Why ‘Cure All Diseases’ Is a Misguided Goal

Toward the end of this year’s Google I/O keynote, Google DeepMind CEO Demis Hassabis declared with a completely straight face that the company hopes to “reimagine the drug discovery process with the goal of one day solving all disease.” That’s the kind of statement the phrase “big, if true” was invented for. But 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 claims in this newsletter, but Hassabis’ remark deserves far more contextualization. Good science communication , something digestible enough for the layperson without unintentionally promoting misinformation , has become increasingly difficult. The researchers in the I/O audience likely understood the claim to mean that advances in AI have dramatically reduced the time needed for medical discoveries. But for the average person, it probably sounded like “Gemini is going to cure every disease because that is the power of AI.” That’s just not how medical breakthroughs work.

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

During the keynote, Hassabis pointed to Google’s AlphaFold and AlphaGenome projects. AlphaFold helps researchers better understand protein structures, which play myriad roles in countless biological processes. Better understanding proteins , or designing novel synthetic ones , could unlock cancer treatments. Recently, scientists found 1,700 new proteins that might do just that. Traditionally, discovering new proteins, their functions, and how they interact with other molecules was a yearslong process. AlphaFold helps dramatically reduce that timeline. Researchers have used it to develop malaria vaccines, discover a key protein behind LDL (or “bad cholesterol”), and understand another protein behind early-onset Parkinson’s disease.

Gemini for Science is a group of AI tools meant to help researchers make new discoveries. Meanwhile, AlphaGenome helps predict mutations in human DNA sequences, potentially explaining why certain diseases occur. But a Nature study notes important limitations: this model hasn’t been validated for personal genome prediction, and it struggles to capture cell- and tissue-specific patterns. These nuances matter to researchers but typically fly over everyone else’s heads.

In many respects, what Hassabis said onstage wasn’t directed at you or me. And here’s some crucial context: these AI models and Gemini for Science tools will not 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 for a currently sick relative or your own lifespan. But as far as rigorous scientific research goes, that’s an ambitious, aggressive estimate.

This isn’t something you have time to explain at a keynote announcing forty bajillion other AI agents and features. The problem is that these statements travel far and have wide-ranging impact. For most of us, AI health has been a craptacular experience of regurgitated metric summaries, hallucinations, and tedious hand-holding. We shouldn’t conflate AI tools for researchers with 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 AI might make the Food and Drug Administration “irrelevant,” arguing 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 might leap to misleading associations, like thinking Google is parroting or lending credence to Kennedy’s analysis.

The Verge has previously reported on why Kennedy’s take on AI in health is flawed. 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 easier and more efficient. But that doesn’t eliminate the need for FDA drug trials, animal testing, or decades-old processes. AI is ultimately a tool that requires expert input and collaboration. 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. When I first outlined the wellness grifter playbook, I said step one is generally to juxtapose a broad fact next to a misleading assertion. I’m not saying Hassabis committed a colossal crime with his keynote statement. 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, a lot gets lost in this 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 possible and hope it finds the appropriate audiences.

There’s a reason 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 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 will also impact clinical research capabilities. So forgive me if, right now, I’m not quite as optimistic as Hassabis.