HN有多少是AI？

HN有多少是AI？
How much of HN is AI?

原始链接: https://lcamtuf.substack.com/p/how-much-of-hn-is-ai

作者对 Hacker News 抱有爱恨交织的情感，虽然经常有负面情绪，但仍将其视为重要的流量来源。最近，他们观察到网站内容发生了显著变化，人工智能相关内容占据了压倒性的地位。对 2026 年 2 月份的五条热门文章分析显示，大部分日期的至少四项内容都与人工智能相关。进一步使用 AI 检测工具 Pangram 的调查表明，这些文章中有很大一部分是由人工智能*撰写*的，该工具能够准确地标记出表现出大型语言模型典型风格模式的文章。作者指出，AI 检测并不需要检测“非人类”写作，而是识别当前 LLM 持续存在的风格特征。他们认为 Pangram 的发现是准确的，强调了一种令人担忧的趋势，即人工智能生成的内容正在饱和一个以前“极客导向”的平台。

一场由一篇质疑网站上人工智能生成内容数量的文章引发的黑客新闻讨论，揭示了一场细致的辩论。用户承认人工智能日益增长的影响力以及由此产生的内容，但反对创建专门的“人工智能”类别，这符合黑客新闻反对内容孤立的理念。一些人建议标记潜在的人工智能创作的文章，而另一些人认为这是一个无法执行的问题，因为黑客新闻本质上是问题的下游。一个更深层次的担忧是人工智能对在线真实的人际联系和创造性激励的影响。一位用户质疑如果在线贡献仅仅被吸收到人工智能训练数据中，其价值何在，并思考了诸如付费墙之类的解决方案——但这些方案被认为与互联网的开放性相悖。最终，许多人认为黑客新闻目前还没有发生显著变化。

原文

I have a complicated relationship with Hacker News. The site is the most important aggregator of geek news and a major source of traffic to this blog. At the same time, it has a fair number of toxic commenters, making it a dependable source of insults hurled in my general direction; if you want a taste, this article has been called “watered-down” and “slop”.

The site is run by geeks and for geeks, so it’s not immune to tech trends; for example, around 2018, it had a fair number of stories focused on cryptocurrencies and NFT. That said, the recent shift feels more profound: almost every day, it feels that the lineup is dominated by stories focused on AI, written by AI, or commented on by AI.

To get a sense of how much of the feed is occupied by AI-related topics — often vendor announcements — I took a sampling of the daily top #5 for February 2026:

So, yep. AI took four out of five spots on Feb 4 and Feb 12, plus arguably the entire line-up on Feb 5 (story #3 was submarine marketing for an AI vendor). The only days without LLM news in the top 5 were February 1 (with the first AI story at #7, then #9), February 9 (first at #8), and February 25 (with AI at #6, #9, #10).

For the second part of the experiment — to figure out which stories are likely AI-written — I tapped into Pangram. Pangram is a remarkably good, conservative model for detecting LLM-generated text. These detectors have a bad rep among techies, but the objections are often based on outdated assumptions or outright misconceptions. For the tools to work, AI writing doesn’t need to be in any way “inhuman”. It’s enough that the default voice of the current crop of LLMs is quasi-deterministic: ask for the same essay twice and you’ll get a stylistically similar result. The individual mannerisms are human-like, but it’s very unlikely that your writing combines the exact same set.

To validate the results, I also reviewed all the flagged stories and I think the findings make sense; if anything, Pangram had a couple of false negatives. To give you a sense of what was flagged, have a look at the #3 story on February 19 (“AI is not a coworker, it’s an exoskeleton”). In my opinion, it has a wide range of red flags.

HN有多少是AI？ How much of HN is AI?

HN有多少是AI？
How much of HN is AI?