As AI systems grow more capable, the bar for human training has risen sharply, and generalist data labelers are being pushed aside. That's according to HireArt's 2025 AI Trainer Compensation Report, which collected information from more than 150 sources, including a survey of active workers, public job postings, and internal data. The study shows that today's AI models demand nuanced reasoning, domain expertise, and multilingual fluency, transforming "data labeling" into specialized cognitive work.
"The agent underneath is actually more universal than we thought." Instead of building new agents for every use case, companies should rely on a single general agent powered by a library of skills, Zhang said. Skills are "organized collections of files that package composable procedural knowledge for agents," Zhang said. They are simply folders that contain whatever an agent needs to complete a task consistently and efficiently.
As artificial intelligence transforms advertising analytics, many organizations are rushing to adopt AI tools, hoping for a "magic button" that instantly democratizes data access. It's an appealing vision: marketing teams asking questions in plain English and getting instant insights without SQL expertise or analyst bottlenecks. But generic AI tools often stumble when confronting the specialized world of advertising analytics. They lack a crucial understanding of attribution models, customer journeys and cross-channel measurement.