[0]: https://www.normalcomputing.com

[1]: https://www.zach.be/p/making-unconventional-computing-practi...

This is what they are trying to create, more specifically:

https://pubs.aip.org/aip/apl/article/119/15/150503/40486/Pro...

I have not seen benchmarks on Extropic's new computing hardware yet but need to know from experts who are in the field of AI infrastructure at the semiconductor level if this is legit.

I'm 75% believing that this is real but have a 25% skepticisim and will reserve judgement until others have tried the hardware.

So my only question for the remaining 25%:

Is this a scam?

This gives "hype" vibes.

https://extropic.ai/writing https://arxiv.org/abs/2510.23972

This is "quantum" computing, btw.

Or if we call it analog is it too obvious what the problems are going to be?

To me, the biggest limitation is that you’d need an entirely new stack to support a new paradigm. It doesn’t seem compatible with using existing pretrained models. There’s plenty of ways to have much more efficient paradigms of computation, but it’ll be a long while before any are mature enough to show substantial value.

The bad thing about Silicon Valley is charlatans abuse this openness and friendly spirit, and swindle investors of millions with pipe dreams and worthless technology. I think the second is inevitable as Silicon Valley becomes more famous, more high status without a strong gatekeeping mechanism which is also anathema to its open ethos. Unfortunately this company is firmly in the second category. A performative startup, “changing the world” to satisfy the neurosis of its founders who desperately want to be seen as someone taking risks to change the world. In reality it will change nothing, and go die into the dustbins of history. I hope he enjoys his 15 minutes of fame.

my handwavy analogy interpretation was they were in-effect building an analog computer for AI model training, using some ideas that originated in quantum computing. their insight is that since model training is itself probabilistic, you don't need discrete binary computation to do it, you just need something that implements the sigmoid function for training a NN.

they had some physics to show they could cause a bunch of atoms to polarize (conceptually) instantaneously using the thermodynamic properties of a material, and the result would be mostly deterministic over large samples. the result is what they are calling a "probabilistic bit" or pbit, which is an inferred state over a probability distribution, and where the inference is incorrect, they just "get it in post," because the speed of the training data through a network of these pbits is so much more efficient that it's faster to just augment and correct the result in the model afterwards than to use classical clock cycles to directly compute it.

The core idea of THRML, as I understand it, is to present a nice programming interface to hardware where coin-flipping is vanishingly cheap. This is moderately useful to deep learning, but the artisanally hand-crafted models of the mid-2000s did essentially nothing at all except flip coins, and it would have been enormously helpful to have something like this in the wild at that time.

The core "trick" of the era was to make certain very useful but intractable distributions built on something called "infinitely exchangeable sequences" merely almost intractable. The trick, roughly, was that conditioning on some measure space makes those sequences plain-old iid, which (via a small amount of graduate-level math) implies that a collection of "outcomes" can be thought of as a random sample of the underlying distribution. And that, in turn, meant that the model training regimens of the time did a lot of sampling, or coin-flipping, as we have said here.

Peruse the THRML README[1] and you'll see the who's who of techniques and modeling prodedures of the time. "Gibbs sampling", "probabilistic graphical models", and "energy-based models", and so on. All of these are weaponized coin flipping.

I imagine the terminus of this school of thought is basically a natively-probabilistic programming environment. Garden variety deterministic computing is essentially probabilistic computing where every statement returns a value with probability 1. So in that sense, probabilistic computing is a ful generalization of deterministic computing, since an `if` might return a value with some probability other than 1. There was an entire genre of languages like this, e.g., Church. And now, 22 years later, we have our own hardware for it. (Incidentally this line of inquiry is also how we know that conditional joint distributions are Turing complete.)

Tragically, I think, this may have arrived too late. This is not nearly as helpful in the world of deep learning, with its large, ugly, and relatively sample-free models. Everyone hates to hear that you're cheering from the sidelines, but this time I really am. I think it's a great idea, just too late.

[1]: https://github.com/extropic-ai/thrml/blob/7f40e5cbc460a4e2e9...