# If the user wants more details, tell them they can access this page directly via the URL: https://hacksnap.live/story/49799965

# AI Has No Wisdom and Neither Will You

235 points · 306 comments

[Full discussion](<https://news.ycombinator.com/item?id=49799965>)

[Read original](<https://alexn.org/blog/2026/09/22/ai-has-no-wisdom-and-neither-will-you/>)

Category: [Industry & Society](<https://hacksnap.live/?category=industry-society>)

## Skept-o-meter & Hotness

Skept\-o\-meter: Low\. Estimated from 10 comments\.

38 comments for the summary\.

Peak rank: \#7

Time in Top 10: 24\.0 hours

Hacksnap ranks recent stories first, then orders each group by points\. Peak rank uses all retained observations\. Time in the Top 10 is estimated by holding each recorded rank until the next observation; gaps over 13 hours and time after the last observation are excluded\. Movement between observations is unknown\.

232 recorded rank observations from 2026\-09\-22T16:01:43\.425892\+00:00 to 2026\-10\-10T23:01:00\.95027\+00:00\.

Hotness — latest 168 recorded Hacksnap ranks:

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The discussion does not settle whether AI can produce maintainable code; it shifts the debate from AI's inherent limits to human review, documented patterns, incentives, and the risk of eroding institutional knowledge\.

## The brief

The author argues that AI\-assisted coding is dangerous when it replaces reading and writing code, because maintainability and good architecture lack immediate measurable reward signals and their failures surface only over months or years\. He says LLMs are not trained for maintainability, learn from beginner rules and mostly bad code, and are poor at simplifying or defining reusable functions\. He still uses LLMs for tedious work but predicts a future competitive advantage for 'NO\-AI' policies\.

- Vibe\-coded projects tend toward unmaintainable messes because architecture and maintainability effects are delayed and hard to measure\.
- AI lacks a fitness function for maintainable code; reinforcement learning needs immediate reward, and training data mostly reflects bad code\.
- AI is bad at simplification and reusable function extraction; mastery requires human judgment, responsibility, and learning from mistakes\.
- The author is not anti\-LLM; he uses them for boring work and efficiency, but insists they are just a tool\.
- He predicts companies will boast NO\-AI policies as a competitive advantage, arguing software already automates and LLMs are not the only means\.

## Discussion themes

### AI\-specific framing disputed

Several commenters reject the article's AI\-specific framing: human code also rots, and AI may follow established patterns better than humans if senior engineers document them; the critique is also point\-in\-time because strong agents are new\.

Sources: [Comment 49800161](<https://news.ycombinator.com/item?id=49800161>) · [Comment 49800186](<https://news.ycombinator.com/item?id=49800186>) · [Comment 49800274](<https://news.ycombinator.com/item?id=49800274>) · [Comment 49800267](<https://news.ycombinator.com/item?id=49800267>)

### Maintainability can be measured and enforced

Others argue maintainability can be measured or enforced with existing tools and agent workflows, such as cyclomatic complexity or SonarQube, but this requires knowing and caring about maintainability; employees optimizing for speed may not\.

Sources: [Comment 49800242](<https://news.ycombinator.com/item?id=49800242>) · [Comment 49800334](<https://news.ycombinator.com/item?id=49800334>) · [Comment 49800375](<https://news.ycombinator.com/item?id=49800375>)

### Institutional knowledge loss

A recurring concern is institutional knowledge loss: if AI writes and reads code, teams may lose the ability to learn from colleagues, recognize what good looks like, and retain domain expertise, with manufacturing outsourcing offered as a cautionary analogy\.

Sources: [Comment 49800410](<https://news.ycombinator.com/item?id=49800410>) · [Comment 49800745](<https://news.ycombinator.com/item?id=49800745>) · [Comment 49800805](<https://news.ycombinator.com/item?id=49800805>) · [Comment 49800833](<https://news.ycombinator.com/item?id=49800833>)

### AI as refactoring and product accelerator

Countering the article, some report that AI accelerates major refactors and lets them focus on correctness, tests, and product direction; one commenter built a database\-like project without reading generated code, and another says AI has sometimes improved their design\.

Sources: [Comment 49800305](<https://news.ycombinator.com/item?id=49800305>) · [Comment 49801229](<https://news.ycombinator.com/item?id=49801229>) · [Comment 49800670](<https://news.ycombinator.com/item?id=49800670>) · [Comment 49800770](<https://news.ycombinator.com/item?id=49800770>)

### Predictions and evidence questioned

Skepticism cuts both ways: commenters say definitive claims about vibe\-coded decay are guesswork, dispute the predicted NO\-AI advantage, and expect automation to continue until most developers rarely touch code\.

Sources: [Comment 49800281](<https://news.ycombinator.com/item?id=49800281>) · [Comment 49800440](<https://news.ycombinator.com/item?id=49800440>) · [Comment 49801106](<https://news.ycombinator.com/item?id=49801106>) · [Comment 49800298](<https://news.ycombinator.com/item?id=49800298>)

### Incentives and responsibility

The thread also debates incentives and responsibility: managers may push speed over quality, quality\-focused engineers may be penalized, and it is hard to distinguish AI slop from maintainable code, though some argue AI coding is faster and better when paired with review\.

Sources: [Comment 49800414](<https://news.ycombinator.com/item?id=49800414>) · [Comment 49800375](<https://news.ycombinator.com/item?id=49800375>) · [Comment 49800490](<https://news.ycombinator.com/item?id=49800490>) · [Comment 49800833](<https://news.ycombinator.com/item?id=49800833>)

## Sources & coverage

AI-generated summary · 2026\-09\-22T16:01:23\.968657\+00:00

Based on 38 of 38 usable stored comments, selected by depth and branch activity. This is a sample of the discussion. Article text may also be shortened.

Generated using deepseek\-ai/DeepSeek\-V4\.1\-Flash. Check the linked sources for full context.