Marc Andreessen on AI product strategy, open source risks, Apple's innovator's dilemma, and M&A climate

absolute murderer's row of clients. Congratulations. Line up. Well, we have Mark Andre joining us. He's live from the TBNN Ultrajum. Welcome to the stream. How you doing, Mark? Hey, what's happening? Great to see you. Yeah, you too. A lot.

It's uh it's a little bit of a slow news day, but uh exciting stuff with GPT opensource. It's not a slow August. I will say 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.

American work. We're setting an example and the and the 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? Fantastic. Going really well.

Um, 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 when it turns out you're 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 we 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 brand risk, but 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 we're approaching the the the touring test, the the uncanny valley.

We're escaping the question like 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 uh from so many different teams has been absolutely insane.

Everybody's like trying to keep up and it didn't used to feel that way at least from my point of view. So my my view it always is there's like these there's this these disconnected, you know, kind of patterns or trends.

There's 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 and and and that process kind of this, you know, kind of sawtooth 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 it keeps happening through recessions and depressions and wars and like all kinds of crazy crazy stuff that's happening. But basically, you know, the the technology keeps getting better.

So there's there's kind of that curve and then and then there's the the sort of enthusiasm curve and and the 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 uh you know for for for the new thing um like if you talk to people who worked on I'm sure you guys have talked to people who work on language models they will tell you that they were surprised the chat GPT was the breakthrough moment because they thought everybody already knew what these models could do for you know 3 years before that and so they were you know they were shocked that it was the chatbot interface that that made the thing go.

Um and so so there there's somewhat of a sort of arbitrary disconnection um between what's actually happening in the substance and then what what what what people are are seeing and feeling. And so it's just it's it's really hard to predict when these things pop.

But also if if you're in this day-to-day, it's it's really hard to tell um you know when things are going to be hot or not uh because it doesn't necessarily map to how much the techn is improving. Yeah, we were just talking about that in the context of uh of Google's new world model.

It's this like generative video game that you can kind of move around in and it feels like Deep Mind 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 igopolistic but consumer seems to be winner or take all.

I guess the question is like how much value uh do you place right now in the AI race to just like moving fast breaking things uh you know uh 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. It feels like now is the time to really push on that. Yeah. Well, first of all I need to correct you. It's It's moving fast and making things. Um, I don't know where I I I don't even know where that came from.

I I I I I I have no idea what you never heard Never heard of it. I mean, didn't really break anything. I I think that's a good point. It really did just move fast and make things. The first things it made were weird, but that was fine. And it failed and it and it hallucinated a ton, but it didn't really break anything.

I don't know. Yeah. I Yeah, I believe in I believe in this case total deaths attributable to uh to chat GPT are still zero. Zero.

So not notwithstanding all of the notwithstanding all the all the catwalling but um yeah so look I think the AI industry in particular has a very acute version of the of the of the sort of challenge that you identified with and and you know and I don't say this negatively just an observation which is that there you know like in sort of a normal technology company you've kind of got engineers who make products and then you've got you know kind of sales people or marketing people who sell them you know in in the AI companies you have this third tier of you know the quote unquote researchers right um and so you know which is which has worked out incredibly well I the researchers have done, you know, they've just done like amazing breakthroughs at these companies, but you know, the the the handoff, you know, there's not necessarily clean handoff from the researchers to to the market.

Um, and so it kind of raises this question of like, okay, like is there is are these companies therefore kind of three, you know, kind of three segment companies where they have research and then they have product development. Um, and then and then they have go to market. Um, and 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 know, you alluded to deep mind, but even more broadly, Google, you know, Google developed the transformer in 2017. Um, and 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 is, you know, kind of all these all these they had all these reasons to not productize it.

U, I talked to somebody senior who was there at the time who and I I asked them, you know, when when could you have had chat GPT with GPT4 level uh output? Um, if you had just got, you know, gone gone flat out starting in 2017. And they said by 2019. Yeah. You know, they they already knew how to do it.

and then you know they've now caught up but it took it took an extra 5 years 5 years to catch up. Um and and so I I think a lot of these companies kind of have that challenge.

Elon as usual of course is is provoking this question is I'm sure you guys talked about but you know he he has now you know with XAI he's now collapsed you know he's eliminated the distinction between research and product.

