人工智能需要第一性原理思维

正确的方向比速度更重要。

The right vector matters more than velocity.

作者： 库尔特·唐纳尔
管理顾问彼得·德鲁克曾教导《从优秀到卓越》的作者吉姆·柯林斯一条彻底改变他经商方式的原则：能做一件事就不要做一百件事。这个洞见看似简单，实则不然——看似无数个互不相干的决定，往往只是同一个决定的不同伪装。

史蒂夫·乔布斯曾因每天穿同一套衣服而臭名昭著，他认为这样可以省去很多穿衣打扮的麻烦。然而，这种思维方式的真正力量远不止于此。它关乎于做出能够彻底排除未来决策类别的战略性决定。

能够有效应对决策疲劳的领导者，其绩效比同行高出22%，由此可见，这一原则至关重要。但在当今人工智能驱动的世界里，它更是不可或缺。

决策疲劳的影响

据估计，普通人每天要做出35000个决定。等到真正需要做决定的时候，他们的脑力往往已经耗尽。这种认知超负荷会损害自我调节能力，导致做出次优选择，每年给全球经济造成数十亿美元的生产力损失。

当认知负荷超过大脑的承受能力时，大脑调节冲动和情绪的能力就会下降，这往往会导致我们做出下意识的反应，变得过度规避风险，或者干脆避免做决定。

对错误问题的正确答案
人工智能并没有减轻我们的决策负担，反而放大了我们做出错误决策的后果。例如，如果你决定在A和B之间做出选择，你可能会构建一个人工智能系统，它能够出色地在A和B之间做出选择，考虑所有相关因素并进行完美优化。

然而，你或许根本无需做出选择，因为同时选择A和B，或者两个选项都不选，可能反而更好。你只是花费了大量的时间、精力和资源，却在为一个完全错误的问题进行优化。例如，一家轿车销量下滑的汽车制造商可能会构建一个复杂的、多变量的AI增强系统，来优化在每个市场生产和向经销商交付哪些颜色的轿车，而更重要的问题是：应该生产轿车、SUV还是卡车？

速度会放大误差
人工智能让我们行动更快。应用程序原型设计、假设验证和解决方案生成正以前所未有的速度进行。但飞机偏离航线一度，每飞行 60 海里，最终就会偏离航线一海里。以人工智能的速度进行开发时，一度的误差会呈指数级增长。

危险不在于人工智能本身，而在于人们更容易钻研细节，为一些根本不需要解决的问题构建复杂的解决方案。我们加快了进程，但除非我们都朝着正确的目标前进，并拥有正确的基本假设，否则加快速度又有什么意义呢？

我亲眼见过这种情况。你可以产出惊人的成果——编写大量代码、构建令人印象深刻的功能、创建漂亮的仪表盘。但如果这些成果没有带来预期的结果，那就是浪费时间和精力。

运用第一性原理思考
这就是第一性原理思维成为你竞争优势的地方。它是一种解决问题的方法，能够将复杂问题分解成最基本、最根本的真理。它摒弃了假设，从零开始构建解决方案，而不是通过类比推理。

当事情开始变得复杂时，就应该停下来，回到最初。我们最初的假设是什么？验证它，如果它是错误的，就能解释为什么后续所有环节都受到影响。

在实施人工智能解决方案或要求团队优化流程之前，请回答以下三个问题：

1. 我们真正想要解决的问题是什么？不是症状，不是眼前的痛点，而是根本问题。

2. 我们对这个问题的原因做了什么假设？质疑这个假设是否正确。

3. 解决这个问题对我们的核心目标有何帮助？如果你无法将这个解决方案与你的主要目标直接联系起来，那么你很可能是在追求错误的目标。

指标错位会使人工智能失去作用
最阴险的错误决策莫过于不同团队朝着不同的目标努力，并使用不同的指标来衡量成功。我曾亲眼目睹一些公司部署了令人惊叹的人工智能技术，但他们的团队成员对成功的定义却各执一词。最终，他们反而成为了实现目标的绊脚石。

