EVERY SPOKEN WORD
30 min read · 6,094 words- 0:00 – 0:58
Intro
- DADhaval Adjodah
The fundamental idea here is to build AI that improves itself, and I think we're getting there.
- OHOwen He
For the first time, we could swap the optimizer as the optimizee, and this leads to a reinvestment of cognitive improvement into itself.
- DADhaval Adjodah
You can think of RSI many different levels of the stack. Prompts, harness, weights, and you can go all the way to basically chips. Our goal here is to optimize all the levels of the stack.
- OHOwen He
Our approach to RSI is that we have a multi-agent system working together that's being recursively improved.
- MCMartin Casado
I'm so impressed by your belief that, like, this idea [chuckles] of a model improving itself-
- DADhaval Adjodah
But it's not a belief.
- MCMartin Casado
It is a dramatic statement, which is-- I think it's the first time I've heard you say it.
- DADhaval Adjodah
Because we have been so thorough, we have not released things until we're super sure it's correct.
- MCMartin Casado
Do you envision a system where the amount of energy that goes into it somehow decreases over time relative to the improvement?
- DADhaval Adjodah
We can't share too much, but one of the very important things that we've tried to do-
- 0:58 – 5:10
What actually counts as RSI
- MCMartin Casado
Welcome both of you, uh, for joining. We're gonna be talking about everybody's favorite topic, which is RSI, Recursive Self-Improvement. Maybe to start, it'd be great to just introduce yourselves and your backgrounds, then we'll talk a little bit about what you're working on now, and then we'll dig into the, the, the content.
- DADhaval Adjodah
Yeah. I, I, I think of myself as like, um, a physicist of some kind. I, I studied that, then I did a PhD in reinforcement learning at the MIT Media Lab. I really like building things, and I'm very excited to be building, um, a cool startup, MakerMaker, with Owen and Grace.
- OHOwen He
My name is Owen. Um, I was, uh, first studying math, and then one day I realized that all mathematicians do is are proving theorems, and I was taking a class called Formal Language and Logic, and they were showing how you can build a theorem prover.
- DADhaval Adjodah
Yeah.
- OHOwen He
And I was like, "Oh, that would be cool. I can just build a machine that prove theorems for me." That's how I got into AI.
- DADhaval Adjodah
Amazing.
- OHOwen He
And then for my PhD, I worked on continual learning and meta learning, then worked for a few years at DeepMind as a research scientist. Now I'm working on RSI.
- MCMartin Casado
I see a lot of pitches come in, and a lot of them use the term RSI. And I'll be like, "Oh, so what do you do?" And they'll say, "We use AI to create AI." "That sounds very interesting. So what do you do?" "We use AI to create GPU kernels."
- DADhaval Adjodah
Mm.
- MCMartin Casado
Which that doesn't, to me, kind of map the definition of what I would consider to be RSI. So maybe can we start by you defining what you think or what you would include in RSI definitionally?
- OHOwen He
Yeah. I think RSI is definitely an overloaded term today. Um, many people when they say RSI, they mean different things. Um, so I think the important thing when you are differentiating them is to ask the question, what is the optimizer and what is the optimizee? Like when you're im-- self-improving, who is improving, right? So, um, there, there are people who are saying, "Okay, we're doing RSI. We have human using AI to develop better AI."
- MCMartin Casado
Yeah.
- OHOwen He
But that to us is just, um, autocatalysis-
- MCMartin Casado
Yeah
- OHOwen He
... in a sense that the improver is still human with, uh, tools, even though these tools are getting better and better, but the ideas and the improvement come from human brains.
- MCMartin Casado
Right. And, and just-
- OHOwen He
Yeah
- MCMartin Casado
... I don't mean to interrupt, but-
- OHOwen He
Yeah
- MCMartin Casado
... to be clear, most technical epochs have had autocatalytic effects, right? Like there's no way you can create a computer chip without a computer, right?
- OHOwen He
Right.
- MCMartin Casado
You could argue that the internet development was sped up by the fact that people that weren't geographically located could coordinate, right? I mean, the steam engine, you could build better steam engines 'cause you could get coal faster, et cetera. So this notion of like using the thing to build the thing faster is not new.
- DADhaval Adjodah
Right.
- MCMartin Casado
And so you're saying something different.
