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Researchers tested an AI system called Jev on 2,029 real phone calls stripped down to pure call structure—no audio or transcripts—and it learned to predict which calls would result in bookings with 94% accuracy by the end, all for $3 in compute costs.
- Jev made 38,012 turn-level forecasts across the calls at 118ms median latency, processing thousands of calls in seconds without any fine-tuning or examples from the company's data.
- The system achieved 0.78 AUC by the halfway point and reached 94% ranking accuracy near the end, learning to separate bookable from non-bookable calls based solely on call structure, timing, and workflow patterns.
- The experiment revealed the AI could extract predictive signal from call metadata alone—turns, tool calls, workflow stages—demonstrating that meaningful patterns exist independent of what's actually said.
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.