Last updated: 2026-04-27
A measured, non-commercial guide to being found in the age of AI agents. Fifteen lessons across five modules: how the search landscape has shifted, how AI agents read a website, structured data and clean HTML, content for machines and humans, off-site signals and freshness, agent protocols (MCP, AP2, NLWeb), measurement, the five-step agent-ready audit, and the first-mover window. No pitches, no upsells, no proprietary frameworks invented for this course.
Self-paced. Honor-based. Not accredited.
Five short modules covering the new vocabulary (GEO, AEO, AIO), how AI agents actually read a website, structured data and clean HTML, writing for both machines and humans, the off-site signals that build trust, the agent protocols already shipping (MCP, A2A, AP2, ACP, NLWeb), measurement that catches what classical analytics misses, and a one-week audit that takes most sites from invisible to recommended.
Each module stands on its own. Together they give a working frame for thinking about AI search optimisation — one you can apply to your own site, push back on, and extend.
Classical SEO, GEO, AEO, and AIO defined. The delegated customer journey. The five signals AI agents use to read a website and the three interaction modes that shape the work.
Schema.org and JSON-LD. Semantic HTML, headings, ARIA, and the text-to-HTML ratio. robots.txt rules for GPTBot, ClaudeBot, and friends. sitemap.xml with lastmod, llms.txt, and AGENTS.md.
The four-question page (what, who, cost, how). Tables, lists, FAQ schema, and HowTo blocks. Topical authority and the category-association flywheel.
Citations, mentions, and off-site signals. Freshness and the last-updated signal. APIs, feeds, and the agent-protocol stack — MCP, A2A, AP2, ACP, and NLWeb.
Prompt testing, server-log analysis, citation tracking, and agent-readability scoring. The five-step agent-ready audit. The first-mover window and how to act without overreacting.
Start with the question that won't go away. From Keynes's 1930 forecast to today's AI wave, there is a single thread — and it is worth pulling on.
This course is written for a general adult audience — no economics background assumed. Terms are defined where used; claims are sourced; and every lesson points you to the primary readings if you want to go deeper. If you have followed the AI debate and want something more grounded than most of the public conversation, this is for you.
This course deliberately draws on a wide set of voices — Keynes (1930), Fisher (1933), Autor, Levy & Murnane (2003), Bresnahan & Trajtenberg (1995), Piketty, Brynjolfsson et al. (2023), Acemoglu & Restrepo, Frey & Osborne (2013), Paul David (1985), Diamandis & Kotler (2015), Darity & Hamilton (2010), Van Parijs, Karabarbounis & Neiman (2014), the ILO, the OECD, the IMF, and the NBER. It also draws on David Shapiro's popular writing, which has done much to bring post-labour economics to a wider audience. The point of the course is to let you read all of them — and then form your own view.
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