# AI's 80-Year Overnight Success and the Agent Economy

**Podcast:** a16z Podcast
**Published:** 2026-04-03

## Transcript

This episode originally aired on the latent space podcast.
Mark Andreessen has watched AI cycle through summers and winters for more than 35 years, from coding in Lisp in 1989 to backing the foundation model companies today.
He argues that the current moment is not another false start, but the payoff from eight decades of foundational research catalyzed by four distinct breakthroughs: large language models, reasoning, agents, and self-improvement.
He also makes the case that the combination of a language model, a Unix shell, and a file system represent one of the most important software architectures in a generation.
Swix and Alessio Fanelli speak with Mark Andreessen, co-founder and general partner at A16Z.
Something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.
Having said that, I think what's actually happened is an enormous amount of tactical progress that built up over time.
And like for example, we now know the neural network is the correct architecture.
And I will tell you, like there was a 60-year run where that was like a, you know, or in 70 years where that was controversial.
And so the way I think about what's happening is basically I think I think about basically the period we're in right now is it's I call it 80-year overnight success.
Right.
Which is like it's an overnight success because it's like bam, you know, chat GPT hits and then and then 01 hits, and then you know open claw hits.
And like, you know, these are open, these are these are like overnight, like radical overnight transformative successes, but they're drawing on an 80-year sort of wellspring backlog, you know, of ideas and thinking.
It's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious hardcore research.
If I were 18, like this is a hundred, this is what I would be spending all of my time on.
This is like such an incredible conceptual breakthrough.
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Hey everyone, welcome to the Lydia and Space Podcast.
This is Alassio, founder of Colonel Labs, and I'm joined by Swix, editor of Laden Space.
Hello, and we're in A16Z with A.
Uh Mark Jesus and welcome.
Yes.
Yes, A and what half of 16?
A one.
Exactly.
Uh apparently this is the the final few days in your your current office.
You're moving across the road.
Uh we're yeah, we have a limit, we have some projects underway.
But yeah, business is actually this is the original.
We're in actually the original office.
We're in the we're in the we're in the whole thing's right.
Yeah, great.
Thank you.
So I have to come out.
Uh this is a, you know, I wanted to pick a spicy start.
In October 2022, I just made friends with Rune.
And uh I wanted to give him something to sort of be spicy about.
And I said, uh, uh it'll never not be funny that A16Z was constantly going, the future is where the smart people choose to spend their time and then going deep into crypto and not in AI.
And that was in October 202, 2022.
And Rune says there was an internal meeting in A16Z to reorient around Gen AI.
Obviously, you have, but was there a meeting?
Well, what was that?
I mean, I don't know.
Look, I've been doing AI since the late 80s.
Yeah.
So I don't know.
Like all that as far as I'm concerned, this stuff is all Johnny Cum lately.
Yeah, no, I mean, look, we've been doing AI our entire existence.
I mean, we've been doing AI machine learning deep, you know, deeply.
We've been doing this stuff way from the beginning.
Obviously, A AI is just core to computer science.
I I actually view them as like quite uh quite continuous.
Um, you know, Ben and I both have computer science degrees.
Um, you know, we we both Ben and I actually both are world enough to remember the actual AI boom in the 1980s.
There was like a there was a big AI boom at the time.
Um, and there was a there's winner names like expert systems, um and the area of like Lisp and Lisp machines.
Um I I coded a Lisp.
I was coding a Lisp in 1989 when that was the language of the AI future.
Um yeah, so this is something that we're like completely you're completely comfortable with and been doing the whole time and are very enthusiastic about.
Is there a strong like this time is different?
Because uh my closest analog was 2016-17 nozzles on the AI boom and it petered out very, very quickly.
Um just in sense of investment, sort of, sort of investment investment excitement.
Although that's really when the NVIDIA phenomenon really it was, I would say it was in that period when it was very clear that at the time it the vocabulary was more machine learning, but it was very clear at that time that machine learning was hitting some sort of takeoff point.
Yeah.
Well, and as you guys, you guys have talked about this at length on your on your thing, but you know, if you really track what happened, I think the real story is it was it was the AlexNet uh basically breakthrough in like 2013.
That was the that was the real knee in the curve.
Um, and then it was obviously the transformer breakthrough in 17.
Yeah, um, and then everything that followed.
But but you know, look, machine learning, you know, there were, you know, look, uh, I mean, look, I've been working, you know, I've been working with one of my you know kind of projects working with Facebook since 2004, um, and on the board since 2007.
And of course, that you know, they they started using machine learning very early.
Um, and you know, have used it basically, you know, for like 20 years for you know, content, you know, feed optimization and advertising optimization, and obviously many, you know, financial services, you know, many, many, many companies, many different sectors have been doing this.
And so it's like one of these things.
It's like it's not a sing, it's not a single thing.
Like it's it's like it's like layers, right?
Um, and and the layers arrive at different paces, but they kind of build up.
Yeah, uh, they kind of build up over time.
And then and then, yeah.
And then look, in retrospect, it was 2017 was kind of the you know, the key, the key point with the transformer.
And then as you guys know, there was this really weird like four-year period where it's like the the transformer existed, and then it was just like let's go.
Yeah.
Well, but but it was but but between 2020, but between 2017 and 2021, I mean, that was the era of which like companies like Google had internal chatbots, but they weren't letting anybody use them.
Yeah.
Right.
And then, you know, and then OpenAI developed chat GPT or GPT 2, and then they told everybody this is way too dangerous to deploy, right?
You know, we can't possibly let normal people normal people use this thing.
And then do you you guys, I'm sure remember AI Dungeon.
Um is so the old for there was like a year where like the only way for a normal person to use GPT 3 was in in AI Dungeon.
Yeah.
And so you you we would do this, you'd go in there and you'd pretend to play Dungeons and Dragons.
And in reality, you're just trying to talk to talk to GPT.
And so there was this, you know, there was this long, you know, and the I, you know, the big big companies, you know, big companies are cautious, and you know, the big companies were cautious.
It did by the way, it took open AI, you know, they they they talked about this.
It took open AI time to actually adjust, you know, kind of re redirect their research path.
I think uh was at Rosewood, right?
Uh the the dinner that founded OpenAI was right there.
Right.
But that that dinner would have taken place in 2018.
The formation of open AI as late as 2018.
Sorry uh no I'm I'm I'm I'm wrong.
Probably should be 20 yeah they just celebrated 10 year anniversary so it is 2025.
Yeah.
That's it so 2015.
Yeah 2015 yeah 2015.
But then uh um Alec Radford did GPT one in what probably 1718 yeah 1718.
So it yeah for the and then and then they didn't really and then GPT three was what 2020 2020 2020 because that became co-pilot immediately even open AI which has been you know the leader of the le of this thing in the last decade you know even they had to adapt and and and lean into the new thing.
And so um yeah I I think it's just this process of basically sort of wave after wave layer after layer you know building on itself and then you kind of get these catalytic moments where the where the whole thing pops.
And obviously that's what's happening now.
Is it useful to think about will there be any AI winter because there's always these patterns like is it endless summer it's something I constantly think about because do I get, do I just like just get endlessly hyped and just trust that I will only be early and never wrong or well are we uh will there be a winter?
So there's something about say the following there's something about AI that has led to this repeated pattern.
Um and and and you you guys know this but it's summer winter summer winter winter, summer winter.
And it goes back 80 years.
Yeah, 80 years.
Uh so the original Neural network paper was 1943, right?
Which is which is amazing uh that it was it was far back that long.
And then there was you if you guys have ever talked about this on your show, but there was this uh there was a big uh there was an AGI conference at Dartmouth University in 1955, 55.
Yeah.
And they got an NSF grant to uh for the all the AI experts at the time to spend the summer together, and they figured if they had 10 weeks together, they could get AGI at the other end.
And they got there, by the way, they got the grant, they got the 10 weeks, and then you know, 1955, you know, no, no AGI.
And like I said, I lived through the 80s version of this where there was a big a big boom and a crash.
And so so there is this thing, and there is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.
Um, and and it's probably on both sides of like the the boom bus cycle, you you kind of see that play out.
Having said that, I think what's actually happened is like just at in you know, and we now know in retrospect, like an enormous amount of technical progress that built up over time.
And like for example, we now know the neural network is the correct architecture.
And I will tell you, like there was a 60-year run where that was like a you know, or even 70 years where that was controversial.
And we now know that that's the case.
And so we we now, you know, everything we're building on today just sort of derives from the original idea in 1943.
And so so in retrospect, we we now know that like these these guys were right.
They, you know, they would get the timing wrong and they thought you know, capabilities would arrive faster, or they were it could be turned into businesses sooner or whatever.
But like they were fundamentally the the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.
And and the and the payoff from from from all their work is happening now.
And so the way I think about what's happening is basically I think I think about basically the the the period we're in right now, is it's I call it 80 year overnight success.
Right.
Which is like it's an overnight success because it's like bam, you know, chat GPT hits and then and then 01 hits, and then you know, open claw hits, and like you know, these are open, these are these are like overnight like radical overnight transformative successes but they're drawing on an 80 year sort of wellspring backlog you know of of ideas and thinking it's not just that it's all brand new it's that it's an unlock of all of these decades of like very serious hardcore research um and thinking look there were AI researchers who spent their entire lives they got their PhD they they worked for they researched for 40 years they retired in a lot of cases they passed away and they never actually saw it work.
Yeah so sad.
It is it is sad.
It is sad and I knew was like the last guy yeah yeah well there were the guys Alan Newell I mean there's tons of John McCarthy you know John McCarthy was like one of the inventors in the field he's one of the guys who organized the Dartmouth conference and you know he taught at Stanford for 40 years and passed you know passed away I don't know whatever 10 10 years ago or something.
Never never actually got got to see it happen.
But like it is amazing in retrospect like these guys were incredibly smart and they worked really hard and they were correct.
So anyway so then it's like okay you know as they say history doesn't repeat but it rhymes it's like okay does that mean that there's going to be another like you know basically boom bug cycle and I I will tell you like look like in a sense like yes, everything goes through cycles and you know people get overly enthusiastic and overly depressed.
And there's there's a time, there's a timelessness to that.
Having said that, there's just no question um so the four most the four most dangerous words in investing is different.
Do you know the 12 most dangerous words of investing?
No.
The four more stay four most dangerous words in investing are this time is different.
