‘Solve all diseases,’ you say?

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‘Solve all diseases,’ you say?

Google DeepMind CEO Demis Hassabis made a bold claim at this year’s I/O keynote. Not so fast!

Toward the end of this year’s Google I/O keynote, Google DeepMind CEO Demis Hassabis declared, with a completely deadpan face, that the company hopes to “reimagine the drug discovery process with the goal of one day solving all disease.”

This is the sort of statement that the phrase “big, if true” was coined for.

What Hassabis was really describing was Gemini for Science, a collection of experimental AI tools designed to encourage researchers to explore and make new discoveries.

I’m often critical of AI health in Optimizer, but Hassabis’ statement is one that deserves a lot more contextualization. Good science communication — something that is digestible enough for the layperson, that doesn’t unintentionally promote misinformation — has become increasingly difficult. Surely the researchers in the I/O audience understood the claim to mean that advances in AI have dramatically reduced the time it takes to make new medical discoveries. But for the average person (and arguably, even science communicators), it probably sounded like “Gemini is going to cure every disease because that is the power of AI.” This is just not how medical breakthroughs work in the real world.

For decades, AI has been an integral part of medical research and discovery. The algorithms that wearables use? That’s AI. Discoveries for noninvasive, wearable detection features? Machine learning, baby. Generative AI is a relatively newer entrant into this area of research, but it holds incredible promise. As part of my job, I often speak with clinical researchers, and many of the breakthroughs in consumer health tech over the years are due in part to AI advances. For example, this meta review found that AI played a major role in reducing the development timeline for the covid-19 vaccinations. That’s something that the entire world benefited from. However, the review also found that significant ethical, logistical, and regulatory challenges remain in using AI like this with regard to algorithmic bias, data privacy, and equitable global access.

In the keynote, Hassabis pointed to Google’s AlphaFold and AlphaGenome projects. The former helps researchers better understand protein structures. This is important because proteins play myriad roles in countless biological processes. Better understanding proteins — or even designing novel synthetic proteins — could be the key to unlocking cancer treatments. (Recently, scientists found 1,700 new proteins that might do just that.) Traditionally, to discover new proteins, what they do, and how they interact with other molecules was a yearslong process. Something like AlphaFold helps to dramatically reduce that timeline. In terms of real-life case studies, researchers have used this model to help develop malaria vaccines, discover a key protein behind LDL (or the “bad cholesterol”), and understand another protein behind early-onset Parkinson’s disease, among other applications.

Meanwhile, AlphaGenome is another model that helps researchers predict mutations in human DNA sequences. The potential for this model is that it may help researchers understand why certain diseases happen, though in a Nature study, Google has noted that there are important limitations. For instance, this model hasn’t been validated or even designed for personal genome prediction, and it struggles to capture cell- and tissue-specific patterns. These are important nuances for researchers, but something that typically will fly over the heads of everybody else.

In many respects, what Hassabis was saying onstage wasn’t directed at you or me. And, some other important context, these AI models and Gemini for Science tools are not going to magically eradicate cancer or every previously “unsolvable” disease in the next three, five, or even 10 years. Something like this is more likely to take *at least *20 years, probably more. You might think that’s a long time — especially in terms of what that means for a currently sick relative, or your own lifespan. But as far as rigorous scientific research goes, that’s an ambitious, aggressive estimate.

But this isn’t exactly something you have time to explain at a keynote where you’re announcing forty bajillion other AI agents and features. The problem is that these statements travel far and have a wide-ranging impact. For the majority of us, AI health has been, thus far, a craptacular experience of regurgitated metric summaries, hallucinations, and tedious hand-holding. We shouldn’t necessarily conflate AI tools for researchers and consumer AI health features, but it’s extremely human to do so.

My gut reaction to Hassabis’ comment was remembering a recent statement from Health Secretary RFK Jr. In a congressional hearing, Kennedy said that AI might make the Food and Drug Administration “irrelevant.” His logic is that AI could help develop and approve new drugs. Compare that to Hassabis’ comment — something with a completely different context — and you can see how the layperson’s reaction may leap to misleading associations. For example, that Google is parroting or lending credence to Kennedy’s analysis.

Not for nothing, The Verge has previously reported on why Kennedy’s take on AI in the health space is flawed. But as a refresher, in an interview with Tucker Carlson last year, Kennedy stated that AI could rapidly accelerate the drug approval process. That’s a broad statement that isn’t wholly untrue. Yes, AI tools have long been used in this space. Yes, newer, more powerful models could make researchers’ and pharmaceutical companies’ processes a lot easier and more efficient. But it doesn’t eliminate the need for FDA drug trials, animal testing, or various processes that have been in place for decades. AI is ultimately a tool that requires expert input and collaboration, and for the millionth time, scientific rigor is not a step that can be skipped willy-nilly.

Context is king, and it’s usually the first thing to go in buzzy soundbites. This is why, when I first outlined the wellness grifter playbook, I said step one is generally to juxtapose a broad fact next to a misleading assertion. To be clear, I’m not saying that Hassabis has committed a colossal crime with his statement during the keynote. Google (and Apple) actually does a lot of clinical research and puts effort into communicating that effort in blogs. But, like a game of telephone, there is a lot that gets lost in this current age of short-form social videos, reduced attention spans, and declining media literacy. I have no solution, other than to try and plug in more context whenever, wherever possible and hope it finds the appropriate audiences.

There’s a reason why sciencewashing is so prevalent today. A few buzzwords or bold statements lend an air of high-tech legitimacy that erases nuance. In Silicon Valley, you can see it in tech bros who attend peptide parties or subscribe to Bryan Johnson’s brand of longevity-focused biohacking. It’s not a huge leap from “AI can solve all diseases” to “track your biometrics, optimize with these supplements, and defeat death.”

Maybe AI will eventually, one day, help solve all diseases. But if it does, the path won’t be clear-cut or simple. A lot can happen in the next 20 years, especially in the political, societal, and cultural milieu that’ll also impact clinical research capabilities — so forgive me if, right now, I’m not quite as optimistic as Hassabis.