How AI Training Built a New Labor Market
Source: Glasp
The infrastructure underpinning AI systems relies on a sprawling, largely invisible workforce—radiologists annotating images, coders generating training datasets, people recording mundane tasks like laundry folding—creating a new category of piecework that sits between gig labor and specialized professional services. This market's growth hinges on three questions: whether companies can extract high-quality training data at scale without paying knowledge workers professional rates, whether regulatory pressures (data provenance, copyright claims, geographic restrictions) will force more transparent pricing, and whether this labor concentrates value upstream in AI companies or distributes it to the humans generating it. The 2026 question is whether this becomes a legitimate labor category with protections or remains a shadow economy of micro-tasking that props up trillion-dollar AI valuations.