Reid riffs on AI adoption, sensitive data, and digital twins

On Part II of last week’s Live Riff, co-hosted with Village Global, Reid fields audience member questions about scale product-market fit, license modeling, data sensitivities, and the future of customer service, along with positive use cases for deepfake technology and the making of Reid AI, his digital twin. He also tells a story from his PayPal days, when Peter Thiel gave him five days to come up with a solution to a problem that was putting money and user trust on the line. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/ For Reid and Allie Miller’s agent experiment: https://www.youtube.com/watch?v=YeLSq9D65m4 For Reid AI giving Reid’s Perugia speech in multiple languages: https://www.reidhoffman.org/perugia-speech/ For the Masters of Scale episode about building trust featuring Daniel Ek: https://mastersofscale.com/daniel-ek-how-to-build-trust-fast/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Reid Hoffman?

  I'm Reid Hoffman.

• 0:01Aria Finger?

  And I'm Aria Finger.

• 0:02Reid Hoffman?

  We want to know what happens if, in the future, everything breaks humanity's way. This is Possible.

• 0:11Aria Finger?

  Hey there, Possible listeners. This episode is part two of our live version of Reid Riffs, which was recorded in New York City on June 24 in partnership with Village Global. If you haven't yet listened to part one, please go check that out. It was last week's episode, and it's really good. In this week's episode, we'll be hearing Reid's response to some listener questions. Hope you enjoy.

• 0:38Speaker 02

  Yes, Faison, I'm traveling from Chicago. So the question I had was, especially in this evolving world of AI, what are the biggest misconceptions founders have about scaling companies quickly?

• 0:50Reid Hoffman?

  Oh, that's interesting.

• 0:53Reid Hoffman?

  The usual general misconception is that scaling is just a straightforward game that's just kind of like the next step. Like, once you establish product market fit, scaling is a relatively, you know, kind of like just, oh, hire more people, have a larger organization, reorganize. And there's a lot of things. One of the reasons why I actually think that the phrase product market fit is interesting and useful, but scale product market fit is what really matters when you're projecting a scale. And what does that look like? But then the question is, is another kind of misconception frequently is like, you've proven everything, and then you just, again, you know,

• 1:37Reid Hoffman?

  add rocket fuel, go scale. Most of the time, you go, I've got a high enough probability of scale product market fit that I'm just going to go. Right? And we see lots of companies like that, doing that in various challenging ways. So, you know, classics from the book are like Uber and Airbnb and others.

• 2:00Reid Hoffman?

  And so, like, for example, very early days, you know, no one, few people here will remember this. But like, in early days, it's, oh, yeah, yeah, Facebook's scaling, a whole bunch of people are using it. But is it going to be a good business? Right, was actually in fact, and they were going, no, we'll figure out the business as we go. It'll, it'll happen. Let's go. And so, so actually, in fact, you will take some scaling risk. You will figure out some questions as you're going. As part of it. Now, in an age of AI, Maybe there's a few other ways to kind of add to that. One is, you know, because of course the general discourse is around size of model and the hyperscalers, there's an over like, okay, can I get like, is size of model the

• 2:45Reid Hoffman?

  thing that really mattered to me? And I think in a lot of businesses, it won't matter. Now, there's this interesting question about how long and what shape will open source models be provided? The reason why there's a lot of open source models right now is because there's a lot of challengers who it's the way they get into the game. But once, you know, open source providers start realizing that they're taking their compute resources, which by the way gets more expensive as it gets larger, and they're providing them to everybody, including competitors and all the rest, I think there will be a, oh, maybe we shouldn't do that, or maybe we'll try to experiment with different kind of license modeling and other kinds of things. So, so you can use open source models, but like, by the way, I think there will be tons

• 3:28Reid Hoffman?

  of competitors, which means that it's kind of like the multi-model thing as a way of doing startup is a kind of a good thing. Because, you know, basically, you know, one of the reasons why what things we're seeing with, you know, the various hyperscaler models now is like, oh, this one's better at this. And then four months later, no, no, this one's now better at this. And, and that's a pattern that's going to continue. And so you have to adjust that variability. And that will be a little bit of this question of how scale plays into what you're building is you have to have the dynamism and being able to, to shift as you need. Now, obviously, we have network effects to defend your business. You can be slower at, at, at adoption. Maybe a really key one is, you know, Apple and artificial intelligence.

