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.

Despite sound science, many recent drug launches are failing. The root cause is not the data but an underinvestment in market conditioning. Cautious investors and tighter budgets mean companies are starting their educational and scientific storytelling efforts too late, failing to prepare the market adequately.

A COVID-19 trial struggled for patients because its sign-up form had 400 questions; the only person who could edit the PHP file was a grad student. This illustrates how tiny, absurd operational inefficiencies, trapped in silos, can accumulate and severely hinder massive, capital-intensive research projects.

The most valuable lessons in clinical trial design come from understanding what went wrong. By analyzing the protocols of failed studies, researchers can identify hidden biases, flawed methodologies, and uncontrolled variables, learning precisely what to avoid in their own work.

While the FDA is often blamed for high trial costs, a major culprit is the consolidated Clinical Research Organization (CRO) market. These entrenched players lack incentives to adopt modern, cost-saving technologies, creating a structural bottleneck that prevents regulatory modernization from translating into cheaper and faster trials.

The process of testing drugs in humans—clinical development—is a massive, under-studied bottleneck, accounting for 70% of drug development costs. Despite its importance, there is surprisingly little public knowledge, academic research, or even basic documentation on how to improve this crucial stage.

A clever legal hack can solve the problem of inaccessible trial data. By purchasing the regulatory documents (Common Technical Documents) of bankrupt biotech firms as assets during liquidation, an organization can legally acquire and then publicly release priceless process knowledge that is otherwise lost forever.

The "golden era" of big tech AI labs publishing open research is over. As firms realize the immense value of their proprietary models and talent, they are becoming as secretive as trading firms. The culture is shifting toward protecting IP, with top AI researchers even discussing non-competes, once a hallmark of finance.

Biotech firms are beginning to selectively disclose clinical data, citing the need to protect R&D from fast-following competitors, particularly from China. This forces investors into a difficult position: either trust management without full transparency or discount the company's value due to the opacity.