Um and so you know of course you know he's pushing this as hard as he can and I think it's a it's a good question for a lot of these other companies kind of how hard they want to push on actually getting these things in fully productized form out to the market. Yeah. Yeah.

On on on Elon's uh like distinction, it feels like there is more research to be done, but it feels like we're we're entering like a new cycle of, you know, 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 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. Um I'm I'm wondering uh more on on that, but also on Apple strategy.

It feels like Apple's been um kind of like, you know, people have been maligning them for not for missing the AI opportunity and Tim Cook's just there on the earnings call being like, "Look, we acquired a couple small companies and seven this year, seven companies.

" But then it seems like they're taking more of like an American dynamism approach. Like there was news today in the journal that they uh that they're investing $100 million in American manufacturing. They're certainly doing stuff.

They're just not chasing the you know the the shiny tennis headline$undred billion dollar capex. Um so I'm wondering about your thoughts on on when you have a you know uh when a when you have a platform uh how hard is it to resist chasing the new shiny object?

Is that the right move or are are there any other things that you think Apple should be uh you know changing their strategy on? Yeah.

So look, Apple's always had this you know very clearly defined strategy that you know Steve Steph Steve and Tim you know working together figured out a long time ago which is you know they they I I forget the exact term but it's it's something like basically they they they invest deeply into the core of what they do.

You know they'll basically work internally on things for many years. They they only actually release things when they feel like they're kind of fully baked. Yeah. Um, right. And and and so as a consequence, they have this thing where and Tim says this, right?

You know, they're rarely first to market with new technologies. You know, they're more often in the category of what, you know, Peter Peter Teal calls last to market. You know, they're, you know, they'll they'll they'll come out whatever, three years later, whatever, 5 years later.

You know, there, you know, there were tablets for years before the iPad. There were, you know, 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 if they do the last mover. The last mover. Yeah. Yeah. Yeah. Sorry.

the the last mover, I guess. Yeah. Well, what I would say is like, look, that that clearly works if you're Apple, right? Um, and so it clearly works if you're Apple, but I would say there's a fine line between that strategy and just and simply becoming obsolete, right?

Um, and so the the problem is like if you're not Apple and you don't have all the other kind of super strengths and, you know, kind of now the market position that Apple has, you know, do you really want to be a company, 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 lean as hard as we can into it.

" Um, and so I I 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, you know, because they, you know, because because they didn't adapt.

I mean, you know, all the other smartphone companies when the iPhone came out, they were like, "Oh, yeah, well, we could do Touch 2, right? You know, we'll just, you know, we'll get to it, right? " Um, and you know, you know, they're gone. Yeah. What was it? Very bold. I remember it was like an iPhone knockoff.

What do you think? You know, right now people are are variety of, you know, shareholders are annoyed at Apple around their reaction to AI LLM. John's annoyed around just like transcription generally, just like super basic stuff. But it doesn't feel like the the uh core business is immediately threatened today.

It feels like it's still on the horizon around these sort of like, you know, eyewear based computing, you know, potentially net new devices that we're that that we'll see from uh, you know, companies like OpenAI over time.

But where do you like like how how real is the threat you know this year uh versus 10 years from today and and kind of what's your framework? Yeah. Yeah.

Well, look, I mean, I think 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 pane of glass in your hand, you know, called a phone. Um, you know, because other things have superseded it.

And, you know, look, everything, you know, everything becomes obsolete at point. So, there will there will come sometime when we're not, you know, carrying phones around and we'll we'll watch movies or people have phones and we'll be like, yeah, look at look at how primitive they were, right?

because because we'll have moved on to other things and whether those things are eye based or you know uh you know other kinds of wearables or whether it's just kind of you know computing happening in the environment um or just you know entirely voice based or you know who knows what it is but um you know there will come a time when that happens you know is that time 3 years from now because there's like some you know huge breakthrough you know from from some company that figures out the the product that obsoletes the phone right away or is that 20 years from now because the phone is just you know such a standard platform for everything that we do in our lives and everything else you know kind of remains peripheral to the phone.

I mean that, you know, that's, you know, that that's the game of elephants that's playing out there. Um, you know, obviously I think, you know, I think it's highly likely that we we'll have a phone for a very long time.

