Conjectures has done a great job showing how incentive systems don't just parallelize search over solutions — they parallelize search over search systems themselves. Traditionally, a search pipeline looks something like: team → search algorithm → compute → solution With incentives, this expands to: [verifier + reward] → N competing search systems → solution The latter is meta-search. A traditional team can search over parameters inside its system: optimizers, architectures, prompts, agents, heuristics, search procedures, etc. But many things remain fixed outside the search. The team itself. Its ingenuity. Its hardware. Its capital. Its energy costs. Its infrastructure. Even the assumptions determining which algorithms it chooses to try. An incentive system can push that boundary outward. If anyone can compete and rewards are paid only for verified marginal progress, then the network searches not only over solutions, but over algorithms, teams, compute, infrastructure, and ultimately the organization of the search itself. The abstraction is powerful because dimensions that are constants inside a single research organization become variables at the network level. That larger search space has a cost. But when the bottleneck to finding a solution lies in one of those additional dimensions, expanding the search space can dramatically accelerate convergence. Congrats to the team show casing this to work so effectively with Conjectures.io