A (2:25)

Well, I think there's a couple elements to this. So one is first of all, Reid was ultimately responding to a thing that was no longer the actual policy.

C (2:33)

Yeah.

A (2:33)

So he said, well 100k a year was a great policy and obviously the Internet had moved on. It's not to me obvious. I wouldn't conclude the same outcome that you just concluded in that I think that you'd have a situation where the Amazons and Googles would probably actually capture the vast portion of the talent in this situation. So it's not clear to me that like startups sort of come out ahead. We're better off from this particular implementation.

C (2:56)

Maybe Amazon and Google who are probably more easy to regulate. But there are a number of organizations that are consultancy.

A (3:04)

Yeah.

C (3:05)

That actually are price sensitive.

A (3:07)

Yes.

C (3:07)

That would be squeezed by this. And I would think that given that they're in the top 15, they make up like four or five of them. That would be a significant freeing up.

B (3:15)

Yeah.

C (3:15)

Higher level.

A (3:16)

I think the my thing would be like if you could just get all the people in the room that have an opinion on this topic and you. But you actually have the practitioners in tech in the room as well and the let's say the most a kind of, you know, you can't even say like right wing because actually I don't think this is even classic Republican. So it was just like the polls. If you got everybody in a room and you say what are we optimizing for? Are we optimizing for. We don't want to have wages go down. That's an interesting thing. Are we optimizing for a particular kind of job? Not going to, let's say certain populations of Americans. Are we optimizing for just ensuring that we only have the highest merit people on the planet coming here, like those are all totally different kind of goals to optimize for. And I think that the framework you end up up with and the system that you end up with should probably, hopefully have like a cohesive sort of strategy behind it. My strategy would be we want the absolute best in the world here. There's not exactly clear that there's a fixed number on that. Some years there might be 5,000, some years there might be 50,000, some years there might Be 80,000. We probably want them to be net positive to wages. So let's agree that, you know, in any given industry or locale wages should go up with this talent pool as opposed to down. So I think that's actually totally reasonable. And you. So you should have the market kind of sort of some marketing market dynamic to that. And you shouldn't be able to kind of game and exploit the talent pools for saying now in Detroit we can go wipe out IT jobs because we can go and offshore those. Like I think you could build a system that basically meets all of those goals while still ensuring that you can get somebody that goes to their master's program in Nam, your state school, they come out of it, they're an AI engineer. They're not yet at the sort of meta is going to pay them $100 million, but they are going to be totally valuable contributors to our economy. It's all sort of positive sum. It's not taking a job from anybody else. It makes us competitive. And I think there's a way to do that without sort of overly, let's say putting constraints in the system that make it maybe so a startup wouldn't be able to kind of economically viably participate in this. And I think 100k per year would be at a point where the startups would be directly impacted.

C (5:18)

Working with a lot of startups, I'm not sure that's the respectfully.

A (5:21)

Respectfully, the kind of startups that Andreessen Horowitz sees are not all of the base of startups in the world.

C (5:27)

So you could quibble about the number. Is it 20k which Keith Rabois said when I thought was very sensible, or is it 100k? I don't know. But like the idea.

A (5:34)

Idea that you wait, Keith throughout 20?

C (5:36)

Keith throughout 20.

A (5:37)

Well, let's just go with Keith number. I think that if Keith rough 20 I think we can be good with a Keith number.

B (5:41)

But I think the number, it's easy to fixate on the number, but you have to always look at what is the number replacing. And I don't think the average person having this debate, other than the people that really work at this, have any idea the amount of productivity that is lost working this system. Yes, I mean the incredible amount of resources. And of course the bigger companies, the ones that you mentioned, have enormous teams that spend all of their energy like literally essentially as lobbyists working this system. And then the back end of that is are all the justifications and all of the management, all of the handling. And now they've just deployed all their resources to manage like in house call centers to deal with getting their employees back to the United States to just deal with it. I do you hit on one Point that I think is really important to this debate that is sort of getting lost, which is there's no doubt that within the big tech world that they, for the past 25 years or so, they really went on this sort of bifurcated curve which is hiring for the people in the office, focusing on say 25 or 30 university departments. And then basically everybody else was like, well, it's so much easier if we just hire huge numbers of people from these eight international locations and schools. And I think a lot of this is missing that a big part of this is, well, the tech industry. Like if you look at intel and where they all went to college, and if you look at the history of Silicon Valley, it's all these people from all the schools in the middle of the country, none of which are like the target recruiting schools by the main tech companies these days. And that's been a place where I think that the big companies have been somewhat lazy. And as a person who spent decades flying to all of these schools and recruiting, there's work that the universities have not done to be better programs and that there's work that the big companies have not done to be clear what it is that why they've stopped recruiting or haven't seen the numbers and that change would be better for everybody to really make.

C (7:42)

I agree. So my expectation, what gets impacted is kind of an even different job set than that, which is if you go to Florida today and you try and get like an IT job for 100k, you just can't. Right. And so that I think is actually the area that's the most directly impacted by the largest large consultant.

B (7:58)

Meaning there aren't jobs that pay 100k or that you just can't find a job.

C (8:02)

They're taken, they just don't exist. Like any sort of IT administrator, like the services, like the basic consulting gigs, like all of that has been saturated. It's very, very tough to get a job between like 80 and 120k in much of the United States because of this. Right. And so this isn't about new grad being a software engineer, because the reality is the expected value of a software engineer over their lifetime is high enough that I think that like the market kind of navigates that, but it's almost these kind of lower level, more like it admin jobs that have been squeezed out. And listen, if we want to bring them back, then I do think, and we don't want to do this kind of arbitrage that a lot of these companies are doing that we're going to have to change the pricing.

A (8:40)

But wouldn't a minimum salary band effectively solve that problem for you?

C (8:44)

Sure. Yeah. Yeah, for sure. Yeah. I think there's a lot of mechanisms to do it.

A (8:47)

Okay.

C (8:47)

I actually agree with you. We should actually talk about the problem that we're trying to solve. So I think we would all agree if you come to the United States, you get a college degree, you should have a visa. Okay. So that's for sure.

A (8:58)

Do we all agree on that?

C (8:59)

Okay. Yes. Okay.

A (9:00)

So the bill. I don't think that the people proposing this strategy actually agree on that.

C (9:06)

Well, so Trump famously said.

A (9:07)

Oh, he famously said a lot of things for sure.

B (9:10)

And then he unsaid it.

C (9:11)

Yeah, exactly.

A (9:12)

I mean this wouldn't be an interesting debate. If we all agree and we have a policy that says if you go from IIT and then you come to Kansas State, then, then you get a.

