Skip to content
AI Data Payout

Guides

Why are AI companies buying company data?

AI models learned from the open web, and the open web does not show how professionals talk to each other, solve problems inside a business, or make judgment calls at work. That knowledge sits in private business records. So AI firms now pay large sums to license it, and businesses that own those records are getting paid.1,2,3,4

By Lead analyst (to be named) · reviewed by Reviewing editor (to be named) · published 14 Oct 2026 · updated 18 Oct 2026

What the open web taught AI, and what it left out

Today's AI models were trained mostly on public writing: websites, books, forums and news. That pile is big but it is running out. Epoch AI, a research group, estimates about 300 trillion tokens of usable public text exist, and that training could use all of it between 2026 and 2032. A token is roughly a word.1

The bigger problem is what the web never showed. Public writing is the finished product: the blog post, the press release, the polished answer. It almost never shows the work behind it. How a dispatcher talks a driver through a missed pickup. How an estimator argues a price with a customer. How a manager decides whether to refund a job that went wrong.3

That is exactly what AI now needs. Surge AI, which builds training for AI agents, says it looks for records covering disagreements and tricky judgment calls. One company's chat history with real variety and depth can be worth more than another company's chat, email and calendars put together.3,5

That knowledge is locked inside businesses, so AI firms pay for it

How professionals really work is written down in one place: inside companies. In email, chat, tickets, job notes and SOPs. None of it is public, and nobody can scrape it. If an AI company wants it, it has to pay the business that owns it.6,7

And it is paying. micro1 has committed $1B over 12 months to buying company data. In the Spirit Airlines bankruptcy, Google bid $10M for the airline's internal emails and messages; the court has not ruled yet. AI labs had already shown they would pay for text they could not take: Reddit disclosed $203.0M in data licensing contracts.4,8,9

Made-up data is not a substitute. MIT Technology Review reported that AI labs hit the limits of public and synthetic data, and that fake records carry tell-tale errors, such as dates that do not exist. Real business records do not.2

What AI firms build with your records

The goal is AI agents: software that does a whole job, like answering a supplier, booking a service call or closing a ticket. Agents learn in training environments, practice worlds that copy real software. The agent tries a task and is rewarded when it gets it right.10

Labs want many more of these practice worlds. Anthropic's leaders reportedly discussed spending more than $1 billion on them in a year. micro1 says it turns company data into environments that mirror real business workflows. Your shop's ordinary paperwork is the raw material.11,12

A modern-day gold rush, and who is cashing in

When buyers with this much money need something only businesses own, a gold rush follows. At least 18 firms, most of them brokers, started advertising for business data between September and mid-October 2026. SimpleClosure has done over 150 data deals for closed startups alone.13,14

Ordinary businesses are already being paid, by the buyers' own accounts. Polyshares lists a $110,000 license for a marketing agency. Pewter says a 60-person accounting firm was paid $350,000. Neither names the company, and both are their own claims, but they show who these firms are buying from: businesses like yours.15,16

AI Data Payout exists to help you get your share. We rank the firms that license business data on their published facts, never on what they pay us, and our quiz matches your business to the ones that fit.

Pros and cons

Pros

  • New money from records you already paid to store: published ranges mostly run $100K to $2M, the buyers' own claims.17,18,19
  • Most buyers charge the owner no fee, by their own published terms.20,21,22
  • Buyers want everyday paperwork, not trade secrets: SOPs, tickets, project histories.6
  • Demand is backed by real budgets, such as micro1's $1B commitment.4

Cons

  • Your records mention real people. One study found 99.98% of Americans could be re-identified from 15 traits.23
  • You often cannot see which AI lab ends up with the data.24
  • An attorney quoted by MIT Technology Review called this kind of data "legally radioactive".25
  • Some software terms limit it: Slack's API terms restrict bulk export and use for AI training.26

What it means for a business like yours

Think about what your shop writes down every day. Dispatch notes, quotes, change orders, supplier emails, service tickets. To you it is paperwork. To an AI firm it is the one thing the open web never had: a record of how a real business gets a job done. Nyne's ads, for example, name old dispatch logs and load histories.27,28

Size still matters. Most buyers want 20-30 or more employees and a few years of records, and a few state no minimum.29,30,31,32,33

Should your business get in now?

Businesses are already licensing their records: micro1 has committed $1B over 12 months to buying company data, and at least 18 firms started advertising for it between September and mid-October 2026.4,13 Our view, not a promise: the money will not be around forever, because prices tend to come down as more businesses sell.4,11 Take the quiz to see which firms fit your business.

Plus a free Seller's Guide when you finish.

Take the quizWho the brokerages are

We feature companies that pay us a referral fee. Our scores and reviews don't change for it. How we earn.

