In Good Company with Nicolai Tangen - Reid Hoffman: Shaping the AI Era, Investing in Transformation and Calling on Europe

Episode Date: February 25, 2026

What's holding back AI adoption in large organizations? Nicolai Tangen speaks with Reid Hoffman, co-founder of LinkedIn, partner at Greylock, and board member at Microsoft. They explore why AI is the biggest tech revolution of our lifetime, how startups are deploying it effectively while large companies take a risk-first approach, and why Europe must get in the game rather than just regulate from the sidelines. Reid shares his contrarian investment philosophy that led to early bets on PayPal, Facebook, and Airbnb, and offers crucial advice: the next generation must become AI native. Tune in for an insightful conversation!In Good Company is hosted by Nicolai Tangen, CEO of Norges Bank Investment Management. New full episodes every Wednesday, and don't miss our Highlight episodes every Friday.  The production team for this episode includes Isabelle Karlsson and PLAN-B's Niklas Figenschau Johansen, Sebastian Langvik-Hansen and Pål Huuse. Background research was conducted by Tobias Hyldmo and David Høysæther. Watch the episode on YouTube: Norges Bank Investment Management - YouTubeWant to learn more about the fund? The fund | Norges Bank Investment Management (nbim.no)Follow Nicolai Tangen on LinkedIn: Nicolai Tangen | LinkedInFollow NBIM on LinkedIn: Norges Bank Investment Management: Administrator for bedriftsside | LinkedInFollow NBIM on Instagram: Explore Norges Bank Investment Management on Instagram Hosted on Acast. See acast.com/privacy for more information.

Hi everyone, I'm Nicola Tangen, the CEO of the Norwegian Soan 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, read. So wonderful to have you here. It's great to be here. And, you know, one of these awesome things about the modern world is,

You know, here I am in Seattle, there you are in Oslo, and we can have a fully robust conversation. It's not quite spanning the globe, but maybe in topic. 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? 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, things 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 massive 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, like in your industry, the discussion of, you know, is it, is it a bubble? I don't think it is. If anything, I don't think it's a bubble in the usual description 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 and I have at least a number of decades ahead of us. And I think that's stunning because in industry and in life and in society, I think the fact that we've now made learning machines as part of our firmament of the humanist world, the society, is landmark. Now you see this from both sides, given that you're on the market. Microsoft Board, which is like, you know, the incumbent, and then you also invest in some of the new, you know, more disruptive companies. How does that shape your way of thinking? Well, you know, the frequent way that people put this kind of conversation is, it's going to more benefit startups, more benefit large companies, you know, et cetera, et cetera. And the answer is massively all. And which one more, I don't know. But I think it's important on the, you know, in kind of the

AI, you know, kind of the revolution that we're doing, that we support both startups and large companies. Both of them have a substantive role in their contribution to industry and society. So, for example, startups can't be doing the kind of things that the frontier models are doing without at least the support of the frontier model companies. So, you know, like, for example, Open AI could only get to its position because Microsoft supported it with compute. And, you know, similarly, you know, that's what's happening with Anthropic and others, as it requires these, the hyperscalers and the massive cloud companies, not only in the work they're doing themselves, but in the work they're doing to support the startups. On the other hand,

the kind of risk and innovation in the startups is the thing that will drive a lot. So, like, example, there's a, another, you know, not, you know, relatively common meme that is, you know, like, well, enterprises are trying to deploy it, and they're not deploying it that well, you know, does that mean that it's overheight? And the answer is, well, if you look at small startups and the way they're deploying AI, it's magical. I mean, the speed at which you can move with a smaller number, with an initial number of employees, the way that you're, you can empower all aspects from team meetings to productivity and all the rest is already like massively in motion. And it's just the question of, well, you know, those

companies will grow and then the large companies will need to either adapt to that, probably with some speed in the next few years, or run the risk of being, you know, kind of horse and buggy companies once the automobile industry starts going. And so I think it's a kind of an, it's a all of it. The benefit of being on the Microsoft board is you see, you know, both what Microsoft's doing and an open AI and, you know, a whole bunch of the different companies that partner with Microsoft, you know, Anthropic and Code and all these others. And, you know, by doing startups, myself and through Greylock and other places, we can also see a number of that kind of the bold edge innovation. Where are you seeing the most kind of genuine, massive transformation

now as opposed to experiments? Well, so, so the short, what? One of the things I tell people, that'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 kids' 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 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, chat CBT, co-pilot, Gemini, you know, et cetera, for a second opinion, then you're also making a mistake. And so there's all a whole bunch 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 fall, 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 in what I'm doing,

