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?
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?
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 Andreessen went live on TPP on this week and today we're dropping that full conversation here on the pod.
Mark gets into it all what's really happening in AI right now, how Apple is playing its hand, the return of open source, and why perfect products can signal the end, not the peak.
He also shares his take on how to break into venture capital in 2025 and what he's actually using AI for 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 paid promotional advertisements, other company references and individuals unaffiliated with A16Z.
Such advertisements, companies and individuals are not endorsed by AH Capital Management LLC, A16Z or any of its affiliates.
Information is from sources deemed reliable on the date of publication, but A16Z does not guarantee its accuracy.
We have Mark Andreessen joining us.
He's live from the TBPN Ultra Drum.
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.
American work ethic.
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?
Fantastic.
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, but it turns out 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 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 approaching the touring test, the uncanny valley.
We're escaping the uncanny valley.
I think, a question looking back over the, you know, maybe 10 or 15 years.
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 used to feel that way, at least from my point of view.
So my view of it always is there's like these, there's this, these disconnected, you know kind of patterns or trends.
There's sort of 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, kind of sawtooth, kind of up to the right key 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 like all kinds of crazy crazy, crazy stuff that's happening.
But basically, you know, the technology keeps getting better.
So 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 new thing?
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 that ChatGPT was the breakthrough moment because they thought everybody already knew what these models could do for you know three years before that.
And 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 when these things pop.
But also, if you're in this day to day, it's really hard to tell when things are going to be hot or not, because it doesn't necessarily map to how much the technology is improving.
Yeah, we were just talking about that in the context 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 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 go 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 take 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 you know Dealing, having like the thick skin to deal with, like the safety constraints and all of the different stuff, obviously not being irresponsible, but just speeding up the organization as much as possible?
It feels like now's the time to really push on that.
Yeah, well, first of all, I need to correct you.
It's moving fast and making things.
Making things, that's right.
I don't even know where that came from.
I have no idea.
Never heard of it.
I mean, Chachibiji didn't really break anything.
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 hallucinated a ton, but it didn't really break anything.
I don't know.
Yeah, I believe in this case, total deaths attributable to chat GPT are still zero.
Zero.
So notwithstanding all the caterwauling.
Yep.
So look, I think the AI industry 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 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 salespeople or marketing people who sell them.
You know, in the air companies you have this third tier of you know, the quote unquote, researchers.
Yeah.
Right.
And so, you know, which is which has worked out incredibly well.
I 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 necessarily.
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 DeepMind, but even more broadly Google, you know Google developed a 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 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 and I asked them you know, when could you have had ChatGPT with GPT-4 level output if you had, just you know, gone flat out starting in 2017?
And they said by 2019.
You know, they already knew how to do it.
And then, you know, they've now caught 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 I'm sure you guys talked about.
But you know he has now, you know, with NXAI he's now collapsed.
You know he's eliminated the distinction between research and product.
Yeah.
Um, and so, you know, of course he, 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 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.
I'm wondering more on that, but also on Apple's strategy.
It feels like Apple's been kind of like, you know, people have been maligning them for not –
We're missing the AI opportunity.
And Tim Cook's just there on the earnings call being like look, we acquired a couple small companies.
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're investing 100 million in American manufacturing.
They're certainly doing stuff.
They're just not chasing the, you know, the shiny tennis ball.
The headline $100 billion CapEx.
Right.
So I'm wondering about your thoughts on when you have a, you know, 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, you know, changing their strategy on?
Yeah, so look, Apple's always had this, you know, very clearly defined strategy that you know Steve and Tim, you know working together, figured out a long time ago, which is, you know, I forget the exact term, but it's something like basically, they invest deeply into the core of what they do.
You know, 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.
Yeah.
Right.
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 Thiel calls last to market.
You know, they'll come out whatever three years later, whatever five years later.
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 it, they'll hit it.
The last mover, the last mover.
Yeah, yeah, yeah.
