Underappreciated fact about frontier AI data: Scale AI projects $2B revenue in 2025. Add Surge, Mercor & others, and total data spend hits around $3.5B: mostly for reasoning models. But multimodal AI? Expect that to be 10x bigger. Driven by video, audio, and beyond.

Our guy @NiphermeDave's content always slaps 🗣️ check it out

Sometimes you just have "one of THOSE" weeks.... Grateful for the ORO community. For anyone that finished a tough week, keep your head up! It's just the beginning. Enjoy your Sunday

A lot of people have been asking us... what does my data power? At ORO, we help power multimodal AI models broadly. Reasoning, Voice, Video, and soon... other verticals 👀 Why Multimodal? Because it's the space that centralized players can't and won't attack. The world of video, audio, long-tail data from wearables.... only a platform like us can aggregate it. And we're the only one working with the leading labs. First-mover advantage in effect.

Another post in our text series... $META in 2024: If you tried to aggregate all the world's high-quality audio data, you'd get 3,000 hours. We need MILLIONS. Who actually knows how to aggregate this data at-scale? To filter out millions of hours to get 10,000 actually usable hours to train the next generation of models? ORO. That's it.

We'll just go out and say it... Most AI data providers aren't actually aggregating useful data. They don't have real researchers at the leading labs training on their data. Learn to separate the signal from the noise. Like we are with our audio data!

Why do serious data providers obsess over infra? 🤔 Because great AI data requires two things: 1️⃣ global-scale collection 2️⃣ autonomous quality verification #1 needs decentralization. #2 depends on powerful infra. We’re the only platform battle-tested on both—collecting private video, audio, and wearable data at scale with the best multimodal labs using it.

A few weeks ago, we shared a peek into our AI data infra. Today, it powers leading multimodal AI labs with extensible pipelines: – scalable data synthesis – model-in-the-loop annotation – granular, customizable search Before us: brittle in-house tools or slow legacy vendors. Now: infra purpose-built for AI at scale.

Most models today are trained on scraped web data... But that’s not where the next performance leaps will come from. The untapped layer? Voice notes, wearable streams, receipts, calendars, diagnostics. Structured, permissioned, and rich with real-world context.

The next LLM breakthrough won’t come from model tweaks. It’ll come from test-time compute on real data: 🔸 dynamic context 🔸 source-level attribution 🔸 live inputs from devices and logs We're creating the bridge between passive models and interactive intelligence 🤝