I spent a few years designing game economies before I spent any time on AI tooling, and the transition has been strange, because I keep watching people rediscover failure modes that mobile games documented years ago.
The current one: bounties for AI-assisted work. Pay people for tasks completed by their agent. Put a leaderboard on it. Watch throughput go up.
Throughput does go up. That is the problem.
Every reward loop has a currency, a source, and a sink. The loop stays healthy while the effort to satisfy the metric is higher than the effort to game it. The moment that inverts, the economy does not slow down - it accelerates in the wrong direction, and it looks like success on the dashboard the whole time.
This is why a "1% drop rate" feels broken to players, why battle passes get abandoned mid-season, and why currency sinks are the least glamorous and most load-bearing part of an economy. Sources are easy. Sinks are the design.
Ask what a submission actually costs.
Before agents, submitting a completed task cost hours. The metric was expensive to satisfy and roughly impossible to game, so nobody bothered building anti-gaming controls. That cost is now close to zero. One prompt produces something task-shaped. It compiles. It has a plausible commit message.
So if you pay per submitted task, you have built a source with no sink. You will get volume, immediately, and it will be indistinguishable from productivity in every chart you have.
The three specific patterns I would expect, all of them rational:
None of that requires bad actors. Design for the employee who understands your metric perfectly and is not trying to cheat, because that person will find the cheapest honest path and take it.
In a game the sink removes currency. In a work system the sink is a decision that can say no, placed between the claim and the reward, and made by somebody who is not the claimant.
Concretely, the parts I would not skip:
Point 4 is where I think most of these systems will actually fail. You can move the bottleneck from doing to reviewing and call it a productivity win, and the reviewers will absorb it silently for about two months.
I do not know what the right payout ratio is, and I am suspicious of anyone who says they do. Games get curve numbers by shipping and watching, and most of them get it wrong twice first.
What I am fairly confident about is the shape: verified outcome, independent decision, evidence attached, review counted. Wagglet's write-up on bounties and rewards for AI work is the most careful version of that argument I have read, including the anti-gaming controls, which is the part usually left as an exercise for the reader.
Run it on one team for a month before announcing it as a revolution. If your throughput triples in week one, that is not the good outcome - that is your economy telling you the metric is cheaper to game than to satisfy.