B (9:22)

Job that became part of the gamification of the system because you would do the IIT thing, then you would get a company sponsorship for a master's degree at some school. So it didn't really accomplish the goal of investing in coming to the US the same way that going to a four year school would have done.

A (9:40)

Wait, wait, why is that? What was the problem?

B (9:42)

Because you ended up getting sponsored by a company.

A (9:44)

Yeah.

B (9:44)

And it changed the whole dynamic of were you seeking out the U.S. or did a company pull you to the U.S. it's a little bit different.

A (9:51)

Okay.

C (9:52)

But I also think so often this discussion goes the direction this one is going on as we focus on like new grad software engineers. And I actually don't think that what is getting impacted.

A (10:00)

Right.

C (10:00)

I really don't. I really think it is like what are the consulting shops do they do admin work. IT work and it's just a different salary band.

A (10:09)

Yeah. I think it's.

C (10:10)

I think these are, these are body shops and like an approach like this directly targets them. I think in a way that's actually quite positive.

A (10:17)

Yeah. I just think there's. I'm then. I mean I would just then favor Keith's approach because I think there is a number in which it becomes. You're then making other trade offs in your business just to be able to.

B (10:27)

Yeah, yeah, yeah, yeah.

C (10:27)

The 100k number is.

B (10:28)

Well, I do think that the dollars. But I do think that just to reinforce this that, that the whole system can do without this immense cost and uncertainty.

A (10:38)

Yep.

B (10:38)

And any solution should really be. If you address that then the whole. The rest of the dynamics will Follow. But as long as it's a huge, complicated, expensive system y then the big companies are going to continue to benefit from it disproportionately.

A (10:52)

Yeah.

C (10:53)

And I will tell you right now like it is much harder for a startup to deal with the lottery system. Yes, it's possible than it would be for them to pay 100k like for the startups.

A (11:02)

But I don't think it changed the lottery system. It's a hunt.

B (11:04)

Right, right. It didn't.

A (11:05)

It's 100k to participate in the lottery system.

B (11:08)

Right.

C (11:08)

Oh I understand. Hopefully, hopefully like number it'll change the calculus of a lot of people that are in the lottery system.

A (11:16)

I just think that I would prefer.

C (11:17)

To remove the lottery system.

A (11:18)

Yeah, I think you can absolutely pull off a system that, that for probably in a shared definition for us and anybody else would say this is clearly a high merit job. It's going to increase the wages in this particular sector on average. And, and we want to make sure that we've got the best talent in the world that comes in to do that. It's not going to drive down wages. We probably want as many of those individuals here as possible. And, and so I think you know, some elements of this are, are intriguing in that they push the conversation forward on the dimension of you know, like the 100k is like a hammer to do that and maybe there's a more nuanced approach that I would certainly prefer.

C (11:54)

But, but again it's important like the, the, the 100k will scale with the skill level. Right. So the higher the skill the more like that is amortized. And so you could argue that this is just kind of a dial to get higher skills.

B (12:06)

Well you, you, the, the once you put a dollar amount on it, you have to keep in mind that people are going to pay it.

C (12:11)

That's right. Yeah. You're going to make up their own adaptive market.

B (12:14)

And, and the skill isn't. Might not be relevant to some people because you know the tricky part.

C (12:19)

Yep, yep.

E (12:19)

I want to segue from labor markets to good open.

A (12:23)

What's the next, what's the next really interesting political topic that we engage in?

E (12:28)

Yeah, exactly. From labor markets to labor productivity with AI offline. Aaron, we were talking about the meter paper and it was, the paper suggested that their developers were actually less productive with AI but that doesn't square with your experience talking to a lot of different starts seeing a lot of different startups and how they're so much more, more productive. So when you talk about where you're seeing startups say they're more productive and why is it happening?

A (12:49)

Yeah, so I'll, I'll first just represent our own case study and then, and then there's the really extreme version. So our own case study is we've adopted a few different kind of AI coding tools. You know, cursor being a super, super popular one internally. And you know, as I talk to people, let's say in the hallway who have, you know, maybe they're trying to get me excited by AI but like I think they know I'm bought in. So the kind of qualitative answers I get from people. And then I'll give you our internal metric. You know, some people say I'm getting, you know, a 20, 30% productivity gain, other people will say 75%. Interestingly, I have not been able to pinpoint the demographic difference on the answers.

C (13:35)

Oh, but this is self reporting.

A (13:36)

This is self reporting.

C (13:38)

How happy are you?

A (13:39)

Yeah, no, but we have internal metrics as well. So about 30% of our code right now is coming from AI. So we've got the 70%. 30%, 30%. So we have some of the kind of pure internal metrics that show this. But what's interesting is that I have a two by two of, I have senior people that are saying that they're getting 75% productivity. I have junior people that are saying they're getting 75% and then vice versa on the 25% let's say. And it's, I haven't been able to quite figure out a pattern maybe except for, and we've talked about this a little bit, but you kind of see this online except for maybe the, the biggest criteria is just the people that actually push the AI to do more, which is sort of this other new kind of psychographic which is just like who is willing to just be like, you know what, I'm going to yolo this, this task and just see what the AI comes up with. And your, your sort of willingness to just do that I think probably somewhat then shows up in the, in the ultimate productivity gain. So that's, that's us as a relatively larger company on the startup side. But what's crazy, and this is the thing that just blows my mind, I will regularly talk to 3 5, 10 person startup founders that, that self report they might be getting somewhere on the order of like 3 to 5 to 10x productivity improvements. And, and the, the big difference is, is that you know, a year ago if we were to have this conversation, the co. The conversation would be about, you know, AI sort of doing type ahead and, and it, you know, it can add maybe like a few lines of code to your productivity per, you know, incremental, you know, sort of unit of work that you give it. And then now obviously the big phenomenon is background agents where I give it a very, you know, detailed prompt, I send it off, it comes back. You know, people talk about it as like a slot machine of like some percent of the time is not going to come back with the right thing. You have to decide which, what you actually, you know, kind of pull in from it. But the kind of startups that are getting like real multiples of productivity gain are just, they're fundamentally engineering in a different way. They're sending off a task, the task goes off, comes back in 20 minutes and then they're really in the, in the business of doing code review, not code writing. And it's going to obviously change, you know, quite a bit of what computer science looks like in the future. And then the only question is like, you know, what are all the things that that's good for? Where does that break down? What, what kind of teams can actually evolve to that state? But that one has been blowing my mind the most recently and I think that kind of fundamentally changes what, what the future of, you know, kind of engineering looks like.

