Marc Andreessen: Why Perfect Products Become Obsolete

0:00are ads purely destructive or negative to the user experience or are they actually have done properly or are they actually the neutral or even positive. You know if you're not Apple, do you really want to be a company that basically sits there and says yeah the world's moving and we're very deliberately not going to leave us as we can and do it. I think there's a lot of survivorship bias in these kinds of strategy discussions where people look at the one company that's able to pull this off and they don't look at the 50 other companies that are in the graveyard because they didn't adapt. Mark and Dresan went live on TPP on this beat and today we're dropping that full conversation here on the pot.

0:32Mark gets into it all, was really happening in AI right now. How Apple was playing its hand, the return of open source in why perfect products can signal the end, not the peak. He also shares his take on how to break a diventure capital in 2025 and when he's actually using AI for a day to day. Let's get into it. This information is for educational purposes only and is not a recommendation to buy, hold, or sell any investment or financial product. This podcast has been produced by a third party and may include pay promotional advertisements, other company references and individuals unaffiliated with A16z.

1:05Such advertisements, companies, and individuals are not endorsed by AH Capital Management LLC, A16z, or any of its affiliates. Information is from sources deep reliable on the date of publication. Basics in Z does not guarantee its accuracy. We have Mark Andreessen joining us. He's live from the TVP and UltraJum welcome to the stream. How you doing, Mark? Hey, what's happening? Great to see you. Yeah, you too. A lot. It's a little bit of a slow news day, but exciting stuff with GPT open source. It's not a slow August. It's not a slow August. We're glad. We were just reflecting that we've taken exactly one day off this summer that was July 4th, and we're showing the Europeans how American companies work.

1:51American workers. We're setting an example, and we have proof of work because we exist on the internet, and you can see us live every day. So we're setting an example. How are you doing? How's your summer going? Add tested going really well. So how long is it going to be until you guys put up avatars that make claims that you're working hard all through the summer? You're on the beach. You might have caught us. I think you'll know better than us as to when the technology gets there. We've been demoing some of the stuff. People have been doing a lot of deep fakes of us, and fortunately all of them have been clockable, so it doesn't feel like a broad risk.

2:24But they're getting closer and closer, and I know that there's going to be a moment where we have to say, hey, that's actually using our name and likeness to endorse something that we don't necessarily endorse. Can you please take that down? So we're approaching the the touring test the the Uncanny Valley. We're I think a question looking back over the you know, maybe 10 or 10 or 15 years was was what moments did you feel like there just was not a lot of action happening because this summer is just the pace from so many different teams has been absolutely insane. Everybody's like trying to keep up and it didn't use to to feel that way, at least from my point of view.

3:03So my view, it always is there's like these, there's these disconnected, you know, kind of patterns or trends, there's sort of the, the sort of day -to -day phenomenon where like engineers show up every day and they make things a little bit better. And then every once in a while, you know, you get a technical breakthrough or a new platform. And that process kind of this, you know, I kind of saw tooth kind of up to the right, kind of process kind of plays out over time, kind of regardless of what else is happening in the world. And so it keeps happening through recessions and depressions and wars and all kinds of crazy crazy crazy stuff that's happening.

3:33But basically, you know, the technology keeps getting better. So there's there's kind of that curve. And then there's the sort of enthusiasm curve and then the adoption curve, you know, which is basically like, when do these things actually show up in the world? And then by the way, when are people actually ready? You know, for the for the new thing. Like if you talk to people who worked on like, I'm sure you guys have talked to people who work on language models, they will tell you that they were surprised that chat GPT was the big through a moment because they thought everybody already knew what these models could do for, you know, three years before that.

4:01And so they were, you know, they were shocked that it was the chatbot interface that made the thing go. And so there's somewhat of a sort of arbitrary disconnection between what's actually happening in the substance and then what people are seeing and feeling. And so it's just, it's really hard to predict from these things pop. But also, if you're in this day to day, it's really hard to tell, you know, when things are going to be hot or not, because it doesn't necessarily map to how much that technology is improving. Yeah, we were just talking about that in the context of Google's new world model.