- OHOwen He
Yes. I think for us, um, in, in the history, right, over civilization, we have this autocatalysis, you know, computer-- using computers to build better computers. But the, the idea for this change, for this improvement, come from human brain. But that human brain was never the improvee, we- never the optimizee.
- MCMartin Casado
Ah, right.
- OHOwen He
Right? Whereas when now, w- for the first time, we could swap the optimizer as the optimizee. So the, the brain of the agents, which is the LM, the model, can be the thing that you're optimizing, and this leads to a compound, this reinvestment of cognitive improvement into itself.
- MCMartin Casado
But there has to be a reason that you decided to take on this hard problem as opposed to what anybody else would do, which is like chew off pieces of the problem.
- DADhaval Adjodah
Yeah.
- OHOwen He
Yeah.
- 5:10 – 6:58
Capability as a numerator and denominator
- DADhaval Adjodah
difference. What's really different here can be explained pretty easily.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
So if you think of capability-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... what we're trying to do here is we're trying to improve the rate of improvement of ca-capability, right? So like the first derivative of capability with respect to time is the thing that everybody's trying to improve.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
Right? And now you can put that to be equal, driven by two things, the n-nominators and the denominator. The nomina-nominators would be performance-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... which is what everybody's trying to improve, and then the denominator is some kind of cost. Let's say capital-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... right, or difficulty.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And this one is pretty s- is pretty set because the higher the capability, the higher c- ... the difficulty of the next step, right? Now, the numerator is the interesting part. For things like steam engines and whatnot, that was fixed.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
Right? So then you end up with a very simple differential equation where-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... you basically plateau.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
Right? Now, if you have a system where the improvee, the things being improved, is also the thing that's doing the improvement-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... you, how-- You, you now have an, a new way to think about it, and the, the simplest case would be that the numerator is also correlated with capability-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... which makes sense, right?
- MCMartin Casado
Yeah.
- DADhaval Adjodah
Because the better you are, the better you are at also improving.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And if you do that, the math cancels out and you basically get constant improvement over time. That's, that could be one level of, of RSI, right? And then there's one more case, which is-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... now that the, the numerator could be correlated with a higher power of capability.
- MCMartin Casado
Yeah.
- 6:58 – 8:32
Closed loop vs takeoff
- DADhaval Adjodah
Hmm.
- MCMartin Casado
Like, for some reason, when people see a, um, a graph where there's like a, a circle with an arrow to another circle-
- DADhaval Adjodah
Yes
- MCMartin Casado
... th- and it goes back, and they look at that closed control system and they say, "Takeoff." [laughs]
- DADhaval Adjodah
Yeah. [laughs]
- MCMartin Casado
Right? Which, I mean, if this is a nonlinear control system-
- DADhaval Adjodah
Mm-hmm
- MCMartin Casado
... it's very difficult to know-
- DADhaval Adjodah
Yes
- MCMartin Casado
... what the hell happens. By the way, most people don't seem to understand this distinction-
- DADhaval Adjodah
Hmm
- MCMartin Casado
... because we very quickly go to, like, takeoff and FOOMing-
- DADhaval Adjodah
Yeah
- MCMartin Casado
... and all that stuff when we talk about these things.
- DADhaval Adjodah
Well, as a network guy, you know that it depends on the exponent of, you know, your, your function-
- MCMartin Casado
Yeah, right
- DADhaval Adjodah
... that's, that's self-referencing.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And it's-- What we found is that it's almost not useful to speculate on what we think could happen, because this is really an empirical problem, and the real way to know what's happening is to test things. And testing things is really, really hard. Like, reward hacking is one of, you know, many problems that we have to deal with. But the, the question you're asking is very empirical, and for some algorithm-
- MCMartin Casado
Oh, interesting. So, but, so I'm a networker, that's how I would've answered.
- DADhaval Adjodah
Yes.
- MCMartin Casado
I would've thought math people would've been like, "We've got this cl- [laughs] we've got this differential equation-"
- DADhaval Adjodah
Yeah.
- MCMartin Casado
"... and I took out my pencil-"
- DADhaval Adjodah
Yeah.
- MCMartin Casado
"... and I wrote down the answer."
- OHOwen He
I mean, it is possible to write down the differential equation, but you still have to empirically dif-, uh, measure the, the variable, the exponent impact.
- MCMartin Casado
So there's no analytical solution.
- DADhaval Adjodah
I mean, honestly, we don't even have a learning theory of deep learning. We don't know, like, fundamentally why it works.