B (16:03)

I think what you said is super interesting. Let me ask you. I think that there's an overlay that goes beyond junior, senior and, and there, and which is, is we're all talking about characteristics that have two. A solution has two really important characteristics right now. One is that it's engineers doing stuff for engineers and, and they understand the domain super, super well. Yeah, and I think that that's a really, really important part and a really big thing that people aren't talking enough about, which is maybe what's going on is that you have AI accelerating for people that work in the domain and are very smart. And then the other is we, we shouldn't forget and this is to your, your self reporting a little bit, but also which is that this is all early adopters and early adopters are very forgiving of mistakes on purpose. And it's a, it's a super interesting dynamic where when something is brand new, a culture around it develops which, which just lets anything happen. I mean, you know, like the early Internet people didn't complain that the Internet was slow.

A (17:07)

Right.

B (17:07)

The only people on the Internet were slow were the late adopters who were like, wow, this is so much slower and, or like take online video which was like, I'm watching this tiny postage stamp video. And the early adopters, like, this is the coolest thing I've ever seen. Everybody else is like, why would I want to watch anything like that? And the same with downloading music. And. And so I really feel like what's going on is just this incredible.

A (17:28)

You remember that you guys, do you remember this watch that you guys made?

B (17:32)

The spot watch.

A (17:33)

Yeah, Spot watches. So in like 2004. Four or three or something.

B (17:37)

Something.

A (17:37)

So I bought one and it used.

B (17:39)

Like FM or FM radio, unused FM radio, white noise. So you get like Stockholm, it's 45 minutes delayed if you're outdoors in a field.

A (17:48)

Yes. And so I had one and I was in this camp, like, this is the coolest thing in the entire world. And obviously this is going to be the most mass market product of all time. And you know, I was like 20 years too early with the Apple watch.

B (18:01)

Turn by turn gps. Like the first time you could put turn by turn GPS in your car, it was super cool. Except for the fact that most cars moved faster than the ability for the computer in the car to calculate when you were going to turn. And so you caught, you just drove around. It was like a U turn machine. And. But I think that it's just, it's so interesting because I think that people use these, the AI tools today and they assume like, well, I don't, I'm not a doctor. I don't know anything about being a doctor. Let me ask you to cure me, right? And diagnose me. And it's like, whoa, that is the worst. And it just like all the people who see failure, like, they're just, they weren't great programmers to begin with.

A (18:38)

Right.

B (18:38)

And they didn't know how to ask. They didn't want to review it. And whereas great programmers or just professional programmers know that code review is really important and, and that's just how you do it.

C (18:49)

I think there's an aspect, well, there's an aspect of this that makes it very difficult to measure. One of them is, and I don't think it's just an early adopter thing like these, these models are so magic that you get dazzled. Oh. So even if it's not what you want, you're like, it was great, you know, and I think it's very easy to that with being productive. Like, it's not what I wanted, but it was amazing. So therefore, therefore it must be great. So, so like maybe over time we just abdicate having an opinion and like the model does everything. But right now, and I see this a lot, people are like, they're so enthusiastic about using AI, but it really hasn't impacted, you know, their output. They're just enthusiastic. The second one is I, I feel like there's almost shadow productivity.

A (19:31)

Oh, sorry, how, how would you, how would you, how would you verify that with the five or ten person company who, who kind of empirically is operating at like a 50 to 100 person company? Like, like you just, you can see the, the sheer, you know, scale of their code and you're like, okay, you could not have done that 10 years ago.

C (19:50)

I actually agree with everything Steve was saying, which is. So listen, and this is anecdotally. Anecdotally, I work with a lot of companies anecdotally, the more senior small teams that use AI are superhuman.

A (19:58)

Yeah, yeah.

C (19:58)

It's like they woke up and they were all fucking Tony Stark. It is unbelievable. And like their productivity is insane. But they're all, they're all super senior.

B (20:05)

You know, and look, they were, they don't, I don't want to take anything at all away, but those companies were also incredibly productive relative to a 10.

A (20:14)

Yes.

B (20:14)

Because there's no code and they're starting from a clean slate.

C (20:17)

No, no, they, they really, they really wake up. Yeah, yeah, for sure. And, and like, you know, they're very senior, but they're also almost to a person where AI skeptics to begin with and are incredibly sober about like the value. And so they, they just use it in these very pragmatic ways.

A (20:31)

There's one other category that I'm seeing because I just want to be intellectually honest on the full spectrum. I'm seeing these 19 and 20 year olds that are like, first of all, I don't know what is in the water at like Stanford, mit, et cetera right now. But like everybody's dropping out. So. Yeah, so like just like literally like people are going there for like a week just to drop out. But there is a tendency of that core, it's the 996 people. And there is this tendency which is, you know, they would have been maybe 10x engineers in a prior world, but now they're like 100x engineers. And so, you know, senior in terms of, in their own kind of relative cohort. But like the, the way that they are building their startups are just like completely different than. It's probably the, If I look at, at, you know, so we dropped out of college, God, that's carried 19 years ago. If I look at how these companies run versus today. It's the biggest change in, in how you start and run a company that I've ever seen.

C (21:27)

Yeah.

A (21:28)

And like, and I think if you looked at like in 1995, if you were to drop out of college versus 2005 when we dropped out of college, I don't think you, like, I don't think like the company building process was all that different.

C (21:40)

The Internet. The Internet fundamentally.

A (21:42)

No, no, post Internet, post Internet.

C (21:43)

Okay. So, so you're, you're building, you have to rewind to the Internet because that.

A (21:46)

Was a. Yeah, we're not in 85. So at 95 you're dropping out to do an Internet startup. Okay. You drop into an Internet startup by 2005. Other than the fact that our resources were in the cloud versus, you know, you'd have to go to a data center. In our case, we actually still went to the data center. Like not that much about the company building process was different. Today, in 2025, everything about how you're starting your company is completely different.

B (22:07)

Because I think that the key, the through line in all that is velocity.

A (22:12)

Yes, exactly.

B (22:12)

And I think that cutoff the Internet increased velocity.

A (22:17)

Yes.

B (22:18)

And AI increases velocity the same way. And I think that that's just, just super, super important to what's going on.

A (22:24)

So basically, if you go back, even.

B (22:26)

With the, in the early Internet, like when you started a company, there was still a lot of old school, like, what's your business plan? What's your plan? We're going to be stealth for two years. There was all of the stuff and it was really Mark and Ben at Netscape that changed the velocity of how companies work. And the cloud was an accelerant to that acceleration of velocity. And, and AI is, is a, a refactoring of how velocity works.

