{"id":916573,"date":"2026-06-30T20:15:41","date_gmt":"2026-07-01T01:15:41","guid":{"rendered":"https:\/\/newsycanuse.com\/index.php\/2026\/06\/30\/solve-all-diseases-you-say\/"},"modified":"2026-06-30T20:15:41","modified_gmt":"2026-07-01T01:15:41","slug":"solve-all-diseases-you-say","status":"publish","type":"post","link":"https:\/\/newsycanuse.com\/index.php\/2026\/06\/30\/solve-all-diseases-you-say\/","title":{"rendered":"\u2018Solve all diseases,\u2019 you say?"},"content":{"rendered":"
\n

This is <\/em>Optimizer<\/a>, a weekly newsletter sent from <\/em>Verge senior reviewer<\/em> Victoria Song<\/em><\/a> that dissects and discusses the latest gizmos and potions that swear they\u2019re going to change your life. This week\u2019s issue is a special early edition tied to <\/em>The Verge\u2019s Google I\/O coverage. You can expect our next issue at its usual time next Friday. Opt in for <\/em>Optimizer here<\/a>.<\/em><\/p>\n

Toward the end of this year\u2019s Google I\/O keynote, Google DeepMind CEO Demis Hassabis declared, with a completely deadpan face, that the company hopes to \u201creimagine the drug discovery process with the goal of one day solving all disease.\u201d<\/p>\n

This is the sort of statement that the phrase \u201cbig, if true\u201d was coined for.<\/p>\n

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

I\u2019m often critical of AI health in Optimizer<\/em>, but Hassabis\u2019 statement is one that deserves a lot more contextualization. Good science communication \u2014 something that is digestible enough for the layperson, that doesn\u2019t unintentionally promote misinformation \u2014 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 \u201cGemini is going to cure every disease because that<\/em> is the power of AI.\u201d This is just not how medical breakthroughs work in the real world.<\/p>\n

For decades, AI has been an integral part of medical research and discovery. The algorithms that wearables use? That\u2019s 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<\/a> found that AI played a major role in reducing the development timeline for the covid-19 vaccinations. That\u2019s 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.<\/p>\n

In the keynote, Hassabis pointed to Google\u2019s AlphaFold<\/a> and AlphaGenome<\/a> projects. The former helps researchers better understand protein structures. This is important because proteins play myriad roles in countless biological processes. Better understanding proteins \u2014 or even designing novel synthetic proteins \u2014 could be the key to unlocking cancer treatments. (Recently, scientists found 1,700 new proteins<\/a> 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<\/a>, discover a key protein behind LDL<\/a> (or the \u201cbad cholesterol\u201d), and understand another protein behind early-onset Parkinson\u2019s disease<\/a>, among other applications.<\/p>\n

\n
\n

\"Another<\/a><\/p>\n<\/div>\n

Gemini for Science is a group of AI tools meant to help researchers make new discoveries.<\/em><\/figcaption><\/p>\n<\/div>\n

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<\/em> study<\/a>, Google has noted that there are important limitations. For instance, this model hasn\u2019t 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.<\/p>\n

In many respects, what Hassabis was saying onstage wasn\u2019t 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 \u201cunsolvable\u201d disease in the next three, five, or even 10 years. Something like this is more likely to take at least <\/em>20 years, probably more. You might think that\u2019s a long time \u2014 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\u2019s an ambitious, aggressive estimate.<\/p>\n

But this isn\u2019t exactly something you have time to explain at a keynote where you\u2019re 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\u2019t necessarily conflate AI tools for researchers and consumer AI health features, but it\u2019s extremely human to do so.<\/p>\n

My gut reaction to Hassabis\u2019 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 \u201cirrelevant.\u201d<\/a> His logic is that AI could help develop and approve new drugs. Compare that to Hassabis\u2019 comment \u2014 something with a completely different context \u2014 and you can see how the layperson\u2019s reaction may leap to misleading associations. For example, that Google is parroting or lending credence to Kennedy\u2019s analysis.<\/p>\n

\n

\"A<\/a><\/p>\n<\/div>\n

Not for nothing, The Verge<\/em> has previously reported<\/a> on why Kennedy\u2019s 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\u2019s a broad statement that isn\u2019t wholly untrue. Yes, AI tools have long been used in this space. Yes, newer, more powerful models could make researchers\u2019 and pharmaceutical companies\u2019 processes a lot easier and more efficient. But it doesn\u2019t 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.<\/p>\n

Context is king, and it\u2019s usually the first thing to go in buzzy soundbites. This is why, when I first outlined the wellness grifter playbook<\/a>, I said step one is generally to juxtapose a broad fact next to a misleading assertion. To be clear, I\u2019m 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.<\/p>\n

There\u2019s 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\u2019s brand of longevity-focused biohacking. It\u2019s not a huge leap from \u201cAI can solve all diseases\u201d to \u201ctrack your biometrics, optimize with these supplements, and defeat death.\u201d<\/p>\n

Maybe AI will<\/em> eventually, one day, help solve all diseases. But if it does, the path won\u2019t be clear-cut or simple. A lot can happen in the next 20 years, especially in the political, societal, and cultural milieu that\u2019ll also<\/em> impact clinical research capabilities \u2014 so forgive me if, right now, I\u2019m not quite as optimistic as Hassabis.<\/p>\n

\n

Follow topics and authors<\/strong> from this story to see more like this in your personalized homepage feed and to receive email updates.<\/span><\/p>\n