Ai里程:未来简历必填 vs 老司机公里数

以下是我今天给孩子写信

原文是英文的，今天故意翻译成中文分享给对育儿有积极主动意识的朋友

亲爱的孩子

关于 AI 时代，有一个想法是我非常想和你们分享的。

想象一下，几十年前，如果你去应聘一份职业司机的工作，雇主可能会问你一个非常重要的问题：

你开过多少公里的车？

因为真正驾驶过 50 万公里，和仅仅“会开车”，代表的是完全不同的经验水平。

我相信，在 AI 时代，也可能出现类似的情况。

过去，雇主招聘一个人的时候，经常会看：

你毕业于哪所大学？你有多少年的工作经验？

而在未来，另外一种经验可能会变得越来越重要：

你真正使用过多少 AI 算力？你烧过多少 Token？

我曾经听到有人提出一个观点：这甚至可能成为未来一种新的“履历”。

我觉得这个想法非常有意思。

过去一年，我估计自己在各种 AI 订阅和使用上已经花了大约 5,000 澳元。

换句话说，我有意识地花了几千澳元购买算力、消耗 Token。

对我来说，这就是我的：

AI Mileage —— AI 里程。

就像一个司机通过不断开车积累行驶里程一样，我也正在通过成千上万次与 AI 的真实互动积累自己的 AI 算力使用经验——和 AI 一起思考、测试它、挑战它、比较不同模型，并且用它解决真实世界里的问题。

所以，当我每个月花钱购买 AI 算力的时候，我并不只是把它看成一笔订阅费用。

我把它看成是在投资自己的：

AI 里程。

等到你们进入职场的时候，我相信，雇主可能不再只关心你在哪里读书，或者你有多少年的工作经验。

他们也可能越来越关心：

你究竟拥有多少真正与 AI 一起工作的经验？

就像过去一个职业司机开过多少公里，可以代表他真实的驾驶经验一样，

在 AI 时代，一个人实际使用过多少算力，也可能成为衡量 AI 原生工作者经验的一种指标。

所以，我给你们的建议其实非常简单：

Start building your AI mileage.

从现在开始，积累属于你自己的 AI 里程。

爱你们的，妈妈

Dears

There is one idea about the AI era that I really want to share with you.

Imagine applying for a job as a professional driver decades ago. One of the most meaningful questions an employer could ask was:

How many kilometres have you driven?

Because driving 500,000 kilometres means something very different from simply knowing how to drive.

I believe something similar may happen in the AI era.

In the past, employers often looked at things such as:

Which university did you graduate from?How many years of work experience do you have?

In the future, another form of experience may become increasingly important:

How much AI compute have you actually used?How many tokens have you burned?

I once heard someone describe this as a possible new kind of résumé, and I think that idea is very interesting.

Over the past year, I estimate that I have spent around AUD 5,000 on AI subscriptions and usage. In other words, I have deliberately spent thousands of dollars buying compute and burning tokens.

To me, that is my AI mileage.

Just as a driver accumulates kilometres on the road, I am accumulating compute through thousands of real interactions with AI — thinking with it, testing it, challenging it, comparing models and using it to solve real problems.

So when I spend money on AI compute, I don’t simply see it as a subscription expense. I see it as investing in my AI mileage.

By the time you enter the workforce, I believe employers may care not only about where you studied or how many years you worked, but also about how much real experience you have working with AI.

The same way kilometres once represented experience for a professional driver, compute may become one measure of experience for an AI-native worker.

So my advice to you is simple:

Start building your AI mileage.

Love,Mum