If you searched "how to sell data to AI companies," you're ahead of most businesses. The data your company generates every day has become an asset someone will pay for. AI labs spent the last two years signing licensing deals that run from tens of thousands of dollars to a quarter billion, and the buying has moved well past famous publishers. Creator video catalogs, call recordings, gameplay footage, support tickets, even a defunct airline's internal archive have all drawn real bids. Read on to find out who's buying, what sells, what it pays, and how to get started without a data team.
Who buys data, and why now
The buyers are AI developers (the frontier labs, the model companies behind voice and video products, robotics and physical AI startups, and enterprises training their own systems). The public internet, the raw material for the last generation of models, is scraped out, and courts have found liability in unlicensed acquisition. So labs now pay for what they can't scrape (authentic, rights-cleared, real-world data). Our guide to who buys company data for AI training names the buyers from the public record and covers how each one is reached.
Licensed data went from an afterthought to a standard procurement channel, which is why sellers have leverage they didn't have three years ago. Our guide to where AI labs source training data covers the full sourcing landscape.
What sells
| What sells | Why buyers pay for it |
|---|---|
| Real-world video | World models and physical AI need footage of real life, not polished stock |
| Voice and audio | Voice agents need natural conversation across accents, settings, and noise |
| Gameplay footage | Interactive environments at scale, the raw material for world models |
| Robotics and first-person video | Task demonstrations teach machines how physical work gets done |
| Business and operational data | Agentic AI learns from real workflows, decisions, and their outcomes |
| Content catalogs | Text, images, and music with clean rights fill modality gaps labs can't scrape |
Most companies overlook the newest category on that list, business and operational data (support tickets, CRM histories, project records, transaction logs). To you it looks like exhaust. To a lab training agentic AI it's the record of how real work gets done, with the outcomes attached, and buyers are paying for it now. The same goes for engineering history. Private repositories with their tickets and reviews are now their own market, and our guide to how to sell source code to AI companies covers it. If you run a software company, our guide to how tech startups and SaaS companies can license their data to AI walks through that stack system by system.
What data sells for
There's no public price list, but the disclosed deals sketch the range. News Corp's catalog went to OpenAI for a reported 250 million dollars plus over five years, Google pays Reddit around 60 million dollars a year, Shutterstock earned 104 million dollars from AI licensing in a single year, and Spirit Airlines' operational archive drew a 10 million dollar bid from Google in bankruptcy court. You're probably not News Corp, and marketplace licensing prices well below headline deals, but the same factors move price at every scale (scarcity, exclusivity, clean rights, and training readiness). Our full breakdown of what AI training data costs walks through those factors from the buyer's side. As a seller, they're your value levers in reverse.
Selling usually means licensing
In almost every case you don't sell your data, you license it. You keep ownership and grant a defined right to use it, for defined purposes, for a defined period, exclusively or not. A non-exclusive license can be licensed more than once, so the first sale is the floor, an exclusive one commands a premium, and terms around permitted uses and model types matter as much as the headline number. Our guide to exclusive vs non-exclusive data licenses covers which of the two to choose and why the same data can earn more than once. Our guide to how AI data licensing deals work breaks down the structures and terms themselves. And if the bigger question is whether licensing makes sense as a revenue line at all, our guide to data licensing as a business model covers why companies do it and when they shouldn't.
It also means privacy and rights work happen before anything ships. Names and personal details get removed, and anything your customers or contracts don't permit stays out of scope. Our owner's guide to licensing company data for AI covers the step-by-step process (data inventory, rights review, deal structure).
How much is your data worth?
It depends, and anyone quoting a number without seeing what you hold is guessing. Value comes from scale and history (years of accumulated records beat a fresh export), uniqueness (data that exists nowhere else), context (records that connect across systems tell a fuller story), visible outcomes (workflows where you can see what happened next), and rights cleanliness. The quickest way to find out is to take Troveo's free, five-minute data value assessment (ten questions). It scores the AI-training value of what your company holds, and you don't need anyone technical to fill it in. Our guide to how much your company's data is worth to AI labs covers the public price points and each factor in more detail. Our guide to how long it takes to license your company's data covers the timeline from signed deal to first payout.
Where Troveo fits
Licensing on your own means finding buyers, negotiating terms, and handling packaging and privacy yourself, which is why most owners go through a marketplace. Troveo has paid out more than 20 million dollars to over 7,000 rights holders licensing video, audio, gaming, robotics, and now business data to AI buyers. You keep ownership and set the terms, you decide what's excluded before anything ships, your company is never named publicly, and you get paid on every sale, not just the first. We remove names and personal details to a documented standard before any buyer sees anything. Start with the data value assessment to see what you have, or talk to us directly.