or if I'm thinking about kind of the different kinds of molecules for therapeutics in Monas AI, company I co-founded the beginning of the year with Siddharamugurgy, you know, what are the different attributes of the different kind of therapeutic molecules, you know, between the, you know, different kinds of small molecules that you use as drugs and pharmaceuticals and what's the history of them been and so where, then you can actually get

pretty deep research. that being said, I'd say 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 kind of a foreshadowing drum to what's going to really happen in legal and medical and a bunch of other things because, 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. So I think that's kind of where you see, you know, the kind of the most, you know, put into work and workflow so far. But, you know, part of the reason, of course, I invested in Manas and co-founded it with Saddamukerji was because it's like, well, but there's these other areas

that people aren't thinking about yet like drug discovery. What does an AI native drug discovery company look like that are part of the thing that I think we will also see simply, you know, amazing, stunning magic on. Are you surprised that we are not seeing productivity gains yet that we're not really seeing it in the productivity numbers?

I'm not surprised yet. I mean, although, you know, of course, what is that old line? I see computers everywhere, but not in the numbers. You know, it's kind of like the, you know, kind of a, like there's a bunch of different things within the, within the kind of GDP side where it's a kind of an odd measurement. Now, that being said, I definitely see it in all the startups. And I think what's in part happening is companies figuring out what to do because most of the things, like the class. way a company experiments with and builds technology is they go, okay, this group of, you know, three, five, ten people are all going to go do a little proof of concept and we're going to see

what comes to that. Well, AI can work in some of those areas, but you have to be selective of what the project is. So, for example, if your project is, and this is one of the things I think, you know, call it within, you know, two-ish years. It could be four to five, but it could already happen now. is any, like basically every organization should be saying, we're recording all of our meetings and we're running an AI on the recording of the meeting, not just for the transcript, but also to do all of the suggested follow-ups. It's like, hey, did you, you mentioned this, you should probably, you should probably let Nikolai know and make sure that that's the case. Or, you know, you should make sure that you get approval from Satya on the following thing,

or this other group is doing this. Like all of that kind of thing is a already, like the technology is there to go. You should already be doing it. I'm doing it. You know, work groups should be doing this. And yet most companies aren't doing that. There is in a massive acceleration in terms of speed, information, risk analysis, you know, updating communication functions, you know, on a whole stack of things,

which would then begin to get into the numbers in any industry. And that's the kind of thing that I think we, you know, like as opposed to a proof of concept, just start doing all the meeting stuff or both. What do you think are the biggest hurdles that you see for large organizations trying to integrate AI effectively? Well, typically most large organizations 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 and market position and capital, 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 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 is 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 could say, well, hey, there's a bunch of unknown risks here.

Like, for example, we're doing the meeting thing that I'm talking, what happened if we have all these transfers to 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? prize in a way that's concerning. And we worry about these probabilistic machines, like do the probabilistic machines misconstrue something?

And then that causes an error. And you can list all the different errors. And you go, oh, we should make sure all the errors 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 Tronheim 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're never going to get on the road. And so I have to say I'm pretty, I have to say I'm pretty impressed by your local knowledge here. Have you actually driven there?

I have not. But it is on my bucket list that come to Norway, not just because of Northern Lights, but the fjords and all the rest. So, so I know a little bit about Norway's geography. A friend of mine is is a Norwegian-born and has been talking to be about sailing in the fjords for at least two decades. Very good. Now, I do think it's interesting

because it's the first time I've seen the potential for compliance officers to kill companies, you know? Yeah. No, exactly. And it's one of the things where you've got to go,

look, is this really a risk? Not could there be something or would there be a bad press article or something else? But it's like, is it really a risk? Because if it's not really a risk, like deploy, iterate and learn, right? And so, like, I actually think, like, it's literally, the real question is just,

when do we get to where it's considered to be weird when you're not having an AI in the meeting, helping you with the various aspects of the meeting? Because, like, you know, part of what we find in the venture community in Silicon Valley and in Greylock is that we record these meetings all the times now because it gives us a set of notes, it gives us easy follow-ups, it gives us something that we can run through AI to say, hey, can you do research on these questions, right, that we came up with? You know, because, you know, part of what, like, like, if people, if this is news to people, you're not using AI enough, which is one of the things we two right now is you can trade off, call it minutes of compute to instant research, like very, very different than what you get

from a Google search and so forth. And so you go, okay, you know, I'm really interested in, you know, what is the pattern of data centers going to evolve and, you know, kind of energy ecosystem and green energy? You can very easily set, you know, the AI deep research queries on this and get some really good answers. Not perfect, not to say errors, but really good answers, like beginnings of answers in like 10 minutes because it does like 10 minutes, 15 minutes,

