This episode originally aired on the Latent Space podcast.
Marc 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 also makes the case that the combination 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 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 you know, 70 years or that was controversial.
The way I think about what's happening.
I think about the period we're in right now.
I call it 80-year overnight success.
It's an overnight success because it's like bam ChatGPT hits and then O1 hits and then OpenClaw hits.
These are radical, overnight transformative hits 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 100, 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 Latent Space Podcast.
This is Alessio, founder of Kernel Labs, and I'm joined by Squeaks, editor of Latent Space.
Hello, and we're in A16Z with A. Mark and Jason, welcome.
Yes.
Yes.
A and what?
Half of 16?
A1.
Exactly.
Apparently, this is the final few days in your current office.
You're moving across the road.
Uh we're, we have a limit of some.
We have some projects underway, but yeah, this 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.
It's beautiful.
Yeah.
Great.
Thank you.
So I have to come out at, 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, 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.
That was in October 2022.
Rune says there was an internal meeting in A16Z to reorient around Gen AI.
Obviously, you had, but was there a meeting?
What was that?
I mean, look, I've been doing AI since the late 80s.
So I don't know.
As far as I'm concerned, this stuff is all Johnny-come-lately.
I mean, look, we've been doing AI our entire existence.
I mean, we've been doing AI machine learning deeply.
We've been doing this stuff way from the beginning, obviously.
AI is just core to computer science.
I actually view them as 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 old enough to remember the actual AI boom in the 1980s.
There was a, there was a big AI boom at the time.
Um and there was a, there was one under names like expert systems um, and they were of like Lisp and Lisp machines.
Um, I, I coded at Lisp.
I was coding a Lisp in 1989 when that was the language of the AI future.
Yeah, so this is something that we'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 my closest analog was 2016, 17.
There was an AI boom.
And it petered out very, very quickly.
Just in terms of investing.
Sort of, sort of.
Investment excitement.
Although that's really when the NVIDIA phenomenon really.
I would say it was in that period when it was very clear that at the time 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, 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 Alex net.
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 you know look, machine learning.
You know they were.
You know look, 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 and on the board since 2007.
And of course, you know they started using machine learning very early 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 more.
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, 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 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 that.
And then, as you guys know, there was this really weird like four year period where it's like the transformer existed.
And then it was just like, let's go.
Yeah.
Well but, but.
But between 2020, 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 open AI developed chat GPT or GPT two.
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 you guys, I'm sure, remember AI Dungeon.
There was a year where the only way for a normal person to use GPT-3 was in AI Dungeon.
And so we would do this.
You'd go in there and you'd pretend to play Dungeons & Dragons.
In reality, you're just trying to talk to GPT.
And so there was this.
You know there was this long.
You know, you know the big companies, you know big companies are cautious and you know the big companies were cautious.
By the way, it took open AI, you know, they, they, they talk about this.
It took open AI time to actually adjust, you know, kind of redirect their research path.
I think it was at Rosewood, right?
The dinner that founded open AI was right there.
Right.
But that dinner would have taken place in 2018.
The formation of OpenAI?
As late as 2018?
Sorry, no, I'm wrong.
They just celebrated a 10-year anniversary, so it is 2025.
So 2015.
Yeah, 2015.
But then Alec Radford did GPT-1 in, what, probably?
17, 18.
17, 18.
And then GPT-3 was, what, 2020?
2020.
Because that became co-pilot immediately, yeah.
Even OpenAI, which has been the leader of this thing in the last decade, even they had to adapt and lean into the new thing.
And so-
Yeah, 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 whole thing pops.
And obviously that's what's happening now.
Is it useful to think about, will there be any winter?
Because there's always these patterns.
Like, is this end of 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 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 guys know this, but it's summer winter summer winter summer winter, and it goes back 80 years, 80 years.
So the original neural network paper was 1943, which is amazing that it was far back that long.
And then there was if you guys have ever talked about this on your show, but there was an AGI conference at Dartmouth University in 1955.
Yeah.
And they got an NSF grant for 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 of the other end.
And by the way, they got the grant, they got the 10 weeks, and then, you know, 1955, no AGI.
And like I said, I lived through the 80s version of this where there was a big boom and a crash.
And so there is this thing.
There is something about AI that causes the people in the field, I would say, to become both excessively utopian and excessively apocalyptic.
And it's probably on both sides of like the boom-bust cycle, you kind of see that play out.
Having said that, I think what's actually happened is like just you know, 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 you know, 70 years or that was controversial.
And we now know that that's the case.
And so we now.
You know, everything we're building on today sort of derives from the original idea in 1943.
In retrospect we now know that these guys would get the timing wrong and they thought capabilities would arrive faster or it could be turned into businesses sooner, or whatever.
The scientists who worked on this over the course of decades were fundamentally correct about what they were doing.
The payoff from all their work is happening now.
And so 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.
Cause it's like bam, you know chat GPT hits, and then, and then, Oh one hits.
And then you know open call hits.
And, like you know, these are open, these are.
These are like overnight, like radical overnight, transformative hits successes, but they're drawing on an 80 year sort of wellspring backlog, you know of, 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 and thinking.
There were AI researchers who spent their entire lives.
They got their PhD.
They've researched for 40 years.
They retired.
In a lot of cases, they passed away and they never actually saw it at work.
Yeah.
So sad.
It is sad.
I think Jeff Hinton was like the last guy.
Yeah.
Yeah.
Well, there were guys, Alan Newell.
I mean, there's tons of John McCarthy.
John McCarthy was like one of the inventors of the field.
He's one of the guys that 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 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, say, 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, bust cycle.
And 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.
Yeah.
The four most dangerous words in investing are this time is different.
Do you know the 12 most dangerous words in investing?
No.
The four most dangerous words in investing are this time is different.
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 LLM's, 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 going to be great for creative writing and creating.
You know Shakespearean sonnets and you know as as rap lyrics or whatever, like it's gonna be great at all that stuff.
But we're not going to be able to harness this to make this relevant in you know coding, or in medicine or in law or in 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 going to be able to actually turn this into something that's going to work in the real world.
And then obviously the coding breakthrough, or basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.