如果你的组织内部不同部门各自为政，那么你的人工智能技术再先进、执行速度再快都无济于事。在加速推进其他所有工作之前，你需要明确真正重要的决策——你的方向。

这并不是说你不应该进行一些充满创意的编程探索，探索各种可能性。但要把这些探索当作概念验证。进行压力测试，确保概念验证朝着解决挑战的方向发展，然后充分发挥你那支人工智能赋能团队的全部潜能。

赢得规模化发展的权利
这里有点违反直觉：有时候你需要先获得许可，才能做到效率低下。正如身着黄色燕尾服的萨凡纳香蕉队创始人杰西·科尔所说：“你需要先做那些无法规模化的事情，才能做那些可以规模化的事情。”

在 Freestar，我们特意安排人手处理一些显然更适合自动化解决的问题。当时感觉效率不高，但正是这种先摸索后学习的过程，让我们明白了高效的方法。我们必须先深入理解问题，才能系统化地构建解决方案。这并非浪费时间，而是第一性原理思维的实践。

做出更好的初始决定
如果使用得当，人工智能可以帮助你更快地检验基础假设。你可以在几天内（而不是几个月）完成解决方案原型设计、数据收集和理论验证（或推翻）。在加速应用人工智能之前，请先掌握以下三个决策框架：

1. 一次决定，处处适用
哪些可以一概而论的重复性决策？记录下来，系统化。这样可以解放团队的认知资源，让他们专注于真正全新的情况。

2. 对这个问题提出质疑
当有人让你在几个选项中做出选择时，停下来问问自己，整个框架是否正确。通常，最佳答案就隐藏在对前提的质疑之中。

3. 衡量真正重要的事
确保整个组织对成功的定义保持一致。如果各个团队追求的指标各不相同，那就已经偏离了方向。

一旦方向正确，人工智能就能成为你的放大器。但如果方向错误，你只会更快地迷失方向。因此，当人工智能让我们在以前做一次决定所需的时间内做出100次决定时，你无需成为速度最快的人；你只需要专注于做出正确的第一次决定。

Kurt Donnell 是 Freestar 的首席执行官。

关于作者
自 2019 年以来，库尔特·唐纳尔 (Kurt Donnell) 一直担任 Freestar 的总裁兼首席执行官。Freestar 是一家为数字媒体出版商提供广告技术服务的公司。在他的领导下，Freestar 从一家总部位于美国的 25 人团队发展成为一家遍布 15 个国家/地区、拥有近 200 名员工的公司，收入增长超过 500%，并被评为美国增长最快的私营公司。

AI requires first principles thinking

The right vector matters more than velocity.

Management consultant Peter Drucker once taught Jim Collins, author of Good to Great, a principle that transformed how he approached business: Don’t make a hundred decisions when one will do. The insight is deceptively simple—what appear to be countless disparate decisions are often the same decision wearing different disguises.

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Steve Jobs famously wore the same outfit daily to eliminate fashion decisions, although the real power of this thinking went far beyond wardrobe choices. It was about identifying strategic decisions that categorically remove entire categories of future decisions.

Leaders who effectively manage decision fatigue outperform peers by 22%, so we know this principle is relevant. But in today’s AI-powered world, it’s become absolutely critical.

THE IMPACT OF DECISION FATIGUE

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The average person makes an estimated 35,000decisions per day. By the time they reach the important ones, their mental fuel tank is often empty. This cognitive overload impairs self-regulation and can lead to suboptimal choices, costing the global economy billions annually in lost productivity.

When cognitive load exceeds capacity, the brain’s ability to regulate impulses and emotions is diminished, often leading to us making knee-jerk reactions, becoming overly risk-averse, or avoiding decisions altogether.

THE RIGHT ANSWER TO THE WRONG QUESTION

AI hasn’t reduced our decision load, but it’s amplified the consequences of getting our foundational decisions wrong. For instance, if you decide to choose between A or B, you might then build an AI system that does an amazing job selecting between A and B, taking in all the relevant factors and optimizing beautifully.

