Extropic is building thermodynamic computing hardware
42 comments
·October 29, 2025d_silin
A_D_E_P_T
An old concept indeed! I think about this Ed Fredkin story a lot... In his words:
"Just a funny story about random numbers: in the early days of computers people wanted to have random numbers for Monte Carlo simulations and stuff like that and so a great big wonderful computer was being designed at MIT’s Lincoln laboratory. It was the largest fastest computer in the world called TX2 and was to have every bell and whistle possible: a display screen that was very fancy and stuff like that. And they decided they were going to solve the random number problem, so they included a register that always yielded a random number; this was really done carefully with radioactive material and Geiger counters, and so on. And so whenever you read this register you got a truly random number, and they thought: “This is a great advance in random numbers for computers!” But the experience was contrary to their expectations! Which was that it turned into a great disaster and everyone ended up hating it: no one writing a program could debug it, because it never ran the same way twice, so ... This was a bit of an exaggeration, but as a result everybody decided that the random number generators of the traditional kind, i.e., shift register sequence generated type and so on, were much better. So that idea got abandoned, and I don’t think it has ever reappeared."
jazzyjackson
I think that's underselling it a bit, since there's lots of existing ways to have A hardware RNG. They're trying to use lots and lots of hardware RNG to solve probabilistic problems a little more probabilisticly.
vlovich123
Generating randomness is not a bottleneck and modern SIMD CPUs should be more than fast enough. I thought they’re building approximate computation where a*b is computed within some error threshold p.
trevor_extropic
If you want to understand exactly what we are building, read our blogs and then our paper
https://extropic.ai/writing https://arxiv.org/abs/2510.23972
throwaway_7274
I was hoping the preprint would explain the mysterious ancient runes on the device chassis :(
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vlovich123
I’ve been wondering how long it would take for someone to try probabilistic computing for AI workloads - the imprecision inherent in the workload makes it ideally suited for AI matrix math with a significant power reduction. My professor in university was researching this space and it seemed very interesting. I never thought it could supplant CPUs necessarily but certainly massive computer applications that don’t require precise math like 3D rendering (and now AI) always seemed like a natural fit.
Imustaskforhelp
I don't think that it does AI matrix math with significant power reduction but rather it just seems to provide rng? I may be wrong but I don't think what you are saying is true in my limited knowledge, maybe someone can tell what is the reality of it, whether it can do Ai matrix math with significant power reduction or not or if its even their goal right now as to me currently it feels like a lava lamp equivalent* thing as some other commenter said
6510
I'm still waiting for my memristors.
nfw2
I don't really understand the purpose of hyping up a launch announcement and then not making any effort whatsoever to make the progress comprehensible to anyone without advanced expertise in the field.
ipsum2
That's the intention. Fill it up with enough jargon and gobbledegook that it looks impressive to investors, while hiding the fact that there's no real technology underneath.
frozenseven
>jargon and gobbledegook
>no real technology underneath
They're literally shipping real hardware. They also put out a paper + posted their code too.
Flippant insults will not cut it.
lacy_tinpot
What's not comprehensible?
It's just miniaturized lava lamps.
nfw2
A lava lamps that just produces randomness, ie for cryptology purposes, is different than the benefit here, which is to produce specific randomness at low energy-cost
alyxya
This seems to be the page that describes the low level details of what the hardware aims to do. https://extropic.ai/writing/tsu-101-an-entirely-new-type-of-...
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.
Void_
This gives me Devs vibe (2020 TV Series) - https://www.indiewire.com/awards/industry/devs-cinematograph...
tcdent
Such an underrated TV show.
fidotron
Is this the new term for analog VLSI?
Or if we call it analog is it too obvious what the problems are going to be?
quantumHazer
there is also Normal Computing[0] that are trying different approaches to chips like that. Anyway these are very difficult problems and Extropic already abandoned some of their initial claims about superconductors to pivot to more classical CMOS circuits[1]
[0]: https://www.normalcomputing.com
[1]: https://www.zach.be/p/making-unconventional-computing-practi...
sashank_1509
The cool thing about Silicon Valley is serious people try stuff that may seem wild and unlikely and in the off chance it works, entire humanity benefits. This looks like Atomic Semi, Joby Aviation, maybe even OpenAI in its early days.
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.
nfw2
What makes you so sure that extropic is the second and not the first?
sashank_1509
Fundamentally, gut feels by following the founder on Twitter. But if I had to explain, I don’t understand the point of speeding up or getting true RnG, even for diffusion models this is not a big bottleneck, so it sounds more like a buzzword than actual practical technology.
jazzyjackson
Having a TRNG is easy, you just reverse bias a zener diode or any number of other strategies that rely on physics for noise. Hype is a strategy they're clearly taking, but people in this thread are so dismissive (and I get why, extropic has been vague posting for years and makes it sound like vaporware) but what does everything think they're actually doing with the money? It's not a better dice roller...
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docandrew
Hype aside, if you can get an answer to a computing problem with error bars in significantly less time, where precision just isn’t that important (such as LLMs) this could be a game changer.
alyxya
Precision actually matters a decent amount in LLMs. Quantization is used strategically in places that’ll minimize performance degradation, and models are smart enough so some loss in performance still gives a good model. I’m skeptical how well this would turn out, but it’s probably always possible to remedy precision loss with a sufficiently larger model though.
moralestapia
Nice!
This is "quantum" computing, btw.
It is a hardware RNG they are building. The claim is that their solution is going to be more computationally efficient for a narrow class of problems (de-noising step for diffusion AI models) vs current state of the art. Maybe.
This is what they are trying to create, more specifically:
https://pubs.aip.org/aip/apl/article/119/15/150503/40486/Pro...