Reid riffs on GPT-5, Figma's IPO, and the end of AOL dial-up

0:28I'm Reid Hoffman. coworker Parth, I don't think slept for the five days leading up to it. Everyone was so excited to see what's to come. And this was sort of a different release than the previous. People who watched the rollout weren't sort of wowed as much by the technological changes. But what happened is this was an all-in-one. They deprecated all the previous models that people had come to know and love, but also be made fun of for whether you should be using 3.0 or 4 or the one that thinks or you know, the one that does all these other things, which I think sort of boggled the minds of some more regular people who are trying to figure out which AI to use.

1:03What is just your first take on the GPT-5 launch? So this might be a paradox, but I was expecting both a little bit of the blowback. And I think the GPT-5 launch is a very good thing. Ultimately, it's almost akin to I wonder how many people remember this. When Facebook launched newsfeed, there were actually protests outside of Facebook's headquarters. I mean, people went truly insane. For people who don't remember, it was crazy. Right. And I was like, nope, newsfeed's a very good idea. It's a good thing. It will continue. Very quickly, everyone went, oh, I really like this. And so that's kind of a little bit of the parallel in terms of what I think about like GBD-5.

1:43I think it's the, when you make these kind of changes where people are familiar with the previous thing, there's a bunch of people who are unhappy about the new thing and want to stay at the old thing, whether it's a changing interface on, you know, your computer or phone or anything else. And it's like, ah, that's just not better because it's different. And, you know, there are a whole bunch of GBD5 increases. I think Ethan Mullick, I think, told me the one that I think is the most relevant broadly, which is there's more already intelligent model direction. And since most people don't actually know how to choose models well exactly which kind of problem and thing they're trying to do the fact that you have a little bit of ai intelligence on oh this one you should use the more difficult thinking model for doing it it doesn't do it all on everything for example still with gbd5 like i find myself going and clicking the deep research button to make sure it's doing that and a little bit of the kind of the fun hack is but think deeply in your prompt as a way of doing it.

2:43Because obviously, to some degree, the intelligence algorithm is also driven by cost for OpenAI. And so it's going to try to get you a good result, but it's going to default to the inferentially cheaper model. I haven't yet seen where the real question on coding and healthcare is, what is the network of thousands to tens of thousands of people and doing it in comparison? Like I haven't yet seen the rollup of that. I know that OpenAI believes that it's trained the current best. And I think a lot of people are like, that's just really good. Now, of course, the other part of the GBD5 launch was the fact that OpenAI said, hey, we've got our current best models that are there through the service.

3:24And then we also made some open source models just to show you that the open source models, we can make ones that are better than anything that's currently out there and enable them and put them out there, I think has also been a very significantly noteworthy thing. I think one thing that's interesting is there's sort of two different directions. One is you want to get the best possible models out there, people on the cutting edge using it. And then you also want really broad adoption. You want people to have good models when they're using the free version. You don't want them to have to think, to your point, about, ah, do I click deep research, do I not?

3:55OpenAI, honestly, with this launch, seems to be winning on both counts. Why is OpenAI ahead of the other frontier labs on this one? Well, the key thing that OpenAI started very early and they built on some work that was done at Google. This was part of Ilya and Greg and Dario. OpenAI went, we are going fully at scale, right? And we're going to think it's just scale compute with various patterns of learning and scale data. and we're going to apply whatever scale team we need to. And we're going to aggregate that and we're going to go for that. And they started that earlier than even Google, which had created the baseline techniques, but was not as convinced about scale, was not as convinced about productization.

4:44And so it was not as fully into it, even though the initial side, and then, you know, we're followed by Microsoft in the combination of doing the deal with OpenAI and then learning and doing that. And so they have the scale path and the learning to how to do the scale path as the advantage, and they continue to blitzscale along. I mean, it is, in a sense, a classic Silicon Valley blitzscaling story, which is bet on the fact that the scale version of this is going to really work. So invest in ways that people think you're crazy. Go after scale team. like yep we're gonna move from 50 people to 500 people really really fast we're gonna put all that together we're gonna put that there do scale data right which is like we're gonna take data from wherever we can legitimately get it you know everywhere on the internet come and crawl everything else we're gonna do whatever deals we can and we're gonna be throwing as much data at this and we're gonna be we're just gonna continue to blitz on all of these runs and what's more even though the chat GPT rollout was a research rollout, was not a launch, once they saw it, they would blitz on the chat GPT deployment as well.