Sorry.
The last move, right?
I guess, yeah, what I would say is like, look, that clearly works if you're Apple, right?
And 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 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?
And so 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 too.
Right.
You know, we'll just, you know, we'll get to it.
Right.
And you know, You know, they're gone.
What do you think?
Blackberry Bold, I remember.
It was like an iPhone knockoff.
What do you think you know?
Right now, people are a variety of, you know shareholders are annoyed at Apple around their reaction to AI LLMs.
John's annoyed around just like transcription generally, just like super basic stuff.
Actually.
But it doesn't feel like the core businesses 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'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 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, you know, because other things have superseded it.
And, you know, look, everything, you know, everything becomes obsolete at this point.
So there will come some time, 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 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.
You know, is that time three years from now?
Because there's, like some, you know, huge breakthrough, you know, from some company that figures out 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 a peripheral to the phone.
I mean, you know, 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 in 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 Andrews & Horowitz maybe a decade ago or something and Chris Dixon showed me the HoloLens and I was like, okay, we're one year away from this being everywhere.
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.
And it feels like we're always there.
But it does feel like Apple did a lot of work on the fundamental, you know, pixel density of the resolution of the display.
And then Meta has 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, here's the other thing.
But you know, I think that's true.
But you'd also say you know I'm, I'm on the on the meta board.
So I'm kind of a dog hunting this one.
But, like the meta, Ray-Ban glasses are a big hit.
Oh, totally.
Right.
Like they're, they're a big, you know, so I think we, we now have a form factor that we know works.
You know, for eye-based wearables, you know, there's not VR and then VR, you know, on top of that.
But you know, just you know the glasses and you know, and then the glasses with camera, you know sort of integrated camera, integrated microphone, integrated speaker.
You know, that's a very interesting platform.
You know, the watch clearly works, by the way, which Apple, of course you know, has played a significant role in making happen.
You know, that now sells in huge volume.
You know, so that's the second data point.
And then, you know, look, I think these, you know, I think some form of AI pin is going to work.
I also think you know headphones are going to get a lot more sophisticated, which is already happening.
And so, you know, you do have these, you know, kind of data points coming out.
And then, yeah look, the trillion dollar question ultimately is are these peripherals to the phone you know which is what they are today or are these replacements for the phone?
And you know yeah, I would say you know 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 the Meta Ray-Bans, 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 do you disagree with that?
Please.
Well, you guys might be too young.
The first iPhone actually was a bad phone.
How so?
For the first two years, it couldn't reliably make phone calls.
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 least of the expectation.
But yeah, I mean, I guess you're right.
So for the first two years it was a classic apple strike, because for the first two years the thing couldn't reliably make phone calls.
And then it turned out there was an issue with the antenna and with how you held it.
I remember that.
And you would disconnect it.
You could basically brick the device.
Yeah.
Based on how you held it.
And somebody emailed.
This is when Steve would respond to emails from random people.
And somebody emailed Steve saying, if I hold the phone this way, it doesn't make phone calls.
And 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 took like five years for the iPhone to find its footing.
It took like two years to get.
I remember also the original iPhone didn't have it in that 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.
So 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's big.
Steve would have hated this, right?
Like how 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 LG products become obsolete at the precise moment they become perfect.
And 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.
And there's been tons of examples of 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 done. if the product has been realized.
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, but are fundamentally different.
And so that is one of the time-honored traditions.
And one of the things you could say about Tim is his willingness to kind of break the mold of Apple only ships perfect products, but being willing to ship the Vision Pro shows a level of determination to kind of stay in the innovation game, which I think is very positive.
Yeah, yeah, yeah, yeah.
That's great.
Updated thinking on open source.
Since we last talked, there's a lot that's been happening yesterday.
OpenAI is an open source company again.
Yes, OpenAI is open again.
Yes.
Yeah.
Yeah, look, very encouraging.
You know, a year ago I was very, you know, I was getting very distressed about, you know, whether open source AI was going to be allowed right.