4:29It's this like generative video game that you can kind of move around in and it feels like DeepMind is just absolutely crushing at the AI research frontier. They have the best world model simulator that you can walk around in. The question is like if they let another lab do the chat GPT thing and just get it out into the consumer three months earlier, they might wind up kind of chasing and trying to catch up if somebody actually figures out how to make it like a dominant consumer product. Now in the enterprise, it's more oligopolistic, but consumer seems to be winner -tick -all. I guess the question is like, how much value do you place right now in the AI race to just like moving fast, breaking things, dealing, having like the thick skin to deal with like the safety constraints and all the different stuff, obviously not being irresponsible, but just speeding up the organization as much as possible.

5:21it feels like now is the time to really push on that. Yeah, well, first of all, I need to correct you. It's moving fast and making things. I don't know where that's right. I don't even know what I'm from. Yeah, I have no idea what you've heard. Never heard of it. I mean, you didn't really break anything. I think that's a good point. It really did just move fast and make things. The first things that made were weird, but that was fine. And it failed and it hallucinated a ton, but it didn't really break anything. I don't know. Yeah, I believe in this case. Total bats are to the Tichet GPT are still zero.

5:50Zero. So that was standing all of it. Now it was standing all the cat are walling. But yeah. Yeah. So look, I think the AI in this field in particular has a very acute version of the sort of challenge that you identified with. And I don't say this negatively, just an observation, which is that they're in sort of a normal type of a machine company. You've kind of got engineers who make products. And then you've got sales people who are marketing people who sell them. In the end companies, you have this third tier if you know the quote unquote researchers. Right. And so, which has worked out incredibly well.

6:21I mean, the researchers have done, you know, they've just done like amazing breakthroughs at these companies. But, you know, the handoff, you know, there's not necessarily a clean handoff from the researchers to the market. And so it kind of raises this question of like, okay, like is there, are these companies there for kind of three, you know, kind of three segment companies where they have research and then they have product development and then they have to go to market. And I think that's a really open issue. I mean, if you, you know, Google's kind of a case study of this, you alluded to DeepMind, but even more broadly, Google developed a transformer in 2017.

6:51And then they basically let it sit on the shelf, right? Because it was a research project. They didn't productize it. They were very worried about, you know, from people I've talked to, they were very worried about the, you know, brand issues and safety issues, you know, kind of all these, they had all these reasons to not productize it. I talked to somebody, senior, who was there at the time, but then I asked them, you know, when, when could you have had GPPT with GPPT 4 level output? If you had just got, you know, gone flat out starting in 2017 and they said by 2019, they already knew how to do it.

7:17And then they've now clawed up, but it took an extra five years to catch up. And so I think a lot of these companies kind of have that challenge. Elon, as usual, of course, is provoking this question, as you guys talked about, but he has now, with the next AI, he's now collapsed. He's eliminated the distinction between research and product. And so, of course, he's pushing this as hard as he can. And I think it's a good question for a a lot of these other companies kind of how hard they want to push on actually getting these things and fully productize form out to the market. Yeah, yeah, on Elon's distinction, it feels like there is more research to be done, but it feels like we're entering a new cycle of just focus on the engineering, focus on the deployment, the applications, let's get all this technology out into the world, let's reap all that benefit, and yes, there will be a different track of fundamental research that's happening somewhere, but it's really, really hard to predict, And so if you have something that's working, just double down and just go really aggressive on it.

8:17I'm wondering more on that, but also on Apple strategy, it feels like Apple's been kind of like, people have been maligning them for not for missing the AI opportunity. And Tim Coak's just there on the earnings call me, like, look, we acquired a couple small companies and seven companies. But then it seems like they're taking more of like an American dynamism approach. which like there was news today in the journal that they're investing $100 million in American manufacturing. They're certainly doing stuff. They're just not chasing the shiny tennis ball. They have a line, $100 billion Catholics.

8:54So I'm wondering about your thoughts on when you have a platform, how hard is it to resist chasing the new shiny object? Is that the right move? Or are there any other things that you think Apple should be changing the strategy on. Yeah, so look, Apple's always had this very clearly to find strategy that Steve and Tim, we're gonna get their figured out a long time ago, which is, for the exact term, but it's something like basically, they invest deeply into the core of what they do. They'll basically work internally on things for many years. They only actually release things when they feel like they're kind of fully baked.

