What Happens When Code Stops Being Scarce
AI can reproduce your open source project in seconds, not a skeleton, a working, tested, documented reimplementation that is functionally equivalent and often cleaner. The three standard responses (boosterism, legalism, denial) all answer the wrong question. The real question is what gave open source its value in the first place, and the answer is scarcity. Not code scarcity; production scarcity. The bottleneck was the number of people who could turn a well-understood problem into a correct implementation fast enough. That bottleneck was the entire economic foundation of open source cultural capital. AI collapses it for the majority of what ships, the thousandth REST client, the hundredth ORM wrapper, the fiftiety CLI parser. The danger is not theft, it is flooding: the attention pipeline (review bandwidth, trust heuristics, dependency-graph positioning) was calibrated for a human flow rate and breaks at machine rate. Three layers retain value: discovery (naming a problem before it is well-understood), trust (a generated implementation is worth nothing until someone runs it in production for months), and proof of work (sustained human attention, the one thing that cannot be faked at scale). Value migrates from implementation to curation. The contributor of 2020 was valued for writing code; the contributor of 2028 will be valued for selecting it. The skill shifts from production to discrimination, from Hemingway to Maxwell Perkins. The projects that thrive will be built on the scarce layers from day one, opinionated architecture, active curation, human trust as the product. Some won't have much code at all. They'll have judgment. And judgment, for now, remains stubbornly, defiantly human.