- MCMartin Casado
Yeah.
- 8:32 – 13:39
Humans as agents in the system
- DADhaval Adjodah
Right? And, and then now you're like, you know, five, six layers on top of deep learning, and so we don't have a good theory. I think the closest things we have are scaling laws, and these are empirical laws.
- OHOwen He
I think one misunderstanding people have about, like, our approach to RSI is that they think the entire system has to be fully automatic and digital. But actually, if you look at our mathematical foundation, we have a multi-agent system, not just one digital agent. And in, in this multi-agent system, human researchers and engineers are actually a special case of these agents. And it's this hybrid system of multiple agents working together that's being recursively improved.
- DADhaval Adjodah
I see.
- OHOwen He
And we don't want to leave out any component to not optimizing them, because, like, if it's possible to optimize the model, why wouldn't we do that? 'Cause you will have more gains if you do include those. Anything that you don't optimize will later become your bottleneck.
- MCMartin Casado
Yeah. So one observation that, um, uh, uh, we have, we have made here is, um, there are claims of, uh, speed is improving, which is actually the case, right? So if you look at, like, the model release time, it is reducing. And so on one hand, you could be like, okay, so clearly we're in some sort of a, you know, a rapidly improving phase. But if you divide by the actual amount of capital raised by these companies, that's no longer the case, right? [chuckles] And so you're like, well, maybe they just raise a whole bunch of money, and then, like, AI allows you to apply that money. So how would you-- This is not for yourself, but how would you encourage people that are e- either evaluating claims about this or even working on them to think about not fooling yourself by throwing more money at the problem? Like, you're actually showing that you're making an improvement here that's meaningful with the denominator.
- DADhaval Adjodah
I think for us it's, it's pretty straightforward. It's because if you get improvement and they just come at more and more resources being poured in, it's not RSI. You have to be able to back your claim of how much improvement you're getting.
- MCMartin Casado
But do you include, like, all the man-hours and experimentation that went into that? Because I, I feel like at some level, like, that, like, it's like, like, they'll talk about, like, the actual flops that go in-
- DADhaval Adjodah
Yeah
- MCMartin Casado
... and money that go in, but the reality is this is, like, the 23rd experiment and-
- DADhaval Adjodah
Yeah
- MCMartin Casado
... there's actually a human ex- burning a lot of mental capital to get to that final configuration.
- DADhaval Adjodah
Yeah.
- MCMartin Casado
Like, do you actually include that in the denominator?
- DADhaval Adjodah
I th- we do, and I think it's, it's good company discipline to do that.
- MCMartin Casado
Yeah, yeah.
- DADhaval Adjodah
And we've tried, again, to very intentionally from the beginning have a culture of tracking where things go, cost, time, all that kind of stuff. I'll share a funny thing. Internally, we have this thing, people ca- talk about leaderboards, you know-
- MCMartin Casado
Gotcha
- DADhaval Adjodah
... which is like score.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
You know what I'm about to say. [chuckles] We have something called a bleeder board-
- MCMartin Casado
Mm-hmm
- DADhaval Adjodah
... where we keep track of the cost of, of experiments of everybody-
- MCMartin Casado
That's amazing
- DADhaval Adjodah
... who's running experiments.
- MCMartin Casado
That's awesome.
- DADhaval Adjodah
You know, and, and everybody, it's the com- everybody any day can look at, like, who's, who's high up on the, on the bleeder board.
- OHOwen He
I can also give an example from AI. Um, I don't know if you know this NanoGPT benchmark people are running.
- DADhaval Adjodah
Yeah. Yeah, sure.
- OHOwen He
It's basically asking you to retrain GPT-2, which when it just came out, it's a very expensive model to train, and it takes weeks, I think-
- DADhaval Adjodah
Yeah
- 13:39 – 17:41
Why most RSI papers don't scale
- MCMartin Casado
I mean, that's a pretty meaningful statement, which is awesome.
- DADhaval Adjodah
What we found is that a lot of the papers that claim to do RSI, when we test it in our internal infra, we find that they don't work. And a lot of it is because they are cool ideas that humans try to introspect and say, "Oh, you know, this is what I think should happen."
- MCMartin Casado
Yeah.
- DADhaval Adjodah
They're complicated. They sound cool. They get you a good paper. But then when we test it at scale, you know, on benchmarks, on real economic problems, they don't scale.