A (22:51)

Yeah. But what was interesting is even in the cloud, like that was this great virtualizer of the physical stuff you would have to deal with.

B (22:58)

But that was a two year buildup.

A (22:59)

Yeah, exactly.

B (23:01)

You had no. You could have customers. Yeah, 100%. As soon as you had code, you could have customers, which I mean plg.

C (23:08)

And a lot of these kind of high velocity startups actually started pre AI. Yeah, but pretty phenomenal. Think about GitHub, think about Slack, think about Figma.

A (23:16)

Yes.

C (23:17)

You know, you have some pretty remarkable companies that came pre AI that drafting on it. And a lot of that was like basically cloud. And then this has fication and then unlocking kind of different us to go to market. Zoom was a great early example.

B (23:27)

This is the Thing that AI is, is an accelerant in building the product. Whereas what running cloud did was accelerate getting paid, which was a thing that used to take two or three years.

C (23:38)

I mean I would. Arguably the cloud also made it much quicker to build a product because you would have like these big.

B (23:42)

Well, it made it much quicker for customers to all have the same one.

A (23:45)

I don't think it made it like five times faster.

C (23:47)

Fair enough.

A (23:47)

So like, like, I think it was like, it was like, yeah, like you can see your website a lot quicker but like I could see a website in 98 pretty quickly. Like.

C (23:55)

Yeah, but well, maybe it is an infrastructure thing. Building a big distributed service. No, no, sure, sure.

A (23:59)

I was not doing distributed really, really, really hard infrastructure in 98. So.

C (24:02)

Yeah, so, but so there's. Sorry, I just want. So, so I think AI productivity is hard to measure for two reasons. The first one I just managed, it's just really dazzling. So I think like people kind of like, they're like, oh, it's amazing. The second one is I think a lot of the productivity is actually hidden and people measure the wrong thing.

A (24:17)

Right.

B (24:17)

So shocking that people measure the wrong thing in productivity.

C (24:19)

That's right.

B (24:20)

Literally the history of productivity measurement.

C (24:22)

But also what happens here is like you have the board and the board is like we need more AI and then so what happens? And they go to like some CTO or some innovations lab and then the innovations lab do AI and so like whatever they like bring in like build some internal tool and it'll fail. Of course that'll fail. Right. But the reality is like this AI wave is so personal. Like, like probably most people in the company are using ChatGPT. Probably there is, you know, some personal assistant, probably, you know, they're using cursor or some coding thing and that's much, much harder to measure just because you know, it's not advertised. And so if you actually look at the reports on like enterprise, things fail. You guys look at like what they were they're measuring. It's like, yeah, clearly some internal project pushed down by the board where they hired like you know, some consultant to do it is going to fail. Those always fail. But that's actually not what's going on. Like the movement that's happening is a very secular.

B (25:10)

Well, it' it, this is, this is the, the, the next time that bottom up adoption is really changing the productivity equation and that's a thing that, that it defies. Big companies do not know how to deal with that because they, they want, they need to control it they worry about safety and security and privacy and all of their corporate rules. And and then also the other thing I think to overlay on that is AI is, is a very unique, if that's not a bad way to say things, innovation in that it's like non deterministic. And so all of a sudden you have this very personal and non deterministic thing which the real problem in a large organization to all these pilots and AI projects is that you can't not just measure, but you don't even want to put out there a non deterministic solution because your whole thing is like well we operate at scale and we have 60 countries. Yeah. We can't put like a customer support solution out if five agents in five languages all have different answers based on the way that the customer or the CS agent asked the question. And I really feel like that is going to be the hugest challenge in large organizations. Figuring out how to adopt things is like they're going to just get all bunched up over non determinism.

A (26:30)

Yes. Yeah. I think that we will have to have some new form of measurement probably in general on this because so much of the, so much of the improvement in productivity will be, will sort of be this like subtle change of like, like I used to go to Google for that.

C (26:47)

No, my EA is writing emails using chat GPT.

A (26:51)

I'm sure that's where is that showing up in. And other than just like again when we talked about this in I think the last podcast of just like we're going to just start to do higher levels of work and that will just end up looking different but you won't be able to be like okay, how do I measure what the productivity was when it's just like I'm working just totally differently. Yeah.

C (27:08)

I mean code I think is such a great example. So like what do people, in my experience the more senior folks actually use AI for code. It's like documentation, writing, testing. I mean it's a lot of the other stuff that may not actually like increase like the shipping schedule but like you get a lot more robust code, a lot more maintainable code, a much better architecture, a much better future forward architecture. So we could be building tremendously better software but still be shipping features at the same velocity.

A (27:37)

Right. We might need to just measure like quality of life as another metric of like literally I'm just happier. I don't have to do.

C (27:44)

Developers don't want to write documentation. Exactly.

B (27:47)

But this is, I, you know it's so, so easy to get accused of hyperbole and, and overstating it and things. But what I think is just so key to what's going on is this, it is what you were talking about, which is like the whole notion of what you're going to do with the job is going to be really different. You know, this is my obligatory super old person thing. But the pre spreadsheet, post spreadsheet is a perfect example of this. The pre spreadsheet, classic example. I didn't say classic. But before spreadsheets you would be a banker and you would be like I'm supposed to help this company get acquired. And you would come up with a financial model and you'd have 50 recent MBAs all churning away with their HP calculators figuring out the financial model and then you go like okay, let's do this again. But if the interest rate changes or if their source of funds change and you're like okay, well that's like a week and everybody has to do it all over again. So you actually, your quality of decision was really bad.

A (28:51)

Yes.

B (28:52)

And so what happened was that just in 1985 that job just completely changed. And by 1990 instead of asking the recent grads do it, you were doing it yourself. I actually remember this absolutely crystal clear. My, my two cousins were University of Chicago MBA grads in 1985. They did not use a computer to when they got their MBAs. And when I was talking about going to Microsoft two years later they were like, well you know, we have these kids who use the computer for us and they, they ended up using computers and stuff. But, but like their whole notion of banking was defined by this multi week turnaround and then all of a sudden it's just more interns doing their Lotus 1, 2, 3 thing. And this is, it gets to the, you know what's going on with code and startups is it's just, there's just like a whole different mindset over how much you can do and how soon and iterate. Figma and Dylan, they're always talking about this like this like Figma is going to now change the trajectory of a design idea to go from like oh, let's iterate over here to like, like let's just do it.

