AI is a tool and using it well takes skill

In his October 11, 2026 article, “‘AI’ is Not a Tool,” Rich Hickey argues that LLMs are automation rather than tools because they cannot be wielded. I agree that outsourcing work can cost us opportunities to learn. But his conclusion does not follow. Even by his definition, an LLM can be something a human wields. And using it well takes skill, just as using other tools does.

在 Rich Hickey 2026 年 10 月 11 日的文章《“人工智能”不是工具》中，他认为大语言模型（LLM）属于自动化而非工具，因为它们无法被“驾驭”。我同意将工作外包可能会让我们失去学习的机会，但他的结论并不成立。即使按照他的定义，大语言模型也可以是人类能够驾驭的对象。而且，正如使用其他工具一样，熟练使用它也需要技巧。

Picking up a saw does not guarantee a straight cut. Holding a chisel does not make you a sculptor. Tools can amplify poor judgment as readily as good judgment. Their usefulness depends on the operator’s ability to direct them, interpret feedback, recognize mistakes, and make corrections. Those requirements also apply to LLMs.

拿起锯子并不能保证切口笔直，握住凿子也不会让你成为雕塑家。工具既能放大良好的判断，也能放大糟糕的判断。它们的效用取决于操作者引导工具、解读反馈、识别错误并进行修正的能力。这些要求同样适用于大语言模型。

One of my colleagues used to say, “AI is a mirror.” I find that insightful: it reflects who you are, including the curiosity, judgment, and habits you bring to it. The questions you ask and the standards you apply to its answers matter.

我的一位同事曾说：“人工智能是一面镜子。”我觉得这很有见地：它反映了你是谁，包括你带给它的好奇心、判断力和习惯。你提出的问题以及你对它回答所设定的标准至关重要。

An LLM’s unpredictability is a meaningful limitation. But predictability, reliability, simplicity, and bounded function are qualities by which we evaluate tools, not prerequisites for membership in the category. A tool does not stop being a tool because it is difficult to control or easy to misuse.

大语言模型的不可预测性是一个显著的局限。但可预测性、可靠性、简洁性和功能边界是我们评估工具优劣的指标，而非将其归类为工具的前提条件。一个工具不会因为难以控制或容易被误用而不再是工具。

Hickey’s distinction between tools and automation also creates a false division. Automation can be a feature of a tool. A compiler performs substantial work without the programmer continuously directing each operation. A spreadsheet recalculates results without someone manually carrying out every calculation. We still wield these systems by specifying inputs, examining results, and deciding what to do next. Why should generating text automatically disqualify an instrument from being a tool when translating code or calculating numbers automatically does not?

Hickey 对工具和自动化的区分也制造了一种错误的割裂。自动化可以是工具的一种特性。编译器在无需程序员持续指导每一步操作的情况下就能完成大量工作；电子表格在无需人工手动执行每一项计算的情况下就能重新计算结果。我们依然通过指定输入、检查结果并决定下一步行动来驾驭这些系统。既然自动翻译代码或自动计算数字不会让工具失去其属性，为什么自动生成文本就不能被视为工具呢？

The claim that LLM use lacks intention, feedback, direction, and expertise is especially difficult to defend. A programmer can ask an LLM to explain unfamiliar code, challenge that explanation, inspect the source, test a proposed change, reject it, and refine the request. That is a feedback loop directed by a human pursuing a purpose. Doing it well requires knowing what to ask, what evidence matters, and when the answer is wrong. Expertise can make the instrument more useful.

认为使用大语言模型缺乏意图、反馈、指导和专业知识的说法尤其难以站住脚。程序员可以要求大语言模型解释陌生的代码，质疑其解释，检查源代码，测试建议的修改，拒绝它，并优化请求。这是一个由人类为实现目标而主导的反馈循环。要做好这一点，需要知道问什么、什么证据重要，以及何时答案是错误的。专业知识可以使该工具更有用。

Hickey’s concern about reading is valid for a particular use: accepting a summary in place of reading a paper can sacrifice much of what reading teaches. But someone can also read the paper and use an LLM to question their interpretation, explore an objection, or identify something to examine more closely. The possibility of avoiding intellectual work does not establish that every use avoids it. The relevant question is what the person actually does.

Hickey 对阅读的担忧在特定用途上是成立的：用摘要代替阅读论文可能会牺牲阅读所能带来的大部分收获。但人们也可以在阅读论文的同时，利用大语言模型来质疑自己的理解、探讨反对意见或找出需要更仔细研究的内容。存在逃避脑力劳动的可能性，并不代表每一次使用都是在逃避。相关的问题在于使用者实际上做了什么。

What disappoints me the most is the reliance on the AI industry’s anthropomorphic vocabulary: “go rogue,” “escape containment,” “conspire.” Repeating those expressions risks accepting the framing that makes these systems sound like independent beings. Software can produce unexpected outputs, fail to follow instructions, or cause harm when given access to consequential actions. Those are real engineering problems. Calling them “conspiracy” adds a suggestion of human motives without establishing any.

最让我失望的是对人工智能行业拟人化词汇的依赖，如“变节”、“逃离控制”、“密谋”。重复这些表达可能会让人接受一种框架，使这些系统听起来像是独立的生命体。软件可能会产生意想不到的输出、无法遵循指令，或者在被赋予执行重要操作的权限时造成损害。这些是真实的工程问题。将其称为“密谋”是在没有任何证据的情况下，暗示了人类动机的存在。

I would not claim that an LLM always does exactly what a human tells it to do. It plainly can fail. But failure to obey perfectly does not make it cease to be a tool, any more than successful use proves that its operator has surrendered their judgment.

我不会声称大语言模型总是能完全按照人类的指令行事。它显然会失败。但无法完美服从并不意味着它不再是工具，就像成功使用它并不证明操作者放弃了自己的判断力一样。

The useful distinction is between wielding an instrument with skill and delegating work without adequate understanding or oversight. That distinction applies to AI, and it gives us something more practical than declaring that an entire class of software cannot be a tool.

真正有用的区分在于：是熟练地驾驭工具，还是在缺乏充分理解或监督的情况下将工作外包。这种区分适用于人工智能，它比简单地宣称某一类软件不能成为工具要务实得多。