.@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.

Etched has raised $700M at a $21B valuation, more than doubling its $10.3B valuation from just a few weeks ago. Etched is an AI chip startup building specialized hardware for inference, essentially trying to compete with $NVDA on the infrastructure used to run AI models at scale. Jane Street led the round and has already received Etched’s first operational server rack, while the company says it has roughly $1B in customer contracts.