However, you may not have needed to choose in the first place, because choosing both A and B, or picking neither option may have been better. You’ve just spent enormous time, energy, and resources optimizing for the wrong question entirely. For example, a car manufacturer with flagging sedan sales could build a complex, multi-variate AI-enhanced system to optimize which sedan colors to manufacture and send to dealers in each market, when the more important question is whether to manufacture sedans, SUVs, or trucks.

SPEED MAGNIFIES THE ERROR

AI allows us to move faster. Prototyping apps, testing assumptions, and generating solutions is happening at unprecedented speed. But a plane off course by one degree ends up a full nautical mile off course for every 60 nautical miles flown. When you’re building at AI speed, a one-degree error compounds exponentially.

The danger isn’t AI, but that it is easier to go down rabbit holes, building sophisticated solutions to problems that don’t need solving. We’ve accelerated accomplishment. But unless we’re all heading toward the correct goal with the right foundational assumptions, what’s the point of going faster?

I’ve seen this firsthand. You can show tremendous output—generate reams of code, build impressive features, create beautiful dashboards. But if it’s not driving the right outcome, it’s a waste of time and energy.

USE FIRST PRINCIPLES THINKING

This is where first principles thinking becomes your competitive advantage. It’s a problem-solving method that breaks complex problems down into their most basic, fundamental truths. It eliminates assumptions and builds solutions from scratch rather than reasoning by analogy.

When something starts feeling complex, that’s your signal to stop and go back to the beginning. What was the first assumption we made? Test that, and if it’s wrong, it explains why everything downstream is compromised.

Before implementing an AI solution or asking a team to optimize a process, answer these three questions:

1. What problem are we actually trying to solve?Not the symptom, not the immediate pain point, but the foundational problem.

2. What assumption are we making about why this is a problem?Challenge whether that assumption is true.

3. What would solving this accomplish for our core goals?If you can’t draw a straight line from this solution to your primary objectives, you’re likely optimizing for the wrong thing.

MISALIGNED METRICS MAKE AI USELESS

The most insidious form of wrong first decisions is when different teams are working toward different goals and using different metrics to measure success. I’ve watched companies implement incredible AI capabilities while their teams speak different languages about what success means. They become their own barrier to achievement.

It doesn’t matter how sophisticated your AI is or how fast you can execute if different parts of your organization are on different vectors. You need clarity on the decision that matters—your true north—before accelerating everything else.

This isn’t to say that you shouldn’t have creative vibe coding sessions to explore the art of the possible. But treat the sessions as proofs of concept. Pressure test, ensure the proof of concept is heading the direction that will solve your challenge, then unleash the full power of your AI-supercharged team.

EARN THE RIGHT TO SCALE

Here’s the counterintuitive part: Sometimes you need permission to be inefficient first. As Jesse Cole, the yellow tuxedo-clad founder of the Savannah Bananas said, “You need to do the unscalable to do the scalable.”

At Freestar, we’ve purposefully thrown people at problems that undoubtedly would be better handled by automation. It felt inefficient in the moment. But doing things the hard way first taught us what the efficient way looks like. We had to understand the problem deeply before we could systematize the solution. That’s not wasted time—that’s first principles thinking in action.

MAKE BETTER FIRST DECISIONS

AI can help you test foundational assumptions faster if you use it correctly. You can prototype solutions, gather data, and validate (or invalidate) theories in days instead of months. Before you accelerate with AI, anchor yourself with these three decision-making frameworks:

1. DECIDE ONCE, APPLY EVERYWHERE

What recurring decisions can you make categorically? Document them. Systematize them. Free your team’s cognitive resources for genuinely novel situations.

2. CHALLENGE THE QUESTION

When someone asks you to choose between options, pause and ask whether the entire framing is correct. Often, the best answer is hidden in questioning the premise.

3. MEASURE WHAT MATTERS

Align your entire organization on shared definitions of success. If teams are optimizing for different metrics, you’re already off course.

Once you’re pointed in the right direction, AI becomes your amplifier. But if you’re aimed wrong, you just get lost faster. So, when AI lets us make 100 decisions in the time it used to take to make one, you don’t need to be the fastest; you just need to focus on getting the first decision right.

Kurt Donnell is the CEO of Freestar.

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