For deals requiring human-like analysis and negotiation but can’t risk information leakage
HealthcareBioOther
Concept
Some high-value markets either hinge on 1) the value of information or 2) on the thoughtful negotiation over terms that can’t be standardized and/or publicly disclosed. Both require LLMs + privacy tech, each of which have only emerged in the last couple years.
Longer Description
Both marketplace categories require the combination of human-like intelligence in analysis & negotiation and privacy during negotiation.
Kenneth Arrow won the Nobel in part for identifying that data is inherently difficult to trade because buyers can't assess its value without first examining it. But it’s very examination means they’ve received all of its value without paying for it.
Even if a market does develop around the data, sellers risk having their data exploited without compensation with limited recourse since detecting misuse remains notoriously difficult, while buyers face adverse selection.
A good concrete example of 2) is spelled out in this article on how much of a joke the RFQ process is for biopharma to select a CRO to operate their clinical trial (one of the most important decisions the company will make). The rest of the article spells out in detail.
Several academic papers have emerged over the last couple years attempting to solve these problems by employing LLMs to value the data in a privacy-preserving block box. Then, the system completes the transaction only if the value exceeds the agreed upon threshold.
A team that builds a company around such marketplaces would require high caliber privacy-preserving guarantees that no information leakage occurs and an understanding of that particular industry’s culture.
Some examples of areas this premise could be useful for beyond clinical trial operations include:
Pharma Licensing & IP Deals: Drug candidates at preclinical or early clinical stages are almost impossible to value without seeing the underlying data (mechanism of action specifics, animal model results, early PK/PD data). BD teams currently solve the trust issue with CDAs and staged data rooms, but it's slow, trust-dependent, and riddled with adverse selection
Robotics data foundry: the Scale AI for robotics plays currently can’tsimplycollect and sell data because it’s currently not well understood what data is useful. They must go all the way to training models themselves to demonstrate its value.
Proprietary Trading Strategies & Alternative Data: alternative data vendors can't demonstrate alpha without revealing the signal, and once revealed, the buyer has received the value. Current solutions are crude — backtests on limited subsets, trial periods with delayed data. The alternative data market is already estimated at $7-10B and growing rapidly, but liquidity is constrained precisely because of this trust problem.
M&A Sourcing: Strategic acquirers looking for targets or private companies exploring a sale both face enormous confidentiality constraints. The acquirer can't reveal what they're looking for without telegraphing strategy to competitors. The target can't reveal they're for sale without destabilizing employees, customers, and suppliers. Investment bankers currently serve as trusted intermediaries, but they're expensive (2-5% of deal value), slow, and have misaligned incentives.
Reinsurance & Specialty Insurance Placement: Reinsurance negotiations are almost entirely bespoke. A cedent (primary insurer) needs to share loss history, portfolio composition, and catastrophe exposure data to get quotes — but that data is deeply proprietary and reveals their underwriting strategy. Reinsurers, in turn, don't want to reveal their appetite, pricing models, or capacity constraints. The current process runs through brokers (Aon, Guy Carpenter, Gallagher Re) and involves months of back-and-forth over non-standard terms
