The AI revolution threatens a ‘token divide’

The internet revolution was accompanied by a digital divide. Now, the artificial intelligence (AI) revolution threatens a token divide.

Access to the internet opened new possibilities for everyone. The eighth grader doing her homework and the local business serving its customers both benefited. Broadband connections gave them information. Now, AI offers them intelligence.

Tokens are the unit through which intelligence is organized, measured, and sold. Access to tokens governs access to intelligence.

The digital divide was about access to and affordability of internet connections. The token divide retains those challenges and adds another: the constant expansion in what AI can do. Closing the token divide requires access and affordability, all while keeping pace with an accelerating frontier of capability.

Affordability of tokens determines the quantity and quality of intelligence that can be obtained. The accelerating advance of AI capabilities deepens the divide because affordable intelligence is only meaningful if it keeps pace with the advancing frontier. In the end, the token divide asks whether access to yesterday’s model is meaningful access at all.   

Today’s generative AI models process inputs and generate output through computational units called tokens. The diverse applications of AI models, ranging across text, data, and images, are all token-based. In English-language text-based applications, a token averages roughly three-quarters of a word. By breaking language into tokens, an AI model can mathematically analyze relationships among pieces of language and generate an intelligent response.

Every query (a “prompt” in AI-speak) to an AI model must be processed as tokens, and every response must be generated as tokens. The longer the prompt and response, and the more extensive the reasoning the model performs, the greater the consumption of tokens and compute capacity. Regardless of whether the user sees a per-token charge, someone bears the cost of their processing.

Advanced reasoning models can consume substantial computation before they produce any output. That compute must ultimately be paid for by the user, the provider, or both. The growth of autonomous AI agents substantially expands this dynamic because they consume tokens and compute on their own initiative.

The flat monthly subscription to an AI chatbot does not stop the meter from running. 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. 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.¹

Token economics favor organizations operating at scale. Large users can negotiate volume or capacity commitments and employ engineers and infrastructure to reduce inference costs through caching and routing. For the small company competing with a bigger company, that can deploy more intelligence per customer, that is a competitive disadvantage. For the student competing with other students whose schools or parents can afford more capable AI tutors, it is an educational disadvantage.

The token is the industry’s current denominator, and pricing schemes may evolve to per task, per agent, or per outcome. Presently, there is an upsurge in routers to direct requests to the least expensive model with adequate capability. Undoubtedly, as the business evolves the billing unit may change as well. But the logic will not. However intelligence is ultimately denominated, it must be paid for. Regardless of whether the meter measures tokens, tasks, agents, outcomes, or compute, the underlying economics remain.

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.

The term “digital divide” was widely popularized in 1995 by Larry Irving, President Bill Clinton’s assistant secretary of commerce for communications and information. Early on, it was about access to and the affordability of a computer and dial-up internet access. By the 2000s, it had become access and affordability of high-speed broadband.

Broadband eventually spread, albeit imperfectly, slowly, and inequitably, because network operators had a business model that rewarded expanding the number of connections. 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.

The AI business model is different. 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. It is a strain that becomes especially acute as autonomous agents perform multistep tasks and consume tokens continuously.

The token divide is more than the digital divide by another name. Its most significant difference is that unlike broadband, the target does not stand still. Flat-price broadband access ultimately helped make the digital divide manageable because a typical connection offered sufficient throughput for most ordinary needs. AI may not have such a plateau. 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.

The meter keeps running while the frontier keeps moving.

Bridging the digital divide often fell to civic institutions. 

Libraries became the on-ramp of internet access for all.  The local library purchased a collection of computers, subscribed to an internet connection, and opened the doors. That free access mattered, for instance, when job applications began to be accepted only online.

Schools followed a similar path. In the early days of the internet, this meant a connection to the computer lab down the hall. As computer literacy and internet access became an increasingly important part of education, connectivity moved out of the lab to each student’s desk.

Rural hospitals faced the challenge of implementing the advantages of the internet in a situation where broadband did not always reach their community.

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. 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.

But metered intelligence inverts the logic of those subsidies.