to compute and does that. And by the way, part of what the frontier models are developing is they're developing the capability for this to work coherently in compute for hours to get to a result. And so, like, for example, you know, some of what my team does, like, for example, where I have a podcast called Possible and we've released Possible in French because we basically had AI, you know, kind of do all the translation and compute. And like he sets it on the evening, he goes to bed, he sets up going in the morning and an evening, goes to bed, guts out in the morning, checks it, sees which things need to be fixed, et cetera, et cetera. That, this is going to become part of a work process. And so having compute be amplifying of our

labor is like, it's here now. It's, it's one of my favorite science fiction author is William Gibson wrote neuromancer and others. One of his quotes, that I really like is the future is already here. It's just unevenly distributed. Well, you have a lot of the future where you live and also in Silicon Valley. Do you think that dominance is under threat? For instance, from immigration policies and so on? Well, for sure.

I mean, look, one of the things that is there's multiple things that are crazy about the current administration global relations. You know, one is, you know, the U.S. as a country for every decade built on being the best superpower for immigration in the entire world. It's part of what's gotten the U.S. to where it is. And Silicon Valley is a microcosm of that. I mean, how much of Silicon Valley has been built by, you know, immigrants from all over the world, including both Western and Eastern Europe, like a lot of very smart people coming to Silicon

Valley and the U.S. from there, also India, you know, also China, et cetera. So the immigration is one. But the next, of course, is that part of our ability to have global industry is because we have good global relations or have had good global relationships that has been, you know, kind of most catastrophically damaged in the entire history since World War II by this. administration, right? I mean, they're going out and saying, you know, give us your lunch money or we're going to tear a few as kind of a bully tactic on this kind of stuff is, is terrible ways to deal with partners, trading allies, and other things. And so that's another area of significant damage. And so, yes, I think there is, there is some of the areas that we've had is historic strengths

are under extreme challenge. Now, that being said, part of what I want Europe to do more of is to get more into the technology game. Like, one of the things I've told various, you know, I take trips through the UK and France and Italy and other places, and I say, look, getting in the AI game directly is important. Let me give you a European metaphor, which is I'm using the word football in the European sense,

not in the American sense, which is, if you think, of AI as a football game, World Cup match between the U.S. and China, and what Europe tries to be as the referee, there's two problems. One is the referee never wins, and two, no one really likes the referee. So you've got to get on the pitch, right? You've got to be doing stuff, and it's really important. And they say, well, we're behind and we don't have the hyperscalers and computers. We'll do deals with the hyperscalers in order to get compute. So what would you, so, what would you do specifically? So now I give you, let's say now, I don't know who I am, but I am somebody with a little power and I say, hey, we can you

Can you fix Europe and AI? What do you do? Well, it's work. It's not easy. One is, I'd say, go-do deals with the various companies at hyperscalers that can build a bunch of compute. Those deals don't necessarily require money. They could just be here. We have facilitated a bunch of energy, data setter permits, ability to build stuff to be to build your business here, you know, and build these data centers. In return for this facilitation, we want to make sure that you are enabling kind of European companies to be able to access that compute, either in building new applications or inference for deploying them. And in

return for that, we also want you to make sure that you're, you know, that some of your AI technology, that if you happen to be building it, is also deployable in a kind of a, a, kind of a a good way within the European sector. And you say, well, but that's still a bunch of U.S. technology. Of course, you're going to have a bunch of U.S. technology, not just the least of which is like the semiconductors, the chip infrastructure, because, you know, Nvidia, obviously, but then TVUs and, you know, other kinds of things as ways of doing this. But there's a tremendous amount of value in the applications.

There's a tremendous amount of value in, like, where do we bring our, you know, kind of areas of competitive advantage? I'm like, for example, if I was, if I was being a, you know, European entrepreneur, I'd say, well, one of the benefits we have of centralized medical systems is we can use that centralized medical system to build a whole bunch of different unique medical applications. And we should do that. And we should, we should dominate that not just within Europe, but globally. I mean, one of the things that, you know, most often is the mistake in how European governments say, well, we just need to have a European one. You know, we just need to have an Austrian one. And it's like, no, no, no, no. technology industry is strongest when it's global. So you want to say which are the ones that we can build that are global? Which are the things that are like Spotify, like Agen, like, you know, etc. And how do we get those kinds of things? Because, you know, here are some.

