AI leaders such as Demis Hassabis and Andrew Ng have made claims that AI can cure all human diseases within a decade. However, experts like cardiologist Dr. Eric Topol and Senior Fellow at Harvard's School of Public Health Dr. Catherine Young dispute these claims, citing the complexity of chronic diseases and the lack of precedent for rapid cures. According to them, 80% of chronic diseases are caused by lifestyle factors rather than genetics, making AI solutions insufficient on their own. The experts warn that exaggerating AI's potential can set unrealistic expectations and distract from more effective approaches to improving human health.
Written by the local model on 2026-09-05,
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“I think it will actually be possible to cure most human disease in ~5-10 years, as crazy as it may sound to ordinary people and frankly to biologists as well (I used to be one!).”
uncertain
I → think → biologists
Truth be told, this is not an uncommon take among AI leaders.
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this → tell → leaders
On 60 Minutes last year, Demis Hassabis, co-founder of Google DeepMind, declared that “we can cure all disease with the help of Al… maybe within the next decade or so, I don’t see why not.”
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I → declare → decade
An estimated 80% of chronic diseases and premature deaths are not driven by our genes, but by the way we live: what we eat, how much we move, how we sleep, how we manage stress and stay socially connected.
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we → estimate → stress
It’s true AI can accelerate drug discovery and personalize nudges for our daily behaviors, but it can’t “cure all disease” while ignoring human nature, free will, and how we live.
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we → ’ → nature
The view among those currently working to lower the disease burden is considerably less utopian than in AI.
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view → work → AI
“Diabetes, Alzheimer’s, heart disease—we don’t have cures for any of those common diseases,” says cardiologist Dr. Eric Topol.
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Topol → have → diseases
“To think that in the next five or 10 years new drugs are going to change everything, there’s no precedent for that.
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drugs → think → that
It’s unrealistic, and it sets the expectations for AI too high.
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it → ’ → AI
It’s hype.”
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It → ’ → ?
It is more than 200 distinct diseases, all with different causes, biology and mechanisms,” explains Dr. Catherine Young, a Senior Fellow at the Harvard T.H. Chan School of Public Health.
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Young → explain → Health
“Scientists who study cancer rarely talk about ‘curing cancer’ because it’s nothing more than an empty slogan.
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it → study → slogan
As Amy Dockser Marcus writes for The Information, we’ve been here before.
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we → write → Information
In 2000, on the heels of the first sequencing of human DNA, Francis Collins, then-director of the Human Genome Project, predicted that “in another 20, 25 years we should be able to prevent or cure most cases of cancer, of diabetes, of heart disease, of multiple sclerosis, of asthma.”
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we → predict → asthma
And yet, here we are, 26 years later, facing a growing epidemic of chronic diseases.
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we → face → diseases
Daphne Koller, CEO of the AI-driven drug development company insitro, calls it the “magic wand” assumption.
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Koller → drive → it
“Hundreds of years into modern medicine,” she writes, “our understanding of most human disease, and much of healthy physiology, is best captured by the parable of the blind men and the elephant; in this case, a really huge elephant.”
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understanding → write → case
The current debate about whether AI can cure all diseases is more than a tempest in a GPTeapot.
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AI → cure → GPTeapot
It points to a larger problem: we are too often focused solely on improving the machines, and too rarely focused on investing energy and resources in improving humans.
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we → point → humans
In 2024, Amodei wrote about the limiting factors that stand in the way of AI transforming the world for the better: “speed of the outside world,” “need for data,” “intrinsic complexity,” “physical laws” and “constraints from humans.”
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AI → write → humans
Of those, I would argue that the constraints imposed by humans will be the ultimate limiting factor.
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constraints → argue → humans
“If for every dollar and every minute that we invest in developing artificial intelligence, we also invest in exploring and developing our own minds, it will be okay,” suggests Yuval Harari in his book 21 Lessons for the 21st Century.
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Harari → invest → Century
“But if we put all our bets on technology, on AI, and neglect to develop ourselves, this is very bad news for humanity.
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this → put → humanity
As we work to realize all the possibilities of AI, the bottleneck that will hold us back from maximizing its benefits will be what it has always been throughout human history: human nature.
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it → work → history
That is why I believe the greatest risk we face is that AI will get better but humans won’t.
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humans → believe → ?
We need the same relentless focus on helping humans tap into their better selves and achieve better health outcomes through healthier daily behaviors as we have on helping the machines achieve better frontier models.
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machines → need → models
What we don't need is hype.
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need → need → What
