Intelligence per Watt: Measuring Intelligence Efficiency of Local AI
1 distinct publishers, one article each in this sample.
Ownership mix: Other: 1
1 publishers · 1 articles · switch to 1-minute for disagreement and framing.
What happened
Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure. Demand growth strains this paradigm faster than providers can scale. Two advances create an…
Why the coverage differs
AI-assisted comparison · labeled · generated just generated or not yet stored · not a verdict
This story is currently covered by only 1 source. Comparison requires at least two sources from different bias positions.
Comparison summary
AI-assisted · Cerebras / Llama · just generated or not yet stored · inspect sources below rather than trusting this alone
This story is currently covered by only 1 source. Comparison requires at least two sources from different bias positions.
Report timeline
Oldest → newest among clustered members. Gaps may mean delayed pickup, not silence.
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Sep 14, 2026, 2:16 AMarXiv.org · CenterIntelligence per Watt: Measuring Intelligence Efficiency of Local AI
Coverage by perspective
Perspective labels are external consensus ratings (AllSides / Ad Fontes / MBFC-style), not WeSearch truth scores. Center is not automatically more accurate.
Center · 1
Headline framing
Vocabulary fingerprints · not a political endorsement
No framing analysis for this cluster yet.
Bias/ownership: published methodology on source profiles · AI text always labeled · no reader paywall · no engagement ranking of news · transparency · contribute Ws · home