And how do we have a like kind of global competitiveness with the edges that we have here? And I think that's the kind of thing that I, you know, basically every, every, you know, European leader who reaches out, I try to help. You, continuing on the topic of ramping and scaling, you wrote the book on Blitzscaling, right? And does that playbook work for AI where the scarce resources are GPUs and electricity and not users? Well, it is actually centrally, like I think, if anything, the AI, so first backup. So for those who don't know, the reason I wrote this book, Blitzscaling, it was. kind of like, what is the lessons I learned from how Silicon Valley builds the technology companies of the future? And if you take the greater Silicon Valley area, like the entire metropolis area, it's seven plus million people.

And that's not all in the tech industry. That's just, you know, in like everything in the Bay Area, which is, you know, a little over twice the size of Ireland. And why does more than half of the global NASDAQ come out of Silicon Valley? And there's a whole set of different things, immigration we mentioned before. But one of them is it's just learning about how to build the technology companies to create the platforms, the networks, the kind of the global technologies of the future through these companies. And the answer is blitzscaling. And the two places in the world that I see demonstrate blitzscaling in really robust general ways are Silicon Valley in China, which is part of the reason why the two technical. And so it's like, look, the more that we have, especially in Europe, but like in the rest of, call it Western democracy world, of ability to build places like Silicon Valley and to have tech center innovations, I think the better off the world is, the better off even Silicon Valley is. So that's the reason why I wrote it. Now, if you look at what's happening with, and the precise definition of blitzscaling is taking the risk of going big in an environment of uncertainty.

That is exactly what's happening with AI. Like if you look at, you know, the discussion around AI bubbles and everyone else, it's like, well, we have such conviction that large-scale training is going to build such interesting learning machines that we're investing, like I think it's roughly like $60 billion to get a gigawatt of compute to do, and, you know, we're trying to get from multiple gigawatts in multiple companies to be able to build this stuff in a way that we can then deploy it through other gigawatts that compute in inference, because this will have such a elevating effect

to everything that we're doing in, think of it as, you know, work, life, and society. And so, but yet, you know, it's like, well, you know, we see open AI making some money, and we see Anthropic making some money, but like, is there any of the rest of it? And that's exactly what Blitzscaling is. So that's what we're doing.

Now, there are, you know, There's limited numbers of GPUs that are growing as fast as they can. There's limited data centers growing as fast as they can. And there's limited energy. And interestingly, energy may end up being the weird, once again, almost back to the days of oil, the geopolitical issue that really kind of puts all this stuff together. But by the way, of course, you also need data and you need talent. And critically, you need a loop of adoption.

and all of those things are part of what, you know, kind of makes the higher revolution. So I think the blitzscaling is, frankly, I'm thinking about writing some of this up in the next couple months, is blitzscaling is there's specifically like all of the rules in the blitzscaling book are being applied, and there's more that are being developed and added. Another thing you talked about is the kind of the splinternet, the fragmentation of the internet. How are we seeing the same type of developments happening now in the AI world, i.e. the race between the US and China mainly? Well, there's this whole question around, like generally speaking,

it's good to have kind of just like a global trading system and a global communication system, a global telecom system, and a global transport system. There's good to have a lot of stuff that's global. And, of course, you need to balance it out, with various issues of national culture, control, sovereignty, et cetera, as ways of doing it. And the natural instinct that most people have, given how important the Internet and digital is, is like, and very few do it other than China, is to kind of say, well, I have my own, and it's kind of federated.

And so I think that the kind of splinternet, there's still a lot of tensions heading in that direction. It's, again, I think, among the problems that the current administration is creating for the American people, American industry, because I think if anything, they're accelerating that. You know, like the tariff policy is a good way to try to incent other people to trade with China versus the U.S. I think that's a deep danger. And then similar on the kind of the Internet side. So I think that's important. Now, that being said, roughly speaking, the things that are successful within global platforms are the things that get the broadest possible base.

So one of the reasons why AI tends to be most naturally trained out of English versus, call it any of the other Western languages, is because the vast majority of the content that's in the data, everything else, allows that to happen. And matter of fact, when I was on the Open AI board and got early access to GPD4, one of the things I did as kind of an experiment is I had GPD4 write some poems in English, then write some poems in Hindi, and then write some poems in English and translate them into Hindi. And then I went to some of my poetically minded Indian friends and said, okay, without a kind of blind taste test, which of these poems are better? and all of the poems that literally the top half where they won the written in English and translated to Hindi because of the ability to have that language generous there.