A (29:56)

Yes. I mean the, the this is, and this is where it's like a, again a weird thing to like think about the percentage of my productivity as an example. And like my, my day is like not representative obviously because I'm bouncing between too many different things. But like there'll be so many times where it's 10pm In a prior world, I would have sent off a task to somebody, you know, you know, some analyst or chief of staff, you know, type, type role, go research this thing, it comes back three days later and then you find the answer. And then now it's obviously just like kick off a deep research, go to cursor to generate a prototype, do some kind of analysis, and then you have it back in 10 minutes or 20 minutes. And I've just compressed whatever that was kind of then going to be as a serially connected to that task is now just fully compressed. And so then by the morning, you're now kicking off whatever that project was again, like nearly impossible for me to peg. Like what is that as a number? It's just like a fundamentally different thing on just what work looks like. And because you just compress so many different steps of a workflow into a single action. And so it's just like a completely different way of thinking about work.

B (31:05)

Where does that fit in for you? In what we were talking about, what I was asking about earlier, which is how does the expertise, your expertise, really contribute to that?

A (31:14)

Yeah.

B (31:14)

And in particular, I think it'd be interesting for people to understand, like when you talk to customers, how do you help them to avoid trying to get people to make AI, make them do jobs they couldn't do in the first place? Because that's an easy point of failure.

A (31:28)

Yeah, I mean it actually is. This, is, this, is this really counterintuitive thing where, and you've talked about specialization on the last one is like the, the, the biggest gains of AI go to people who have some degree of expertise in an area to know what is actually true, what is not going to work, what should I integrate from the output of this AI? You know, like, what are the 2% of things that maybe are hallucinations or, you know, took the data in the wrong direction. If you don't have a deep understanding of your particular space or field or domain, you aren't able to then have the right judgment to make all those decisions. So I think the experts just get more powerful in this world. And so I would, that's why I'm not even like convinced that you can tell a college student to learn anything different than ever any other period in history. Be really good at a particular field, and then AI is merely a turbocharger of your capability in that particular field. But if I didn't just generally know the things I know about SaaS, which is obviously a really weird expertise, but I'm okay. At understanding SaaS, then, then the things I give to, you know, a deep research agent that I then go incorporate back into work wouldn't make sense. Like to me, I wouldn't have the, I wouldn't have all the context for like that one thing that it mentioned. How do I like form that into the overall strategy? But because I have some understanding of, of this particular industry that just makes me way more productive. So, so I don't think expertise goes away at all and I think any, any of the experts in their particular area just become more powerful. Powerful.

C (33:04)

So we actually have a fair bit of anecdotal market data on this. So it's very, very interesting. So if you take like a lot of these, let's just take kind of a non text example like image or video and if you look at the customer base for any of the popular platforms, very interesting. So if you, if you draw a dollar at random that's monetized, it's from a professional and for obvious reasons because you know, like, you know, they can produce, if you draw a user at random, it's, it's casual and it's in the tail.

A (33:31)

Right.

C (33:31)

And so it's fairly clear that you know, this is a prosumer movement from monetization. And you know, I'm associated with a number of companies that work with like say professional designers or professional creatives. They spend just as much time on the AI tools as they would on traditional tools. Yeah, it just turns out the output is far more rich and like, you know, you know, it's, it tends to be, they have a. Yeah, but it's still human taste. There's still like very specific requirements. And so I think, I think, you know, if there ever is an ads model that ever shows up for AI, I think that there's going to be a long tail of people that want to use AI, but they actually don't have the financial incentive to do it or it isn't tied to like, you know, their actual job. And we're already starting to see that bifurcate out and there's going to be another subsection that do. And my sense is, let's say if I'm say, say I'm writing like whatever, I'm a casual developer and I'm writing a 3D game and I want to have like a 3D asset. I've got one of two choices. I can have AI created for me or I can, you know, contract a professional to do it. Like me as a developer, I'm not going to create Even if I use AI, it's not going to be a great 3D asset. Right. And so I think that, you know, you're going to have that same light that you have today is either you can hack something up yourself or you can go with a professional, and the professional will be using AI.

A (34:43)

And I think what's super exciting is that AI has created a third category, which is, I'm not trying to do this as a job. I'm never going to monetize this. But there's some product utility gain to me that is worth 20 bucks a month. So my ability to now generate prototypes when I'm by myself just brainstorming again, I'm not going to do anything in the ultimate delivery of that into any functional code, but it's worth $20 for me to be able to go and realize the thing that I'm thinking about. And so there's just all new ways of capturing tam, because there's all this utility that gets unlocked.

C (35:21)

And there's one more thing that's worth noting, which is people that are interested in Aerith in an area get there through AI. Right.

A (35:26)

Yeah.

C (35:27)

It was like, this is the number one opportunity for somebody, you know, that wants to enter an area to do it as an AI native. Right. Because a, like, the tool actually can teach you.

A (35:34)

Yes.

C (35:34)

You know, and then there's just such a paucity of, of talent out there that you can fill.

B (35:38)

And this is also, I mean, this is the history of, of productivity in general, which is you. You. The more tools that you have available, first the experts use them and then more people are able to become experts. And, and I, and I as long. But that's part of your managing your own career. I love that point you made about like, you still have to be really good at something and, and I think that, that, you know, if you want to be good at finance or sales, you. You should become really good at it and assume you're going to use AI for that.

A (36:09)

Yeah.

B (36:09)

And you're going to be better than the people pretending. And I think that that's very much like if you just take, you know, we were going back and forth about, you know, oh, it makes a PowerPoint slide deck. Well, it turns out to make a good PowerPoint deck is still a skill and people still pay McKinsey huge amounts of money for better PowerPoint decks with better pictures.

C (36:28)

I contract a lot of work for videos and art, and I have for a very long time as part of different companies here at A16Z, I have seen the Shift for contracting something that uses traditional techniques to AI Dollar amounts are the same. And so again this is Jevons Paradox all over again. You spend just as much time. It's just the output tends to be more dazzling or whatever it is.

A (36:51)

And you should. More versions of it. They should run more simulations and other.

C (36:54)

That's right, you've got more iteration with them. You've got more control. Like I can listen, I can have a video where like and I want a dragon to fly out of the sky, you know, so like you have like a lot more control as, as the customer. But for sure, you know, this is not somehow like dropping the cost of output.

E (37:11)

I want to. Were you about to jump in? I want to circle back to a point you made earlier about that there are 20 year olds who are building companies in new ways. Because remember a few years ago, I think Patrick Carlson and a few others were asking, hey, where are all the Gen Z super successful founders? REM remember that? And of course there was Dylan Field and Alexander Wang, but their companies took a few years to really work. But now we're seeing the cursor founders, the more core founders sort of get to massive scale in a very short period of time. And maybe it was that the foundation model companies required a certain level of experienced founder because of the fundraising amounts and maybe the applications are more conducive to younger founders. But what's your reflection on this?

