Data-native capital is coming

Author: Yoni Rechtman (Slow Ventures / 99D newsletter) URL: https://99d.substack.com/p/data-native-capital-structure One-line: Corporate data — historically expensed, buried in goodwill, and ignored on balance sheets — is now monetizable for AI training, and capital markets are on the verge of financializing it the way junk bonds restructured corporate debt.

Key claims

• Data has always been invisible on balance sheets — expensed when created, ignored during continuous operations, buried in goodwill on acquisition. AI training finally created a real buyer willing to pay real prices for what companies generate incidentally as a byproduct of doing business.
• The junk bond / LBO analogy is the thesis: a new accounting reality (data as an asset, not an expense) will drive financial engineering and new capital structures. Securitization transmuted illiquid one-off assets into liquid, legible ones; data financialization may do the same for corporate information exhaust.
• Four early financial instruments are already emerging: (1) cash advances against anticipated data sales via private credit vehicles; (2) hard assets that generate sellable data to fund acquisition of more hard assets; (3) PE firms pre-selling a target’s data before close to offset part of the purchase price; (4) data contracts large enough to replace a VC funding round entirely.
• Data creates financial AND operating leverage simultaneously — a new secondary revenue stream and a driver of new business models, in the same way advertising was for the internet era. Rechtman frames “data allocation” as the emerging counterpart to capital allocation.
• The bear case is real but the moment is still real: data is non-rivalrous, budgets may not last, and prices will be competed down as more sellers wake up. But even in that scenario, current prices are real and the window is open to those who move now.
• “Data-native companies” — analogous to internet-native or AI-native companies — are businesses built from the ground up around data as a core asset class, fully incorporating that belief into operating assumptions (build processes that generate valuable data rather than just use it).
• Once market-clearing prices are established, book values adjust: valuations and acquisition multiples will eventually reflect data as an asset on the balance sheet, creating a wave of near-term arb for early movers and restructuring deals for later ones.

Notable quotes

“There are trillions of dollars hidden in the computers. It’s expensed when created (doing the normal work of running a business), ignored on continuous operations (as it’s accumulated), and buried in goodwill (when acquired). Data has always just kind of faded away on balance sheets and in M&A pricing.”

“We now know what it’s all always been for (training models!), which means it’s now practically, not just theoretically, valuable.”

“If I’m right, this is the new junk bonds that will spur the new LBO.”

“We are at the start of a sea change in technofinance.”

“There are/will be data-native business models and capital structures around financializing this hidden asset.”

“Making the choice between keeping data internal vs. exposing it externally (and how) is akin to the choice between returning capital to shareholders and letting it compound (and how). The best CEOs will always be brilliantly creative allocators.”

How it connects

• Capital Allocation — “data allocation” as a new dimension alongside capital allocation; the best CEOs will be brilliantly creative at both
• Yoni Rechtman — author; Slow Ventures partner focused on AI’s second-order financial effects
• AI Agents — the training-budget demand that makes data monetizable
• Private Credit — cash advances against data sales are among the first financial instruments emerging from this shift
• Securitization — historical analogy: transmuting illiquid assets into liquid, legible, tradeable ones
• Financial Engineering — the thesis is that data financialization follows the same playbook as junk bonds and securitization in restructuring corporate finance