Reid Hoffman, LinkedIn Founder: 99% of People Are Using AI Wrong

Most people are really underutilizing today's AI capabilities, blinding themselves with emotional reactions like AI slop or other kinds of things. But the really interesting use is this agentic framework. There won't be individual human contributors anymore. There will be human managers of groups of agents. It can work while you're asleep. But here's the thing that's a little strange. AI has a negative PR perception in the US and Europe and a positive one everywhere else. And one of my greatest worries for the US and Europe is to say, 'No, it's really important we slow this down.' And in Asia, you go to Japan, you go to China, they're all like, 'Oh my god, this is the future. We want to be on it.' Part of what made the US and Europe so strong globally was the industrial revolution, embracing it early. And so, it's kind of like the reverse revenge of the industrial revolution. Maybe the rest of the world is going to embrace AI and the cognitive industrial revolution earlier. And so it's a huge moment for this kind of wakeup call. If they don't reinvent themselves, then the dinosaur decay has started. Part of the reason why people have negative is because they say, 'Oh, the tech companies are doing this to me. I'm going to lose my job.' And that's the entire story is 99% of the human race. So I understand. But the question is, how do you get to the 99%? Show me benefit. Show me how I can be amplified and using. So you go, okay, AI can be for me. And I think that's the way you persuade people. And it isn't by a story of words. All right. How you get trust is by seeing value. And so the unequivocal thing that I said to all people is it's super important to have reads.

Thank you very much and a pleasure to be here.

First, most people are really underutilizing today's AI capabilities. There's everything from the people who aren't doing it at all for a whole variety of reasons. Lack of curiosity, blinding themselves with emotional reactions like AI slop or other kinds of things. And so, they're not really digging into the pretty amazing capabilities. Then there's a relatively not small group that are kind of using it as a search replacement, which is hey, I get a more coherent answer like is it okay for my dog to eat coconut, or other kinds of things, which gives you a very precise and detailed answer that you can follow up on to check because these things always try to be pretty helpful, so they hallucinate sometimes, although the hallucinations have gone way down in the last couple years, and that's a bulk of use. But the really interesting use is for today and capabilities which most people wonder is this agentic framework of actually doing work, because what all of the labs have been doing is how do you get amplified in terms of how you operate. So for example, given coding has been the massive thing. All of the software developers who are using this actually have a large screen that they have multiple agents running on and they're kind of doing more orchestration. They're like, okay, you're doing this, you're doing this, you're doing this. This is the handoffs. Look, this one's got stuck on something. I'm doing, oh, this one's finished. This one needs to get the next one. You know, this kind of thing. Including and this is obviously one of the things that has worked for the economics and capitalism is it can work while you're asleep. So one of the most classic things these coding developers do is they have big problems that they set when they're going to go to sleep. So they get up in the morning and the thing has been working for eight hours and has a substantive piece of work left. Now you'd say because it's agents and coding and it's only agents coding, but actually coding is a lens into all knowledge work. It's a form of reasoning and any place where you can close the digital loop, and that isn't just writing, that isn't just code, that isn't just images, that isn't just video, it's other things too. Part of the reason why there are startups in law and all the rest is a place that you can be amplified today and you can actually be doing that, and the underdeployment of that capability. I can tell when people are starting to get into it because it's like, oh gosh, I'm using cloud code or Codex and I'm staying up to two in the morning getting stuff done, or I've been really diving into ceramics and now I have a ceramics app because I can do that, and it doesn't have to be like, it's now doable as an amplification for the things I'm doing in ceramics. And that's the kind of thing that you're beginning to see. But that's still a very tiny percentage of the working people. And that's what today looks like. It's all changed so incredibly quickly. And I don't think many of us realize just how quickly this amazing technology was going to advance.

Well, not as a surprise, part of that is the acceleration of capabilities and the digital closing loop. So not only will coding continue to get a lot better, but more and more people who are not coders will be deploying coding to do things, the ceramics app, bunch of other things. And those coding capabilities will then go into all kinds of workflows because you create agentic workflows. So what you'll begin seeing, which you see in a small number of people now, is people going like within 3 years, and I think one year you'll begin to see the broader based adoption of this. There won't be individual human contributors anymore, there will be human managers of groups of agents. It doesn't mean that the people aren't contributing some pretty important things that are not just managing the agents but also context awareness and everything else, but that management of a group of agents will then become an understood developing pattern of work, and every place where you can do the digital closing the loop will be there, and that's part of what will be growing out. Now, in particular, I think you'll begin to see because we've begun in the last year to really see takeoff in coding. I think you'll see the coding go to the next level and then it bringing all the other knowledge workflows into the same kind of things that coding has been doing this year.