9:31Right, and so as a consequence, they have this thing where, Tim says this, right? They're really first market with new technologies. they're more often in the category of Peter Teele calls last to market. They'll come out whatever three years later, whatever five years later. They were tablets for years before the iPad. There were smartphones for years before the iPhone. Folding phones. They're about to do a folding phone. It's like 10 years into that technology. I'm sure they do. The last mover, the last mover. Yeah, yeah, yeah. Sorry. The last mover. I guess, yeah, what I'm gonna say is that clearly works if you're Apple, right?

10:04And so it clearly works if you're Apple. But I would say there's a fine line between that strategy and just simply becoming obsolete, right? And so the problem is like if you're not Apple, and you don't have all the other kind of super strengths and kind of now the market position that Apple has, do you really want to be a company? If you're not Apple, do you really want to be a company that basically sits there and says, yeah, the world's moving and we're very deliberately not going to lean as far as we can into it? And so I think there's a lot of survivorship bias in these kinds of strategy discussions for people look at the one company that's able to pull this off and they don't look at the 50 other companies that are in the graveyard because they, you know, because they didn't adapt.

10:38I mean, you know, all the other smartphone companies when the iPhone came out, they were like, oh, yeah, well, we could do touch too, right? You know, we'll just, you know, we'll get to it, right? Um, and, you know, you know, they're gone. What do you think? What do you think? I remember if it's like an iPhone knock on it. What do you think? Yeah, you know, right now people are, are variety of, you know, shareholders are anointed Apple around their reaction to AI, I'll lens John's annoyed around just like transcription. Generally just like super basic stuff. But it doesn't feel like the core business is immediately threatened today.

11:13It feels like it's still on the horizon around these sort of like, you know, I wear based computing, you know, potentially net new devices that we'll see from, you know, companies like OpenAI over time. but where do you, like, how real is the threat, you know, this year versus 10 years from today and kind of what's your framework? Yeah, well, I mean, I think it's the biggest ultimate danger. I mean, the biggest ultimate danger is very clear, which is just like at what point do you not carry around a panoplasm in your hand, you know, call the phone, you know, because other things have superseded it.

11:45And you know, look, everything, you know, everything becomes absolutely at the point. So there will come some time to make sure when we're not carrying phones around, We'll watch movies where people have phones and we'll be like, yeah, look at that. Look at how primitive they were. Right. Because we'll have moved on to other things. And whether those things are eye -based or other kinds of wearables or whether it's just kind of computing happening in the environment or just entirely voice -based, who knows what it is. But there will come a time when that happens. Is that fine? Three years from now, because there's like some huge breakthrough from some company that figures out the product that obsolete the phone right away or is that 20 years from now?

12:21because the phone is just such a standard platform for everything that we do in our lives and everything else kind of remains a peripheral to the phone. I mean, that's the game of elephants that's playing out there. Obviously, I think it's highly likely that we'll have a phone for a very long time. Having said that, it is exciting that there are companies that are going directly at that challenge and whoever cracks the code on that will be the next apple. And by the way, that may end the fullness of time be Apple itself. They may be the company that figures that out. Yeah, I remember being at a board meeting at the injuries and horror wits maybe a decade ago or something and Christyxin showed me the hollow lens and I was like, okay, we're one year away from this being everywhere.

13:01And I feel like today I'm still in the like, yeah, VR, it's definitely one year away. The next quest I'm going to be wearing daily. And it feels like we're always there, but it does feel like Apple did a lot of work on on the fundamental, the pixel density of the resolution of the display. And then Meta's been doing a ton of work on just getting it light and affordable. Like it feels closer than ever. But you know, you always got to wait until you see the churn numbers until you really call the game, right? Well, you say it was like, but you know, I just, I think that's true. But you'd also say, you know, I'm on the, on the Meta board side, kind of a dog on this one.

13:36But like the Meta Raybank glasses are a big hit. Oh, totally. Like they're big, you know. So I think we now have a form factor that we know works for I base variables is, you know, there's not VR and then VR on top of that. But, you know, just the, you know, the glasses and, you know, and then the glasses of camera, you know, sort of integrated camera, integrated microphone, integrated speaker. Yeah. You know, that's a very interesting platform. You know, the watch clearly works, by the way, which Apple, of course, you know, is way to significantly rolling making happen. You know, that now sells in huge volume.

