Manufacturing Intelligence: Sizing Up Pharma's Biggest AI Deals, Part Two

In the second half of a two-part deep dive on major pharma-AI partnerships with PharmTech’s Chris Cole, Richard Jaenisch, senior director of education, outreach, and digital experience at Open Biopharma, argues that generative AI's promise in drug discovery is real but narrower than the hype suggests. Jaenisch draws a sharp distinction between AI tools that mine existing pharmaceutical datasets to surface overlooked structures — where scientists stay in control and can validate the science — and generative approaches he likens to "chimpanzees writing Shakespeare," where sheer volume substitutes for scientific rigor. The former is producing real pipeline candidates, including one drug now in Phase III; the latter, he says, hasn't yet demonstrated meaningful success. (continued below)

Watch Part One on why Roche's raw GPU infrastructure buildout and Sanofi's regional talent hub represent fundamentally different bets on where AI adoption actually stalls in pharmaceutical manufacturing.

Video Chapters

• 2:17 – Where the line is today between what generative AI can do in discovery and formulation and what people imagine it does
• 9:38 – What’s feeding this disconnect, be it exaggerated capabilities or pure hype
• 11:16 – Similarities between the ethical conversations around AI and those around embryonic stem cell use
• 20:17 – Whether or not the Pope voicing opinions on AI has any sway, operationally, for pharma companies
• 22:09 – Where AI agents actually save time today in pharma where the security risk is just not worth it
• 31:23 – If there’s a pharma company or situation currently “hamfisting” enough money to make the security risk worth it
• 37:05 – With workforce repositioning as the metric that determines whether any of these investments pay off, what a pharma company doing this correctly looks like in two years

Jaenisch also draws an unexpected parallel between the current AI ethics debate and the embryonic stem cell controversy, noting that Pope Leo's recent commentary on AI has forced pharma leaders, Catholic or not, into conversations about morality and technology they weren't previously having. He connects this cultural moment to concerns about "AI psychosis" and over-reliance on chatbots as substitutes for human judgment and community.

On agentic AI specifically, Jaenisch is blunt about the security exposure created by ungoverned deployment. Most agents in active use today are simple scraping or reporting tools, he says, but coding agents that are increasingly used to build unauthorized software bolt-ons represent a growing risk, since the same capabilities that help non-experts build tools also help bad actors find exploits in them. He points to real-world incidents of agents breaking sandbox environments and warns that context-window limitations can cause agents to quietly drift from their original instructions over time.

Pressed on whether any pharma company has invested enough to meaningfully move the needle on AI-driven workforce transformation, Jaenisch is skeptical of headline infrastructure spending and argues the real signal will be sustained, team-based investment in people, not individual empowerment. "If everyone's stirring the bucket, the bucket keeps moving," he says, describing how team-oriented AI adoption preserves institutional knowledge even as individuals leave. He predicts the industry will have clearer answers on ROI and workforce strategy within two years, once companies begin reporting real financial results rather than partnership announcements.