Chapter 1: What is the seductive promise of superintelligent AI in curing cancer?
I think this is a false choice we're forced to make quite often in the discourse. It's like we either get our cancer cures and then we have to take on the risks of unemployment, extinction, X, Y, and Z. There's another path here where we get our cancer cures and we don't take that on, right? Like there's a different option on the table that I think often kind of gets pushed aside.
Hey everyone, and welcome to Your Undivided Attention. This is Tristan Harris. One of the most common arguments you hear from people racing to super intelligent AI is that it'll be able to cure cancer. It's incredibly powerful. It will do all these wonderful things like, you know, will help us cure cancer. It may help us to eradicate tropical diseases.
We are working to build tools that one day can help us make new discoveries and address some of humanity's biggest challenges, like climate change and curing cancer. I think one day maybe we can cure all disease with the help of AI. not help with cancer, not improve treatment, but cure cancer. Now, that's obviously an incredibly powerful and seductive promise.
And everybody listening to this right now likely knows someone who's died of cancer. It kills almost 10 million people per year.
Chapter 2: Why might the belief that AI can cure cancer be a false promise?
I lost my mother to cancer in 2018. This is a very personal topic. And that's why this promise is so potent and why we need to examine it. Because if the technology really can cure cancer, then anyone who stands in the way of it, anyone who wants to slow it down, even because of the serious risks, is essentially letting people die.
This is the idea of the invisible graveyard you hear about from the accelerationists. Think of all the people that we might be able to save by racing forward. In fact, the biggest risk is not going fast enough, they argue. But what if it isn't actually capable of solving cancer in the way it's been described?
What if we're being sold a false promise to justify a dangerous race and just to make a handful of people incredibly wealthy and powerful and avoid regulation? Our guest today argues that this is some of what is happening. Dr. Amelia Jaworski is a physician, public health researcher, and director of the Futures Program at the Future of Life Institute.
She's worked across scientific research, clinical trials, tech startups, and AI policy. And she recently wrote a paper called How AI Can and Can't Cure Cancer, in which she argues that the promise of superintelligence curing cancer falls apart under scrutiny, and that we can't use this false promise to justify the peril that we're currently facing. We're going to link to that in the show notes.
This is a deeply personal conversation for both of us.
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Chapter 3: How does personal experience shape the guest's views on cancer treatment?
Emilia also lost a parent to cancer. So hear her criticism of this promise as coming from a place of real concern and not just cynicism. It also comes from the belief that AI can be really revolutionary for medicine, but not in the way we're building it today. So, Emilia, welcome to Your Undivided Attention.
Thank you so much for having me, Tristan.
So first, I'll just say, Amelia and I are friends, and she's an incredible ally in this work. We were at the South by Southwest conference earlier this year. And you talked about the work that you've been doing on AI and cancer. And I was struck by how personal this is for you, since you lost a parent to cancer.
Before we get into your arguments, can you just talk about your experience of that and how it shaped your thinking?
Yeah. So when we hear the promise of AI in cancer, it triggers in all of us a personal experience because all of our lives have been touched by some sort of loss to cancer. And for me, it was deeply personal that I lost my father to cancer. I lost my father to cancer over a decade ago.
And when I sat down to write this essay and really think about examining the ASI to cure cancer promise, I went back through the medical literature to see how much progress had been made since the time my father passed to where we are today. And the reality is the survival rate is almost exactly the same as it was over a decade ago.
And so the problem of progress in oncology is probably one of the most urgent of our time and one of the most noble things we can deploy capital in service of solving and our talent in service of solving. But I think it's really important to examine whether putting that capital into a race to superintelligence is the best way to save the lives of our loved ones.
Yeah, I mean, having lost my own mother in 2018, Aza co-hosted this podcast. He lost his father to pancreatic cancer. I just want to establish, I think it goes without saying, anybody who has this in their family with a loved one wants to accelerate anything that will save their life, anything that has a chance. And yet there's so many issues that you find out about.
There's all these new things that are coming to market, but then they're not actually even available to your spouse or your loved one when they get this.
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Chapter 4: What are the limitations of AI in addressing complex diseases like cancer?
So in addition to the personal experience with loss and cancer, I having a background as a clinician and having gone to medical school, you also experience it from the other side, the frustration of providers about how limited of a toolkit they have to actually help people and encountering it sort of over and over again, day in and day out, having to deliver news of loss to families.