A library cannot pay for inference once given that every user session runs the meter. The advantage of free use becomes the burden of continuously recurring costs.

Schools face a similar challenge as intelligence becomes a per-student usage cost. 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.

The rural hospital now has connectivity but needs intelligence. As patient volume and medical need drives AI usage, the quantity and quality of the inference can vary based on what is affordable.

For public institutions, the token divide creates a recurring cost of unknown size. 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. The token divide has no such advantage. Every use the institution seeks to enable now carries a cost that grows to become an obstacle against that enablement.

In 2025, large language model (LLM) “inference prices [fell] rapidly but unequally across tasks,” according to Epoch AI, a nonprofit AI research institution. The rate of price declines on a per-token basis ranged from nine to 900 times per year, the report found. 

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. 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.

Such absolute comparisons understate the complexity of understanding token pricing. Because a token is a proprietary rather than a standardized unit, each company’s model creates them differently. The result is that a million tokens from one provider can represent meaningfully more or less than a million tokens from another.

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.

Usage effect

The 19th century British economist William Stanley Jevons identified a behavior that resurfaces today in the token economy. 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. Applied to tokens, it means that while per-token prices may fall, increased usage drives total token spending upward. Lower unit costs invite more uses, longer and more complex interactions, and increased autonomous activity.

Frontier effect

The token gap is a moving target. Even amid price cuts, the powerful new frontier models are typically priced at a premium, often with usage limits. On July 30, OpenAI cut its lower tier by 80%, its middle tier by 20%, and its frontier model by zero. Advantage accrues to those who can afford today’s frontier.

Relative-position effect

The measure of the token divide is the relation between the top and bottom of capabilities. 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. Falling prices can improve everyone’s absolute position without eliminating relative inequality. Perhaps yesterday’s model is sufficient for an eighth grader’s homework or the small firm’s customer service, but that misses the point. The token divide is a competitive condition. The student is measured against others. The firm bids against rivals. The measurement of both is conducted based on the intelligence each brings to the table. 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. Intelligence has no such plateau. When the ceiling is constantly rising, “good enough” is constantly slipping behind those who can follow the expanded intelligence upward.

Concentration effect

When token prices decline but the tollbooth simply moves to other layers of the AI stack, cheap tokens and concentrated control arrive together. Competition among model providers, cloud companies, and chip suppliers can drive down the cost of intelligence. Yet, while there appears to be growing competition among models, the infrastructure necessary to produce intelligence is increasingly concentrated. 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. The headlines about data center construction foretell the AI pricing meter moving from models to the computing power to run those models. 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.   

Markets will continue to deliver abundant intelligence; whether they deliver equitable intelligence is something falling prices cannot answer. It is the reason the token divide will not fix itself.

As chairman of the FCC from 2013 to 2017, I oversaw the federal government’s principal effort to bridge the digital divide. The solutions Congress established and we applied were derivative of policy developed in the 1930s to deal with electricity. The Rural Electrification Administration, cooperative utilities, and ultimately lifeline rates brought power to every farm and household. Applying similar strategies helped attack the digital divide.

The four effects discussed above differentiate the token divide from the earlier divides over electricity and the internet. 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.

A household’s demand for electricity levels off once the lights, refrigerator, and other applications are powered. The same holds true once a certain level of network bandwidth is achieved. The quality of the service being delivered was also constant across users. 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. Neither of these conditions holds true for intelligence. Demand for intelligence does not level off—it grows with each new capability and each new application. A kilowatt may be a kilowatt in terms of what it delivers, but all tokens are not created equal in their capability.

The digital divide had an identifiable finish line: an affordable connection. 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. 

Here are three reasons that the solutions of the relatively quiet past are not sufficient for the exponential future.

Capital expense vs. operating expense

The digital divide ended up being attacked by the application of capital. New capital construction expanded the opportunity to connect across a wider fiber and spectrum footprint. 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.

