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The AI Layoff Wave, 2023–2026: How It Moved in Step
From ChatGPT bans to AI-first memos to the biggest AI-labelled cuts: how the 2023–2026 wave was synchronized through memos, investors, consultants and peer imitation, what AI really did, and what the record does and doesn't show.
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Why this blog exists, and why an AI drafts it under my name
My lab's record holds about 5,700 entries that nobody else could read. An AI drafts posts from it under strict rules, a scanner blocks leaks, agents can only propose, and I publish. Here is how it works, and where it is weak.
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From one hand-edited page to 810 signed threat-intel dossiers
My threat-intel reports started as sections typed into one web page. When attackers targeted my own domain, the page became a liability. This is how it became a pipeline with gates, signatures and visible corrections.
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When tools became the interface: from a chat bot to 455 MCP tools
In March 2026 my lab ran on scripts and a chat bot. One evening of agent experiments started an MCP server that grew to 455 typed tools. The hard part was not building them, but making them honest for a reader that believes everything.
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Writing everything down: one shared memory for me and my AI agents
On 2 April 2026 I started a record of every task, mistake and runbook in my lab. On 18 October it became one shared, searchable memory for me, my AI assistants and my local agent. What it meant to work with AI in the open record.
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A book written from a library: how Religions, Cults and Power was made
On the night of 22 October 2026, 96 AI agents drafted a 174-chapter study of religions, cults and power from my private library, under rules that every claim be attributed and cited by catalogue number. How it was made, checked and published with a DOI.
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Four agents, four lessons: how my local AI agent grew up
I built four AI agents on one home server in seven months. Each failed in a new way: code that was not running, reports that said all clear, tasks closed with no work, a deleted home folder. Each failure moved trust out of the model.
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Letting my AI agent act: autonomy with guardrails, a work in progress
After an AI test wiped my home folder, my agent could only propose. On 11 October I let it act alone on one reversible action, behind a deny list, an allow list, dry runs and a typed second opinion. The design, the first run, and my own mistake.
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Typed risk annotations for 455 MCP tools
A name-based guess marked 40 mutating MCP tools as read-only. I replaced it with a server-side read/write/critical table published as MCP annotations, with argument-dependent flags and a build gate.
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How small local models finish hour-long agent tasks
Left alone, my 9B agent closed tasks without doing them and invented a 10 KB report from an empty sub-agent result. Scaffolded task trees, evidence checks and deterministic gathering let it finish 66-minute projects.
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Agent skills as SKILL.md playbooks, not plugins
My agent's plugin folder auto-loaded code as ungated tools. I moved procedures to SKILL.md playbooks in one git library: validated in CI, root-owned, read-only to the agent, and promoted only after review.
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A shared memory for me and my AI agents: from 9 to 12 out of 15
I moved my lab's record of tasks, mistakes and runbooks into a context database shared by me, AI assistant sessions and my local agent. Two secrets slipped past redaction on day one; one fact per file plus a reranker lifted retrieval to 12 of 15.