Critical knowledge on how to run clinical trials is not formalized in textbooks or courses but is passed down through a slow apprenticeship model. This limits the spread of best practices and forces even highly educated scientists to "fly blind" when entering the industry, perpetuating inefficiencies.

When a promising ALS drug failed Phase 2 trials, the company shut down. The drug's original founder, Dr. Ari Azhir, still believed in the science, repurchased the asset and all its data, and ultimately uncovered its true potential, leading to a new FDA application.

When a billion-dollar drug trial fails, society learns nothing from the operational process. The detailed documentation of regulatory interactions, manufacturing, and trial design—the "lab notes" of clinical development—is locked away as a trade secret and effectively destroyed, preventing collective industry learning.

Cuban identifies a massive, overlooked opportunity: acquiring the intellectual property (patents, data, designs) from millions of defunct businesses. This "dead IP" could be aggregated and sold at a high premium to foundational model companies desperate for unique training data.

While staying private can offer strategic advantages, particularly for future M&A, the biotech industry lacks a mature private growth capital market. Companies needing hundreds of millions for late-stage trials have no choice but to go public, unlike their tech counterparts.

Startups with legal claims as assets can sell portions of their cases to litigation finance firms. This provides immediate, non-dilutive capital to fund operations, de-risking the business model while waiting for lengthy legal proceedings to conclude.

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 unique dataset without massive cost, target the aggregated, non-identifiable 'exhaust data' from software, payments, and telematics companies. These firms often undervalue this data, which they may have been deleting, and might provide it cheaply or exclusively.

After reacquiring a "failed" ALS drug, Neuvivo's team re-analyzed the 200,000 pages of trial data. They discovered a programming error in the original analysis. Correcting this single mistake was a key step in reversing the trial's outcome from failure to success.