人工智能的杠杆悖论
The Leverage Paradox in AI

原始链接: https://www.indiehackers.com/post/lifestyle/the-leverage-paradox-ksRiX6y6W7NzfBE57dzt

## 人工智能杠杆悖论 约翰·梅纳德·凯恩斯预测,21世纪的技术进步将带来每周15小时的工作时间,但这一预测与现实大相径庭。我们并没有减少工作,反而常常工作*更多*,这并非因为缺乏进步,而是因为进步的广泛普及。这就是“杠杆悖论”:技术提供了更强的能力,但竞争迫使我们利用这种杠杆仅仅是为了*维持*自己的地位。 人工智能是最新例子。它就像一个“自动扶梯”,让任务更容易,但同时也提高了门槛——每个人都能使用,加剧了竞争。这导致了大量低质量、同质化的内容(“人工智能垃圾”),因为人们优先考虑速度而非实质。 在这个环境中,仅仅*使用*人工智能是不够的。关键在于避免自满。作者建议采取“百次草稿”的方法——将人工智能的输出视为需要大量改进的粗稿——并努力创造真正独特的东西,一种在人工智能生成的大量同质化内容中脱颖而出的“紫牛”。最终,抵制贡献“垃圾”的冲动,并优先考虑质量和独特性,将决定谁能在人工智能时代蓬勃发展。

## AI 中的杠杆悖论:摘要 最近 Hacker News 上的一讨论集中在“杠杆悖论”应用于 AI 的问题上——即虽然 AI 提供了提高生产力的工具,但大多数领域的竞争性质意味着个人最终需要付出同样的努力来*保持*自己的位置。 基本上,增加杠杆作用并不会导致更少的工作,而是导致保持竞争力的工作标准提高。 对话强调了关于*如何*使用 AI 的争论。 一些人提倡迭代改进,将 AI 视为需要仔细监督的编码助手,而另一些人则试图寻找“一次性”解决方案,通常会导致混乱、不稳定的代码。 人们对依赖 AI 的开发人员可能出现的技能退化表示担忧,但也关注到潜在的解决问题能力提高。 一些评论员将讨论与现有的经济概念(如杰文斯悖论(效率提高导致消费增加)和“红皇后竞赛”(仅仅为了保持位置而不断努力))进行了类比。 最终,讨论表明,仅仅*使用* AI 并不够; 通过质量、信任或独特的方法实现差异化——成为“紫牛”——对于在 AI 驱动的世界中取得成功至关重要。
相关文章

原文

In 1930, the economist John Maynard Keynes predicted that automation and other expressions of technological progress would lead to a 15-hour workweek by the early 21st century.

In his essay Economic Possibilities for Our Grandchildren, he wrote:

The economic problem may be solved, or be at least within sight of solution, within a hundred years. … Thus for the first time since his creation man will be faced with his real, his permanent problem — how to use his freedom from pressing economic cares, how to occupy the leisure, which science and compound interest will have won for him.

Now that we're coming up on the 100th anniversary of that essay, it's safe to say Keynes' prediction wasn't just wrong in magnitude, but wrong in direction as well.

Many of us today work longer hours than people did in the 1930s, especially today's high earners. And that's not because technology failed to advance, but because the advances were available to everyone. This is a distinction with a difference because for most of us, the purpose of work isn't just to do better in life. It's to do better than others.

This is the leverage paradox. New technologies give us greater leverage to do more tasks better. But because this leverage is usually introduced into competitive environments, the result is that we end up having to work just as hard as before (if not harder) to remain competitive and keep up with the joneses.

Enter AI

On the left is a small staircase labeled "Before AI" features two people racing each other toward the top. On the right is a much larger escalator labeled after AI, featuring more than half a dozen people racing each other toward the top.

Before AI, we had to beat our competitors to the top of a staircase. It was tricky to climb because we had to ascend the steps manually, writing our own words, designing our own graphics, producing our own sounds, and coding our own software.

Now, AI has turned that staircase into an escalator. Which is easier to climb! Yay!

Except… the leverage paradox means we all have to climb way higher to get to the top while competing against many more people.

Those who understand this paradox — not just intellectually but in a deep, intuitive way — are the few who will make it to the top of the escalator. And those who don't will be the ones at the bottom.

We're watching this unfold in real time:

We can all now produce blog posts, social media images, lifelike videos, and even halfbaked web apps in under 5 minutes.

Problem is, the lion's share of this content is utter slop. Including stuff that might've been acceptable before the web got flooded by AI models that all talk the same, draw the same, think the same, and hallucinate the same.

It feels like AI slop is everywhere:

  • Search engine results pages filled with low-effort, SEO-farm articles

  • An endless conveyor belt of Studio-Ghibli-style images on social feeds

  • AI reply spam on every comment section

  • Faceless channels on YouTube, TikTok, and Instagram splicing lazy stock footage with same AI narration voices from ElevenLabs

It's easy to recognize this problem and to make snarky comments about it online. But as the saying goes:

You aren't stuck in traffic; you are traffic.

Resisting the urge to turn your brain off and output an additional unit of AI slop for the internet to roll its eyes at is hard. Because it's hard to adopt a new technology while continuing to put in the same hours and the same effort you put in before the technology was introduced.

But that's why the leverage paradox is the leverage paradox. And that's why only a small fraction of us will get ahead with AI while the rest of us fall behind.

Winning in the AI era

I have two pieces of advice for indie hackers who don't want to get seduced into complaceny with all the new AI tools:

1. "One-hundred-shot" your projects

One of the main reasons for all the AI slop is that everyone's trying to "one-shot" everything. Which is a huge missed opportunity.

Generative AI gives us incredible first drafts to work with, but few people want to put in the additional effort it requires to make work that people love.

So instead of trying to one-shot your next blog post or code implementation, start thinking like an actual craftsman and prepare to add 10 or even 100 extra coats of paint to the starting material given to you by some AI model.

2. Be a purple cow

Seth Godin's classic marketing concept has never been more useful to indie hackers than it is today:

When my family and I were driving through France a few years ago, we were enchanted by the hundreds of storybook cows grazing on picturesque pastures right next to the highway.

Then, within twenty minutes, we started ignoring the cows. … Cows, after you’ve seen them for a while, are boring. They may be perfect cows, attractive cows, cows with great personalities, cows lit by beautiful light, but they’re still boring.

A Purple Cow, though. Now that would be interesting.

Ghibli images are cows. AI replies are cows. Most AI videos are cows. And the same goes for much of the rest of the code and content produced by AI.

So make your stuff stand out. It doesn't have to be "better." It just has to be different.

联系我们 contact @ memedata.com