# AI Agents, Scaling Laws, and Organizational Evolution

**Podcast:** Latent Space: The AI Engineer Podcast
**Published:** 2026-04-03

## Transcript

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, or even 70 years where that was controversial.
And so the way I think about what's happening is basically, I think about basically the period we're in right now is it's, I call it, 80-year overnight success, right?
Which is like...
It's an overnight success because it's like, bam, you know, chat GPT hits and then O1 hits and then, you know, open claw hits.
And like, you know, these are open, these are like overnight, like radical overnight transformative successes, but they're drawing on an 80 year sort of wellspring backlog, you know, of ideas and thinking.
It's not just that it's all brand new, it's that it's an unlock of all of these decades of like very serious hardcore research.
If I were 18, like this is 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 Squix, 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.
We have some projects underway.
Actually, this is the original.
We're in actually the original office.
We're in the whole thing.
It's beautiful.
Great.
Thank you.
So I have to come out.
I wanted to pick a spicy start.
In October 2022, I just made friends with Rune.
And I wanted to give him something to sort of...
be spicy about.
And I said, it'll never not be funny that A16Z was constantly going, the future is where the smart people choose to spend their time, and then going deep into crypto and not in AI.
And that was in October 2022.
And Rune says there was an internal meeting in A16Z to reorient around Gen AI.
Obviously you have, but was there a meeting?
What was that?
I mean, I don't know.
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.
Yeah, 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 continuous.
Ben and I both have computer science degrees.
Ben and I actually both are old enough to remember the actual AI boom in the 1980s.
There was a big AI boom at the time.
And there was one of their names, like Expert Systems, and they were of Lisp and Lisp machines.
I coded in Lisp.
I was coding in 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.
It 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.
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.
Well, and as you guys have talked about this at length on your thing, but, you know.
If you really track what happened, I think the real story is it was the AlexNet basically breakthrough in like 2013.
That was the real knee in the curve.
And then it was obviously the transformer breakthrough in 17.
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.
on the board since 2007.
And of course, they started using machine learning very early and have used it basically for like 20 years for content feed optimization and advertising optimization.
And obviously, many financial services, many, many, many companies, many different sectors have been doing this.
And so it's like one of these things.
It's not a single thing.
It's like layers, right?
And the layers arrive at different paces, but they kind of build up.
They kind of build up over time.
And then, yeah, and then look, in retrospect, it was 2017 was kind of the key point with Transformer.
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 between 2017 and 2021, I mean, that was the era of which companies like Google had internal chatbots, but they weren't letting anybody use them.
Yeah.
Right.
And then OpenAI developed ChatGPT2, and then they told everybody, this is way too dangerous to deploy.
We can't possibly let normal people use this thing.
And then you guys, I'm sure, remember AI Dungeon.
So there was like a year where the only way for a normal person to use GPT-3 was in AI Dungeon.
Yeah.
And so we would do this.
You'd go in there and you'd pretend to play Dungeons and Dragons.
In reality, you're just trying to talk to GPT.
And so there was this, you know, there was this long, you know, the big companies, you know, big companies are cautious and, you know, the big companies were cautious.
By the way, it took OpenAI, you know, they talk about this.
It took OpenAI time to actually adjust, you know, kind of redirect their research path.
I think it was at Rosewood, right?
The dinner that founded OpenAI 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.
It should be 20.
They just celebrated a 10-year anniversary, so it is 2025.
Yeah.
So 2015.
Yeah, 2015.
Yeah, 2015.
But then Alec Radford did GPT-1 in what?
Probably 17, 18.
17, 18.
And then GPT-3 was, what, 20?
2020, because that became co-pilot immediately.
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, 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 an AI winter?
Because there's always these patterns, like is this endless summer?
It's something I constantly think about because Do I 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.
And you guys know this.
Summer, winter, summer, winter.
Summer, winter, summer, winter.
And it goes back.
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.
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 at the other end.
And by the way, they got the grant, they got the 10 weeks, and then 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 and 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, there was a 60-year run where that was like, or even 70 years where that was controversial.
And we now know that that's the case.
And so everything we're building on today sort of derives from the original idea in 1943.
And so in retrospect, we now know that these guys, they would get the timing wrong, and they thought capabilities would arrive faster, or it could be turned into businesses sooner or whatever.
But they were fundamentally, the scientists who worked on this over the course of decades were fundamentally correct about what they were doing.
And the payoff from all their work is happening now.
And so the way I think about what's happening is basically, I think about basically the period we're in right now is it's, I call it 80 year overnight success, right?
Which is like, it's an overnight success because it's like, bam, you know, chat GPT hits and then O1 hits and then, you know, open claw hits.
And like, you know, these are open, these are like overnight, like radical overnight transformative.
successes, but they're drawing on an 80-year sort of wellspring backlog 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 very serious hardcore research and thinking.
Look, 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 work.