And so that's going to be one of the things that was we get to the splinternet is going to say, well, actually, in fact, we're going to have to rely upon the languages for the inference engine everywhere else that have the vast majority of the data for the training of these things. It doesn't mean that we can't ultimately get to

a Norwegian system, a Swedish system, you know, you know, et cetera, et cetera. But like, it's going to be, to get the high quality system, you're going to start with some of the inferences. Now, in a classic European thing, you say, well, you don't have to just do English, you're going to English and Chinese and a few others, and we can be pluralistic in how to,

in case there's any latent, you know, kind of cultural biases and so forth, to try to be kind of a little bit more of a pluralist understanding, which is one of the things that I love about what Europe's been doing in kind of the post-World War II era, and that could be something that is a feature rather than a challenge for what's going on. But you have to kind of navigate those things. And so I think the tendencies the splinternet will still be there,

but need to be resisted substantially in at least some vectors. When you look at the amount of capital flowing into data centers and chips and energy and the circularity, and the new kind of debt issuance and so on, what are your reflections when it comes to the speed of the development here? Speed and magnitude. Yeah, so it's a natural worry to say something like Nvidia invests in a company,

puts a bunch of money in a company, the company then buys more Nvidia chips, and then Nvidia invest in other countries that buy more Nvidia chips. And this would be a classic bubble if the only use of those Nvidia chips was doing things that didn't have, kind of call it were separate economic productivity. Like if you didn't have this view where part of what I think is coming with AI is we're going to get intelligence with the scale and 24 by 7 availability of electricity.

So intelligence is going to be as available as electricity. Well, that's from these compute centers. And so the fact that they're building all this computer, isn't like its own little isolated bubble that's going to collapse, it actually is creating the compute infrastructure that we already see, even in these very early days,

a tremendous amount of demand for it. Because at a company like Microsoft, each GPU is kind of being allocated between like inference where customers want it, because basically you could sell everything to customers. Two is internal groups that want to do R&D themselves,

in building it. And three, you know, it's kind of like groups that want to do, you know, like deploy things in terms of their products. So you've got, you've got, you've got all these competing for it. And so even if you suddenly ramped down the training speed a great deal, there would still be a lot of economic demand for the commute. Now, a little bit of how I think about this is it's kind of, I don't think. think the AI bubble as getting to a collapse is, you know, is actually, in fact, a real worry. The AI bubble leading to potential pricing corrections is definitely, of course, possible because you go, well, but look at the astronomical prices and so when you say, well, but actually,

in fact, you know, like if you created a data center for, call it $60 billion, you know, you're still going to run the data center and you can run it profitably, but you may not run profitably from a cost basis of $60 billion if all of a sudden, you know, like the level of demand takes the premium off it, right? It could be that as a risk. But that's not a bubble correction. That doesn't create the kind of contagion and debt and banking and other kinds of things that people most worry about with bubbles. And that's the reason why I'm not that concerned with the current worries about, you know, is it circular round trips or other kinds of things? Because what I actually see it is kind of an evolution takeoff to getting a compute infrastructure

there that we have intelligence available, essentially with the same scale and an always-on availability that electricity has. Related to this, what do you think the primary computing device would look like in the future. I'm not sure. I mean, the kind of stuff that Johnny I, obviously, working on, open AI is working on some things. Have you had a look at it? I haven't.

Johnny Ive got purchased in the company after I was on the board. It's a little bit of kind of classically with technology is like, will there be something that surprises you, the answer is absolutely yes. Can you predict the surprise much harder and an easy way to look foolish? I do think that there's a real value in all of the scale compute that's going on. So the fact that there is a tremendous amount happening through massively centralized data servers is, I think, a continuing trend. Now that being said, you know, like, for example, take a little bit of what the electric vehicle revolution is. Electrical vehicle revolution is not just electric, it's a, and you know, the green, all the rest, but it's also putting a data center

with sensors on wheels. And that data sensor with, data center with sensors means all kinds of things. There doesn't mean autonomous vehicles, but also means a much more real-time map for, you know, mobility, emergency management, you know, a bunch of other things that are all there. I mean, imagine, like, for example, you have a whole bunch of autonomous vehicles and you need to get the emergency vehicle through currently, if it's got gridlock on a highway, you just kind of hoge, drive on the side of the road, side streets, et cetera, et cetera, you literally could go, AVs, you know, clear the way, and they all kind of go, okay, and clear a channel, and the fire trucks can get through, the ambulances can get through, you know, et cetera, et cetera.

And so, you know, this is all part of all of that part of revolution. And, of course, that will have a lot of local compute and local compute areas. Now, I guess what I would say is I think it's too early for the whole glasses revolution being the next social thing. I mean, a variety of companies are doing some pretty interesting work there. I don't know about these pins and other things. I mean, the classic thing tends to be is, hey, if it works pretty well with your phone, we're all pretty acculturated to the phone, right? So what does that mean exactly is kind of an interesting question. I do think that the notion of like an always on AI assistant for all of us is just a question of when and how than if.

