Proprietary data, public reasoning, and the future of quality

In a recent interview, Bob McGrew, former head of research at OpenAI, posed a question that gets to the heart of where we are right now: “How valuable will your proprietary data be compared to what your competitor’s infinitely smart, infinitely patient agents can estimate from public data?”

It’s a fair question, and an important one. As AI systems learn to reason across massive amounts of public information, the moat once created by proprietary data is shrinking fast. Even industry-specific models trained on so-called “secret” datasets often struggle to outperform general-purpose systems that simply reason better.

At Data Quality Co-op, we see this as a pivotal moment. Proprietary data still matters, but its value now lies less in exclusivity and more in stewardship. The future advantage won’t come from owning the most data, but from maintaining the data the world can actually trust. What sets information apart now isn’t that it’s hidden, but that it’s verified, permissioned, and aligned with reality.

Marc Ryan captured this shift well in his recent essay, “Why Vibe Insights is the Future,” when he wrote that “reasoning, not just raw data, is now proving to be a game changer.” I agree. But reasoning without reliable inputs is just speculation at scale. The real differentiator will belong to those who make their data reliable enough to reason with.

That reliability comes from structure, not secrecy. Clean, verified, human-grounded data provides the calibration points that keep synthetic systems honest. It’s how we teach models what truth looks like. Proprietary datasets built from permissioned, transparent collection methods still carry unique value, precisely because they represent consented reality; the context, nuance, and trust that public data, however vast, can only approximate.

As our industry experiments with synthetic augmentation,we need to apply the same discipline we once reserved for sample sourcing. Synthetic data is a tool, not a substitute for sample. It’s powerful when it’s used intentionally and within clear boundaries. Boundaries like small, transparent augmentation, capped and auditable use, and validation through fidelity, utility, and privacy checks. And it needs a feedback loop that measures drift and maintains alignment over time.

McGrew’s comments remind us that in the age of reasoning models, data quality and governance (not data volume) define competitive advantage. Proprietary data may no longer guarantee a moat on its own, but coordinated, trustworthy infrastructure still can. That’s the foundation we’re building at Data Quality Co-op: a clearinghouse that connects buyers, suppliers, and platforms in a continuous loop of benchmarking, validation, and improvement.

If reasoning models are the future of AI, then clean, comparable, and transparent data is the infrastructure that keeps them grounded. The question isn’t whether proprietary data still matters. It’s whether we can make it matter for the right reasons.