A (37:47)

Well, the founder, I don't remember exactly the date at which he mentioned that, but I do think there was a period between in the sort of mid 2010s to early 2020s where we were actually in kind of a bit of a lull as an industry. And the reason for that was we kind of did check off a lot of boxes of the core things that people needed in the world.

B (38:15)

World.

A (38:16)

And so we checked off like a lot of the like. Once you have Slack, you don't need five other chat tools. Once you have Zoom, you don't need five other video conferencing tools. And so it gets kind of derivative, you know, past these kind of core platforms. And so once you had like SaaS kind of check off all the major like things you do at work. And then in the consumer world we like we had ways of delivering food and listening to music and watching videos. So like there's like not an infinite set of things that we do as consumers. Then what is the 20 year old founder supposed to work on? Like they're gonna, it's like you have pretty finite opportunities as Compared to in the mid 2000s, let's say the whole world was open, you could start anything. And because every single category had to be reinvented, kind of post mobile post, you know, kind of cloud maturity. So we now have that era in AI. And that is why I'm like so unbelievably pumped up. And it's because you have a complete reset of the landscape where there is incumbent advantage in distribution. But that is it. There's no other real advantage.

B (39:19)

Well, there's a bunch of disadvantages, yes.

A (39:21)

And then there's a bunch of disadvantages, but I do. Yeah, go ahead.

B (39:23)

Sorry.

A (39:23)

No, no, no, but like, I mean, you know where I'm going. So you have this, you have the exact makings of a landscape where new startups can come in and do things that incumbents either can't or there's no obvious incumbent to even do that thing because again, you're taking maybe like services and turning them into AI labor. And there was no software incumbent previously to even attempt to do that. And then you have incumbents that have a whole lot of complexity in terms of their ability to go and execute in some of these spaces and they're not going to retool their entire internal engineering workflows to move at 10x the pace. And so a brand new startup can go and do that and then instantly get the scale of a larger company. So it's the first time in history where you have none of the disadvantages of a big company. And the traditional advantage you have as a big company is you have scale and you have distribution scale because you can look at a feature and you say, we're going to go build that next month. And obviously it's harder because there's like, you know, there's just lots of complexity to that, but at least you have the human power to go do that. Now as a startup you instantly have scale because, you know, background agents, et cetera. And so then it's a distribution game. And a lot of these pieces of software can go viral now in a way that wasn't possible 10 or 15 years ago. So we've kind of neutralized a lot of the incumbent advantages. And so thus it's a ripe opportunity for brand new startups. Often will be people just like coming right out of college saying, hey, it's my first time building a company, like they're crazy enough to not know how hard it is, so they'll jump right into markets that otherwise we would assume are like the market's already solved for. There's no way that you're going to build a company and you'll just have new startups that actually go and do it and actually produce real, you know, real companies in these spaces.

B (40:58)

Yeah, because, I mean, this is just so critical because what's really happening is this is why, you know, it's an actual platform ship. So Silicon Valley has seen this movie many times before, and that's why often there's a lot of this, you know, is this crying wolf or not? Because everybody knows that when there's a platform shift, that's the moment in time that startups are at an advantage. And so each time there's a platform shift, like, everybody's like, oh, this is it. This is going to reinvent everybody. And then it doesn't. And people get really like, oh, it's always incumbents. But historically, like, the advantages to incumbents are wildly overestimated. And really, I mean, this is this one where, you know, like, you know, was, did the Internet undo Microsoft or not undo Microsoft is a super interesting thing because, of course, there's a $3 trillion company now, but not on the Internet in a way that you think about the Internet. Like, none of the consumers, none of the platforms, none of the assets that we had in the 90s became Internet assets. I mean, even if you look at Azure today, it's an amazing accomplishment. It's not running Windows anywhere.

A (42:05)

Right.

B (42:06)

And I think that that's why, you know, it's not crazy to go, wow, is this going to be good or bad for Google? Because there's a bunch of stuff that becomes really, really difficult if you don't make transition. And then it turns out historically, even if you do make the transition, you really didn't, and you just have to wait for time to pass. Well, and this is like intel with the GPU, like, they. They missed the GPU in 2005, right? And they missed the opportunity to buy the company to do the work or whatever. And they kind of missed the data center, too. It just took a longer time to figure out that they missed that as well.

A (42:40)

And we have a pretty narrow definition of, like, disruption in the sense of, like, we expect that the incumbent has to lose this new start. And that never happens. It never happens. And so it's the whole radio, tv, you know, theater, you know, movie analogy. It's like, no, it turns out that Microsoft can be a $4 trillion company and you can have all these new categories emerge that maybe Microsoft should have owned if everything was, like, perfectly analogous to the desktop days. But they just don't. And it all works together as one sort of ecosystem because it just turns out like software did eat the world and these markets are actually just like a hundred times larger than what we realized. And so incumbents can grow and then you have new disruptors that sort of emerge along the way.

C (43:21)

Yeah, anytime you bring in a new technology that brings the marginal cost down, then like the market's going to expand and like the incumbents can do it. I will say incumbents are very bad when new user behaviors and buying behavior show up. In particular, they don't know how to cater to it. AI is definitely a new user behavior and a new buying behavior. And so this is very much an advantage of startups just because, you know, to change a large company around a new user behavior cuts to the entire company. Everything from basically marketing all the way to support in the back end. That's just too much of a lift.

A (43:50)

I mean if you look at the, if you look at the best practices of even how you would create an agent, in the last 18 months I think we've gone through two to three architecture pattern changes. And so it's just like, you know, I can barely keep up as a mid sized company. I can't imagine if you just had so many more people you had to like organize around that.

C (44:13)

Disruptive new technologies require people to understand how to use them in consumption in different ways. That evolves over time as you get best practices like that sort of flexibility can only come. It's actually a very interesting question of how Microsoft actually did copilot to begin with because it was actually one of the first ones that these products was very successful. I learned recently it was created by OpenAI so that kind of explains it well.

A (44:34)

But you had NAT in there, you had a startup person and you, and, and conveniently he was a startup guy.

C (44:39)

So 100% even then. By the way, it's remarkable that it came out of Microsoft.

