Between us, we have six children. The oldest is 12. None of them has a phone yet, and none has asked us whether artificial intelligence is going to ruin their lives.
They will.
They will learn that some of the people building the most powerful AI systems have warned that the technology could become dangerous in ways we cannot yet control. They will hear predictions that many of the jobs they might want in the future may disappear. They will encounter arguments about truth, power, safety, and whether our institutions can possibly keep pace with machines advancing faster than our ability to govern them.
We will not be able to tell them that any of it is nonsense. We don’t think it is.
But another danger looms over this moment. As the conversation around AI grows darker, we risk becoming so consumed by what might go wrong that we stop paying attention to what is beginning to go right.
That is not an argument for complacency. The risks are real and demand immediate action. It is an argument for something we think is becoming equally necessary: pragmatic optimism.
We work in one of the most failure-prone fields there is, trying to understand disease and determine which medicines might work in human beings. From where we sit, on the front lines of investing in and creating medicines, we are beginning to see AI change what is possible.
Drug development has always been a brutal business. Historically, roughly nine out of 10 therapies entering human testing fail. A successful medicine can take a decade or more and billions of dollars to develop. But of course, those statistics obscure the real cost. A failed or missing medicine is a patient still waiting.
For most of modern drug development, scientists have started with a hypothesis about biology, searched for a molecule that might affect it, tested that molecule and, overwhelmingly, discovered along the way that they were wrong. AI is beginning to change that story.
We have had the wonder of watching this shift in human insight and discovery direction being force-multiplied by AI up close. We recently watched a team of researchers use AI to design antibodies from scratch against some of the hardest targets in medicine, unlocking approaches that have stymied some of the most sophisticated groups in pharma. Nearby, another group of only five scientists used an AI-driven experimental system to move from an idea for a challenging new medicine to demonstrating what we believe are unique cancer-treating capabilities in a primate in less than six months. Programs with these ambitions would conventionally require teams of specialists working for years.
What is changing is not simply the tools available to help us make predictions about human biology. It is the speed of the discovery process itself: propose an idea, build it, test it in the physical world, learn from what happened, and begin again. The scientific method itself is beginning to accelerate, and the number of scientific inquiries, opportunities to advance health per dollar spent, is beginning to scale.
Last month, Merck and Moderna reported that a personalized cancer vaccine for melanoma significantly delayed the disease from spreading. The result is remarkable on its own, and studies are already underway in other settings. What we find particularly exciting is how this growing clinical evidence, combined with AI tools and automated labs, can open the field to more competitors. The evidence reduces uncertainty about the approach, while these tools can lower the cost and time required to pursue it. Together, they can bring more teams and more capital into our fight against cancer and expand the number of promising ideas that will be tested at an unprecedented rate.
We are at the very beginning.
Anyone claiming that AI has solved drug discovery is selling something. Of the 117 AI-enabled drug programs in human trials last year across 63 companies. Not one has been approved. Biology has humbled generations of brilliant scientists, and AI will not repeal biology. Models will be wrong. Drugs will fail. Patients will respond in ways nobody predicted.
But we see green shoots and a remarkable new calibration of direction, not a destination. Direction is the whole game in fighting disease and improving human health. AI is beginning to give us better ways of finding it.
That is the source of our optimism. Not the belief that the dangers surrounding AI are exaggerated, or that medicine is about to become easy. We believe neither. It comes from watching a technology begin to alter a field in which meaningful progress has historically been painfully slow.
Our children will inherit all of this. They will live with the disruptions we are debating now and others none of us has anticipated. They deserve leaders who take those risks seriously and institutions willing to act before every consequence is known, and before some of the most dangerous become irreversible.
They also deserve to know what else was happening at this moment.
Diseases we could not understand are now yielding more of their secrets. Machines can now search for possibilities no human mind can find alone. Medicines are being designed differently. Diseases too rare or complicated to command sufficient attention are becoming more tractable.
And if all of that started to compound, the baseline of human health our children inherit started to be different from the one we inherited ourselves.
We don’t yet know what AI will take from our children’s generation. From where we sit, we are beginning to see what it might give.
And that is a story worth telling too.
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