Having said that, it is it is exciting that there are companies that are going directly at that challenge. Um, and you know, who whoever cracks the code on that will be the will be the next Apple. And by the way, that that may in the fullness of time be Apple itself.

You know, they they may be the company that figures that out. Yeah.

I remember being at a board meeting at Andre and Horowitz maybe a decade ago or something and Chris Dixon showed me the hollow lens and I was like okay we're one year away from this band everywhere and and 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.

Um, and and it feels like we're always there, but it does feel like Apple did a lot of work on the on the fundamental uh, you know, 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 uh, you know, you you always got to wait until you see the churn numbers until you really call the game, right? Well, here's the other thing. But, you know, I think that's true. But you'd also say, you know, I'm on the on the meta board, so I'm kind of a a dog hunt in this one.

But like the meta rayband glasses are a big hit. Oh, totally right. Like like they're a big, you know, so I think we we now have a form factor that we know works, you know, for for for eyebased wearables. This, you know, there's not VR and then VR, you know, on top of that.

But, um, you know, just the, you know, the glasses and, you know, and then the the glasses with camera, you know, sort of integrated camera, integrated microphone, integrated speaker. Yep. You know, that's a very interesting platform.

Um you know the watch clearly works by the way which Apple of course you know is played a significant role in making happen you know that now sells in in in huge volume um you know so that's the second data point and then you know look I think these you know these these I I think some form of AI pin is going to work um I also think head you know headphones are going to get a lot more sophisticated which is already happening um and and so you you know you do have these you know kind of data points coming out and then yeah look the the trillion dollar question ultimately is are these are these peripherals to the phone um you know which is what they are today or are these replacements for the phone and it you We we yeah I would say we you know we have we allow we 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.

Yeah, I always think about the value of like narrowing the aperture on these new technologies. Like with with the the meta ray bands, 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 text messages and then it was an iPod. But I do you disagree with that, please? Well, you you guys I don't you guys might be too young. The first iPhone actually was a bad phone. How so?

Because for the first two years I couldn't reliably make phone calls. I I had I 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 the expectation. But yeah, I mean I guess you're right.

So for for the first it was a classic Apple store because the first for the first two years the thing couldn't make reliably make phone calls and then it turned out there was an issue with the antenna and with with how you held it and there was a famous Steve email. Yeah.

Youard it and you would and you would disconnect it. you could basically brick the device from based on how you held it. And somebody emailed, this is when Steve would would respond to emails from random people. And somebody emailed Steve saying, "If I, you know, hold the phone this way, it doesn't make phone calls.

" And he's like, "Well, don't hold it that way. " Yeah. Right. So, so, so even there it was like, Yeah. And people, you know, people forget it took like five years for the iPhone to find its footing. It took like two years to get the And remember also the original iPhone didn't have it didn't have broadband uh data.

It it was on it was on the the old 2G uh 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 down, 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, right? Like you look at the Vision Pro and it's like, well, the battery is big. Steve would have hated this, right?

Like how he never would have shipped this. and that being constrained and 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 is a real challenge. So I would say there's a correlary to that.

One of the things I've observed over the years is I I think technology products become obsolete at the precise moment they become perfect. And and to your point what I mean by perfect basically is like yeah it's like the perfect idealized complete product. Like it does everything you could possibly ever imagine.

Everything a customer could imagine everything you as the technology developer can imagine. It's absolutely perfect. Um and there's there's been tons of examples of this o over the last 50 years.

um where it's like the absolute perfect permanent 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 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 um you know but but are fundamentally different.

So, you know, that is one of the time honored traditions and, you know, one of the, you know, one of the, you know, things you could say about, you know, Tim is, you know, his willingness to kind of break the mold of Apple only ships perfect products by, you know, be willing being willing to ship the, uh, you know, the vision pro.

Um, you know, you know, shows a level of determination to kind of stay in the innovation game like that, which I think is very positive. Yeah. Yeah. Yeah. Yeah. That's great. Um, updated thinking on open source since we last talked. Uh, there's there's a lot that's been Open AI is an open source company yesterday. Yes.

Open AI is open again. Yes. Yeah. Yeah. Look, very encouraging. You know, a year ago, I was very, you know, I was I was getting very distressed about open, you know, whether open source AI was going to be allowed. Uh, right. It was even going to be legal.