B (44:45)

What's always remarkable is when, when something new and defining comes from these big companies, you 100% of the time you look into it and you're like, it was basically skunk works. Basically they had nothing to lose and it didn't interfere. Like the ipod is this classic one people or the iPhone even. People always talk about like the brave. It's like, like their computer business was dead, dead, dead. Like there was, it was like 3% share and going nowhere. So like it, it was like a Hail Mary. The ipod was a Hail Mary. And then the phone, they weren't in the phone. Business, it didn't matter. And the fact that they made a phone, a little computer is what Nokia was like, whoa, what is that? And, and I think people really need to wrap their heads around just the fact that to your point that the, the big companies stay around a very, very long time. And this is something I've seen a bunch of people in the past couple of weeks. You know, oh, it's basically Christensen innovators. Of course, nobody's ever read the book. And Clay is a great guy. You know, he was down the hall when I was teaching there. And the thing is that this is a book like about two and a half inch disk drives. Yeah. And a bunch of crazy.

C (45:52)

I really don't think it applies to modern.

B (45:54)

But the thing that he missed, aside from he missed the cost and low high end and thing, but was also that, that the companies don't evaporate. Your point about that is really important. And so what it does is there's this shadow that everybody is worried about. And so you just see it constantly. Well, like I assume like you, when a company is like, we're not worried about if Google does this, that's what you want to hear.

A (46:17)

Right?

B (46:17)

Because like it's just, I mean, and you live because you, you were like the classic, you know, Steve Jobs said you're a feature and here not just that you do. There's like two old companies that do this stuff.

A (46:27)

Fortunately, he only said that to Drew, so.

B (46:28)

Right, right.

A (46:30)

I got the gains version of.

C (46:32)

Yeah, yeah, yeah.

A (46:33)

But no, but the one thing that is very timeless about, about Clay would be, would be the thing that has the thing that does transcend floppies or whatever. The whatever thing is, is the incumbent doesn't want to do something that's against their business model working.

B (46:47)

Of course, of course.

A (46:48)

And that, that part is fully timeless. And to your point, it's actually very rare. Whenever we say these companies disrupted themselves, it's almost never the case that they disrupted themselves. It's the case that they, they went after a market that they had no actual market share in and it just worked.

B (47:04)

Which was the development tools for Microsoft because there was no business in Microsoft development tools anymore because nobody was writing Windows programs. So really it was like, what could we do for the cloud?

C (47:15)

Honestly, it's even a little bit of a silly discussion to do this with AI. AI is very disruptive. So we're like, okay, well then it allows startups to work against incumbents. But even in non disruptive technologies, startups often have a play against them. So for Example every year I'm an infrastructure investor every year for the last 10 years after AWS reinvent, I move into therapist mode. And all of my founders, all of my founders and they're launching my open source, they're competing with. It happens every single time. And I'm always like, you know what, you'll be fine. I can't think of one company AWS has ever put out a business by launching a service. And you know what? They've all done? Been fine. And so in some ways, even in like the normal like state of business without massive disruption, startups still have.

B (47:58)

Yeah, it was. Don't build a SQL Server and compete with Oracle directly. Don't build a word processor. Those are things that were evergreen for a long time.

C (48:05)

Never do a spreadsheet. Like there's a few things that.

A (48:07)

There are some categories where people should call us first. They won't change.

B (48:12)

There's a far side that we should, we should definitely show people that it's a really. Can I attend? Why do you want 10 cents for your startup? So I could buy a loaf of bread and beat you over the head with it for such a dumb idea. It's a good one.

C (48:24)

We should have a call in on this thing, right?

A (48:27)

Oh yeah. Here's my startup. The thing though that, the other thing that I just don't think we've had, at least I don't know of a modern kind of case study for, is again this opening up of non software TAM for software. So there's not even incumbents in the classic software sense. The incumbents are, are, are really just professional services categories of work. And so, and so it's really for the first time ever, you're packaging up intelligence for a particular domain and workflow. And so there's no software company you're competing against dollars.

C (49:00)

But it could be the vertical company, right? Like yes, you know, like you have to become an ag company but then.

A (49:04)

But the vertical company will probably also be your customer. Yeah, but they're also your customer. So it's actually this amazing thing where, where the people you're probably disrupting on paper are actually the primary users of your technology.

C (49:16)

Take advantage.

A (49:17)

Yes. And so then it's like, it's like there's really no inherent competition until, until you know, eventually like more companies flood that space to do that idea.

C (49:24)

So this plays out in practice. If you have a company, an AI company that goes after say like agriculture or construction, they end up like realizing the competitive set are agriculture and construction. The buyer knows how to price Things like agriculture, construction, they end up becoming basically agriculture and construction companies. And then they end up doing exactly what you're saying is selling to the agriculture because they're not good at that.

B (49:46)

There's a whole world. In fact, the earliest PC software was extremely vertical. Like if you actually look at the TRS 80 catalog from the early 1980s or late 1970s, it would be like, this is crop rotation software. Literally like, okay, this is what you should do. And the salesperson for Tandy would show up up in Nebraska and sell crop rotation software. And then there was like, I run a dentist's office and this is scheduling for a dentist's office. And what happened was, and this is what I think is going to happen is these professional services organizations are. There are going to be some that are like computer savvy.

A (50:23)

Yeah.

B (50:23)

And today they're really good at using existing. They're just going to go, you know what, we should just build like a company. Huge numbers of these verticals are just going to be existing pro serve that turn into.

A (50:34)

I think, I think it's an incredible time where, where if. Let's just pretend that you just are committed to not building a software company. Like you don't want to do that or you don't maybe have the team to do that. So you're going to build like a real world company. It's an incredible time. If you started from scratch with now AI as your foundation, if you wanted to be a new systems integrator, and your whole point is that we are a systems integrator, but we use Claude code or we use cognition or we use cursor to get the output, you will have such an advantage over any incumbent because the incumbent is not going to be able to rebuild that. So I've seen, seen examples of people building new ad agencies because obviously like if you can do literally a million dollar ad campaign for $5,000, somewhere between those two numbers, you can charge the customer. So there's like this incredible time where you can just be building all new kinds of companies from the ground up, leveraging the breakthroughs that we've now seen.

B (51:27)

In AI that actually did. That was an early Internet thing that did really happen, which particularly in the advertising space. And I think it's going to happen in everything that's technology, text and everything which was. There were these digital native ad agencies that just knew how to use Flash.

A (51:39)

Right, right.

B (51:40)

And they got like they would get bought for a billion dollars.

C (51:42)

The same thing happened with Social, by the way.

B (51:44)

Yeah, exactly. What did you think of this survey that was how many people use AI every week. I thought this was pretty interesting.

A (51:50)

Yes. What was the, what was your, what was your Pew response?