So sad.
It is sad.
I think Jeff Hinton was like the last guy.
Yeah, yeah.
Well, there were the guys, Alan Newell.
I mean, there's tons of John McCarthy.
John McCarthy was like one of the inventors in the field.
He's one of the guys who organized the Dartmouth Conference.
And he taught at Stanford for 40 years and passed away, I don't know, whatever, 10 years ago or something.
Never actually got to see it happen.
But it is amazing in retrospect.
These guys were incredibly smart and they worked really hard and they were...
correct.
So anyway, so then it's like, okay, you know, as they say, history doesn't repeat, but it rhymes.
It's like, okay, does that mean that there's going to be another, like, you know, basically boom, 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.
So the foremost, the foremost dangerous words in this time is different.
Do you know the 12 most dangerous words in investing?
No.
The four most dangerous words in investing are different.
The 12 most dangerous words.
And so I'll tell you what's different.
Now it's working.
I mean, look, there's just no question.
And by the way, I'll just give you guys my take.
LLM is from basically the chat 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 the hallucination rates are way too high, and this is going to be great for creative writing and creating Shakespearean sonnets as rap lyrics or whatever.
It's going to be great at all that stuff, but we're not going to be able to harness this to make this relevant in coding or in medicine or in law or in fields that 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 over the, basically the coding breakthrough that kind of catalyzed over the holiday break was kind of the third step in that.
But it was like, all right, if, you know, if Linus Torvalds is saying that the AI coding is not better than he is, like, that's never happened before.
That's the benchmark.
Yeah.
That's never happened before.
And so now we know that it's going to sweep through coding.
And then we know, you know.
We know that if it's going to work in coding, it's going to work in everything else, right?
Because that's like the hardest, in many ways, that's the hardest example.
And now 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, LOMs, reasoning, agents.
And then now RSI.
And they're all actually working.
And so I'm just like, I'm jumping out of my shoes.
Like, this is it.
Like, this is the culmination of 80 years worth of work.
And this is the time it's becoming real.
I'm completely convinced.
I think the anxiety that people feel is like during the transistor era, you had Morse Law.
And it's like, all right, we understand why these things are getting better.
We understand the physics of it.
With AI, it's...
It's so jagged in the jumps.
Like you said, it's like in three months, you have this huge jump.
And people are like, well, this can keep happening, right?
But then it keeps happening.
It will keep happening.
And so how do you think about also timelines of what we're building?
I think we always have this question with guests, which is like, should you spend time building harness for a model versus the next model just going to do it one shot in the latent 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.
One is, look, Moore's Law was what we now call a scaling law.
Moore's Law was a scaling law.
For your younger viewers, Moore's Law was every chip.
Chips either get twice as powerful or twice as cheap every 18 months.
It's gotten more complicated in the last few years, but that was the 50-year trajectory of the computer industry.
And then, 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 bucks.
And so that was a scaling law.
And then key to any scaling law, including Moore's Law and the AI scaling laws is they're not really laws.
They're predictions.
But when they work, they become self-fulfilling predictions because they set a benchmark and then the entire industry, 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 their predictions and then they're motivating catalysts for the research work that is required to be.
And by the way, also the investment dollars are required to basically keep the curves going.
And look, it's going to be complicated, and it's going to be variable, and there are going to be walls that are going to look like they're fast approaching, and then they're going to be – 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 been happening a lot.
And then, look, there's going to be times when it looks like the walls have – the laws have petered out, and then they're going to pick up again and surge.
And then it appears what's happening to the eyes, there's now multiple scaling laws.
There's multiple areas of improvement.
And I think – 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.
You know, like, for example, there's probably some scaling law around world models and robotics that we don't fully understand, you know, kind of acquisition of data at scale in the real world that we don't fully understand yet.
So that one will probably kick in at some point here.
There's a bunch of really smart people working on that.
And so, yeah, I think the expectation is that, you know, the scaling laws generally are going to continue.
Yeah, 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 kind of 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.
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.
8 billion people making collective decisions on planet Earth is not a simple process of just like – You see this happening now.
It's like a bunch of the AI CEOs have this thing, which is just like, well, there's just this, they just all have this kind of thing when they talk in public where they're just like, well, there's this 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.
It depends.
Look, there's no question people are going to like – there's no question there are going to be companies.
It's already happening.
There are companies that think that they're building value on top of the models and then they're just going to get blissed 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 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 OpenAI 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 uh these build outs and uh you know capital investment yeah so just start by saying so i lived through the dot-com crash um and i can tell you stories for hours about the dot-com crash and it was horrible no it was awful it was it was it was apocalyptic by the way that a lot of the dot-com crash was actually at the time it was actually a telecom crash it was a bandwidth crash like the thing that actually crashed that wiped out all the money with the telecom companies Global Crossing.
I'm from Singapore, and they laid so much cable over our oceans.