Um the 12 most dangerous words.
And so, like I'll tell you what's different.
Like now it's working.
Like there's just no, I mean, look, there's just no question.
And by the way, I'll just give you guys my take.
Like LLMs, like from basically the Chad GPT moment through to spring of 25.
I think you could still, I think well-intentioned, well-informed skeptics could still say, oh, this is just pattern completion, and oh, these things don't really understand what they're doing, and you know, the hallucination rates are way too high.
And you know, this is gonna be great for creative writing and creating, you know, Shakespearean sonnets and you know, as rap lyrics or whatever.
Like it's gonna be great at all that stuff, but we're not gonna be able to harness this to make this relevant in, you know, coding or in medicine or in law or in you know, you know, kind of fields that you know kind of really really matter.
And I think basically it was the reasoning breakthrough.
It was 01 and then R1 that basically answered that question and basically said, oh no, we're gonna be able to actually turn this into something that's gonna work in the real world.
And then it obviously the coding breakthrough over the or basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.
We're just like, all right, if if you know, if Linus Torvalds is saying that AI coding is not better than he is, like that's that's never happened before.
That's the benchmark.
Yeah, that's never happened before.
And so now we know that it's it's gonna sweep through coding and and then and then we we know, you know, we know that if it's gonna work in coding, it's gonna work in everything else, right?
It's just that then because that's that's like that's like that's like the hardest in many ways, that's the hardest example, and now everything else is gonna be uh a derivative of that.
And then on top of that, we just got the agent breakthrough with, you know, with open claw, which is fantastic, which is amazing and incredibly powerful.
And then we just got the the um the auto research, uh, you know, the the self-improvement, you know, we're now into the self-improvement breakthrough.
And so the so the way I think about it is we've had four fundamental breakthroughs and functionality, LLMs, reasoning uh agents, um, and then uh and and then now uh RSI.
Um and they're all actually working.
Um and so I'm I'm just as you can tell I'm jumping out of my shoes.
Like this is like this is it like this this is the culmination of 80 years worth of worth of work and this is the time it's becoming real.
Yeah I I'm completely convinced.
I think the anxiety that people feel is like during the transistor era you had Morse law and it's like all right we understand why these things are getting better.
We understand the physics of it.
With AI it's it's so jagged in like the jumps where like you said it's like in three months you have like this huge jump like and people are like well this can keep happening right but then it keeps happening it'll keep happening.
And so like how do you think about also timelines of like what's we're building I think we always have this question with guests which is like you know should you spend time building harness for a model versus like the next model just going to do it one shot in the latest base.
And how does that inform like how you think about the shape of the technology.
You know you talk about how it's a new computing platform.
If you have a computing platform then like every six months it like drastically changes in what it looks like it's hard to build companies on top of it.
Yeah so so a couple things.
So one is like look the the Moore's law was what we now call a scaling law.
Like Moore's law was a scaling law.
And for your younger viewers, more more's law was every chip, chip chips either get twice as powerful or twice as cheap every every 18 months.
And that, and that, and then you know that it's gotten more complicated in the last few years.
But like that, that was like the 50-year trajectory of of uh of the computer industry.
And then, and then by the way, and that's what took the mainframe computer from a 25 million dollar current dollar thing into you know the phone in your pocket being, you know, a million times more powerful than that, like that, you know, for for 500 bucks.
And so that that was a scaling law.
And then and then and then key to any scaling law, including Moore's law and the AI scaling laws is, you know, they're not really laws, right?
They're they're they're they're predictions.
But when they work, they become self-fulfilling predictions because they they set a benchmark and then the entire industry, right?
All the smart people in the industry kind of work to make sure that that actually happens.
And so they they kind of motivate the breakthroughs that are required to keep that going.
And then and in chips, that was a 50-year, you know, that was a 50-year run, right?
And it was amazing.
And it's still happening in in in some areas of uh of chips.
I think the same thing is happening with the the core scaling laws, the core scaling laws in in AI.
You know, they're not they're not really laws, but like they they are basically they're predictions and then they're motivating catalysts for the research work that is required to be and and by the way, also the investment uh dollars um uh you know required to basically keep you know keep the curves going.
And and look, it is it's gonna be complicated and it's gonna be variable, and they're you know, there are gonna be walls that are gonna look like they're fast approaching, and then they're gonna be, you know, engineers are gonna get to work and they're gonna figure out a way to punch through the walls.
And obviously that's you know, that's been happening a lot.
You know, and then look, there's gonna be times when it looks like the walls have, you know, the the laws have petered out, and then they're gonna they're gonna pick up again and surge and then and then and then it it appears what's happening to the eye is there's now multiple, you know, multiple scaling laws.
Um, there's multiple areas of improvement.
And I think, you know, I don't know how many more there are already yet to be discovered, but there are probably some more that we don't know about yet.
You know, they like, for example, there's probably some scaling law around um world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.
So that that that one will probably kick in at some point here.
There's a bunch of really smart people working on that.
Um so yeah, I I think the expectation is that the you know, the the scaling laws generally are going to continue.
Yeah, the the pace of improvement will continue to move really fast.
Um to your question on like what to build.
So I I'm a complete believer the scaling laws are going to continue.
I'm a complete believer the capabilities are going to keep getting amazing, um, you know, leaps and bounds.
Uh the part where I kind of part ways a little bit with I would what I described as the AI purists, um, you know, which is which I would characterize as like the people who are in many ways the smartest people in the field, but also the people who spend their entire life like in a lab, um, and have to have say have very little experience in the outside world.
Um, the the nuance I would offer is the outside world of eight billion people and institutions and governments and companies and economic systems and social systems is really complicated.
Um, and um and doesn't, you know, it it eight billion people making collective decisions on planet earth is not a simple process of like just like you see this happening now.
It's like a bunch of the AI CEOs have this thing, which is just like well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's this these obvious set of things that society needs to do.
And then they're like, society's not doing any of those things, right?
And it's like, how can society not, you know, what whatever their theory is, how can society not see X, Y, Z?
And the answer is, well, society is number one, there's no single society.
It's like eight billion people, and they like all have a voice and they all have a vote, like at the end of the day of how they they react to change.
And then, you know, it just that like it's just human reality is just really complicated and messy.
Um, and and so the specific answer to your question is like, as usual, it depends.
Um, you know, it it depends.
Look, there's no question people are gonna like, there's no question they're gonna be companies, it's already happening.
There are companies that think that they're building value on top of the models, and then they're just gonna get blissed by the by the next model.
There's no question that's happening.
But I think there's no question also that just the process of adaptation of any technology into the real and into the real messy world of humanity is is just going to be messy and complicated.
It's it's not going to be simple and straightforward, it's gonna be messy and complicated.
And there are going to be a lot of companies and a lot of products, um uh and in fact, entire industries that are gonna get built that to basically actually help all of this technology actually reach real people.
The amount of capital going into these companies, I mean, Dario talked about it on the Dork Cash podcast, and Dorkesh was like, Why don't you just buy 10x more GPUs?
And he's like, Because I'm gonna go bankrupt if the model doesn't exactly hit the the verborman's level.
How do you think about that?
Also, as a risk on, you know, you guys are investors and no banana and think of machines and world apps.
It seems like we're leveraging the scaling loss at a pretty high rate.
Like, how comfortable, I guess, do you feel with the downsize scenario?
Like, and say, like, thanks, Peter Al.
You think you can kind of like restructure uh these build-outs and uh, you know, capital investment?
Yeah, so I should start by saying, so I live through the dot com crash.
Um, and I can tell you stories for hours about the dot-com crash, and it was horrible.
No, it was awful.
It was it was it was apocalyptic.
Uh by the way, the a lot of the dot-com crash was actually at the time, it was actually a telecom crash, it was a bandwidth crash.
Like the the thing that actually crashed that wiped out all the money was the telecom companies.
Global crossing.
Global global, yeah.
So I'm from Singapore and they they laid so much cable over our oceans.
Well, actually, there it was a scaling law in the dot com era, and it was literally the the US Commerce Department put out a report in 1996, and they said internet traffic was doubling every quarter.
And actually in 1995 and 1996 internet traffic actually did double every quarter.
And so that became the scaling law and so what all these telecom entrepreneurs did was they went out and they raised money to build fiber anticipating that the demand for bandwidth is going to keep doubling every quarter.
Doubling every quarter though is like you know grains of chess on the chessboard like at some point the numbers become extremely large right and and and it really and really what happened was the internet the internet by the way continuously kept growing basically since inception it is you know this it's continuously grown.
It's never shrunk.
And it's grown really fast compared to anything else you know in in in human history but it wasn't doubling every quarter as of 1998 1999.
And so there was this gap in the expectation of what they thought was a scaling law versus reality.
And that's actually what caused the dot com crash which was the it they they weigh over companies like global crossing way overbuilt fiber which is sort of the by the way fiber telecom equipment you know so all the all the networking gear you know and then and then by the way the actual physical data center.
So like that was the beginning of the of the of the data center build and then and then data center overbuilt.
And so you had that but it was it was literally I think it was like two trillion dollars got wiped out right it was like it was like a big it was and by the way, the other the other subtlety in it was the internet companies themselves never really had any debt because tech tech companies generally don't run on debt, but the telecom companies run on debt, physical infrastructure companies run on debt.
And so the companies like Global Crossing not just raised a lot of equity, they also raised a lot of debt.
So they're highly levered.
And so then you just do the thing.
It's just like, okay, you have a highly levered thing where you're you're just over you're overbuilding capacity.
Demand is growing, but not as fast as you hoped, and then boom, bankrupt, right and and then it's and then it's like they say about the hotel industry, which is it's always the third owner of a hotel that makes money.
It has to go bankrupt twice, right?
You have to wash out all of the overoptimistic exuberance before it gets to actually a stable state and then it makes money.
So by the way, all of those data centers and all of those, all the fiber that they're in use it's all in use today, but 25 years later.
But it it took and actually the elapsed time was it took 15 years.
It took 15 years from 2000 to 2015 to actually fill it fill up all that capacity.
The cautionary warning is the the overbuild can happen.
Um and and and and you know, you you get into this thing where basically everybody, everybody who basically has any sort of institutional capital is like, wow, it's just I I don't know how to invest in these crazy software things, but for sure I can put build data centers and for sure I can buy GPUs and I can deploy, you know, compute grids and and all these things.
