Polyshares

Enterprise data lab

[Nathan Mathews @rollupceo]

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

[Charles Rollet @CharlesRollet1]

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:

[Corey Ganim @coreyganim]

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)

[Ryan Lock “RL” @RyanLockAI]

https://t.co/uD5WCHrEAh