For both the model owner and the user, the token divide is an operating expense rather than a capital expense challenge. Society was able to build its way through the digital divide. Once on the other side of the connectivity challenge, the cost to access the next nonsubscription webpage was invisible to the user. The token economy is the reverse: Access consumes tokens and drives costs.

Moving target

Broadband policy relied on a benchmark of sufficiency. The FCC’s definition expanded from downlink speeds of 25 Mbps in 2015 to 100 Mbps in 2024, a fourfold increase. The scale of computation pushing the AI frontier is moving on a radically different trajectory. Between 2010 and 2024, the computing power used to train frontier models grew more than 100 million-fold, doubling roughly every six months. This makes it difficult to develop a definition of sufficiency in token access. At the exponential growth of AI, any benchmark would be out of date almost immediately. Worse, a subsidy pegged to such a baseline risks institutionalizing the divide by codifying yesterday’s intelligence while the market moves on.

Quantity and quality

The ability to purchase throughput above 100 Mbps doesn’t necessarily produce a smarter internet. The ability to purchase more AI tokens, however, produces better outcomes, deeper research, more agents, and more sophisticated reasoning.

Token access determines how often and for what AI can be used. The volume of intelligence a user can afford can affect the value of the output. Whether the user can reach the most capable models can determine the quality of intelligence available to them.

A prescriptive “to do” checklist is inappropriate at this point in AI history. This is, however, the right time to begin investigating causes and solutions.

One thing is clear, however: What worked in the digital divide may be inadequate for the token divide. Policymakers cannot conflate efforts to deal with tomorrow’s token divide with remedies designed for yesterday’s digital divide.

Such an investigation must produce a better understanding of the realities of AI economics and their impact on consumers and competition. 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. 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.

The path to token divide policy begins with measurement. 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. Absolute counts go stale almost immediately. A relational sufficiency standard avoids that. 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.  

Once the metrics are in place, it becomes possible to assess various mitigations. Because new times challenge us to think anew, this becomes an expansive exercise. Candidates for consideration could include but not be limited to the following.

Institutional support

Support for schools, libraries, and rural health facilities was foundational to attacking the digital divide. We found that modernizing digital divide programs was an ongoing challenge. We learned, for instance, that a program for institutional support required empowering local institutions with the leverage to deal with corporate power. When some local internet service providers took advantage of government support to demand high rates from schools, the FCC stepped up to give schools the competitive leverage of building their own last-mile connection to the internet. The volume discount asymmetry of token pricing invites similar expansive initiatives, including but not limited to government buying consortiums and leveraging of government purchasing.

Open-weight safety

Open-weight models whose core components are openly released offer low-cost access to intelligence. These models frequently originate in China, raising national security concerns. That by definition they are open to modification by anyone further increases their risk. For the benefits of open-weight models to be widely accessible, such problems must be addressed in a standardized manner. 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.

Public compute

A user can obtain an open-weight model for little to nothing. But “free” weights do not mean free intelligence as the model still needs compute power. Publicly supported compute can help break the chokehold of concentrated and costly compute. Early efforts are worthy of support and analysis. 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.

Transparency

We have nutritional labeling for our food, and the FCC requires broadband providers to post consumer labels. 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. When I asked for a report on the tokens used in support of this paper, the information was unavailable to me as a subscriber. It is such uncertainty that makes the token divide real for librarians, school administrators, and businesses.

Nondiscrimination

Historically, owners of key infrastructure have used access to its capabilities to control competition. 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. 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. 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.

Competition

Marketplace competition is at the heart of the world’s most successful economy. But competition requires a supporting scaffold of rules. 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. The AI marketplace is currently controlled by a handful of corporate titans that are making their own rules for their own benefit. Successful markets are markets with rules. Policies addressing the integrated activities of the dominant AI firms would help to bridge the token divide.    

Democratizing AI

The internet democratized access to information. The digital divide taught us, however, that technology does not democratize itself alone. Attacking the digital divide was an effort to make that democratization a reality through broad participation. Democratizing AI goes beyond that to address whether access remains relevant as intelligence itself advances. The answer to that question may depend on whether we prevent the emergence of a new divide hidden inside the humble token.