Actually, there was a scaling law in the dot-com era, and it was literally 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 is 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.
Really, what happened was the internet, by the way, continuously kept growing basically since inception.
It's continuously grown.
It's never shrunk.
It's grown really fast compared to anything else in human history, but it wasn't doubling every quarter as of 1998, 1999.
There was this gap in the expectation of what they thought was a scaling law versus reality.
That's actually what caused the dot-com crash, which was companies like Global Crossing way overbuilt fiber.
Which is sort of, by the way, fiber, telecom equipment, so all the networking gear.
And then, by the way, the actual physical data centers.
That was the beginning of the data center build and the data center overbuild.
And so you had that, but it was literally, I think it was like $2 trillion got wiped out, right?
Jesus Christ.
It was like a big, and by the way, 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, okay, you have a highly levered thing where you're just over building capacity.
demand is growing, but not as fast as you hoped, and then boom, bankrupt, right?
And then it's like they say about the hotel industry, which is it's always the third owner of a hotel that makes money.
It has to go bankrupt twice, right?
You have to wash out all of the overoptimistic exuberance before it gets to actually a stable state, and then it makes money.
So by the way, all of those data centers and all of those, all the fiber that- They're in use.
It's all in use today, but 25 years later.
But it took, and 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 know, 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, you know, compute grids.
and all these things.
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 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 the money are like the bluest chip of companies.
And so back in the, like Global Crossing was like, it was like an entrepreneur.
It's like a new venture, but like the money that's being deployed now at scale is Microsoft and, you know, and Amazon and Google and Facebook and NVIDIA and, you know, these, these, these, and now, you know, by the way, OpenAI and Anthropic, which are now like, you know, really serious size, you know, as companies with, you know, very serious revenue.
These are very large scale companies with like lots, lots of cash, lots of debt capacity that they've never used.
And so this is institutional in a way that that really wasn't at the time.
And then the other is, at least for now, every dollar that's being put into anything that results in a running GPU is being turned into revenue right away.
And you guys know this, everybody starved for capacity.
Everybody starved for compute capacity and then all the associated things, memory and interconnect and everything else.
data center space.
And so every dollar right now that's being put in the ground is turning into revenue.
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.
It's true.
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.
Right.
We're not even getting the good stuff.
But getting the good stuff is just, even if technical progress stops.
Once there's a much bigger build of GPU manufacturing capacity and memory, 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 much better.
And then, as you know, there's just a million ways to use this stuff.
There's just a million use cases for this.
This isn't just sending packets across a thing, whatever, and hoping that people find something to do with it.
This is just like, oh, we apply intelligence in every domain of human activity.
And then it works incredibly well.
Here's what I know.
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.
The entire supply chain is sold out or selling out.
We're just going to have chronic supply shortage for years to come.
um there's going to be a response from the market that's going to result in an enormous you know it's happening now an enormous flood of investment in a new fab capacity and you know everything else to be able to do that at some point the supply chain constraints will unlock you know at least to some degree that will be another accelerant to industry growth when that happens because the products will get better and everything will get cheaper um and so 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 And so I know the capabilities are like really real and serious.
I also know that the technical progress is not going to stop.
It is accelerating.
Like the breakthroughs are tremendous.
I mean, even just month over month, the breakthroughs are really dramatic.
And so, you know, 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 like somehow disappoint in, you know, at least for years to come.
I think it would be essentially suicidal to make that bet.
It was at Michael Burry.
That's an interesting guy, huh?
We'll pick on a guy.
Let's pick on one guy.
Well, because he did.
He came out with it.
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, but just 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 and very profitably.
And so 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 like literally never happened before.
Like it's never been the case that you have an older model chip that becomes more valuable, not less valuable.
And again, that's an expression of 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 usually they advise like four to seven years.
And maybe you sort of realistically haircut it down to two to three.
But actually, it's going up and not down.
And I think that's the dream.
We are finding utilization.
Utilization solves all problems.
You can find use cases for even memory, we're having a shortage.
And even the shittier versions of memory that we do have, we are finding use cases for it.
So that's great.
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?
Well, 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.
And like, it may rise dramatically, right?
So what is, and then is, you know, like the big model companies are subsidizing heavily right now, right?
And so what's the...
What will be the average person's per day, per month token cost 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, you guys probably have friends.
I have friends today who are paying $1,000 a day for cloud tokens to run OpenClaw.
And so, OK, $30,000 a month.
And by the way, those friends have like 1,000 more ideas of the things that they want their claw to do.
And so you could imagine there's latent demand of up to, I don't know, $5,000 or $10,000 a day of tokens for a fully deployed personal agent.
And obviously, consumers can't pay that.
But it gives you a sense of the future scope of demand.
And so 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 like the entire chip ecosystem is just going to get- Wait for network constraints.
That would be the killer.
It's all bottlenecking potentially for years.
And so I think that Brad, and I think it's actually possible.