Well, I think people are both taking it not seriously enough and too seriously. The not seriously enough is a lot of people not actually engaging, realizing that we've created the best learning and amplification technology in human history. That we're entering into the cognitive industrial revolution and this will be faster and larger impact than the industrial revolution. So that's the not seriously enough. And the too seriously tends to be a little bit of the science fiction thing like, oh my gosh, in 2 years half of white collar people will be out of work, or that kind of thing as ways of not understanding human organizations and what humans can bring in and all the rest. I do think there will be a lot of workforce transition, but what I actually think is that a lot of people's jobs will get replaced by people using and managing groups of AI, and so that transition is difficult, but it's not like AIs are doing everything three years from now.

Yeah, there might be a three-year thing where that happens, but my hazard bet will be that we will actually have decades still of humans fitting in because if I see the pattern of where we're going, we already have super intelligence. There's already things that ChatGPT, Claude, Copilot, Gemini can all do that we can't do - speed, depth of access to the internet, kind of exhaustiveness of research, a bunch of different things including working on certain problems. For example, I haven't coded for decades and I can create interesting things with code now given AI. That's all kind of superpowers. On the other hand, what you discover is your ability to have context awareness, to understand when things are going off the rails or what real true excellence looks like, is still substantially better, and I think we'll likely be there for a while because while these things have this jagged edge of super intelligence capability, they also have weirdnesses. Even the super intelligent great models today still screw up on various kind of prime number problems.

And part of that screw up is that you ask it a simple prime number problem and it gives you an answer and you say that's wrong. Give me another one and it goes, oh sorry, yeah, here's the answer. It'll keep doing that 20 times, 100 times. Whereas human beings, and this is part of what I mean by context awareness, the third time I give you the wrong answer, I go, look, I know I don't have this and I stop. I know I need to change my game. And the models currently haven't really demonstrated any non-brittle, clunky way of doing that. And that's just one instance where I think human capabilities may add in for quite a long time and maybe for a very long time.

Well, as I think you know, it's even more relevant although the pattern changes a little bit. So a lot of blitz scaling was lessons from Silicon Valley about why within a 30 mile radius of the peninsula of Silicon Valley is over half of the NASDAQ market cap. And one of the things that I was told recently that I thought was interesting was that 91% of the non-Chinese AI market cap is in Silicon Valley. And actually if you remove Anthropic and OpenAI, that only drops to 70%. And so it's a huge amount of what's being created there. And blitzscaling was the lessons by which these technology companies of the future are built so that as many places in the world can learn, adapt, participate. I want to help the creation of Silicon Valley. Now Silicon Valley is everywhere. So blitzscaling was in part one of the things that was most uncomfortable was once you discover scale product market fit, how does it go to the moon and to Mars and beyond, like Toy Story to infinity and beyond. How does it go that way? And the answer was by really scaling the organization and taking a whole bunch of risks in product market fit, in scale of hiring and business operations and technology development and so forth in order to realize that scale opportunity at speed before others. Now how blitz scaling changes in the era of AI is that it goes from pure scaling of organization, by the way it will still have some scaling of organization, to the deployment of AI doing and redoing work at intensity. Because one of the many different things that AI brings to changing the game for how you build scale technology is you might build something for a couple weeks and then throw it away and build something new, and that might be your way of doing it. Whereas previously, you really were trying to build on top of each new thing and then refactoring it. And you'll still be doing that with AI, but you'll be building at faster, higher uncertainty, higher risk, trying things, throwing it away, going again. And you'll be still scaling a human organization. Obviously, the number of agents that you'll be scaling and how you'll be operating will be a multiple of human beings because each human being will be managing teams potentially up to thousands or even tens of thousands of agents.

