Early AI training involved simple preference tasks. Now, training frontier models requires PhDs and top professionals to perform complex, hours-long tasks like building entire websites or explaining nuanced cancer topics. The demand is for deep, specialized expertise, not just generalist labor.

The role of an AI Product Manager is legitimate and highly compensated, as confirmed by Google's Director of AI Product. Job postings and salary data sites like Levels.fyi reflect the high demand and experience required for these positions in a competitive industry.

To move beyond general knowledge, AI firms are creating a new role: the "AI Trainer." These are not contractors but full-time employees, typically PhDs with deep domain expertise and a computer science interest, tasked with systematically improving model competence in specific fields like physics or mathematics.

Despite general AI hype, the demand for AI Product Managers (AIPMs) is real, reflected in median compensation packages that are now competitive with top-tier software engineering roles in major tech hubs like the Bay Area.

In a group of 100 experts training an AI, the top 10% will often drive the majority of the model's improvement. This creates a power law dynamic where the ability to source and identify this elite talent becomes a key competitive moat for AI labs and data providers.

The market places a significant premium on product managers with AI skills. Data shows compensation bands for AI PM roles are 30-40% higher than their non-AI counterparts, with senior roles reaching well into the high six-figures and even millions.

Experience alone no longer determines engineering productivity. An engineer's value is now a function of their experience plus their fluency with AI tools. Experienced coders who haven't adapted are now less valuable than AI-native recent graduates, who are in high demand.

The key technical skill for an AI PM is not deep knowledge of model architecture but a higher-level understanding of how to orchestrate AI components. Knowing what AI can do and how systems connect is more valuable than knowing the specifics of fine-tuning or RAG implementation.

The dramatic increase in "AI PM" job listings isn't just about new roles. It's a marketing tactic. Companies use the "AI" label to attract top talent, and candidates adopt it to signal value and command higher salaries, creating a feedback loop.