I mean, generally inference costs are going to keep coming down, but I think the, let's put it this way, the rate of decline, I think may level out here for a bit because of these supply constraints.
And then at some point, maybe the lab stops subsidizing so much and that again will be an issue.
And so there's just going to be so much more demand for inference than can be satisfied, you know, kind of with the centralized model.
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.
It's quite amazing.
The 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 other motivators.
There's other motivators, which is just like, okay, how much trust are the big centralized model providers?
How much trust are they building in the market versus how much are, at least in certain cases with some people for certain use cases, people being like, well, I'm not willing to just turn everything over.
So there's all the trust issues.
By the way, there's also just straight up price optimization.
There's many uses of AI where you don't need Einstein in the cloud.
You just need a smart local model.
There's also performance issues where you're going to want your doorknob to have an AI model in it to be able to do access control.
Obviously, 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 you're going to have, and then you're going to, 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.
Look, number one, I do think we care who makes it.
I would say this.
The previous presidential administration wanted to kill it in the US.
Oh, yeah.
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 in the council?
PCAST?
Yes, and PCAST, yeah.
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 Chinese have a very – the various Chinese companies have a very specific reason to do open source, which is they don't – 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 kind of view, I think, open source AI as a bit of a loss leader against basically domestic paid services and then kind of ancillary products.
They're very excited about it.
By the way, I think it's great.
I think it's great that they're doing it.
I think DeepSeek was like a gift to the world, I think.
The great thing about 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.
The paper.
The paper and the code.
For example, I thought this was amazing.
OpenAI comes out with 01, and it's an amazing technical breakthrough, and it's just absolutely fantastic.
But of course, they don't explain how it works in detail, and then of course, they hide the reasoning traces.
And then everybody's like, okay, this is great, but who's going to be able to replicate this?
Are other people going to be able to do this?
Is there secret sauce in there?
And then R1 comes out and it's just like, there's the code and there's the paper.
And now the whole world knows how to do it.
And then three months later, every other AI model is adding reasoning.
And so you get this kind of double, even if the Chinese models themselves are not the models that use, the education that's taken place to the rest of the world, the information diffusion 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.
There already is.
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 in different ways, you know, and then obviously both X and then Meta where I'm involved, you know, both have huge, you know, huge attempts to, you know, to kind of leapfrog underway.
And then you've got a whole fleet of startups, new companies, including a whole bunch that we're backing that are trying to come out with different approaches.
And then you've got whatever it is.
I don't know.
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.
Right.
Yeah.
But does that include, that includes like Moonshot?
Yes.
Kimmy, DeepSeek, ZAI, Quinn, O-One 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 model yet.
Yeah.
But at least, you know, C-Dance is very inspiring and presumably they have more stuff coming and Tencent probably has more stuff coming and so forth.
And so, so like, look, here would be a thing you can anticipate, which is there are not, these markets, they're not going to be, between the US and China right now, there's like a dozen primary foundation model companies that are like at scale at some level of like critical mass.
It's not going to be a dozen in three years, right?
Just because 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 a whole bunch of those guys that are going to have to figure out alternate strategies.
And I think open source is one of those strategies.
And so I think you could see a whole – I think the questions like who's going to do open source, I think that could change really fast.
I think that's a very dynamic thing.
I think it's very hard to predict what happens.
And I think it's very important.
NVIDIA is doing a lot.
Well, I was going to say, well, exactly.
And then you got NVIDIA.
And then, you know, just to get an industrial, there's an old thing, a business strategy, which is called commoditize the complement.
That's right.
And so if you're Jensen, it's 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.
Bam.
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.
He was in what?
Oh, he was in Vienna.
And then where is he now?
He's moving to SF.
Okay.
All right.
Okay.
There we go.
And then, yeah, the pie guy.
All right.
The pie guys are European.
Their buddies in Australia.
Mario is also there.
Right.
And are they, yeah, they haven't announced yet any sort of change or have they?
No, they have a company there.
Okay.
Good.
Good.
Good.
Yeah, good.
Anyway, 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 end.
Yeah, 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 1970 to, I don't know, it still is very important, but from 1970 through to basically the creation of Linux, which is basically this thing we used to call the Unix mindset.
Because there were all these different theories, all these different operating systems and mainframes, and then all these Windows and Mac and all these things.
But kind of behind it all was this idea of the Unix mindset.
And the Unix mindset was this thing where basically you don't have these, like in the old days, The operating system that made the computer industry really work in the 1960s was this thing called OS 360, which was this big operating system IBM developed that was supposed to basically run everything.
It was this giant monolithic architecture in the sky.
It was like a giant castle of software.
By the way, it worked really well and they were very successful with it, but it was this huge castle in the sky.
It was this thing that was almost unapproachable, which is like you had to be inside IBM or very close to IBM and you had to really understand every aspect of how the system worked.
