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Introduction: When Physicians Start Teaching Machines to Think
A quiet transformation is unfolding at the intersection of medicine and artificial intelligence. Seasoned professionals are no longer just using AI tools — they are actively shaping how these systems think, reason, and make decisions. Among them is Dr. Alice Chiao, a former emergency medicine instructor at Stanford University, who now spends her time doing something far more futuristic: training AI chatbots to diagnose and respond like a real doctor. What began as a behind-the-scenes technical process has rapidly evolved into a multibillion-dollar industry that may redefine how expertise, labor, and intelligence itself are valued.
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Dr. Alice Chiao once taught human medical students; today, she teaches machines. She is part of a fast-growing workforce of professionals training AI systems using reinforcement learning — a method where experts evaluate and correct AI responses so models improve through trial and error. This new service economy, supporting major AI labs, is already estimated to be worth at least $17 billion.
Chiao works with Mercor, a company that connects AI developers with experts across fields such as medicine, law, finance, sports, and even wine tasting. These professionals can earn hundreds of dollars per hour teaching AI how to perform complex, domain-specific tasks. Chiao sees her role as essential to ensuring medical AI tools are accurate, safe, and understandable for everyday users.
Major AI developers like OpenAI, Google, and Anthropic rely on vast human feedback networks to refine their models. According to Mercor CEO Brendan Foody, these firms deploy “large armies” of human reviewers to teach AI the difference between useful and harmful outputs.
The rise of AI has also intensified fears of job displacement. Market anxiety surged after Anthropic released industry-specific AI tools and after a viral essay warned of mass workforce disruption. Critics argue companies like Mercor may accelerate the shift from stable careers to gig-based knowledge work.
Chiao rejects the idea that she is training a machine to replace doctors. Instead, she believes AI can remove administrative burdens — reading scans, filling charts, and taking notes — allowing physicians to focus on patients. She emphasizes that AI lacks the intuition developed through years of human interaction and clinical experience.
When training models, Chiao uses real cases from her decades in emergency and primary care. She corrects responses that are misleading, unsafe, or overly alarmist. Mercor’s experts score AI outputs using detailed rubrics, feeding those evaluations back into the system.
Beyond medicine, Mercor hires heavily in software engineering, finance, and law. Some domains, like comedy, have proven harder to teach. Even comedians from the Harvard Lampoon struggled to define universal standards of humor for machines.
Mercor itself has grown at an extraordinary pace. Founded just three years ago by Foody and co-founders Adarsh Hiremath and Surya Midha, the company pivoted from recruiting to AI training. It now pays out over $1 million per day to experts and is valued at more than $10 billion, according to Pitchbook. Competitors such as Scale AI, backed by a massive investment from Meta and led by founder Alexandr Wang, highlight how central human feedback has become to AI development.
Foody, now one of the youngest tech billionaires since Mark Zuckerberg, argues that making people more productive with AI is key to solving global challenges — from climate change to cancer — even as debates over automation continue.
What Undercode Say:
The rise of expert-trained AI reveals a paradox at the heart of the automation debate. The more advanced AI becomes, the more it depends on deeply human judgment. Reinforcement learning is not cheap labor; it is premium cognitive work. Companies like Mercor are monetizing expertise itself, turning decades of professional intuition into structured feedback loops for machines.
This model reframes AI not as a purely technological breakthrough, but as a socio-economic system built on invisible human scaffolding. Every “smart” medical response, legal suggestion, or financial insight is often the result of countless human corrections behind the scenes. Far from eliminating experts, frontier AI is temporarily making them more valuable — especially those capable of translating messy real-world judgment into teachable rules.
However, this dependence raises uncomfortable questions. When expertise becomes gig-based and fragmented, who owns professional knowledge? If doctors, lawyers, and engineers train models that later undercut their own bargaining power, the short-term payouts may mask long-term erosion of traditional careers. The shift from institutional authority to platform-mediated expertise could weaken professional norms that took decades to build.
In medicine, the stakes are especially high. AI systems trained by clinicians like Chiao may soon become the first stop for health advice — effectively a new, algorithmic front door to care. While this can democratize access to information, it also risks normalizing machine-mediated triage in systems already strained by cost pressures. Once insurers and hospitals see reliable AI assistance, the temptation to reduce human staffing will be strong.
Yet the article also highlights a critical safeguard: experts who see AI as augmentation, not replacement. If physicians remain embedded in model training and oversight, AI could meaningfully reduce burnout by stripping away administrative overload. The danger lies not in AI itself, but in who controls its deployment and incentives.
Mercor’s meteoric valuation signals investor belief that “human-in-the-loop” AI is not a temporary phase but a permanent layer of the AI stack. Ironically, the path to autonomous systems runs directly through human judgment. The future of work may not be humans versus machines, but humans continuously teaching machines — often without public recognition — to behave acceptably in society.
Ultimately, this economy exposes a truth Silicon Valley rarely admits: intelligence does not scale cleanly. It must be curated, corrected, and culturally localized. As long as AI lacks lived experience, it will remain dependent on people like Dr. Chiao to keep it grounded in reality.
Fact Checker Results
The article’s claims about Mercor’s valuation and payout scale align with statements attributed to Pitchbook and company leadership.
Descriptions of reinforcement learning and expert feedback accurately reflect standard AI training practices.
No evidence suggests current medical AI tools can replace licensed physicians, supporting Chiao’s cautionary stance.
Prediction
Over the next three years, expert-driven AI training marketplaces will expand rapidly, especially in regulated fields like healthcare and law. As AI becomes the default interface for information, professionals who help shape its judgment will gain influence — but only temporarily. Long term, pressure will grow for clearer regulations, transparency around human training labor, and new ethical standards governing how expertise is converted into machine intelligence.
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Reported By: edition.cnn.com
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