01 · Discover

Finding problems worth building

shippedprinciple → decision → workflow → artifact

Principle

AI can generate a hundred product ideas before lunch. That does not make ideation free — it makes choosing expensive. The failure mode shifts from “we couldn’t build it” to “we built the wrong thing beautifully.”

Problems worth building share three traits: recurring pain (not one-off annoyance), reachable users (you can observe behavior, not just opinions), and honest economics (someone will pay or repeatedly choose your solution over the default).

The decision

DEC_004

  Curiosity (wide)
│
▼
AI synthesis ──► hypotheses (written, falsifiable)
│
▼
Human contact ──► behavior + counts (not vibes)
│
▼
Problem bet (narrow) ──► build or kill
Discovery widens with AI; commitment narrows with evidence.

Signals that matter

SignalStrongWeak
Pain”I do this every week and hate step 3""That sounds cool”
WorkaroundSpreadsheet, hack, paid alternativeNothing — they don’t care
CommitmentTime, money, intro to a colleaguePolite interest
UrgencyDeadline, cost, risk they nameGeneric “eventually”

AI is excellent at turning messy notes into a table like this. You still own the column assignments.

Workflow

  1. Write the problem before the product — one sentence: who, when, what breaks. No feature list yet.
  2. Run an AI pass on public sources: competitors, forums, job posts, reviews. Output: map of how people already cope.
  3. Talk to five humans who match the who — Mom Test rules: past behavior, not future promises.
  4. Falsify — what would prove you wrong in two weeks? If you cannot name it, you are still brainstorming.
  5. Promote or kill — one problem bet goes to validation (next chapter). The rest go in a graveyard doc with what you learned.

Tooling

Perplexity, Claude, NotebookLM for synthesis. Calendly and a voice memo for interviews. A spreadsheet beats a Notion board for counts.

Common mistakes

Artifacts

Further reading