Then the Unix Sky is originally out of AT&T and then out of Berkeley.
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 a prompt and a shell.
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 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 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 um you know normal people don't need it didn't need to necessarily know about it but like if you were doing like system architecture application development you you knew all about it And then, you know, it's been in the background ever since.
And, you know, look, your Mac still has a Unix shell, you know, kind of in there and your iPhone still has a Unix shell kind of buried in there somewhere.
So they're kind of in there.
And then, you know, the Windows shell is kind of, you know, sort of a weird derivative of that.
But, you know, but look, the Internet runs on Unix and then smartphones.
Actually, both iOS and Android are Unix derivatives.
And so, you know, kind of Unix did end up winning.
But anyway, and then we just started taking that for granted.
And then, 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?
Which is- The best kind.
The best kind.
They weren't obvious at the time or somebody else would have done them already.
And so there is 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.
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.
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, 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.
uh already and so like the full power of your computer is available at the command line level um and then it turns out it's really easy to expose other functions as a command line interface and so like this whole idea where we need like mcp and these like products fancy protocols whatever it's like no we don't we just need like a command command line thing so that's the architecture and then it turns out what is your agent your agent is a bunch of files stored in a file system and then there's the thing that just like completely blew my mind when i write my head around it as a result of this which is like okay This means your agent is now actually independent of the model that 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 states stored in the files will be retained.
Yeah, different instruction set, but you just compiled it.
Right, exactly.
And it's all right.
It's like swapping out a chip 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 swap out the heartbeat, 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 its files.
And then there's a person.
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 you can instruct your agent migrate yourself to a different uh runtime environment migrate yourself to a different file system migrate yourself to a different you know like 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 is the agent actually has full introspection and actually it actually knows about its own files and it could rewrite its own files right which by the way is basically no widely deployed software system in history where the the thing that you're using actually has full introspective knowledge of how it itself works and is able to modify itself like that 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 completely blew my mind when I wrapped my head around it, which is you can tell the agent to add new functions and features to itself, and it can do that.
Right.
Extend yourself, like extend yourself, give yourself a new capability.
Right.
And so, and so literally it's just like, you run into somebody at a party and they're like, oh, I have my open claw, do whatever, connect to my eight sleep bed.
And it gives me better advice than 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 then the next thing you know, it has this new capability.
And so you can have it upgrade itself without having to do anything other than tell it that you wanted to do that.
And so anyway, the combination of all this is just like a Massive, incredible.
I mean, it's just incredible.
If I were 18, 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 trivial.
Of course, it's going to be able to use your computer.
It has full access to the shell.
Right.
And then, and then you just, you, you give it access to a browser and then you've got the computer and the browser and off and away it goes.
And then you've got all the abilities of the browser also.
And so, and so the capability unlock here is profound.
My friends who are, you know, deepest into this are having their claw do like, like literally like a thousand things in their lives.
They have new ideas every day.
They're just like constantly throwing new challenges at the thing.
And by the way.
It's early, and these are prototypes, and as you guys know, there's security issues.
There's a bunch of stuff to be ironed out, but the unlock of capability is just incredible.
I have absolutely no doubt that everybody in the world is going to have at least 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 close to posting their tweets on claw XAI or whatever, you know?
I do think that that is how, you know, we get into some danger there in terms of like alignment and whether or not we want these things to run.
You guys know rentahuman.com?
Yeah, rentahuman.com.
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 that you're seeing agents similar today so 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, and to be clear, this was totally different.
We didn't have the capabilities we have today, but we didn't have the language models underneath this.
But we did have this idea that human readability actually mattered a great deal.
And specifically in those days, it was not so much English language, but there was a design decision to be made between binary protocols and text protocols.
Basically, every basically old school systems architect that had grown up between the 1960s and the 1990s basically said, 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 a 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 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.
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 maxer.
Yeah, exactly.
Yeah, yeah, yeah.
Basically, it's just like, well, yeah.
Well, actually, this was actually the conscious thing, which basically says just assume a future of infinite bandwidth, build for that.
And then basically what it was, it was a bet that 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 and process everything.
And then, of course, a lot of websites are financed to databases.
And so you wanted to unleash the underlying latent power of whether it was an Oracle database or 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, OK, 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 it's 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, okay, I need to build a new kind of application.
So the first thing I need to do is build a new programming language.
And then the next thing I need to do is build a new operating system.
And the next thing I need to do is I need to build a new chip.
And they kind of want to reinvent everything.
And 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 I think the web did that for those reasons.
And I think it's the same thing now that's happening.
It's a good perspective on the web.
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.
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'll be like oh okay python is kind of slow type script you know it's like yeah as 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 every language and i think they're going to be good at translating from any language to any other language like 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 Java or any of this stuff.
And so everything I ever did, I was managing my own memory at the level of C.
And then I'm still from the generation.
I knew assembly language.
