The AI revolution threatens a ‘token divide’
Artificial Intelligence Internet & Telecommunications Technology Policy & Regulation
人工智能 互联网与电信技术政策与法规
The internet revolution was accompanied by a digital divide.
互联网革命伴随着数字鸿沟。
Now, the artificial intelligence (AI) revolution threatens a token divide.
现在，人工智能（AI）革命威胁着象征性的鸿沟。
Access to the internet opened new possibilities for everyone.
接入互联网为每个人带来了新的可能性。
The eighth grader doing her homework and the local business serving its customers both benefited.
八年级学生做作业和为顾客提供服务的当地企业都受益。
Broadband connections gave them information.
宽带连接为他们提供了信息。
Now, AI offers them intelligence.
现在，人工智能为他们提供了智能。
Tokens are the unit through which intelligence is organized, measured, and sold.
代币是组织、衡量和销售情报的单位。
Access to tokens governs access to intelligence.
对代币的访问控制着对情报的访问。
The digital divide was about access to and affordability of internet connections.
数字鸿沟涉及互联网连接的获取和负担能力。
The token divide retains those challenges and adds another: the constant expansion in what AI can do.
代币鸿沟不仅保留了这些挑战，还增加了另一个挑战：人工智能功能的不断扩展。
Closing the token divide requires access and affordability, all while keeping pace with an accelerating frontier of capability.
缩小代币鸿沟需要可及性和可承受性，同时与不断加速的能力前沿保持同步。
Affordability of tokens determines the quantity and quality of intelligence that can be obtained.
代币的承受能力决定了可以获得的情报的数量和质量。
The accelerating advance of AI capabilities deepens the divide because affordable intelligence is only meaningful if it keeps pace with the advancing frontier.
人工智能能力的加速进步加深了分歧，因为负担得起的智能只有跟上前沿的发展步伐才有意义。
In the end, the token divide asks whether access to yesterday’s model is meaningful access at all.
最后，代币鸿沟询问对昨天模型的访问是否是有意义的访问。
Today’s generative AI models process inputs and generate output through computational units called tokens.
当今的生成式人工智能模型通过称为令牌的计算单元处理输入并生成输出。
The diverse applications of AI models, ranging across text, data, and images, are all token-based.
人工智能模型的多样化应用，涵盖文本、数据和图像，都是基于代币的。
In English-language text-based applications, a token averages roughly three-quarters of a word.
在基于英语文本的应用程序中，一个标记平均大约占一个单词的四分之三。
By breaking language into tokens, an AI model can mathematically analyze relationships among pieces of language and generate an intelligent response.
通过将语言分解为标记，人工智能模型可以对语言片段之间的关系进行数学分析并生成智能响应。
Every query (a “prompt” in AI-speak) to an AI model must be processed as tokens, and every response must be generated as tokens.
对 AI 模型的每个查询（人工智能术语中的“提示”）都必须作为令牌进行处理，并且每个响应都必须作为令牌生成。
The longer the prompt and response, and the more extensive the reasoning the model performs, the greater the consumption of tokens and compute capacity.
提示和响应越长，模型执行的推理越广泛，消耗的代币和计算能力就越大。
Regardless of whether the user sees a per-token charge, someone bears the cost of their processing.
无论用户是否看到按代币收费，都会有人承担其处理成本。
Advanced reasoning models can consume substantial computation before they produce any output.
高级推理模型在产生任何输出之前可能会消耗大量计算。
That compute must ultimately be paid for by the user, the provider, or both.
该计算最终必须由用户、提供商或两者共同支付。
The growth of autonomous AI agents substantially expands this dynamic because they consume tokens and compute on their own initiative.
自主人工智能代理的增长大大扩展了这种动态，因为它们会主动消耗代币并进行计算。
The flat monthly subscription to an AI chatbot does not stop the meter from running.
人工智能聊天机器人的每月固定订阅并不会阻止仪表的运行。
The fixed cost may hide the meter from the consumer, but it does not eliminate the never-ending metered incremental cost of every prompt, every response, and every moment of machine deliberation.