Um and so you you know, you if you're a pessimist, you could look at this and you could say, wow, this is like really set up to be able to basically replicate you know what we went through, what we went through in 2000.
Obviously, that would be bad.
The counter argument, which is the one I agree with, which is the counterargument on the other side is a couple things.
One is the companies that are investing all the the companies that are investing the money are like the bluest chip of companies.
And so back back back in the in the document, like global crossing was like an it was like an entrepreneur, it's like a new venture.
But like the money that's being deployed now at scale is Microsoft and you know and Amazon and Google, right?
And Facebook and NVIDIA, and you know, these these and now, you know, by the way, OpenAI and Anthropic, which are now at like you know, really serious size, um, you know, as companies with you know very serious revenue.
These are very large-scale companies with like lots, lots of cash, lots of debt capacity that they've they've never used.
And so this is institutional in a way that that really wasn't at the time.
And then the other is at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.
Like, so and you guys know this, like everybody starved for capacity, everybody starved for compute capacity, and then you know, all the associated things, memory and interconnect and everything else, um, data center space.
And so every dollar right now that's being put in the ground is turning into revenue.
And in it, and in fact, I actually think there's an interesting thing happening, which is because everybody starved for capacity, the models that we actually have that we can use today are inferior versions of what we would have if not for the supply constraints.
Um right, suppose a hypothetical universe in which GPUs were 10 times cheaper and 10 times more plentiful, the models would be much better because you would just allocate a lot more money to train and you'd just build better models and they would be better.
Um, and so we're we're actually getting the sandbag version of the technology.
No, well, everything we use is quantized because the the labs have to keep the the full versions, right?
Like we're not even getting the good stuff.
Yeah.
But the but getting the good stuff, it's it's just even if technical progress stops, once there's like a much bigger build of like GPU manufacturing capacity and memory, you know, all the things that have to happen in the course of the next five or 10 years.
Once it happens, even the current technology is gonna get gonna get much better.
And then, as you know, like there's just like a million ways to use this stuff.
Like, there's just like a million use cases for this.
Like it, you know, this isn't just sending packets across a thing, whatever, and hoping people find something to do with it.
This is just like, oh, we apply intelligence into every domain of human activity, and then it works like incredibly well.
Um, here's what I know.
Here's what I know.
Um, in the next three or four years, it's like somewhere between three or four years out, basically everything is selling out.
So, like the the entire supply chain is is is sold out or selling out.
And so there's no like it we're just gonna have like chronic supply shortage for you know for years to come.
Um, there's going to be a response from the market that's going to result in an enormous, you know, it's happening now, an enormous flood of investment in a new fab capacity and every you know everything everything else to be able to do that.
At some point, the supply chain constraints will unlock, you know, at least to some degree.
That will be another accelerant to industry growth when that happens because the products will get better and everything will get cheaper.
Um, and so I so I know that's gonna happen.
I know that you know the deployments, you know, the the actual use cases are like really compelling.
And then, like I said, you know, with reasoning and agents and so forth, like I know they're just gonna get like much, much better from here.
And so I I know the capabilities are like really real and serious.
I also know that the technical progress is not going to stop.
It it is excellent, it is accelerating.
Like the breakthroughs are tremendous.
I mean, even just month over month, the breakthroughs are really dramatic.
And so I, you know, I think if you were a cynic and there there are cynics, you can look at 2000, you can find echoes, but I can't even imagine betting it that this is gonna like somehow disappoint in you know, at least for years to come.
I think it would be essentially suicidal to make that bet.
Um, it was that Michael Burry.
Uh that's an interesting one.
We'll pick on a guy, we'll pick let's pick on one guy.
We'll pick well because he did.
He he came out with it, was it was the He doesn't mind?
It was the NVIDIA short, right?
It came out with the NVIDIA short.
And then if you guys probably talked about this, but just the the analysis now that like the current models are getting better faster at such a rate that if you are running an NVIDIA, if you're running an NVIDIA inference chip today that's three years old, you're making more money on it today than you did three years ago.
Because the pace of improvement of the software is is faster than the than the depreciation cycle of the chip.
And then my understanding is Google is running, I don't think I don't know exactly what I this is rumors that I've heard, or maybe it's public, but um I think Google's running very old TPUs very profitable.
Yeah, very and very profitably.
Um and so it actually turns out as far as I can tell, it's actually the opposite of the Burr thesis, is actually he was actually 180 degrees wrong.
It's actually the the the old NVIDIA chips are getting more valuable, which is something that's like literally never happened before.
Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable.
And again, that's an expression of that just in ferocious pace of software progress, ferocious pace of capability payoff that you're getting on the other side of this.
And so I just the idea betting against that, like it's like an invitation to get your face ripped up.
One of my early hits was like modeling the lifespan of the H100 and H200s and going like you know, usually they advise like four to seven years, and it was, you know, maybe you sort of realistically care cut it down to two to three, but actually it's going up and not down.
And and uh that's I mean, that's I think that's the dream.
Uh we are finding utilization.
And I think utilization solves all problems.
Like you can you can find use use cases for even like the poor, like even memory we're having a shortage, right?
And and you even like the the shittier versions of the of memory that we do have, we are finding use cases for it.
So like that's great.
Yeah.
How how important is open source AI and kind of like edge inference in a world in which you have three years of supply crunch?
Like do you think in the like you know, if you fast forward like five years, like how do you think about inference uh in the data center versus at the edge?
Well, so just to start, yeah.
So I think I think open source is very important for a bunch of reasons.
I think edge edge inference is very important for a bunch of reasons.
I I think just practically speaking, if we're just gonna have fundamental construct supply crunches for the next, I mean, you you guys know if you just project forward demand over the next three years relative to supply.
One of the dismaying predictions you can do is what's gonna what's gonna happen to the cost of inference in the core uh over the next three years, and like it may rise dramatically, right?
Like so so what it and then he's is you know like the the big model companies are subsidizing heavily right now, right?
And so so what's the what will be the average person's you know per day per month token cost, you know, three years from now to do all the things that they want to do.
And I I don't know, it's gonna be I mean, I have you guys probably have friends.
I have friends today who are paying a thousand dollars a day for open claw for claw tokens to run open claw, right?
And so okay, $30,000 a month, right?
And and by the way, those those friends have like a thousand more ideas of the things that they want their claw to do, right?
And so you could imagine there there's like latent demand of up to I don't know, five or ten thousand dollars a day of of tokens for a fully deployed, you know, person personal agent.
And obviously consumers can't pay that, right?
And so so, but it gives you a sense of the few of the few of the future scope of demand, right?
And so you so even even if there's a 10x improvement in price performance, that's still you know goes to a hundred dollars a day, which is still way beyond what people can pay.
So there's just gonna be like ferocious demand.
By the way, the agent thing, the other interesting thing is I think the agent thing, so up until now, a lot of the constraints of GPU constraints, I think the agent thing now also translates into CPU constraints, right?
CPU and memory, yes, CPU memory, right?
And so like the entire chip ecosystem is just gonna get within network constraints.
That will be the killer.
It's all bottlenecks and potentially for years.
And so I I think the Brad, and and I think it's actually possible.
I mean, generally inference costs are gonna keep coming down, but I think the let's put it this way, the rate of decline I think may level out here for a bit because of these supply constraints.
And then at some point, maybe the lab stops subsidizing so much, and that that that again will be an issue.
And so there's just gonna be so much more demand for inference than than can be satisfied, um, you know, kind of with the centralized model.
And then and then you know you guys know this, but like all the just the dramatic, I mean, just the dramatic innovations that have happened in the Apple Silicon to be able to do uh inference is quite amazing.
A level of effort being put, like the open source guys are putting incredible effort into getting, you know, the this recurring pattern where the big model will never run on a PC, and then six months later, oh, it runs on a PC, right?
It's like amazing.
And there's very smart people working on that.
So there's all that.
And then look, there's also, you know, there's also like other motivators.
There's other motivators, which is just like, okay, how much trust are the big centralized model providers, you know, how much trust are they building in the market versus, you know, how much are, you know, at least for in certain cases with some people for certain use cases, people being like, well, I'm not willing to just like turn everything over.
So there, there's all the trust issues.
Um, by the way, there's also just like straight up price optimization.
There's many uses of AI where you don't need Einstein in the cloud.
You just need like a smart local model.
There's also performance issues where you want to high, you know, you want, you know, you're gonna want your doorknop to have an AI model in it, you know, to be able to, you know, do um, you know, to be able to do access control.
Um, obviously, like everything with a chip is gonna have an AM model in it, and a lot of those are gonna be local.
Um so yeah, no, like I think I think you're gonna have and then you're gonna, by the way, also wearable devices, you know, you don't want to do a complete round trip.
You want, you know, you you whatever your smart devices are, you want it to be like super low latency.
Yeah.
The question is, do we care who makes it?
One of the biggest news this week was the collapse of AI2, the Allen Institute, one of the actual American open source model labs.
Um, and uh I'm not that optimistic on American open source.
Like you you guys invested in Mr.
Al and Ms.
Troll's doing extremely well outside of China, that's about it.
Yeah, we'll see.
We'll see.
I look I number one, I do think we care.
I do think we I do think we care who makes it.
Um I would say this, the the the previous presidential administration wanted to kill it in the US.
Like they wanted to drown in the bathtub.
Um and so they wanted to kill it.
So at least we have a government now that actually like actually wants it, wants it to happen.
And you're on the council.
And the new and the PCAS, yeah.
So that the, you know, this administrative for whatever other political issues people have, which are many, you know, this administration has, I think, a very enlightened view, and in particular, an enlightened view on AI, and in particular on open source AI.
Uh, and so they're very supportive.
Um, my read is the Chin the Chinese have a very the various Chinese companies have a very specific reason to do open source, which is they they they don't fundamentally they don't think they can sell commercial uh AI outside of China right now, and or at least for specifically not not in the US for a combination of reasons.
And so they they kind of view I think open source AI as a bit of a loss leader against basically domestic uh you know paid paid services and then kind of you know, kind of ancillary products, you know, they're that they're very excited about it.
By the way, I think it's great.
I think it's great that they're doing it.
Um, you know, I think Deep Seek was like a gift to the world.
Um, I think the great thing about open source, open source, the the the impact of open source is felt two ways.
One is you you get the software for free, but the other is you get to learn how it works, right?
And so, like the paper.
