Introducing ORO — a new way to contribute private data to frontier AI and get rewarded for it, with privacy built in. If ChatGPT is how you use AI, ORO is how you build it.

You might be wondering why we've been more quiet in the past month or so.... Long story short 👀 Season 2 is going to be crazy. Renewed purpose, refined focus, more insight into our core customers. Stay tuned for new updates 🫡

Everyone's talking about RL environments. And you're too scared to ask at this point, "what is an RL environment"? Think of it like a world where an agent lives where the current state, rewards, and goal are defined. New way to scale intelligence. Hardest part of defining RL

Two weeks ago, OpenAI released GPT-5. Much of the buzz is about its coding model, now competing with Anthropic’s Claude Code. Its new skills though? Gained via training on real-world coding data. What if that same data was open to any researcher building next-gen models? That’s

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

Hot take: current staffing platforms won’t survive the next gen of AI data. AI data gravity is shifting to RL + RFT for enterprises—aligning models + agents to new, high-value use cases. The playbook: own uniquely valuable data at scale → build RL environments with enterprise

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

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

Thanks to YOU, our community, we are one of the leading platforms aggregating data from wearables, social platforms, and more. We will make sure to reward ALL users who have provided high-quality, useful data that model providers want. It's ALL about quality, not quantity.

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!

The vast majority of data providers, even modern and competitive ones, can't actually define what "data quality" is. What "high-fidelity data" looks like specifically for their field — video, audio, text, etc To do that, you'd actually have to form your own thesis. Most won't

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

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.

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 🤝

What’s the biggest reason people don’t contribute data to AI?

AI infrastructure is becoming its own industrial revolution. 🔸 Meta is building data centers the size of Manhattan 🔸 Google’s upgrading hydropower plants 🔸 Microsoft is restarting nuclear reactors To power smarter systems, you need smarter infrastructure.

AI agents are already embedded in company workflows like handling sales, support tickets, even underwriting. But most teams still don’t trust them to run without human supervision. Why? The data layer isn’t ready. Infrastructure’s fragile. Privacy controls are missing.