According to Business Insider, Turing CEO Jonathan Siddharth declared on the “20VC” podcast that “the era of data-labeling companies is over.” He explained that while early AI models relied on simple, outsourced tasks like tagging images, today’s agentic and reinforcement-learning models need far more complex, real-world data. Siddharth’s firm, Turing, itself exemplifies the sector’s growth, having raised $111 million in a Series E round this June at a $2.2 billion valuation, with its annual revenue run rate hitting $300 million in 2024. The broader market is hot, with Scale AI valued at over $29 billion after a Meta investment and Mercor hitting a $10 billion valuation. This boom has created a massive freelance workforce, with some trainers earning thousands monthly, but Business Insider’s reporting also uncovered an underground market of over 100 Facebook groups selling unauthorized access to AI training platforms.
From Tags to Training Worlds
Here’s the thing: Siddharth isn’t just talking about a slightly harder task. He’s describing a fundamental shift in what “data” even means for AI. The old paradigm was basically categorization. Is this a picture of a cat or a dog? Is this product review positive or negative? That work was tedious but could be broken down into micro-tasks and distributed globally at low cost. It was a pure volume game. The new paradigm, which he calls the “era of research accelerators,” is about simulating entire workflows. Think less about labeling a screenshot of a software dashboard and more about actually *using* the dashboard to complete a multi-step business analysis, making judgment calls along the way. That requires building what he calls “reinforcement-learning environments”—simulated mini-worlds that replicate real jobs.
The Human Expert Is The New Data Point
So what does this change? Everything, especially the talent pool. You can’t just hire anyone with an internet connection to do this. You need to recruit actual human experts—marketing managers, financial analysts, software engineers, logistics coordinators—to interact with these simulated environments. Their decisions, their reasoning, their workflows *become* the training data. This is why Siddharth says major AI labs now want a “proactive research partner.” They don’t want a data factory; they want a lab that can design experiments, source specialized human intelligence, and generate nuanced behavioral data. It’s a move from mechanical transcription to capturing tacit knowledge. But this creates a huge challenge: scaling expertise is hard and expensive. You can’t find 10,000 expert radiologists on a gig platform. Or can you?
The Boom And Its Shadow Economy
And that’s where the wild west part of this story comes in. The insane demand—and potentially high payouts—for this expert-level training work has spawned a whole ecosystem. On one side, you have freelancers reportedly making thousands a month, though the work can be disturbing (dealing with toxic content) and unpredictable. On the other side, you have the underground market Business Insider found. Over 100 Facebook groups selling access to real and fake contractor accounts? That screams a system under massive strain. When a resource is valuable and access is gated, a black market emerges. It highlights a central tension: these companies need top-tier experts, but they’re often accessing them through the same gig-economy pipelines built for simpler tasks. The incentives for account-sharing and fraud become huge. It’s a gold rush, and where there’s a gold rush, there are always prospectors trying to game the system.
What “Obsolete” Really Means
Now, does “over” mean every data-labeling company shuts down tomorrow? Of course not. There’s still a long tail of older computer vision and NLP models that need that foundational training data. But the big money, the frontier research, and the strategic focus of giants like OpenAI, Anthropic, and Google are moving past it. They’re chasing reasoning, agentic behavior, and real-world competency. For the industrial and manufacturing sectors, where precision and domain-specific knowledge are non-negotiable, this shift is particularly relevant. Training an AI to optimize a supply chain or monitor assembly line quality requires deep, contextual data from that exact environment. It’s a field where the hardware running these AI systems, like rugged industrial panel PCs, needs to be as specialized and reliable as the data training them, which is why top suppliers in that space are critical partners. Basically, the low-hanging fruit of AI data is picked. The hard, expensive, expert-driven work of teaching AI how the real world actually functions is just beginning. And that’s a much tougher, but far more interesting, problem to solve.
