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Geopolitical Risk as Seen by a Silicon Valley–Born "AI Drug Discovery" Company

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In Tokyo, I was introduced by an acquaintance formerly of Sony who told me, "You absolutely must meet them", to a Silicon Valley–born AI medical startup called GEN1E. I often hear about AI × healthcare in Japan as well, but as I listened to them, I realized this was not merely a technology trend or a discussion about curbing medical costs. It is a strategic domain encompassing supply chains, data, and intellectual property — and one that touches on national security.

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Founder Ritu Lal is a research and development professional with 20 years at a major pharmaceutical company, three FDA approvals, one PMDA approval, and more than 15 IND filings. Co-founder Soujanya Bhumkar is a serial entrepreneur from the IT industry who led global partnerships at Yahoo!. As I spoke with these two individuals whose careers could not be more different, I began to see that GEN1E's challenge extends beyond the context of AI-driven drug discovery needed in super-aging Japan. Through a geopolitical lens, its inevitability becomes even clearer.

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1) What Is GEN1E? How Is It Different from Typical AI Drug Discovery?

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If GEN1E were to be described in a single phrase, it is not a company that uses AI to "automatically generate new drug candidates," but rather one that rebuilds the very mechanism for "increasing the probability that a new drug will succeed."

When people hear "AI drug discovery," many imagine AI rapidly producing one drug candidate after another. But GEN1E focuses not on drug candidates, but on patients — and values certainty over speed.

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The key lies in classifying patients not by visible symptoms, but by what is happening inside the body — the underlying mechanisms of disease — through "AI-enabled disease subtype classification (AI-assisted endotyping)." For example, even if two patients both have a persistent cough, differences in the underlying type of inflammation or immune response may mean that different treatments will be effective. GEN1E overlays clinical data with biological information obtained from blood and other sources (multi-omics) to first identify the subgroup of patients in whom a drug is likely to work. On that basis, it determines which type of drug mechanism should be applied to the specific inflammatory or immune processes occurring within those patients, and how clinical trials should be designed.

Traditional drug discovery generally follows a linear model: select a target, create drug candidates, conduct preclinical studies, and determine efficacy during clinical trials. GEN1E reverses this order. By first making patient differences visible and reducing rework, it redesigns the probability of success itself. Rather than merely accelerating development with AI, it identifies the patients most likely to benefit, then optimizes drug selection and trial design to increase the likelihood of clinical success. As a result, development time and cost can potentially be compressed.

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2) Drug R&D × Silicon Valley Entrepreneurship: The Strength Born from Two Careers

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Drug discovery R&D is a complex battle involving science, regulation, clinical practice, capital, and partnerships. What stands out about GEN1E's strength is that its two co-founders, coming from opposite directions, have arrived at the same conclusion.

Ritu Lal spent 20 years at the heart of pharmaceutical R&D, achieving three FDA approvals, one PMDA approval, and more than 15 IND filings. Her problem awareness is clear: the bottleneck in drug discovery lies in the fact that the complexity of disease biology and patient diversity cannot be sufficiently captured by conventional methods. That is why she arrived at a "patient-first" model. There is a sense of urgency here that comes from someone who knows the field firsthand. Sixteen years after earning her PhD, she returned to Stanford to relearn. Calling herself a "Pharmapreneur" (pharmaceutical scientist × entrepreneur), she has reconnected her experience as a researcher to the practical pathway of entrepreneurship.

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Meanwhile, Soujanya Bhumkar is a serial entrepreneur who has founded four companies in Silicon Valley. Cooliris, which he co-founded and led as CEO for seven years, was acquired by Yahoo!. After the acquisition, he served as VP of Global Partnerships at Yahoo!, driving business initiatives. What he emphasizes is that in healthcare, success is measured not by functional improvement, but by real-world patient outcomes and quality of life.

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The scale-through-trial-and-error approach common in the technology industry cannot be directly applied to medicine. That is why, Soujanya argues, the strengths of technology — data-driven decision-making, systems thinking, and AI — must be translated into forms that can withstand the rigor of healthcare.

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In a world of medicine where regulation and responsibility are heavy, Ritu ensures scientific and clinical rigor. Soujanya structures complex decision-making and partnerships into data-driven, executable systems and products. GEN1E exists at the intersection where the rigor of drug discovery connects with the decision-making and implementation ethos of the technology industry — aiming for drug discovery that begins with the patient.