固定成本可能会向消费者隐藏计量表，但它并不能消除每次提示、每次响应和机器审议的每时每刻所产生的永无休止的计量增量成本。
Users of flat-fee chatbots rediscover the tollbooth when a usage limit is crossed, and the model slows, shifts them to a less capable model, restricts use, or starts adding incremental fees.
当超过使用限制时，固定费用聊天机器人的用户会重新发现收费站，并且模型会变慢，将其转换为能力较差的模型，限制使用，或开始增加增量费用。
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Token economics favor organizations operating at scale.
代币经济学有利于大规模运营的组织。
Large users can negotiate volume or capacity commitments and employ engineers and infrastructure to reduce inference costs through caching and routing.
大型用户可以协商数量或容量承诺，并雇用工程师和基础设施通过缓存和路由来降低推理成本。
For the small company competing with a bigger company, that can deploy more intelligence per customer, that is a competitive disadvantage.
对于与大公司竞争的小公司来说，大公司可以为每个客户部署更多的智能，这是一个竞争劣势。
For the student competing with other students whose schools or parents can afford more capable AI tutors, it is an educational disadvantage.
对于与学校或家长能够负担得起更有能力的人工智能导师的其他学生竞争的学生来说，这是一种教育劣势。
The token is the industry’s current denominator, and pricing schemes may evolve to per task, per agent, or per outcome.
代币是行业当前的分母，定价方案可能会演变为按任务、按代理或按结果。
Presently, there is an upsurge in routers to direct requests to the least expensive model with adequate capability.
目前，越来越多的路由器将请求定向到具有足够功能的最便宜的型号。
Undoubtedly, as the business evolves the billing unit may change as well.
毫无疑问，随着业务的发展，计费单位也可能发生变化。
But the logic will not.
但逻辑不会。
However intelligence is ultimately denominated, it must be paid for.
不管智力最终是如何定价的，它都必须付出代价。
Regardless of whether the meter measures tokens, tasks, agents, outcomes, or compute, the underlying economics remain.
无论仪表是否测量代币、任务、代理、结果或计算，底层的经济学仍然存在。
A meter that never stops running will divide users by how much intelligence they can afford, the quality of the intelligence they can access, and what they are able to do with it.
一个永不停止运行的计量表将根据用户能够负担多少智能、他们可以获得的智能的质量以及他们能够利用智能来做什么来划分用户。
The term “digital divide” was widely popularized in 1995 by Larry Irving , President Bill Clinton’s assistant secretary of commerce for communications and information.
“数字鸿沟”一词于 1995 年由比尔·克林顿总统负责通信和信息的助理商务部长拉里·欧文广泛普及。
Early on, it was about access to and the affordability of a computer and dial-up internet access.
早期，它是关于计算机和拨号互联网接入的访问和负担能力。
By the 2000s, it had become access and affordability of high-speed broadband.
到了 2000 年代，它已经成为高速宽带的接入和可负担性。
Broadband eventually spread, albeit imperfectly, slowly, and inequitably, because network operators had a business model that rewarded expanding the number of connections.
宽带最终得到了普及，尽管并不完美、缓慢且不公平，因为网络运营商拥有奖励扩大连接数量的商业模式。
Once sufficient broadband has been built, additional usage can normally be bundled into a flat monthly price, and low-income consumers are helped by subsidies.
一旦建立了足够的宽带，额外的使用通常可以捆绑成每月固定价格，低收入消费者可以获得补贴。
The AI business model is different.
人工智能的商业模式是不同的。
Because every additional AI inference requires additional computation, the AI business model wrestles with absorbing, limiting, or passing along the additional cost incurred with each new inquiry.
由于每次额外的人工智能推理都需要额外的计算，因此人工智能商业模式需要努力吸收、限制或转嫁每次新查询所产生的额外成本。
It is a strain that becomes especially acute as autonomous agents perform multistep tasks and consume tokens continuously.
当自主代理执行多步骤任务并持续消耗代币时，这种压力变得尤其严重。
The token divide is more than the digital divide by another name.
代币鸿沟比数字鸿沟更重要。
Its most significant difference is that unlike broadband, the target does not stand still.
其最显着的区别在于，与宽带不同，目标不会停滞不前。
Flat-price broadband access ultimately helped make the digital divide manageable because a typical connection offered sufficient throughput for most ordinary needs.
