Jev is a new AI model that only outputs structured data instead of human language, making it dramatically faster (70-500ms vs seconds) and enabling real-time applications like playing Doom. The author argues this speed advantage could become a new computational primitive for AI, though he suspects competitors can replicate it using simpler inference tricks on existing models.

• Jev generates all structured output in a single forward pass instead of token-by-token, achieving 70-500ms response times compared to seconds for standard LLMs, fast enough to play real-time video games.
• The speed advantage likely doesn't require novel model architecture—you can achieve similar results by prefilling responses and generating only one constrained token with existing LLMs, suggesting Jev lacks a substantial technical moat.
• Structured output could unlock entirely new use cases beyond chatbots by injecting "100ms worth of dirt-cheap intelligence" at decision points throughout applications, though Jev won't match frontier LLMs in raw capability.

Etched announced a working inference chip and over $1 billion in signed contracts after emerging from stealth with $800 million raised and a $5 billion valuation. Their rack-scale system, built on TSMC’s N4P process, is already running models like DeepSeek, Qwen and Llama, and production is ramping at new Taiwan and San Jose facilities.

• Etched has working A0 silicon on TSMC's N4P process and a rack-scale system already running DeepSeek, Qwen, Mamba and Llama models
• The company raised $800M total, including a $500M round in December at a $5B post-money valuation, backed by Peter Thiel, Karpathy, Hinton, Fei-Fei Li and major trading firms
• It has over $1 billion in signed customer contracts despite being just under three years old
• Etched is building out Taiwan production and a San Jose HQ (data center, test house, NPI lab) with a goal of gigawatt-scale manufacturing by 2027

The article compares cost-plus and value-based pricing for AI inference resellers, showing how cost-plus margins shrink as inference commoditizes while value-based charges per outcome retain durable margins. It also covers cost-optimization tactics—model routing, caching, distillation—and explains why bring-your-own-key customers break cost-plus but still fit value-based and optimization models.

• Cost-plus pricing on inference collapses as models commoditize since customers can switch to cheaper raw API calls once they spot the markup
• Value-based pricing (Sierra charging per resolved ticket, Devin's Agent Compute Units) decouples revenue from inference costs by charging for outcomes instead
• Distillation—training a sub-8B "student" model from a "teacher" model—can cut per-call costs (e.g., $1.00 to $0.70) while creating a proprietary edge that's harder to copy than caching or routing
• Bring-your-own-key customers break cost-plus pricing entirely but still work under value-based or platform-fee/optimization models