Last updated: 2026-05-10
A measured, non-commercial introduction to running a small AI agency. Twenty-two lessons across six modules: the structural shape of the field, the unit economics behind every honest decision, market choice, coordinated service-stack design including platform setup and channel automation, legal and AI-specific risk, sales without manipulation, retention, and the exit landscape. No pitches, no upsells, no proprietary frameworks invented for this course.
Self-paced. Honor-based. Not accredited.
Six short modules covering the structural shape of an AI agency, the unit economics behind every honest decision, how to pick a market, what components form a coordinated service stack, the legal and AI-specific risks operators must manage, how to sell and retain, and the exit options available to mature practices. Practical frame, no jargon, no salesmanship.
Each module stands on its own. Together they give a working frame for thinking about a small AI agency — one you can test against your own situation, push back on, and extend.
What this kind of practice actually does, why standalone tools rarely sustain it, and how productisation creates defensibility.
Unit economics for service practices: customer acquisition cost, lifetime value, payback period, contribution margin, and the AI-specific cost trap.
Where money already moves in small businesses, how to validate demand cheaply, and why coordinated stacks beat single offerings.
Re-engaging dormant lists, building a reputation flywheel, recovering missed enquiries, layering paid acquisition, configuring all-in-one operating platforms, and automating direct messages and outbound on social and professional networks — each tagged with its unit-economics implication.
The legal and compliance landscape (GDPR, PECR, TCPA, AI Act) and AI-specific operational risks (hallucination, escalation, audit trails).
Outbound and inbound channels, diagnostic conversations, pricing models, retention mechanics including net revenue retention, failure patterns, and the exit landscape including roll-ups and the vertical-SaaS pivot.
Start with what this kind of business actually is. The opening lesson clarifies who buys, what they buy, and why the underlying need is older than the technology that has lately come to address it.
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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