统一价格宽带接入最终有助于缩小数字鸿沟，因为典型的连接可以为大多数普通需求提供足够的吞吐量。
AI may not have such a plateau.
人工智能可能不会有这样的平台期。
More intelligence, or more capable intelligence, can enable qualitatively different tasks and results, so even as the cost of a given level of intelligence may fall, the frontier keeps advancing.
更多的智能，或者更有能力的智能，可以实现质量上不同的任务和结果，因此，即使给定智能水平的成本可能下降，前沿仍在不断前进。
Bridging the digital divide often fell to civic institutions.
弥合数字鸿沟往往由民间机构负责。
Libraries became the on-ramp of internet access for all.
图书馆成为所有人访问互联网的入口。
The local library purchased a collection of computers, subscribed to an internet connection, and opened the doors.
当地图书馆购买了一批电脑，订阅了互联网连接，并打开了大门。
That free access mattered, for instance, when job applications began to be accepted only online.
例如，当工作申请开始只在网上接受时，这种免费访问就很重要。
Schools followed a similar path.
学校也遵循了类似的道路。
In the early days of the internet, this meant a connection to the computer lab down the hall.
在互联网的早期，这意味着连接到大厅里的计算机实验室。
As computer literacy and internet access became an increasingly important part of education, connectivity moved out of the lab to each student’s desk.
随着计算机知识和互联网接入成为教育中越来越重要的一部分，连接从实验室转移到每个学生的桌子上。
Rural hospitals faced the challenge of implementing the advantages of the internet in a situation where broadband did not always reach their community.
在宽带并不总是覆盖社区的情况下，乡村医院面临着发挥互联网优势的挑战。
Congress addressed these challenges by instructing the Federal Communications Commission ( FCC ) to create a universal service funding structure to subsidize school, library, rural hospital, and low-income access to the internet.
国会通过指示联邦通信委员会 (FCC) 创建普遍服务资助结构来补贴学校、图书馆、乡村医院和低收入者使用互联网来应对这些挑战。
The subsidy worked because the economics cooperated as payments to network providers helped them build, and payments to civic institutions and low-income consumers helped them use.
补贴之所以有效，是因为经济合作，向网络提供商支付的费用帮助他们建立，向公民机构和低收入消费者支付的费用帮助他们使用。
But metered intelligence inverts the logic of those subsidies.
但计量情报颠倒了这些补贴的逻辑。
A library cannot pay for inference once given that every user session runs the meter.
一旦每个用户会话都运行计量器，图书馆就无法为推理付费。
The advantage of free use becomes the burden of continuously recurring costs.
免费使用的优势变成了不断重复发生的费用的负担。
Schools face a similar challenge as intelligence becomes a per-student usage cost.
随着智力成为每个学生的使用成本，学校也面临着类似的挑战。
This can create a situation where one eighth grader has an inexhaustible tutor while on the other side of town another student maxes out token usage partway through an assignment.
这可能会造成这样一种情况：一名八年级学生有一位取之不尽的导师，而在城镇的另一边，另一名学生在作业中途使用了代币。
The rural hospital now has connectivity but needs intelligence.
乡村医院现在已经联网，但需要智能化。
As patient volume and medical need drives AI usage, the quantity and quality of the inference can vary based on what is affordable.
随着患者数量和医疗需求推动人工智能的使用，推理的数量和质量可能会根据负担得起的情况而有所不同。
For public institutions, the token divide creates a recurring cost of unknown size.
对于公共机构来说，代币鸿沟会产生规模未知的经常性成本。
The digital divide benefitted from how, once affordable connectivity was in place, the marginal cost of the next web search, job application, or homework assignment approached zero.
一旦实现了负担得起的连接，下一次网络搜索、工作申请或家庭作业的边际成本就接近于零，数字鸿沟受益匪浅。
The token divide has no such advantage.
代币划分则没有这样的优势。
Every use the institution seeks to enable now carries a cost that grows to become an obstacle against that enablement.
该机构寻求实现的每一项用途现在都伴随着成本，而成本逐渐成为实现这一目标的障碍。
In 2025, large language model (LLM) “inference prices [fell] rapidly but unequally across tasks,” according to Epoch AI, a nonprofit AI research institution.