And so, and I think, you know, we're basically through that at this point. I say we're through that in the US. Um, you know, we'll we'll see about we'll see about the rest of the world.

Um, and then look, you know, the US China thing is obviously a big deal, but it, you know, I think it's been net positive for the world that China has been been so enthusiastic about open source AI coming out of China, uh, which has been great.

And then yeah look open leaning hard into this um you know and releasing what you know what they did is I is I think fantastic um both because of of what they released which is great but also just the fact that they are now you know willing to do that and then Elon reaffirmed overnight that he's going to you know open source you know start open sourcing previous versions of Grock um and so yeah so we you know we we we we seem to be we seem to be in the timeline where open source AI is going to happen.

Um you know right now you know what you I think what you would say is it kind of lags the leading edge proprietary implementations by you know six months or something like that. Um but 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 uh Chinese open source models specifically? Uh 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 uh 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. I think that's great.

Uh, at the same time, it made me very it made me much more appreciative of AI safety research and capability research and actually being able to interpret what's going on and and say definitively this model is going to behave weird in this weird way. Uh, like the Manurion candidate problem.

We haven't found any of that, but it certainly seems like something we'd want to keep an eye on. But in from your perspective, like what what are the what are the risks that we need to be aware of going into a world where China is really pushing hard into open source?

Yeah, there's two there's two and you identified them, but let's let's let's talk about both of them.

Um, so the so the phone home thing is the is the easy one which is you can put a you know you can packet sniff you know a network and you can tell when the thing is doing that and you and and plus you can go you can go in the code and you can see when it's doing that and so you can validate you can validate that that's either happening or not happening and I think that you know that's important um uh but you know I think people are going to people are going to are going to figure that out.

You you can kind of get that problem practically. Yeah. Um the the the bigger issue is um we we have this term in the field uh right now called open weights. Um and um open weights is a loaded term. Uh it uses the open term from open source. But of course with open source the thing is you you can actually read the code.

Um you know with open weights you have you know just a giant file full of numbers as you said that you you can't really interpret.

And then what you don't what you don't have what what most what most of the open source open weights models don't have including you know deepseat specifically what they don't have is they don't have open data right um or open corpus right so you you can't actually see the training data that went went into them um and of course you know most of the people building models are kind of obscuring what that you know what that training data is in various ways um and and so when you get an openweight model you know the good news is the the the software source is open the 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 um inside the weights.

And so I I think that that is going to be a bigger and bigger issue which is like okay how the thing behaves like yeah what what has it actually been trained to do um and what restrictions or directives has it been given in the training um you know that are embedded in the weights that that you need to be able to see.

Um you know this is I would say this is coming up as sort of I would say a global issue um you know which you know we worry about when these models come from China other countries worry when these models come from the US right which is right so one of one of the phrases you'll hear when you talk to people kind of outside the US is kind of this this phrase people are kicking around which is not my weight's not my culture okay right right or or by the way for that matter not my weights not my laws right um which is like okay like what actually is this thing going to do right and to your point that Chinese models for example might you know ever criticize, you know, communism or something.

I can tell you the American models have all kinds of constraints also. Yeah. Uh right, implemented, you know, usually by a very specific kind of person uh in a very specific location in the US.

Um and so, you know, I think that this is a this is a general issue and and and we're going to have to see basically people's tolerance levels uh being willing to run open weights models where they don't fundamentally have access to the data.

And then correspondingly, I think what we'll see is more open source developers also doing open corpus open data so you can see what's actually in them. Yeah.

Um obviously open source is very important in terms of just distributing intelligence broadly uh giving people the ability to run their own models and and really fine-tune them and have control. Uh there's also the big push just to make frontier models and high capability models free.

One model is you charge for the premium, you give the free away. It's a premium model. That's what we're seeing at most of the labs right now. There's also this kind of uh spectre on the horizon of potentially putting ads in LLMs and what that would do to the world.

Jordy got in a little dust up with Mark Cuban on the timeline uh deciding whether or not it would be a net good to put advertising in LLMs. What might happen that might be bad there?

Uh do you my yeah my my point broadly was that ads have been uh an incredible way to make a variety of products and services online free and just saying like default just no ads would would potentially um you know be incredibly destructive um but uh yeah curious your framework.

