Silicon Valley’s AI ‘tokenmaxxing’ obsession has a big problem – and philosophers saw it coming
, ,

硅谷对AI“代币最大化”的狂热痴迷存在一个大问题——而哲学家们早就预料到了

Silicon Valley’s AI ‘tokenmaxxing’ obsession has a big …

Victoria Lorrimar, Director, Centre for Technology and Human Futures, University of Notre Dame Australia Tim Smartt, Senior Research Fellow, Philosophy, University of Notre Dame Australia

What makes for a good life? Simple: grinding through tokens.

什么是美好生活?很简单:不断地“刷”代币。

Some time earlier this year, an employee at tech giant Meta built a system to track how much each staff member was using artificial intelligence (AI) .

今年稍早,科技巨头Meta的一名员工建立了一个系统,用于追踪每位员工使用人工智能(AI)的程度。

Named “Claudeonomics” after the Claude chatbot, the system created a leaderboard ranked by the number of tokens each user was exchanging with AI models, with leaders given titles such as “Token Legend”. (Tokens are tiny chunks of text, each around four characters long, that language models use for processing.)

该系统以聊天机器人Claude命名为“Claudeonomics”,它创建了一个排行榜,根据每位用户与AI模型交换的token数量进行排名,并将领先者授予“Token传奇”等称号。(Token是文本的微小片段,每个片段大约包含四个字符,语言模型用它们进行处理。)

Meta is not alone in its fascination with “tokenmaxxing”: AI labs OpenAI and Anthropic, e-commerce company Shopify, and tech investment firm Sequoia capital are all reportedly monitoring AI usage and rewarding heavy users, some of whom burn billions of tokens in a week.

Meta并非唯一一个沉迷于“tokenmaxxing”的公司:AI实验室OpenAI和Anthropic、电商公司Shopify以及科技投资公司红杉资本(Sequoia capital)都据报道正在监控AI使用情况,并奖励重度用户,其中一些用户每周消耗的token数量高达数十亿。

Reducing a person’s performance to a single metric can be appealing for management in large corporations. But the choice of what to measure isn’t a neutral one – and if we’re not careful, it can start to rewrite our vision of what we actually value.

将一个人的表现简化为一个单一指标,对于大型企业管理层来说可能很有吸引力。但选择衡量什么本身就不是一个中立的选择——如果我们不小心,它可能会开始重写我们对真正价值的认知。

The score keeps the score

代币决定了分数

One of the more full-throated advocates of tokenmaxxing is Jensen Huang, chief executive of chipmaker Nvidia, who envisions a future in which tech employees negotiate high token budgets and consume tokens at rates commensurate with their salaries. Around 80% of those tokens are currently processed via Nvidia’s chips, so Huang’s enthusiasm makes sense.

芯片制造商英伟达(Nvidia)首席执行官黄仁勋是“代币最大化”(tokenmaxxing)的坚定支持者之一。他设想的未来是,科技员工可以协商获得高额的代币预算,并以与薪水相匹配的速率消耗代币。目前,大约80%的代币是通过英伟达的芯片处理的,因此黄仁勋的热情是合理的。

But is token consumption a helpful metric for those of us who do not profit directly from AI processing volume?

但是,对于那些没有直接从人工智能处理量中获利的我们来说,代币消耗是否是一个有用的衡量指标?

In a recent book, The Score, philosopher C. Thi Nguyen analyses the rise of metrics throughout modern society and offers some helpful insights.

在他最近的一本书《The Score》中,哲学家C. Thi Nguyen分析了现代社会中指标的兴起,并提供了一些有益的见解。

As Nguyen emphasises, what we measure shapes our goals. We develop metrics as tools of convenience; they standardise our measurement of values so we can compare large numbers of otherwise disparate things.

正如Nguyen强调的,我们衡量什么,就塑造了我们的目标。我们发展出指标作为便利的工具;它们标准化了我们对价值的衡量,使我们能够比较大量原本不相关的事物。

This standardisation comes at the expense of variation and distinctiveness, Nguyen argues. In business, it can make workers seem interchangeable.

Nguyen认为,这种标准化是以牺牲变化性和独特性为代价的。在商业领域,它可能会让员工看起来可以互相替代。

Determining which employees in a large organisation are consuming the most tokens in a week is fairly straightforward. But it tells us nothing about the quality or impact of their work.

确定一个大型组织中哪位员工在一周内消耗了最多的代币是相当直接的。但这并不能告诉我们他们工作的质量或影响。

Bad metrics, bad results

糟糕的指标,糟糕的结果

In the past, questionable metrics have contributed to dramatically bad outcomes.

过去,可疑的指标促成了极其糟糕的结果。

Prior to the 2008 global financial crisis, for example, many financial institutions had sophisticated systems of measures designed to incentivise selling as many loans as possible, as quickly as possible. Perhaps unsurprisingly, many of those loans turned out to be far riskier than anyone realised.

例如,在2008年全球金融危机之前,许多金融机构都建立了复杂的衡量体系,旨在激励尽可能快地出售尽可能多的贷款。不出所料,其中许多贷款的风险远超任何人的预料。

Nguyen emphasises that these types of metrics can tempt us into thinking they are unavoidable. But one of the central lessons of moral philosophy is that we ought to pause at moments like these and ask a couple of basic questions: what is a good life, and what values are actually worth chasing?