And so for me, this is deeply personal to me, both in terms of my life, but also in terms of my career. And also in sort of a parallel hat that I've worn in this AI policy conversation for the better part of a decade now, have seen these two worlds, which is biomedical innovation and the ASI race. And to me, hearing over and over and over again, AI is going to cure cancer.
Like we must build ASI because it's going to cure cancer. And yet that promise going entirely unexamined, just kind of being taken at face value that if we want to save lives and if we want to cure cancer, that this is the thing that we have to do. And I strongly believe that that is not actually the best way to start saving lives today.
And for listeners, ASI is artificial superintelligence, which is an AI system that is more intelligent and powerful than all of humanity's intelligence combined. You are not anti-AI for cancer. You just think there's a totally different approach we could be taking.
And first, we have to understand the problems with our current approach and then give people the hope that there actually is a totally different way we could be applying AI. that would actually get us to the outcomes that we're all looking for, as opposed to false promises to sell investors and keep pumping up your data centers.
Yes, I'm incredibly excited about the potential for AI and this general moment that we're in for progress in oncology. I remain really hopeful and excited about what the future has ahead.
For me, that's sort of three ingredients, which is one, supporting all of the AI tools that are being developed in specific areas of oncology that are making things go faster, cheaper, better, unlocking new capabilities. the exciting research that's happening in biology. So there's really exciting science that's happening that's sort of discovering totally new ways to think about the problem.
And so figuring out how do we support those scientists doing that good work and getting their discoveries out of the lab and into the clinic faster. And then thinking about how can we actually realign and redesign the system that we have and identify where the parts are in the current system that are either holding up progress or even taking it in the other direction, right?
And so I think that kind of tripartite approach is one that makes us well-suited to make a lot of progress in oncology in the next decade.
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Chapter 5: How does the current healthcare system impact cancer treatment progress?
Let's just steel man for a second. So why would they say it could cure cancer? I mean, it seems intuitive. AI understands language, patterns in language. So just the same way you can understand patterns in text and generate chat GPT essays, you could understand patterns in DNA and understand, you know, oncology.
Let's just steel man for a second why people believe because it's not like it's wrong, but it's seductively false and kind of an optical illusion, almost like a magic trick.
So we hear a lot about the ways that AI is helping advance progress in medicine in kind of the here and now, which it is, and it is going to be instrumental in doing so. But it's not chat GPT that is unlocking that progress.
It's scientists building bespoke models off of highly curated data sets to actually solve a specific problem, whether that be drug design or whether that be predicting toxicity, the list goes on.
So I think one piece to start is the AI will cure cancer promise, surfs a little bit on the AI progress that's already being made with tools and smaller models, and kind of bundling that as evidence as to why ASI will help solve the problem, because if the AI could get so much better, imagine how much better results we could be getting, right?
So that could be an image of a mammogram for breast cancer, or it could be blood test results, right? And then getting sufficient measurement of that phenomenon into a data set. And so can we generate a data set that captures all of the variability that we see in when we measure that phenomenon that's sufficiently representative?
And then can we apply intelligence to unlock insights that previously humans did not see or were unable to do at scale? And so in medicine, we're seeing this happen across many domains where we have good data.
So when we talk about early detection of breast cancer, AI is amazing at that because we have lots of great images that are high quality and curated by human radiologists of what is and what isn't breast cancer. So in that domain, AI does very well when it has the data to work with and that data is sufficiently representative of the phenomenon that we would like to study.
Right. So we have lots of mammograms. We have lots of results that confirm whether that mammogram did have a cancer or not, which means you can train a more and more accurate model. That one's solid. Correct. So what are some of the other narrow AI applications that Word's helping?
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Chapter 6: What alternatives to superintelligent AI could effectively improve cancer treatment?
Being able to really just lean into the chemistry part of biology, even more so than biology itself, to design new molecules, to design new drugs. So that also is, I'd say, an area that's quite exciting. And then there's clinical AI. So AIs that are actually being used in the operating room when they're excising tumors, right? And trying to figure out if they have a margin or not.
And that's because there's imaging databases of what a margin looks like, right? That an AI can look at and say, okay, I think we've got it or we haven't gotten it. I would just highlight those three examples. And each of those are not being developed within large companies. They're all being developed either by small startups or even academic institutions.
Whereas the ASI promise is saying, let's just digest everything. Let's take all knowledge and put it into one big giant model and see what insights it can derive from that model, right? And so the idea here is the more and more data we put into this, the more and more capable systems we can make. And one day we'll make a system that is more capable than humans.
And then thus we'll be able to do types of reasoning or types of insights that humans would not really be able to do or discover. And assuming in that set is a cure for cancer.
