利用 AI 消除信息“再语境化”成本：按受众自动适配汇报格式
TBM 437: AI and the Recontextualization Tax
Here’s an area where I am optimistic about AI (for now, at least).
这是一个我对 AI（至少目前）持乐观态度的领域。
So many companies flatten things to meet the needs of the person with 1) the most power, 2) the lowest appetite for detail/seeing real-world complexity, and/or 3) the least amount of time to actually work through details and nuance. For good (and less good) reasons, a CEO says, “This is too in the weeds. There’s no way we are doing this much!” That one statement will kick off one of a couple of things:
许多公司为了迎合以下三类人的需求而将事物扁平化：1) 权力最大的人；2) 对细节/现实复杂性的容忍度最低的人；以及/或 3) 实际处理细节和细微差别的时间最少的人。出于好与不好的原因，CEO 会说：“这太琐碎/深入细节了。我们不可能做这么多！”仅这一句话就会引发以下几种情况之一：
- Investing huge amounts of time in flattening reality to get that report to the “right” level
- Not investing that time and just winging it last minute. A reasonable survival mechanism.
- 投入大量时间将现实扁平化，以便将报告调整到“合适”的层级
- 不投入这些时间，而是临时抱佛脚、凑合应付。这是一种合理的生存机制。
Sometimes #1 is shouldered by lots of people in the org. And sometimes the burden is shouldered by just a few people. But it boils down to few/many people translating one version of reality to another, simplified, flattened version of reality. The recontextualization tax can be massive. So massive, in fact, that companies often slip into just winging it because no one in their right mind would want teams dedicating that much time to pretending things work one way in order to get “simple” overviews, “simple” cascades that “roll up perfectly,” etc.
有时，组织中的许多人共同承担第 1 点所述的工作；有时，只有少数人承担这一负担。但归根结底，就是少数人或多数人将一种现实版本翻译/转换为另一种简化、扁平化的现实版本。这种“重新语境化税”可能非常巨大。事实上，它如此巨大，以至于公司往往选择直接凑合应付，因为任何理智的人都不愿让团队花费那么多时间去假装事情以一种方式运作，从而获得“简单”的概览、“简单”的层层分解，而这些分解又能“完美汇总”等等。
If you’ve ever been in these high-level meetings, you understand that the desire for simplicity and “less” is most often a survival mechanism. It isn’t petty. It reflects a real cognitive load barrier. They are hoping to “manage by exception” instead of viewing everything.
如果你曾参加过这些高层会议，你就会明白，对简单性和“更少内容”的追求通常是一种生存机制。这并非小事一桩，它反映了一种真实的认知负荷障碍。他们希望“例外管理”，而不是查看所有内容。
Which brings me to AI.
这就引出了 AI。
An area AI excels in is doing the grunt work of recontextualization and exception hunting (provided, of course, it has the raw context and that context is reasonably up to date). Specifically, if you have teams working in highly effective, emergent, varied ways, but you really want to flatten that information more artfully into one format, AI is a good tool.
AI 擅长的一个领域是执行重新语境化和异常查找的苦力工作（当然，前提是它拥有原始上下文，且该上下文 reasonably up to date）。具体来说，如果你的团队以高效、涌现且多样的方式工作，但你确实希望更艺术地将这些信息扁平化为一种格式，那么 AI 是一个很好的工具。
Say I have 10 stakeholders, and each stakeholder likes information “a certain way.” Pre-AI, I didn’t have many options. I could:
假设我有 10 个利益相关者，每个利益相关者都喜欢以“某种特定方式”获取信息。在 AI 出现之前，我没有太多选择。我可以：
1. Try to persuade them all to look at information the same way
1. 试图说服他们都以相同的方式看待信息
2. Make 10 versions of every presentation so that we could have effective meetings
2. 为每份演示文稿制作 10 个版本，以便我们能够进行有效的会议
This is why roadmapping tools almost never travel around orgs: you have so many stakeholders/audiences, and they each have different needs out of the roadmap, which means that it is far easier to just PPTX or Miro it up ad hoc every time.
这就是为什么路线图工具几乎不会在整个组织中流通的原因：你有太多的利益相关者/受众，他们对路线图有着不同的需求，这意味着每次临时用 PPTX 或 Miro 拼凑起来要容易得多。
Now, you can let a team work however they want to work and write a skill for each of those audience segments, tailored to how they understand product, think about roadmaps, care about roadmaps, etc. It doesn’t matter if you want to basically work in GitHub issues and Google Docs; you can pretend that you work in nice swimlane roadmaps with clear deliverables and an orderly flow. Or in a outcome-oriented “move the metric” kind of way. Or whatever fits your fancy, and your audiences way.
现在，你可以让团队按照他们想要的方式工作，并为每个受众群体编写相应的技能，这些技能针对他们理解产品、思考路线图、关注路线图等方式进行了定制。无论你实际上是想在 GitHub issues 和 Google Docs 中工作，都可以假装自己在拥有清晰交付物和有序流程的漂亮泳道式路线图中工作；或者以结果导向的“推动指标”方式；或者任何符合你喜好以及你的受众群体的方式。
At the new job, we don’t want to force different clients to play by our rules. We also don’t want to hyper-customize Linear to become a super-abstracted, “respond to every client workflow” tool. Clients want things their way and they come first. We want things our way so we can make our clients awesome. So my first area of focus is figuring out how to put together skills that translate how we need to work to a language that is empowering for every client and doesn’t force them to use words/concepts that they don’t want to use.
在新工作中，我们不想强迫不同的客户遵循我们的规则。同时，我们也不想过度定制 Linear，使其成为一个高度抽象化、“响应每种客户工作流”的工具。客户希望按他们的方式行事，且他们的需求优先。我们希望按我们的方式来运作，以便让客户变得出色。因此，我首先关注的领域是弄清楚如何组合技能，将我们需要的工作方式转化为一种赋能语言，使每位客户都能受益，且不强迫他们使用他们不愿使用的词汇/概念。
AI is great at this.
AI 在这方面表现出色。
I get all the high-end AI use cases, but a lot of the value is doing something you wanted to do forever but didn’t have enough hours in the day to make happen.
我了解所有高端 AI 用例，但许多价值在于做那些你一直想做却因每天时间不足而无法实现的事情。
You don’t reinvent the wheel. Just do what you knew you should do, but couldn’t.
无需重新发明轮子。只需去做那些你早就知道应该做但却没能做到的事。
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力