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.

This is where ORO comes in. We’re building a system that unlocks the next frontier of AI by turning private data from a liability into a usable, protected, and valuable asset. Not by asking for trust. But by designing a protocol where privacy and scarcity are enforced at the compute layer.

Privacy Built into the Compute Layer

At ORO, privacy isn’t a UI toggle. It’s embedded in the way data flows through the system — from the moment it’s collected to the moment it’s used for training or inference.

Here’s how it works:

• Your data is encrypted at rest and in transit, but more importantly, it’s never exposed during computation. Instead, we use Trusted Execution Environments (TEEs) to ensure that raw data is processed only within secure, hardware-isolated environments where no human, platform, or node operator can see it.

• These enclaves allow AI models to run over your data and return results without ever leaking the underlying information. Computation happens inside the vault, not on an open server.

• In distributed cases, we also support Multi-Party Computation (MPC), where data can be split across nodes and jointly computed without ever being reconstructed.

• Through verifiable compute and cryptographic attestations, every operation is logged, tamper-resistant, and auditable. You don’t have to trust that it was handled properly - you can verify it.

This isn't privacy by legal agreement. It's privacy by architecture that’s enforced cryptographically, not contractually.

You Don’t Have to Trust Us — You Can Verify

Most platforms treat privacy as a marketing claim. At ORO, it’s a verifiable property of the system.

When your data is processed, it happens inside secure hardware environments that generate cryptographic attestations. Cryptographic attestations are tamper-proof receipts that prove your data was handled exactly as specified, with no leaks or side access. These attestations are publicly auditable and tied to the specific enclave configuration, so you don’t have to take anyone’s word for it - not even ours.

Even if a node is compromised, the data inside a TEE stays sealed. If a node tries to access raw data or skip fair payment, the operation fails by design. There’s no trusted middleman and no centralized authority, just secure compute and guaranteed enforcement.

That’s the core promise of ORO: privacy that doesn’t depend on trust, but on math, hardware, and protocol.

Privacy Isn’t Just Protection. It’s What Makes Data Valuable

When data leaks, it becomes worthless. That’s the paradox at the heart of today’s AI pipelines: the more valuable the data, the less incentive there is to contribute it because once it’s exposed, it loses all scarcity, all control, and all future upside.

ORO flips that dynamic.

By ensuring that private data remains protected and non-replicable, even during computation, we preserve its scarcity which means we also preserve its economic value. It can’t be scraped, copied, or resold. It retains integrity and provenance.

This creates a new kind of data market: one where contributors don’t give data away, but contribute it securely and repeatedly while getting compensated fairly each time. And because ORO enforces these rules at the protocol level, no platform or buyer can circumvent them.

In this system, privacy isn’t just protection - it’s what enables private data to become a real, recurring asset in the AI economy.

Built for an Open, Intelligent Future

We’re entering a world where AI systems will mediate everything: how we search, learn, create, diagnose, and collaborate. But without private, high-quality data, these systems will remain narrow, brittle, and biased.

ORO is built for that next frontier. Not just to protect individual privacy, but to unlock a new kind of intelligence infrastructure where everyday people can contribute to, shape, and benefit from the development of AI.

That means building a system where users don’t have to trade control for utility. Where privacy is enforced by default. Where data remains rare, useful, and valuable across time - not just exploited once and discarded.

We believe the most powerful AI systems of the future won’t be built on scraped web data or surveillance.

They’ll be built on a new social contract. One that starts with cryptographic guarantees, economic alignment, and privacy by design.

About ORO

ORO is a decentralized intelligence platform unlocking high-quality private data for frontier AI. Through a consumer app and a privacy-preserving protocol, ORO enables individuals to contribute existing and net-new data, while ensuring that raw inputs remain private and contributors are fairly rewarded when their data powers AI models.

Backed by a16z crypto, Delphi Ventures, and NEAR Foundation, we’re building toward a future where private data fuels AI progress and everyone shares in the upside.

Our team brings deep expertise in AI, cryptography, and distributed systems, with backgrounds at Stanford AI Lab, Scale AI, Replit, Salesforce, Google DeepMind, Amazon AI, and Binance. Together, we’ve built AI products used by millions, designed large-scale data infrastructure, and advanced privacy-preserving model training.