So I could drop down and do things right on the ship.
And so all of us, we've always lived in a world in which software is like this precious thing that you have to think about very carefully.
And it's really hard to generate good software.
And there's only a small number of people who can do it.
And you have to be very jealous in terms of thinking about how do you allocate?
What are your engineers working on?
And how many good engineers do you actually have?
And how much software can they write?
And how much software can human beings kind of maintain?
And I think all those assumptions are being shot right out the window right now.
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 it's written and you just tell the thing, all right, I want the right now, I want the rest version.
Or, you know, security, you know, security, we're about to, by the way, we're about to go through computer security is about to go through the most dramatic change ever, which is number one, like every single latent security bug is about to be exposed.
So 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 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.
And that has 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 software in Rust, you just tell the bot you want all your software in Rust.
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 its optimal language be and let it design it.
True.
Okay.
Here's a question.
Are you even going to have programming languages in the future?
Or the AI is 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?
Yeah.
Or are they just coding binary directly?
Did you see there's actually an experiment?
Somebody just did this thing where they have a language model now that actually emits model weights for a new language model.
Right.
And so will the bots...
Just predict the weights.
Yeah.
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 doing a simulation of a simulation and a simulation inside of the weights.
Yeah, very inefficient.
But look, LLMs are already incredibly inefficient.
My favorite thing, ask Claude to add 2 plus 2 equals 4.
It's like whatever, billions and billions of times more inefficient than using your pocket calculator.
But yeah, the payoff is so great of the general capability.
And 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 depth 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.
It was not that long ago that 99% of humanity was behind a plow.
What are people going to do if they're not plowing fields all day to grow food?
It just turns out there's much better ways for people to spend time than plowing fields.
Yeah, dude's growing.
Exactly.
Talking to their friends.
Look, I'm not an absolutist and I'm not a utopian.
To be clear, I have an 11-year-old and he's learning how to code.
I think it's still a really good idea to learn how to code and so forth.
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 it's going to do it in whatever way is most optimal for it to do it.
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.
And so it's going to be the engine of interpretability to explain itself.
And I just am 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.
Well, I'm curious, if that's true, then shouldn't the model 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 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 the Py OpenClaw thing.
It's like, oh, I built all the software using the OpenAI model and I'll switch to the Anthropic model, but the Anthropic model doesn't understand the thing.
So it feels like there still needs to be some obstruction.
But maybe not.
Maybe that's the lock-in that the model providers want to have.
I'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, giving you 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 burden to do a chip or something.
And now they reverse engineer to get a version that runs on their Mac.
And so if you reverse it, if you're reversing 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, basically.
And so I just think it's this thing where it's just like, By the way, everything we're describing is something that human beings in theory could have done before, but it was just always cost and labor prohibitive.
I learned how to reverse engineer.
Human beings can reverse engineer binaries.
It's just for any complex binary, you need 1,000 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 yeah right um and if you don't have the human limitations anymore then all of a sudden you have and it's not that you 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 ones just talking about protocols was it you or someone else i forget my internet history who said that like the biggest mistake that we didn't figure out in the early days was payments yes Is that you?
Yes.
It was a 402 payment required.
We have a chance now.
I don't think we're going to figure it out.
I don't know.
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 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 just, I mean, it's just, I think it's now obvious.
It's like, obviously AI agents are going to need money.
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 0.1% if that, but yeah.
Oh, today.
Yeah, yeah, yeah.
But think forward.
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 yet.
My friends who are the most aggressive users of OpenClaw just have given their Claws bank accounts.
and credit cards.
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 behalf.
It's just completely obvious.
And again, the number of people who have done that today to your point is like, I don't know, probably 5,000 or something.
It'll grow.
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?
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.
Log everything, you know, just watch it.
Watch the logs.
let's actually find out what the thing can do.
And the way to find out what the thing can do is just like- Try everything.
Yeah, let it try everything.
Let it unlock everything.
By the way, that's how you're 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 the first like 20 minutes.
But I think the contribution that they're making to the future of our species is amazing.
It's like gentleman science.
Yes, yes, yes.
Experimental yourself.
It's Ben Franklin out with trying to get lightning strikes.
his balloon and seeing if he gets electrocuted.
Yeah.
It's Jonas Salk with the polio vaccine, right?
Injecting it.
Yes.
So yes, I think we should have like, we should have like flags and like, we should have like monuments to the people that just let open club run their lives.
More anecdotes.
I was like, what are the craziest or interesting things that people listening to this should go home and do?
I mean, this is, the extreme thing is just like the straight YOLO.
Like just, yeah, turn your life away.
That's a general capability.
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 is just, it's just absolutely amazing.
The number of stories on, I just don't want to violate people's, you know, obviously personal situations, but, you know, one of the things OpenCloud is really good at is hacking into all this stuff in your LAN.
It's really good.
So, you know, Internet of Things, aka Internet of Shit.
