Pick a depth. Each prompt opens in your AI pre-loaded with the lesson. Click a row to preview the prompt.
The team that runs more experiments wins — not because every experiment succeeds, but because learning rate compounds. Experiment velocity is the number of rigorous tests a team ships per month; most teams running one or two per quarter will always lag a team running eight, even if both have equal insight quality. The bottleneck is usually process, not ideas.
Model the compound learning advantage of a high-velocity team over 6 months.
function learningCompound(experimentsPerMonth: number, successRate: number, liftPerWin: number, months: number) {
let baseline = 1.0; // normalized conversion rate
let wins = 0;
for (let m = 0; m < months; m++) {
const monthlyWins = Math.floor(experimentsPerMonth * successRate);
wins += monthlyWins;
baseline *= Math.pow(1 + liftPerWin, monthlyWins);
}
return { totalWins: wins, cumulativeLift: ((baseline - 1) * 100).toFixed(1) + "%" };
}
const slowTeam = learningCompound(1, 0.3, 0.05, 6); // 1 exp/mo, 5% lift/win
const fastTeam = learningCompound(8, 0.3, 0.05, 6); // 8 exp/mo, same win rate
console.log("Slow team:", slowTeam); // { totalWins: 1, cumulativeLift: "5.0%" }
console.log("Fast team:", fastTeam); // { totalWins: 14, cumulativeLift: "97.9%" }