We were just like all right if, if you know, if Linus Torvalds is saying that the AI coding is not better than he is, like that's, that's never happened before.
That's the benchmark.
That's never happened before.
And so now we know that it's, it's going to sweep through coding.
And then, and then we, we know, you know, we know that if it's going to work in coding, it's going to work in everything else.
Right.
It's just that, because that's that's like, that's like, That's like the hardest.
In many ways, that's the hardest example and how everything else is going to be a derivative of that.
And then, on top of that, we just got the agent breakthrough with OpenClaw, which is fantastic, which is amazing and incredibly powerful.
And then we just got the auto research, the self-improvement.
We're now into the self-improvement breakthrough.
And so the way I think about it is, we've had four fundamental breakthroughs in functionality LLMs, reasoning agents.
And then, and then now RSI and they're all actually working.
And so I'm, I'm just, I'm jumping out of my shoes.
Like this is like, this is it.
Like this is the culmination of 80 years worth of worth of work.
And this is the time it's becoming real.
Yeah.
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 so jagged in like the jumps, right?
Like you said, it's like in three months, you have like this huge jump.
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 worth 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 leading space?
And how does that inform how you think about the shape of the technology?
You talk about how it's a new computing platform.
If you have a computing platform, then every six months it drastically changes in what it looks like.
It's hard to build companies on top of it.
Yeah, so a couple of things.
So one is like, look, Moore's law was what we now call a scaling law.
Like Moore's law was a scaling law.
And for your younger viewers.
Moore's law was every chip chips either get twice as powerful or twice as cheap every every 18 months.
It's gotten more complicated in the last few years, but that was the 50-year trajectory of the computer industry.
By the way, that's what took the mainframe computer from a 25 million current dollar thing into the phone in your pocket, being a million times more powerful than that for 500.
And so that was a scaling law.
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 predictions.
But when they work they become self-fulfilling predictions because 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 kind of motivate the breakthroughs that are required to keep that going.
And in chips, that was a 50-year run, right?
And it was amazing.
And it's still happening in some areas of chips.
I think the same thing is happening with the core scaling laws in AI.
They're not really laws, but they are basically predictions, and then they're motivating catalysts for the research work that is required to be.
And by the way, also the investment dollars required to basically keep the curves going.
And look, it's going to be complicated and it's going to be variable and they're, you know, they're going to be walls that are going to look like they're fast approaching, and then they're going to be.
You know, engineers are going to get to work and they're going to 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 going to be times when it looks like the walls have, you know, the, the, the laws have petered out and then they're going to, they're going to pick up again and surge.
And then, and then, and then it appears what's happening to the eyes.
There's not multiple, you know, multiple scaling laws.
There's multiple areas of improvement.
I don't know how many more there are yet to be discovered, but there are probably some more that we don't know about yet.
For example, there's probably some scaling law around world models and robotics that we don't fully understand.
Acquisition of data at scale in the real world that we don't fully understand yet.
That one will probably kick in at some point here.
There's a bunch of really smart people working on that.
I think the expectation is that these scaling laws generally are going to continue.
The pace of improvement will continue to move really fast.
To your question on what to build, 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, leaps and bounds.
The part where I part ways a little bit with what I would describe as the AI purists, which I would characterize as the people who are, in many ways, the smartest people in the field, but also the people who spend their entire life in a lab.
And I would say have very little experience in the outside world.
The nuance I would offer is the outside world of 8 billion people and institutions and governments and companies and economic systems and social systems is really complicated and doesn't change.
8 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 were just like well, there's this 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, 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 8 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 react to change.
And then, you know, it just like, it's just human reality is just really complicated and messy.
And so the specific answer to your question is like, as usual, it depends.
You know, it depends.
Look, there's no question people are going to like, there's no question they're going to be companies.
It's already happening.
There are companies that think that they're building value on top of the models and they're just going to get blessed 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 messy world of humanity is just going to be messy and complicated.
It's not going to be simple and straightforward.
It's going to be messy and complicated.
And there are going to be a lot of companies and a lot of products and, in fact, entire industries that are going to get built to basically actually help humanity.
All of this technology actually reach real people.
The amount of capital going into these companies.
I mean, Dario talked about it on the DoorCash podcast and DoorCash was like why don't you just buy 10x more GPUs?
And he's like because I'm going to go bankrupt if the model doesn't exactly hit the performance level.
How do you think about that also as a risk on?
You know you guys are investors in open AI and thinking 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 downside scenario?
Like and say, like things, Peter out, you think you can kind of like restructure these build-outs and uh, you know, capital investments.
I lived through the dot-com crash.
I can tell you stories for hours about the dot-com crash.
It was horrible.
It was awful.
It was apocalyptic.
By the way, at the time, a lot of the dot-com crash was actually a telecom crash.
It was a bandwidth crash.
The thing that actually crashed that wiped out all the money was the telecom companies.
Global Crossing.
I'm from Singapore.
They laid so much cable over our oceans.
Actually, there was a scaling law in the dot-com era.
The U.S.
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 was going to keep doubling every quarter.
Doubling every quarter, though, is like grains of chess on the chessboard.
At some point, the numbers become extremely large.
And really what happened was the internet, by the way, continuously kept growing, basically since inception.
It's continuously grown.
It's never shrunk.
And it's grown really fast compared to anything else 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 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 overbuild.
And so you had that, but it was, it was literally, I think it was like $2 trillion 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 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, OK, you have a highly levered thing where you're just you're overbuilding capacity.
Demand is growing, but not as fast as you hoped.
And then boom, bankrupt.
Right.
And then, 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 to wash out all of the over-optimistic exuberance before it gets to actually a stable state, and then it makes money.
By the way, all of those data centers and all the fiber that they're in use.
It's all in use today, but 25 years later.
But actually, the elapsed time was it took 15 years.
It took 15 years from 2000 to 2015 to actually fill up all that capacity.
The cautionary warning is the overbuild can happen.
And you get into this thing where basically everybody who basically has any sort of institutional capital is like wow, it's just, I don't know how to invest in these crazy software things.
But for sure I can build data centers and for sure I can buy GPUs and I can deploy compute grids and all these things.
