AI assistants seem to be everywhere, yet paid AI subscriptions remain unusual among American consumers. A Harvard Business School working paper estimates that 2.9% of US consumers paid for at least one major generative AI service as of April 2026. Its authors also found relatively low annual customer retention among the major providers they examined. In other words, using an AI chatbot and deciding to keep paying for one are very different behaviours. That gap could shape the price, quality and future business models of tools increasingly used for work and everyday life.
The finding comes from a study by Mark Egan, Dominic Russel, Claire Shi and Vincent Yao, described on co-author Claire Shi's research page. It is a working paper, not a settled forecast of the market's ultimate size. Nevertheless, its evidence offers an unusually useful counterpoint to the constant announcements of more powerful models. The companies may be able to impress a large audience while still having to persuade people that a monthly charge delivers something they cannot get free or from a competitor.
Paid AI Subscriptions Are Still a Minority Choice
The researchers analysed credit and debit card data covering 174 million consumers and 114 billion transactions. Those are the scale of the underlying payment records, not the number of people who purchased AI subscriptions. From this material, they estimated the portion of US consumers paying for a major generative AI service in April 2026. The conclusion was a small minority. The analysis is therefore about observed paid adoption, not how frequently everyone uses AI, how many people have free accounts or how satisfied they are with the technology.
That distinction matters whenever a company boasts about the reach of its free chatbot. A user may ask dozens of questions, try an image generator and return every week without ever becoming a subscriber. There may be good reasons for this. Free services can be useful enough for casual tasks. People may also prefer to wait until an expensive feature solves a problem they genuinely face. A popular product can have substantial usage and still struggle to turn that popularity into consistent subscription revenue.
The authors found that paid adoption was higher among younger and higher-income consumers. Nearly all subscribers in their data chose basic tiers priced at $30 a month or less. That does not establish that wealthier customers always value the technology more, or that price is the only reason somebody pays. It suggests, more cautiously, that the current subscription market is shaped by how people weigh their own needs against a recurring bill. A model can be impressive without providing enough additional value to justify another item on a household budget.
The result is also a reminder that consumer spending and enterprise AI spending are not the same market. A business might buy access for staff, pay for software that embeds models or use an API without appearing as a conventional consumer subscription in the way the study measures it. The 2.9% figure should not be recycled as the share of all people using AI or as the proportion of all AI revenue coming from households. Those are different questions requiring different datasets.
Why Customers Switch Between Chatbots
The paper reports annual retention rates ranging from 20% to 45% across major AI providers. Retention describes whether customers remain with a service across a specified interval, not whether every customer disliked it. Some subscribers may finish a short project, move to a cheaper product or decide that the free tier is sufficient. Others may use more than one paid service. Low retention creates a business challenge because providers must replace departing customers while continuing to invest in new capabilities.
The researchers also observe that switching rises around major model releases. That is understandable in a market where a new version may suddenly become better at a task someone cares about. A writer might test an assistant for editing, while a developer tries another for code. The winning choice in one month need not remain the winning choice forever. Model releases can therefore shift demand quickly, although the study does not mean every announcement results in a mass migration.
In their economic model, consumers appear more attached to having useful AI assistance in general than to any single provider. The authors interpret this as evidence of competitive pressure and relatively close substitutes among services. That is an important difference from markets where a person is locked into a particular platform by years of accumulated material or a hard-to-move network of contacts. The research suggests the major providers may have to keep competing on capability and price instead of assuming that an early subscriber will stay indefinitely.
Still, the world of AI subscriptions changes fast. Providers add memory, integrations, business functions and distinctive tools that could make a service harder to replace. A person who has carefully built a workflow around one assistant may face more switching friction than a casual user asking factual questions. The study covers a particular period and set of major services. It cannot tell us whether retention will improve or worsen after those services become more deeply embedded in daily routines.