Because, you know, the kind of like, for example, I don't know if your listeners track the fact that, you know, I'm one of these people who made a digital version of myself, read AI and demo it in various ways. And part of what I was doing that is because normally when people talk about deep fake, they tend to say, oh, that's just bad. It's like it's imitation. It's going to be used for like cyber hacking and fishing and and political misinformation. By the way, those are all legitimate issues, serious ones. But I was like, okay, let's play with it because there's always something we can steer towards that's good. And one of the things I realized by creating read AI is that it's a small number of years where like voicemail goes away because what happens when when someone calls Nikolai, Nikolai AI answers and says, hey, you know, and I.

And then it says, oh, it's read. And, oh, this is really important. Let me see if I can interrupt him. Hold on. I'll get, like, hold on a second. Let me second. I was like, no, actually, he's in a board meeting right now. Can I take a message?

He'll probably be able to call you back. I don't know, seven o'clock, eight o'clock. Would that work for you? You know, that's what it's going to become, right, as part of it. And by the way, part of the kind of the digital avatar version is it's, it's the representation of you in terms of how you're doing it. And so that's, you know, part of what I think is saying. And that will also be edge computer. And so you're asking what are the AI devices in the future?

And it's kind of like every single device that has a, has anything of compute capability will start adding AI to it. And some devices that don't currently have compute capability will have compute capability added into them in order to do it. Like, for example, you can imagine, you know, pretty easily like washing machines as part of being green and saving electricity. You kind of say, hey, just set it up to run when the grid has already got a, you know, has already got a surplus of energy. And so I'm being green about, you know,

using energy that that is otherwise unused and not creating the spikes that cause the whole expense of the thing. And then you'll have, you know, simple compute in your washing machine and dryer, you know, as instances of doing this. So anyway, so like, I think perhaps the answer is actually everywhere, as I think about your question. I think it's just so much fun to be alive. Yes. So exciting, no? Yes.

No, no. It's, look, we've created the most amazing technology in human history, more, better learning device than the book. Now, of course, it builds on them. But it's just, it's, you know, like, for example, people like to talk about, and I think this is mostly just people who misunderstand. Like, oh, is there's going to be a huge spike in energy because of use of AI, electricity, and is that going to be an climate.

impact? And the answer is, well, if you're using intelligence, you can use intelligence to mitigate climate impact. And so as long as you're using some of this electricity, AI electricity for being smart about the climate, you're going to be a net benefit. And as a tangible example, Google, which runs some of the best data centers in the world, when they applied their AI technology, figured out how to get savings of 40% in their data center. And that's not going to, you know, these old inefficient grids and all the rest of this stuff, which would be a very natural way to really get a lot more energy efficient using intelligence. Reid, let's change tax area a bit.

And we'll put on a little jingle here so that people understand that actually we are moving slightly to another side here. Now, you've seen so many entrepreneurs in your life. What are the common characteristics of great entrepreneurs? entrepreneurs. 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? 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 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 entrepreneurial journey. They have to be able to take risk smartly. Frequently, the issue is like, oh, just take risk. It's like, no, no, no. 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. And so you have to allow errors and risk. And you correct for them, right?

It doesn't mean you allow them forever, but you kind of, you go out and you play it. So that has to be another thing they're capable of. Another one is you have to be able to learn the journey. And the learning the journey is lots of things. It's like you have a product or service idea. Can you assemble talent? Can you assemble capital? Can you do the initial things to get your business together?

Can you then be growing and scaling your company, which means scaling the executives and scaling yourself, you know, and doing all of that. And that kind of, we have this phrase in American English, multi-tooled athlete is really important. Now, part of that for an entrepreneur, whether she or he is doing this, is to be kind of like, okay, that's not my tool, but I can hire someone who has that tool. Because you're assembling, like part of it is entrepreneurial companies and entrepreneurs launched through networks. It's one of the reasons why, like I helped set up, you know, kind of this program called Silicon Valley comes to UK. because it was like, you know, bring the Silicon Valley network and learnings into the UK. And we've done that occasionally in other places too, Japan, France, other places.

And it's the, because we want more of this kind of understanding, but you need that kind of, not just with the founders, but you'll also need it with the executives and early employees and much of other things in order to play that out. And the founder needs to be, you know, kind of knowing that that's what they're looking for, that they're always trying to assemble the strongest possible network to realize this vision that is evolving and changing as the market changes and as scale changes. And so that's the set of attributes you want. But sometimes, by the way, and then you get to where are the places you must have a complete edge.

