🏎️ Iteration speed is the advantage
In the age of AI, the advantage isn’t having the perfect idea — it’s the the speed at which you iterate. Whoever makes ten laps around the problem while the other person is still planning the first gets further. Each iteration is a data point: you find out what works, adjust, and start from a higher baseline in the next round.
🧠 The central idea
AI has lowered the cost of trying. Before, each draft took hours; now it takes minutes. Those who understand this stop trying to get it right on the first try and start aiming for more iterations per hour.
- •Each iteration is information — not waste.
- •Each cycle raises your baseline for the next one.
- •More cycles per hour beat one “perfect” cycle per week.
🧪 Fast, Ugly Prototype — Move Beyond the PoC
Don’t try to plan the perfect version in your head. Make a ugly prototype today, see where it breaks and fix what broke. The never-ending spreadsheet PoC teaches you nothing; a draft running in the real world teaches you in minutes. Fail fast and small before you fail slowly and big.
✓ What to DO
- ✓Put together a rough version that runs today.
- ✓Let it break — the error shows you the next step.
- ✓Fix only what broke, and run it again.
✗ What NOT to do
- ✗Planning the final version before testing.
- ✗Getting stuck on a polished PoC that never gets off the ground.
- ✗Wait until you have "everything ready" to start.
🚲 Teaching AI Is Like Learning to Ride a Bike
Working with AI is a feedback loop, just like learning to ride a bike. You don't read the manual and ride perfectly—you get on, lose your balance, correct yourself, and with every lap your body calibrates. It's the same with AI: you ask, get a response, adjust your request, and with each cycle you get closer. At some point you "take off the training wheels" and fly.
The feedback loop that calibrates AI to your way of working — illustrative diagram.
⌨️ Shortcuts, voice, less mouse
Whoever iterates quickly reduces the friction of each round. Keyboard shortcuts take their hands off the mouse; the voice dictation is usually much faster than typing when passing context to AI. The operator matters too: the same tools deliver more in the hands of someone who has mastered the shortcuts.
Learn your tool’s shortcuts
Every time you take your hand off the mouse, you save seconds; over a day, they add up to minutes.
Use voice to provide context
Speaking 5 sentences is faster than typing—and you give AI more detail.
Train the operator, not just the tool
The same AI delivers much more in the hands of someone who has reduced friction.
🎯 Tie 1 automation to 1 business metric
Iterating quickly without a target turns into motion without progress. Anchor each automation in a business metric — your North Star metric. Not “I used AI today,” but “I increased tickets resolved per day,” “I increased appointments per week,” or “I reduced the refund rate.” The number tells you, without opinion, whether the iteration was worth it.
📈 North Star examples by area
- •Support: tickets resolved per day.
- •Sales: qualified appointments per week.
- •Operations: % for refunds or rework.
- •Content: pieces published on time per month.
💡 Practical tip
Before automating, write down today’s number. After a week of iterating, compare. Without the "before," you’ll never prove the "after"—and it’s hard to defend your work.
🛑 Know When to STOP Iterating
Iterating quickly has a dangerous twin: iterating forever. Define your definition of done before you start — the criterion that says "enough, it's good enough to deliver." Without it, each round becomes an excuse for one more, the scope grows (scope creep), and you never deliver.
✓ Stop when…
- ✓It met the “done” metric you defined beforehand.
- ✓The final passes tweak details no one notices.
- ✓The gain from one more iteration became smaller than the cost.
✗ Signs of scope creep
- ✗"Just one more tiny tweak" for the tenth time.
- ✗Adding new requirements that weren’t there at the start.
- ✗Polishing what’s already done instead of delivering.
🏁 The "done" rule
Write a "done" sentence before you start: what needs to be true for you to deliver. Once it’s met, stop—even if there’s room to improve. Delivering and measuring is worth more than polishing in the dark.
Self-check (optional): Which habit most sets apart people who stand out in the age of AI?
✅ Module Summary
Next Module:
2.3 — Taste & Judgment