Where the Next $1T Company Will Be Built
The next trillion dollar company will likely be built in healthcare. Here's why and what it will look like
The next trillion-dollar company will be in tech for health. It will not be a drugmaker like Eli Lily (~$970B market cap). Nor will it be an insurance company like UnitedHealth (~$300B market cap).
It will be the intelligence layer that makes medicine and healthcare learn.
Every generation produces a company that solves a critical problem, or reorganizes a fundamental system of human progress. Standard Oil organized the energy economy, which led the industrial age. IBM organized information, starting the computing revolution. Eli Lilly organized biology, transforming chemistry into modern pharmaceuticals. Microsoft organized productivity, making computers essential to daily work and life. Apple organized experience, turning technology into an extension of human intuition and creativity. Google organized knowledge, mapping the world’s information and making it universally accessible. Amazon organized commerce, building the digital infrastructure of consumption. Meta organized connection, reshaping how billions communicate. Tesla organized mobility, electrifying cars. NVIDIA organized intelligence, supplying the computational infrastructure behind the AI boom.
Each of these companies emerged from a simple insight. That an enormous, inefficient system with strong human demand was waiting to be made intelligent.
Modern healthcare stands on the edge of that same realization.
Nothing is Bigger than This.
The world spends over $11 trillion each year on healthcare. That’s 10% of global GDP. The pharmaceutical sector alone accounts for about $1.7 trillion, with nearly $200 billion flowing into drug discovery and development. Yet despite this scale, progress remains agonizingly slow.
More than 30,000 clinical trials run every year, costing over $120 billion. Fewer than 8% of drugs that enter human testing ever reach patients. Each failure represents years of effort, billions in sunk cost, and therapies that never make it to the people who need them.
But the bottleneck is not just biological uncertainty. The system around it is inefficient. Trial data is fragmented. Operational processes are outdated. Insights are trapped inside silos that can’t communicate. The most advanced industry in the world still runs its most expensive function on infrastructure that doesn’t learn.
If clinical development became as intelligent and adaptive as discovery has, the effect would cascade across the entire health economy. Better trials mean faster cures, lower costs, and dramatically higher R&D productivity. This isn’t incremental value. It’s systemic value.
AI for Discovery, Intelligence for Delivery
AI has already transformed the front end of medicine. Models now design proteins, predict molecular interactions, and generate novel drug candidates in silico.
But discovery isn’t where most drugs fail. They fail in delivery. They fail in the fragile, complex, data blind process of proving that a molecule works in humans.
What would it mean to make that process intelligent?
Imagine clinical trials that can forecast failure before it happens, detect data drift in real time, simulate alternative trial designs, and learn continuously from every study ever run. The data exists. The knowledge exists. But it is locked inside systems never built to learn from themselves.
We already have AI for discovery. What we need is intelligence for delivery.
A platform that connects biological insight with operational foresight.
A system that helps science see itself clearly and improve with every iteration.
A learning engine for the most important experiments we run as a species.
This single idea, making clinical trials self-improving, is one of the largest untouched opportunities in the global economy. It is the infrastructure layer medicine is missing.
When Experimentation Gets Cheap
Every major technology wave begins when experimentation becomes cheap. Cloud computing made building software cheap. Startups replaced incumbents. Open financial APIs made fintech cheap . New institutions were born. Falling sequencing costs made biology cheap to explore. Biotech exploded.
The same shift is coming to healthcare. When clinical validation becomes faster, cheaper, and more predictable, the structure of drug development will be rewritten. Small, technical teams will be able to pursue ideas that once required billions. Innovation will no longer be gated by capital. It will be gated by intelligence.
That moment is when a trillion-dollar company emerges: when friction between scientific discovery and clinical proof disappears.
The Curve That Stopped Rising
For a century, U.S. life expectancy climbed steadily, from forty-seven years to nearly seventy-eight. Then, around 2000, the curve flattened. Despite record spending and scientific breakthroughs, we barely added a single year in two decades.
The problem isn’t compassion or funding. It’s that our clinical system doesn’t compound knowledge.
We treat disease, but we don’t learn from it. Each new therapy starts from zero instead of from accumulated understanding.
If clinical trials could learn from themselves, if every failure increased the probability of the next success, the health curve would rise again. Medicine would stop moving linearly and begin compounding, just as software and AI have.
The Missing Layer in Medicine
Between the biotech lab and the patient’s bedside lies a missing layer:
an intelligence that helps medicine learn as fast as it discovers.
A company built to fill this gap would not compete with drug manufacturers. It would become the connective tissue of the entire health economy. The platform that makes trials adaptive, predictive, transparent, and continuously improving.
Even modest improvements in trial success rates could unlock hundreds of billions in value, accelerate timelines by years, and reshape how the entire system learns and scales.
This company does not yet exist at scale. But it should. And when it does, it will redefine the economics of healthcare for the next century.
The Next Industrial Revolution in Medicine
Colonel Eli Lilly helped build the foundation of industrial trust in pharmaceuticals. The idea that medicine could be standardized, reliable, and safe. Biotechnology turned biology into engineering. The next revolution will turn experimentation itself into intelligence.
When that happens, medicine will stop inching forward and start learning exponentially. Clinical trials will shift from bottleneck to engine. And the company that transforms the chaos of clinical data into clarity won’t just be valuable. It will be unstoppable.
The next trillion-dollar company will not be one that only invents new drugs. It will be the one that invents a new way for medicine to learn.
It will not be built by the usual healthcare crowd. Apple, Meta, and Google were not built by the incumbents of their era. The establishment actually cannot build it because they are optimized for system preservation, not reinvention. The builders will be outsiders. They will look more like entrepreneurs than executives. They will move fast, experiment aggressively, and treat healthcare inefficiency not as an acceptable status quo but as a solvable engineering problem. They will not be patient, nor will they tolerate stasis. They will start like a startup and become the builders of a system medicine has never seen before. They might have started already.
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Sources
World Health Organization. Global Spending on Health
https://www.who.int/publications/i/item/9789240064911Centers for Medicare & Medicaid Services. National Health Expenditure Data
https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data/historicalSeyhan, A.A. (2019). Lost in Translation: the Valley of Death Across Preclinical and Clinical Development
A New System for Moving Drugs to Market. Issues.org
PwC. The Future of Health
https://www.pwc.com/us/en/industries/health-industries/library/future-of-health.html