Well, some industries, like you've got to be a really good salesperson. Some industries, you've got to be a real good finance person. Some industries you have to be a really good engineer. Some industries you have to be a really good product person. And one of the things I look at when different, you know, companies is, is this person a fit to this problem now and is it scales? And so, like, for example, one of the kind of aphorisms that tends to circulate around Silicon Valley is, oh, you want to invest in people who started coding before they were 12 years old. And by the way, that's a good sign for it. They're deeply immersed in software.

They have reflexes, software tech. They understand it. understand kind of what the different patterns are and how they're evolving. But like, for example, Brian Chesky, the CEO and co-founder of Airbnb, there's not that. He's a design person. But so, you know, classic, that's a classic aphorism. There's a lot that's good to it. But I look at Brian, I go, he's perfect for Airbnb because he's approaching the marketplace, the experience of travel, the experience of staying somewhere, the experience of hosting somewhere, as a design thing where he comes from RISD. And the design attribute is actually in fact,

the right attribute. So it's kind of different sets. And that's part of the reason why not just different fitness functions, but different kinds of ways of entrepreneurs are better at different kinds of businesses. Some are very good at enterprise, another suck at enterprise. Some are very good at consumer, and many suck at consumer. So it's kind of a different set of configurations. So now you sit in your Greylock office and in I Come. How long time does it take for you to decide whether I'm worth backing or not? Well, it depends a little. bit. Ideally, like I did with the Airbnb folks, I had reference check them before I met with them. So I interrupted them. I think it was a few minutes into the pitch. And I said, I'm going to

make you an offer to invest. Let's take the rest of this pitching session as a work session. And let's work together on this stuff. So you get to see me. I get to see you. We can see if this is a good match. And it's one of the reasons why they declined a financing offer that was twice my valuation in order to work with me because they said, hey, this is like he is actually helping us solve these problems. That's actually, in fact, really valuable. So that, you know, can happen. Airbnb is an example. You know, Zinga, Facebook. You know, these are other companies that I, you know, basically made an investment decision in the first minutes in. Sometimes it takes days because sometimes you've got a sense of, okay, I need to reference check afterwards.

Sometimes it's, okay, I wonder what the competitive landscape looks like here, or are there other people developing technology like this? Or do I think that this technology will bear fruit at the right sequence? Because usually the ideal thing in a venture investment is, for my kind of stuff, is at day zero, it looks crazy, like some of my partners thought Airbnb was crazy. At year two and three, it looks feasible. And year five, it looks, five to seven, it looks obvious, right? And so you go, okay, well, that's your time frame for the evolution. Now, occasionally it's longer.

Like one of the Greylock investments is Roblox, and that took longer. You know, it does happen. But it's like, that's kind of a classic time frame. And AI is obviously compressing this in some ways. So this is a pre-AI kind of juristics and time frame. Now, you said that it's more damaging to pass on a great investment than to back a poor one. And I guess in the old day, it was about protecting your downside. Then in the venture capital world, it was about not missing out on the big thing.

But it seems like the whole world is now moving into the VC way of thinking, we must not miss out on the next great thing. But instead of the tickets being a million dollars, they're like $500 million, right? Yeah, exactly. Well, it's still true that on the venture world and generally speaking investing in tech, if you can get into some of the great ones, that's all that matters. And missing one of those matters a lot more. Now, that doesn't mean if you're presented with 20 investments and none of them are the great ones and you put all your money into that, that it's not going to be horrific for you.

You have to get some of the great ones. Like the corollary to that statement is if you're not getting some of the great ones, better not to play at all. right and so and that's one of the reasons why people tend to you know a lot of capital goes towards open AI goes towards anthropic you know goes towards you know the ones that they go well these ones I think are very very likely to work you know kind of as a direction and so um but I think so I think it's still the case that it's you know like if I think this could be one of the great ones then I tend to have to talk my way out of investing,

then talk my way into investing. But the challenge of that is that, you know, you have to have a pretty good sharp edge about, is it really possibly one of the great ones? I mean, like, more or less, like, you see all kinds of silly stuff where people talk themselves into things where you're like, no. the universe in which that's one of these great tech investments is kind of the universe in which like, you know,

30 volcanoes in the world go off at the same time. It's not impossible that that happens. Oh, my God. It's a totally different world. And so you have to have a good sense of, is it actually, in fact, realistically possible? And if so, then, you know, with some energy on it, because the vast majority of companies obviously fail,

and the vast majority of companies do not become industry-defining, transforming companies. And the vast majority of venture capital companies don't make particularly much money, but a few at the very top make a heck of a lot of it, right? Exactly. Because they get the first pick, I guess. It's the main reason, or what is the main reason? So McKinsey did a study a couple decades ago, and they found that what happened is the top-tier Silicon Valley venture firms,

they didn't deliberately, they don't directly collaborate with each other, but they tend to signal and draft off each other. And that's, you know, one of the things we're identifying the really top companies and top teams. It's like a, it's, again, one of the ways to analyze Silicon Valley investing is through networks. Networks entrepreneurs, networks of executive talent, but also networks of angel investors and networks of venture capitalists. And it's that network. Tell us about your PayPal network. Oh, right.