Yeah so I should start by saying like whenever I personally use internet service I always try to buy the premium version of it that doesn't have ads. Um, right. And so if if I can like live personally inside an ad for universe and pay for it, like that's great.

Um, and I I'll freely admit, you know, whatever level of, you know, hypocrisy or in congruence, you know, kind of kind of kind of results from that. But no, the point is choice. The point is choice. Well, the point is the point is exactly what you said. It's affordability.

So the the problem is if you really want to get to f if you want to get to a billion and then five billion people, um, you you you can't do that with a paid offering. Like it just at any sort of reasonable price point. It's just not possible. uh the you know global per capita GDP is not high enough for that.

People don't have enough income for that at least today. Um and and so if if you want to get to you know if you want the if you want the Google search engine or the Facebook social app or the whatever AI you know Frontier AI model to be available to 5 billion people u for free. Um you you need to have a business model.

You need to have an indirect business model and and and ads is the obvious one. Um, and so I I do think if you know if if if you take some principal stand against ads, I think you unfortunately are also taking a stand against against against against broad access just in the way the world works today.

And then and then look the other the other really salient question is um you know the same question that the companies like Google and Facebook have been dealing with for a long time which is um are ads purely destructive or negative to the user experience or are they actually if done properly are they actually either neutral or even positive right and and this was something that you know Google I think to their credit figured out very early which is you know a a wellargeted ad at a specifically relevant point in time is is actually content like it actually enhances the the experience right because the obvious case you're searching on a product there's an ad, you can buy the product, you click to buy the product.

That was actually a useful piece of functionality. Um, and so, you know, can you can you have ads or or or other things that are like ads or look like ads, you know, different kinds of referrals, you know, mechanisms or whatever.

Can you have them in such a way that they're actually additive to the to the product experience? Uh, and you can just like with search and with social networking, you could imagine lots of examples of that. People will, you know, people will, you know, they'll whine around in lots of different ways.

But I think it, you know, I think that hasn't been a bad outcome overall. Um and I think that uh I think it's entirely possible that that's what what happens with with these models as well. Yeah.

So uh kind of similar kind of question what what should be legal kind of trying to create legal frameworks on on a number of issues with AI. Uh there's been a number of IP cases that have been working their way through the courts. What can labs use to train models etc. There's been some good outcomes recently.

Sam also was talking about how a lot of people are using AI as like a confidant, like a, you know, a friend, things like that. And he mentioned that currently your chats are not privileged, they can be used in in in a in a lawsuit or or other uh situations.

uh how how optimistic are you that our sort of legal system in the US can get some of these issues right where maybe it can't just be you know total free markets kind of lawless whatever goes you know so in the case of training data I think that there I mean there's a bunch of these copyright you know kind of lawsuits happening right now there's you know the big New York Times open the I1 and there's you know been a bunch of others um I I think in that for that particular problem my guess is that problem ultimately has to be solved through legislation um it's It's it's ultimately a legislative question.

The reason is because it goes to the nature of copyright law itself, you know, which which is legislation and and and of course, you know, the the the content industry is already claiming that of course, you know, using using copyrighted data to train, you know, without permission and without paying is is is sort of, you know, they they believe illegal on its face, you know, due to violation copyright law.

The counter-argument to that, which, you know, which we believe is, well, it's not copying, right? There's there's a distinction between training and copying, just like in the real world, there's a distinction between reading a book and copying the book, you know, as a person.

And so there there's going to need I I think, you know, the courts are trying to grapple with that. There's a whole bunch of cases. There's jurisdictional questions. You know, probably ultimately Congress is going to have to figure out a a um you know, figure out an answer on that.

And by the way, the president has kind of, you know, thrown down that gauntlet in his I think the speech he gave last week or two weeks ago. Um you know, where he said that, you know, Washington probably needs to deal deal with that as an issue. Um so that's one on the on the on the um on the on the privacy thing.

I I think that that one feels like it's a Supreme Court thing. Um to me it feels like that's the kind of issue say the Supreme Court and the in other words like whether for example your trans transcripts are are considered your property and whether they're protected against you know warrantless search and seizure.

Um and and the observation I would make there is if you look at the march of technology over time.

So the the constitution has like very clear, you know, fourth, fifth amendments, you know, very specific rights around the, you know, the things that are yours, you know, such as, you know, your home, you know, being in your home, you know, by the way, the thoughts in your head, right?