And so if you're a pessimist, you can look at this and you can say wow, this is 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 counter on the other side, is a couple of 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 in the, in the doc, like global crossing was like, it was like an entrepreneur.
It was like a new venture but like the money that's being deployed now at scale as Microsoft and you know an Amazon and Google and Facebook and Nvidia, and you know these these, these and now you know, by the way, open AI 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 lots of cash, lots of debt capacity that they've never used.
This is institutional in a way that that really wasn't at the time.
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 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 it, And in fact, I actually think there's an interesting thing happening, which is because everybody's 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.
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 training and you'd just build better models and they would be better.
And so we're actually getting the sandbag version of the technology.
No, everything we use is quantized because the labs have to keep the full versions.
We're not even getting the good stuff.
But getting the good stuff is 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 going to get, going to 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 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 incredibly well.
Here's what I know.
In the next three or four years.
It's like somewhere between three or four years out.
Basically everything is selling out.
So the entire supply chain is sold out or selling out.
And so there's no...
We're just going to have a chronic supply shortage for years to come.
There's going to be a response from the market that's going to result in an enormous flood of investment in a new fab capacity and 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.
And so I know that's going to happen.
I know that, you know, the deployments, you know, 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 going to get like much, much better from here.
I know the capabilities are really real and serious.
I also know that the technical progress is not going to stop.
It is accelerating.
The breakthroughs are tremendous.
Even just month over month, the breakthroughs are really dramatic.
I think if you were a cynic and there are cynics you can look at 2000.
You can find echoes.
But I can't even imagine betting that this is going to somehow disappoint in at least four years to come.
I think it would be essentially suicidal to make that bet.
Um, it was at Michael Burry.
Uh, that's an interesting guy.
We'll pick on a guy.
We'll pick, let's pick on one guy.
Well, cause he did, he came out with it.
It was, it was, he doesn't mind.
It was the Nvidia short, right?
He came out with the Nvidia short.
And then you guys probably talked about this, which is the analysis now that the current models are getting better faster, at such a rate that 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 faster than the depreciation cycle of the chip.
And then my understanding is Google is running I don't know exactly what these are rumors that I've heard, or maybe it's public but I think Google's running very old TPUs very profitably.
It actually turns out, as far as I can tell, it's actually the opposite of the Burry thesis.
He was actually 180 degrees wrong.
The old NVIDIA chips are getting more valuable, which is something that's literally never happened before.
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 the just 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 of betting against that like it's like an invitation to get your face ripped off.
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 haircut 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 even like the, the shittier versions of memory that we do have we are finding use cases for it.
So like, that's great.
Yeah.
How important is open source AI and edge inference in a world in which you have three years of supply crunch?
If you fast forward five years, how do you think about inference in the data center versus at the edge?
So just to start, yeah, so I think open source is very important for a bunch of reasons.
I think edge inference is very important for a bunch of reasons.
I think, just practically speaking, if we're just going to have fundamental supply crunches for the next, I mean, 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 going to happen to the cost of inference in the core over the next three years.
It may rise dramatically.
The big model companies are subsidizing heavily right now.
What's the, what will be the average person's, you know, per day, per month, token costs.
You know, three years from now, to do all the things that they want to do.
And I don't know, it's going to be, I mean, I have, you guys probably have friends.
I have friends today who are paying a thousand dollars a day for open clock for cloud tokens to run open clock.
Right.
And so, OK, $30,000 a month.
Right.
And, by the way, 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's like latent demand of up to I don't know, five or ten thousand dollars a day of tokens for a fully deployed personal agent.
Obviously, consumers can't pay that, but it gives you a sense of the future scope of demand.
Even if there's a 10X improvement in price performance, that still goes to 100 a day, which is still way beyond what people can pay.
So there's just going to 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 have been GPU constraints.
I think the agent thing now also translates into CPU constraints, right?
CPU and memory, yes.
CPU and memory, right?
And so the entire chip ecosystem is just going to get With the network constraints.
That would be the killer.
It's all bottlenecked, and potentially for years.
And so I think it's actually possible.
I mean generally, inference costs are going to keep coming down, but I think, 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, again, will be an issue.
And so there's just going to be so much more demand for inference than than can be satisfied.
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 inferences is quite amazing.
A level of effort being put, like the open source guys are putting incredible effort into getting you know this recurring pattern where the big model will never run on a PC and then six months later 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, there's 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's all the trust issues.
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.
You know you want.
You know you're going to want your doorknob to have an AI model in it.
You know to be able to.
You know, do you know to be able to do access control.
Obviously like everything with a chip is going to have an AI model in it.
And a lot of those are going to be local.
And so yeah no, like I think, I think you're going to have, And then, by the way, also wearable devices.
You know you don't want to do a complete round trip.
You want, you know, whatever your smart devices are, you want it to be like super low latency.
The question, 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.
And...
I'm not that optimistic on American open source.
Like you guys invested in Mistral and Mistral is doing extremely well outside of China.
That's about it.
Yeah, we'll see.
We'll see.
Number one, I do think we care who makes it.
I would say this.
The previous presidential administration wanted to kill it in the U.S.
They wanted to drown in the bathtub.
And so they wanted to kill it.
So at least we have a government now that actually wants it to happen.
And you're on the council.
And the new PCAST, yeah.
So, for whatever other political issues people have, which are many, this administration has, I think, a very enlightened view and in particular, an enlightened view on AI and in particular, on open source AI.
And so they're very supportive.
My read is the various Chinese companies have a very specific reason to do open source, which is fundamentally they don't think they can sell commercial AI outside of China right now, or at least specifically not in the US, for a combination of reasons.
And so they, they kind of view.
I think open source AI is a bit of a loss leader against basically domestic, you know paid paid services and then kind of you know kind of ancillary products, you know they're, they're very excited about it.
By the way, I think it's great.
I think it's great that they're doing it.
You know, I think deep seek was like a gift to the world.
I think, The great thing about open source, open source, the impact of open source is felt two ways.
One is 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 the code 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, 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 a secret sauce in there?
And then our one 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 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.
There are a bunch of American open source AI model companies.
I mean, look, there's going to be tremendous competition among the primary model companies.