14:03You know, so that's the second data point. And then I think these, you know, these, these, I think some form of I think the way I can is going to work. That's what the hedge funds are going to get a lot more sophisticated, which is already happening. And so you do have these kind of data points coming out. And then, yeah, look, the trillion dollar question ultimately are these peripherals to the phone, which is what they are today or are these replacements for the phone. And I would say we have, I think we have a lot of invention coming, both from new companies and from the incumbents who are going to try to figure that out.

14:32Yeah, I always think about the value of like narrowing the aperture on these new technologies, with the Meta Raybans, I feel like the fact that they aren't also trying to be a screen is actually a feature, not a bug. And I always go back to the iPhone. Like, it was first and foremost a phone. And people bought it because it could make calls. And then it could make calls messages. And then it was an iPod. But do you disagree with that, please? Well, you guys might be too young. The first iPhone actually was a bad phone. How so? Then for the first two years, it couldn't reliably make phone calls.

15:06I had like the third one and a friend had one, but I feel like it was still like people were carrying cell phones, and that was the at least of the expectation. But yeah, I mean, I guess you're right. So for the first year, it was a classic Apple Store, because the first two years, the thing couldn't make fun, reliably, make fun of them, and then it turned out, there was an issue with the antenna and with how you held it, and there was a, I remember that email. Yeah, you would, and you would disconnect it. Yeah, you could basically brick the device. Yeah, based on how you held it, and somebody emailed, this was what Steve would respond to emails some random people and somebody emailed Steve saying, if I, you know, hold the phone this way, it doesn't make phone calls.

15:37And he's like, well, don't hold it that way. Yeah. Right. Yeah. So even there, it was like, yeah, and people, you know, people forget it. It took like five years for the F on the finance footing. It took like two years to get the remember. Also, the original iPhone didn't have, it didn't have broadband data. It was on, it was on the old 2G. It was called the AT &T edge network. So it didn't have broadband data. And then of course, it didn't have an App Store, right? It was completely locked out. Right. So the challenges, the challenges for Apple now is that people are so used to perfection with the device, that launching a product that isn't perfect, like is embarrassing.

16:11Right, like you look at the Vision Pro, and it's like, well, the battery's big. Steve would have hated this, right? Like, he never would have shipped this and that being constrained and not being able to innovate because you're tied to this like impossible standard of being on whatever generation 17 of the iPhone and perfecting every element is a real challenge. So I would say there's a corollary to that. One of the things I've observed over the years is I think to algae products become obsolete at the precise moment that they become perfect. And did you hear my way? What I mean by perfect basically is like, yeah, it's like the perfect idealized, complete product.

16:46Like it does everything you could possibly ever imagine. Everything a customer could imagine, everything you as the technology to the whole of the American imagine, it's absolutely perfect. And there's been tons of examples in this over the last 50 years where it's like the absolute perfect, It seems to be the permanent version of that product and then it just turns out that's actually the point of obsolescence Because it means creativity is no longer being applied right into that platform You're just like there's just nothing else to do. You're just like you're you're done Right, the product has been realized and then and then the cycle is what happens to your point the cycle is other people come in With completely different approaches completely different kinds of products that are broken and weird in all kinds of ways You know, but but are fundamentally different and so you know that is one of the time under traditions and one of the things you could say about Tim is his willingness to break the mold of Apple only shifts for perfect products, but being willing to shift the vision pro, it shows a level of determination to kind of stay on the innovation.

17:38I like that. I think it's very positive. Yeah, yeah, yeah, that's great. Updated thinking on open source since we last talked. There's a lot that's been opening eyes open source company. Yes, opening eyes open again. Yes. Yeah. Yeah. It looked very encouraging. You know, a year ago, I was very, you know, I was getting very distressed about open, you know, whether open source say I was going to be allowed. Right. It was even going to be legal. And so, and I think, you know, we're basically through that at this point. Right. So I'm going to say, we're through that in the US. You know, we'll see about, we'll see about the rest of the world.

