Original Reddit post

Putting aside questions about the company, its founders’ crypto background, possible hype, or potential grift, I’m trying to understand whether the technical idea itself makes sense. My current understanding: For a large LLM or reasoning model like Claude, ChatGPT, or Gemini, generating a token looks roughly like this:

1. Tokenise the input “The capital of France is” becomes something like: [The] [capital] [of] [France] [is]
2. Turn the tokens into vectors Each token becomes a large list of numbers.
3. Run the vectors through many transformer layers The model performs attention and other operations. This involves huge numbers of multiply-and-add operations, especially matrix multiplications. This is where most of the compute happens.
4. Produce scores for possible next tokens For example: text Paris: 12.7 Lyon: 5.1 London: 3.8 banana: -2.4
5. Convert the scores into probabilities For example: text Paris: 96% Lyon: 1% London: 0.2%
6. Choose the next token The software selects a token from that distribution. It might choose: Paris
7. Repeat The model adds “Paris” to the context and runs again to generate the next token. Reasoning models may also generate many hidden intermediate tokens before giving the final answer. So my understanding is that choosing the final token is not the expensive part . Generating a random number and selecting from the probability distribution is relatively cheap. The expensive part is the huge neural-network calculation needed to produce the scores and probabilities. Therefore, if Extropic were only saying: “We can use thermal noise instead of a digital random-number generator for the final token choice,” that would not be a major breakthrough. It would improve only a small part of the workload. I think their actual idea is more ambitious. Today’s LLMs roughly do: input → huge deterministic calculation → probability distribution → sample Extropic seems to be exploring different probabilistic or energy-based models, where much of the computation is represented by interacting stochastic variables. Their hardware uses physical noise and connections between pbits to let the system evolve toward useful probability distributions. So instead of a GPU digitally simulating every part of a probabilistic process, the chip would build a physical stochastic system and let the hardware’s behaviour perform part of the computation. The potential benefit is therefore not: “Thermal noise makes random numbers cheaper.” It is more like: “Redesign AI models so useful computation can happen through physical stochastic dynamics instead of so many deterministic matrix multiplications.” If that is correct, Extropic’s chips would not be simple drop-in replacements for GPUs running today’s transformers. The bigger bet is that new AI architectures designed for this hardware could perform useful tasks while using much less energy. Is this a fair summary? And where, specifically, would thermodynamic sampling replace the expensive operations currently performed by a transformer? That is the part I’m still struggling to understand. submitted by /u/rosemaryoannah

Originally posted by u/rosemaryoannah on r/ArtificialInteligence