Clinical Development Needs Shared Trust, Not Just Another Central Coordinator Clinical development has a paradox: it takes many independent organizations to run a program, yet coordination and records are often managed through sponsor-led or program-specific systems. The sponsor, often working with a contract research organization (CRO), oversees the program and coordinates its activities. When a subsequent program involves different organizations, prior records, partner qualifications, and approval workflows are not automatically reusable. Teams can end up rebuilding parts of the same operational groundwork. A better coordinator alone does not solve this problem. When records depend on one party’s control, other participants must decide whether and how to rely on them. A decentralized architecture can reduce that dependence by allowing participants to share responsibility for maintaining and verifying records under jointly agreed rules. Decentralization changes who maintains the record and sets the rules, not who does the work: The record of who performed and approved each step is maintained and verified jointly. The rules for how programs run are agreed by the participants and applied consistently to all of them. The work stays with each organization, which carries out its own tasks without surrendering operational sovereignty. This does not replace governance. It must operate alongside clear institutional responsibilities and shared definitions of progress. When it does, the record and rules no longer belong to any single program, allowing clinical programs to move from bespoke projects to reusable rails: Network value: Every program adds partners, validation capacity, and verified contributions to the network, making subsequent programs faster, less costly, and easier to coordinate. Accountability: Every result remains traceable to those who produced and approved it, and verifiable by every actor that relies on it. In clinical development, the challenge is making coordination reusable across programs with distinct protocols, institutional responsibilities, and site-level integration. Life AI helps design, validate, and deploy clinical programs. Its shared infrastructure and coordination layer is designed to reduce the need for repeated bilateral negotiations, bespoke compliance work, and site-by-site integration. The goal is to make operational groundwork more reusable across programs through a neutral protocol, while each organization retains its own regulatory responsibilities. What would it take for competing sponsors to rely on the same verifiable record?

The next phase in the evolution of networks will be shaped by what they can coordinate, not just what they can verify. Stablecoins improved how value moves between parties. Real-world assets made ownership more transparent and programmable. Healthcare AI presents a different coordination challenge: aligning technology companies, healthcare providers, experts, communities, patients and autonomous agents around shared outcomes. Blockchain can provide auditable records of onchain ownership and transactions, while AI agents are beginning to execute tasks across systems. Around this infrastructure, Web3 is developing trust primitives such as onchain identity, attestations and agent reputation. These primitives can establish provenance and record participation, but they do not by themselves determine how contribution should be attributed or evaluated within a specific context. This creates a new coordination question: how can networks attribute and evaluate meaningful contribution across participants? This shift from Proof of Ownership to Proof of Contribution is the principle behind Proof of Serving Life (PoSL). PoSL is currently applied through Life Unity, Life AI’s community layer, where contribution is evaluated across three dimensions: Proof of Work reflects verified activity and community-building efforts. Proof of Commitment considers the depth and duration of participation. Proof of Impact evaluates whether those efforts translate into real usage and observable outcomes within the ecosystem. Together, these signals connect activity, continuity and outcomes. The objective is not to create a universal reputation score, but to make contribution more structured, attributable and explainable within a defined context. As healthcare AI becomes more collaborative, networks will need more than proof of identity and records of activity. They will also need a reliable way to understand how different participants contribute to shared processes and outcomes. Blockchain made ownership and transactions verifiable. AI is making action autonomous. As healthcare AI evolves, making contribution legible becomes an important coordination challenge.

Healthcare AI will not scale if every program has to rebuild the same coordination layer. The opportunity is to move from bespoke projects to reusable rails. Across healthcare and drug development, every program must coordinate a complex operating layer: institutions, clinical evidence, validation workflows, regulatory pathways, privacy requirements, access rights and deployment partners. When that operating layer is rebuilt program by program, execution remains slow, fragmented and difficult to scale. For Life AI, shared infrastructure becomes the reusable rail for coordinating AI, biotech, clinical validation and real-world deployment across programs. But “reusable” does not mean every program uses the same model or workflow. A stroke prevention program, an oncology workflow, a metabolic disease program or an autism intervention study may each require different domain logic, partners, evidence standards and deployment paths. What can be reused is the infrastructure underneath. These rails appear across several core infrastructure components: Purpose-built world models for domain-specific healthcare programs. Multi-organization infrastructure for privacy, ownership and access transparency across multi-party healthcare environments. Clinical validation pathways for moving from insight and study design toward evidence, validation and real-world deployment. Coordination primitives for identity, role, capability, permission, evidence, decision-making, accountability and feedback loops. Together, these components define how participants enter a program, what they are allowed to do, what evidence supports their work, how decisions are made and how outcomes return to the system.a Over time, each deployment can strengthen the next. More partners, evidence, validation workflows and real-world feedback can accumulate into shared infrastructure. That is how healthcare AI moves from isolated deployments to infrastructure that compounds across programs.

