At KAMI Think Tank, we love cutting through the AI hype to showcase where the technology actually stands and how to use it meaningfully within the life sciences. Now, we're bringing that same clarity with our community to a brand new podcast: From Models to Medicine.
Every week, we sit down with a real practitioner in the life sciences, whether they're working in the lab, leading innovation at a biotech, or building tools to better serve scientists. We discuss how they cut through the hype of AI and practically leverage the tool in their scientific workflows. Glad to have you here.
Episode 28: The Algorithm Has a Women's Health Problem
We sit down with Geri Stengel (https://www.linkedin.com/in/geristengel/), Forbes contributor, founder of Ventureneer, and co-chair of the Women's Health Technology Coalition, who has spent years reporting at the intersection of AI and women's health. Geri opens with a number that frames everything: from 1977 to 1993, the FDA recommended excluding women of childbearing age from clinical trials. That's nearly half of the core historical dataset that AI models are trained on.
We get into the specialized companies building clinical-grade tools on narrow, targeted women's health data, why women are actually consenting to share their data at rates of up to 97%, and a systemic problem most people haven't heard of: search engines and social media platforms are routinely flagging standard medical terms like menstruation and menopause as adult content. That suppression drives up acquisition costs, distorts fundraising, and ultimately widens the health disparity gap. It's one of the more eye-opening conversations we've had on the show.
Show notes:
https://ventureneer.com/ (https://ventureneer.com/)
https://www.forbes.com/sites/geristengel/ (https://www.forbes.com/sites/geristengel/)
https://geristengel.substack.com/ (https://geristengel.substack.com/)
14 Oct 2026
Episode 27: The Memory Code AI Is Starting to Crack
For this episode, we sit down with Kristeen Pareja-Navarro (https://www.linkedin.com/in/kapn/), co-CEO of Engram Bio, whose path from a farming family in the Philippines to peptide engineering for Alzheimer's disease at the Buck Institute is one of the more remarkable stories we've heard on the show. Kristeen walks us through her work trimming the KIBRA protein into a small functional peptide that rescues memory impairment in mice, and how tools like AlphaFold 3 and ESMFold have compressed years of structural biology work into days.
But she's equally candid about where AI falls short. Intrinsically disordered proteins still break the models. AI summaries miss the nuances buried in figures and methods. And no amount of compute can answer the question the field is still wrestling with: how does memory persist when every cell in your body eventually turns over. A grounded, honest conversation about what AI is genuinely changing in early-stage drug development and what it isn't.
Show Notes:
https://www.biorxiv.org/content/10.64898/2026.06.03.729735v1.full.pdf (https://www.biorxiv.org/content/10.64898/2026.06.03.729735v1.full.pdf)
https://www.nature.com/articles/s41586-025-09429-6 (https://www.nature.com/articles/s41586-025-09429-6)
7 Oct 2026
Episode 26: Bigger Models Won't Save Biomanufacturing
We sit down with Shawn Manchester (https://www.linkedin.com/in/shawn-manchester-04931289/), CEO of TripleBar, whose path from yeast DNA repair to metabolic engineering at MIT to biomanufacturing AI gave him a front-row seat to one of biotech's most underappreciated problems.
Everyone funds discovery. Nobody funds manufacturing. And yet a molecule that can't be made reliably and cheaply at scale never reaches a patient. Shawn makes the case that biomanufacturing is where the real bottleneck lives, and why it's finally getting the AI attention it deserves. It's a refreshingly grounded conversation about what biological AI actually requires to work.
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