While AI has mastered verifiable tasks with clear right answers, its future growth depends on human experts training models in subjective fields where 'good' is not easily defined. Companies are now sourcing professionals to act as 'verifiers' that teach AI nuanced, domain-specific judgment.
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AI startup Mercore's valuation quintupled to $10B by connecting AI labs with domain experts to train models. This reveals that the most critical bottleneck for advanced AI is not just data or compute, but reinforcement learning from highly skilled human feedback, creating a new "RL economy."
To ensure accuracy in its legal AI, LexisNexis unexpectedly hired a large number of lawyers, not just data scientists. These legal experts are crucial for reviewing AI output, identifying errors, and training the models, highlighting the essential role of human domain expertise in specialized AI.
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.
AI excels where success is quantifiable (e.g., code generation). Its greatest challenge lies in subjective domains like mental health or education. Progress requires a messy, societal conversation to define 'success,' not just a developer-built technical leaderboard.
LLMs have hit a wall by scraping nearly all available public data. The next phase of AI development and competitive differentiation will come from training models on high-quality, proprietary data generated by human experts. This creates a booming "data as a service" industry for companies like Micro One that recruit and manage these experts.
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.
The frontier of AI training is moving beyond humans ranking model outputs (RLHF). Now, high-skilled experts create detailed success criteria (like rubrics or unit tests), which an AI then uses to provide feedback to the main model at scale, a process called RLAIF.
If AI were perfect, it would simply replace tasks. Because it is imperfect and requires nuanced interaction, it creates demand for skilled professionals who can prompt, verify, and creatively apply it. This turns AI's limitations into a tool that requires and rewards human proficiency.
AI models have absorbed the internet's general knowledge, so the new bottleneck is correcting complex, domain-specific reasoning. This creates a market for specialists (e.g., physicists, accountants) to provide 'post-training' human feedback on subtle errors.
The emerging job of training AI agents will be accessible to non-technical experts. The only critical skill will be leveraging deep domain knowledge to identify where a model makes a mistake, opening a new career path for most knowledge workers.