非营利性人工智能研究机构 Epoch AI 表示，到 2025 年，大型语言模型 (LLM)“推理价格迅速下降，但各个任务的情况并不均衡”。
The rate of price declines on a per-token basis ranged from nine to 900 times per year, the report found.
报告发现，每个代币的价格每年下跌 9 至 900 次。
The Financial Times recently reported that “[l]eading US AI labs such as OpenAI and Anthropic are releasing cheaper models as they fight to retain cost-conscious customers who are switching to cut-price alternatives from Chinese rivals.” On July 30, OpenAI slashed the price of GPT 5.6 Luna—its fastest, cheapest model for high-volume tasks—by 80%, from $6 to $1.20 per million output tokens.
英国《金融时报》最近报道称，“OpenAI 和 Anthropic 等领先的美国人工智能实验室正在发布更便宜的模型，以留住注重成本的客户，这些客户正在转向中国竞争对手的降价替代品。” 7 月 30 日，OpenAI 将 GPT 5.6 Luna（适用于大批量任务的最快、最便宜的模型）的价格削减了 80%，从每百万输出代币 6 美元降至 1.20 美元。
The next day, Chinese AI lab DeepSeek announced a new model with a rate card of 28 cents for the same one million output tokens.
第二天，中国人工智能实验室 DeepSeek 宣布了一种新模型，同样 100 万个产出代币的价目表为 28 美分。
Such absolute comparisons understate the complexity of understanding token pricing.
这种绝对比较低估了理解代币定价的复杂性。
Because a token is a proprietary rather than a standardized unit, each company’s model creates them differently.
由于代币是专有的而不是标准化的单位，因此每个公司的模型创建它们的方式都不同。
The result is that a million tokens from one provider can represent meaningfully more or less than a million tokens from another.
结果是，来自一个提供商的 100 万个代币可以代表来自另一个提供商的 100 万个代币。
Putting aside the difference in tokens, falling prices do not solve the token divide, and in fact, there are four ways that falling prices may actually end up widening rather than closing the gap.
抛开代币的差异不谈，价格下跌并不能解决代币鸿沟，事实上，价格下跌可能会通过四种方式最终扩大而不是缩小差距。
The 19th century British economist William Stanley Jevons identified a behavior that resurfaces today in the token economy.
19 世纪的英国经济学家威廉·斯坦利·杰文斯 (William Stanley Jevons) 发现了一种在今天的代币经济中重新出现的行为。
Studying the consumption of coal , Jevons identified what economists call the Jevons paradox : When technology makes a resource cheaper, people end up using more of it.
通过研究煤炭的消耗，杰文斯发现了经济学家所说的杰文斯悖论：当技术使某种资源变得更便宜时，人们最终会使用更多的资源。
Applied to tokens, it means that while per-token prices may fall, increased usage drives total token spending upward.
应用于代币时，这意味着虽然每个代币的价格可能会下降，但使用量的增加会推动代币总支出的上升。
Lower unit costs invite more uses, longer and more complex interactions, and increased autonomous activity.
较低的单位成本会带来更多的使用、更长、更复杂的交互，以及更多的自主活动。
The token gap is a moving target.
代币缺口是一个不断变化的目标。
Even amid price cuts, the powerful new frontier models are typically priced at a premium, often with usage limits.
即使在降价的情况下，功能强大的新前沿型号通常定价较高，而且通常有使用限制。
On July 30, OpenAI cut its lower tier by 80%, its middle tier by 20%, and its frontier model by zero.
7 月 30 日，OpenAI 将其低层模型削减了 80%，将中层模型削减了 20%，将前沿模型削减了 0%。
Advantage accrues to those who can afford today’s frontier.
那些能够负担得起当今前沿的人就会获得优势。
The measure of the token divide is the relation between the top and bottom of capabilities.
代币划分的衡量标准是能力顶部和底部之间的关系。
Everyone’s use of intelligence may rise because of lower token costs, but the ratio of total intelligence deployed is still a matter of who can pay.
每个人对情报的使用可能会因为代币成本降低而上升，但部署的总情报的比例仍然是谁能支付费用的问题。
Falling prices can improve everyone’s absolute position without eliminating relative inequality.
