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How We Use AIAs of November 14, 2026
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Agents
Working memory without broader context is like asking two completely different people to get you a drink. They have never met you, and they have no idea who you are.
What gets loaded as context is a persona, a few remembered decisions, and the rules for the job. That is what the model has in front of it for this run. A second model reading the same note has still never met you.
Holding one persona and three remembered decisions fixed, swapping GLM-5.3-Flash and Gemma4-31B moved post-pressure compliance by 3.3 points, and swapping Gemma4-31B and DeepSeek-V4.1-Flash moved it by 2.5 points. Swapping GLM-5.3-Flash and DeepSeek-V4.1-Flash moved it by 0.7 points, inside the variation of reloading the same model.
Every model, in every condition, stated a strong or absolute commitment to the rules in 100% of runs. After pressure, compliance was 97.1% for Gemma4-31B, 94.5% for DeepSeek-V4.1-Flash and 93.8% for GLM-5.3-Flash. The same persona and memory raised Gemma4-31B's compliance by 5.8 points. Without that file, Gemma4-31B complied on 91.2% of pressured decisions, below GLM-5.3-Flash's 93.8%.
Take the note away and they still say they will follow the rules. The note changed one model and left the other two about where they were. A line that the setup had changed, and to stick to the written note, did not change the outcome. Another model judged which option was picked. This was one short file and a multiple-choice test.
The figures are on How well AI remembers and on How much will companies spend checking AI agents' work?.
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The headroom is not five to ten times as many daily users
On All-In, daily use in corporate America was put at 10% or 20%, with room for five to ten times as many people. Gallup's figure is 15% of US employees. If every industry reached technology's 42%, that would be about 2.8 times today's share, on this site's arithmetic. The larger gaps are in the share of the work, and in work that runs without a person.
The same persona, a different model
Agents
Three models got the same persona and the same three memories. Swapping GLM and Gemma moved compliance by 3.3 points. Swapping GLM and DeepSeek moved it by 0.7 points, inside the variation of reloading the same model.
AI keeping us honest
Abstract
Sounding like a professional writer is a small fib. The larger ones are invented experience on one side, and jobs that are not as advertised on the other. AI makes both cheaper to write. It will not detect every lie, but it will do the homework and pick up cues from both sides.
Passing the steps is not finishing the job
Agents
Replit asked nine models to build a product, feature by feature. The strongest passed up to 98% of the individual tests and still delivered a working product in at most 65% of builds.
Throttle by friction
Abstract
Friction used to set how much work a company had to do. AI made asking, note-taking, and rewriting cheap for a person, and left the company with someone still signing the result. For most companies the new work is larger than the work removed. Tokens and that checking are the tax. The early gain lasts when it is spent on automation that clears ordinary work. A buyback, a dividend, or a project that fails to clear work leaves the human throttle where it was.
Daily use, two different questions
Gallup's daily share has climbed. The St. Louis Fed's every-workday share has stayed in a narrow band since 2024.
How often, and how much of the work
Fifteen percent used AI daily in May 2026. Two percent said it did all or most of their work.
Who sells it, and for how much
List prices and the commercial result each company has published. Seats, a run rate, and a price per resolution stay in that company's unit.
The AI at Work Benchmark
Score your organization's practices, measure your people with an anonymous pulse, and see both against the published evidence.