• 4:12Reid Hoffman?

  You know, it's kind of, you know, the, if anyone's got a good result from Apple Intelligence, I'd be curious. I haven't yet heard one. But, you know, they have such a network effect lock-in to the business, they have time to play it and obviously amazing devices and all the rest. So anyway, so those are some reflections.

• 4:32Aria Finger?

  Awesome. Thank you. And we're going to dig deeper into how different industries approach AI. Is Priya Murali here?

• 4:38Speaker 03

  Hi, that's me, I'm Priya.

• 4:39Aria Finger?

  Oh, stand up.

• 4:40Reid Hoffman?

  Hi, yes, great.

• 4:41Speaker 03

  Reed Aria, thanks so much for being here. From Cobalt ID, my question is for founders building in cautious, data-sensitive industries, how can founders go about earning the trust they need to access buyer-sensitive data? And what are some of the missteps you see them take that commonly break that trust?

• 5:01Reid Hoffman?

  Great question. And obviously, one of the other challenges is because we have a general atmosphere of mistrust of tech companies overall, and what people are doing with data, and how that's being described. That's an additional difficulty on this particular game. But I did actually have an episode on this in Masters of Scale. And maybe it was the program, was the Daniel Ek and Spotify one, might have been the Build Trust Quickly. And to some degree, this, solving this kind of problem can be a competitive edge and differentiation for your business. You know, to give you an example, we were, in a, in a kind of a similar way, one of the questions early days in PayPal was, how do you get people to trust using PayPal

• 5:41Reid Hoffman?

  to do transactions? And I won't name the executive that came up with the idiot idea. But the idiot idea was the buyer protection program, which was, we guarantee your purchase up to $2,000, and basically with no conditions. And it was kind of classic software person who had no understanding of classic industry. And so you'd have people on eBay where, like, Ari and I would be colluding, and she'd put up a plasma TV, and I would buy it, and she wouldn't ship it to me. And then I would get my money back paid by PayPal, and the losses were hemorrhaging. And so this problem landed on my desk as a, oh my God, we have to shut this down, maybe everyone's going to lose confidence in PayPal, maybe it's

• 6:23Reid Hoffman?

  going to go away. I was like, okay, look, how many days can you give me to solve this problem? And, you know, Peter Thiel was like, five. And I was like, fine, I will go try to solve this problem. And what we came up with was, what we did is we reconstituted the PayPal buyer's protection solution. But instead of guaranteeing you $2,000 in every situation, come hell or high water, we should switch to doubling the eBay insurance. Which meant we still had a buyer protection program. And of course, it was on eBay, so it was fair that it was that. So if you use PayPal, you could double the insurance that you got with eBay.

• 7:05Reid Hoffman?

  But of course, what you had to do then do is show that the eBay insurance, we like offloaded a whole cost structure on eBay. Because like you had to prove that eBay thought that

• 7:16Speaker 04

  Brilliant.

• 7:17Reid Hoffman?

  You was worth doing. And then you show that the thing was actually 370 versus 250, and we'd pay the 120. Right? So and those are instances of kind of how to think about how the how to build trust. Part of the reason Spotify could come out of Europe and not the US is because it guaranteed Denmark and Scandinavia as an area. And buying it in, so like for example, getting insurance, Lloyds of London, other kinds of things as ways of doing that. Basically, it's fundamentally to building trust quickly. It's like, again, ideation, creative. But it's what's the kind of thing that will give the relevant constituencies confidence

• 7:56Reid Hoffman?

  that you're holding yourself accountable to the failure points or worries they might have, you know, buying something on PayPal or anything else. And that's the kind of thing you're looking at. And a lot of people also just don't understand. It's like, like one of the funniest conversations I had a number of years ago is, this was a Silicon Valley person. What has Google given me for my data? And it was like, well, free search.

• 8:26Reid Hoffman?

  Yeah, so there's a lot of misconceptions.

• 8:28Speaker 04

  Not that I don't love everyone in the room. This is my favorite AI person in New York, Allie Miller. Are you here?

• 8:35Reid Hoffman?

  Hey, and Ally and I did a podcast, which she had a great idea, so it was fun.