Um, uh, you know, that the government can't just like come in and take. They can't, you know, they can't just come in and search your house without a warrant. You know, they can't like, you know, put you in a jail cell and beat you until you fess up.

Like, you know, there there are, you know, we we have constitutional protections against the government being able to basically, you know, take information, you know, fundamentally. um uh you know as well as possessions.

Um and then basically what happens is every time there's a new technology that creates a new kind of sort of you know thing that you own, you know, thing that's yours, thing that you would consider to be private thing that you wouldn't want the government to be able to take without a warrant.

You know, out out of the gate, law enforcement agencies just naturally go try to get those things because they're ways to solve crimes and, you know, it feels like that that's a legal thing to And then basically the courts come in later and they you know rule one way or the other and basically say no that that actually is also a thing that is protected against uh you know warrantless for example warrantless search um you know warrantless wiretapping.

And so I I feel like that you know this is the latest of probably I don't know 20 of those over the last 100 years.

Um and you know I don't know which way it'll go but I think it's it's going to be a key thing because as you know people are are already telling these models you know lots lots of things that they're you know that that are very personal. Okay lightning round quick questions.

as we're letting you get out of here in a couple minutes. Um, we're in this age of spiky intelligence. Models are great at some things and then terrible at others. Where are you actually getting value out of AI right now? Where is it falling down for you? Where are you how are you using AI day-to-day? Yeah.

So, I I I have two kind of I don't know bar barbell approach. Um, one is for for serious stuff. I love the deep research capabilities. Yeah.

Um, and so and I'm doing this in a bunch of models, but like the ability to basically say I'm interested in this topic and then I just I just felt like write me a book and I, you know, I'm kind of hoping for the longest book I can get. I always tell like go longer, go longer, more sophisticated.

Um, you know, but the the leading edge models now they're getting up to like 30 page PDFs. Um, you know, that are like completely well formulated, you know, basically long form long form essays. Um, you know, with just like incredible richness and depth.

Um, and you know, if it's 30 pages today, I'm sort of crossing my fingers that it'll get to, you know, 300 pages coming up here in the next few years.

Um, and so I, you know, I'm able to basically have the thing generate enormous amounts of of reading material with just like I think incredible richness and depth and complexity. Um, and then and then on the other side of the barbell is humor.

Um, and I've I've posted some of these to my my X feed over over the last couple years, but I think these models are already much funnier than people give them credit for really. Um, I think I think they're they're actually quite highly entertaining.

Um, a while ago I post I had specific specific formats like you know the chatting back and forth be Mark Andre you know that that formats take a dip in my pool in my office. They're really good. So they're really good at green text u that works really well.

But the the the for some reason the ones I find hysterical are the I have it right screenplays um you know for like TV shows or or or plays or movies. Um and um I I posted I had it right a new season of the HBO Silicon Valley you know set 10 years later. Yep.

Um, and I had it write like an entire I had it write like 10 10 scripts for an complete season. And of course, I just said, you know, make it like Silicon Valley except, you know, it's happening at in 2021 at kind of peak woke.

Um, and I thought it was I think it's you know, I'll sit there at 2 in the morning just like laughing my ass off at how funny this thing is. Um, and so I think these things are actually are actually already like extremely funny.

They're extremely entertaining when they're when they're uh, you know, when they're used in that way. And I I I do I I do enjoy that a lot. And I generate a lot of those uh that that I don't post which Stay in the group chats. They're your property. Yeah, hopefully the fourth amendment holds on these. It's great.

I have one last question. Go for it. And then I've got one more. Uh how do you get a job as a venture capitalist in 2025? Um so I think I mean look the the best way the best way to do it is to have a a track record early as somebody who is like in the loop specifically on new product development.

Um and so somebody who you know be be like deeply in the trenches um at one of these new companies in one of these spaces. um you know participate in the creation of of a great new product uh and and and a great new company and you know really demonstrate that you know how to do that.

Um you know there's there you know there there are great VCs who have not done that but you know I think that is sort of a foundational skill set uh you know for working with the kinds of founders that that you want to work with who are going to who you know are going to want you to have you know kind of very interesting things to say on that um as I think that you know still the the the best way to do it.