It was even going to be illegal.
And so, and I think, you know, we're basically through that at this point.
We're through that in the U.S.
You know, we'll see about the rest of the world.
And then look, you know, the US-China thing is obviously a big deal, but you know, I think it's been net positive for the world that China has been so enthusiastic about open source AI coming out of China.
Mm-hmm. which has been great.
And then yeah, look openly, leaning hard into this, you know, and releasing what you know, what they did is, I think fantastic, both because 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 reconfirmed overnight that he's going to.
You know open source.
You know start open sourcing.
Previous versions of Grok.
Um, and so yeah, so we, you know we we we, we seem to be, we seem to be in a 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 for criteria 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 Chinese open source models 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, its phone and home if it runs into this specific thing?
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.
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 going to 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 want to keep an eye on.
But from your perspective, 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.
And you identified them, but let's talk about both of them.
So the phone home thing is the easy one, which is you can packet, sniff a network and you can tell when the thing is doing that.
And plus, you can go in the code and you can see when it's doing that.
And so you can validate that that's either happening or not happening.
And I think that's important.
But I think people are going to figure that out.
You can kind of gate that problem practically.
The bigger issue is we have this term in the field right now called open weights.
And open weights is a loaded term.
It uses the open term from open source.
But of course, with open source, the thing is you can actually read the code.
You know, with open weights, you have, you know, just a giant file full of numbers, as you said, that you can't really interpret.
And then what you don't have, what most of the open source, open weights models don't have including, you know, deep seek.
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 um, and of course, you know most 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 open weight 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 doesn't phone home, but you don't actually know what's happening um, inside the ways, 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, 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.
Other countries worry when these models come from the US, right?
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 are kicking around, which is not my weights, not my culture.
Right.
Right.
Or by the way, for that matter, not my weights, not my laws.
Yeah.
Right.
Which is like, OK, 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.
I can tell you the American models have all kinds of constraints also.
Right.
Right.
Implemented, you know, usually by a very specific kind of person at a very specific location in the US.
And so, you know, I think that this is 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 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.
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.
One model is you charge for the premium, you give the free away.
It's a freemium model.
That's what we're seeing in most of the labs right now.
There's also this kind of specter 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 deciding whether or not it would be a net good to put advertising in LLMs.
What might happen that might be bad there?
Do you have a 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, just no ads would potentially, you know, be incredibly destructive.
But yeah, curious your framework.
Yeah.
So I should start by saying like whenever I personally use an internet service, I always try to buy the premium version of it that doesn't have ads, right?
And so if I can like, live personally inside an ad-free universe and pay for it like that's great.
And I'll freely admit, you know, whatever level of hypocrisy or incongruence you know, kind of results from that.
No, the point is choice.
The point is choice.
Well, the point is exactly what you said.
It's affordability.
So the problem is, if you really want to get to, if you want to get to a billion and then five billion people, you can't do that with a paid offering like it, just at any sort of reasonable price point.
It's just not possible.
You know, global per capita GDP is not high enough for that.
People don't have enough income for that.
At least today.
And so if you want to get to, you know, 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 for free, you need to have a business model.
You need to have an indirect business model and as is the obvious one.
And so I do think, if you take some principle stand against ads, I think you unfortunately are also taking a stand against broad access, just in the way the world works today.
And then look.
The other really salient question is the same question that the companies like Google and Facebook have been dealing with for a long time, which is 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?
And this was something that Google, I think to their credit, figured out very early, which is a well-targeted ad at a specifically relevant point in time is actually content like it actually enhances the experience right because it's 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 other things that are like ads or look like ads you know, different, 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?
Um, and you can imagine, just like with search and with social networking.
You can imagine lots of examples of that.
People will, you know, the people will, you know, the whiner and in lots of different ways.
But I think, 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 with these models as well.
Yeah.
So uh, kind of similar kind of question what should be legal?
