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- Hacker News
- I really don't get why they decided to choose usb-b-3.0 and usb-micro-b-2.0 female for this? Usb-c is so much cheaper and more common at this point. Why bother with using such old plugs, especially so when one plug could do the job of 2?by WorldPeas
- This looks really amazing if not unbelieveable to the point where it is almost too good to be real.
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?
by rvz - I doubt it’s a scam. Beff might be on to something or completely delusional, but not actively scamming.by unsupp0rted
- I mean it sure looks like a scam.
I really like the design of it though.
by KaiserPro - Too good to be true, incomprehensible jargon to go along...by arisAlexis
- i've followed them for a while and as just a general technologist and not a scientist, i have a probably wrong idea of what they do, but perhaps correcting it will let others write about it more accurately.
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 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.
by sashank_1509 - What makes you so sure that extropic is the second and not the first?by nfw2
- This gives me Devs vibe (2020 TV Series) - https://www.indiewire.com/awards/industry/devs-cinematograph...by Void_
- Such an underrated TV show.by tcdent
- That's what they're trying to do, yeah. To give off a cool vibe I mean. To raise more money. There is nothing even remotely as cool in their real (or not) product. I was very excited when they started specifically because of their cool branding, but the vibe quickly wears off.by vortegne
- I listened to the Hinton podcast few days ago, he mentioned (IIRC) that "analog" AIs are bad because the models can not be transfered/duplicated in a lossless way, like in .gguf format, every analog system is built differently you have to re-learn/re-train again somehow
Does TSUs have to same issue?
by est - 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...
by quantumHazer - 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.by vlovich123
- I'm still waiting for my memristors.by 6510
- 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
- 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.
by alyxya - 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.by nfw2
- What's not comprehensible?
It's just miniaturized lava lamps.
by lacy_tinpot - 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.by ipsum2
- 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
- can you play doom on it, yet?by helicone
- Could you explain to me how you could reasonably justify not citing even one of Normal Computing's works? I can't imagine you're unaware of them or their works. You cite the Thermodynamic Computing group paper.by chermi
- I was hoping the preprint would explain the mysterious ancient runes on the device chassis :(
- 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...
by d_silin - The article linked by you uses magnetic tunnel junctions for implementing the RNG part.
The Web site of Extropic claims that their hardware devices are made with standard CMOS technology, which cannot make magnetic tunnel junctions.
So it appears that there is no connection between the linked article and what Extropic does.
The idea of stochastic computation is not at all new. I have read about such stochastic computers as a young child, more than a half of century ago, long before personal computers. The research on them was inspired by the hypotheses about how the brain might work.
Along with analog computers, stochastic computers were abandoned due to the fast progress of deterministic digital computers, implemented with logic integrated circuits.
So anything new cannot be about the structure of stochastic computers, which has been well understood for decades, but only about a novel extremely compact hardware RNG device, which could be scaled to a huge number of RNG devices per stochastic computer.
I could not find during a brief browsing of the Extropic site any description about the principle of their hardware RNG, except that it is made with standard CMOS technology. While there are plenty of devices made in standard CMOS that can be used as RNG, they are not reliable enough for stochastic computation (unless you use complex compensation circuits), so Extropic must have found some neat trick to avoid using complex circuitry, assuming that their claims are correct.
However I am skeptical about their claims because of the amount of BS words used on their pages, which look like taken from pseudo-scientific Star Trek-like mumbo-jumbo, e.g. "thermodynamic computing", "accelerated intelligence", "Extropic" derived from "entropic", and so on.
To be more clear, there is no such thing as "thermodynamic computing" and inventing such meaningless word combinations is insulting for the potential customers, as it demonstrates that the Extropic management believes that they must be naive morons.
The traditional term for such computing is "stochastic computing". "Stochastics" is an older, and in my opinion better, alternative name for the theory of probabilities. In Ancient Greek, "stochastics" means the science of guessing. Instead of "stochastic computing" one can say "probabilistic computing", but not "thermodynamic computing", which makes no sense (unless the Extropic computers are dual use, besides computing, they also provide heating and hot water for a great number of houses!).
Like analog computers, stochastic computers are a good choice only for low-precision computations. With increased precision, the amount of required hardware increases much faster for analog computers and for stochastic computers than for deterministic digital computers.
The only currently important application that is happy even with precisions under 16 bit is AI/ML, so trying to market their product for AI applications is normal for Extropic, but they should provide more meaningful information about what advantages their product might have.
by adrian_b - by TYPE_FASTER
- 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.by jazzyjackson
- 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.by vlovich123
- 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."
by A_D_E_P_T