Infrastructure only matters when it changes what organizations can do together. One example is our work with Kalbe Farma and its subsidiary, KALGen Innolab, on StrokeGENME, a genetics-powered platform that translates DNA insights into individualized stroke-prevention plans, delivered through Kalbe’s nationwide ecosystem in Indonesia. Bringing StrokeGENME from genetic insights to real-world healthcare delivery brought together capabilities that already existed across the partnership: KALGen’s laboratory capabilities, Kalbe’s healthcare expertise and reach, and Life AI’s technology platform. Kalbe did not need to become a technology company. Life AI did not need to build a clinical network in Indonesia. KALGen already had the laboratory capabilities required on the ground. No single organization needed to own the entire pathway. Each organization could contribute what it was already good at, bringing an existing capability into the same pathway. That leverage changed the path to deployment. The program has since reached real-world deployment, with timeline reduced from approximately 24 months to 3 months, and was subsequently accepted by regulator. That combination is central to how Dr. Tuan Cao, Co-Founder and CEO of Life AI, described the partnership: “Our partnership with Kalbe amplifies impact, pairing AI with a healthcare leader built to reach millions.” The challenge is making those capabilities work together without rebuilding the path from scratch every time. How much of the coordination between technology, laboratories, clinical expertise and healthcare delivery can become reusable? Because if every new collaboration starts from zero, it remains a project. When the path itself becomes reusable, partnership starts to become infrastructure. That is one of the questions we are continuing to build around at Life AI.

Today in Singapore, we took a question we have been working on at Life AI to a much bigger room. As AI accelerates drug discovery, how do we build the operating infrastructure to move what we discover through clinical validation, evidence, and real-world execution? Our Co-Founder & CEO Dr. Tuan Cao shared Life AI’s perspective on that question today at the NVIDIA Inception Grand Challenge Finale. And much of the value lies in what follows: new perspectives, sharper questions, and meaningful connections across the industry. Singapore extended the rails we’ve been laying, bringing our work into a broader conversation across AI, drug development, and healthcare. And that is exactly where we want to be.

What kind of operational infrastructure does coordination across the healthcare value chain actually require? Eight years of building clinical programs across Southeast Asia, working with governments, hospital networks, and pharmaceutical companies, produced one consistent finding: the challenge is not the absence of capable AI. It is the absence of operational infrastructure that makes coordination reusable across programs. Without that infrastructure, coordination remains highly program-specific, especially in drug development, where multiple key players must work together across the development process. The infrastructure this requires has three properties. It has to be reusable. Clinical programs should move from bespoke projects to reusable rails, with shared coordination rails replacing bespoke integration at every site. It has to compound. Every application should strengthen the model, the network, and the protocol, building greater capacity across the ecosystem over time. It has to enable coordination among independent actors. Hospitals, doctors, labs, pharma sponsors, regulators, patients, and AI builders can contribute services, validation, and clinical execution without surrendering operational sovereignty. At Life AI, we are building an operating infrastructure for drug development around these requirements, bringing together AI-driven discovery, wet-lab screening, and clinical validation.

Why is validation so difficult to accelerate? Because unlike discovery, validation cannot be completed in isolation. An AI-generated drug candidate still has to move through key players across the healthcare value chain, including pharma teams, clinical sites, hospitals, clinicians, patients, and regulators. Each operates with different timelines, evidence requirements, compliance constraints, and operational priorities. A candidate can move forward on one front while remaining constrained elsewhere, whether by patient recruitment, study execution, evidence generation, or the decisions required to advance it. This makes validation more operationally complex than discovery. Discovery can be accelerated within a model, platform, or controlled environment, while validation depends on how effectively these key players coordinate and execute across the development process. With more candidates entering development, that operational complexity compounds, requiring more evidence generation, clinical execution, patient participation, and coordinated decision making across organizations. As AI accelerates discovery, the need for better coordination and the infrastructure to support it becomes increasingly important downstream.

AI is accelerating drug discovery. But the pressure is shifting downstream, toward the work required to validate what discovery produces. Across science and life sciences, AI is moving deeper into discovery. Anthropic is expanding AI into scientific research, Isomorphic Labs is scaling AI-first drug design and development, and Discovery Loop is building systems to automate experimental loops. As these capabilities advance, more targets can be explored, more molecules can be designed, and more potential candidates can be generated. The discovery layer is becoming faster and more expansive. But accelerating discovery does not automatically accelerate the path to validation. A promising candidate still has to be evaluated, tested, and supported by sufficient evidence before it can move forward. Clinical validation is where this downstream pressure becomes especially visible. The gains from faster discovery can begin to narrow if the path to validation remains slow and difficult to scale. For Life AI, this raises a critical question: How do we make sure the path to validation can keep pace as AI accelerates discovery?

AI is moving faster than the systems around it. In healthcare, that gap matters. But what makes that gap so difficult to close? “How do we turn AI intelligence into accountable action across fragmented healthcare systems?” It is also a question that has increasingly shaped how Life AI thinks about healthcare AI. Speaking at NVIDIA GTC Taiwan 2026, Life AI Co-Founder and CEO Dr. Tuan Cao @tuan_lifeai put it this way: “In other industries, if you have the best model, you win. But in healthcare, what actually matters is the coordination of so many key players across the healthcare value chain. That is why running a clinical trial in the US is so expensive. It costs about $20 million to $100 million to run a Phase III clinical trial, and for every patient, we pay about $500,000 because the man in the middle has to coordinate so many different players: the pharma, the hospital, the patient, and the doctor. So coordination of all of those key players is actually one of the main bottlenecks.” The challenge is not simply advancing AI, but understanding what it takes to make AI work across the complexity of healthcare. The NVIDIA Inception Grand Challenge 2026 gives Life AI a timely platform to bring this infrastructure question into a broader AI conversation.

1/6 Asia is living longer. But too many of those years are spent in poor health. That is the central tension explored in a recent Future of Asia Podcast from the McKinsey Health Institute. So what stands between living longer and living healthier? 🧵 Here are some numbers and insights worth looking at.