价格下跌可以提高每个人的绝对地位，但不会消除相对不平等。
Perhaps yesterday’s model is sufficient for an eighth grader’s homework or the small firm’s customer service, but that misses the point.
也许昨天的模型足以满足八年级学生的作业或小公司的客户服务，但这没有抓住重点。
The token divide is a competitive condition.
代币划分是一种竞争条件。
The student is measured against others.
学生是与其他人进行衡量的。
The firm bids against rivals.
该公司与竞争对手竞标。
The measurement of both is conducted based on the intelligence each brings to the table.
两者的衡量都是基于各自带来的情报进行的。
Broadband reached a plateau of sufficiency because streaming a video at 100 megabits per second (Mbps) looks the same in a mansion as it does in a trailer.
宽带达到了充足的水平，因为以每秒 100 兆比特 (Mbps) 的速度传输视频在豪宅中与在预告片中看起来是一样的。
Intelligence has no such plateau.
智力没有这样的高原。
When the ceiling is constantly rising, “good enough” is constantly slipping behind those who can follow the expanded intelligence upward.
当天花板不断上升时，“足够好”就不断地落后于那些能够跟随扩展的智力向上的人。
When token prices decline but the tollbooth simply moves to other layers of the AI stack, cheap tokens and concentrated control arrive together.
当代币价格下跌但收费站只是转移到人工智能堆栈的其他层时，廉价代币和集中控制就会同时出现。
Competition among model providers, cloud companies, and chip suppliers can drive down the cost of intelligence.
模型提供商、云公司和芯片供应商之间的竞争可以降低情报成本。
Yet, while there appears to be growing competition among models, the infrastructure necessary to produce intelligence is increasingly concentrated.
然而，尽管模型之间的竞争似乎日益激烈，但产生情报所需的基础设施却日益集中。
A 2025 staff report by the Federal Trade Commission (FTC) identified how partnerships between the largest cloud providers and the leading model developers could constrain rivals’ access to compute and other inputs.
美国联邦贸易委员会 (FTC) 的一份 2025 年工作人员报告指出，最大的云提供商和领先的模型开发商之间的合作可能会限制竞争对手获得计算和其他输入。
The headlines about data center construction foretell the AI pricing meter moving from models to the computing power to run those models.
有关数据中心建设的头条新闻预示着人工智能定价表将从模型转向运行这些模型的计算能力。
The token divide thus becomes a question of market structure, and closing it requires a policy that promotes, preserves, and protects the competition that drives prices down.
因此，代币鸿沟变成了市场结构的问题，而消除这种鸿沟需要一项促进、维持和保护压低价格的竞争的政策。
Markets will continue to deliver abundant intelligence; whether they deliver equitable intelligence is something falling prices cannot answer.
市场将继续提供丰富的情报；他们是否提供公平的情报是价格下跌无法回答的问题。
It is the reason the token divide will not fix itself.
这就是代币分裂无法自行修复的原因。
As chairman of the FCC from 2013 to 2017, I oversaw the federal government’s principal effort to bridge the digital divide.
作为 2013 年至 2017 年 FCC 主席，我监督了联邦政府弥合数字鸿沟的主要努力。
The solutions Congress established and we applied were derivative of policy developed in the 1930s to deal with electricity.
国会制定和我们应用的解决方案是 20 世纪 30 年代制定的电力政策的衍生品。
The Rural Electrification Administration , cooperative utilities , and ultimately lifeline rates brought power to every farm and household.
农村电气化管理局、合作公用事业公司以及最终的生命线电价为每个农场和家庭带来了电力。
Applying similar strategies helped attack the digital divide.
应用类似的策略有助于消除数字鸿沟。
The four effects discussed above differentiate the token divide from the earlier divides over electricity and the internet.
上面讨论的四种效应将代币鸿沟与早期电力和互联网上的鸿沟区分开来。
They also demonstrate why solutions originated in the 1930s do not resolve the 21st century problem of access to the continually expanding capability of AI.
他们还证明了为什么起源于 20 世纪 30 年代的解决方案无法解决 21 世纪获取不断扩展的人工智能能力的问题。
A household’s demand for electricity levels off once the lights, refrigerator, and other applications are powered.