Well, generalizing the last 10 years, I do think the two places that get the scale technology story most correct are Silicon Valley and China. I'm trying to help Europe, Japan, other places build us out in various ways because I think it's better for the whole world and better for those regions. But basically Silicon Valley continues to have the network, the information, the learning, the venture capital. It attracts talent from everywhere in terms of building these software technology scaleups and obviously that bleeds into other things. So there's also biology and other kinds of things happening. But I think one of the things that's funny is if you look at the last 20 years, there's always a new article about so-called peak Silicon Valley and it's always proven to be wrong 3 to 5 years later. Like, oh, housing's too expensive or the taxation thing has killed it or it's too woke and we're all moving to Miami, and then within a couple years it's like, nope, Silicon Valley has continued to massively grow, and Miami is still basically just Cryptoville.

Well, the normal thought will be can we get an exclusive partnership or access by which we leverage that in return, and I don't think any of these AI models are really going to provide that. And what's more, partially because a lot of startups start with something that's thin because that's part of the reason why software startups are so good. Look at the technology we started LinkedIn with, it was very thin. It's gotten deeper over time, but it's still mostly a network effects business. So I think that the notion of partnering with them will broadly be beyond the scope and irrational for most startups. But partnering is different than building upon. It's a little bit like you might build upon the mobile app stores of iOS and Android and not partner with them, but still build something that's pretty effective. And so similarly in terms of these models, you want to do what is classic around the parallel of cloud services, which is you might start intensively with Azure or AWS or G-Cloud, but you're ultimately going to want to have some diversity across them so that the pricing and dependency and your ability to do that. You'll want the same thing in terms of models ultimately, not necessarily at the beginning, but that'll be part of your strategy. And then what you're going to be looking for is how do you build something that once it's built is very valuable that the models can't just go, oh, we added that as a feature and your whole business goes away. There are various techniques to try to do that, some of them are network effects, some of them are enterprise integration, but you're going to want to be doing that. So building on top of them can make a lot of sense, but you're going to have to build a substantive business that isn't just a wrapper on the model.

So, I think it's a good thing for tech. It's part of the Schumpeter creative destruction, which is it isn't that once you've innovated or created a business, you're good for all time. It's part of the S&P churn rate which might be much quicker in this accelerated universe, and it's a thing to be smart about. Entrepreneurs, 90 plus percent of entrepreneurial businesses fail without really getting off the ground, and that's because you have a bad theory of the game, you're unlucky in your market timing, you're unlucky in your competition, all kinds of different things. You can't pull together the resources to execute against it. And that's fine. For example, I saw this back in the day at GPT-3. Hey, I'm building a copywriting ad business on top of it. And you're like, well, but its writing capability is very good. What are you doing beyond just saying I'm giving you a front end to GPT-3? And so when GPT-4 came out, the whole business got disrupted. You have to be having an entrepreneurial theory of the game. That's why it is that the AI companies, the hyperscalers and AI companies themselves doing it, or other people just go around you and use it directly. For example, it's like, well, I built a great app with cloud code and now everyone's going to buy my cloud code app. Well, could they just point cloud code at it and say build me that app? And the answer is yes, in which case you're like, okay, so why would they pay you a lot of money for it because the token cost of cloud code or Codex doing that is not going to be that high. So you have to have that more detailed theory. But by the way, I have continued to invest in various AI companies along with Greylock and others, and so there are smart people doing this, but you have to have that thought on the go to market, that thought on the predictability of it, the thought on network effects, the thought on enterprise integration, the thought on what kind of loops like data loops and other things are really something that creates unique value. Then if you're one of the AI companies that say I really need to get into this, they go, hey, we're going to come and offer to buy you for a huge premium because that's how we get into the market, and that's how we compete with our competitors. And that's what you have to be having as an entrepreneur.

I do. And I think part of the thing that AI causes is it changes a landscape in which skills really matter. So for example, when you're doing graphic design, it used to be, oh, I have a lot of hand-eye coordination to be able to draw very well. And it's like, well, AI can draw very well. That hand-eye coordination doesn't need to go down. But your visual thinking and what your bar is for what is really good goes up. So it changes the landscape of which skills are really important. The landscape is now like, oh, it's super important to have the taste, to have the judgment about what excellence looks like, and not just in visual but obviously in code and writing and then the hand drawing skill goes down. So if you were just a hand drawing skill person, you're going to have to figure out how to bring that into AI and have AI amplify, and say, okay, I take these skills on and I still add something on top of AI. But like the taste, the orchestration, the judgment is another one in addition to context awareness that I think will persist for decades.

