Human intelligence data could become the next critical input for AI

By Madhu Rajputra Peravalli, CEO, Troogue

AI has learned from more human-created content than any system in history.

Every book it could access. Every line of code. Every image, video, forum post, documentation page and conversation pattern. It still does not know how you think.

That’s the gap nobody’s talking about enough.

Knowing what someone has written is not the same as knowing how they apply it. A doctor working a case with half the information and two competing risks. An engineer staring at a broken system, deciding in seconds which alerts matter and which are noise. A manager trades off budget, speed and quality – knowing there’s no clean answer, just a less wrong one.

None of that lives in a document. It lives in the moment someone acts.

I call this human-intelligence data. 

Structured evidence of how people actually demonstrate capability, judgement, adaptability, and problem-solving – not what they claim on a resume, not what a personality test infers, and not what a monitoring tool captures while nobody’s looking. Content tells you what someone knows. Capability data tells you how they use it.

We already know human input matters to AI – that’s the entire premise behind preference learning. Humans rank outputs, flag what’s useful, and teach models what “good” looks like. Human-capability data is the next rung up that ladder.

Here’s why this matters right now, not in five years: AI stopped being a question-answering tool. It’s doing the work. Writing the code, running the analysis, managing the workflow. And when AI does the work, the thing that decides whether it succeeds isn’t information retrieval anymore. It’s judgement.

An agent can execute a task flawlessly and still miss the point of the task. The final answer is not the whole story. The decisions on the way are there.

This is also where benchmarks start to break. The moment a model saturates a benchmark, that benchmark stops telling you anything real. You need something that evolves – tasks grounded in actual constraints and actual outcomes, not a leaderboard.

The unit I’d propose: a capability event. The problem. The constraints. The action taken. The reasoning behind it. Expert evaluation of that reasoning. The outcome. And critically, did the person get better after feedback? String enough of these together, and you get something benchmarks never gave you: a real signal for evaluation, for domain-specific training, for agent behaviour, and for human-AI collaboration.

One assessment tells you nothing. The pattern across many tells you everything.

I’ve seen this idea surface in a place you wouldn’t expect: talent marketplaces. Most of them stop at the transaction – matching a profile to a job, collecting the fee, and moving on. The real question nobody’s answering is simpler and harder: does this person actually have the capability the role demands?

Answering that means giving up on static profiles. It means tracking capability across the full loop, opportunity, evaluation, selection, deployment, performance, feedback, and growth. Most platforms cut the story off right after “hired”. The real intelligence lives in what happens after.

Now the part I won’t skip past, because it matters more than the opportunity: this data can go wrong fast. Human-intelligence data must never become a rebrand for surveillance. If it isn’t built on informed consent, real transparency, and the right for a person to challenge or correct what’s inferred about them, it isn’t worth building.

Trust will be the actual moat here. Not the biggest dataset. Not the most data points per person. The company that wins this is the one people trust enough to let it happen, because the connection between demonstrated capability and real outcome was earned, not extracted.

AI has learned everything from what humans wrote. The next chapter is learning from what humans do. Not more information about the world. Better evidence of how people navigate it.