• 8:40Speaker 04

  Oh, good. Okay, well, then let me put you on the spot and see if you have good ideas, Reed.

• 8:45Reid Hoffman?

  No, I never do.

• 8:47Speaker 04

  I would like to know what signals we should be looking for and perhaps on what timeline where AI is good enough at embedding itself. This is part of the software writing, but AI embedded itself inside of business and technical processes. So much so that the painfully slow human behavior of adoption and adaptation no longer matters.

• 9:16Reid Hoffman?

  Great question, not a surprise, given that you do this AI leadership quite well over a number of things, and I will essay to try to be not dumb in my answer. What you're essentially looking for is where are there coherent loops that can move much faster that don't have organizational change or organizational hiring as part of them? Part of the reason why I invested in Sierra with Brett Taylor is because I think the front end to customer service is more easily modular as of all from how it interfaces with the rest of the company. Obviously, there is a periodic connection, and there's a way that you can very quickly move it to being the front end to how you're interfacing with the company and that kind of thing.

• 9:59Reid Hoffman?

  And so it's one of the reasons why I think the customer service thing is one of the places where it will really kind of speed up what that change is. One of the ones that I think will be interesting to see how it plays out, and one can play out multiple hypotheses, good range for multiple startups like sales, and part of it is like just like customer service, you could say, well, that's kind of inbound, what about outbound with sales? Of course, you know, people might get really pissed off about being called by AI agents, might get legislation, other things, a bunch of different risks and how that plays out, you know, thing, but that's another one where a modular function where you can get that loop going. Like, one of the classic things is like, you know, the Bezos two pizza teams and other

• 10:43Reid Hoffman?

  kinds of things that what the coherence of a smaller group can accomplish if the groups work working pretty intensely will now have a higher throw weight, and there's always, that's part of why, reason why you have dumb bar number, you know, 150 is like, how much do people hold in mind? One interesting, I think, prediction, actually, this is a pretty fun thing for everybody in the room to do. Think about what year you will never have a professional meeting where you're not having an AI agent listening to you and playing a role.

• 11:18Reid Hoffman?

  And that's like the literally every single meeting that you're doing that has any kind of professional thing. By the way, you might also do it when you are having a talk with your kids and or else that's a different question. But and I don't think that year is too far off. And if that's the case, this is one of the things I wrote for the MIT Tech Review like a decade ago as an anticipation of where AI is coming. If that's the case, then the the scalable coordination between teams might get a lot easier because it's literally like, you know, this team is having this meeting and this team is having this meeting. And the the AI agents go, oh, wait a minute and immediately create notification and

• 12:00Reid Hoffman?

  like or even it was like two hours later, like that kind of transition and how the speed and how that operates will be interesting. That being said, the way the accelerations will happen will be where the loops close in tight ways and where people feel competitive need. So part of the reason why AI adoption has been so slow right now is that most people don't feel competitive need. But like once, for example, you know, I'm sitting here, you know, doing my coding and I see Aria ten Xing my speed and delivery.

• 12:36Aria Finger?

  What now, Reid?

• 12:37Reid Hoffman?

  Yeah, with, yes, exactly. And I'm like, how am I not working? Oh, she's being much smarter about how she's using AI. I'm going to then start using AI too, and that will be another thing that will drive it. But obviously, it's a very good question in a complex space.

• 12:49Aria Finger?

  Well, I mean, a lot of Ali's work is working with Fortune 500 companies to have them adopt AI more quickly. And I could imagine that perhaps 90% of this room is already there, can't imagine a work day without AI listening constantly. And yet I think other people are going to be sort of dragged, A, kicking and screaming, B, only when they absolutely have to because of market need. That being said, as a parent, I mean, last week I was having a conversation with my nine year old. We were talking about monarchy because of the No Kings marches and we were explaining what monarchy was and he goes, what are some other arcies? And I was like, oh, I don't know. And then I was like, oh, patriarchy. And I explained what it was. And he goes, oh, it's

• 13:29Aria Finger?

  so good that that doesn't exist anymore. And I was like, ooh, I wish an AI was listening to that conversation and could have just piped in like, Aria, here's how to explain patriarchy to your nine year old. So I think as a parenting agent, like, there's, there's hope.

• 13:51Aria Finger?

  I think we have time for about two more questions. Esther in the front row.