Well, one of the things to understand about the internet is that offering products through the internet, the other product is only a URL, a click away, an app download, etc. So that can be a small advantage that they found you versus others, but the other thing is discoverability. So that's part of the reason why you want to have a very good sense of what a great product is for that problem solution. It's kind of like one, you have to ask the right questions. Two, you have to have a sense of what real quality looks like. And by the way, one of my aphorisms that I'm famous for is if you're not embarrassed by your first product release, you've released too late. That is still correct because it's the trajectory towards great. It doesn't necessarily mean you wait for four years to have great coming out. It's like no, no, no. You want to be learning and engaging to get scale product market fit and which things really work and not. So you want to have a fast learning curve towards that. And then of course, one of the perennials for entrepreneurship is your go to market, your distribution. You have to have a differential theory of that. One of the most common failings of quality, tasteful knowing what the right problem is and the right great solution is, is understanding that the go to market is as important as your product in order to succeed.

Distribution is great and it's extremely important, and it's one of the reasons why by the way in existing companies it's not necessarily game over for them. If they don't reinvent themselves, then the dinosaur decay has started. And this is actually part of what has gotten the SaaS apocalypse wrong, because it's which SaaS companies are embracing and doing AI will be most likely part of the future, at least on a risk coefficient, and those that are not are almost certainly not part of the future on an accelerating decay. And it's the similar thing in terms of distribution, which is you say, 'Hey, I've got distribution that allows me to use AI and to amplify and to do it.' If you kind of go, 'Oh, I'm not going to use any AI,' then my guess is your distribution may persist for a while, but you will start arcing down some. And that's part of the reason why the AI reinvention, the unequivocal thing that I say to all people is start playing with it, start using it and understand how it helps you get to better upside. Not just better cost savings, that's interesting too, but better upside. And that is key. And distribution helps you get into that loop much faster.

Here's the thing that's a little strange. AI has a negative PR perception in the US and Europe and a positive one everywhere else, including Asia and so forth. And one of my greatest worries for the US and Europe is to say, 'No, it's really important we slow this down.' I mean, this gets to the idiocies in the US of like, 'Let's ban data centers.' And it's like, look, there's nothing that says any particular region should adopt a data center if they don't want it. And I very much get that the local community should ask for good things that help the local communities, economic upside and jobs and other kinds of things, and also noise pollution and other things that are important to get right. But that's part of the future. And in Asia, you go to Japan, you go to China, you go other places, they're like, 'How are we part of the future?' But not just Asia, you go to Central and South America, you go to Africa, you go to the Middle East, they're all like, 'Oh my god, this is the future. We want to be on it.' And so you've got this kind of like maybe it's the reverse revenge of the industrial revolution. Part of what made the US and Europe so strong globally was the industrial revolution and embracing it early. And maybe the rest of the world is going to embrace AI and the cognitive industrial revolution earlier, and then you'll have a pattern of modern reverse colonization from the US and Europe from everywhere else adopting AI much more fervently. So there's a tale of two regions in AI perception. Part of the reason I state this bluntly is of course I want the US and Europe to embrace and to be a leader in the cognitive industrial revolution. I think it's great societies, great sets of social and societal values, and we want those to be at the leading table in the world if not leading the table. And so it's a huge moment for this kind of wakeup call. You would think, especially in the US, if they're leading the turbocharge and the storytelling and the big picture vision and making AI the most amazing technology, they would be very positive about it. Europe, where I'm originally from, I'm originally from the UK. Sorry to the listeners listening to my British voice every single day. But they're having a few more positive signals around AI and what it can actually do.

The ultimate thing is to engage and see value. Part of the reason why people have negative is because they say, 'Oh, the tech companies are doing this to me. I'm going to lose my job and AI is going to be doing the job.' And that's the entire story. And by the way, if that's the entire story, you don't tend to be broad-minded and say, 'Hey, my society and my community needs to work.' Most people tend to go, 'Well, if it doesn't work for me, then it doesn't work for anyone else.' Obviously, there's a certain amount of narcissism and egoism in that perspective that is 99% of the human race. So I understand. But the question is, how do you get to the 99%? Show me benefit. Show me how I can be amplified in using it. Show me that even if it's a job transition, which I don't really want to sign up for, it feels like a reduction in