The paper, the paper, and and the code, right?
And the code.
And so, like, for example, I thought this was amazing.
So, open AI comes out with a one, and it's an amazing technical breakthrough, and it's just like absolutely fantastic.
But of course, they don't explain how it works in detail.
And then, of course, they hide the they hide the reasoning traces, right?
And then everybody's like, okay, this is great, but like who's going to be able to replicate this?
Are other people going to be able to do this?
You know, is there secret sauce in there?
And then R1 comes out and it's just like there's the code and there's the paper, and now the whole world knows how to do it.
And then, you know, three months later, every other AI model is adding reasoning.
And so you get this kind of double, like, even if the Chinese models themselves are not the models that get used, the education that's taken place to the rest of the world, the information diffusion, you know, is incredibly powerful.
So that happens.
And then I don't know, we'll see we'll see.
You know, there are a bunch of American, you know, open source, you know, AI uh model companies.
I mean, look, there's going to be tremendous, you know, there already is.
There's, you know, there's gonna be tremendous there's tremendous competition uh among the primary model companies.
You know, there's depending on how you count, there's like four or five, you know, big co-model companies now that are, you know, kind of neck and neck uh in different ways.
Um, you know, and and and um, you know, and then obviously both both X and then MetaWrite evolved are you know both have huge, you know, huge attempts to, you know, kind of to kind of get lead prog underway.
And then you've got you know a whole fleet of startups, new companies, including a whole bunch that we're back in that are, you know, trying to kind of come up with different approaches, and then you've got whatever it is, I don't know.
How many, how many like mainline foundation model companies are there in China at this point?
It's probably six.
It's five tigers, is what they call it.
Uh Quinn is in in questionable because it there's change in leadership.
Right.
But yeah.
But that does that include that that includes like Moonshot.
Yes, Kimmy, Deep Seek, uh uh ZAI, um, Quinn, O one is uh in there.
Right.
And then um ByteDance, and then you see.
Byte Dance would be like the next year.
They weren't as prominent.
They weren't having a but now you know.
Yeah, but they're in at least you know, C Dance is very inspiring, and presumably they have more stuff coming, and Tencent probably has more stuff coming and so forth.
And so, like, look, here would be a thing you could anticipate, which is there are not these markets, there are not going to be between the US and China right now, there's like a dozen primary foundation model companies that are like at scale at some level of like critical mass.
It's not gonna be a dozen in three years, right?
Like it just because these industries don't bear a dozen, it's it's gonna be three or you know, there's gonna be three or four big winners or maybe one or two big winners.
And so there's gonna be like a whole bunch of those guys that are gonna have to figure out alternate strategies.
Um, and I think like open source is one of those strategies.
And so I I think you could see like a whole uh uh I think the questions like who's gonna do open source.
I think that could change really fast.
I think that that that's a very dynamic thing.
I think it's very hard to predict what happens.
And and I think it's very important.
NVIDIA's doing a lot.
You want to do it?
Well, I was gonna say, well, exactly.
And then you got NVIDIA, and then you know, just again, an industrial front this there's an old thing, a business strategy, which is called uh commoditize commoditize the compliment.
And it's right.
And so if your Jensen is just kind of obvious, of course you want to commoditize the software.
Yeah, and he's, and to his enormous credit, he's putting enormous resources behind that.
And so maybe it's maybe it's literally NVIDIA.
And I think that would be great.
Yeah, yeah.
Uh narrative violation to European projects uh in the beginning.
Bam.
I'm hosting my uh Europe uh conference soon, and I got both of them.
They got us.
They got us.
Okay.
Wait a minute.
Where was Peter?
So where was Steinberger when he did all those in Austria?
Yeah, yeah.
He was in VM.
Oh, he was in Vienna, and then where is he now?
Uh he's moving to SF.
Okay.
Okay.
All right.
Okay.
There we go.
And then, yeah, the Pi guy, right?
The Pi guys are European.
Yeah, they're okay.
Everybody's in Australia.
Mario is also there.
Right.
And are they, yeah, they haven't announced yet any sort of change change to, or have they?
No, they're a company there.
Okay, okay.
Okay, okay.
Yeah, yeah.
Um, good.
Anyway, I think Pi and OpenClaw are very important software things.
And and I just wanted you to just go off on what do you think?
Yeah.
So I think in the combination of the two of them, I think is one of the 10 most important software.
Open cloud got all the attention, but right, talk about Pi.
Pi Py's kind of the end, yeah.
Pi Pi is kind of the architectural breakthrough.
For those of us who are older, there was this whole thing that was very important in the world of software, basically from like 1970 to, I don't know, it still is very important, but like 19 from 1973 to like basically the creation of Linux, which is basically this thing we used to call like the Unix mindset.
Like so, so because there were all these different you know, theories, there are all these different operating systems and mainframes and and then you know, all these Windows and Mac and all these things.
And then there was this but kind of behind it all was this idea of kind of the Unix mindset.
And the Unix mindset was this thing where basically you don't have these like in the old days, like like the operating system that like made the computer industry really work, like in the 1960s, was this thing called OS 360, which was this big operating system that IBM developed that was supposed to basically run everything.
And it was this like giant monolithic architecture in the sky.
It was like a, you know, it's like a giant castle um of software.
And by the way, it worked really well, and they were very successful with it, but like it was this huge castle in the sky.
But it was this thing, it was almost unapproachable, which is like you had to be kind of inside IBM or very close to IBM, and you had to really understand every aspect of how the system worked.
And then the the Unix skies originally out of ATT and then out of out of Berkeley, um, you know, came out and they said, no, let's have a completely different architecture.
And the way architecture is going to work is we're gonna have we're gonna have a prompt and a sh and a shell.
And then and then we're gonna all the functionality is going to be in the form of these discrete modules, and then you're gonna be able to chain the modules together.
And so, like the the up, it's almost like the operating its operating system itself is going to be a programming language.
Um and then that led to the the sort of centrality of the shell.
Um, and then that led to sort of uh, you know, basically chaining together Unix tools, and then that led to the emergence of these these scripting languages like Pearl, where you you could basically kind of very easily do this.
And then the shells got more sophisticated.
And then and then and then look, like, you know, that that number one, that worked.
And that that was the world I grew up in.
Like I was I was a Unix guy, you know, sort of from call it 1988 to, you know, kind of all the way through my work.
And it worked really well.
It it's in the background.
Um, you know, nor normal people don't need to didn't need to necessarily know about it.
But like if you were doing like system architecture application development, you you you knew all about it.
Um and then, you know, it's been in the background ever since.
And you know, like your Mac still has a Unix shell, you know, kind of in there, and your iPhone still has a Unix shell kind of buried in there somewhere.
So they're kind of in there.
And then, you know, the Windows shell is kind of a you know, sort of a weird derivative of that.
But um, you know, but look, the internet the internet runs on Unix um and then smartphones.
Actually, both iOS and Android are Unix derivatives, and so you know, kind of Unix did end up winning.
But but anyway, we and then we just started taking that for granted.
And then and then so so basically the the way I think about what happened with Pi and then with OpenClaw is basically what those guys figured out is always say the the great breakthroughs are obvious in retrospect, right?
Which is the best kind.
The best kind they weren't obvious at the time, or somebody else would have done them already.
Um and so there is a like a real conceptual leap, but then you look at it sort of the backwards looking, and you're just like, oh, of course like the the to me those are always the best breakthroughs.
Well actually language models themselves are like that.
It's just like oh next token completion.
Oh of course yeah what other objective mattered yeah exactly but but like right but she's even saying it wasn't obvious until somebody actually did it right and so the conceptual breakthrough is real and deep and powerful and very important.
And so the way I think about Pi and OpenClaw is it's basically marrying the the language model mindset to the unit to the Unix basically shell prompt mindset.
And so it's it's basically this idea that what what so what is an agent right and is as and as you know like many smart people have been trying to figure out what an agent is for for for decades and they've had many architectures to build agents and the whole thing and it turns out what is an agent.
So it turns out what we now know is an agent is the following it's so it's language model and then above that it's a bash it's a bash shell.
So it's a Unix shell and then it's and then the agent has access uh has access to to the shell in you know hopefully hopefully in a sandbox maybe in maybe in a sandbox.
So it's it's the model um it's the shell um and then it's a file it's a file system um and then the state is stored in files and then you know there's the markdown format for the you know for for the files themselves and then and then there's basically what in Unix is called a cron job there's a loop and then there's a heartbeat for the there's heartbeat and and the thing basically wake wakes up wakes up.
So it's basically LLM plus shell plus file system plus markdown plus cron.
And it turns out that's an agent and and and every part of that other than the model is something that we already completely know and understand.
And in fact it turns out the like the latent power of the Unix shell is like extraordinary because basically like all like there's just like an there's just enormous latent power in the shell.
There's enormous numbers of Unix commands there's enormous number of command line interfaces into all kinds of things already in the you know your entire I mean your entire just to start with your computer runs on a shell if you're running a Mac or a or a phone your compute your computer's running on a shell uh already and so like the full power of your computer is available at the command line level.
And then it turns out it's really easy to expose other functions as a command line interface.
And so like that this whole idea where we need like MCP and these like protocol fancy protocols, whatever it's like no we don't we just need like a command command line thing.
So that's the architecture and then it turns out what is your agent?
Your agent is a bunch of files stored in a file system.
And then there's the thing that just like completely blew my mind when I wrapped my head around it as a result of this which is like okay this means your agent is now actually independent of the model that is running on because you can actually swap out a different LLM underneath your agent and your your agent will change personality somewhat because the model is different but all of the state stored in the files will be retained different instruction set but you just compiled it.
Right exactly and it's all right it's like right swapping out a ship and recompiling.
But it's it's still it's still your agent with all of its memories um and with all of its capabilities.
And then by the way you can also swap out the shell uh so you can move it to a different execution environment that is also is also a bad shell.
By the way you can also switch out the file system right and you can and you can and you can swap out the the the heartbeat for the the cron framework the the loop the the agent framework itself.
And so your agent basically is basically at the end of the day, it's just it's just its files.
And then and then there's of course the claw.
Yeah, it's it's basically it's it's just the files.
Um and then by the way, as a consequence of that, the agent it's and then the agent itself, it turns out a couple important things.
So one is it, it's it can migrate itself, right?