Well, so I thank you. I usually refer to as PayPal Network, as you know, most people refer to as PayPal Network. as you know, most people refer to as PayPal Mafia. It's like, it's not criminal. It's a network. So one of the things that made the PayPal network so noteworthy is PayPal sold to eBay in 2002, which was kind of the, you know, kind of the mid to kind of two-thirds of the way through the internet winner, maybe midway.

And so you ended up with all these people who had some money and believed in the Internet. and would go and create new companies. LinkedIn, my own, also YouTube, you know, Yelp, whole number of different companies got created in this time. And then the ability to do angel investing in a variety of these companies. And that was part of the reason why I was viewed to be

such an awesome network, because what we would do in, you know, 2003, is we'd be calling each other saying, hey, did you see this company, this project, this looks really good. It could be one of the things that's really good. You know, that's part of how, you know, I reconnected with Chad and Steve at YouTube, you know, and other things.

And that's part of what made, you know, kind of so many good companies coming out of that initial group of founders and investors from the PayPal network. And I think they, you know, it's one of the things that has now been, because these get learned in Silicon Valley. is that part of what happens is, you know, venture capital firms go and look at and they say, hey, you know, will this company X, you know, say Roblox be a generative thing of a bunch of new people coming out and which I should go see which are the people who are going to come out of that and be the next generation of founders and so forth.

And I should go research them. And that's another of the learning loops in Silicon Valley. So anyway, PayPal was one of the early icon ones. 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 for a general theme? Well I'd say broadly including LinkedIn

of course in this investment as an 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 it currently in your company, et cetera, et cetera.

And so, you know, that was a LinkedIn thing. And you could never get the network to 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 they'll never be money in college students. And sure, 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 models bad. For Airbnb, it's, oh, it's really strange that you're going to rent a room or apartment or, you know, or a house from a stranger. And what is the trust, how do you build the trust and how does that happen? And, you know, part of the

the theory there is actually, in fact, there's such demand for better, more unique experiences than hotels and at different price points and 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 way of doing it. Zinga is like people actually want to play casual games with each other. Gaming is not just a, not just a hardcore. People say, well, no, no, no, this is a halo and a, you know,

and the hardcore gaming is, no, no, there's this new category of games that social networks enable for how it is. And you basically go through, you know, almost all of my investments. And it's that, I don't know, 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. Right. But that's the thing that leads to the industry transforming successes. Given all that and given all your experience, what is your advice to young people?

Well, the longer version of it is my very first book is called The Start Review, which came out of a commencement speech I gave to my high school. So it's precisely, it's take all the entrepreneurial lessons that I learned from Silicon Valley. because, by the way, I think we're all becoming, need to be much more entrepreneurial in times of disruption. Doesn't mean you need to start a company. If it's right for you, great. But only a small number of people should start companies. But you have to lead your career in a much more entrepreneur way, as opposed to like the, oh, I go apprentice at IBM, then I work my way up, you know, career ladder of type X within the company. And then we get a job at another company and so forth. But it's like, industries are changing, job tracks are changing. You know, what is a marketer going to look like in

five and 10 years versus what a marketer looks like today given AI. It's going to be totally different. So you have to be entrepreneurial and learning and using the tools. And so that's part of the kind of the startup view and entrepreneurship, of which there's a lot more depth in the startup view. But the central thing, of course, is the, like one of the things for the next X years that young people should pitch firms on is I'm a native AI user. You need to be AI transformed. Here's the way that my experience with AI can come help you in your organization. Right. And I think that's a very important thing to do because all of these companies are going to be looking at these going,

shit, we need this transformation. Who is AI native? And one of the things I gave this talk in Bologna a couple of years back is, you know, you guys are Generation AI. That's the thing you should be leaning into and deploying for your path to work in life success. Good advice to Generation AI. Yes. Reed, you kind of feels like part of Generation AI as well.

So it's been just tremendous to talk to you. Wow, what a story. What an experience. Just be so much fun. I think you can opt to join it, which I'm trying to. I try to use AI in new ways every week. Absolutely. Well, I have, I finished my name with AI, so I'm kind of part of it.

Yes. But a big thanks.