The most valuable AI assets are not models, but proprietary data from years of solving domain-specific problems. This 'scar tissue'—like knowledge from undocumented APIs or complex integrations—is painful to acquire and impossible for competitors to replicate quickly, creating a durable competitive moat.

A new market has emerged where defunct startups sell their entire operational histories—including codebases, internal communications, and go-to-market data—to AI labs and data brokers. This creates a new form of salvage value, turning years of failed effort into a valuable corpus for training next-generation models.

Beloved but financially underperforming brands like Bumble, Snap, and Pinterest are now key M&A targets. The likely acquirers are not traditional tech or PE firms, but AI companies like OpenAI seeking to rapidly acquire large user bases and proprietary data sets to train their models and scale distribution.

A powerful go-to-market strategy is for an AI company to buy a legacy business (e.g., a debt collector) with existing clients but declining revenue. This allows the startup to bypass the difficult early sales process, immediately deploy and refine its AI, and use the acquired firm's client roster as a launchpad.

In an era of commoditized LLMs, the real competitive advantage lies in unique, proprietary datasets. These datasets, when combined with AI models, create a defensible moat that software alone cannot replicate. This is why major tech companies are aggressively acquiring data-rich companies.

With public data exhausted, AI companies are seeking proprietary datasets. After being rejected by established firms wary of sharing their 'crown jewels,' these labs are now acquiring the codebases of failed startups for tens of thousands of dollars as a novel source of high-quality training data.

To build a data advantage against giants like Meta, AppLovin bought gaming studios not to enter the gaming business, but to create a closed loop for gathering proprietary data. This data was used to train its recommendation engine, and the studios were later divested.

The company's initial app to find friends' games "stunk," but its core recommendation engine had a very high response rate. This technology was salvaged and became the foundation for the entire AppLovin advertising business, proving value can be found in failed projects.

In the current M&A landscape, data-centric startups are more valuable than application-layer companies. Acquirers, particularly large tech firms, need proprietary data sets to train, run, and customize their AI models. This demand makes companies with unique data assets highly attractive takeover targets, with some seeing a tenfold increase in inquiries.