And so you're you you can instruct your agent, migrate yourself to a different uh runtime environment, migrate yourself to a different file system, migrate yourself to a different, you know, like swap out the language model, your agent will do all that stuff for you.
And then there's the final thing, which is just amazing, which is the agent is the agent actually has full introspection and actually it actually knows about its own files and it can rewrite its own files, right?
Which by the way, is basically no widely deployed software system in history where the the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself.
Like that, that I mean, there have been toy systems that have had that, but there's never been a widely deployed system that has that capability.
And then that leads you to the capability that just like completely blew my mind when I wrap my head around it, which is you can tell the agent to add new functions and features to itself, and it can do that.
Right.
Extend yourself, like extend yourself, give yourself a new capability, right?
And so, and so literally it's just like you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eight sleep bed and it gives me better advice and sleep.
And you go home at night and you tell your claw, or if they're at the party, by the way, you tell your claw, oh, add this capability to yourself, and your claw will say, Oh, okay, no problem.
And it'll go out on the internet and it'll figure out whatever it needs, and then it'll go out to claw code or whatever, it'll write whatever it needs, and then the next thing you know, it has this new capability.
And so you don't even have to like you can have it upgrade itself without even having to, without having to do anything other than tell it that you want it to do that.
And so, anyway, so the the combination of all this is just, I mean, this is just like a massive, incredible, I mean, it's just incredible.
Like if I if I were if I were 18, like this is a hundred, this is what I would be spending all of my time on.
This is like such an incredible conceptual breakthrough.
And again, people are gonna look at it and they already get this response.
People are gonna look at it, they're gonna say, oh, well, where's the breakthrough?
Because these the all of these components were already known before.
But but this is the key.
The key to the breakthrough was by using all these components that were known before, you get all of the underlying capability that's buried in there.
And so all and so for example computer use all of a sudden just kind of falls trivia trivially of course it's gonna be able to use your computer it has full access to the shell right and then and then you just you you give it access to a browser and then you've got the computer and the browser and and off and away it goes and then you've got all the abilities of the browser also.
And so and so the capability unlock here is profound.
My friends who are you know deepest into this are having their claw do like like literally like a thousand things in their lives.
They have new ideas every day they're just like constantly throwing new challenges at the thing and by the way it's early and you know these are you know these are prototypes and there's you know as you guys know there's security issues.
Yeah and and so you know there's a bunch of stuff to be ironed out but the the unlock of capability is just incredible.
Yeah.
And I I have absolutely no doubt that everybody in the world is gonna is gonna have at least you know an agent like this if not an entire family of agents and we're gonna be living in a world where I think it's almost inevitable now that this is the way people are going to use computers.
I was gonna say for someone who is deeply familiar with social networks the next step is your claw talking to mic law posting on claw Facebook uh posting their jobs on claw linkedin and close posting their tweets on claw XAI or whatever, you know.
Um, I do think that that is how uh you know, we we get into some danger there in terms of like alignment and whether or not we want these things to to to run.
You guys know about rent rent a human.com.
Yeah, the rented.
Yeah, yeah, yeah.
Yeah.
I mean it's Fiverr, it's task drivet, sure, of course, uh, mechanical turk.
Yeah, but flipped.
Right.
The agent hiring the people.
Which of course is gonna happen.
Right.
It's obviously gonna happen.
I'm curious if you have any thoughts on the engineering side.
So when you build the browser, the internet, you know, just a bunch of mostly plain text file plus some images.
And today the every website and app is like so complex, and like somehow, you know, the browser kept evolving to fed that in.
Are there any design choices that were made like early in the browser and kind of like the internet and the protocols that you're seeing agents similar today's like, hey, this thing is just not gonna work for like this type of new compute, and we should just rip it out right now.
There were a whole bunch, but I'll give you a couple.
So one is um then we didn't, you know, to be clear, like this this was not, you know, this was totally different.
We didn't have the capabilities we have today, but because we didn't we didn't have the language models underneath this.
But um, we did have this idea that human readability actually mattered a great deal.
Um, and and so and specifically in those days, it was it was not so much English language, but it was there there was a design decision to be made between binary protocols and text protocols.
And basically every every every basically old school systems architect that had grown up between like the 1960s and the 1990s basically said, you know, the internet is the what do you know about the internet?
It's star for bandwidth.
You you just you do you have these very narrow straws?
Uh you know, look, people, when we did the work on mosaic, like pe people who had the internet at home had a 14 kilobit modem, right?
So you're you're trying to like hyper-optimize every bit of data that travels over the network.
And so obviously, if you're gonna design a protocol like HTTP, you're gonna want it to be binary, you know, highly compressed binary protocol for maximum efficiency, and you're gonna want to have it be like a single connection that persists, and you're, you're the last thing you're gonna want to do is like bring up and tear down new connections.
And you definitely you're not gonna not gonna want a text protocol.
And so, of course, we said, no, we actually want to go completely the uh the other direction.
It's obviously we only want text protocols.
Um, by the way, same thing in HTML itself.
We want HTML to be relatively verbose.
You know, we want the tags to actually be like human readable.
Um, we want to use the most inefficient things possible.
Yeah, we want to do the inefficient, we'll do the inefficient things.
You're the original token maxer.
Yeah, exactly.
Yeah, yeah, yeah.
Basically, it's just like well, yeah.
Well, actually, this was this was actually the kind of conscious thing, which basically says just like assume assume a future of infinite infinite bandwidth, build for that.
And then basically what it was is it was a bet that it was a bet that if the system was, if the if the latent capabilities of the system were powerful enough and that was obvious enough to people that would create the demand for the bandwidth that would cause the supply of bandwidth to get built that would actually make the whole thing work.
And then specifically what we wanted was we wanted everything to be human readable because we at the engineering level, we wanted people to be able to read the protocol coming over the wire and be able to understand it with their with their bare eyes without having to like disassemble it or whatever, right?
You have it converted out of binary, right?
And so the the the all the you know, HTTP and everything else were it was always uh text protocols.
Uh and the same thing with HTML.
And in many ways, some people say that the key breakthrough in the browser was the view source option, um, which is every web page you go to, you could view source, which means you could see how it worked, which means you could teach yourself how to build right new uh to build new web pages.
There was that.
So human readability, um, you know, and again, human readability in those days still meant technical, you know, specs.
You know, now it means English language, but that there's an incredible latent power in giving everybody who uses the system the option to be able to drop down and actually understand and see how it's working.
And that worked really well for the web.
And I think it's working really well for AI.
That was one.
Um, what was the other?
Um a big part of the idea of web servers was to actually surface the underlying latent capability of the operating system and to be able to surface the uh also the underlying latent capability of the database.
Because basically, what was a web server?
What is a web server fundamentally architecturally?
It's it's it's the operating system.
So it's it's the operating system's ability to, you know, it's running on top of an OS.
So it's the OS's ability to manage the file system and do everything else that you want to do, process everything.
Um, and then of course, a lot of early, you know, a lot a lot of websites are front-ends to databases.
Um, and so you wanted to you wanted to unleash the underlying latent power of whether it was an Oracle database or some other, you know, some other Postgres or whatever, whatever it was.
Um, and so a lot of the function of the web server was to just bridge from that internet connection coming in to be able to unlock the underlying power of the OS in the database.
Uh and again, people looked at it at the time and they were like, well, is this really does this really matter?
Like, is this important because we've had databases forever and we've always had you know user interfaces for databases, and this is just another user interface for a database.
It's like, okay, yeah, fair enough.
But on the other side of that, it's just like this is now a much better interface to databases and one that 8 billion people are going to use and is going to be like far easier to use and far more flexible.
And and and you're not just gonna have old databases, now you have a system where people can actually understand why they want to build, you know, a million times more database apps than they had in the past.
And then the number of databases in the world exploded.
And so again, this goes to this thing of like building building in layers.
Some of the smartest people in the industry look at any new challenge and they're like, okay, um, I'm I need to build a new kind of application.
So the first thing I need to do is build a new programming language.
Right.
And then the next thing I need to do is build a new operating system.
Right.
And then the next thing I need to do is I need to build a new chip.
Right.
And they they kind of want to reinvent everything.
And I've I've always had maybe it's just, I don't know, pragmatic mentality or something, or maybe an engineering over science mentality, but it's more like, no, you have just like all of this latent power uh in the existing systems.
And you don't want to be held back by their constraints, but what you want to do is you want to kind of liberate that power and open it up.
Yeah.
And so I think I think that and I think the web did that for those reasons.
And I think it's the same thing now that's happening.
It's a good perspective on the web.
The programming languages is another good thing.
We have Brett Taylor on the podcast, and we were talking about Rust and you know Rust is memory safe by the fault.
And so why are we teaching the model to not write memory unsafe code, just use thrusts, and then you get it for pre.
How much do you think there's like time to be spent like recreating some of these things instead of taking them for granted?
I'd be like, oh, okay, Python is kind of TypeScript.
You know, as they as imperfect as they are, they are the lingua franca.
I mean, I think this is gonna change a lot because I don't think the models care what language they program in.
And I think they're gonna be good at programming in every language, and I think they're gonna be good at translating from any language to any other language.
Like, okay, so this gets into the coding side of things.
I I think we're going through a really fundamental change.
And then look, I grew up hand, you know, I grew up hand coding, you know, I grew up hand coding.
Everything I did was actually everything I did actually was written in C.
I wasn't back in the days.
I wasn't even using C.
So I or like Java or any of this stuff, right?
Uh and so um I everything, everything I ever did, I was like managing my own memory at the level of C.
And then I, you know, I I'm still from the generation that you know, I knew assembly language, and you know, I I, you know, um, so I I could drop down and do things uh right on the chip.
And so we we've just we've all all of us, we've always lived in a world in which software is like this precious thing that like you have to think about very carefully, and it's like really hard to generate good software, and there's only a small number of people who can do it, and like you have to be very like jealous in terms of thinking about like how do you allocate, like what are your engineers working on, and how many good engineers do you actually have and how much software can they write, and how can how much software can human beings you know kind of maintain?
And I think like all those assumptions are being shot right out the window right now.
Like I think they're I think those days are just over.
And I think the new world is like actually high quality software is just like infinitely available.
And if you need new software to do XYZ, like you're just gonna wave your hand and you're gonna get it.
And then if it's if you don't like the languages written in, you just tell the thing, all right.
I want the right, now I want the rest version.
Um, or you know, security, you know, security.
