.@Etched raised $1 billion in 26 days, and its valuation jumped from $10.3 billion to $21 billion. But look at the order: - @JaneStreetGroup tested its hardware - Installed the first rack - Then led a $700 million round Jane Street runs tens of thousands of GPUs, builds custom compilers and hardware, and operates machine-learning systems where tiny latency gains can have enormous economic value. It is close to an ideal customer for testing Etched’s claims. Etched originally built Sohu, a Transformer-only chip that sacrificed GPU-like flexibility for speed and efficiency. Now it calls its system "architecture-agnostic" and says it runs Llama, DeepSeek, Qwen, and Mamba, a state-space model. If Etched kept the efficiency of specialization while escaping the Transformer-only trap, Jane Street may have validated something rare: specialized hardware without crippling rigidity. The GPU once won by finding the right middle ground between flexibility and specialization. Has Etched found the next one? I unpacked the hardware, the funding, and what remains unproven in the new Attention Span episode.