Founder → AI-augmented operator
For: A founder running a small company who needs to use AI across the full job — research, customer work, product decisions, hiring, comms — without delegating judgment to it.
You will be able to: You can use AI as a real collaborator for the founder job, ship AI features without embarrassing yourself, and avoid the predictable "we built an AI thing and it broke" outcomes.
Steps in order
- 01
Getting started with AI: a 30-minute starter path
A guided reading order if you are new to AI. What to learn first, what to skip, and the three habits that separate people who get value from AI from people who give up after a week.
— Start here. Even if you have been using ChatGPT for a year, this fixes the orientation gaps.
6 min·/learn/ai/get-started - 02
How large language models work
The mental model that fixes most prompting confusion — prediction, training, inference, why hallucinations happen, why prompt phrasing matters so much. For the operator who wants to understand the mechanism.
— The mental model. Founders who skip this make scoping mistakes that cost months.
10 min·/learn/ai/foundations - 03
What AI is good at, and what it still gets wrong
A blunt capability map. The categories of work where AI is reliable, the categories where it bluffs, and the in-between where it works if you verify.
— The capability map. Half of "should we build this with AI?" resolves here.
7 min·/learn/ai/get-started - 04
How to verify AI output before you trust it
AI is fluent enough to make wrong answers sound right. A practical checklist for catching hallucinations, broken facts, and silent drift — for code, copy, research, and analysis.
— For a founder, an AI mistake that ships to customers is a brand risk. Verification matters more, not less.
7 min·/learn/ai/verify-and-trust - 05
Talking to users before you build anything
How to find 10 real prospects, what to ask, what NOT to ask. The Mom Test in practice.
— Customer interviews are upstream of any AI feature decision. Get this habit right first.
6 min·/learn/building-shipping/validation - 06
Killing ideas early without ego damage
The signals that say "stop." How to walk away from your own idea before you sink three months into it.
— The corollary to talking to users. Most AI feature ideas should die in week 1, not month 6.
6 min·/learn/building-shipping/validation - 07
Fine-tuning vs RAG vs prompting: which one fits your problem
The three ways to make a model behave better for your case — cost, persistence, updateability, when to use each, and when to mix them. With the decision matrix and the math for "is fine-tuning worth it."
— The architecture choice. Most founder "should we fine-tune?" debates resolve here.
9 min·/learn/ai/foundations - 08
How AI agents work (and where they break)
The minimum that makes something an agent (LLM + tools + loop). What agents are good for, the six predictable failure modes, the autonomy spectrum, multi-agent vs single, and what to log in production.
— When to build an agent vs a workflow. The failure-mode section is the scoping checklist.
10 min·/learn/ai/foundations - 09
AI for solo founders: doing the work of a small team
Landing pages, support, design, customer research, code. Treating AI as your team rather than your tool.
— The applied chapter. Brings the foundations together for the founder job.
8 min·/learn/ai/use-cases-by-role
When you finish this path
You can use AI as a real collaborator for the founder job, ship AI features without embarrassing yourself, and avoid the predictable "we built an AI thing and it broke" outcomes. For the next step, browse other paths or the full library.