- MCMartin Casado
Right.
- DADhaval Adjodah
And what we found is that the s- the simpler thing that closes the loop is the thing that scales. I would say one thing that we do that's probably unique is that we always start with a very grounded mathematical framework that, that basically lays out the map of the whole space.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And the reason is because this space is so large that if you don't do that and you don't have this very principled way of doing it, you get lost very quickly in this complexity.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And the other reason is that often when you have this really complex system, there's two extremes of how you can build it. You can take an approach where you basically build like 200 different systems-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... and you hope that, like, this, this, this, you know, what is it? The Rube Goldberg machine will kind of work out.
- MCMartin Casado
[laughs] Yes. Yes.
- DADhaval Adjodah
Or you take the other way, and you're, you kind of basically spend some time thinking about these 200 things, and maybe you realize, you know, there are actually just 12, um, abstractions that generalize to these 200 things. And so what we have tried, um, to, to create a culture of, let's really think from first principles. Let's, you know, look at the math, and then we realize what are the assumptions we make to be practical, because you have to do both, and then y- we see what works. And, and when you do that, you then can see that, oh, you know, these, like, multiple papers or ideas are actually, you know, subclasses of the same general framework.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And if you do that well, you can scale.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And I think, um, that's, that's one thing that I see that's missing and that's hard to do and, and takes a very unusual group of people to, to really, like, make that work.
- OHOwen He
I think Richard Feynman said o-once that there are two kind of sciences. There, there's stamp collections and then there are physics.
- MCMartin Casado
Yeah.
- OHOwen He
So without a unified math framework, all this research are sort of just stamp collections. You could-- Like, a lot of this paper we're reading, they're just ideas inspired by some human behavior, and then they try to mimic that and say, "Oh, look, we have RSI." But then if you don't put into a unified system, you don't know if the, the overall system is actually improving or you're, you're just, like, locally applying an idea that, that's getting better. So what we have is that we're putting all these components that you could possibly recursively improve into a unified framework and look at it from a holistic view so that improving one part, um, does not negatively impact the other part. Right?
- DADhaval Adjodah
I see. I see.
- OHOwen He
So, so for example, there are people who are doing recursive improvement just on harness, and then there are people who are only doing on weights.
- MCMartin Casado
Yeah.
- OHOwen He
But weights and harnesses are not isolated component. They have impact on each other.
- DADhaval Adjodah
Y-you can think of RSI many different levels of the stack, right? You can think of, um, prompts, um, harness, weights-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... and you can go all the way to basically chips, right?
- MCMartin Casado
Yeah, sure. Yeah.
- DADhaval Adjodah
And our, our goal here is to optimize all the levels of the stack, right?
- 17:41 – 24:00
Why they chose inference
- DADhaval Adjodah
make it work.
- MCMartin Casado
Can you talk a little bit about the problem domain that you're going after just to prove that this works?
- DADhaval Adjodah
Um, we are trying very hard to pick areas of the AI space that are as close as possible to AI itself. Again-
- MCMartin Casado
I see
- DADhaval Adjodah
... we try to be recursive.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And if you think about it, there's, there's not that many problems that are super close to AI and gives you improvement on both the numerator and denominator.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And one of those is the inference problem.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
Right. Because the more you can spend on, um... Sorry, the more you can get improvements and, and efficiency out of inference, the, the more everything kind of unlocks, right?
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And it's not just, um, for technical reasons, but also the most promise of both the numerator and den-denominator being improved, right?
- MCMartin Casado
It kind of breaks the mind, right? So like just, just for everybody that's listening to this, there's two improvements going on here, and one of them is the ability of the model to make inference faster, and then the second one is now that the inference is faster, it can do that faster. [chuckles]
- DADhaval Adjodah
Exactly.
- MCMartin Casado
Right?
- DADhaval Adjodah
Yes.
- MCMartin Casado
And again, that does not necessarily mean takeoff. That can still, you know, asymptote-
- DADhaval Adjodah
Yep
- MCMartin Casado
... at some point.
- DADhaval Adjodah
Yeah.
- MCMartin Casado
But it's just wild to think of this kind of compounding impact.
- DADhaval Adjodah
Yeah, yeah. We're a startup. We're not a research lab, and one of the reasons we chose it is because everybody in the world right now wants to serve their own model.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And, and everybody wants to use the latest, for example, open weight model, Kimi K3 or whatever just coming out, and they want to be able to serve it as efficiently as possible, right? So it's, it's an economically extremely important problem right now.
