Data beyond brokerage

1. Key Themes

Theme: The AI data boom is a historic cash-flow opportunity, but probably not a durable business

Unlimited buyer budgets are creating overnight fortunes

Model labs, AI app companies, and robotics labs are the demand engine, and spending power is currently outrunning supply.

"Fortunes are being made overnight feeding the voracious, seemingly endless demand and budgets for data from the model labs, well funded AI app companies, and robotics labs. The data business is probably the single most lucrative opportunity in the world right now."

The demand is likely temporary, and the specific demand is likely to churn

Rechtman sees risk both at the macro level (budgets get cut) and the micro level (buyers' needs shift).

"That might be a temporary condition, and the eventual cost pressures from being public make it more likely to be that way. Or if it does last, it seems likely that 'what they want' will change/turn over often enough such that even if you have 'the right tokens' today you're unlikely to have them tomorrow."

Implication: treat it as a short-lived cash trade or a funding source

"So if you're going to chase this money, you probably need to plan on doing that either as a short lived, high FCF, low TV trade (an S-Corp) or as a way to fund something else."

Theme: Data sales are a new form of capital, not a revenue line

Data sales as a fourth funding source alongside debt, equity, and cash flow

"Businesses can fund themselves through debt, equity, or cash flows. Now data sales are this new source of funding to mix in (FCF for cash-burning companies). Think of data sales as a new source of capital rather than a potential revenue stream."

The accounting treatment is broken, so data assets are likely undervalued

Selling data monetizes an asset that was never on the balance sheet.

"Right now, selling data basically creates money from nothing. You sell an asset that was never accounted for - manna from heaven." "Companies are very likely trading at valuations that don't account for the values of their data."

The decision framework: "are you doing X to sell data, or are you selling data to fund X?"

"So: are you doing X to sell data, or are you selling data to fund X? In the short term they may look the same."

Theme: Data as "exhaust" creates new business models, and possibly a new ad-like model

Four places to look for data-as-exhaust businesses

"Basically any company with broad consumer reach should at least be considering data sales as a way to fund expansion... This is especially true/tantalizing in games and entertainment, where you can generate world model data." "Same goes for really any company with physical operations; strap a camera on and start getting some ego data!" "Probably the most interesting set of companies are those who can actually get PAID to acquire data. AI transformation companies can likely be great partners to their customers (who already pay them)..."

Data could become the next advertising-style subsidy model

"Anyone who wants to use data to offer price cuts or even pay customers to use services. This is likely how data emerges as a new business model akin to advertising."

Durable value will come from new structures, not Web 2.0/SaaS playbooks

"To the extent data sales themselves are a lasting business with terminal value, it'll be through new capital structures and business models rather than rerunning web 2.0 and SaaS models."

2. Contrarian Perspectives

Pure data brokerage is a bad business despite being the hottest market

Consensus sees a gold rush; Rechtman sees a trap unless you have a compounding advantage. The risk is being "bent" to a single customer's roadmap.

"If there's not really natural or organic ways for you to get or generate data... then it costs you focus and resource allocation, and ultimately bends you to the roadmap of a customer who might go away tomorrow." "I'm relatively less bullish on straight up brokerage businesses that don't have a theory on some compounding advantage to create durable/terminal value."

Brokerages may lack terminal value but still be great exits

The cash flow and strategic value make them easy to fund and sell, so the play is to harvest.

"I don't think there's a ton of TV in data brokerage, BUT they are clearly so cash flow generative and so strategic that they get funded well and acquired easily. Someone will buy Mercor for a lot of money, both for its AI talent, its data, to impair competitors, etc."

AI app companies generally should NOT sell their data

Selling exhaust can mean selling your moat, because data is non-rival: once sold, the buyer keeps a copy forever.

"Data is non-rival. Once you sell it the lab has a copy forever, so when does selling your exhaust mean selling your moat? It's highly contextual." "For AI apps businesses, it probably does not make sense. Harvey should never sell data about legal workflows to the models. That data is its prime differentiator and eventual source of margin."

3. Companies Identified

Mercor

• Description: AI talent and data company serving model labs.
• Why mentioned: Example of a cash-generative, strategic data business that will be an easy acquisition.
• Quote: "Someone will buy Mercor for a lot of money, both for its AI talent, its data, to impair competitors, etc."

Harvey

• Description: AI legal software company.
• Why mentioned: Case study in who should not sell data, since legal workflow data is its core differentiator.
• Quote: "Harvey should never sell data about legal workflows to the models. That data is its prime differentiator and eventual source of margin."

Slow Ventures

• Description: Venture firm where the author is a partner (≈$325M fund, pre-seed/seed focus).
• Why mentioned: The author's firm, which is actively backing companies in this space.
• Quote: "We have a number of companies working on this in different areas, using or contemplating data sales to fund go to market for other products/services/opportunities across consumer apps, agents, and asset heavy businesses."

Context Acquisition Company

• Description: A concept/thesis previously written about by 99D, with more coverage promised.
• Why mentioned: Referenced as related reading on companies funding themselves via data.
• Quote: "See also, the Context Acquisition Company (more to come soon)."

4. People Identified

Yoni Rechtman

• Description: Author; partner at Slow Ventures leading pre-seed and seed rounds.
• Why mentioned: Author and host of the data brokers dinner.
• Quote: "I'm a generalist investor looking for weird takes on important stories: N-of-1 companies taking non-obvious approaches to markets that matter."

Leeor Mushin

• Description: Co-host of the NY dinner for data brokers and buyers.
• Why mentioned: Partnering with the author on a convening of the data market.
• Quote: "Next month Leeor Mushin and I are hosting a dinner in NY for data brokers and buyers."

Shelby

• Description: Writer the author cites on the "selling data to fund X" idea (surname not given).
• Why mentioned: Recommended further reading on the framework.
• Quote: "Shelby wrote really well about thi[s]."

5. Operating Insights

Pass the "X vs. data" test before taking data revenue

Decide whether data is exhaust from a primary business or the product itself, because the second path ties you to customers' shifting needs.

"If data sales are an exhaust from some primary thing, it makes it much more economically viable to climb that hill. If you get the model-labs-monopoly-money, use it to fund something rather than as the goal unto itself."

Pitch data sales as revenue, but plan for them as capital

Present it the way the market wants to hear it, while internally treating it as non-recurring funding.

"For the moment, founders should obviously talk/think about data sales as revenue because it's what the market wants to hear."

Sell historical data, protect production data, and extract cash early

Distinguish what you can safely sell, and if you run a pure brokerage, plan your exit.

"There's also a difference between historical data and production data. If you operate a business/asset, you could sell the data once while continuing to accumulate / create new data through usage." "The alternative is to be in a high cash flow trade of TBD duration, in which case you should keep an eye on the exits. Look to get cash out of those businesses either through dividends or a strategic acquisition."

6. Overlooked Insights

Non-core corporate data is a hidden asset

Companies may sit on valuable data they don't consider sensitive, which creates an opening for services firms that help monetize it, particularly screen-recording and computer-use data.

"does an industrial co really regard its accounting practices as trade secret?... We've met a lot of companies working on this within screen recording and computer use data specifically."

Public-market cost pressure could be what ends the data spending boom

The author flags that buyers becoming public companies is a specific catalyst for budget discipline.

"the eventual cost pressures from being public make it more likely to be that way."