Kind of trying to create legal frameworks on a number of issues with AI.
There's been a number of IP cases that have been working their way through the courts.
What can labs use to train models, et cetera?
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 friend, things like that.
He mentioned that currently your chats are not privileged.
They can be used in a lawsuit or other situations.
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 total free markets kind of lawless, whatever goes?
So in the case of training data, I think that there's a bunch of these copyright lawsuits happening right now.
There's the big New York Times opening, I won, and there's been a bunch of others.
I think for that particular problem.
My guess is that problem ultimately has to be solved through legislation.
It's ultimately a legislative question.
The reason is because it goes to the nature of copyright law itself. you know, which is legislation.
And of course you know the content industry is already claiming that.
Of course you know, using copyrighted data to train, you know, without permission or without paying is sort of, you know they believe, illegal on its face, you know, due to violation of copyright law.
The counter argument to that, which, you know, which we believe is, well, it's not copying.
Right.
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's going to need 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 you know, figure out an answer on that.
And, by the way, the president is kind of, you know, thrown down that gauntlet in his, I think, the speech he gave last week or two weeks ago, you know where he said you know Washington probably needs to deal with, deal with that as an issue.
So that's one on the on the on the on the on the privacy thing.
I think that that one feels like it's a Supreme Court thing to me.
It feels like that's the kind of issue says Supreme Court.
And in other words like whether, for example, your chance transcripts are considered your property and whether they're protected against, you know, warrantless search and seizure.
And the observation I would make there is if you look at the march of technology over time, so 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.
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 right.
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 are, you know, we have costumes for protections against the government being able to basically, you know, take information, you know, fundamentally, you know, as well as possessions.
You know 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's a legal thing to do.
And then basically the courts come in later and they, you know, rule one way or the other and basically say no, that actually is also a thing that is protected against.
You know warrantless, for example, warrantless search.
You know wireless wiretapping.
And so I feel like you know, this is the latest of probably I don't know 20 of those over the last 100 years.
And you know, I don't know which way it'll go, but I think it's going to be a key thing because, as you know, people are already telling these models.
You know lots of things that they're you know that are very personal.
Okay, lightning round, quick questions.
We're letting you get out of here in a couple minutes.
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?
How are you using AI day to day?
Yeah, so I have two kind of, I don't know, barbell approach.
One is for serious stuff.
I love the deep research capabilities.
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.
You know what, 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 it like, go longer, go longer.
More sophisticated, you know, but the leading edge models, now they're getting up to like 30 page PDFs.
You know that are like completely well formulated, you know, basically long form, long form essays.
You know it's just like incredible richness and depth.
And you know, if it's 30 pages today I'm sort of crossing my fingers it'll get to you know, 300 pages coming up here in the next few years.
And so I'm able to basically have the thing generate enormous amounts of reading material with, just like I think, incredible richness and depth and complexity.
And then on the other side of the barbell is humor.
And I've posted some of these to my next feed over the last couple of years.
I think these models are already much funnier than people give them credit for.
Really?
Yeah, I think they're actually quite highly entertaining.
Is it specific formats?
Like prompters just chatting back and forth?
Be me, be Marc Andreessen, you know, that format.
Take a dip in my pool in my office.
They're really good.
So they're really good at green text.
That works really well.
But for some reason the ones I find hysterical are the I haven't read screenplays, you know for like TV shows or plays or movies.
And I posted, I had it right, a new season of the HBO Silicon Valley, you know, set 10 years later.
And I had it right like an entire day.
I had it right like 10 scripts for a complete season.
And of course I just said, you know, make it like Silicon Valley, except you know it's happening in 2021, a kind of peak woke season.
And I thought it was just.
I think it's just.
You know, I'll sit there at two in the morning and just like laughing my ass off at how funny this thing is.
And so I think these things are actually already like extremely funny.
They're extremely entertaining when they're, you know, when they're used in that way.
And I do enjoy that a lot.