Yeah, like feel the growth, be immerse yourself in the growth, the the the the the aggressive growth environment and then you'll be able to identify it when you see it from afar. Yeah, that's right. Last question for me.

State of M&A in your mind, how are you advising, you know, companies uh where where you're on the board or just the portfolio broadly around what they should expect now and and in the near future? You mean in terms of whether you can get things approved or basically? Yeah. Yeah. Yes.

So look, approval still appro approval is not a slam dunk. There was there was you know there was a I just saw there was a medical device company this morning you know where the the acquisition was not allowed by the FTC. So um you know look there is still scrutiny.

It's you know it's obviously a very different political regime in Washington.

But you know this is this is not an admin you know by by their own statements this is not an administration that believes in total affair um M&A and it definitely wants to you know in in their view maintain a a very healthy level of market competition. Um, yeah.

How many do you expect do you expect certain companies to be negatively impacted by the Figma story, right? You have this deal gets blocked, successful, you know, IPO, Lena Khan is taking a victory lap.

uh you know many people were responding and and joking saying you know someone Lena cuts off the arm of a pianist and they endure and can create a masterpiece and then um and so I expect and then you look at the example with you know Roomba I think it was where where Roomba had a deal with Amazon it was blocked and and the company has just been shambles ever since.

So my concern is that people look at Figma and say you should be independent. You just figure it out. Nothing can go wrong. Yes. Yeah. Miscon lap was very disconcerting. Um and and for exactly the reason you said which is survivorship bias.

Um right which is you you you pick the one that worked out and then you know it's the it's the airplane the red dots in the airplane mean you know you you you ignore the 50 that are in the ground uh that you've never heard of.

Um, and so that that was very disconcerting because that, you know, it's sort of the central planning fallacy, which is like we make centrally planned economic decisions. We have one example, you know, it's like in Europe it's like, yeah, well, the bottle caps actually don't fall off the bottle, right?

Like, you know, it works, right? It's like, okay, but do you want to live you want to live in an economic regime in which that, you know, the government has dictated bottle cap design? The answer is clearly no.

uh cuz the downside consequences or even even looking at that uh you know the Chinese model which is you know people can say they're picking winners but to get to maybe picking a winner you have this intense bloodbath of competition where you know teams need to rise to the top and sort of prove themselves before they get any of that real like you know meaningful state benefit.

Yeah that's right. And so you just you just yeah you just you just have this adverse election survivorship bias thing where you just you don't pay attention to all the collateral damage. So I I I I do think that mentality is like super super dangerous.

Um and so yeah look I I think companies just have to be very thoughtful about this both acquirers and the acquirees.

um you know and the big thing is if you're selling a company like you just need to anticipate that you might you might not get it through and if you don't there sort of they like okay number one is there like a big enough breakup fee right are you going to get you know paid for the you know paid for the the the you know the damage that you're going through um you know is and and how is that structured on the one hand and then two is yeah look do you have the kind of company culture that's going to be able to withstand that um and and is your business you know strong strong enough to be able to be able to get through that and it's it it is a real risk and something worth you know taking very seriously yeah and that's that that's why it felt emotion we were at I see last week it felt emotional this that that the the Figma team was was able to like effectively just like restart the business and say like we're we're we're taking this all the way.

So if you talk the way to think about it, if you talk to any really successful company, what they'll tell you is, yeah, over the years we had these like crucible moments in which like we almost died, right?

But we like pulled together and we pulled it off and then that became like, you know, one of these central kind of mythical events in the history of the company that we always refer to and like my god, we got through that and we're so strong and tough and we've been forged in fire and now we can do anything.

And it's like, yeah, that's great. And then there's 50 other companies that hit those crystal moments, blew up and died. So, yeah, like it's it's all of the quote lessons learned on this stuff, they're all conditional on on like survival. Um, and so they they these things need to be taken incredibly seriously.

Um, you know, which which the great CEOs do. Yeah. Well, thanks so much for joining. We'll let you get back to your day. We already five minutes over. Next time we have to book five hours because this is fantastic. I got 10% of the way the first 24hour TV. Yeah, we would love to have you again.

Uh, enjoy the rest of your day. We'll talk to you soon, Mark. Have a great day. Bye. Thank you, guys. Thank you. Up