Tech • AI • Robotics • Game
The Huge Business of Business Takeovers
5/10TL;DR
A fast-growing market is emerging around the sale of corporate data to AI developers, with advocates arguing that internal business records could become a major hidden asset worth millions for small and mid-sized companies.
KEY POINTS
A new market for internal company data
The central thesis is that AI agents need more than public internet content to become useful for businesses. To deliver relevant outputs, they must be trained on real operational data such as emails, workflows, recruiting processes, customer exchanges and internal decision-making. That creates demand for datasets drawn from thousands or even millions of companies, not just one-off public sources.
A concrete example priced at $600,000
One striking case involved Micro One, which reportedly approached Hampton, a US-based community business, with an offer to buy anonymized internal data for $600,000. The proposal covered email exchanges, recruiting processes, podcast workflows and community discussions, while leaving the operating business intact. The case is presented as evidence that a company may be able to monetize its internal knowledge without selling the company itself.
The model extends beyond social platforms
Large deals for data licensing have already appeared in consumer internet businesses. Reddit’s agreement with OpenAI, often cited at around $60 million to $100 million, is viewed as an early marker that proprietary data has become a strategic asset. The argument now is that this logic is spreading from media and social platforms to traditional industries whose records are largely offline and fragmented.
Traditional firms may be deeply undervalued
The biggest upside may lie in so-called non-sexy businesses such as accounting firms, real estate agencies, plumbers, hair salons and other service companies. These businesses often change hands at around one to two times annual revenue, yet they may also hold years of structured operational knowledge. That creates a new valuation angle: buyers could acquire a company for its cash flow and its data asset at the same time.
Buying struggling or dead companies for their archives
A second model targets businesses that are failing or already closed. Even when operations collapse, companies can retain vast archives in CRMs, shared drives, email servers and cloud tools. Those records may still be valuable for AI training, especially if acquired cheaply and cleaned, organized and packaged for resale.
Intermediaries and aggregators could profit most
A major opportunity may sit with brokers that source, anonymize, clean and bundle data before selling it to AI labs. Rather than selling one company’s records at a time, an aggregator could combine many businesses from the same sector into a single dataset. That could support specialist funds that buy multiple firms, standardize the data and then sell industry-specific training material at scale.
Failure data could become a product
Another idea is to build datasets focused on why businesses fail. By aggregating records from thousands of bankruptcies, restructurings or near-failures, a company could create AI tools for risk audits, turnaround advice or management consulting. The same data could also be sold to developers building models for HR, restructuring, financing or founder decision support.
A succession crunch in France adds urgency
France may be especially exposed to this shift. The discussion points to roughly 370,000 businesses expected to change hands by 2030, while only about 130,000 would find a buyer at the current pace. If many of those companies cannot be sold on traditional terms, their operational archives could become an alternative source of value for acquirers.
Sectors with large hidden reservoirs of data
Several industries stand out for the depth of their records: transport and logistics, postal services, energy, real estate management, retail chains and old industrial groups. These sectors capture information on consumption, maintenance, shipping, tenant decisions, repairs and local demand patterns. Much of that data is difficult for outsiders to access, which increases its strategic value.
Limits and ethical concerns remain significant
The model depends on anonymization, legal transfer rights and clear consent, and some operators may reject transactions on ethical grounds. There is also a distinction between real operational data and synthetic data, with the former seen as much more valuable. Still, the broader expectation is that proprietary corporate data will become one of the most contested inputs in the next phase of AI competition.
CONCLUSION
The emerging trade in internal business data could reshape how companies are valued, bought and financed, especially in fragmented service sectors. If demand from AI labs keeps rising, corporate archives may become a core asset class rather than a byproduct of daily operations.
Ask a question