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."

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.

Companies like Intercom and Cursor are proving that fine-tuning open-weight models on specific, "last-mile" user interaction data creates cheaper, faster, and more accurate models for vertical tasks (like customer service or coding) than general-purpose frontier models from labs like OpenAI.

For specialized, high-stakes tasks like insurance underwriting, enterprises will favor smaller, on-prem models fine-tuned on proprietary data. These models can be faster, more accurate, and more secure than general-purpose frontier models, creating a lasting market for custom AI solutions.

Achieving state-of-the-art AI performance requires a massive, bespoke data generation process. This involves thousands of human experts—from legal specialists to management consultants—creating specific examples, rubrics, and chain-of-thought explanations, forming a new and rapidly growing data industry that is the true engine of progress.

Instead of relying solely on massive, expensive, general-purpose LLMs, the trend is toward creating smaller, focused models trained on specific business data. These "niche" models are more cost-effective to run, less likely to hallucinate, and far more effective at performing specific, defined tasks for the enterprise.

The "agentic revolution" will be powered by small, specialized models. Businesses and public sector agencies don't need a cloud-based AI that can do 1,000 tasks; they need an on-premise model fine-tuned for 10-20 specific use cases, driven by cost, privacy, and control requirements.

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.

Mercore's $500M revenue in 17 months highlights a shift in AI training. The focus is moving from low-paid data labelers to a marketplace of elite experts like doctors and lawyers providing high-quality, nuanced data. This creates a new, lucrative gig economy for top-tier professionals.