← In Good Company with Nicolai Tangen
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Transcript15 lines
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Hi, everybody.
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Tune in to this short version of the podcast, which we do every Friday. For the long version, tune in on Wednesdays.
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Hi, everyone.
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I'm Nicola Tangen, the CEO of the Norwegian Sovereign Wealth Fund, and I'm here today with Reid Hoffman, who is the co founder of LinkedIn, partner at Greylock, board member at Microsoft, and one of Silicon Valley's most influential thinkers. And today we are basically going to talk about everything that's going on. AI, human potential, all the things you've been up to. Reid, so wonderful to have you here.
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It's great to be here.
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Now, Reid, you've seen multiple tech cycles, from web 1.0 to the current AI boom. Just how does it stack up compared to what you've seen before?
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Well, look, each new tech cycle, and even if you do a bit of history and you kind of go back to printing, press and other kinds of things as, as early versions of this is new and impressive and builds upon the old and part of the current, you know, AI, just, you know, massive acceleration, much bigger than, much quicker, much larger, more impact than anything else is because it builds on the Internet, it builds on the cloud, it builds on, you know, kind of the massive amount of data we have and the mass amount of commute we have, which then makes it possible to build these amazing learning machines. And so I think it's obviously the largest now in all large things, you know. You know, like in your industry, the discussion of, you know, is it, is it a bubble? I don't think it is. If anything, like, I don't think it's a bubble in the usual discussion of, you know, could it get to a collapse. But the impact upon all of society is probably going to be the biggest of our lifetimes. And that's presuming that, you know, you and I have, have, have, have at least a number of decades ahead of us. And, and I think that's stunning because in industry and in life and in society, I think the fact that we've now made like learning machines as part of our firmament of the humanist world, the society, is, is landmark.
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Where, where are you seeing the most kind of genuine massive transformation now as opposed to experiments?
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Well, so, so the short. One of the things I tell people, it's probably useful here too, is if you're not finding the current frontier models to be useful in some substantive way to do that, like, for example, useful in your work, not just, you know, create a sonnet for your kid's birthday or, you know, take a picture of what's in your fridge and ask for what a recipe could be, which are great, but in some substantive way that involves information analysis, research, decision support, etc. Then you're not trying hard enough. And in fact, you know, one of the things that I think for the frontier models is if you're engaging in a substantive medical decision and you're not using you or your doctor are not using, you know, ChatGPT, Copilot, Gemini, you know, etc. For a second opinion, then you're also making a mistake. And so there's all a whole bunch of, of substantive into individual uses. I myself, you know, probably use, you know, kind of serious AI, not simple queries, not like, oh, you know, when was, you know, when did you know the following, like chart all the different, when all the different cryptos started and so forth. But like, you know, research, like light research things, but like deep ones. Like, you know, like if I'm working on a book like my book Super Agency, you know, what would a historian of technology give me a serious critique and what I'm doing or if I'm thinking about kind of the different kinds of molecules for therapeutics. Now, that being said, I'd say the, the probably leading adopters are a whole bunch of stuff in coding because coding gives you a, A, engineers understand this, international adopters. B, it gives you a. It's a precision in information work that, by the way, is a, is a kind of a foreshadowing drum to what's going to really happen in legal and medical and a bunch of other, you know, other areas of precision. You know, here is coding precision. Both coding precision will be used for legal, medical, educational, et cetera, but also will be the pattern by which the similar kind of precision in those areas will also be flowering and developed.
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What do you think are the biggest hurdles that you see for large organizations trying to integrate AI effectively?
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Well, typically, most large organizations for, with a rational basis kind of start with a like risk first, you know, avoid downside first, gain upside second. And part of the reason is because a large organization usually has a whole bunch of assets, not just brand market position and capital and the way that's developed over the years and decades to be efficient and have a market position and so forth. And so it has a position to say, hey, don't take risks on these things or, or choose these risks very selectively. But that leads to a general. And that's part of the reason why you tend to do a proof of concept as a little thing on the side that leads to a, like, don't introduce anything until you've run all the risks to zero. And one of the things that with AI is it will say, well, hey, there's a bunch of unknown risks here. Like for example, we're doing the meeting thing that I'm talking, that I'm talking about, well, what happened if we have all these transcript meetings, Is that going to increase legal liability? Is that going to increase information bleed and flow? And you know, might some of this information get outside of the enterprise in a way that's concerning. And, and we worry about these probabilistic machines, like do the probabilistic machines misconstrue something? And then that, that, that causes an error and you can list all the different errors and you go, oh, we should make sure all the error are brought to zero before we do anything. And you're like, well, that's a little bit like saying, you know, I'm going to, you know, drive from Oslo to Trondheim and I'm going to, I'm going to, I'm going to get all of the, I'm going to eliminate every risk before I get on the road. And you're like, yeah, it's not going to work. You'll never, never going to get on the road.
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You've seen so many entrepreneurs in your life. What, what are the common characteristics of great entrepreneurs?
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One good thing for many entrepreneurs is there isn't just one archetype, right? Since, you know, again, we're talking European, you know, there may be multiple Jungian archetypes for this and, but you know, important characteristics are to be super ambitious, right? If because you don't shoot for the stars, you don't even get to, you can't get to the moon as a way of doing it. To be both like, like kind of believe in, you know, that kind of huge outside capability, but also learning and adjusting. It isn't believe against any data and belief, but it's like, it's a, hey, I think I can do this. Because, you know, one of the definitions of entrepreneurship is your plans outstrip your current resources. Because almost by definition that's true for all entrepreneurs. Every stage along their along entrepreneurial journey, they have to be able to take risks smartly, frequently. You know, the issue is like, oh, just take risk. It's like, no, no, no, like, like risk blow you up all the time. But there is no entrepreneurship without risk. It's one of the challenges with a general European framework because they say, well, we want to minimize risk because we really like the stability of what we have and you're like, well, but there is no innovation without risk and there is no innovation without making errors.
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If you were to look at your most successful investments, PayPal, Facebook, Airbnb and so on, what was it that you saw that other people didn't see? Is there a general theme?
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Well, I'd say broadly, including LinkedIn, of course, in this investment entrepreneur is I saw why a number of smart people would think it was a dumb investment, and I saw why I thought I was right. So it's the contrarian and right thing. So, you know, in the case of LinkedIn, everyone thought there was no such thing as a professional network. People wouldn't put their, you know, their CVs online. There wouldn't be a utility of collaborating with people other than currently in your company, et cetera, et cetera. And so that, you know, that was a LinkedIn thing, and then you could never get the network to scale, scale to do it. In the Facebook case, people said, oh, yeah, there's a lot of activity, but it's all college students. And there'll never be money in college students. And sure, they'll, they're, you know, the amount of pure raw generation of time is important, but for college students, who's going to pay for it? The advertising market's not very good. College students aren't going to pay. Business model is bad for Airbnb. It's, oh, it's really strange that, you know, that you're going to rent a room or apartment or, you know, or a house from a stranger. And, and what is the trust? How do you build the trust and how does that happen? And part of the theory, there is actually, in fact, there's such demand for better, more unique experiences than hotels and at different price points in different locations. And it enables a network of entrepreneurship in the hosts for doing it, that, that will actually evolve to a kind of product that then becomes a brand name like Xerox or Kleenex or that kind of stuff in terms of how it operates, because people now refer to as an Airbnb as a, as a way of doing it. You basically go through almost all of my investments and it's that, oh, no, here's why a bunch of smart people think it doesn't work. And here's what I'm betting on, that does work. Now, by the way, some of my failures are in that too, because my bet was wrong. But that's the thing that leads to the industry transforming successes.