18:10And then look, you know, the US China thing is obviously a big deal. But you know, I think it's been that positive for the world that China has been been so enthusiastic about open source say coming out of China, which has been great. And then Yeah, look, opening out leaning hard into this, and releasing what they did, as I think fantastic. Most because of what they released, which is great, but also just the fact that they are now willing to do that and then you want to reconfirm overnight that he's gonna start open sourcing previous versions of GROC. And so yeah, so we seem to be in the timeline where open sourcing AI is gonna happen.

18:41Right now, what you would say is it kind of lags the leading edge proprietary implementations by six months or something like that. But I think that, you know, that's a good, if that's the status quo that continues, I think that would be a very good status quo. What are the rough edges that we need to kind of sand down when we're thinking about Chinese open source model specifically? Is it, we need to do some fine tuning on top of them to add back free speech or do we need to watch for back doors? Say it's phone and home if it runs into this specific thing. Like the Chinese open source thing, it was remarkable because I feel like it really does accelerate the pace of innovation because everyone gets to see, Oh, this is how reasoning works.

19:19I think that's great. At the same time, it made me much more appreciative of AI safety research and capability research and actually being able to interpret what's going on and say definitively this model is gonna behave weird in this weird way. Like the Manchurian candidate problem. We haven't found any of that, but it certainly seems like something we'd wanna keep an eye on, but from your perspective, like what are the risks that we need to be aware of going into a world where China is really pushing hard and open source? Yeah, there's two. There's two. And you identify them, but let's let's let's talk about both of them.

19:50So the phone home thing is the easy one, which is you can put up, you know, you can pack a sniff of, you know, a network and you can tell when the thing is doing that. And plus you can go and you can go in the code and you can see what is doing that. And so you can validate it. You can validate that that's either happening or not happening. And I think that, you know, that's important. But, you know, I think people are going to people are going to figure that out. You can kind of that problem practically. The bigger issue is we have this term in the field right now called OpenWates. OpenWates is a loaded term.

20:22It uses the open term from open source, but of course, the open source, the thing is you can actually read the code. With OpenWates, you have just a giant file full of numbers. As you said, you can't really interpret. Then what you don't have, what most of the open -weight models don't have, including deep sea, specifically what they don't have is they don't have open data, right? Or open corpus, right? So you can't actually see the training data that went into them. And of course, most of the people building models are kind of obscuring what that training data is in various ways. And so when you get an open -weight model, the good news is the software source is open.

20:58The good news is you can run it on your machine, you can verify that it doesn't phone home, but you don't actually know what's happening inside the ways. And so I think that is going to be bigger and bigger issue, which is like, okay, how the thing behaves like, yeah, what has it actually been trained to do and what restrictions or directives has it been given in the training, you know, that are embedded in the weights that you need to be able to see? You know, this is, I would say this is coming up as sort of, I would say, a global issue, you know, which, you know, we worry about when these models come from China.

21:26Other countries worry when these models come from the US, right? So one of the phrases you'll hear when you talk to people kind of outside the US is kind of this phrase, people kicking around, which is not my weight, not my culture. right? Right? Right? Or by the way, for that matter, not by ways, not by laws. Right? Which is like, okay, like what actually is this thing going to do? Right? And to your point, the Chinese models, for example, might never criticize communism or something. Tell you, the American models have all kinds of constraints also. Right? Implemented usually by a very specific kind of person, in a very specific location in the US.

21:59Yeah. And so I think that this is a general issue. And we're going to have to see basically people's tolerance levels being willing to run open weights models where they don't fundamentally have access to the data. And then, of course, finally, I think what we'll see is more open source developers also doing open -core -pissed open data. So you can see what's actually in them. Yeah. Obviously, open source is very important in terms of just distributing intelligence broadly, giving people the ability to run their own models and really fine tune them and have control. There's also the big push just to make frontier models and high capability models free.

22:36One model is you charge for the premium, you give the free away, it's a freemium model, that's what we're seeing at most of the labs right now. There's also this kind of spectre on the horizon of potentially putting ads in LLMs. And what that would do to the world, it's already got in a little dust up with Mark Cuban on the timeline deciding whether or not it would be a net good to put advertising in LLM's, what might happen that might be bad there? What do you have to take? Yeah, my point broadly was that ads have been an incredible way to make a variety of products and services online free, and just saying like default, you just know ads would potentially be incredibly destructive.