I disagree with Dario on most things, but this is spot on: “At this point, saying that AI will cure cancer is more a cliché than it is inspiring, and most people think it is deceptive. The thing that will work is actually curing cancer.”
The thing that frustrates me is that some people often draw a straight line from smarter models → cures, without acknowledging how much infrastructure is missing in between. We’ve still got a lot of work to do, people.
The regulatory bottleneck gets a lot of attention. But the bigger bottleneck may be that in many places, we’re lacking the right biological data. AI can’t reason its way to cures if we haven’t generated the data required to understand the disease in the first place.
Ironically, that’s why I’m most bullish on AI making dramatic progress in cancer first: decades of investment have produced extraordinary datasets across genomics, pathology, imaging, clinical outcomes, and more.
For complex chronic diseases, much of that infrastructure simply doesn’t exist yet. The good news is that many companies (including
@ChronicleBioAI) are racing to build it.
But model intelligence and biological infrastructure are going to have to scale together. I really do believe AI can cure all diseases but only if we build the infrastructure to translate intelligence into cures at the same pace that models improve (which is a high bar!). Otherwise, we risk having superintelligent AI with an incomplete picture of human biology, and delay that promise by years.