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 Claw in a loop.
I have it wake up frequently and have it watch me.
Just tell him, 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 because, you know, I have his health data and I know that he hasn't been getting enough sleep.
And so it's really good that he's getting sleep.
I really hope he gets his full whatever, you know, five hours of REM 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 being a bot is just very focused.
It's just like his reason for existence is to watch Joe sleep.
And then I was talking to my friend who did this.
On the one hand, it's like, all right, this is weird and creepy.
And maybe this is taking over my life.
And then the other thing is like, you know what?
If I had a heart attack in the middle of the night, this thing literally would freak out and call 911.
There's no question this thing would figure out how to alert medical authorities and probably summon SWAT teams and do whatever would be required to save my life.
And so it's like, yeah, that's happening.
What else?
It's a company, Unitry, that makes the robot dogs.
I actually have one at home, which is actually really fun.
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 Unitry 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, and 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 like is a complete idiot when it comes to climbing the stairs, but it will happily teach you quantum mechanics.
Right.
In like a plummet English accent.
It's just like absolutely amazing.
Jagged intelligence.
Yeah.
Talk about jagged.
Now, obviously what's going to happen in the future is they're going to connect together, but right now it's, and so right now it's not that useful.
And so I have a friend who has one of these who had his claw basically hack in and rewrite the code, rewrite new firmware, rewrite new firmware for the, for the unit robot.
And now it's, now it's an actual pet dog for his kids.
How do you do that before or after, like the motion?
Yeah, you said it's completely different.
He said it's a complete transformation.
And whenever there's an issue in the thing now, the claw just rewrites the code.
It goes in and does the code.
And so it kind of goes to your thing here.
So all of a sudden, this is the way we want to think about AI coding.
AI coding is not just like writing new apps.
It's also going in and rewriting all the old stuff that should have worked that never worked.
And so I think basically, I think the internet of shit is basically over.
I think everything, there's a potential here where all these devices in your house that have been basically marginal or basically dumb, all of a sudden they might all get really smart.
Smart home.
You have to decide if, yes, there are horror movies of which this is the premise.
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 in the whole thing.
To have that unlock without a human being having to go do any of that work.
I'm waiting for a story, 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, I don't think you can do this, but you know, this is a real, are you really, you know, are you really sure?
And you told me last night you really don't want me to let you do this.
So 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 you go?
When you can pass the test, I will open the fridge door for you.
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 the same thing.
It's a virtual world version.
It's a physical world version.
So the virtual world version is the bot problem.
We're just like, you know, the internet is just like a wash in bots.
The internet's a wash in fake people.
It has been forever.
By the way, a lot of that has to do with lack of money, you know, and so this is...
My spicy take was these two are the same thing and corporations are people too, you know.
Interesting.
Yeah, yeah, yeah.
Okay.
So a bank account is proof of human.
Yeah, okay.
Yeah, until you give the bots bank accounts.
Yeah, exactly.
So, okay.
Yeah, so there's that.
But yeah, look, every social media user knows this.
The bot problem is a big problem.
The bot problem has been a big problem forever.
It's a huge problem, and it's never really been confronted directly at any point.
By the way, the physical world version of this is the drone problem.
And so we've known for 20 years now that the asymmetric threat, both in actual military conflict, but also in just security on the home front, the big threat is the cheap attack drone, the cheap suicide drone with a bomb.
And we've known that forever.
And by the way, it's very disconcerting how every office complex in the world is unprotected from drone attacks.
Every stadium, every school, every prison.
Sure.
Okay, we've known that.
We've never done anything about it.
What are you going to do about it?
Yeah.
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.
Kinetic personal force fields.
Exactly.
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 something 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 is it's the virtual version of the problem and it's the physical version of the problem.
The virtual version, 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.
If the bots can pass the Turing test, then you can't screen for bot.
You can't have proof of not a bot.
What you can have is you can have proof of human.
You can have cryptographically validated, this is definitely a person.
Then you can have cryptographically validated, this is definitely something that a person said, this video is real.
right uh just to double click on uh do you think alex blania with world do you think he's got it or is there an alternative Oh, so I mean, there's going to be, I think there'll be, I think many people will try.
We're one of the key, you know, participants in the world, in the world project.
Yeah, so we're partisans.
But yeah, I think, so we think world is exactly correct.
Okay.
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 bot, you have to do proof of human.
To do proof of human, you need, you need biological validation.
You needed to start with this was actually a person, right?
Because otherwise, you have bots signing up as fake people, right?
So you have to have like something, you have to have a biometric, and then you have to have crypt.
graphic validation and then the ability to do the lookup.
Then by the way, the other thing you need was that you also need selective disclosure.
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 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.
You're going to need to validate a proof of age.
you know, to be able to legally operate, right?
And so 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 things.
That's a tricky one.