A Better Model Does Not Guarantee a Higher Price
From a business perspective, the obvious temptation is to charge more as models improve. Yet the study's findings point to a complication: when several products become capable of doing broadly similar jobs, technical progress may also increase competition. A subscriber who considers alternatives close enough may simply move when the price rises. Providers could face growing costs while finding it hard to extract more money from consumers. That is one reason financial success cannot be inferred directly from benchmark results.
There is a separate problem of visibility. Consumers encounter AI in search engines, office suites, customer support and mobile devices without necessarily choosing or paying for a standalone assistant. These embedded tools may reduce the perceived need for another subscription. They can also introduce new users who later decide they need a specialist product. The direction of that effect depends on the task, the quality of the included tools and the friction of switching. Broad claims that free AI will inevitably destroy paid subscriptions are no more justified than claims that every occasional user will eventually subscribe.
Harvard Business School's summary of the study places the findings within research on consumer demand and competition. Its authors use transaction records and an economic model, so their discussion of future pricing power involves assumptions as well as observed behaviour. That is normal for serious economic research, but readers should distinguish recorded purchases from the model's projection of how providers might react. The former tell us what happened in the study period; the latter help explore possibilities.
What the Numbers Mean for Someone Paying Today
For an ordinary subscriber, the practical lesson is not to abandon a useful tool. It is to ask what the paid plan actually accomplishes. Does it handle work that a free tier cannot? Are the extra features used often enough? Does the product save enough time or provide a reliable capability that justifies the price? Comparing the same real task across a few services can be more informative than judging them by advertisements or headline model rankings.
A monthly fee is easiest to overlook when the service sits beside other subscriptions. That is especially true if somebody purchased access for a demanding project that has since ended. The question of value is personal. A researcher, a small business and a casual user can have entirely different reasons for paying. LiveAIWire previously examined how much users think generative AI is worth, a related but distinct question from the actual payment records in this paper.
There is also a cost to switching that does not appear on a monthly bill. Moving saved instructions, testing familiar workflows and learning a new interface takes effort. A cheaper alternative may be poor value if it requires repeated corrections. At the same time, reluctance to switch can keep people paying for a product they no longer need. Both possibilities deserve a sober assessment based on the work being done rather than loyalty to a brand name.
The study also invites scepticism about services that promise one subscription can replace several unrelated tools. An attractive demonstration may conceal feature limits or require another paid service underneath. Our earlier report on paying twice for AI considered the risk of overlapping subscriptions and embedded costs. Consumers benefit when providers clearly explain what is included and what triggers additional charges. Complexity can make price comparison harder even when competition between underlying models is strong.
The Future Could Favour the Customer, but It Is Not Settled
If people can change suppliers without much penalty, companies have stronger incentives to improve their products and avoid excessive pricing. That is the optimistic reading of the authors' competition findings. It is not a guarantee that prices will fall, because the future also depends on computing costs, regulation, market concentration and the creation of features that users value enough to pay for. It is possible for a market to be fiercely competitive while still demanding significant investment from all participants.
Privacy and trust matter alongside performance. Someone might reject a free tool if its information practices are unsuitable for confidential material, or prefer a paid product that offers stronger controls. LiveAIWire's examination of the hidden costs of free AI offers a reminder that the cheapest advertised price is not always the whole cost of a service. These factors may eventually prove important sources of differentiation, even if the current paper finds that many consumers see the largest assistants as increasingly interchangeable.
The most revealing number in the Harvard research is perhaps not the adoption rate on its own, but the contrast between the enormous cultural attention paid to AI and the much smaller share of people paying directly for an assistant. Model makers can keep demonstrating spectacular abilities. Turning those abilities into a product people willingly renew is a different test. The companies that succeed may not simply be the ones with the loudest announcement; they may be those whose tools solve an everyday problem so reliably that cancelling feels inconvenient.
About the Author
Stuart Kerr is Technology Correspondent at LiveAIWire, covering artificial intelligence, cybersecurity and the social impact of emerging technology. LiveAIWire is an independent, human-led technology publication using AI-assisted research, editorial production and original AI-assisted editorial illustrations under his direction.