5:55And that's part of the reason why OpenAI maintains its lead. And part of, you know, in a baseline OpenAI strategy is to say, we're ahead on this scale compute, the deployment, et cetera. Let's just keep doing that. Well, I think one thing that's interesting is, you know, chat GPT seems like such an overnight success. Like you said, they did the research launch and then turned into an actual launch, such like a classic Silicon Valley hockey stick. And I'd say another company that has been in the news recently that has that also classic, you know, nowhere, nowhere, nowhere, nowhere, and then everywhere is Figma.

6:28They famously took nearly a decade to go from their MVP to, you know, somewhat mass adoption. And for those of you who don't know, it's sort of the design tool of choice for startups and large enterprises. And they were famously blocked by Adobe's attempted acquisition. And they just went public recently, one of the biggest one day gains in stock market history. And so they're one of those companies that took over 10 years to get to a real revenue place. And yet with AI, we're seeing companies go to 100 million ARR in a year or two. Are the figmas of the world, the sort of slow growth until you get mass adoption, are they the same in the past?

7:05Do you have to have revenue immediately in these sort of AI days? Well, I think you certainly don't need to have revenue immediately. I mean, that's part of what venture capital and capital markets about. The part of what Silicon Valley has understood in venture capital well before all the rest of the world. Like actually, in fact, proving your strategic value in various ways and then potentially getting the revenue. Now, you always have a revenue thought, you know, you have to get there for long term, but like getting there kind of strategically first, like people don't remember, this was early criticisms of Facebook folks like, oh, it's great for college students, but I'll never make any money, right?

7:41Four years of commentary on Facebook, you know, as an example. And it's multiple through these things. And it's actually understanding that getting to strategic position first as your leading edge where revenue may be trailing. And look, I have a soft spot in my heart, not just for Figma, which is awesome. And Dylan and the whole team are great, but also like this whole, oh, it's nothing, it's nothing, it's nothing. Oh my God, it's everything. You know, to some degree, this is a Greylock VC special, you know, because there's not only Figma, there's LinkedIn, which is very similar, which is dead relevant.

8:11Oh, amazing. You know, there's Roblox, you know, oh, trivia little kids thing, trivial little kids thing. Oh, major platform. You know, we tend to do this a lot at Greylock and that pattern will continue, right? Now you want to get to strategic value as soon as possible. And, you know, one of the problems with slow bakes is if someone gets ahead of you on a slow bake, then you could sort of die out in the wilderness and never get to your hockey stick. Now, those of us who have been close to Figma, inclusive myself, inclusive John Lilly, inclusive of, you know, a number of other people, we knew Figma was awesome and was coming.

8:47And it's partially because it's a product-focused, like more and more of these things are product - focused entrepreneur. And Dylan was an intern at LinkedIn. You know, we've known Dylan a long time. And the notion of that design is the front end to how you work generally across a number of different things is part of it. It's not just like, oh, it's just like Adobe. And it's like, well, actually, there's obviously some questions about where you use Adobe and some questions where you use Figma. But it's also a design approach to collaborative work. And that's the thing that makes Figma, you know, kind of interesting.

9:21in this. And, you know, Dylan's one of those very product founder, true north, choose the thing you're doing. How does it make happen? And so we just couldn't be more delighted for Figma, you know, having its first moment on the public stage with its IPO. I mean, Dylan seems truly like one of those founders who's not chasing the latest hot thing, but super smartly integrating AI as appropriate, making sure he's true to this sort of core of designers that they've always served. But by the way, one other funny thing to say about Dylan, just because it's like your questions reminding me of how much you like Dylan.

9:59When I did the Bitcoin rap battle, Dylan was one of the people we had in the audience, right? Oh, that's awesome. And he had as part of one of the people doing the reactions to Satoshi versus Hamilton. Anyway. We'll make sure to include the link to the Bitcoin rap battle in the show notes because it's amazing, hilarious, and prescient because it was many years ago. So speaking of another sort of hot company, Perplexity has this sort of eye-popping rise in valuations, at least, from$520 million to over $18 billion in just over a year. And they also just made headlines with a$34.5 billion offer to acquire Chrome.

10:35A lot of people think this is just a marketing stunt. They want to grab attention. What is your read on this offer to acquire Chrome? Is it real or is it just marketing? Well, if Google turned around and said, sure, I think perplexity would do it. So it's real in that regard. I think it's not real in the regard that completely speculating is an outsider. I think that maybe perplexity might have reached out to Google beforehand before it made a public offer or might not have, who knows. And I'm pretty sure Google's not interested in this for a whole wide variety of reasons. And, you know, I think that given Perplexity is one of the efforts that's trying to compete with Google on what does AI mean for search?

