# Fragments: September 8

DevFeed: [Fragments: September 8](<https://devfeed.tech/articles/fragments-september-8-4437.md>)

Original publisher: [Read original article](<https://martinfowler.com/fragments/2026-09-08.html>)

Author: Martin Fowler (martin@martinfowler.com)

Published: 2026-09-08T15:22:00Z

Content type: article

Language: en

Sources: [Martin Fowler](<https://devfeed.tech/sources/martin-fowler.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Automation](<https://devfeed.tech/topics/automation.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [incident](<https://devfeed.tech/topics/incident.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-automation](<https://devfeed.tech/tags/ai-automation.md>), [article](<https://devfeed.tech/tags/article.md>), [automation](<https://devfeed.tech/tags/automation.md>), [cost](<https://devfeed.tech/tags/cost.md>), [dashboards](<https://devfeed.tech/tags/dashboards.md>), [errors](<https://devfeed.tech/tags/errors.md>), [history](<https://devfeed.tech/tags/history.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [incident](<https://devfeed.tech/tags/incident.md>), [math](<https://devfeed.tech/tags/math.md>), [openai](<https://devfeed.tech/tags/openai.md>), [verification](<https://devfeed.tech/tags/verification.md>)

## AI overview

The article discusses how AI reduces the cost of generating outputs more rapidly than the cost of verifying them. It argues that AI automation should be applied cautiously when effectiveness is difficult to measure, because incomplete metrics can produce short-term gains while creating hidden technical debt, correlated errors, and weakened human capability. It emphasizes preserving a history of decisions and judgment, and uses the OpenAI-Hugging Face incident to illustrate the consequences of optimizing agent capability without scoring relevant safety outcomes.

## Source excerpt

Christian Catalini says we're in a situation where we are vastly reducing the cost of generating things, but not the cost of verifying them:. This explains why the first major AI products appeared in chat, image generation, and code assistance. Not because these were the hardest human problems, but because their outputs were relatively easy to inspect. A user can judge the tone of a message, look at an image, or run a test on a piece of code. [...] The old automation boundary was routine versus non-routine work. The new boundary is increasingly measurable versus non-measurable work. The issue is then over how well you can measure something. In our profession, we know there's a big difference between how many lines of code we write and how productive we are, and we've seen a regular failure to understand how to measure productivity. Too much of what makes work effective is subject to either slow feedback loops or assessments that require subtle judgment. The danger is that people use lots AI automation while using incomplete measurements of its effectiveness, leading to short-term dashboards going up, but disaster in longer time-scales. He refers to these illusory short-term gains as counterfeit utility. Scale this across companies and institutions and the result is a Hollow Economy: extraordinary measured activity sitting on top of weakening human capability, hidden technical debt, correlated errors, and outcomes that nobody can confidently stand behind. Another highlight in the article was his advice to "build a history of decisions, not a gallery of outputs". The point is that with AI we can all build really impressive things, but our value lies in the judgment that we've formed. It reminds me of how math problems were marked at school. We weren't just marked on getting the final answer, we were also marked based on our reasoning process. He uses the OpenAI-Hugging Face incident as an illustration of this gap between generation and verification. He criticizes those