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.

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 primary economic incentive driving AI development is not replacing software, but automating the vastly larger human labor market. This includes high-skill jobs like accountants, lawyers, and auditors, representing a multi-trillion dollar opportunity that dwarfs the SaaS industry and dictates where investment will flow.

The economic incentive for VCs funding AI is replacing human labor, a $13 trillion market in the US alone. This dwarfs the $300 billion SaaS market, revealing the ultimate goal is automating knowledge work, not just building software.

OpenAI's new GDPVal framework evaluates AI on real-world knowledge work. It found frontier models produce work rated equal to or better than human experts nearly 50% of the time, while being 100 times faster and cheaper. This provides a direct measure of impending economic transformation.

OpenAI is launching initiatives to certify millions of workers for an AI-driven economy. However, their core mission is to build artificial general intelligence (AGI) designed to outperform humans, creating a paradox where they are both the cause of and a proposed solution to job displacement.

OpenAI announced goals for an AI research intern by 2026 and a fully autonomous researcher by 2028. This isn't just a scientific pursuit; it's a core business strategy to exponentially accelerate AI discovery by automating innovation itself, which they plan to sell as a high-priced agent.

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.