• 13:57Speaker 05

  So this is not a political question, but.

• 14:02Reid Hoffman?

  So this is not a political answer?

• 14:04Speaker 05

  Exactly. But it is an economic one. There's a lot of value created, whether it's Google or who else, by basically what's a collective asset of content on the internet. What do you think of the idea of some kind of like mining rights or water, water rights or taxes that would, LLMs, not, not people who use their own data sets for training, but the large language models and, I don't know, maybe some other things, where they would fundamentally pay some kind of taxes on the revenues they generate using the collective asset. And then that would go to teachers and childcare workers and people who are now desperately underpaid.

• 14:45Reid Hoffman?

  I think the, the trying to figure out how to tax on all the data stuff, it's such a fast moving thing. Like, one of the things that currently seems like a pattern is the larger scale you get on the mixture of expert models, the less general data it actually needs. And so then which general data and all the rest is one instance. Like, what the patterns are of how data fits into this is challenging. And I also believe in simplicity. Like, I think it's kind of like, you know, probably fine to say, hey, look, there's, because of technological leverage, some companies are going to have enormously profitable business models. Let's have a little bit more tax there and direct it towards public goods. Right?

• 15:28Reid Hoffman?

  Like, I think it's just simpler to do it something like that, you know, kind of wherever it is. Like, one of the kind of underlying tensions is because, you know, labor and capital, and then when you get services labor, services labor gets a lot less kind of scale leverage. And yet, services labor really matters in terms of how we care for each other and so forth. So, we go, okay, as that balance changes, because one of the things that AI does is it does change some of the labor calculus towards capital, it's like, okay, should we try to figure out how to get some more adjustment to services labor? One of the mistakes I think most policymakers do is they try to be overly coding specific. It's almost like the mistake in software of hard variable coding, right?

• 16:10Reid Hoffman?

  Like, it's just like, no, let's just kind of, let's try to do a general program across that. So I'm positive on that. I don't think it needs a justification of, well, these things can't work unless they use what is this currently common good. I think they live in society, right? And it's important that we have a society that brings people along.

• 16:31Aria Finger?

  Yes, right there.

• 16:32Speaker 06

  Hi, Reid. Thank you so much for coming today and for inspiring many of us to start companies. The way you inspired me is by posting the video of you talking to your digital twin last year. And I thought that this is so cool, and this is the future of sort of AGI interface. So my question is, could you share a little bit about your experience building this digital avatar? What were your learnings? What are some of the things that could go right about this technology? And what advice you could give in terms of split scaling for someone building in this space and trying to compete with incumbents like Cajun and Synthysia? Thank you.

• 17:10Reid Hoffman?

  Well, blitzscaling will be more specific and hard, so I'm not going to give much of a good answer there. But, you know, it's a good question to be asking. It's like, when is your probability of the set of things of scale product, market fit, business model, etc. coming together can convince people to raise enough capital and go? And what blitzscaling has set as a clock is what your competitors look like. It's one of the problems that you have is you're like, if you're doing a capital spend competition with hyperscalers, that's a very difficult, risky business. You better get it right. Now, on the Read AI thing, it was funny because the idea basically came along because I have as a general principle that one of the things that people happen to happen to do is kind of technology good,

• 17:53Reid Hoffman?

  technology bad versus how do you shape technology to be good and how do you shape technology to be less bad? And one of the things that I find very frustrating about most tech critics is they go bad. And you're like, well, maybe in some ways, but the question is, is always a question of sequencing, dynamism, which things do you fix first, which things do you improve first? It's like, wow, you just said medical agent. And sometimes that medical agent's going to say something that's wrong to this person. And you're like, well, yeah, but you have to look at this kind of thing of, well, how often did that person have got nothing? How often will that person got wrong some other way and all the rest? So you have to kind of look at it at kind of a systemic basis. The real question is, how do you how do you shift it? And when I was thinking through it, I was like, oh, yeah.

• 18:36Reid Hoffman?

  And there's this technology called deep fakes, which is almost like labeled bad, bad. And and the only positive case that most people can imagine for it now is like making a younger Tom Hanks, you know, playing in a movie or or doing CG or something. And I was like, wow, and all the rest of it's terrible, evil, evil, evil. And I was like, well, is that true? And I went, well, okay, well, let's start experimenting with it. Because I understand that this one's harder, because obviously, you know, you can abound by the tremendous number of different harms in deep fakes, everything from fiscal to misinformation, fake news, to, you know, revenge porn, all the rest of stuff. It can be really, really bad. And so the, I was like, I was thinking about it and I said, well, let's just start experimenting.