Depending on how you count, there's like four or five big co model companies now that are, you know, kind of neck and neck uh, in different ways.
Um uh, you know, and and um, you know, and then obviously both, both X and then meta, where I'm involved are, you know, both have huge, you know, huge attempts to, you know, to kind of leapfrog 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 come up with different approaches?
And then you've got whatever it is.
I don't know how it.
How many mainline foundation model companies are there in China at this point?
It's probably six.
It's Five Tigers, is what they call it.
Quinn is questionable because there's change in leadership.
But does that include Moonshot?
Yes.
Deep Seek, ZAI, Gwen01 is in there.
Right.
And then ByteDance.
ByteDance would be like the next tier.
They weren't as prominent.
They don't have a leading audience.
Yeah, but at least you know CDance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming, and so forth.
And so...
Look, here would be a thing you can anticipate.
Between the US and China.
Right now there's a dozen primary foundation model companies that are at scale at some level of critical mass.
It's not going to be a dozen in three years.
These industries don't bear a dozen.
There's going to be three or four big winners, or maybe one or two big winners.
And so there's going to be like a whole bunch of those guys that are going to have to figure out alternate strategies.
And I think like open source is one of those strategies.
And so I think you could see like a whole, I think the questions like who's going to do open source I think that could change really fast.
I think that that's a very dynamic thing.
I think it's very hard to predict what happens.
And I think it's very important.
NVIDIA's doing a lot.
Well, I was going to say, well, exactly.
And then you've got NVIDIA, and then just to get an industrial, there's an old thing in business strategy which is called commoditize the complement.
And so if your Jensen is just kind of obvious, of course you want to commoditize the software.
And to his enormous credit, he's putting enormous resources behind that.
And so maybe it's literally NVIDIA, and I think that would be great.
Yeah.
Narrative violation to European projects.
Damn.
I'm hosting my Europe conference soon and I got both of them.
They got us.
They got us, Mark.
Wait a minute.
Where was Peter?
So where was Steinberger when he did it?
He was in Vienna.
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 pie guy, right.
The pie guys are European.
Yeah.
Their buddy is in Australia.
Mario is also there.
Right.
And are they, yeah, they haven't announced yet any sort of change changed or have they?
No, they're, they have a company there.
Okay.
Okay.
Good.
Yeah.
Um, yeah.
Anyways, I think Pi and OpenClaw are very important software things.
And I just wanted you to just go off on what you think.
Yeah.
So I think the combination of the two of them, I think, is one of the 10 most important software.
OpenClaw got all the attention, but talk about Pi.
Pi is kind of the idea.
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 from 1970 through to like basically, the creation of Linux, which is basically this thing we used to call like, the Unix mindset.
Like so, because there were all these different you know theories, all these different operating systems and mainframes, and then you know all these Windows and Mac and all these things.
And then there was this 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, 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 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 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, how the system worked.
And then the Unix guys originally out of ATT and then out of Berkeley, 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 going to have, we're going to have a prompt and a shell, and And then all the functionality is going to be in the form of these discrete modules.
And then you're going to be able to chain the modules together.
And so it's almost like the operating system itself is going to be a programming language.
And then that led to the sort of centrality of the shell.
And then that led to sort of, you know, basically chaining the other Unix tools.
And then that led to the emergence of these scripting languages like Perl, where you could basically kind of very easily do this.
And then the shells got more sophisticated.
And then, and then, and then looked 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's in the background.
Normal people didn't need to necessarily know about it.
But if you were doing system architecture or application development, you knew all about it.
And then it's been in the background ever since.
And look, your Mac still has a Unix shell in there and your iPhone still has a Unix shell buried in there somewhere.
So they're in there.
And then the Windows shell is a weird derivative of that.
But look, the internet runs on Unix.
And then smartphones.
Actually, both iOS and Android are Unix derivatives.
And so Unix did end up winning.
But anyway, and then we just started taking that for granted.
So basically the way I think about what happened with Pi and then with OpenClaw is basically what those guys figured out is.
I always say the great breakthroughs are obvious in retrospect right.
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, to me those are always the best breakthrough.
So 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 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 language model mindset to the Unix, basically shell prompt mindset.
And so it's basically this idea that what is an agent, right?
And, as you know, many smart people have been trying to figure out what an agent is 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.
So it's a language model, and then above that, it's a bash shell.
So it's a Unix shell.
And then the agent has access to the shell, hopefully in a sandbox, maybe in a sandbox.
So it's the model, it's the shell, and then it's a file system.
And then the state is stored in files.
And then there's the markdown format for the files themselves.
And then there's basically what in Unix is called a cron job.
There's a loop, and then there's a heartbeat.
And the thing basically 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 every part of that, other than the model, is something that we already completely know and understand.
And in fact, it turns out the latent power of the Unix shell is extraordinary.
Because basically, 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 your entire.
I mean, just to start with, your computer runs on a shell.
If you're running a Mac or a phone, your computer is running on a shell already.
And so 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 this whole idea where we need MCP and these fancy protocols, whatever it's like no, we don't.
We just need a 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 completely blew my mind when I wrap my head around it as a result of this, which is like OK, This means your agent is now actually independent of the model that it's running on, because you can actually swap out a different LLM underneath your agent.
And your agent will change personality somewhat because the model is different, but all of the state stored in the files will be retained.
MARK MIRCHANDANI- Different instruction But you just compiled it.
Right, exactly.
And it's all right.
It's like swapping out a ship and recompiling.
But it's still your agent with all of its memories and with all of its capabilities.
And then by the way, you can also swap out the shell.
So you can move it to a different execution environment that is also a bash 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 heartbeat for the cron framework, the loop, 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 there is, of course, Yeah, it's basically, it's just the files.
And then, by the way, as a consequence of that, the agent and then the agent itself.
It turns out a couple important things.
So one is it can migrate itself, right?
And so you can instruct your agent.
Migrate yourself to a different runtime environment, migrate yourself to a different file system, migrate yourself to a different.
You know like.
We 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 actually has full introspection.
It actually knows about its own files, and it can rewrite its own files.
Which, by the way, is basically no widely deployed software system in history, where the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself like 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 they 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 in 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 cloud code or whatever.