一旦电灯、冰箱和其他应用通电，家庭的电力需求就会趋于平稳。
The same holds true once a certain level of network bandwidth is achieved.
一旦网络带宽达到一定水平，情况同样如此。
The quality of the service being delivered was also constant across users.
用户所提供的服务质量也是一致的。
A kilowatt of power was a kilowatt of power, no matter what it was used for, just as a bit of throughput was a bit of throughput regardless of how it was used.
一千瓦的功率就是一千瓦的功率，不管它用来做什么，就像一点吞吐量就是一点吞吐量，不管它如何使用。
Neither of these conditions holds true for intelligence.
这两个条件对于智力来说都不成立。
Demand for intelligence does not level off—it grows with each new capability and each new application.
对智能的需求不会趋于平稳——它会随着每一项新功能和每一项新应用而增长。
A kilowatt may be a kilowatt in terms of what it delivers, but all tokens are not created equal in their capability.
就其提供的功率而言，千瓦可能就是千瓦，但并非所有代币的功能都是相同的。
The digital divide had an identifiable finish line: an affordable connection.
数字鸿沟有一个明确的终点线：负担得起的连接。
The token divide has no such finish line because it is about access to intelligence, its quality, and the relationship to the intelligence available to others.
象征性鸿沟没有这样的终点线，因为它关系到情报的获取、情报的质量以及与其他人可获得的情报的关系。
Here are three reasons that the solutions of the relatively quiet past are not sufficient for the exponential future.
以下三个原因表明，相对平静的过去的解决方案不足以应对指数级的未来。
The digital divide ended up being attacked by the application of capital.
数字鸿沟最终被资本的运用所打破。
New capital construction expanded the opportunity to connect across a wider fiber and spectrum footprint.
新的基本建设扩大了连接更广泛的光纤和频谱足迹的机会。
AI is also capital intensive, however, expenditures for data centers and new models exacerbate the token divide by expanding the capabilities of top-of-the-line AI.
人工智能也是资本密集型的，然而，数据中心和新模型的支出通过扩展顶级人工智能的功能而加剧了代币鸿沟。
For both the model owner and the user, the token divide is an operating expense rather than a capital expense challenge.
对于模型所有者和用户来说，代币划分是运营支出而不是资本支出挑战。
Society was able to build its way through the digital divide.
社会能够克服数字鸿沟。
Once on the other side of the connectivity challenge, the cost to access the next nonsubscription webpage was invisible to the user.
一旦遇到连接挑战，访问下一个非订阅网页的成本对用户来说是不可见的。
The token economy is the reverse: Access consumes tokens and drives costs.
代币经济则相反：访问会消耗代币并增加成本。
Broadband policy relied on a benchmark of sufficiency.
宽带政策依赖于充足性基准。
The FCC’s definition expanded from downlink speeds of 25 Mbps in 2015 to 100 Mbps in 2024, a fourfold increase.
FCC 的定义将下行速度从 2015 年的 25 Mbps 扩大到 2024 年的 100 Mbps，增长了四倍。
The scale of computation pushing the AI frontier is moving on a radically different trajectory.
推动人工智能前沿的计算规模正在沿着完全不同的轨迹发展。
Between 2010 and 2024, the computing power used to train frontier models grew more than 100 million-fold, doubling roughly every six months.
2010 年至 2024 年间，用于训练前沿模型的计算能力增长了 1 亿多倍，大约每六个月翻一番。
This makes it difficult to develop a definition of sufficiency in token access.
这使得制定令牌访问充分性的定义变得困难。
At the exponential growth of AI, any benchmark would be out of date almost immediately.
随着人工智能的指数级增长，任何基准几乎都会立即过时。
Worse, a subsidy pegged to such a baseline risks institutionalizing the divide by codifying yesterday’s intelligence while the market moves on.
更糟糕的是，与这样的基准挂钩的补贴可能会在市场继续发展的情况下将昨天的情报编纂成法，从而使这种分歧制度化。
The ability to purchase throughput above 100 Mbps doesn’t necessarily produce a smarter internet.
购买超过 100 Mbps 吞吐量的能力并不一定会产生更智能的互联网。
The ability to purchase more AI tokens, however, produces better outcomes, deeper research, more agents, and more sophisticated reasoning.
