While crucial, the slow, administrative, and sometimes political process of defining "responsible AI" is becoming a deterrent for pharma companies. Aditya Gherola argues that regulators must move faster to provide clear guidelines, preventing the concept from becoming a roadblock to critical innovation in drug discovery.
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Drug developers often operate under a hyper-conservative perception of FDA requirements, avoiding novel approaches even when regulators might encourage them. This anticipatory compliance, driven by risk aversion, becomes a greater constraint than the regulations themselves, slowing down innovation and increasing costs.
Eroom's Law (Moore's Law reversed) shows rising R&D costs without better success rates. A key culprit may be the obsession with mechanistic understanding. AI 'black box' models, which prioritize predictive results over explainability, could break this expensive bottleneck and accelerate the discovery of effective treatments.
When addressing AI's 'black box' problem, lawmaker Alex Boris suggests regulators should bypass the philosophical debate over a model's 'intent.' The focus should be on its observable impact. By setting up tests in controlled environments—like telling an AI it will be shut down—you can discover and mitigate dangerous emergent behaviors before release.
AI delivers the most value when applied to mature, well-understood processes, not chaotic ones. Pharma's MLR (Medical, Legal, Regulatory) review is a prime candidate for AI disruption precisely because its established, structured nature provides the necessary guardrails and historical data for AI to be effective.
The 'FDA for AI' analogy is flawed because the FDA's rigid, one-drug-one-disease model is ill-suited for a general-purpose technology. This structure struggles with modern personalized medicine, and a similar top-down regime for AI could embed faulty assumptions, stifling innovation and adaptability for a rapidly evolving field.
While seemingly promoting local control, a fragmented state-level approach to AI regulation creates significant compliance friction. This environment disproportionately harms early-stage companies, as only large incumbents can afford to navigate 50 different legal frameworks, stifling innovation.
The FDA's traditional focus on risk avoidance overlooks the inherent risk of delay. Unnecessary bureaucratic steps, like months of animal trials, prevent dying patients from accessing potentially life-saving treatments. The cost of inaction is measured in lives lost.
Silicon Valley's economic engine is "permissionless innovation"—the freedom to build without prior government approval. Proposed AI regulations requiring pre-approval for new models would dismantle this foundation, favoring large incumbents with lobbying power and stifling the startup ecosystem.
An FDA-style regulatory model would force AI companies to make a quantitative safety case for their models before deployment. This shifts the burden of proof from regulators to creators, creating powerful financial incentives for labs to invest heavily in safety research, much like pharmaceutical companies invest in clinical trials.
Dr. Jordan Schlain frames AI in healthcare as fundamentally different from typical tech development. The guiding principle must shift from Silicon Valley's "move fast and break things" to "move fast and not harm people." This is because healthcare is a "land of small errors and big consequences," requiring robust failure plans and accountability.