- MCMartin Casado
Just for the sa-s-sake of discussion, if, if I, if I wanted to make the inference for Kimi K3 faster, would I use Kimi K3 in a recursive way, or would I use a separate model and system to do that for Kimi K3?
- DADhaval Adjodah
To improve the serving stack of Kimi K3, you mean?
- MCMartin Casado
Yeah.
- DADhaval Adjodah
In our... Well, our system, um, is not just using one LLM. You have many different LLMs that are doing many different things-
- MCMartin Casado
Oh, so you're doing-
- 24:00 – 25:42
"It's not a belief, it's happening"
- MCMartin Casado
I'm so impressed by your, your belief that, like, this idea [chuckles] of a model improving itself-
- DADhaval Adjodah
But it's not a belief No, you've got it correct. That's amazing.
- OHOwen He
I mean, also it's a question of what metric you use to, to view the progress, right? So if you use, um, I don't know, benchmark accuracy-
- DADhaval Adjodah
Yeah
- OHOwen He
... it's upper bounded by 100%. You're never going to see exponential improvement on that-
- DADhaval Adjodah
Of course. Yeah
- OHOwen He
... because it eventually bottleneck at 100%.
- DADhaval Adjodah
Of course.
- OHOwen He
So, so for a long time, we had this question, how do we even know if something has take off, right, w- if we don't have the right metric?
- DADhaval Adjodah
It's not a belief that this is happening. I think we're already seeing glimmers of that happening.
- MCMartin Casado
But it is a dramatic statement, which is I, I think it's the first time I've heard you say it. So wait, is this like a, has, has this been kind of, you know, a, a continuous result, or is it relatively recent that you're starting to see glimmers of this?
- DADhaval Adjodah
No, we-we've been seeing like in, in these results kind of grow and grow and grow over the past few months.
- MCMartin Casado
That's amazing. How do you have confidence in the results given, you know, like it's not working yet and you don't know? [chuckles]
- DADhaval Adjodah
No, it's not belief. It's, it's empirical measurement.
- MCMartin Casado
That's good.
- DADhaval Adjodah
But, but also as a, as a company, there's a, there's a very funny thing that, that is happening, which is because we have been so thorough, we have not released things until we're super sure it's correct, right? And every few months, you know, there's like an announcement of something RSI related. We, we look at it, we're like, "We should have released our results or something." And then we look at it, we try to replicate it, and we realize, oh, it's noise. You know, it's cherry-picking. And so it's very, very hard to, to know in this, in this sea of, you know, AI, I don't know what you want to call it, like everybody's trying to do AI RSI stuff to know what's
- 25:42 – 29:39
The Viceroy of Variance
- DADhaval Adjodah
legit or not. The, the technical answer is you have to be really, really good at measuring things. So for example, we have one of our engineers that we, we, we named him the Viceroy of Variance.
- MCMartin Casado
[laughing]
- DADhaval Adjodah
That's, that's a real thing. Like his job-
- MCMartin Casado
That's so good
- DADhaval Adjodah
... his job is to go and like destroy the results we have and find what's wrong. And we even have little like Slack emojis that we have. There's one where like there's like red laser stuff coming out of his eyes when like he finds that there's bad variance in stuff. One of the things that we found is that, you know, when, when you're dealing with a new benchmark or a new problem of economic value that you want to apply this to, so far we have not found a replacement for what we call quality time. Literally running hundreds of experiments, looking at it, developing an intuition, trying to figure out what is wrong, what is not wrong, what, what is, what is just variance, what is not. What is this weird thing that actually shows that there's, you know, some kind of reward hacking like two levels down, the agent that built the agent that built the agent-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... did hack something and now you have these weird results.
- MCMartin Casado
Right.
- DADhaval Adjodah
It's not easy.
- MCMartin Casado
So when I have dealt with, you know, nonlinear control systems, there's a bunch of-- in, in networking, there's a bunch of degenerate behavior you have to deal with.
- DADhaval Adjodah
Yes.
- MCMartin Casado
It's not, it's not like just convergence and divergence. You have things like oscillations.
- DADhaval Adjodah
Yes.