Agency. This is part of the reason I wrote the book Super Agency because it's like how you can gain agency and lean into it and do that. However, the big tech companies are deploying their AI agents. But to you say, okay, even if I don't want to be doing this job and am now being forced into it and doing it, what are the benefits I get? Well, one benefit is obviously a huge exoskeleton of the mind and the capabilities, which is great, but also part of the reason why I would like to see it deployed with speed and vigor is like a medical assistant that runs for free on every smartphone because you go, 'Oh, I'm doing this job transition.' But unlike where it used to be only the 0.1% or 0.01% of humanity could have a doctor on call 24/7 to answer something for their kids, their parents, their partner, their pets, etc., that exists now and to deploy that effectively. And by the way, the same thing for a legal assistant in terms of, you know, most people, they sign a lease, they don't know how to read the lease and deal with it. AI agents can do that, can help with that today. You know, educational tutors and helping do jobs, all the rest of this stuff. So you go, okay, I get it. I can gain agency. AI can be amplification intelligence. AI can be for me. And I think that's the way you persuade people. And it isn't by a story of words. It isn't by a hey, just trust me and I'm trustworthy, so you should just follow me. It's by getting them to engage and getting them to use it and seeing value for themselves. And that's part of the reason why it's like engage, engage, engage, engage is the short answer. And by the way, when you're a leader in a company or a society for doing this, it's get people to engage. They see the value.

Yes. Well, and also part of how you engage a thousand percent and part of how you get trust is by seeing value. Obviously, we live in very chaotic times. People tend to trust institutions, leaders a lot less, you know, across not just the political spectrum, but also the corporate spectrum and institutions even, you know, you see nutty groups of people not trusting doctors and so forth, which is insane, you know, like the antivax stuff and all the rest. But part of how trust is built is by this has been helpful. This has built useful things into my life. A medical assistant helping me with my work. And that's the kind of thing where people start going, 'Oh, now trust is being rebuilt.'

So, this is precisely the thing that for eight plus years I've been telling antitrust folks, which is if we were five to seven large tech companies heading to three, I would have an antitrust concern. But literally, I was like, we're five to seven heading to 10 to 15. This was from eight years ago. And they're like, well, which ones? You're like, well, you don't necessarily know. Like, I wouldn't have necessarily predicted Nvidia. Nvidia is one. I thought OpenAI was coming. I wouldn't have necessarily picked Anthropic, but Anthropic is clearly there now too with a nearly trillion dollar valuation and a basis of coding. Now I do think enterprise, I think Anthropic will make a lot of progress in enterprise by the way. I think Microsoft has a whole bunch of services and everything else there, so we'll also be vigorous there. And you know, I'm not saying this as a proponent, I'm just saying this as I'm principally a venture capitalist, you know, investing in these things. But I think there's going to be more. Like, for example, you say, well, is it only OpenAI, Anthropic? Because you got the consumer with OpenAI and you've got coding amplification and a set of enterprise implications. And the answer is I am nearly certain that three to four years from now we'll go, oh yeah, there was this third. And this is one of the things that's great about entrepreneurship now. What's good about that is it creates a lot of value for society and a lot of value for entrepreneurship, a lot of value for society because they're competing with each other, so they're offering a huge amount of productivity at low prices. It's one of the things where I think there's likely to be at least four frontier models, if not maybe five to eight, in which case, hey, as an entrepreneur or society, you can play out between them. And so I think that's likely to play. Then there's a whole bunch of things on top of that that allow entrepreneurs to then compete with each other and so forth, just as the scale companies are competing with each other. When I'm creating a new business, my new business, I either succeed myself and create a new star in the pan in the constellation, or if I've created something of value, I can sell to somebody. And when there's 10 to 15, I can sell to someone much more valuable. This creates a cycle of human and financial capital investing in more and more of these things going, because if I go, hey, if I succeed at all, I get something of a return. I don't have to create the new OpenAI, the new Anthropic. I can create something valuable that's smaller and it's still worth it for the team and for the capital. And that, I think, is the universe that we have clear sight into.