B (51:54)

Well, it turns out like the number of people, it's like up to 75% of adults are using it many times per week. And of course you just see it when you use Google search. It's all self reported so you don't really know, but it was pretty interest. I pulled a 1999 Pew study on Internet usage.

A (52:09)

As you would.

B (52:10)

As I would like. Basically in 1999, like half the country owns computers.

A (52:16)

Yeah.

B (52:16)

And they were all online.

A (52:18)

Yeah.

B (52:19)

Even four years post Netscape, it was like still half the country.

A (52:22)

Yeah.

B (52:22)

So you look at that as being slow or being fast.

A (52:25)

It's fast. Back then, I mean that felt fast.

C (52:27)

Actually we had to spend $3,000 buying your computer.

B (52:30)

But still, you know, if you discount the search AI.

A (52:34)

Yes.

B (52:34)

There's still, you know, activation energy to, to figure out what this new thing is. Nobody knows how to ask questions to a blank edit control. Like make me smart about something.

A (52:43)

I think the, the, I mean the adopt, the universal adoption of this as a consumer technology and then bleeding into prosumer is, is it exceeds anything I've ever. It's unbelievable experience and I think it is, it will just fundamentally change people's sort of daily patterns. Like my sister not in tech at all, teacher, like she was in town and she was like, yeah, I was asking chat this question and like I had to like do a double take. I was like chat. And I was like, oh, chatgpt. That's what they, that's what normal people call this. And it's just like, it's just like, it's completely pervasive as just a standard technology. And so that to me is just like, okay, we now have the conditions laid for the next phase which is, and we've now seen this for a couple decades which is consumer adoption now goes first and then it gets basically pulled into the enterprise because you go to work and you're like why can't I ask questions of my enterprise systems the way that I can everything else in the world and why am I not getting that same level of productivity gain? And then you again have the kids coming out of college that like they only know how to do homework. With ChatGPT, they come into the workforce and they're like why would I spend two weeks writing this report when I just came out writing essays in an hour? Like obviously something has to kind of give on this. So this is why this is just lays the foundation for why we're going to see be just a massive upgrade cycle in the enterprise.

B (54:00)

Also, to build on your point earlier, like about distribution and the reason that distribution is not the advantage that it used to be is because it already exists on 7 billion phones. And so at every other platform shift there was an upside to getting new distribution that didn't exist before and but you had to overcome that. Like you had to get like the Internet to people who didn't have the Internet before. You had to get SaaS to people who do not. Now everybody has all of the ingredients right now.

A (54:28)

Anytime your business strategy relies on Comcast showing up in a neighborhood.

B (54:32)

Exactly.

A (54:32)

For, for me to get distribution like that, you're going to have some problems. So this is a very different trend.

C (54:37)

Honestly. Last quick point on this is like for the first time in a very long time we're seeing brand effects with an early technology. And what I mean by that is if you look at like whatever the major model providers, you know, how much better are they from each other? Like you know, maybe, you know, a little bit, maybe not like you know it changes all the time. But you actually clear leaders if they break out early just because people learn like the household line, people learn mid journey, people know OpenAI Etc. So these markets are so big, they're growing so fast that like, you know, if you become a leader in your segment, people will just adopt but, but.

B (55:08)

Don'T discount, you know, like the early leaders of search were like Excite and Yahoo. And so there's like this is just to not discourage people. Right. Like it's so early that names that you never heard of existed before Google and that's going to be really important. Yeah, it's never the first people.

E (55:23)

When we look at mobile, there were big companies built, you know, like Uber and WhatsApp and Instagram and TikTok. But the biggest beneficiaries were Facebook and Google. In AI, do we think it will be different that sort of the biggest companies in the world 10 to 20 years from now will be created, you know, after ChatGPT or, or will it be similar that what's your time frame, you know, post 2019? I don't know.

A (55:48)

No, no, I mean 10 to 20 years or so said oh yeah, sure. Okay, so we can't know if we're wrong until we do this podcast intended. Okay. I think this is so boring, but I think it's going to look like what we saw in something like SaaS or cloud, which is the incumbents get bigger. But then there's all of these new categories that we would not have been able to predict. And then there's lots of 10 and 20 and 50 and $100 billion companies that also emerge and then over time those will just continue to similar to mobile.

C (56:16)

And some don't make the transition.

A (56:18)

Yeah, yeah. Some will go down on a relative basis because they're or like their market wasn't as ripe for agentic kind of workflows. But I think that you can kind of say, you know, maybe this is then for another conversation is just like if you have a current system of record that that has a set of workflows on it, where agents make sense to make that workflow much more powerful. That's a good position to be in. But it, I bet you that if we look back in 10 or 20 years from now, the vast majority of things agents do don't relate to just those things, things that we are currently looking at because there's just so many more fields that are now open. And so all of those use cases I would favor the disruptor and, or insurgent and then in the, in the today's spaces I would kind of favor the incumbent on the margin. But the markets are so large that all you're going to see kind of growth in all of them.

B (57:06)

There's a key attribute across all of those which you know is sort of like thought leadership or like who, who is really setting the agenda for what people are talking about. And I think that's the thing that really changes, changes. The incumbents become bigger but nobody wakes up in the morning wondering what they're up to.

A (57:24)

Right.

B (57:24)

Nobody starts to wonder, well, if they're going to do it, we need to understand it. And that's the shift. And you could think of it in the enterprise space or the business space, like what do the CIOs, who do they wake up thinking about? And that, that was a huge shift that sort of goes under the radar and in the consumer space it just like it becomes the. I understand I use chat GPT at school, I, I need chat GPT and there's nothing you could do about it as a company.

C (57:48)

I think the more provocative question is, is are there any LA that will use this to get ahead? And we've seen this in the past, right, like will Cisco do something interesting? Oracle is making some kind of crazy moves. Are we going to see those that missed social?

A (58:02)

No, I think everybody missed Oracle as an example from three years to today. You would not have been like, oh.

C (58:08)

Definitely stuff had its moment of you didn't know the future and then it used Cloud and Azure to kind of come back. And so this actually is an opportunity for laggards that are behind, behind the curve to come back.

A (58:18)

Yeah, to the Cisco point. Like, data centers are sexy. Like, it turns out that we're just going to be building out lots of AI factories everywhere. So you're going to get more scale from parts of the stack that we stop paying, like Broadcom. Like, again, people were not going to.

C (58:33)

End up running there. Everybody looked at Jensen and maybe somebody else.

E (58:36)

So we'll table the rest for the next conversation.

C (58:38)

Thank you so much. Thanks for doing.

D (58:41)

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