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buherator
buherator
Edited 6 hours ago
I could not find an existing phrase so I'm inventing a new one:
"Yes Man Design" is the principle of implementing a system so it always reports desired results and hides any errors. This often serves to achieve faster adoption than competing, more honest designs, forcing out alternatives.
I use the #LLM tag for no particular reason.
"Yes Man Design" is the principle of implementing a system so it always reports desired results and hides any errors. This often serves to achieve faster adoption than competing, more honest designs, forcing out alternatives.
I use the #LLM tag for no particular reason.
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Ramin Honary
ramin_hal9001@fe.disroot.org
@buherator
from this article: https://leehanchung.github.io/blogs/2026/04/05/the-ai-great-leap-forward/
During the Great Leap Forward, provinces competed to report the most spectacular grain yields. Hubei reported 10,000 jin per mu. Guangdong said 50,000. Some counties claimed over 100,000 — physically impossible numbers, rice plants supposedly so dense that children could stand on top of them. Officials staged photographs. Everyone knew the numbers were fake. Everyone reported them anyway, because the alternative was being labeled a saboteur. The central government, delighted by the bounty, increased grain requisitions based on the reported yields. Farmers starved eating the difference between the real number and the fantasy.
Every province, every village, every household was expected to close the gap with industrialized Western nations by sheer force of will. Peasants who had never seen a factory were handed quotas for steel production. If enough people smelt enough iron, China becomes an industrial power overnight. Expertise was irrelevant. Conviction was sufficient.
The mandate today is identical, just swap the nouns. Every company, every function, every individual contributor is expected to close the AI gap. Ship AI features. Build agents. Automate workflows. That nobody on the team has ever trained a model, designed an evaluation system, or debugged a retrieval system is beside the point. Conviction is sufficient.
One team reports their AI copilot “reduced development time by 40%.” The next team, not to be outdone, reports 60%. A third claims their AI agent “automated 80% of analyst workflows.” Nobody asks how these were measured. Nobody checks the methodology. Nobody points out that the team claiming 80% automation still has the same headcount doing the same work. The numbers go into a slide deck. The slide deck goes to the board. The board is delighted. The board increases investment.
Your AI usage is now a KPI. You are being evaluated on how much grain you reported, not how much grain you grew. This is Goodhart’s Law at organizational scale: when a measure becomes a target, it ceases to be a good measure. The metric was supposed to track whether AI is making the company better. Instead, the entire company is now optimizing to make the metric look better. The beatings will continue until adoption improves.
from this article: https://leehanchung.github.io/blogs/2026/04/05/the-ai-great-leap-forward/
During the Great Leap Forward, provinces competed to report the most spectacular grain yields. Hubei reported 10,000 jin per mu. Guangdong said 50,000. Some counties claimed over 100,000 — physically impossible numbers, rice plants supposedly so dense that children could stand on top of them. Officials staged photographs. Everyone knew the numbers were fake. Everyone reported them anyway, because the alternative was being labeled a saboteur. The central government, delighted by the bounty, increased grain requisitions based on the reported yields. Farmers starved eating the difference between the real number and the fantasy.
Every province, every village, every household was expected to close the gap with industrialized Western nations by sheer force of will. Peasants who had never seen a factory were handed quotas for steel production. If enough people smelt enough iron, China becomes an industrial power overnight. Expertise was irrelevant. Conviction was sufficient.
The mandate today is identical, just swap the nouns. Every company, every function, every individual contributor is expected to close the AI gap. Ship AI features. Build agents. Automate workflows. That nobody on the team has ever trained a model, designed an evaluation system, or debugged a retrieval system is beside the point. Conviction is sufficient.
One team reports their AI copilot “reduced development time by 40%.” The next team, not to be outdone, reports 60%. A third claims their AI agent “automated 80% of analyst workflows.” Nobody asks how these were measured. Nobody checks the methodology. Nobody points out that the team claiming 80% automation still has the same headcount doing the same work. The numbers go into a slide deck. The slide deck goes to the board. The board is delighted. The board increases investment.
Your AI usage is now a KPI. You are being evaluated on how much grain you reported, not how much grain you grew. This is Goodhart’s Law at organizational scale: when a measure becomes a target, it ceases to be a good measure. The metric was supposed to track whether AI is making the company better. Instead, the entire company is now optimizing to make the metric look better. The beatings will continue until adoption improves.
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buherator
buherator
@ramin_hal9001 Right, I actually read this one just forgot about it! Thanks for the reminder!
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It sounds like an old pattern we call ”blowing sunshine upstairs”. Possibility related?
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