Right, so this is like if I read not just the entire internet, but all biology textbooks, had access to every science lab, had a robot arm doing lots of studies, plus integrating it with the GPT-7 trained data center, you know, with Sam Allman's Stargate cluster, that's just combining so much information that it's going to magically find all the needles in all the haystacks.
Kind of that vision of ASI, finding cures to cancer, right?
Correct, yes.
What actually is cancer?
So this is where the AI to cure cancer piece breaks down, is what is cancer and what is a cure? And those are two actually really fuzzy terms, even for the experts in the arena. So when we think about cancer in the early days, The way you thought about cancer was like, there's some cell, it gets a mutation, it goes rogue, and it makes a tumor, right?
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Chapter 7: How can AI be utilized to enhance data collection and patient outcomes?
And so the story of cancer has been, as we push science forward, we've uncovered more and more complexity to the disease, not less. So there hasn't been sort of a march towards a simplifying or unifying hypothesis. It's been a march towards an ever more complex and individualized type of disease. So fundamentally, when we think about the complexity of cancer, it is sort of a shadow self.
And there is a book I highly recommend folks read called The Emperor of All Maladies that really delves into this problem of why this is the most complex disease of all, because it is something that is co-evolving with us. It's dynamic, it's complex, and it's highly individualized. So
compared to other things like treating the flu or treating high blood pressure, right, which are more static biological processes relative to cancer. Like, this is really the big one in terms of complexity.
Okay, so let's go back to the promise made by CEOs. You have Dario Amadei from Anthropic who talks about compressing 100 years of biological progress into five to 10 years by creating what he calls a country of geniuses in a data center that are all dedicated to that. And that's obviously a really compelling idea.
I mean, just to go into that thought experiment, imagine the last 100 years of scientific progress. Just like see that in your mind's eye, all of the things that we got over the last 100 years. Now imagine that coming in the next 10 years, scientifically. That's like magic. This is sort of the science accelerator button.
It's what leads Ajay Akotra to say, this is why AI is like 24th century technology crashing down on 21st century society. But what is the problem with this argument of 100 years of biological progress?
I would say there's three main problems with that argument. The first one is in science, we actually have been accelerating knowledge and intelligence. We have an oversupply of human scientists relative to what we can actually resource in terms of experimentation. So the doubling rate of medical knowledge has gone from 50 years in the 1950s down to 73 days by some estimates.
We have an oversupply of scientists relative to number of lab benches and pipettes and people we can resource. And despite that acceleration and knowledge, we've noticed that therapeutics approved to actually help people have remained markedly flat. We actually haven't made commensurate progress. So
the intelligence that we've gained hasn't really been coupled to actually moving the needle on saving people's lives.
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Chapter 8: What are the risks of pursuing superintelligent AI without addressing existing healthcare issues?
So you argue, though, it's not only wrong, it's actually dangerous. Can you speak to that?
Yeah, so there is a danger to waiting and hoping that some future genie is going to solve a problem, which is in some ways the essence of what the ASI promises. It's sit, wait, hold tight, don't do anything in the here and now. In the future, there's going to be a cure for all of these problems. The reality is people are dying today, right? People need solutions today.
We need to actually be unblocking progress and moving the needle today. So there's the temporal piece of this where it's like people who have cancer don't have time to wait on the future, right? Even if that were to be true. The second piece of this that's really important to think about is we don't live in a world of infinite capital.
If we'd lived in a world of infinite resources and one bucket wasn't coming out of another, then there's a different argument to be made. But we're seeing that biotech is at a 10-year low in terms of venture funding of new ideas. And venture funding is really where you see the new breakthrough, exciting, high-risk types of projects that really can move the needle for patients.
We're living in a time where we're reducing our investments in sort of basic science, in science infrastructure, in data collection. And so the essence here is if we're going to take money away from doing the things we know will unblock progress, then we better be really confident that that is actually the fastest way to save lives.
Can you speak to the amount of resources that are currently going into accelerating ASI versus how much is going into, you know, let's say cancer research?
If you look at the amount of money going into building ASI and the infrastructure associated with that, that's an unprecedented amount of money in terms of investment in a technology. In 2026 alone, they're looking at $540 billion plus, right?
And if we want to compare and contrast that to, let's say, the National Cancer Institute, which was a pretty good barometer of what are we investing in the public in the basic science and understanding and moving the needle in oncology, that's only $7.2 billion.
So it is a fraction of the amount on an annual spend that we're spending on actually solving the problem of curing cancer as opposed to an ASI spend.
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