We're about to, by the way, we're about to go through computer security is about to go through the most dramatic change ever, which is number one, like every single latent security bug is about to be exposed.
Right.
So it's we're gonna have like the in we're we're we're set up here for like the computer security apocalypse for a while.
But but but on the other side of it, now we have a coding agents that can go in and actually fix all the security bugs.
And so how how are you gonna secure software in the future?
You're gonna tell the tell the bot to secure it and it's gonna go through and and fix it all.
And so this thing that was this incredibly scarce resource of high quality software is just going to become a completely fungible thing that you're just gonna have as much as you want, right?
Um and and that has like, you know, that has like tons and tons of consequences.
In some sense, the answer to the question that you posed, I think is just somewhat, I don't know, simpler or something or straightforward, which is just if you want all your software in Rust, you just tell the bot you want all your software in Rust.
Like things that used to be like hard or even like seem like an insurmountable mountain to get to get through all of a sudden, I think become very easy.
I think Brett had a theory that there would be a more optimal language for LLMs.
And so the contention is uh there isn't.
Like just don't bother, just whatever humans already use.
LLMs are perfectly capable of porting.
I think we're pretty close to being, I don't know if this would work today.
I think we're pretty close to being able to ask the AI what would its optional optimal language be and let it design it.
It's true.
Okay, here's a question.
Are you gonna even gonna have programming languages in the future?
Um, or the AI are the AI is just gonna be emitting binaries.
Let's assume for a moment that humans aren't coding anymore.
Let's assume it's all bots.
The bot what levels of intermediate abstraction do the bots even need?
Yeah, or are they just coding binary directly?
Did you see there's actually an experiment?
Somebody just did this thing where they have a they have a uh language model now that actually emits model weights for a new language model, right?
And so will the bots predict the weights.
Well, yeah, will the bots literally be emitting not just coding binaries, but will they will the will they actually be admitting weights for for new models directly directly?
And conceptually, there's no reason why they can't do both of those things.
Uh like architecturally, both of those things seem completely possible.
Very inefficient.
You're basically very inefficient.
Simulation of a simulation in a simulation inside of the weights.
Yeah.
Yeah, very inefficient.
But like, look, LLMs are already like incredibly uh inefficient.
Ask an uh favorite thing, ask Claude to add two plus two equals four, right?
It's just like, you know, it's like, you know, it's it's it's like whatever, billions and billions of times more inefficient than using your pocket calculator.
But but but yet the the the payoff is so great of the general capability.
And so anyway, like I I kind of think in 10 years, like I'm not sure.
Yeah, like I'm not sure there will even be a salient concept of a programming language um in the way that we understand it today.
And in fact, what we may be doing more and more is a form of interpretability, which is we're trying to understand why the bots have decided to uh structure uh code in the way that they have.
I mean, if you play it through, you don't need browsers then like that's the death of the browser.
Well, so I I would take it a step further, which is you may not need user interfaces.
So who is gonna use software in the future?
Other bots, the other bots.
Yeah, yeah.
And so you still need to, I don't know, pipe information in and out.
Really?
Well, what are you gonna do then?
Are you sure?
You're just gonna log off and touch grass.
Whatever you want, exactly.
Isn't that better?
I want software to do stuff for me.
Isn't that but isn't that better?
I mean, I look I you know, I don't know, look, like you know, you know, you know, the arguments here, you know, it was not that long ago that 99% of humanity was behind a plow.
Right, right.
And what are people gonna do if they're not plowing fields all day to grow food, right?
And it just turns out there's like much better ways for people to spend time than plowing fields do scrolling.
Uh exactly exactly, you know, talking to their friends.
And look, and I'm not an absolutist and I'm not a utopian.
And I and to be clear, like I've I have an 11-year-old and he's learning how to code, and like I'm, you know, I think it's still a really good idea to learn how to code and so forth.
But I just if you project forward, you just have to think forward to a world in which it's just like, okay, I'm just gonna tell the thing what I need and it's gonna do it.
And then and then it's gonna do it in whatever way is most optimal for it to do it.
Yeah, unless I tell it to do it non-optimally, like if I tell it to do it in Java or in Rust or whatever, it'll do it, I'm sure.
But like if I'm just gonna tell it to do it, it's gonna do it in whatever way is like the optimal way to do it.
And then I and then if I need to understand how it works, I'm gonna ask it to explain to me how it works.
Right.
And so it's gonna be doing its own interpretation, it's gonna be the engine of interpretability to explain itself.
And I I just am not convinced that that I'm not I'm not convinced that in that world you have these historical the goals of the abstractions will be whatever the boss needs out what the human state.
Yeah, yeah.
That well, I I'm curious, like if that's true, then shouldn't the models providers be building some internal language representation that they can do extreme kind of like RL uh and reward modeling around because it's like today they're kind of like tied to like TypeScript and Python because the users need to write in that language versus they can have their own thing internally and like they don't need to teach it to anybody, they just need to teach their model.
And I think that's how you get maybe the version between the models, like going back to like the Pi open cloud thing.
It's like, oh, I built all the software using the open AI model and now switch to the enthropic model, but the enthropic model doesn't understand the thing.
So I would I it feels like there still needs to be some obstruction, but maybe not.
Maybe that's the lock in that the model providers want to have.
I don't know.
I'm not even sure that's lock in though, because why can't the second model just learn what the first model has done?
Like exactly.
Okay, so okay, give you an example.
So yeah, as you know, models can now reverse engineer software by it's it isn't the whole thing now where people are reverse engineering like Nintendo game binaries.
Yeah.
So you you have like we've seen a bunch of reports like this where somebody has like a favorite game from the 1980s and the source code is like long dead, but they have like a binary burden to a chip or something, and other reverse engineer to get a version of the rest of their Mac, right?
And so if you reverse it, if this is what I kind of say, if you're reversing like x86 binaries, then why can't you reverse engineer whatever they create?
Yeah, and because we're all on a Unix-based system, it has to be reversible because it needs to run on the target.
Yeah, yeah, yeah.
Yeah, yeah, basically.
And so I just I just think it's this thing where it's just like, and by the way, and everything we're describing is something that human beings in theory could have done before, but just with like but with enormous where we're but it was just always like cost and labor prohibitive.
Reverse engineer like I learned how to reverse engineer human beings can reverse engineer binaries.
Yeah, it's just for any complex binary, I need like a thousand years to do it.
But now with the model, you don't.
And so all of a sudden you get the you get these things, or another way to think about it is so much of human built systems sort of compensate for the human limitations.
Yeah, right.
Um, and if you don't have the human limitations anymore, then all of a sudden you have and it's not that you you won't have abstractions, but you'll have a different kind of abstraction.
Yeah.
I have two topics to bring us to a close, and uh, you could pick whichever ones.
Uh just talking about protocols.
Uh was it you or someone else?
Uh I forget my internet history.
We said that like the biggest mistake that we didn't figure out in the early days was payments.
Yes.
That's what was that you?
Yes.
40202 payment required.
We have a chance now.
I don't think we're gonna figure it out.
I don't know.
Like, what's your take?
Oh, I think we will.
Yeah.
No, now I think it's gonna happen for sure.
Yeah.
Yeah, and there's two reasons it's gonna happen for sure.
One is we actually have internet native money now in the form of crypto.
Stable coins stable coins and crypto.
And this is I I think this is the grand unification basically of AI and crypto, uh, is what's about to happen now.
Um I think AI is the crypto killer app, I think is where where this is really gonna come out.
Um and then the other is it's just it, I mean, it's just I think it's now obvious.
It's like obviously AI agents are gonna need money.
And it's already happening, right?
If you've got a claw, if you've got a claw and you want it to buy things for you, you have to give it money uh in some form.
I would say the adoption is probably like 0.1% if if that, but yeah.
Oh, today, yeah, yeah, yeah.
But think forwards.
Like, where is it going?
Forward thinking.
The ultimate principle of everything and and everything that I think I we we do is it's the William Gibson quote, which is the future is already here, it just isn't distributed, you know, isn't it isn't distributed yet.
My friends who are the most aggressive use users of of of OpenClaw just like have given their clause bank accounts and credit cards.
Um and and not only have they done it, it's obvious that they needed to do it because it's obvious that they needed to be able to spend money on their it's just completely obvious.
And so, and again, like so the number of people who have done that today, to your point is like I don't know, probably 5,000 or something.
But that's how these things start.
Actually, I mean, since uh you keep mentioning and by the way, open claw, by the way, if you don't give it a bank account, it's just gonna break into your quote, you know, it's gonna be breaking.
It's gonna break into your bank account anyway and and take your money.
So you you might as you might as well do it.
You might as well do it.
Uh by the way, I really love, I gotta tell you, I really love the phenomenon.
I love the yellow.
Um, I'm not doing it myself to be clear, but but I love the people that are just like, yeah, but what is it?
Skip skip.
They just dangerously which by the way, it's a Facebook thing.
Okay.
In Facebook, they they have this culture to name the thing dangerous so that you are aware when you enable the flag that you're opting into a dangerous thing.
Okay, good.
And they brought it into open AI.
And of course, that makes it enticing.
Sam runs codecs uh with skip permissions on on his laptop.
Yes, a hundred percent.
And so I I think I think the way to actually see the future is to find the people who are doing that.
There's a madness, you know, they're log everything, you know, just watch it, watch the logs.
But like, let's actually find out what the thing can do.
And the way to find out what the thing can do is just like try everything.
Yeah, let it try everything, let it unlock everything.
By the way, that's how you're gonna find all the good stuff it can do.
By the way, that's also how you're gonna find all the flaws.
I think the people who turn that on for bots are like they're they're like martyrs to the progress of human civilization.
Like I feel very bad for their descendants that their bank accounts are gonna get looted by their bots in the first like 20 minutes.
But I think the contribution that they're making to the future of our species is amazing.
It's like gentlemen's science.
Yes, it's yes, yes, experimentally.
It's uh Ben Franklin out with the trying to try trying to get lightning to strike his uh his balloon and seeing if he gets electrocuted.
Yeah.
It's uh Jonas Salk with the polio vaccine.
Yeah, injecting it.
Yes.
So yes, I I I think we should have like a glow, we should have like flags and like we should have like monuments to the people that just let open claw run their lives.
Well, more anecdotes are like what are the craziest or interesting things that people listening to this should go up, go home and do.