- MCMartin Casado
And this is a very multidimensional problem. So do you have to deal with all of that stuff, like potential oscillations and-
- DADhaval Adjodah
Absolutely
- MCMartin Casado
... meaning, meaning like it'll, it'll get good and then it'll get bad, it'll get good and-
- DADhaval Adjodah
Absolutely
- MCMartin Casado
Okay.
- DADhaval Adjodah
And then, and then on top of that, you have noise.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
LLMs are fundamentally super stochastic.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
So what we find is that specific agent on a specific benchmark, you know, there's levels of noise that sometimes are unpredictable, and some of them are extremely like weird. Like you even have some weird noise that comes from like this GPU and this other GPU. They're both, you know, B200, but have slightly different behaviors like on the physics, and you can see some of that being replicated at the high level. So that's why it has to be an empirical thing.
- OHOwen He
So from that, you could already see that intelligence is not a single scalar. It's more like a multi-dimension vector. And there will be domains and field where this RSI will give you takeoff, and there will be dimensions where you might plateau or you will have relatively slow improvement. And I think the, the, the defining factor is what is the slowest component in it. So you're an optimization, um, person. You know that there's this Amdahl's law-
- MCMartin Casado
Of course, yeah
- OHOwen He
... where, you know, uh, like the component, the, the speed up you can get by optimizing a component is-
- MCMartin Casado
That if it-- you just take it, you just take it to zero, yeah.
- OHOwen He
Yeah.
- MCMartin Casado
Nothing else dominates.
- OHOwen He
Exactly. So, so the digital part we could optimize and accelerate, you could get like a thousand times faster or a million times faster. But if it's only 20% of your overall time, you're only going to get 20% speed up, right?
- 29:39 – 31:51
The garden hose company in Wisconsin
- MCMartin Casado
or-
- OHOwen He
Mm
- MCMartin Casado
... uh, how many different robots I would need to-
- OHOwen He
Yeah
- MCMartin Casado
... to solve. So do you think that we're getting any closer to actually having bounds? 'Cause this, I think this is such a critical question for all of us to answer.
- DADhaval Adjodah
One of, one of the internal jokes we have in the company is that the, the secret to RSI is somewhere in a garden hose company in Wisconsin. And, and the joke here is that, uh, if Einstein hadn't discovered relativity-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... he, um, would not have these, these laws that tell you how speed and, you know, contraction of time works.
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And if some unfortunate engineer had to design the GPS system, they would have had to find a, find like a correction of time, and they probably would have said like, "No, no, it's clearly not the fabric of space-time that's contracting," right?
- MCMartin Casado
[laughing]
- DADhaval Adjodah
But I don't know why, but this is the formula I've devised empirically.
- MCMartin Casado
It's like, uh, some absolute, you know-
- DADhaval Adjodah
Yeah, yeah. Exactly
- MCMartin Casado
... like some fudge factor.
- DADhaval Adjodah
Yeah, exactly. Right? And then, and then people later would have been like, "Holy shit, this is, this is like space-time contracting," right?
- MCMartin Casado
[laughs]
- DADhaval Adjodah
And, and we have this theory that, you know, like RSI right now is, is being thought of let's, let's solve all these like super hard kind of, um, fashionable problem. But what we think is that the complexity of the world is such that the, the real challenges probably exist in spaces that are not being, um, applied to, right?
- MCMartin Casado
Yeah.
- DADhaval Adjodah
And so that's the joke, that there is some workflow somewhere in a garden hose company in Wisconsin-
- MCMartin Casado
That applies it
- DADhaval Adjodah
... that if we solve it, we get-
- MCMartin Casado
Yeah
- DADhaval Adjodah
... we get, you know, foom.
- MCMartin Casado
I love it. I just wanna quickly summarize. So you're going after RSI, and in, in your case, this is not autocatalytic effects. This is actual RSI, where the system improves the full system. And for you, it's an actual system, which includes the harness and the weights. Um, if you think about the problem as you've got a numerator and denominator, the numerator is like how good the system gets and the denominator is how much, um, energy goes into it. You believe you're seeing glimmers of the sign where both improve at the same time using this type of approach. Is that correct?
- DADhaval Adjodah
Yep.
- OHOwen He
Yep.
- MCMartin Casado
When can we, uh, uh, expect to hear more from you?
- DADhaval Adjodah
Um, very soon.
- MCMartin Casado
Amazing.
Episode duration: 32:06
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Transcript of episode 3Q1mdn5ZGvM