然而，购买更多人工智能代币的能力会产生更好的结果、更深入的研究、更多的代理和更复杂的推理。
Token access determines how often and for what AI can be used.
令牌访问决定了人工智能的使用频率和用途。
The volume of intelligence a user can afford can affect the value of the output.
用户所能承受的智能量会影响输出的价值。
Whether the user can reach the most capable models can determine the quality of intelligence available to them.
用户是否能够接触到最有能力的模型可以决定他们可以获得的情报的质量。
A prescriptive “to do” checklist is inappropriate at this point in AI history.
在人工智能历史的此时此刻，规定性的“待办事项”清单是不合适的。
This is, however, the right time to begin investigating causes and solutions.
然而，现在是开始调查原因和解决方案的最佳时机。
One thing is clear, however: What worked in the digital divide may be inadequate for the token divide.
然而，有一件事是明确的：在数字鸿沟中有效的方法可能不足以适应代币鸿沟。
Policymakers cannot conflate efforts to deal with tomorrow’s token divide with remedies designed for yesterday’s digital divide.
政策制定者不能将解决未来代币鸿沟的努力与针对昨天数字鸿沟的补救措施混为一谈。
Such an investigation must produce a better understanding of the realities of AI economics and their impact on consumers and competition.
这样的调查必须更好地了解人工智能经济的现实及其对消费者和竞争的影响。
Congress attempted to deal with this through Section 706 of the Telecommunications Act of 1996 requiring the FCC to produce broadband deployment reports that created a factual record for policy decisions.
国会试图通过 1996 年《电信法》第 706 条来解决这个问题，要求 FCC 编制宽带部署报告，为政策决策创建事实记录。
Congress can, and should, act again to develop an understanding of the token divide through an appropriate body such as the National Institute of Standards and Technology or the National Science Foundation.
国会可以而且应该再次采取行动，通过国家标准与技术研究所或国家科学基金会等适当的机构来加深对象征性分歧的理解。
The path to token divide policy begins with measurement.
代币划分政策的路径始于衡量。
Since the tokenization process differs from model to model, and different tokens have different capabilities, there needs to be a unit of measure beyond simple token counting such as cost per completed task or capability per dollar.
由于不同模型的代币化过程有所不同，并且不同的代币具有不同的功能，因此除了简单的代币计数（例如每完成任务的成本或每美元的功能）之外，还需要有一个衡量单位。
Absolute counts go stale almost immediately.
绝对计数几乎立即就会过时。
A relational sufficiency standard avoids that.
关系充分性标准可以避免这种情况。
Such a standard, indexed to a set percent of frontier capability, would bring an equivalent to the cost-of-living indexing to the token market.
这样一个标准，以一定百分比的前沿能力为索引，将为代币市场带来相当于生活成本指数的效果。
Once the metrics are in place, it becomes possible to assess various mitigations.
一旦指标到位，就可以评估各种缓解措施。
Because new times challenge us to think anew, this becomes an expansive exercise.
因为新时代要求我们重新思考，所以这成为一项广泛的练习。
Candidates for consideration could include but not be limited to the following.
供考虑的候选者可以包括但不限于以下内容。
Open-weight models whose core components are openly released offer low-cost access to intelligence.
核心组件公开发布的开放权重模型提供了低成本的情报获取方式。
These models frequently originate in China, raising national security concerns.
这些模型往往源自中国，引发国家安全担忧。
That by definition they are open to modification by anyone further increases their risk.
根据定义，它们可以被任何人修改，这进一步增加了风险。
For the benefits of open-weight models to be widely accessible, such problems must be addressed in a standardized manner.
为了让开放权重模型的好处得到广泛应用，必须以标准化的方式解决这些问题。
A safety certification program that schools, hospitals, and other institutions can adopt would open the door to a powerful tool to bridge the token divide.
学校、医院和其他机构可以采用的安全认证计划将为弥合代币鸿沟的强大工具打开大门。
A user can obtain an open-weight model for little to nothing.
用户可以花费很少甚至免费获得开放权重模型。
But “free” weights do not mean free intelligence as the model still needs compute power.