It is a tricky one, but you're going to, you're going to, there's no reason, like if somebody's checking on your credit, somebody shouldn't, I'll 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, 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, 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 drones and counter drones.
Our drones are better than their drones.
It's supposed to 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 years.
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.
um impact of it the other idea you talked about is bourgeois capitalism and how you know this 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 um and you give elon a gi You can run a lot more things at once.
That's right.
And then you have the social contract.
And I know you received a clip of Sam Allman saying, we're rethinking the whole thing.
And you're like, absolutely not.
Yes.
And I was in an event with Sam last night.
And he actually said in the last couple of weeks, he felt like now people are taking that seriously.
So I'm just curious, like how you're seeing the structure of organization changing, especially when you invest in early stage companies.
And yeah, just like how the impact of.
work structure and all of that is playing out.
Yeah.
There's a whole bunch of times.
I know.
Yeah.
By the way, we'd be happy to spend more time, but we could spend more time on all that.
Just for people who haven't followed this, this term managerial comes from this thinker in the 20th century, James Burnham, who is one of the great 20th century political thinkers, societal thinkers.
He was writing in the 1940s, 1950s.
He said the whole history of capitalism until that point had been in two phases.
Number one had been what he called bourgeois capitalism, which was thinking about it as name on the door.
like Ford Motor Company because Henry Ford runs the company.
It's like a dictatorial model and Henry Ford just 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 at a time in the day.
He said the second phase of capitalism was what he called managerial capitalism, which was the creation of a professional class of managers that are trained not to be like car experts or to be whatever experts in any particular field, but are trained to be experts in management.
And then that led to the importance of Harvard Business Schools and management consulting firms and all these things.
And then you look at every big company today and 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, come work in tech.
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.
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.
He said, look, the professional management class has its downsides.
They're not necessarily experts at doing the thing.
They're not as inventive.
They're not going to create the next breakthrough thing.
He's like, whether you think that's good or bad or whatever, it's what's going to be required.
Basically, that's what happened.
He wrote that book originally in 1940.
Over the course of the next 50 years, basically, up till today, managerialism basically took over everything.
What I'm describing is basically how all big companies run and how all governments run and how large-scale nonprofits run and 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.
We start these companies in the old model.
We start them out in the Henry Ford model.
We start them out with a founder or a founder with colleagues, but there's a founder CEO.
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.
It's a bet that basically by relighting this name on the door kind of thing, this new innovative thing with 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.
By the way, and of course, venture being what it is, sometimes that works, sometimes it doesn't, but we're constantly doing that.
I've always viewed it my entire life as we're raging against the dying of the light.
We're constantly trying to fight off managerialism, just basically swamping everything and everything getting basically boring and gray and dumb and old.
We're trying to keep some level of energy and vitality in the system.
AI is the thing that would lead you to think, wow, maybe there's a third model.
A 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.
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 this.
What is the thing that these bots are really good at?
They're really good at doing paperwork.
They're really good at filling out forms.
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, 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, and then the other challenge is going to be for the, for the incumbents that are managerial to figure out like, okay, what does that mean?
Cause now they're going to, they're, 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 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 in growth and impact, they can kind of like keep going if not accelerating.
That's for sure the hope.
The challenge and look, the AI 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 and consumer cornucopia of everything and it's going to be great.
I hope that's true.
That's the current kind of utopian vision.
I hope that's true.
The problem is, it 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.
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.
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 they're robotics.
If you go look at a modern dock like in Asia, it's all robots.
If you go to American dock, it's like all still 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.
Number one, we learned that even a union as small as 25,000 people still has tremendous political stroke.
We also learned that it actually turns out the dock workers union has 50,000 people in it because they have 25,000 people working at the dock.
They have 25,000 people during full paychecks sitting at home.
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 have their jobs guaranteed in perpetuity, but they only have to report to work in an office one day per month.
And so there are entire office buildings in Washington, D.C.
that are empty 29 out of 30 days of the year that are still operating and we're all still paying for it.
And then what they do, it turns out what the employees do is they're very smart in this way.
And so they figure out 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.
You see where I'm heading with this.
This is locked in.
This is locked in in a way that has nothing to do with, and people say capitalist, it's anti-capitalistic.
It's restrictions on trade.
It's restrictions on the ability to change the workforce.
So much of our economy, 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 through 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 through 12.
There's no chance.
The teachers are 100% opposed to it.
It's 100% not going to happen.
You see what I'm saying?
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 is just wired in.
And so we're going to be lucky.
As a society, we're going to be lucky if AI adoption happens quickly.
Because if it doesn't, we're just going to have a stagnation.
Also, Mark, I know you got to run.
Yeah, I don't know.
Or stay welcome.
But it was such a pleasure talking to you.
We're truly living in an age of science fiction coming through life.
Yes.
Yes.
Could not be more exciting.
Really, thank you, Mark.
Awesome.
Thank you.
That's it.
Thank you.
That's it.