Growth Is a Search Problem

You start in fog, on a dark map, with an objective and no knowledge of which direction is uphill. The craft has always been a loop: hypothesize, move, observe, update. Hill climbing is one part of that loop. It can take you to a local peak, or up the wrong mountain if the proxy is wrong. One company optimized registered users. Another optimized people who actually came back.

AI changes the economics of the loop, not the geometry of the terrain. Agents can analyze, propose, ship, and interpret. You can now climb the wrong hill much faster. A rigid roadmap assumes the order of tests matches the size of the opportunities. Bandits waste less traffic on losers. A better strategy spends a small slice of budget mapping lever sensitivity first: poke the map, then climb where it makes a difference.

When experiments become cheap, the scarce inputs are aim, evidence, and exploration budget. High-uncertainty tests often score poorly on ICE, which is often why they should run. The worst test is not a loser. It is the one that leaves you knowing exactly what you knew. Optimization asks how to improve the current approach. Search design asks which directions are worth learning. Strategy asks which mountain is worth summiting.

This Ledger Entry expands how the reader thinks by treating growth teams as architects of search, not as faster hill-climbers, and by making information gain the unit of exploration rather than immediate lift.