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a 50-person company’s slack and email data is selling for $300-400K right now, and the labs are calling brokers on fridays saying they don’t care what it costs. here’s everything from our conversation with data broker ryan lock (sponsored by Ahrefs): ryan sold his solar company ($35M revenue, 1,000 reps) last year and stumbled into data brokering when someone told his co-founder he could license his company data. the first offer gave him two days to accept or it exploded. his co-founder negotiated it up 4x. that gap is the whole business: the labs are desperate, business owners have no idea what they’re holding, and nobody’s advising the sell side. he launched in july, went viral once, and was doing 15 meetings a day with business owners. team of 13 now. every single lab reached out to him inbound through X. the model is simple: buy data assets from companies, package them into training sets for frontier and mid-tier labs. what they want is operational data: email, slack, drive, accounting, shopify, klaviyo. raw text, not photos or video. the part nobody understands: it’s not volume, it’s context. a customer email that gets pinged into slack that gets resolved in a ticket is a complete story a model can learn from. switch from slack to discord two years ago and lose the chain? the data’s worth a fraction. ryan’s own solar data was nearly worthless because everyone just picked up the phone. his co-founder’s remote slack-native team got the full ask. the numbers: average deal is $300-400K for a 50-100 person company, deals go up to $2-4M for the big ones. quick valuation proxies are number of software tools, years of full-context data, and headcount, because a hundred people generate idea-discussion-approval-feedback loops that five people in one office never write down. the de-identification alone costs $10-50K per terabyte, done by a third party who never lets ryan touch the raw files. and don’t try to scrub it yourself: tony becomes “employee six” everywhere consistently, or the whole set is destroyed. ryan’s analogy for margins: raw data is oil at $100 a barrel, the labs turn it into jet fuel at $2,000, and the broker takes the risk that the barrel has no engine inside. the spirit airlines bankruptcy sold its data to google for $10M, which he thinks is low for the largest public deal. the data companies are all talked about as very profitable, but the real edge is procurement on demand: he forecasts what the labs will want next and ranks his pipeline by acquisition priority. current requests: circuit board CAD files, architecture firms, heavy construction, and video game 3D development. the gotchas before you sell: never sign a perpetual exclusivity. perpetual use is standard (nobody can track files down the chain), but exclusivity should be termed, usually two years, or you’re locked out of a future revenue stream forever. and if you’re selling your business in the next year or two, a $500K data deal can become a due diligence landmine on a $20M exit. tom’s referrals keep saying exactly that. micro one pays $50-75K referral fees because they’re playing a different game. ryan pays 6% and tells brokers to charge the seller for running the process instead. where it goes next: ryan thinks the buyer pool expands past the frontiers to enterprises training their own models. a 20-person HVAC company only knows what it knows, but add 150 years of industry data and you’ve got a vertical model, the harvey-for-law playbook in every trade. the video side is wilder: labs pay $10-20 an hour for people to wear headcams doing manual work, and there are state-sponsored warehouses in china and every low-cost country where workers rotate through sewing and dishwashing stations for robot training. jacky’s fraud prediction: 500 fake chinese companies with active linkedin pages. ryan’s answer: if the simulation is that good, the data’s probably valuable anyway. bonus: jacky’s own slack community, 500 people and 20K messages a month, has already gotten offers. “no amount of money, well, that’s a lie, I’d take my pants off for seven figures.” the over/under for ryan’s next 12 months was set at $300M gross, and micro one just passed a billion annualized. tom asked to become a master affiliate and jacky immediately called it master and slave. tony’s response: “the white guy’s still on top. we went back a hundred years.” the industry is a gold rush and the shovels are already being sold. NGMI with @itstonyyu and @tomwang24, guest @RyanLockAI. watch/listen ↓

Excited to announce I’m building @Polyshares with @RyanLockAI Polyshares is an enterprise data lab focused on operational and physical AI training data We’ve quickly grown to an 8 figure run rate by being the best procurement partner for AI labs In the past week alone, we’ve onboarded 250+ companies, 200+ referral partners, and 7 new team members. If you’re interested in data, let’s jam

Companies are sitting on vast troves of internal data without realizing its value. Data brokers are now hunting for these businesses, and it's getting competitive. @RyanLockAI compares it to the Texas oil boom of the early 1900s:

This guy did $1,000,000 in 30 days as an AI data broker. His model: frontier labs have used all the data on the internet and need private company data to keep improving. His firm finds businesses with the right data then de-identifies it, packages it, and sells it to the labs. @RyanLockAI runs one of the hottest startups in this space (Polyshares) and walked me through his entire business model on the pod. Here's what I learned: 1. Making an intro to a data buyer is only step one. Technical due diligence on how the business actually operates is where the value gets built. 2. Labs buy the full workflow. Client email to internal Slack debate to resolution, including the fixes the team rejected. 3. He targets companies with 50+ employees, 3+ years on the same software, US-based, English. Below 50 employees = fixed legal and diligence costs kill the deal. 4. Remote-first white collar wins. If the work happens on the phone, the lab just sees "ticket created, ticket closed." 5. Depth of history beats raw volume. 20 years in business means nothing if they switched software 3 years ago. 6. Never let sellers de-identify their own data. Names become "employee 632" so relationships survive. DIY versions wreck the value. 7. Use a third party to de-identify. They hold the raw files for about a week, then delete them. That kills the biggest objection. 8. Never sign a perpetual license. 24 months of exclusivity is the typical buyer ask. 9. Time the exclusivity to the exit. Selling in 2 years? Match the term so the new owner inherits a dataset they can license again. 10. Demand rotates. A few weeks ago labs wanted heavy construction. Now it's video game companies. His 2 key takeaways: 1. Act like a sell-side broker, not a lead seller. Organize and present the owner's data like an M&A deal and you can earn an advisory fee on top of the finder's fee. 2. The buyer pool could explode. If enterprises start training their own models, buyers go from maybe 10 labs to hundreds of thousands. Ryan is doing this at a level most of the people hyping it on X are not, and we had a blast going deep on it. Go follow @RyanLockAI (Also available on the Build With AI podcast wherever you get your pods)