For a controversial strategic shift, a co-founder's "moral authority" is invaluable. They can absorb the risk of looking foolish and give up their responsibilities ("Legos") to spearhead a new initiative. This allows them to champion a new direction with a level of credibility that can overcome internal skepticism.
Unlike labor-dependent services that get more expensive, prescription drugs offer a unique societal ROI because they eventually go generic and become cheaper. This deflationary aspect is a powerful, underappreciated argument for investing in drug development, as successful medicines provide compounding value to society over time.
Powerful AI models for biology exist, but the industry lacks a breakthrough user interface—a "ChatGPT for science"—that makes them accessible, trustworthy, and integrated into wet lab scientists' workflows. This adoption and translation problem is the biggest hurdle, not the raw capability of the AI models themselves.
A significant portion of biotech's high costs stems from its "artisanal" nature, where each company develops bespoke digital workflows and data structures. This inefficiency arises because startups are often structured for acquisition after a single clinical success, not for long-term, scalable operations.
The next decade in biotech will prioritize speed and cost, areas where Chinese companies excel. They rapidly and cheaply advance molecules to early clinical trials, attracting major pharma companies to acquire assets that they historically would have sourced from US biotechs. This is reshaping the global competitive landscape.
The pharmaceutical industry is often misunderstood because it communicates through faceless corporate entities. It could learn from tech's "go direct" strategy, where leaders tell compelling stories. Highlighting the scientists and patient journeys behind breakthroughs could dramatically improve public perception and appreciation.
The past few years in biotech mirrored the tech dot-com bust, driven by fading post-COVID exuberance, interest rate hikes, and slower-than-hoped commercialization of new modalities like gene editing. This was caused by a confluence of factors, creating a tough environment for companies that raised capital during the peak.
A key value of AI agents is rediscovering "lost" institutional knowledge. By analyzing historical experimental data, agents can prevent redundant work. For example, an agent found a previous study on mouse models that saved a company eight months and significant cost, surfacing data from an acquired company where the original scientists were gone.