阮强调,这类指标可能会诱使我们认为它们是不可避免的。但道德哲学的一个核心教训是,在这样的时刻,我们应该停下来,问自己几个基本问题:什么是美好的生活?哪些价值观真正值得追求?

Huang and others usually don’t present tokenmaxxing as an answer to these question. But that’s how it functions. What is worth devoting your professional and creative energy to? Simple: grinding through tokens.

黄等人通常不会将“代币最大化”(tokenmaxxing)作为这些问题的答案。但它就是这样运作的。值得投入你专业和创造性精力的是什么?很简单:不断地挖掘代币。

A new vision of the good life?

对美好生活的全新愿景?

Silicon Valley has, of late, produced a striking number of manifestos and quasi-constitutions.

近来,硅谷产出了一批数量惊人的宣言和准宪法。

Consider Anthropic’s Claude’s Constitution, published in January 2026, which sets out the company’s aspirations for its model’s values and speech. Or look at venture capitalist Marc Andreessen’s Techno-Optimist Manifesto, which makes the case for ambitiously accelerating technological advancements in the service of promoting human flourishing.

考虑一下Anthropic于2026年1月发布的《Claude宪章》,该宪章阐述了公司对其模型价值观和言论的期望。或者看看风险投资家马克·安德森的《技术乐观主义宣言》,该宣言主张雄心勃勃地加速技术进步,以服务于促进人类繁荣。

Some of the most influential texts in the history of moral and political philosophy take this form. Thomas Jefferson wrote one – the US Declaration of Independence. Karl Marx and Friedrich Engels wrote another – The Communist Manifesto.

历史上一些最具影响力的道德和政治哲学文本也具有这种形式。托马斯·杰斐逊写了一部——《美国独立宣言》。卡尔·马克思和弗里德里希·恩格斯写了另一部——《共产党宣言》。

One way to view these Silicon Valley proclamations, and trends like tokenmaxxing, is as repackaging familiar commonplaces of corporate life – recasting mission statements and key performance indicators in a loftier register. But another is to see them as attempts to do something far more ambitious: sketch the outlines of a new and far-reaching vision of the good life.

将这些硅谷的宣言,以及“tokenmaxxing”之类的趋势,视为对企业生活常见陈词滥调的重新包装——将使命宣言和关键绩效指标提升到更高的层面。但另一种看法是,将它们视为更宏大尝试:勾勒出一种全新且深远的“美好生活”愿景的轮廓。

On that view, the metrics used to measure progress against the vision matter. Tokenmaxxing, for example, is already creeping beyond the bounds of the tech industry – one report from the Wharton School at the University of Pennsylvania suggests many organisations are prioritising staff AI usage and spending as metrics.

从这个角度看,衡量与愿景相比的进步所使用的指标至关重要。例如,“tokenmaxxing”已经超出了科技行业的范畴——宾夕法尼亚大学沃顿商学院的一份报告指出,许多组织正将员工AI使用和支出作为优先指标。

Metrics can be useful – if we’re careful

指标很有用——如果我们小心的话

Metrics do have their place in an ordered and complex society. There are many instances in which we might happily defer to the scores produced by simple metrics, trading nuance for convenience. Aggregate ratings on product or restaurant review sites, for example, can simplify our decision-making, even if they aren’t tailored to our specific preferences.

在一个有序和复杂的社会中,指标确实有其存在的价值。在许多情况下,我们可能会乐于接受简单指标产生的评分,用便利性来取代细微差别。例如,产品或餐厅评论网站上的综合评分可以简化我们的决策过程,即使这些评分并未根据我们的具体偏好量身定制。

The problem is what Nguyen calls “value capture” – when we uncritically allow external metrics to determine our own goals and behaviour. Resisting this process involves questioning what is being measured and reframing it.

问题在于阮(Nguyen)所说的“价值捕获”——当我们不加批判地让外部指标来决定我们自己的目标和行为时。抵抗这一过程,就需要质疑正在衡量什么,并重新构建我们的认知框架。

Instead of counting tokens, for example, we might use an equivalent metric such as energy consumption. Energymaxxing might sound more like conspicuous wastage, rather than improved performance.

例如,我们不计算代币(tokens),而是可以使用等效指标,例如能耗。能耗最大化听起来更像是明显的浪费,而不是性能的提升。

Counting tokens is one measure of AI activity, which is itself intended as a measure of productivity, which in turn leaves aside the question of what is being produced. Not only is tokenmaxxing a dubious metric in itself, but it may also distort our vision of what matters.

计算代币是衡量人工智能活动的一种指标,而这种指标本身旨在衡量生产力,而生产力又忽略了“到底生产了什么”这个问题。代币最大化不仅本身就是一个可疑的指标,它还可能扭曲我们对“什么才是重要”的认知。

Victoria Lorrimar receives funding from the John Templeton Foundation.

Victoria Lorrimar 接受约翰·坦普尔顿基金会的资助。

Tim Smartt does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Tim Smartt 不受任何从本文中受益的公司或组织的雇佣、咨询、拥有股份或获得资金,并且除了其学术任命之外,未披露任何相关隶属关系。

Read more