I mean, this is this is the this is the the extreme thing is just like the straight yellow, like just yeah, turn turn your life.
That's a general capability of there like a specific story that was like wow, and and everyone in a group chat just lit up.
I mean, like, you know, so there's tons of there's already tons of health.
Um, you know, there's the health dashboard stuff is just is just personal health absolutely amazing.
The number of stories on um I just don't want to violate people's, you know, obviously personal anonymous.
But um, you know, one of the things OpenClaw's really good at is hacking into all the stuff in your LAN.
Uh it's really good at it.
So, you know, internet of things, aka internet of shit.
Yeah.
Like super insecure, but great.
Discoverable.
Yeah, it's like discoverable.
Open claw is happy to scan your network, identify all the things, and then my my my friends who are most aggressive at this are having open claw take over everything in their house.
Yeah.
It takes over their security cameras, it takes over their their, you know, their whatever their access control systems, it takes over their webcams.
I have a friend whose claw watches him sleep.
Put a webcam in your bedroom, put the put the claw, put the claw on a loop, uh, have it wake up frequently and have it watch, and just tell it, watch me sleep.
And and I've I've seen the transcripts, and it's literally like Joe's asleep.
This is good.
This is good that Joe's asleep because you know I have I have his health data and I know that he hasn't been getting enough sleep.
And so it's really good that he's getting sleep.
I really hope he gets his full whatever, you know, five hours of round sleep.
Uh Joe's moving.
Joe's moving.
Um uh Joe might be waking up.
This is a real problem.
If Joe wakes up now, he's gonna ruin his sleep cycle.
Oh, okay.
It's okay.
Joe just rolled over.
Okay, he's gone back to bed.
Okay, good.
All right, okay, I can relax.
This is fine.
He's monitoring the situation.
Situation.
And and being a bot, like, you know, is just like very focused, right?
It's just like, uh, this is like his reason for existence is to watch Joe sleep.
And then, and then I was talking to my friend who did this, is like, you know, on the one hand, it's like, all right, this is weird and creepy.
Um, and I need to, I need to uh maybe this is taking over my life.
And then the other thing is like, you know what?
If I had a heart attack in the middle of the night, this thing literally would like freak out and call 911.
Like, there's no question this thing would figure out how to like alert medical authorities and like probably summon SWAT teams and like do whatever would be required to save my life, right?
And so it's like, you know, like yeah, like that's happening.
Well what else um if um uh it's a company, Unitry, uh, that makes the robot dogs.
Um, uh I actually have one at home, which uh is it's actually really fun.
The Chinese companies.
The Chinese companies are so aggressive at adopting uh new technology, but they don't always like listen take the time to really package it, package it and maybe think it all the way through.
And so so the you at least the United dog I have.
So it it has an old non-LLM just control system, which by the way is not very good.
It markets well but it in practice is it's not that good.
It has trouble with stairs and so forth.
And so it's not quite what it should be.
But then the language model thing comes out in the voice so they they add so they add LLM capability and then they they add a voice mode to it.
But but that LM capability is not at all connected to the control system.
So you've got this schizophrenic dog that like is a complete idiot when it comes to climbing the stairs but it will happily teach you quantum mechanics right in like a plumbing English accent right like it's just like absolutely amazing.
Jagged intelligence yeah yeah talk about jagged and then now obviously what's gonna happen in the future is is they're gonna connect together but but right now it's it's and so right now it's not that useful.
And so I I have a friend who has one of these who had his claw basically hack in and rewrite the code write new firmware write new firmware for the the unit robot.
And now it's now it's an actual pet dog for his kids.
You should do the before after like the motion yeah it's you said it's completely different.
He said it's a complete transformation and whenever there's an issue in the thing now the claw just like rewrites the code you know you know you go in and you do does the code and so it's it kind of goes to your thing here.
And so so like all of a sudden this is why we want to think about AI code AI coding is not just like writing new apps.
It's also going in and rewriting all the old stuff that should have worked that never worked.
And so like I I think I think basically I think the internet, the internet of shit is basically over.
Like I think everything there's a potential here where like all these devices in your house that have been like basically marginal or you know, basically dumb, you know, like all of a sudden they might all get really smart.
Now smart home.
You have to decide if yes, there are horror movies in which this is of which this is the premise.
And so you have to decide if you want this.
But but but this is the first time I can say with confidence, I now know how you could actually have a smart home with 30 different kinds of things with chips and internet access where it actually all makes sense.
It all works together and it's all coherent in the whole thing.
And to have that unlock without a human being having to go do any of that work, like I'm I'm waiting for a sorry mark.
Uh I can't let you open that fridge door.
You know, like exactly, exactly.
Yes, yes.
Because you're not supposed to eat right now.
I have all of it.
So I have every thread of health information, you know, and I know you think you're doing, you know, da-da-da.
And I don't think you can do this, but you know, if this is a real are you really, you know, are you really sure?
And you know, you told, you know, you told me last night you really don't want me to let you do this.
So, you know, I'm sorry, but the fridge door is locked.
Um the fridge doors.
Exactly.
And by the way, I know you're supposed to be studying for a test.
And so why don't we why don't you go when you can pass the test, um, I will open the fridge door for you.
Yeah.
Final protocol, and then we can wrap up.
Uh proof of human.
Yes.
Right?
Yeah.
That's the last piece that we got to figure out.
Yeah.
So I would say there's there's two massive, I would say, um, uh, sort of asymmetries in the world right now where we've known these asymmetries exist and we we societally have been unwilling to grapple with them.
And I think they're both tipping right now.
And and they're they're they're they're the same thing.
It's a virtual world version, it's a physical world version.
So the virtual world version is is the bot problem.
We're just like, you know, the internet, internet is just like a wash and bots.
The internet's a wash and fake people.
It has been forever.
Um by the way, a lot of that has to do with lack of money, you know.
And so this, you know, this is the this is the my spicy take was these two are the same thing, and corporations have people too, you know.
So interesting.
Yeah, yeah, yeah.
Okay.
So a bank account is proof of human.
Yeah, okay.
Yeah, until you until you give the bots make accounts.
Yeah, exactly.
So, okay, yeah.
Yeah, so there's that.
But yeah, look, look, the bot, I mean, every social media user knows this.
The bot the bot problem is a big problem.
You know, the bot the bot problem has been a big problem forever.
It's it's a huge problem, and it's never really been confronted directly, like at any point.
By the way, the physical world version of this is the drone, the drone problem.
Um, right.
And so we we've known for you know, we've known for 20 years now that the asymmetric threat, both in milit military in actual military conflict, but also in just like security, like like you know, security on the home front, the big threat is is the cheap attack drone, right?
The the the cheap the cheap suicide, you know, drone with a bomb.
And we've known that forever.
And by the way, like, you know, it's very disconcerting how like every you know, every office complex in the in the cut, you know, in the world is like unprotected from drone attacks.
Um, every every stadium, every school, every prison, like it's like okay, we've known that.
We've never done anything.
What are you gonna do about it?
Yeah, one possibility is just leave leave them unprotected forever and live in a world of like asymmetric terrorism forever.
The other is take the problem seriously and figure out the set of techniques and technologies required to be able to deal with that, whether those are lasers or jammers or early warning systems or you know, personal force fields.
Kinetic personal force doing uh personal force fields.
Exactly.
And in both cases, the these are these are economic asymmetries.
These are economic asymmetries, right?
Because it's really cheap to field a bot, but it's very hard to tell something a bot.
It's very cheap to field a drone, it's very hard, it's very expensive to defend against a drone.
But you see what I'm saying?
Is it's it's it's the it's the virtual version of the problem and it's the physical version of the problem.
Uh the virtual version of the problem, what we what we need quite literally is proof of human.
The reason is because you're you're not you're not gonna have proof of bot.
The the especially now that the bots are too good.
The the the bots can pass the Turing test.
And if the bots can pass the Turing test, then you can't you can't screen for bot.
You can't have proof of not a bot.
But what you can have is you can have proof of human.
You can have you know cryptographically validated, this is definitely a person.
And this is and then you can have cryptographically validated, this is definitely like something that a person said.
This video is real, right?
Uh just to double click on uh do you think Alex Blania with world, do you think he's got it?
Or is there an alternative?
Oh, so I mean there's gonna be, I think there'll be, I mean, many people will try.
We're one of the key, you know, participants in in the world in the world project, and I just know I don't know.
Yeah, yeah.
So we're we're partisans.
But yeah, I I think so we think world is exactly correct.
Okay.
And the reason is it it has it has to be it, it has to be proof of human.
It it has because you can't do proof of not bot, you have to do proof of human.
To do proof of human, you you need but you need biological validation.
You need it to start with this was actually a person, right?
Because otherwise you have bots signing up as fake people, right?
And so you you have to have like something, you have to have a bio biometric, and then you have to have cryptographic validation and then the ability to do to do to do the lookup.
And then by the way, the other thing you need, which that you you also need selective disclosure.
Um, so you need to be able to do proof of human without reviewing all the underlying information.
By the way, another thing you're gonna need, you're gonna need proof of age, right?
Because there's all these laws in all these different countries now around you need to be 13 or 16 or 18 or whatever to do different things.
And so you're gonna you're gonna need a you know, sort of validated proof of age, um, you know, to be able to legally operate, right?
And so that that's coming, and then you're gonna want like proof of credit score and you know, proof of like, you know, a hundred other things.
That's a tricky one.
It is a tricky one, but you're gonna you're gonna there's no reason like if somebody's checking on your credit, somebody shouldn't put this give you an example.
Somebody shouldn't need to know your name in order to be able to find out whether you're credit worthy, right?
I see independently verifiable inf pieces of information, pieces of information, select selectively disclosed, and this is the answer to the privacy problem at large, which is I I only need to prove what I need to prove at that moment.
So like you're gonna need that.
And I I think their their their architecture makes sense.
So that needs to get solved.
I think language models have tipped the bots are now too good.
Uh and and so they're undetectable.
And so as a consequence, you we now need to go confront that problem directly.
And then like I said, and then the other problem is we we need to go actually confront the drum problems.
The Ukraine conflict has really unlocked a lot of thinking on that.
And now the um and now the the the Iran situation is also unlocking that.
And so I think there's gonna be just like this incredible explosion of of both drone and counter drones.
Our drones are better than their drones.
Yeah.
And counter encounter drones.
I think we're gonna sneak in one more question.
