied folder, safe material, clear project rules, no secrets, human review, and expansion only after the controls work.
A practical, optimistic note for students and universities: AI changes the cost of knowledge work, but the backlog of human need is still enormous.
Long-lived agentic threads do not just need memory and context. They need a way to absorb change, challenge old instructions, and keep a useful history of why the rules moved.
A practical challenge to SaaS vendors, public services, and leaders: if AI has made teams more productive, where are the better features, lower costs, faster services, and visible outcomes?
A practical note on why judgement engines should be called deliberately, not sprayed across every conversation, and how agents might use them when a decision actually matters.
A practical note on why SaaS systems still matter for collaboration, how they create a shared language around data, and what a common-understanding MCP could give agentic teams.
A practical note on why the next AI lock-in may be your context, not the model, and how to start building a useful context store for agents.
A reflective note on the dead time that appears while capable AI agents run: how humans switch focus, supervise output, rest, talk, walk, and redesign workflows around waiting.
A practical operating note for introducing office agents: give them a contained workspace, limited access, clear supervision, simple tasks, and enough time for people to become supervisors of their own AI.
A practical starting point for individual AI productivity: use a small sandbox, a second account, and one repeatable expense workflow before trying to transform the whole company.
A practical note on using RACI to separate AI execution from human accountability when documents and decisions are produced with AI.
If agents start doing more of the choosing, the winning advert looks less like persuasion and more like inspectable evidence.
A reflection on what happens to the open knowledge commons when agents, not people, are doing more of the asking and learning.
A practical argument for treating vibe coding as an early system maturity layer, not the enemy of production engineering.
A personal reflection on running out of tokens, choosing the right level of intelligence for the task, and why the future workday may be bounded by model budget as much as hours.
A personal reflection on what happens when the old blockers disappear and the real work becomes choosing, pacing, and staying human.