Exactly. And I think one of the good things about being public is pension funds can participate. Every citizen, you know, with economics can participate. And we have the additional visibility of public companies to know what's going on, you know, know where society may need to nudge it one direction or other versus speculation. Like the classic, there's all this kind of random speculation. A classic one is, you know, AI data centers are using lots of water and that's just basically not true. They're actually not even using that much electricity yet today because the electricity prices are more of a function of underinvestment in grids, you know, fluctuations of energy supply cost, you know, Iranian war, etc., etc. They will scale and so electricity costs will go up, but also AI applied to it can make grids more efficient, can make data centers more efficient, can make home appliances more efficient. And so you're like, well, with AI applied to it, maybe it could actually even with a massive scale of intelligence. So there's all of this speculation because it's private and then people just want to take a political position. Whereas when in public companies, you can go what's true and you can see it. And that's one of the virtues of more and more of these companies going public.

I'll say one characteristic that they might feel uncomfortable as in both of them that is useful is, and I can say a couple characteristics, but they're both very principled. And part of that principle is the reason why they come out and say, 'Oh my gosh, like for example, Dario, we're gonna have a white collar bloodbath in, you know, two years from now because it was three years from last year.' And you're like, 'Okay, Dario, people get panicked and say that and they don't hear it the way you mean it and so forth.' But what he's meaning is it's very important in the construction of these technologies that it isn't just me doing this solo but some broader-based inclusion of society in the dialogue about what's important to build, what's important not to build, how should this be kind of society accretive, etc. And it's like, hey, wake up. There's a big jobs thing coming and I want to engage people in the discussion. And by the way, a similar thing for Sam. I haven't talked to him about the 5% sovereign wealth fund idea for OpenAI, which I think sovereign wealth funds are a great idea. It's one of the things that we underdeliver in the US and I think is good in parts of Europe and good in parts of Asia. Because I think it's actually sovereign wealth funds part of how to put capital on side of the people. And so I think sovereign wealth funds are good things to do. And so I think that's a good thing and that's part of again the how do I engage society and be socially positive, have some authenticity across that and some commitment to that. And like for example with Sam, who's obviously spent a couple years being attacked by various quarters for untrustworthiness. Like part of what I point out to him is like look, Sam put money in early into experimenting with universal basic income privately to see if this was going to be an important tool for society in an age of AI prosperity. Doesn't mean there isn't some complications around it, but he was investing in it. It isn't just words, it isn't just talking about, he was actually putting money and effort behind it. And that's part of where I think both of them have this kind of principled both impact for society positively and legitimacy by engaging society in the dialogue, which I think is important recognitions of where they are very valuable. And some other AI leaders are less good.

The short answer is they get a lot of political and other flak that is ignorant, unwarranted, hostile, and uninformed. That doesn't mean, and then because of course classically how someone will attack my statement is they'll say you're saying that they're perfect and they're angels and like no, I have not met a perfect person, right, so myself included, nobody is perfect. And so the question is net strongly positive and help them with amplifying the strengths and minimizing the weaknesses. And they're both really good people, they care about society. And so like for example, unlike xAI, which is trying to engage the whole, you know, porn ecosystem to try to get some market share. Like they actually care about the impact on humanity for what's going on. And so they are quality leaders.

Well, actually, it's the end of this year. I'm just not standing for re-election, but yes.

That's a great question. Satya is, I think, an amazing, I think I in my Masters of Scale podcast kind of really popularized the term refounder and has done a spectacular job with Microsoft. I mean, I think amongst the best of all the scale companies. And I think there's kind of a whole set of skills, but I think let's call three of them out. So one of them is he thinks in terms of who has the right natural platforms to do these scale problems. Like what is the scale knowledge work look like across large companies, across societies, etc. What is the way that workflow happens and how is that provisioned to enable companies effectively? And part of his thing is as opposed to replacing companies, it's how, because they're all his customers, he is the most aligned for how do you amplify them and what is a set of things that they bring in as considerations? How do you up their movement speed? All the rest of that. And sometimes he's slower because he's addressing like proprietary intellectual property within company, proprietary information. Like for example, one of the things I learned from Satya in bucket one was, hey, if AI is providing new employees, it's actually really important that these new employees actually work for the company. They don't work for a different company who takes all that IP and goes and does other things, but actually works for that company even if the AI is built by a separate company. So that's one area. The second is making scale organizations play. And part of the scale organizations is, you know, part of what the rebuilding the jet engine as you're flying is continuing these just world-class businesses in enterprise productivity software and a whole bunch of other things, but also rebuilding as you're going. And how do you rebuild the organization? And it's part of the growth psychology. Like the very first corporate executive offsite that I went to at Microsoft was, you know, kind of saying, hey, we really have to follow Carol Dweck's growth psychology. We have to earn our right in this. We don't just take it as a natural given. And how do you rebuild that in the culture of the company? And then the third, which I think people have seen, but Satya has done a stunning job of kind of corporate partnerships and not just acquisitions: Minecraft, LinkedIn, GitHub, etc. But also like the OpenAI deal. Like lots of people thought he was being a fool working with a nonprofit that had no business focus, no business acumen, no product development, etc. But he was going to go do that. And he put his first table stakes was a billion dollars in order to do that and did that. And you know that will go down as one of the genius moves by both Sam and Satya in history.