It'll write whatever it needs and 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 wanted to do that and so anyway.
So 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, This is what I would be spending all of my time on.
This is such an incredible conceptual breakthrough.
And again, people are going to look at it, and they already get this response.
People are going to look at it, and they're going to say, oh, well, where's the breakthrough?
Because all of these components were already known before.
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, for example, computer use all of a sudden just kind of falls trivially.
Of course, it's going to be able to use your computer.
It has full access to the shell.
And then you give it access to a browser and then you've got the computer and the browser 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 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 is, you know, as you guys know, there's security issues.
Yeah.
And so you know there's a bunch of stuff to be ironed out, but the unlock of capability is just incredible.
And I have absolutely no doubt that everybody in the world is going to have, at least you know, an agent like this, if not an entire family of agents.
And we're going to 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 going to say for someone who is deeply familiar with social networks the next step is your Claw talking to my Claw posting on Claw Facebook, posting their jobs on Claw LinkedIn and posting their tweets on Claw XAI or whatever.
I do think that that is how we get into some danger there in terms of alignment and whether or not we want these things to run.
You guys remember Rentahuman.com?
Yeah, Rentahuman.
I mean, it's Fiverr, it's TaskRabbit.
Sure, of course.
Mechanical Turk.
Yeah, but flipped.
Right, the agent hiring the people.
Which, of course, is going to happen.
It's obviously going to 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 fit that in.
Are there any design choices that were made like early in the browser and kind of like the internet and the protocols?
They you're seeing agents similar today?
It's like, Hey, this thing is just not going to 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, and 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 we didn't have the language models underneath this.
But, um, we did have this, the idea of that human readability actually mattered a great deal.
And so, and specifically in those days it was it was not so much English language, but it was 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, what do you know about the internet?
It's star for bandwidth.
You have these very narrow straws.
When we did the work on Mosaic, people who had the internet at home had a 14 kilobit modem.
You're trying to hyper optimize every bit of data that travels over the network.
And so obviously, if you're going to design a protocol like HTTP, you're going to want it to be binary.
You know, highly compressed binary protocol for maximum efficiency.
And you're going to want to have it be like a single connection that persists.
And the last thing you're going to want to do is like bring up and tear down new connections.
And definitely you're not going to want a text protocol.
And so, of course, we said, no, we actually want to go completely the other direction.
It's obviously we only want text protocols.
By the way, same thing in HTML itself.
We want HTML to be relatively verbose.
We want the tags to actually be human readable.
We want to use the most inefficient things possible.
Yeah, we want to do the inefficient things.
You're the original Token Max.
Yeah, exactly.
Yeah.
Yeah.
Yeah.
Basically, it's just like well yeah well actually, this was, this was actually the conscious thing which basically says just like assume, assume a future of infinite, infinite bandwidth built 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, 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 bare eyes, without having to disassemble it or whatever, have it converted out of binary.
And so all the HTTP and everything else, it was always text protocols.
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, 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 new web pages.
There was that.
So human readability, and again, human readability in those days still meant technical specs.
Now it means English language, but 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.
What was the other?
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 also the underlying latent capability of the database.
Because basically, what was a web server?
What is a web server fundamentally?
Architecturally, it's the operating system.
So 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.
And then, of course, a lot of early, you know, a lot of websites are front-end databases.
And so 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 it was.
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 and the database.
And again, people looked at it at the time, and they were like, well, does this really matter?
Is this important?
Because we've had databases forever and we've always had user interfaces for databases, and this is just another user interface for a database.
And 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 far easier to use and far more flexible.
And you're not just going to have old databases.
Now you have a system where people can actually understand why they want to build a million times more database apps than they have in the past.
And then the number of databases in the world exploded.
And so again, this goes to this thing of building in layers.
Some of the smartest people in the industry look at any new challenge and they're like OK, 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 the next thing I need to do is I need to build a new chip.
Right.
And 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 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.
And so I think the web did that for those reasons.
And I think it's the same thing now that's happening.
It's a great 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 default.
So why are we teaching the model to not write memory unsafe code?
Just use Rust and then you get it for free.
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 slow.
Python TypeScript.
You know, it's like, yeah.
As imperfect as they are, they are the lingua franca.
I mean, I think this is going to change a lot, because I don't think the models care what language they program in.
And I think they're going to be good at programming in every language.
And I think they're going to be good at translating from any language to any other language.
Okay, so this gets into the coding side of things.
I think we're going through a really fundamental change.
And look, I grew up hand coding.
Everything I did actually was written in C. Back in the day.
I wasn't even using C++ or like Java or any of this stuff.
Right.
And so everything, everything I ever did, I was like managing my own memory at the level of C.
And then I you know, I'm still from the generation that you know I knew assembly language and you know I, you know, so I could drop down and do things right on the ship.
And so 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 there, 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 X Y Z, like you're just going to wave your hand and you're going to get it.
And then, if it's, if you don't like the language is written and you just tell the thing all right, I want the right now.
I want the rest version.
By the way, computer security is about to go through the most dramatic change ever, which is number one.
Every single latent security bug is about to be exposed.
We're set up here for the computer security apocalypse for a while.
But on the other side of it now we have coding agents that can go in and actually fix all the security bugs.
And so how are you going to secure a software in the future?
You're going to tell the bot to secure it, and it's going to go through 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 going to have as much as you want.
Right.
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 simple or something, or straightforward, which is just if you want all your suffering and rest, you just tell the bot you want all your suffering and rest.
Things that used to be hard or even seem like an insurmountable mountain 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 there isn't.
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 optimal language be, and let it design it.
It's true.
Okay, here's a question.
Are you even going to have programming languages in the future?
Or are they just going to be emitting binaries?
Let's assume for a moment that humans aren't coding anymore.
Let's assume it's all bots.
What levels of intermediate abstraction do the bots even need?
Or are they just coding binary directly?
Did you see there's actually an experiment?
Somebody just did this thing where they have a language model now that actually emits model weights for a new language model.
And so will the bots literally be emitting not just coding binaries, but will they actually be emitting weights for new models directly?
And conceptually, there's no reason why they can't do both of those things.
Architecturally, both of those things seem completely possible.