• 19:21Reid Hoffman?

  I mean, literally, the way we kicked it off was, let's just do something and then see. Um, and you know, so like, for example, if you watch the first one, uh, it ends with me saying, well, I thought I'd hate this more than I do. And then as we started iterating, this is one of the things about always be thinking about a positive outcome. So last year I gave a speech at Perugia that we then had Read AI give in nine languages. And it was kind of, uh, cause of human connection. Um, and it was kind of amusing to watch myself speak Hindi, Chinese, Japanese, Italian, you know, languages, which like, I don't think I know a single word of Hindi. And so that was kind of an instance of positive use cases.

• 20:00Reid Hoffman?

  And then it made me start thinking about like, all right, you know, my likely thing is, is like, what, like, we won't have voicemail anymore. You know, I'll have Read AI that's essentially answers the phone and does the initial talking and it has actually a better interface for that. And I was like, oh, right. That's the kind of thing that will, so I mean, and those will be some of the positive use cases of this. And look, obviously you can argue with like, well, wait, there's so many negatives on this one versus the positives and what the issues and the negatives definitely need to be managed. But that's how I got into it. And part of it is a general principle of part of the thing you have to do as an entrepreneur is when most people think it's idiot bill and you think you have a good idea. That's potentially a great idea. That's part of the contrarian and right. The trick, of course, it's easy to be contrarian sometimes, uh, harder to be right, but that's the kind of thing to, to play out.

• 20:45Reid Hoffman?

  Anyway.

• 20:46Aria Finger?

  One of the really fun things about Read AI was that we also just used like commercially available technologies. We like called Eleven Labs an hour one and we worked with Reece Feature and, and more recently, Hey Gen. So like truly it was not because we had a technological edge or anything else. It was just like, let's try this. And now I will say, you know, not surprising, Read gets asked to give. Speeches and go to events, you know, probably 20 times a day. And I used to just say no to all of them. And now, especially when they're in a foreign country, I say, oh, you know, Reed's unavailable. But if you would like Reed AI to give a three-minute speech in French, like, we can arrange that. And more often than not, people say yes.

• 21:28Aria Finger?

  And so, you know, before they would get nothing, and now they get like a little dose of like, oh, this is so cool because I'm using this technology to, again, be more human. You're not usually having someone from Silicon Valley speak in French. So it's been really fun to see all the positive use cases.

• 21:42Reid Hoffman?

  Do you have a count top of mind? Because I've kind of lost count of the number of conferences.

• 21:46Aria Finger?

  Oh, I mean, like, over 100.

• 21:48Reid Hoffman?

  Yeah.

• 21:48Aria Finger?

  Like, literally Reed AI speaks way more than you do.

• 21:51Reid Hoffman?

  Yes.

• 21:52Aria Finger?

  And now, actually, this is the best. No offense. I'm, I do, I've gotten at least five just emails that are like, hey, can Reed AI speak? And I'm like, hey, Reed's unavailable. And they're like, no, I said Reed AI. And I'm like, oh, okay.

• 22:06Reid Hoffman?

  By the way, totally happy to be replaced with this contact.

• 22:13Reid Hoffman?

  Possible is produced by Wondra Media Network. It's hosted by Ari Finger and me, Reid Hoffman. Our showrunner is Sean Young. Possible is produced by Katie Sanders, Edie Allard, Tanasi Delos, Sarah Shlead, Vanessa Handy, Elia Yates, Paloma Moreno Jimenez, and Mulia Agudelo. Jenny Kaplan is our executive producer and editor.

• 22:36Aria Finger?

  Special thanks to Surya Yalamanchili, Saieda Sapieva, Ian Alice, Greg Beato, Parth Patel, and Ben Ralas. And a big thanks to Jennifer Whiting, Sheila Goodman, Ben Casnoca, and the whole Village Global team, Robert Kingsley, Jerry Madlumbaya, Samuel Henriquez, The Ritz-Carlton, and of course, Vincent Lucero.