Yes. But one of the tricks that I had to do is because I was on the board of both at the time, my role was a trust building that I abstained in the votes of both boards. Everyone was very clear about where my position was and being on both boards. But still, even in that circumstance, part of what you can do is say, well, when person one tells person two statement X or statement Z, you can tell person two, yes, that's true, right? Like, you know me and I'm telling you it's true. And then vice versa and back and forth. So you go, ah, you can establish trust much more quickly because you can go, okay, as opposed to the normal thing of, hey, is there something I'm being taken advantage of here? Do I have to put in a bunch of warrants and representations, do due diligence on the contract? I can rely upon that it's true. And then you say, look, these are the set of things. And by the way, I will of course be abstaining because it's important not just to appear completely high integrity and trustworthy, but the substance of it too. And the substance needs to be that I'm just like, look, I'm not voting on this, and I'm not voting on it because the decision needs to be made independent of because I'm in both organizations.

It's kind of insane. Because it ranges anything from giving world leaders advice on artificial intelligence strategy to working with a bunch of different venture funds, Greylock and others, in terms of investments, to some of the AI companies that I've co-founded, so Manus AI trying to cure cancer and drug discovery with AI, and Inflection, and what are those doing, to doing new investments. I've done some new investments in the last month. Usually I don't get around to announcing specifics, to generating content, like the book Super Agency, the podcast Possible, Masters of Scale, etc., to doing things for fun and also by the way being informative in AI. Like for example, I made a Fourth of July record for celebrating America's 250th anniversary of reinvention and invention because I think that's one of the important things to understand about American culture. So I do all this stuff and any particular week is a triage across these: which things are most important, is there an immediate time demand, is there something afoot, can I put more time into, you know, etc., etc. And that's the wildly ambitious scope across all.

Yes. And you know, people say, 'Well, you know, this is a holiday.' I'm like, 'Oh, yeah. I guess it's a holiday for most people.'

And no weekends. I mean, occasionally a day with friends or something, but yes.

Yeah, eight hours.

Yeah. I get a minimum six with a target of eight.