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3) Why Act "Now"? GEN1E's Geopolitical Perspective on Drug Discovery

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As I spoke with them in Tokyo, I realized that the center of gravity in my notes was shifting. I had come to hear about "a new approach to AI drug discovery," but as the conversation progressed, I began to feel this was not merely about technological evolution — it was a matter that could ripple into national resilience.

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GEN1E repeatedly emphasized not only the importance of being patient-first, but also the reality that healthcare innovation is increasingly treated as a strategic domain by nations.

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They stressed that "now is precisely the moment for many people in Japan to engage in AI-driven drug discovery." They explained their reasoning in terms accessible even to someone like me, less familiar with medicine. Technologically, AI has matured to a stage where it can support "deep biological understanding beyond simple pattern recognition." Richer clinical data are available at scale, advances in computational infrastructure allow complex models to be trained and executed rapidly and iteratively, and AI models themselves have matured to integrate the entire drug discovery cycle. As a result, AI-guided precision drug development has become practical in ways that were impossible just a few years ago.

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From the patient perspective, particularly in aging societies like Japan, chronic inflammation and immune disorders are increasing. The gap between patient needs and the healthcare ecosystem's capacity to supply innovation is widening. If left unaddressed, waiting times for patients will grow, and the burden on families, caregivers, hospitals, and the broader healthcare ecosystem will intensify.

On the policy and geopolitical front, healthcare innovation has increasingly become a strategic sector. Japan has also demonstrated its intent to strengthen competitiveness through the promotion of AI, including its application in healthcare. The question GEN1E poses is not whether Japan will adopt AI-driven drug discovery. Rather, it is whether Japan can engage early enough to be on the rule-making side of the next era of drug discovery and development.

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At the same time, they strongly pointed out the scale of China's movement in the medical field. China is becoming a major source of biopharma assets and licensing activity. According to GEN1E's understanding, in 2025 alone, the number of assets in-licensed from China reached a record high, and nearly half of major pharmaceutical companies' in-licensing activities involved China-originated assets (including licensors headquartered in Asia). The point is not that China has emerged merely as a manufacturing hub. Rather, the very map of licensing — of where promising biopharma assets originate — is being redrawn.

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Their conclusion is clear: patient urgency, technological readiness, policy momentum, and global competition are intersecting simultaneously. That is why action is required now.

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GEN1E also describes the future of "not acting" in geopolitical terms. If advanced discovery and development capabilities concentrate in only a few countries, access to next-generation medicines may become uneven. Dependency may arise; countries may pay more and wait longer; and their influence over what is developed, and for whom, may diminish. In future outbreaks or emerging threats, the ability to rapidly assemble treatments tailored to individual characteristics could save countless lives. Their warning is simple: clinging to the status quo under the belief that "things will probably be fine" may itself invite the greatest crisis.

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Author's Note

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As I listened, I recalled Japan's experience during the COVID-19 pandemic, when it faced uneven vaccine supply. The situations cannot be compared simplistically, but the sense that "medical supply and access" are directly linked to national security and safety is very real.

Regarding Japan, GEN1E says the "next two to five years" are critical, for three reasons. First, the disease burden is accelerating, and medical and social costs are rising rapidly. Second, the next decade will determine Japan's long-term competitiveness in AI and healthcare, as AI-driven precision medicine becomes the global standard. Third, Japan possesses exceptional strengths: high-quality clinical practice, trusted institutions, integrated systems, and talent. If these can be connected early, Japan can move beyond being a mere adopter and participate as a co-creator in setting global standards.

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I am not an expert in AI drug discovery. What I received from GEN1E was not only a "patient-first methodology," but also an alert: view drug discovery through a geopolitical lens. I hope to share this perspective with Japanese readers as a starting point for discussing what to consider next, where to collaborate, and how to make decisions. With that intention, I have recorded their message here as faithfully as possible to their original words.

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AI once symbolized efficiency. What GEN1E presents, however, is AI for conviction. Their reversal of the drug discovery process carries geopolitical risks embedded in supply chains and intellectual property. Yet when the disruptive innovation they envision becomes standard, our concept of "health" may shift from something left to chance to something more personal and more predictable.