And I generate a lot of those, That I don't post.
Stay in the group chats.
It's probably good idea.
Your property.
Yeah.
Hopefully the fourth amendment holds on these.
Yeah.
That's great.
I have one last question.
Go for it.
And then I've got one more.
How do you get a job as a venture capitalist in 2025?
So I mean look, the best way to do it is to have a track record early as somebody who is like in the loop, specifically on your product development.
And so somebody who you know be like, deeply in the trenches at one of these new companies, in one of these spaces you know participate in the creation of a great new product.
And a great new company and, you know, really demonstrate that you know how to do that.
You know, there's there, you know, there are there are great VCs who have not done that.
But you know, I think that is sort of a foundational skill set, you know, for working with the kinds of founders that you want to work with, who are going to you know are going to want you to have you know kind of very interesting things to say on that, as I think you know still the best way to do it.
Yeah like.
Feel the growth, immerse yourself in the growth, 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 MA in your mind, how are you advising companies where you're on the board or just the portfolio broadly around, what they should expect now and in the near future?
You mean in terms of whether you can get things approved?
Basically, yeah.
Yeah, yes.
So look, approval still, approval is not a slam dunk.
There was.
You know there was a.
I just saw there was a medical device company this morning.
You know where the acquisition was not allowed by the FTC.
So, 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 not admit.
And by their own statements, this is not an administration that believes in total laissez-faire.
M&A definitely wants to, in their view, maintain a very healthy level of market competition.
Do you expect certain companies to be negatively impacted by the Figma story?
You have this deal gets blocked, successful IPO.
Lena Kahn is taking a victory lap.
And so I expect.
And then you look at the example with Roomba.
I think it was where Roomba had to deal with Amazon.
It was blocked and the company has just been in 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, I mean, it's kind of taking a victory lap was very disconcerting.
And for exactly the reason you said, which is survivorship bias right, which is you pick the one that worked out.
And then you know it's the, it's the airplane, the red dots the airplane.
You know you ignore the 50 that are in the ground that you've never heard of.
And so that was very disconcerting because that you know 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.
Like, you know, it works.
Right?
It's like okay, but do you want to live in an economic regime in which the government has dictated bottle cap design?
The answer is clearly no.
Because the downside consequences.
Or even looking at the Chinese model, which is people can say they're picking winners, but to get to maybe picking a winner, you have this intense bloodbath of competition where 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 selection, uh survivorship bias thing, where you just you don't pay attention to all the collateral damage.
So i i do think that mentality is like super, super dangerous um, and so yeah look, i think companies just have to be very thoughtful about this, both acquirers and inquirees uh, 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, they're sort of like okay, number one, is there like a big enough breakup fee, right?
Are you going to get paid for the damage that you're going through?
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?
And is your business, you know, strong enough to be able to get through that?
And it is a real risk and something worth, you know, taking very seriously.
Yeah, and that's why it felt emotional.
We were at NICE last week.
It felt emotional that the Figma team was able to like, effectively just like restart the business and say, like we're taking this all the way.
Yeah.
If you talk to any really successful company, what they'll tell you is yeah, over the years, we have these like crucible moments in which, like we almost died.
Right.
But we like pull 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 forging fire and now we can do anything.
And it's like, yeah, that's great.
And then there are 50 other companies that have those crucible moments blew up and died.
Right.
And so, yeah, it's all of the lessons learned on this stuff.
They're all conditional on like survival.
And so these things need to be taken incredibly seriously, you know, which the great CEOs do.
Yeah.
Well, thanks so much for joining.
We'll let you get back to your day.
We already gave you five minutes over.
Next time we have to book five hours because this is fantastic.
I got 10% of the way through.
Let's do the first 24-hour TVP.
Yeah, we would love to have you again.
Marathon.
Enjoy the rest of your day.
We'll talk to you soon, Mark.
Have a good day.
Thank you, guys.
Thank you.
Thanks for listening to the A16Z podcast.
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