My father practiced medicine for 53 years. He was still seeing patients until the last few months of his life. Pancreatic cancer took him from us at 81.
I grew up in a medical family. My mother was a critical care nurse, and I spent nights and weekends roaming the hospital as a kid. I saw sickness and disease firsthand. I also saw what they did to families. The hospital was a place of both hope and sadness, and that has stayed with me.
My grandfather and great-grandfather were medical doctors too. Our family helped establish Thompson Hospital, one of the early hospitals in my hometown of Mount Vernon, Illinois. Medicine was part of our family’s life long before I came along.
I’ve been surrounded by this for most of my life. I have enormous respect for what medicine can do, and a deeply personal reason to want it to do more. My father spent decades caring for other people. I wanted more time with him.
That’s what I was writing about in my last article. I want the diagnosis that changes a family’s life to become something medicine knows how to handle. And that wish extends to families I will never meet.
Now I want to take that thought further. Suppose we become dramatically better at preventing and curing the cancers, cardiovascular diseases and infections that cut lives short. People get years they would otherwise have lost. Their partners keep them. Their children get more time with them.
What does the world look like after that?
Those people will need homes, food, water and energy. They’ll need friendships and a reason to get up in the morning. They’ll have to decide what to do with time they never expected to have. And some may be watching other people receive treatments they cannot access themselves.
Giving people more life would change far more than medicine.
The ambition is real
Demis Hassabis’s company, Isomorphic Labs, has made its mission explicit: “solve all disease” with the help of AI. Google recently announced his move to chair of Google DeepMind and chief scientist of Alphabet, while continuing to lead Isomorphic. This is a stated research ambition, with people and resources being committed to it. Isomorphic Labs, Google’s announcement.
There is already a significant example of making the underlying science widely available. Google DeepMind and EMBL-EBI jointly developed the AlphaFold Database, which offers open access to more than 200 million protein structure predictions. That gives researchers a resource they can use to investigate biology. It does not, by itself, establish that a proposed treatment works in a patient. EMBL-EBI.
AlphaFold is only part of this story. Google DeepMind’s AlphaGenome helps researchers explore how changes in DNA may affect the way genes work. That includes regions that help control when genes are active and how much they’re used, even though those regions don’t directly code for proteins. Google DeepMind’s overview.
The work was published in Nature. AlphaGenome can analyze up to a million DNA letters at a time and predict several kinds of effects on gene regulation. In one example, it reproduced a known mechanism linking mutations to a form of leukemia: those changes could switch on a gene called TAL1. That is a concrete way to investigate how a change in DNA may contribute to disease. DeepMind’s research explanation.
I want researchers to have every useful tool we can give them. If they can narrow down what is going wrong, they have a better starting point for experiments and potential treatments. AlphaGenome’s predictions still need testing, and the model has not been validated for direct clinical use. Research limitations.
We still need to be clear about what we mean by all disease. A mission statement cannot give us a reliable date when medicine will have finished its work. Influenza viruses, for example, continue to evolve; better treatments would still exist in a world where pathogens change. CDC.
Curing major diseases would also leave questions about biological aging. “Old age” is not a separate clock that simply takes over once cancer and heart disease are gone. Aging involves accumulating damage and changes in function, with overlapping conditions that contribute to frailty and falls. Preventing one disease does not automatically resolve the others. WHO.
So the future worth examining is one where far fewer people die prematurely from disease, and many more remain healthy for longer. We don’t need to promise immortality to recognize how much that could change.
Who gets the extra years?
This question is already with us.
Tuberculosis is preventable and curable. WHO estimates that it nevertheless killed 1.23 million people in 2024. Drug resistance complicates treatment, and only about two in five people with drug-resistant TB accessed treatment that year. Malaria is also preventable and curable. Discovering how to treat a disease and ending its toll on humanity are plainly different achievements. WHO: tuberculosis, WHO: malaria.
That should shape how we talk about the next generation of medicines. AlphaGenome’s API is free for non-commercial use, giving researchers access to another tool for investigating disease. AlphaGenome documentation.
I want that generosity to carry through to the people who need treatment. An openly available protein database or research model doesn’t decide what a medicine developed with that knowledge will cost, where it will be manufactured or which patients will receive it first.
Imagine two people with the same diagnosis. A new treatment could help them both, but one has access through their location, coverage or wealth, and the other doesn’t. Every year that passes means something different to each family.
If people can buy extra decades that others cannot access, inequality starts being measured in years together.
Development, manufacturing and delivery require resources. Early supply may be limited. Those are problems to work through, and I want access to be part of that work from the beginning.