FAQ

Questions, answered with sources

  • The open web is close to used up, and it never showed how professionals actually work. Business records do, so AI firms pay to license them to train AI agents.1,2,10

  • Emails show the back-and-forth of real work, including disagreements and judgment calls, which public web pages rarely show. Surge AI says that variety is what it looks for.3

  • Mostly they build training environments, practice worlds where AI agents learn real tasks and are graded. micro1 and Surge AI both describe this use.12,5

  • Epoch AI estimates about 300 trillion tokens of usable public text, which training could use up between 2026 and 2032 if current trends hold.1

  • AI companies do use made-up (synthetic) data, but MIT Technology Review reports labs hit its limits. Synthetic data has errors, such as dates that do not exist, that real archives avoid.2

  • Published ranges for an operating company mostly run $100K to $2M, by micro1's, Ventra's and Cognor Labs' own claims. Closed startups typically get $10K-$100K.17,18,19,14

  • We are an independent directory. We rank the firms that license business data on published facts, and our quiz matches a business to the firms we work with whose criteria fit. We feature the firms that pay us a referral fee; a fee never changes a score or a review.

  • Every business is different, so this one is best talked through with the buyer you're working with. Their team goes over it with you before anything is signed.

Before you sign

Questions to talk through with your buyer

Every business is different, so these are best talked through with the buyer you're working with. Their team goes over them with you before anything is signed.

Questions for this kind of business

  • How do CCPA, GDPR, UK GDPR and state privacy laws apply, and is de-identified data still personal information?
  • Do customer, supplier or platform contracts, NDAs or API terms restrict this?

Questions for any buyer

  • Is the license exclusive, and for how long?
  • Can you sublicense the data, and to whom?
  • Which AI labs will receive it, and will you tell us?
  • How long do you keep it, and how is it deleted?
  • What is your liability cap, and who indemnifies whom?
  • Will the contract ban re-identification of people in the data?
  • Who removes personal data, by what method, and do we approve it first?

What changed since we first published

  • 14 Oct 2026 Rewritten as our flagship explainer, with the argument for why the open web was not enough.

How this page was made: drafted with AI help from the sources below, each checked; a buyer's claims about itself are its own. Updated October 2026. How we rank.

Sources (33)

  1. 1.Epoch AI, published 6 Jun 2024. Accessed 14 Oct 2026.
  2. 2.MIT Technology Review, published 13 Oct 2026. Accessed 14 Oct 2026.
  3. 3.MIT Technology Review, published 13 Oct 2026. Accessed 14 Oct 2026.
  4. 4.The Next Web, published 9 Oct 2026. Accessed 14 Oct 2026.
  5. 5.MIT Technology Review, published 13 Oct 2026. Accessed 14 Oct 2026.
  6. 6.micro1 Enterprise Data Partnership page. Accessed 14 Oct 2026. Company's own claim.
  7. 7.Mercor Data page. Accessed 14 Oct 2026. Company's own claim.
  8. 8.Bloomberg Law, published 17 Aug 2026. Accessed 14 Oct 2026.
  9. 9.TechCrunch (Reddit S-1), published 22 Feb 2024. Accessed 14 Oct 2026.
  10. 10.TechCrunch, published 16 Sep 2025. Accessed 14 Oct 2026.
  11. 11.TechCrunch, published 16 Sep 2025. Accessed 14 Oct 2026.
  12. 12.The Next Web, published 9 Oct 2026. Accessed 14 Oct 2026.
  13. 13.Meta Ad Library (our review). Accessed 14 Oct 2026.
  14. 14.Forbes, published 16 Apr 2026. Accessed 14 Oct 2026.
  15. 15.Polyshares track record page. Accessed 14 Oct 2026. Company's own claim.
  16. 16.Pewter website. Accessed 14 Oct 2026. Company's own claim.
  17. 17.micro1 company referral page. Accessed 14 Oct 2026. Company's own claim.
  18. 18.Ventra company data page. Accessed 14 Oct 2026. Company's own claim.
  19. 19.Cognor Labs partners page. Accessed 14 Oct 2026. Company's own claim.
  20. 20.Mercor Data page. Accessed 14 Oct 2026. Company's own claim.
  21. 21.Polyshares website. Accessed 14 Oct 2026. Company's own claim.
  22. 22.Ventra terms of service, published 30 Sep 2026. Accessed 14 Oct 2026. Company's own claim.
  23. 23.Rocher, Hendrickx and de Montjoye, Nature Communications, published 23 Jul 2019. Accessed 14 Oct 2026.
  24. 24.MIT Technology Review, published 13 Oct 2026. Accessed 14 Oct 2026.
  25. 25.MIT Technology Review, published 13 Oct 2026. Accessed 14 Oct 2026.
  26. 26.Hunton Andrews Kurth, published 5 Aug 2025. Accessed 14 Oct 2026.
  27. 27.Meta Ad Library, Nyne.ai, published 4 Oct 2026. Accessed 14 Oct 2026. Company's own claim.
  28. 28.micro1 Enterprise Data Partnership page. Accessed 14 Oct 2026. Company's own claim.
  29. 29.Cognor Labs partners page. Accessed 14 Oct 2026. Company's own claim.
  30. 30.Nyne sell-company-data page. Accessed 14 Oct 2026. Company's own claim.
  31. 31.micro1 company referral page. Accessed 14 Oct 2026. Company's own claim.
  32. 32.Return My Time Sell My Data page. Accessed 14 Oct 2026. Company's own claim.
  33. 33.Troveo business data page. Accessed 14 Oct 2026. Company's own claim.