He’s probably wrong on medicine.

Ray Kurzweil writes:

After working in the field [of AI] for 61 years—longer than anyone else alive—I am gratified to see ai at the heart of global conversation. Yet most commentary misses how large language models like Chatgpt and Gemini fit into an even larger story. ai is about to make the leap from revolutionising just the digital world to transforming the physical world as well. This will bring countless benefits, but three areas have especially profound implications: energy, manufacturing and medicine.

This is from, “Ray Kurzweil on how AI will transform the physical world,” The Economist, June 17, 2024. (gated)

Kurzweil makes his case well.

Another excerpt:

By contrast, AI can rapidly sift through billions of chemistries in simulation, and is already driving innovations in both photovoltaics and batteries. This is poised to accelerate dramatically. In all of history until November 2023, humans had discovered about 20,000 stable inorganic compounds for use across all technologies. Then, Google’s gnome AI discovered far more, increasing that figure overnight to 421,000. Yet this barely scratches the surface of materials-science applications. Once vastly smarter AGI [artificial general intelligence] finds fully optimal materials, photovoltaic megaprojects will become viable and solar energy can be so abundant as to be almost free.

Energy abundance enables another revolution: in manufacturing. The costs of almost all goods— from food and clothing to electronics and cars— come largely from a few common factors such as energy, labour (including cognitive labour like r&d and design) and raw materials. AI is on course to vastly lower all these costs.

Where he falls short is on medicine. It’s not that he doesn’t make a good case that in a relatively unregulated market, AI could easily have huge positive effects on the kinds of drugs that we put in our bodies. It’s that he seems unaware of the immense power that the Food and Drug Administration has over what drugs we will be allowed to have.

He writes:

Much more laboratory research is needed to populate larger simulations accurately, but the roadmap is clear. Next, AI will simulate protein complexes, then organelles, cells, tissues, organs and—eventually—the whole body.

This will ultimately replace today’s clinical trials, which are expensive, risky, slow and statistically underpowered. Even in a phase-3 trial, there’s probably not one single subject who matches you on every relevant factor of genetics, lifestyle, comorbidities, drug interactions and disease variation.

Digital trials will let us tailor medicines to each individual patient. The potential is breathtaking: to cure not just diseases like cancer and Alzheimer’s, but the harmful effects of ageing itself.

This will happen only if the FDA backs off in a substantial way. Let’s hope, but don’t let hope overrule painful learning from experience.