can do much more, but the early efforts have been less than successful because they over marketed its capabilities.
X: You say in your paper you are not making precise predictions, but rather pointing out directions.
VK: Right. When innovation is involved, you can’t predict. You can speculate. So I call everything I write speculation, not prediction.
X: That doesn’t mean everybody reads it that way.
VK: No. So this is why I pick my words carefully. Even if they’re misinterpreted because people can’t tell the difference, at least the really intelligent people read it correctly.
X: So talk about the longer-term directions.
VK: I would stick with the forecast I gave in that paper. The rough analogy I did—the first picture with the seven generations of cell phones. My first cell phone was mounted on the passenger side of my car, on the floor board. Weighed about 20 pounds. It literally looks like a sewing machine. And then there was the Motorola Brick [Editor’s note: its full name was the DynaTac 8000X.] And then there was the simpler phone, then the flip phone. And then came the smart screen. Then came the BlackBerry, and then the iPhone. So, technology innovation cycles, because they rely on technologists, are about two to three years. And if you imagine seven generations of iteration on medicine, it’s 20 years—give or take 10. It could be a little faster, it could be a little slower. That’s my view of how medicine will progress.
X: Are we still in the first generation then?
VK: I think so. What will version 7 of medicine look like? I can see today’s iPhone is nothing like my sewing machine phone, which was v0. Successive iteration is how large changes cumulate in technology. If you think of a 20-pound phone and compare it to today’s iPhone, that difference or delta is what v7 of medicine will have from today’s early, clumsy digital medicine.
X: What about the broader implications of A.I. beyond medicine?
VK: I wrote my blog on A.I. in medicine and other areas almost six years ago. At that point, it was more ‘this should happen.’ I didn’t know what Google was doing, and frankly Google didn’t have that emphasis on it six years ago. But it was clear that there was a range of possibilities. I think of it as new veins of gold to mine. And if you spent enough time looking, you’d find more and more veins. And that’s what happened the last six years. We discovered a lot of these veins, a few very promising ones.
So there’s this A.I. gold rush, and lots of components and technologies are being built. We’ll soon be able to combine those pieces in unusual ways that offer amazing capability. There’s nothing that requires human judgment that machines don’t have a chance at doing much better, other than where we can’t get enough training data. Now I suspect even when you have very sparse data, machines will do better than humans. But that remains to be seen.
X: What’s this mean for people and the future of work and jobs?
VK: I spoke recently at a National Bureau of Economic Research meeting on the economic implications of A.I. Every economist’s answer has been that education will improve employment—and if machines are coming, we should just get more educated. But I contend that may no longer be true. If machines get smarter, more knowledgeable, and better at judgment than humans, then education doesn’t help. So we have to fundamentally rethink our assumptions. When machines are smarter than humans, what do you do?
X: That sounds pretty bleak. You’ve said you are a technology optimist, though, so solve that one. How do we do that?
VK: I am a technology optimist. Well, people don’t need to work, for those who don’t want to. [Editor’s note: For more on Khosla’s view on the future of work, see this Forbes article.] I enjoy working, so I’ll still keep working. But the guy who’s the garbage man, he won’t do that. The garbage truck will drive around, pick up the garbage without the person. I was just reading this book, one of my favorite books recently, called Scale by Geoffrey West at the Santa Fe Institute. He worked at a brewery in England at age 15—he’s a physicist now. He said every few minutes he was supposed to pick up a crate and load it on a truck. He did it for 12 hours a day and he got paid about a shilling a day. Now, nobody needs to do that job.
Selected Khosla Ventures Healthcare and A.I. investments
Khosla Ventures has a more extensive list of investments in healthcare than is currently posted on its website. Below is a slide showcasing the firm’s current healthcare portfolio. Most of these companies, Khosla Ventures says, have an A.I. component.
And here is another way the firm classifies its healthcare investments:
New science
Inflammatix, Sema4, DarwinHealth
Leveraging data to build an advantage and enable upskilling
AliveCor, Bay Labs, Zebra Medical Vision, Atomwise
Building things that can touch consumers directly
Ginger.io, AliveCor, Forward, Oscar
Better diagnostics and sequencing—going from analog to digital and opening up new avenues
Genalyte, Apton, Color Guardant, Whole Biome, Atomwise