11:16Like, I think it would be very low on Google's list. And so how does a company like Perplexity, which certainly has users and has name recognition and people are talking about it, but they're going up against the behemoths. Like, how do they differentiate? Is it specialization in the long term? Like, what's the long play for them? Like, I'm not close enough to perplexity to have a specific answer for perplexity. You know, it's pretty amazing what they've accomplished. You know, it's take risks that the incumbents won't take. It's move fast on various things. It's try different distribution strategies.

11:52It's do product direction that, for example, the incumbents can't really do because they're also servicing their current customers as a ways of doing it. you know it's kind of the classic christiansen innovators dilemma which is you know initially the thing seems small or less relevant but you bet right and it grows large i think all of those things would be naturally things to consider within the perplexity kind of set you know i did think by the way the the marketing stunt was clever now it's a clever move that also you know comes at google's expense so it's not the kind of thing that you know google's got to be particularly happy about as a stunt, but, you know, all fair.

12:31Right. Typical incumbent versus entrant, figure out what you can do. So I want to end talking about one of the true OGs in the space. I remember very clearly, I think I was in eighth grade and I made an all stars with a Z, 23, I am chat name, and it was all the rage among me and my eighth grade friends. And just recently, AOL finally said no more to the dial-up connection that we all know so well. Those of us a certain age can probably even sort of sing the dial-up song. So what does this mean for the cycles of technology? Like it was used for 30 years. Do you think these cycles are getting shorter or no?

13:09We're still going to see these technology companies being used 30 years into the future, just like AOL was. Well, one thing you're entertaining with your particular eighth grade self is back then, the wisdom was anyone who claimed they were a teenage girl was actually a 30-year-old man. Fair, fair. So you were actually one of the actual teenage girls. That's correct. That's correct. I think the short thing is that people over-predict in the new things the death of the old. Like a classic one is when mobile started growing, people said PCs are over. And what happens is PCs grow, like mobile grows a lot more, but like PCs have continued, right?

13:47And actually, in fact, if you ask me, would they be continuing 10 years from now? The answer is yes. I think they will be. Because when it fits something that's really useful, it actually continues. It has incumbents. It has a bunch of integrations, like not just in enterprise, but in people's lives and all the rest as ways of doing this. I myself, as you know, use, of course, I'm old, use PCs and laptops and all the rest more than I use mobile. I actually deliberately use mobile somewhat just to be familiar with the experience and to know where that's going. But I think as people begin to use these things for more intensive activities, for example, how many people write their PhD on their mobile device?

14:27And it's like not that many. Obviously, the mobile device goes with you everywhere and you go, oh, I can respond to this email right now and all the rest. Like that's part of the whole thing. So that's part of the growth you see. And this is, I think, a very standard part of people's misunderstanding and technology. Like, for example, like one of the memes right now is vibe coding is going to wipe out productivity software. And it's like, well, actually, in fact, what I think you'll see is productivity software will continue. And then vibe coding is going to add on to it and add a bunch, either at a lot or a little.

14:57But it's not going to like suddenly like productivity software is going to go away. Totally. And so it's kind of that that that's the pattern that people need to understand. Now, partially because we venture capitalists and we public markets, when you look at all the valuation of the rails, you're betting on the 10 plus year future in terms of how this stuff plays. And so that's why you go, well, if it's going to grow massively, I want to bet on the things that are growing. So I want to bet on mobile or I want to bet on maybe vibe coding. And that's the kind of way that this plays out. But it's a very standard pattern that what happens is it persists raw.

15:30And then, by the way, when it dies, it dies very quickly in smaller number of years. And it's mostly because the organization no longer has the economic throw weight to keep it going. It's like, okay, this class of mainframes, there's just not enough revenue. And then that kind of mainframe goes away. I'm sure there's probably still some IBM mainframes around somewhere or other, right? But that's the way the death kind of happens. And it's like, okay, the economics around dial-up just don't work anymore. So, okay, so there'll still be some smaller ISPs probably doing this in various regions. But generally speaking, everyone's like, why use dial-up?

16:07Let's just try to do other connectivity solutions. And to your point, so often technology just makes the market bigger. It's like people have been talking about the death of the book forever. And it's like, no, no, no, the book is still with us. We just also have e-books and audio books and websites and mobile sites and all the rest of it. And so we'll pour one out for AOL, but it's certainly not the death of the big technology company. Reid, thank you so much. Always a pleasure. Yep, always look forward to next.

17:01Guria Yalomonchili, Sayida Sepieva, Ian Alice, Greg Beato, Parth Patil, and Ben Rallis.