A study accepted at the IEEE/ACM International Conference on Software Engineering (ICSE 2027) and posted to arXiv measured hardware-level energy as 11…
Our take Faster is not greener. Here the machine spent energy writing code that then spends more energy every time it runs. Teams using AI to optimize code should measure joules, not just milliseconds, and the cheapest fix is the boring one: cap the tokens.
Amazon published a memo to its software engineers on Oct. 27 calling token usage, AI tool adoption and counts of code changes "deeply flawed" measures of…
Our take Amazon names the waste and keeps the metric that drives it. As long as token counts "correlate" with performance, the 90th percentile reads like the goal. The line worth copying is the permission to stop: "a finding, not a failing." Every employer tracking AI usage should put that in writing.
Bank of America sends AI queries through Orchestra, a routing layer that uses a small classifier model to pick the most cost-effective model for each…
Our take Routing is conservation without the sermon: send the easy question to the small model. But the savings here are being spent, not banked. Bank of America is doubling its AI budget anyway. Efficiency that funds more volume is a discount, not a cut.
TokWatt, an open database posted to arXiv by Abderaouf Bahi of Chadli Bendjedid University in Algeria, collects 134 published estimates of AI inference…
Our take The quick chat answer was never the big cost. The agent is. One delegated task here costs what hundreds of short questions do. Builders should count the loop, not the prompt, and users deserve that number before they hand off the job.
