George Church: belief

Transcript context

…Yesterday I had dinner with a bunch of biotech founders. I mentioned that I was going to interview you tomorrow. Somebody asked, “Wait, how many of the people here have worked in George's lab at some point or worked with him at some point?” I think 70% or 80% of the people raised their hand. One of the people suggested, “Oh, you should ask him, how does he spot talent?” Because it is the case that many of the people who are building these leading companies or doing groundbreaking research have been recruited by you, have worked in your lab. So how do you spot talent? Well, I'm glad you framed it as spotting talent. I've heard at least one meme that all you have to do is show up and you'll get into my lab, which is definitely not true. First of all, there's a lot of self-selection. Frankly, we're an acquired taste. Technology development is not at all the same skill set as regular biology where you pick a gene, you pick a disease, you pick a phenomenon, and you hammer away at it for your whole life. This is more like you make a library where you have a million members of the library that are going to fail and maybe one or two will succeed. It’s a very different attitude. It's much more engineering, but it's even different from most engineering. Engineering doesn't usually use libraries that way, millions and billions of components that are non-random but many of them will fail. So the question was selection criteria. There's self-selection. The next thing is, in the interview, I typically tell them that I'm looking for people that are nice. I'm not necessarily looking for geniuses. We end up with a lot of geniuses. That's wonderful. But nice, I think, is highly predictive of how well you will do in the lab and afterwards. As a consequence, I think we have a kind of international set of alumni that are quite nice to each other even though they're supposedly in cutthroat fields. And I think they're nice to other people as well. So nice is one criteria. Multidisciplinarity. It's hard to build a multidisciplinary team from disciplinarians. If you have two people that each know two languages or two skills, even if they don't have anything in common, they have shown that they can learn a new skill and then they'll each add the skill that connects them. That's the third thing. Those are the three main things I would say. Final question. Given the fast pace of AI progress—your point taken, that we should be cautious of this technology, but by default I expect it to go quite fast and there not being some sort of global moratorium on AI progress… Given that's the case, what is the vision? We're going to very plausibly have a world with genuine AGI within the next 20 years. What is the vision for biology given that fact? If AI were 100 years away we could say we've got this research we're doing with the brain or with gene therapies and so forth which might help us cope or might help us stay on the same page. Given how fast AI is happening, what is the vision for this bio-AI co-evolution or whatever it might look like?…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.