The question is how quickly a successful treatment becomes something an ordinary person can actually receive. A breakthrough’s first patient and its millionth patient both matter.
More people, living longer, on the same planet
It’s reasonable to ask what happens to demand for food, fresh water, housing and power. Holding other things equal, fewer deaths means more people alive than there would otherwise have been.
But the size of the change depends on births as well as deaths, on how much longer people live, and on how widely the medical gains reach. Resource demand also depends on consumption and waste. A useful projection needs assumptions about all of that, rather than a single guess about how long everyone might live.
We would need to examine different scenarios: gradual gains, rapid gains, benefits concentrated in a few countries, benefits reaching most people. Each would create different pressures, and those pressures would arrive in particular communities, farms and water systems.
Could AI help us meet them? There are concrete reasons to explore that. FAO’s WaPOR initiative uses satellite data to help improve agricultural water management and support more reliable yields. The International Energy Agency describes AI applications for forecasting renewable generation, managing electricity networks and improving efficiency. These are useful directions for increasing what existing resources can provide. FAO, IEA.
AI also consumes resources. The IEA’s 2025 analysis projects that global data-centre electricity demand will more than double by 2030, with AI a major driver. We have to account for those costs alongside potential savings. IEA.
An irrigation model may tell a farmer how to use less water; the farmer still needs the equipment and money to act on it. The same gap between an idea and a working system exists with fresh water and electricity. Investment in treatment plants, pipes and power infrastructure has to accompany the software.
People who remain healthy could also keep contributing to those systems. The extra years would bring capacity and experience as well as demand.
I want us working on that alongside better medicine. People surviving would be an achievement to accommodate, with serious planning for the lives they’ll go on to lead.
A longer life needs people in it
One possibility I find especially moving is that we could grow older with more of our friends still around.
We often imagine longevity as an individual achievement: I get another twenty years. But imagine your partner getting them too. Imagine the friends who know you best remaining healthy enough to make plans with you. That changes the meaning of the extra time.
WHO identifies bereavement, retirement, poor health and limited community resources among the factors that can contribute to loneliness and social isolation. Neither is inevitable in older age, and both affect younger people too. Preventing some losses and preserving independence could help, but would not address every cause of disconnection. WHO.
Your children might still live far away. Your marriage might end. You could be healthy and have very little contact with anyone who knows you well. Those possibilities deserve attention in a conversation about longer life.
I want more years with people in them.
That means thinking about the places where friendships form and continue: neighborhoods people can move around in, activities they can afford, and opportunities to belong after they leave a job. It also means leaving room for older people to begin something new, including relationships.
My father was still seeing patients at an age when people are often assumed to have finished contributing. His life gives me a concrete way to think about this: being able to remain involved, with people who know you and work that matters to you. Someone else might want a completely different kind of life at 81. I want more people to have the health and opportunity to make that choice.
We would have to reconsider the shape of a life
Much of our planning follows a familiar sequence: education, work, retirement. Substantially longer healthy lives would put pressure on that sequence.
Someone might return to school at 65 because they have time to use what they learn. Careers could become less final. A person who spent years caring for a family might have a much larger opportunity to pursue something of their own.
There would also be difficult questions about paying for longer lives. Pension systems would have to respond to the duration of retirement, the number of people contributing and the resources available. Employers would need to make room for both experienced people who want to stay and younger people trying to get started.
I’d be disappointed if our first response to better health were simply to add another twenty years to everyone’s working life. More choice is part of the benefit I’m hoping for. How much choice people actually gain would depend on how the economic gains are shared.
Longer lives could also affect the transfer of wealth and responsibility between generations. If people hold assets or senior positions for much longer, what happens to the opportunities available to those coming after them? Those are questions for institutions as well as individuals.
None of this makes treating disease less worthwhile. It means success would give us substantial work to do elsewhere.
I want us to prepare for success
I believe AI can help us make enormous progress against the diseases that take people from us. I want that ambition to be taken seriously, including the possibility that progress changes ordinary life in ways we haven’t prepared for.
The gains could be extraordinary: less suffering, families staying together longer, and fewer years spent organizing life around illness. People could keep their independence and have time to attempt things they once thought they had missed their chance to do.
Who receives those benefits, and what surrounds them, will matter enormously. We can start thinking about that before the science reaches every destination we hope for.
When someone gets their life back, I want them to have a place to live, people to share it with and something they’re looking forward to.
I wanted more time with my father. Somewhere, another family is about to want exactly the same thing.
I want us to give them a better answer.
AI engineer, builder, and author of The Age of Imagineering.