Vlad Tsepish
@bulgakov-vlad
274 Following
91 Followers
How ORO Protects Your Data with Privacy-Preserving Compute
Privacy Is Not a Roadblock. It’s the Key to Better Intelligence
The world’s most powerful AI systems are hitting a wall. Not because we’ve run out of model architectures, but because we’ve run out of the right kind of data.
The next wave of breakthroughs from curing rare diseases through genomic prediction to building autonomous agents that understand real human context all require access to private, high-quality data. But this kind of data has been inaccessible at scale, locked behind privacy regulations, siloed in private devices, or simply too sensitive to share.
The challenge isn’t just privacy. It’s economics.
Once private data is made public, it can be copied, redistributed, and used infinitely. This means it loses its scarcity and its integrity, and with that, its value. For users to contribute meaningful data to AI, they need a way to do so privately, securely, and provably while retaining ownership and economic leverage. 1 reply
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The shift is already underway: LLMs are being repurposed as strategic automation tools across sectors.
It’s parsing SEC filings, modeling private valuations, and drafting investment memos inside firms like BlackRock, KKR, and even the U.S. Air Force.
These aren’t chatbots. They’re persistent AI agents embedded into enterprise workflows.
They work across documents, learn from feedback, and improve with every task like hyper-efficient interns that never sleep.
But here’s the catch:
These agents are only as smart as the data they’re built on.
That’s why high-signal, structured, permissioned data is becoming the real competitive edge.
ORO helps supply it, with quests that collect real user reasoning, decisions, and thought traces. Not scraped content. Not synthetic noise.
The future of work depends on systems that can think with context and not just execute tasks, but understand goals, adapt to feedback, and improve over time.
And context starts with people.
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