Very inefficient.
You're basically very efficient.
A simulation of a simulation in a simulation inside of weights.
Yeah, very inefficient.
But like, look, LLMs are already like incredibly inefficient.
My 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 like whatever billions and billions of times more inefficient than using your pocket calculator.
But yet the payoff is so great of the general capability.
So anyway, I kind of think in 10 years I'm not sure there will even be a salient concept of a programming language in the way that we understand it today.
And, in fact, what we may be doing more and more as a form of interpretability, which is we're trying to understand why the bots have decided to structure code in the way that they have.
I mean, if you play it through, you don't need browsers then.
That's the death of the browser.
Well, so I would take it a step further, which is you may not need user interfaces.
So who is going to use software in the future?
Other bots.
Other bots.
Yeah.
You still need to, I don't know, pipe information in and out.
Really?
Well, what are you going to do then?
Are you sure?
You're just going to log off and touch grass?
Whatever you want.
Exactly.
Isn't that better?
I want software to do stuff for me.
But isn't that better?
I mean look, you know I don't look like.
You know, you know the arguments here.
You know it was not that long ago that 99 of humanity was behind a plow.
And what are people going to do if they're not plowing fields all day to grow food?
And it just turns out there's much better ways for people to spend time than plowing fields.
Dude's growing.
Exactly.
Talking to their friends.
Look, I'm not an absolutist and I'm not a utopian.
And to be clear, like 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 going to tell the thing what I need and it's going to do it.
And then, and then it's going to do it in whatever way is most optimal for it to do it.
Yeah.
Unless I tell it to do it non-optimally.
If I tell it to do it in Java or in Rust or whatever, it'll do it, I'm sure.
But if I'm just going to tell it to do it, it's going to do it in whatever way is the optimal way to do it.
And then if I need to understand how it works, I'm going to ask it to explain to me how it works.
Right.
And so it's going to be doing its own interpret.
It's going to be the engine of interpretability to explain itself.
And 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 need, not what the humans need.
Yeah.
Yeah.
Well 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 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 pie open cloud thing.
It's like Oh, I built all the software using the open AI model and I'll switch to the entropic model, but the entropic model doesn't understand the thing.
So I, it feels like there still needs to be some obstruction, um, But maybe not.
Maybe that's the lock-in that the model providers want to have.
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?
Exactly.
Okay, give me an example.
As you know, models can now reverse engineer software binaries.
Isn't it the whole thing now where people are reverse engineering Nintendo game binaries?
Yeah.
I've seen a bunch of reports like this, where somebody has a favorite game from the 1980s and the source code is long dead, but they have a binary burned into a chip or something and now they're reverse engineering to get a version that runs on their Mac.
Right.
And so if you reverse it, 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.
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, but with enormous, but it was just always like cost and labor prohibitive reverse engineering.
I learned how to reverse engineering. human beings can reverse engineer binaries.
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.
You get these things, or another way to think about it, is so much of human.
Built systems are to compensate for the human limitations.
Right.
And if you don't have the human limitations anymore, then all of a sudden you have, And it's not that you won't have abstractions, but you'll have a different kind of abstraction.
I have two topics to bring us to a close and you can pick whichever one.
So just talking about protocols, was it you or someone else?
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.
Was that you?
Yes.
It was a 402, 402 payment required.
We have a chance now.
I don't think we're going to figure it out.
I don't know.
Like, what's your take?
Oh, I think we will.
Yeah.
No, now I think it's going to happen for sure.
Yeah.
And there's two reasons it's going to happen for sure.
One is we actually have internet native money now in the form of stable coins and crypto.
And this is I think this is the grand unification basically, of AI and crypto is what's about to happen now.
I think AI is the crypto killer app, I think is where this is really going to come out.
And then the other is I think it's now obvious.
It's like obviously AI agents are going to need money.
And it's already happening, right?
If you've got a claw and you want it to buy things for you, you have to give it money in some form.
I would say the adoption is probably like 0.1% if that, but yeah.
Oh, today.
Yeah, yeah, yeah.
But think forward.
Like, where is it going?
Forward thinking.
The ultimate principle of everything and everything that I think we do, is the William Gibson quote, which is the future is already here.
It just isn't distributed.
It isn't distributed yet.
My friends who are the most aggressive users of OpenClaw just like, have given their clause bank accounts.
And and and 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 5000 or something.
But that's how these things start.
Actually, I mean, since you keep mentioning it.
And, by the way, OpenClaw.
By the way, if you don't give it a bank account, it's just going to break into your court.
It's going to break into your bank account anyway and take your money.
So you might as well do it.
You might as well do it.
By the way, I really love, I got to tell you, I really love the phenomenon.
I love the YOLO.
I'm not doing it myself, to be clear, but I love the people that are just like, what is it, Skype?
Skip dangerously.
Which by the way, it's a Facebook thing.
Okay.
Because in Facebook they have this culture to name the thing dangerous so that you are aware when you enable the flag that you are opting into a dangerous thing.
Okay, good.
They brought it into OpenAI.
And of course, that makes it enticing.
Sam runs Codex with skip permissions on his laptop.
Yes, 100%.
And so I think the way to actually see the future is to find the people who are doing that.
There's a .
Log everything, you know, just watch it and 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, yeah, let it try everything.
Let it unlock everything.
By the way, that's how you're going to find all the good stuff it can do.
By the way, that's also how you're going to find all the flaws.
I think the people who turn that on for bots are like.
They're like martyrs to the progress of human civilization.
Like I feel very bad for their descendants that their bank accounts are going to get looted by their bots in first like 20 minutes, but i think the contribution that they're making to the future of our species is amazing.
It's like gentleman science.
Yes it's yes yes, it's uh ben franklin out with the trying to trying to get lightning to strike his uh, his balloon and seeing if he gets electrocuted.
Yeah, It's Jonas Salk with the polio vaccine.
So yes, I think we should have flags and we should have monuments to the people that just let OpenCloud run their lives.
More anecdotes.
What are the craziest or interesting things that people listening to this should go home and do?
I mean, this is, this is the, this is the extreme thing.
It's just like the straight Yolo.
Like just, yeah.
