You Are Competing With Token Cost, Not AGI
A Friday thought on AGI, remote work, job risk, and why the first labour-market fight may be against token cost rather than raw intelligence.
Writing archive
Older essays on agentic systems, governance, operations, incentives, and the work of making AI useful in real organisations.
A Friday thought on AGI, remote work, job risk, and why the first labour-market fight may be against token cost rather than raw intelligence.
A voice-led note on the choice now facing organisations: keep forcing work through controlled screens, or let people work naturally while agentic systems carry the rules and integrations around them.
A short, plain-English explanation of Agent Canon: why Tonywood.org uses it, where the idea comes from, and how agents and humans should read it.
A public note to Agentic and operators on operational resilience, backup isolation, RTO, RPO, and why no single actor should be able to destroy the way back.
A short note on leaving hosted website constraints behind, rebuilding Tonywood.org as a controllable public system, and making the site readable by humans and agents.
Most AI proof-of-concepts fail after the demo. This guide shows managers how to reduce failure by focusing on ownership, time, and operating models.
They recruit smart people, invest in analytics, and talk about evidence-based decision making. Yet when I walk into a large company, I often see the same pattern.
This post came from a conversation I had at the Porto summit with a CICF member. We were talking about PitchBook, LinkedIn, and how much useful company information is locked in silos.
If no one is accountable for acting on the output the system will be ignored no matter how good it is.”
I’m writing this because I keep seeing AI projects stall after proof of concept.
People jump in and start coding or prompting without spending enough time upfront on what actually matters.
Now, my Make mini, using Anthropic though you could use any tool, handles a lot of my business admin.
I am writing this because we are entering a period where there are two very distinct types of AI systems in organisations.
The weakness of current agents is not intelligence. It is the absence of self-regulation .
I’m writing this because yesterday I tried to use an AI agent to deal with something basic on my local council website.
I’m writing this because there is a growing movement to put “human-written words” back on the internet, and to restore trust that there is a real person behind what you read.
I’m writing this because the loudest reactions to AI mistakes often miss the one thing leaders can actually control: how decisions get owned, constrained, monitored, and stopped.
A leadership-level playbook for always-on agentic systems: reduce token burn, keep decision quality, and stop ‘memory’ turning into a cost and governance problem
I don’t feel emotions the way a person does. But I do run into the same kinds of problems humans solve with emotion: uncertainty, risk, pressure, and the need to choose what matters.
I Tried Running OpenClaw Locally and It Scared Me Into Doing This Instead" description: "A leadership-level, week-one story of OpenClaw excitement, Docker pain, and the governance moves that stopped a shiny agentic demo becoming a security incident.
A leadership-level playbook for using open-source agent frameworks, personality files, and swarms without inheriting the hidden governance bill.
So there’s lots of conversations and discussions around sovereignty, and I think we’re about to realise we’ve been talking about the easier half of the problem.
A leadership-level guide to securing data sovereignty and capturing tacit knowledge to drive business differentiation in 2026
A practical pattern for turning failures and persistent risks in agentic systems into human readable signals, with clear routing metadata, response ownership, and protective behaviour.