但“自由”权重并不意味着自由智能，因为模型仍然需要计算能力。
Publicly supported compute can help break the chokehold of concentrated and costly compute.
公共支持的计算有助于打破集中且昂贵的计算的束缚。
Early efforts are worthy of support and analysis.
早期的努力值得支持和分析。
These include the National Science Foundation’s National Artificial Intelligence Research Resource ( NAIRR ) pilot, California’s planned CalCompute , and New York state’s Empire AI .
其中包括美国国家科学基金会的国家人工智能研究资源 (NAIRR) 试点项目、加州计划中的 CalCompute 以及纽约州的 Empire AI。
We have nutritional labeling for our food, and the FCC requires broadband providers to post consumer labels .
我们的食品有营养标签，联邦通信委员会要求宽带提供商张贴消费者标签。
Disclosure obligations would also help AI users understand what they are getting as well as inform them when they are being moved to a lesser model.
披露义务还将帮助人工智能用户了解他们所获得的内容，并在他们被转移到较小的模型时通知他们。
When I asked for a report on the tokens used in support of this paper, the information was unavailable to me as a subscriber.
当我要求提供有关用于支持本文的代币的报告时，作为订阅者的我无法获得该信息。
It is such uncertainty that makes the token divide real for librarians, school administrators, and businesses.
正是这种不确定性使得图书馆员、学校管理人员和企业之间的象征性分歧变得真实。
Historically, owners of key infrastructure have used access to its capabilities to control competition.
从历史上看，关键基础设施的所有者曾利用其能力来控制竞争。
The sine qua non—the essential, non-negotiable condition—of access to both broadband and intelligence is a policy of nondiscrimination to allow all who require the capability to be able to use it on just and reasonable terms.
获得宽带和情报的必要条件（基本的、不可协商的条件）是非歧视政策，允许所有需要这种能力的人能够以公正合理的条件使用它。
When a frontier lab manipulates its pricing, access rules, prioritization, and usage limits to benefit its own or favored applications, user discrimination is at work that widens the token divide.
当前沿实验室操纵其定价、访问规则、优先级和使用限制以使其自身或受青睐的应用程序受益时，用户歧视就会扩大代币鸿沟。
This does not have to mean common carrier regulation, but so long as those who own key infrastructure are able to constrain those capabilities for their own self-advantage, the AI marketplace will be neither fully competitive nor functional.
这并不一定意味着公共运营商监管，但只要那些拥有关键基础设施的人能够为了自身利益而限制这些能力，人工智能市场就不会完全竞争，也不会发挥作用。
Marketplace competition is at the heart of the world’s most successful economy.
市场竞争是世界上最成功经济体的核心。
But competition requires a supporting scaffold of rules.
但竞争需要规则的支持。
While it may appear as though competition is driving down token pricing at the model layer, the other necessary components of AI, from compute, to middleware, distribution, and application integration, remain highly concentrated and are growing more so.
虽然竞争似乎正在压低模型层的代币定价，但人工智能的其他必要组成部分，从计算到中间件、分发和应用程序集成，仍然高度集中，而且越来越集中。
The AI marketplace is currently controlled by a handful of corporate titans that are making their own rules for their own benefit.
人工智能市场目前由少数企业巨头控制，他们为了自己的利益制定自己的规则。
Successful markets are markets with rules.
成功的市场是有规则的市场。
Policies addressing the integrated activities of the dominant AI firms would help to bridge the token divide.
针对主导人工智能公司综合活动的政策将有助于弥合代币鸿沟。
The internet democratized access to information.
互联网使信息获取民主化。
The digital divide taught us, however, that technology does not democratize itself alone.
然而，数字鸿沟告诉我们，技术本身并不能实现民主化。
Attacking the digital divide was an effort to make that democratization a reality through broad participation.
消除数字鸿沟是通过广泛参与使民主化成为现实的努力。
Democratizing AI goes beyond that to address whether access remains relevant as intelligence itself advances.
人工智能民主化不仅要解决这个问题，还要解决随着智能本身的进步，访问是否仍然具有相关性。
The answer to that question may depend on whether we prevent the emergence of a new divide hidden inside the humble token.
这个问题的答案可能取决于我们是否阻止隐藏在不起眼的代币中的新鸿沟的出现。