Turn your life.
That's a general capability.
Like a specific story that was like, wow.
And everyone in the group chat just lit up.
I mean, like you know.
So there's tons of there's already tons of health.
You know, there's the health dashboard stuff.
It's just, it's just absolutely, absolutely amazing.
The number of stories on.
I just don't want to violate people's.
You know, obviously personal but um, you know, one of the things open clubs are really good at is hacking into all this stuff in your land.
It's really good.
So, you know, internet of things, AKA internet of shit, like super insecure, but great.
It's discoverable.
It's discoverable.
OpenClaw is happy to scan your network, identify all the things.
And then my friends who are most aggressive at this are having OpenClaw take over everything in their house.
It takes over their security cameras.
It takes over 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 in a loop.
I have it wake up frequently and have it watch it and just tell it, watch me sleep.
And I've seen the transcripts and it's literally like Joe's asleep.
This is good.
This is good that Joe's asleep.
Cause 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 around sleep.
Joe's moving.
Joe's moving.
Joe might be waking up.
This is a real problem.
If Joe wakes up now, he's going to 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.
Monitoring the situation.
And, and being a bot, like, you know, it's just like very focused, right?
It's just like, ah, this is like, it's reason for existence is to watch Joe sleep.
And then and I was talking to my friend who did this it's 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, maybe this has taken 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.
What else?
It's a company, Unitree, that makes the robot dogs.
And I actually have one at home, which is actually really fun with the Chinese companies.
The Chinese companies are so aggressive at adopting new technology, but they don't always take the time to really package it and maybe think it all the way through.
At least the Unitary dog I have.
It has an old non-LLM control system, which, by the way, is not very good.
It markets well, but in practice, it's not that good.
It has trouble with stairs and so forth, so it's not quite what it should be.
But then the language model thing comes out in the voice.
So they add LLM capability, and then they add a voice mode to it.
But that LLM capability is not at all connected to the control system.
So you've got this schizophrenic dog that is a complete idiot when it comes to climbing the stairs, but it will happily teach you quantum mechanics in a plummy English accent. it's just like absolutely amazing intelligence yeah yeah talk about dragon and then now obviously what's going to happen in the future is they're going to 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 yeah write new firmware for the for 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, you said it's completely different.
He said it's a complete transformation.
Whenever there's an issue in the thing now, the claw just rewrites the code.
It kind of goes to your thing here.
So all of a sudden, this is where we want to think about AI coding.
AI coding is not just 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 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 the premise.
And so you have to decide if you want this.
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 and all works together and it's all coherent and the whole thing.
And to have that unlock without a human being having to go do any of that work.
Like you know, It's like I'm waiting for a sorry mark.
I can't let you open that fridge door.
Exactly, exactly.
Yes, yes.
Because you're not supposed to eat right now.
I have all of, yes, I have every shred of health information.
You know, and I know you think you're doing, you know, dah, dah, dah.
And I think you do this, but you know, 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.
Open the fridge doors.
Exactly.
And by the way, I know you're supposed to be studying for a test.
So why don't we, why don't you go when you can pass the test, I will open the fridge door for you.
Yeah.
Final protocol, and then we can wrap up.
Proof of human.
Yes.
Right?
Yeah.
That's the last piece that we got to figure out.
Yeah.
So I would say there's two massive, I would say, sort of asymmetries in the world right now, where we've known these asymmetries exist and we societally have been unwilling to grapple with them.
And I think they're both tipping right now.
And they're they're they're, they're the same thing as virtual world versions.
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.
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, this is my spicy take was these two are the same thing, and corporations are people too, you know.
Interesting.
Yeah.
Yeah.
Okay.
So a bank account is proof of human.
Yeah.
Okay.
Yeah.
Until you, until you give the bots bank accounts.
Yeah, exactly.
So, okay.
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 problem, right?
And so we've known for you know, we've known for 20 years now that the asymmetric threat, both in military, in actual military conflict, but also in just like security, like you know, security on the home front, the big threat is the cheap attack drone right.
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, you know, in the world, is like unprotected from drone attacks.
Um, every, every stadium, every school, every prison, like it's like, yeah, Okay, we've known that.
We've never done anything about it.
One possibility is just leave them unprotected forever and live in a world of 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 Personal force fields.
And in both cases, 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 somebody a bot.
It's very cheap to field a drone.
It's very expensive to defend against a drone.
But you see what I'm saying?
It's the virtual version of the problem, and it's the physical version of the problem.
The virtual version of the problem, what we need quite literally is proof of human.
The reason is because you're not going to have proof of bot, especially now that the bots are too good.
The bots can pass the Turing test.
And if the bots can pass the Turing test, then 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 cryptographically validated.
This is definitely a person. and then you can have cryptographic invalidator.
This is definitely like something that a person said.
This video is real, right?
Just to double click on.
Do you think Alex Blania with Worlds?
Do you think he's got it or is there an alternative?
Oh, so I mean, there's going to be, I think there'll be, I think many people will try.
We're one of the key participants in the world, in the world project.
So we're partisans, but yeah, I think, so we think world is exactly correct.
And the reason is it has, it has to be, it has to be proof of human.
It has, because you can't do proof of not bought.
You have to do proof of human.
To do Proof of Human, you need biological validation.
You need it to start with, this was actually a person.
Because otherwise, you have bots signing up as fake people.
So you have to have something.
You have to have a biometric, and then you have to have cryptographic validation and then the ability to do the lookup.
And then, by the way, the other thing you need, which you also need selective disclosure.
So you need to be able to do proof of human without revealing all the underlying information.
By the way, another thing you're going to need, you're going to 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 going to need, 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 going to want like proof of credit score and you know proof of like you know a hundred other.
That's a tricky one.
It is a tricky one, but you're going to.
You're going to there.
There's no reason, like if somebody is checking on your credit, somebody shouldn't 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.
I see.
Independently verifiable pieces of information.
Pieces of information.
Selectively disclosed.
And this is the answer to the privacy problem writ large, which is I only need to prove what I need to prove at that moment.
So you're going to need that.
And I think their architecture makes sense.
So that needs to get solved.
I think language models have tipped... The bots are now too good.
