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."

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.

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.

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.

Companies like OpenAI and Anthropic are spending billions creating simulated enterprise apps (RL gyms) where human experts train AI models on complex tasks. This has created a new, rapidly growing "AI trainer" job category, but its ultimate purpose is to automate those same expert roles.

The demand from AI labs for high-skilled professionals (engineers, lawyers, doctors) to create evals and training data created a historic business opportunity. Mercor capitalized on this by creating an expert labor marketplace, becoming the fastest-growing company in history.

The value in AI services has shifted from labeling simple data to generating complex, workflow-specific data for agentic AI. This requires research DNA and real-world enterprise deployment—a model Turing calls a "research accelerator," not a data labeling company.

Data is becoming more expensive not from scarcity, but because the work has evolved. Simple labeling is over. Costs are now driven by the need for pricey domain experts for specialized data preparation and creative teams to build complex, synthetic environments for training agents.