Well, so maybe about 18, 24 months ago, I started doing the voice piloting stuff because obviously the voice as you just mentioned is super important and as a baseline. I still kind of give that to people's advice because in engaging with these AI agents, raw amount of pure amount of perspective and data is more important and we type too slowly. Everyone speaks, even very fast typists, you speak much faster and you want to get into, or you're just giving out every piece of context you can think of and trust that it will assemble that in a way. So just as a plus one to your thing, now for two trends. I'd say one of them is people tend to think of the coding agents as building coding applications, mobile apps and coding applications, but actually in fact what I think is amplification. Think about it for any particular kind of work you're doing. Let's take a natural easy step, which is like say for example my investing business doing financial analysis. So one of the things that you do is you can stick in a business plan, even a seed stage early stage, and say build me a financial model of this. And then by the way, it might not do it at one shot. This is part of agents, but rotating through it, and that financial model will be actually using coding assistance, not just coding assistance to set up the Excel deep spreadsheet which it can do, but iterating through the code variations of it, doing research and bringing in databases from the internet, looking for like, hey, I need some data like this, where can I get that? And you're like, oh yeah, maybe if I call somebody I can get some data that I could then ingest into this. So that coding amplification as kind of one is not just you now writing an app for your podcast or something else as ways of doing this, but also various very focused micro areas of work and ways of doing reason and thinking. That's one. And I think we are people are doing that, but it's a little bit like the quote that's ascribed to William Gibson, which is the future is already here, just unevenly distributed. That dynamic is it's very thinly distributed right now, needs to be much broader. The second is to think a little bit about what's the way that you are present in the various networks that are the amplification of you. And I'll give a very ready answer on this, but it's not to say it's different for different people and so forth. So one of the things that I have a lot of people that I've met over decades of working that I really value, and a little bit classic to Dunbar's number is well you can only really maintain a relationship with maximum 150 people. That's actually been not true because of social networks for a while, but it's also now really not true because of AI. And as a micro thing, like for example, part of when I create a record of AI music to celebrate America's 250th birthday and the kind of Fourth of July, I then also create using AI custom one-off printed versions that have the person's face on them and other kinds of things. Because you know, people who get this know that they are part of the group that I think of, part of the group that I care about, that even though I may have been five years since I've talked to them because I've been busy building, trying to cure cancer and other things, lots of things going on, that there's still someone I care about. There's still someone who I go through the effort to get included, make sure that gets to them, gets to their address, has a brief touch point. This is among the kinds of things that AI enables. Obviously, people think about as, oh, it can answer my email for me. And yes, it can do that. And by the way, that's valuable in a number of different contexts, but by the way, you still want to maintain that human connection. And as part of that human connection, you want that to be reinforcing your human connection with them. So it's not like they didn't think I went and custom painted Eric's face on it, right? They understand that I was using an AI tool to do it, but I was thinking about Eric and I sent that to Eric as a kind of a way of doing it. And they're like, 'Ah, great.' That then it becomes by the way an artifact that is part of the treasured presence in our interaction. And so whatever the version is for people, it's exploring this form of creativity and human connection reinforcing way of doing it. Not talk to my agent, don't talk to me, like some kind where you're very busy and all that, like you got a bunch of people pitching you to be on your podcast, you don't know them. That's a good start. But also amplifying the connection you have with people that you know, value, trust, respect, want to go through life and work together.

Truly, I don't really have a favorite guest. I have some more favorite and some somewhat less favorite, but it's been just spectacular and great. And you know, for anyone who's not listened to it, Brian Chesky, who was my very first one, was a great approach of how he designed all of life and experience was spectacular. I still remember what happens when you like if the pope were arriving somewhere, what is the experience the pope has and how do we replicate that for you, right? And was like, wow, never thought of that, right? And like kind of great. So that would be Masters of Scale.

I changed my mind fairly constantly with updating both data and thinking. And I think it's a great question because I think it's important everyone do that. I guess what I would say is I think it's a story of slightly slower AI adoption than I'd like. One of the things that I'd been predicting from many years ago, I wrote for the MIT Technology Review that we have AI agents that be listening to every meeting, giving analysis and following up. And I think because of the human thing, it will be slow that adoption. My team, we record every meeting and we use AI and have a bunch of custom AI agents for research follow-ups, etc. to amplify us. But like I even see startups that are not doing that yet and startups tend to be the boldest. And I think everyone needs to get there because it's a team amplifier as well. And I think I just was like if you had asked me, I would have thought this year would be the year that that would just blitz scale into being. And I think it will grow, but maybe it's next year that it'll be building scaling.

I think one of the things that's probably most important is where do you think my startup plan, my theory of the game, is least likely to fail and what do you think about my proposed solution to it and how might I stress test that solution more? And do you think there's something that I should be paying more attention to, so focused on as opposed to it's going to work, it's going to work, it's okay, it's good in my work. What is the thing that's most likely to blow me up and how do I navigate that?

Well, so I have created Manus AI for drug discovery and thank you for referring to Ned and my Vanderbilt commencement speech. I would say there's a whole raft of different kinds of applications. One of the ones to really do is say, hey, look, there's going to be just a flood of digital AI applications. So, you know, whether it's productivity, coding assistance, all that's all great and some of those will work really well, but there'll be a lot of competition. One of the biggest issues in entrepreneurship is trying to pick the right competition. It's part of the reason why we went to drug molecules. So to think about what's something that's a simple small step off AI that can move with speed, because if you say well I'm going to build robots that are going to be doing manufacturing, it's like well does it take building each new robot in each new manufacturing center and is that going to move really slowly? Some people are going to do that successfully, but what's the thing that is just that one step off that you have a unique edge to? And that's the kind of thing that I would recommend people do. And that's part of the reason why Manus AI has been the thing I've been doing most intensely over the last two years.

My pleasure. That would be awesome.