Extropic TSU Spurs Probabilistic AI Hardware Debate

The instinct is right on energy efficiency, but the post treats algorithm, memory, and hardware as if one breakthrough can solve all three.

• –Predictive coding can approximate backprop on some graphs, but it does not eliminate credit assignment or optimization complexity.
• –BitNet b1.58 is ternary weights and low-bit math, not probabilistic neuron firing, so the post blends quantization with randomness.
• –Extropic's pbits already cover the random two-state hardware angle, but TSU is still aimed at probabilistic sampling, not a drop-in LLM accelerator.
• –Re-prompting and on-the-fly weight updates are closer to iterative inference or online learning, which can help quality but also add latency and forgetting risk.
• –The real gap is manufacturable probabilistic silicon plus tooling, not just a clever noise source.