Guillaume Verdon

There's always more 999s of P safety that you can gain, you know, 999.9999% safe. Maybe you want another 9, oh, please give us full access to everything you do, full surveillance. And frankly, those that Our proponents of AI safety have proposed like having a global panopticon, right? Where you have centralized perception of everything going on.

There's always more 999s of P safety that you can gain, you know, 999.9999% safe. Maybe you want another 9, oh, please give us full access to everything you do, full surveillance. And frankly, those that Our proponents of AI safety have proposed like having a global panopticon, right? Where you have centralized perception of everything going on.

And to me, that just opens up the door wide open for a sort of big brother 1984-like scenario. And that's not a future I want to live in.

And to me, that just opens up the door wide open for a sort of big brother 1984-like scenario. And that's not a future I want to live in.

And to me, that just opens up the door wide open for a sort of big brother 1984-like scenario. And that's not a future I want to live in.

Yeah, I mean, you know, originally we weren't going to announce last week, but I think with the doxing and disclosure, we got our hand forced. So we had to disclose roughly what we were doing, but... Really, Xtropic was born from my dissatisfaction and that of my colleagues with the quantum computing roadmap.

Yeah, I mean, you know, originally we weren't going to announce last week, but I think with the doxing and disclosure, we got our hand forced. So we had to disclose roughly what we were doing, but... Really, Xtropic was born from my dissatisfaction and that of my colleagues with the quantum computing roadmap.

Yeah, I mean, you know, originally we weren't going to announce last week, but I think with the doxing and disclosure, we got our hand forced. So we had to disclose roughly what we were doing, but... Really, Xtropic was born from my dissatisfaction and that of my colleagues with the quantum computing roadmap.

Quantum computing was sort of the first path to physics-based computing that was trying to commercially scale. And I was working on physics-based AI that runs on these physics-based computers.

Quantum computing was sort of the first path to physics-based computing that was trying to commercially scale. And I was working on physics-based AI that runs on these physics-based computers.

Quantum computing was sort of the first path to physics-based computing that was trying to commercially scale. And I was working on physics-based AI that runs on these physics-based computers.

But ultimately, our greatest enemy was this noise, this pervasive problem of noise that, you know, as I mentioned, you have to constantly pump out the noise out of the system to maintain this pristine environment where quantum mechanics can take effect. And that constraint was just too much. It's too costly to do that.

But ultimately, our greatest enemy was this noise, this pervasive problem of noise that, you know, as I mentioned, you have to constantly pump out the noise out of the system to maintain this pristine environment where quantum mechanics can take effect. And that constraint was just too much. It's too costly to do that.

But ultimately, our greatest enemy was this noise, this pervasive problem of noise that, you know, as I mentioned, you have to constantly pump out the noise out of the system to maintain this pristine environment where quantum mechanics can take effect. And that constraint was just too much. It's too costly to do that.

We were wondering, as generative AI is eating the world, more and more of the world's computational workloads are focused on generative AI, how could we use physics to engineer the ultimate physical substrate for generative AI? From first principles of physics, of information theory, of computation, and ultimately of thermodynamics.

We were wondering, as generative AI is eating the world, more and more of the world's computational workloads are focused on generative AI, how could we use physics to engineer the ultimate physical substrate for generative AI? From first principles of physics, of information theory, of computation, and ultimately of thermodynamics.

We were wondering, as generative AI is eating the world, more and more of the world's computational workloads are focused on generative AI, how could we use physics to engineer the ultimate physical substrate for generative AI? From first principles of physics, of information theory, of computation, and ultimately of thermodynamics.

And so what we're seeking to build is a physics-based computing system and physics-based AI algorithms that are inspired by out-of-equilibrium thermodynamics or harness it directly to do machine learning as a physical process.

And so what we're seeking to build is a physics-based computing system and physics-based AI algorithms that are inspired by out-of-equilibrium thermodynamics or harness it directly to do machine learning as a physical process.

And so what we're seeking to build is a physics-based computing system and physics-based AI algorithms that are inspired by out-of-equilibrium thermodynamics or harness it directly to do machine learning as a physical process.