And so they're undetectable.
And so as a consequence, we now need to go confront that problem directly.
And then...
And like I said, and then the other problem is we need to go actually confront the drone problem.
The Ukraine conflict has really unlocked a lot of thinking on that.
And now the Iran situation is also unlocking that.
And so I think there's going to be, just like this, incredible explosion of both drone and counter drone.
Our drones are better than their drones.
As long as we keep it that way.
Yeah.
And counter drones.
I think we can sneak in one more question.
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 you talked about the lag between a new technology and kind of like the GDP impact of it.
The other idea you talked about is bourgeois capitalism and how you know it's kind of managerial class was needed because of this complexity.
And I think if you bring it into the fold, you have like much higher leverage of people.
So like if you have, you know, the Musk industries and you give Elon a GI, you can run a lot more things at once.
And then you have the social contract and I know you were to a clip of Sam Allman saying um, we're rethinking the whole thing.
And you were like, absolutely not.
And I was at an event with Sam last night and he actually said in the last couple of weeks it felt like now people are taking that seriously.
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.
Yeah.
I
By the way, we'd be happy to spend more time, but we could spend more time on all that.
So just for people who haven't followed this, so this term managerial comes from this thinker in the 20th century, James Burnham, who is one of the great kind of 20th century political thinkers, societal thinkers.
And he sort of said, and he was writing in like the 1940s, 1950s And he said kind of the whole history of capitalism until that point had been in two phases.
Number one had been what he called bourgeois capitalism, which was think about as like name on the door, like Ford Motor Company, because Henry Ford runs the company.
And Henry, it's like a dictatorial model.
And Henry Ford just like tells everybody what to do.
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 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 car experts or to be whatever experts in any particular field, but are trained to be experts in management.
And then that led to the importance of Harvard 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 problem with bourgeois capitalism is it doesn't scale.
Henry Ford doesn't scale.
And so if you're going to run capitalist enterprises that are going to have millions to billions of customers, they're going to be operating a level of scale and complexity that's going to 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 going to create the next breakthrough thing.
But he's like, whether you think that's good or bad or whatever, it's what's going to be required.
And basically that's what happened.
Right.
And so he wrote that book originally like 1940.
You know, over the course of the next 50 years, basically managerialism well, I mean today up till today 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 large scale nonprofits run and kind of everything.
You know everything runs.
Basically what Venture Capital does is we basically are a rump sort of protest movement to that to try to find the next Henry Ford, or just to say Elon Musk, or the next Elon Musk, or the next Steve Jobs, the next Bill Gates, the next Mark Zuckerberg.
And so we start these companies in the old model, right?
We start them out as in the Henry Ford model.
And so we start them out with a founder or a founder with colleagues, but there's a founder CEO.
And then 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 going to be able to do.
And so it's a bet that basically, by relighting This sort of name on the door, you know kind of thing, this new innovative thing with like a king, monarchical political structure, that they're going to be able to innovate in a way that the incumbent is not going to be able to, because the incumbent is being run by managers.
Right.
And, by the way, and of course, venture being what it is.
Sometimes that works, sometimes it doesn't, but we're constantly doing that.
Right.
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 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 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 Steve Jobs plus AI is the best of both.
Right.
Because 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 do all 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 I mean, you know what is the thing?
That these bots are really good, 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 think I think the a hundred percent, I think the answer, the answer very well might be to get the best, the best of both worlds by doing this.
And then the challenge is going to be twofold.
The challenge is going to be for the innovators to really figure out how to leverage AI to actually do this.
Right.
And then the other challenge is going to be for the incumbents that are managerial to figure out like okay, what does that mean?
Because now they're going to be facing a different kind of insurgent competitor that has a different set of capabilities than they're used to.
And so 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 GDP and 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 and growth and impact, they can kind of like keep going, if not accelerating.
Yeah.
That's for sure the hope.
The challenge, and look, the utopian view is, of course, and that's going to be the future of the economy, and it's going to grow 10x and 100x and 1,000x, and we're entering this regime of much higher economic growth forever. consumer cornucopia of everything.
It's going to be great.
I hope that's true.
That's the current utopian vision.
I hope that's true.
The problem goes back again.
The real world is really messy.
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.
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 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.
By the way, to work for the government, you have both civil service protections and you have public sector unions.
You have two layers of insulation against ever getting fired for anything or anything ever changing.
I'll give you another example.
The dock workers went on strike a couple of years ago because of robotics.
If you go look at a modern dock like in Asia, it's all robots.
If you go to an American dock, it's all steel guys dragging stuff by hand.
The dock workers went on strike.
It turns out there are 25,000 dock workers working on docks in America.
It turns out they have incredible political power because 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 as small as 25000 people still has like tremendous political stroke.
We also learned that they it actually turns out.
The dock workers union has 50000 people in it because there's 20.
They have 25000 people working at the docks.
They have 25000 people during full paycheck 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 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 do they have their jobs guaranteed in perpetuity, but they only have to report to work in an office one day per month.
There are entire office buildings in Washington DC that are empty 29 out of 30 days of the year, that are still operating and we're all still paying for it.
It turns out what the employees do is they're very smart in this way.
They come in on the last day of a month and the first day of the next month.
And so 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 where I'm heading with this.
This is like locked in.
Right.
This is like locked in in a way that has nothing to do with like, and people say capitalist, 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.
So much of our economy is, 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.
K-12 schools in the United States, they're a literal government monopoly.
How are we going to 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.
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-12.
There's no chance.
The teachers are 100% opposed to it.
It's 100% not going to happen.
So you see what I'm saying is there's this massive slippage that's going to take place.
Both the AI utopians and the AI doomers are far too optimistic.
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, nope.
So much of how the existing economy works, it's just like wired in.
And so we're going to be lucky as a society.
We're going to be lucky if AI adoption happens quickly, right?
Because if it doesn't work, we're just going to have a stagnation.
Also mark, i know you gotta run yeah, we all know or stay welcome, but uh, it was such a pleasure talking to you.
Uh, we're truly living in an age of science fiction coming to real life.
Yes yes, could not be more exciting really.
Thank you mark, with you guys awesome, thank you.
That's it good, thank you.
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