Um I'm trying to tie together a lot of things that you said over the year.
So at the Milken Institute debate with Teal, which is amazing.
Um, you talked about the lag between a new technology and kind of like the GDP um impact of it.
The other idea you talked about is bourgeois capitalism and how you know it's kind of managerial class what's needed because of this complexity.
And I think if you bring the I into the fold, you have like much higher leverage of people.
So, like if you have, you know, the Musk industries, um, and you give Elon a GI, you can run a lot more things uh at once.
That's right.
And then you have the social contract, and I know you retweeted a clip of Sam Almond saying, um we're rethinking the whole thing, and you're like, absolutely not.
Yes, understand uh and I will I was at an event with Sam last night, uh, and he actually said in the last couple of weeks, if how like now people are taking that seriously.
Yeah.
So I'm just curious like how you're seeing uh the structure of organization changing, especially when you invest in early stage companies.
And um, yeah, just like how the impact of work structure and uh all of that is playing out.
Yeah.
So there's a whole bunch of there's a whole bunch of time I know, yeah.
We could spend, I mean, by the way, we'd be happy to spend more time, but we could we could spend more time on all that.
So just so for people who haven't followed this, so the this this this term managerial comes from this thinker in the 20th century, James Burnham, who um just one of the great kind of 20th century political thinkers, uh, societal thinkers.
And he sort of said, as as and he was writing in like the 1940s, 1950s.
Um, and he said kind of the the whole history of capitalism up until that point had been in two phases.
Number one had been what he called bourgeois capitalism, which was think about it as like name on the door, like Ford Motor Company, because Henry Ford runs the company.
Um and Henry, it's like a dictate dictatorial model, and Henry Ford just like tells everybody what to do.
And he said the problem with bourgeois capitalism is it doesn't scale because Henry Ford can only tell so many people to do so many things, and then he runs out of time in the day.
And so um he said the second phase of capitalism was what he called managerial capitalism, which was the creation of a professional class of managers that are trained not to be like car experts or to be whatever experts in any particular field, but are trained to be experts in management.
And then that led to you know the importance of like Harvard business, you know, business schools and management consulting firms and all these things.
And then you look at every big company today, and like most of the executives and most of the Fortune 500 companies are not domain experts in whatever the company does, and they're certainly not the founders of those companies, but they're professional managers.
And in fact, in the course of their careers, they'll probably manage many different kinds of businesses.
They'll rotate around and they might work in healthcare for a while and then work in financial services and then go work in something else, you know, come work in tech.
And what Burnham said is he said that transition is absolutely required because the the the problem with bourgeois capitalism is it doesn't scale.
Henry Ford doesn't scale.
And so if you're gonna run capitalist enterprises that are gonna have millions to billions of customers, um, you're gonna need to you they're gonna be operating a level of scale and complexity that's gonna require this professional management class.
And he said, look, the professional management class has its downsides.
Like they're not necessarily experts at doing the thing, they're not as inventive, you know, they're not gonna create the next breakthrough thing.
But he's like, whether you think that's good or bad or whatever, is what's gonna be required.
And basically that's what happened, right?
And so uh he wrote that book originally in like 1940.
You know, over the course of the next 50 years, basically managerialism, well, I mean, today, up till today, managerial managerialism basically took over everything.
And you know, what I'm describing is basically how all big companies run and how all governments run and how our large-scale nonprofits run and kind of everything, you know, everything runs.
Basically, what what venture capital does is we basically are a rump uh sort of protest movement to that to try to find the next Henry Ford, or just to say Elon Musk, or the or the next, or the next Elon Musk, or the next Steve Jobs, the next Bill Gates, the next Mark Zuckerberg.
And so we we we we start these companies in the old model, right?
We we we start them out as as as as like in the Henry Ford model.
And so we start them out with a founder or a or a or a founder with with colleagues, but you know, there's the founder CEO.
Um, and then we basically bet that we basically bet that the startup is going to be able to do things specifically innovate in ways that the big incumbents in that industry are not gonna be able to do.
And so it's a bet that by basically by relighting this sort of name on the door, you know, kind of thing, this new innovative thing with like a king monarchical uh political structure, um, that they're gonna be able to innovate in a way that the incumbent is not going to be able to because the incumbent is is being run by managers, right?
And and and and by the way, and of course, venture being what it is, sometimes that works, sometimes it doesn't, but but we're constantly doing that.
But I've always viewed it my entire life as like we're like raging against the dying of the light.
Like we're we're we're we're sort of constantly trying to fight off managerialism, just basically swapping everything and everything getting basically boring and gray and dumb and old, right?
And we're trying to keep some level of energy vitality in the system.
AI is the thing that would lead you to think, wow, maybe there's a third model, right?
And and maybe maybe and way to think about it would be maybe it's a combination of the two.
Maybe the new Henry Ford or the new Elon or the new C jobs plus AI is the best of both, right?
Because it's it's it's sort of the spark of genius of the name on the door model, the Henry Ford model, but then it's give that person AI superpowers to do all the managerial stuff and let the boss drill the managerial stuff.
That may be the actual secret formula, and we've never even known that we wanted this because we never even thought it was a possibility.
But yeah, I mean, you know this.
That what is the thing that these bots are really good at?
They're really good at doing paperwork.
Like they're really good at filling out forms, like they're really good at writing reports.
They're really good at reading.
They're really good at doing all the managerial work.
Like they're amazing at it.
And so, yeah, so I I think I think the I 100%, I think the answer, the answer very well might be to get the best best of both worlds by doing this.
And then the challenge is gonna be twofold.
The challenge is gonna be for the innovators to really figure out how to leverage AI to actually do this, right?
Um, and then and then the uh the other challenge is gonna be for the for the incumbents that are managerial to figure out like, okay, what does that mean?
Because now they're gonna they're they're gonna be facing a different kind of insurgent competitor that has a different set of capabilities than they're used to.
And so that the the it this really, I think is going to force a lot of big companies to kind of figure out innovation.
Either I say figure out innovation or die trying.
Do you feel like that structure accelerates the impact on the actual GDPN economy?
If you look at SpaceX, it's like the growth is like so fast.
And like instead of having these companies kind of like peter out in growth and impact, they can kind of like keep going if not accelerating.
That's for sure the hope.
Um, the the challenge, and and you know, and look, the AI utopian view is of course, of course, and and and that's gonna be the future of the economy, and it's gonna grow 10x and a 100x and a thousand X, and we're gonna enter in this regime of like much higher economic growth forever and consumer cornucopia of everything.
And it's it's gonna be great.
And I and I hope that's true.
I hope that's that's like the you you know, that's the current kind of utopian vision.
I hope that's true.
The problem is goes back again, the real world is really messy.
Um, and I'll give you an example of how the real world is really messy.
It requires 900 hours of professional certification training to become a hairdresser in the state of California.
Um, so it's like 35% of the economy, something like that, you have to get some sort of professional certification to do the job, which is to say that the the professions are all cartels, right?
And so you have to get licensed as a doctor, you have to get licensed as a lawyer, you have to get licensed as a you have to get into a union.
Um, by the way, to to work for the government, you need to be you you have both civil service protections and you have public sector unions.
You have two layers of insulation uh against ever getting fired for anything or anything, anything ever changing.
I'll give you another example.
The the dock work, the dock workers went on strike a couple of years ago because they're you know, robot robotics.
You know, if you if you go look at a modern dock like in Asia, it's all robots if you go to the American dock it's like all still guys dragging strike dragging stuff by by hand.
The dock workers went on a strike it turns out there are 25,000 dock workers working on uh on docs in America it turns out they have incredible political power because it's a it's what it's one of these unified blocks of things they won their strike and so they got commitments from the dock owners to not implement more automation.
We learned a couple of things in that so number one we learned that even a union the smallest 2500 people still has like tremendous political stroke.
We also learned that they it actually turns out the dock workers union has 5000 people in it because there's 20 they have 2500 people working at docks they have 2500 people during full paychecks sitting at home from prior union agreements from prior union agreements.
I'll give you another great example there are government agencies there are federal government agencies where the employees right of have civil service protections and they're in public sector unions there are entire federal government agencies that struck new collective bargaining agreements during COVID where not only are they have their jobs guaranteed in perpetuity but they only have to report to work in an office one day per month.
And so there are entire office buildings in Washington DC that are empty 29 out of 30 days of the year that are still operating and are still we're all still paying for it.
And so and then what they do, it turns out what the employees do is they're very they're very smart in the in in this way.
And so they figure out they come in on the last day of a month and then the first day the next month and so and so they're so they're in there, they're in the office two days per 60 days, which means these buildings are empty for 58 days at a time.
And you see what I'm saying, you see where I'm heading with this.
Like this is like locked in, right?
This is like locked in in a way that has nothing to do with like and people say capitalists it's like anti-capitalistic.
It's like it's it's basically it's restrictions on trade, it's restrictions on the ability to like change the workforce.
And so much of our economy is is, you know, the the I'm I'm describing the entire healthcare system.
I'm describing the entire legal profession.
I'm describing the entire housing industry.
I'm describing the entire education system, right?
K through 12 schools in the United States, they're a literal government monopoly.
How are we gonna apply AI in education?
The answer is we're not, because it's a literal government monopoly, it is never going to change the end, and there is nothing to do.
By the way, you can create an entirely new school system.
Like that's the one thing you can do, is you can do what alpha school is doing.
You can create an entirely new school system.
Other than that, you're not going to go in and change what's happening in the American classroom like K through 12.
There's no chance.
The teachers are 100% opposed to it, it's 100% not gonna happen.
So you see what I'm saying, is like that there's this like massive slippage that's gonna take place.
Both the AI utopians and the AI doomers are far too optimistic, right?
You see what I'm saying?
Because they believe that because the technology makes something possible that 8 billion people all of a sudden are going to change how they behave, and it's just like no.
So much of how the existing economy works is just it's just like wired in.
And so we're gonna be lucky as a society, we're gonna be lucky if AI adoption happens quickly.
Right.
Because if it doesn't, what we're just gonna have is stagnation.
Also, Mark, I know you got to run.
Yeah, well no, or still welcome, but uh, it was such a pleasure talking to you.
Uh, we're truly living in an age of science fiction coming to your life.
Yes, yes.
Could not be more exciting.
Yeah, really thank you, Mark you guys.
Awesome.
Thank you.
Good, thank you.
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