Edition #1: Evolution of Health - When AI Stops Assisting and Starts Leading
The Signal: When AI Starts Leading
For thirty years, AI assisted.
It ranked candidates. It flagged anomalies. It suggested the next step. A human decided. A human signed off. A human took responsibility.
That model just changed.
In June 2025, a paper published in Nature Medicine described something that hadn’t happened before: a drug where both the biological target and the molecular compound were identified and designed by AI, completing Phase 2a clinical trials with measurable efficacy results. The drug is called rentosertib. The company is Insilico Medicine. The disease is idiopathic pulmonary fibrosis, a progressive lung condition with no current therapy capable of reversing it.
Traditional drug discovery takes 4 to 6 years to get from target identification to a preclinical candidate. Insilico did it in 18 months. The Phase 2a trial enrolled 71 patients across 21 sites. Patients receiving the highest dose showed a mean improvement in lung function of 98.4 mL. The placebo group declined 20.3 mL. That’s not a rounding error. That’s a direction.
What AI led: finding the target, designing the molecule. What AI didn’t touch: the biology. The trial still took 11 months. Human bodies don’t run on software cycles. That distinction matters, and I’ll come back to it.
But rentosertib isn’t a one-off. Since 2021, Insilico’s platform has nominated more than 20 preclinical candidates. Nine have received IND approval. Multiple Phase 1 trials are active across different disease areas. The model is generating a pipeline at a pace traditional pharma can’t replicate, at a fraction of the cost.
That’s not a drug story. That’s an infrastructure story
We’ve seen this pattern before. When the Human Genome Project completed in 2003, everyone focused on the promise: personalized medicine in five years. Most people missed what was actually happening. The capital wasn’t betting on a specific drug. It was betting on the platform that would find drugs faster. Illumina, founded in 1998, dominated the sequencing market for the next two decades. The leaders who won didn’t move first on the application. They moved first on the layer underneath it.
The capital is making the same bet today. It just doesn’t look the same from the outside.
This has nothing to do with pharma.
AI is shifting from assistant to leader in drug discovery. That shift is already visible in the data. But that paper in Nature Medicine, the one that marks the exact moment, doesn’t come for most industries. The shift happens quietly, in process decisions and vendor pitches and pilot programs that nobody calls historic. And by the time it’s obvious, the infrastructure layer has already been built by someone else.
The Application: The Infrastructure Bet
Insilico proved AI can lead drug discovery. Here’s what happened next.
Eli Lilly inaugurated LillyPod in March 2026: a $1 billion supercomputer built with over 1,000 NVIDIA Blackwell Ultra GPUs and 9,000+ petaflops of capacity. This isn’t a research experiment. It’s a five-year infrastructure commitment to run millions of drug hypotheses in parallel. What Insilico proved with a startup budget, Lilly is building at industrial scale. Even more telling: Lilly is developing TuneLab, a platform that would let other biotech companies access its discovery models. If that scales, the barrier to AI-led discovery drops for the entire sector.
One caveat the field deserves: AI has not improved pharma’s roughly 90% clinical failure rate. It changed the front end, speed and cost of finding candidates. Whether those candidates are better candidates is still an open question.
The platform is the asset. Not the drug.
The Noise: “10 Years to 18 Months”
“AI compresses drug discovery from 10 years to 18 months.”
You’ll hear this in conferences. It’s in pitch decks. It’s technically true and practically misleading.
What AI compressed: the discovery phase. Finding the target, designing the molecule. That compression is real, documented, and significant.
What AI didn’t compress: the biology. The rentosertib Phase 2a trial ran for 11 months, across 71 patients, at 21 sites. Clinical trials take years because that’s how long it takes to observe what a molecule does inside a human being. And most of them still fail.
You can’t iterate a tomato in two-week sprints. You can’t iterate a drug in the human body in 18 months.
The headline collapses two very different things into one clean number. AI improved the front end. The back end follows biology’s clock, not software’s.
The Question: What’s Already Shifting
What technology shifts are already on your team’s radar today, not because you went looking, but because you’re seeing them?
If they answer immediately and without hesitation, you have signal. If they pause, that’s your homework.
What I’m Watching
Quantum computers solving real medical problems this month. The Q4Bio competition (Wellcome Leap) reached its final round with six teams demonstrating that today’s imperfect quantum machines, combined with classical processors, can solve real healthcare problems. Infleqtion is using quantum computing to detect cancer signatures in datasets too large for classical solvers. Algorithmiq is redesigning an oncology drug already in Phase II using quantum-classical hybrid architecture. The narrative says quantum is 10-15 years away. For specific problems in molecular simulation and medical data analysis, partial quantum advantage already exists in 2026. The timeline isn’t uniform, and healthcare is where it’s arriving first.
Humanoid robots crossing the price threshold. Tesla plans 50,000 Optimus units in 2026 at $20,000-30,000 each. Figure AI raised $1 billion and launched Figure 03 for mass manufacturing. The conversation about robots has been about capability for years. The conversation that actually matters is about cost. At these price points, mid-size warehouses and logistics operators can run the numbers. That’s a different wave than “robots in factories.”
Post-quantum cryptography becoming a mandate, not a recommendation. The US government set deadlines for federal agencies to migrate to quantum-safe encryption. Most private sector organizations haven’t started the conversation. The gap between what regulators expect and what organizations have done is widening quietly. If your data needs to stay confidential for the next decade, this is already your problem.
This is Wave Lens. Emerging tech without the hype. Real signals for strategic decisions.
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Javier D’Ovidio
Wave Lens