Are universities on the brink of an Uber-like revolution?

Lionel Page

Sep 14, 2026

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This post is part of a series on the challenges posed by the rise of AI.1 I am fairly optimistic about the possible benefits of AI for society, but at the same time, one should appreciate that such a technology has the potential to disrupt existing institutions. In this post, I describe a possible scenario of how AI could upend one of the sectors most exposed to a technology that is making access to knowledge easier and cheaper: higher education.

In 2024, the educator James Bedford posted a diagram titled “The AI Wheel of Death” which represented the challenge AI and its use by students and educators might represent for universities. The diagram points to the risk of universities being squeezed out of the education process as their added value relative to a simple use of AI becomes progressively less clear.

This “wheel of death”, proposed by James Bedford and William Scates Frances in 2023 and updated by Bedford in 2025, illustrates how AI might progressively erode the current role of universities.

In this post, I develop this idea by presenting a fictitious scenario to illustrate how an AI revolution in higher education could unfold.

It is 2028, and Elon Musk is looking for another domain where to make a big impact with his technological innovations. Higher education catches his attention. AI opens up the possibility of providing personalised teaching at a much lower cost than conventional universities.

Musk has one of the world’s leading AI models, Grok, and the financial resources to develop the platform and fund its initial losses. He is therefore in a privileged position to attempt a bold disruptive move in higher education. Within months, his team announces Grok University, a suite of AI learning programmes promising to deliver “the best of human knowledge, personalised for every student, at a fraction of the cost of a traditional university degree.”

It launches with programmes in mathematics, statistics, computer science and engineering. Students pay US$1,000 per year, plus examination fees. Completing the equivalent of a three- or four-year degree costs around US$4,000. This cost is only a small fraction of the annual tuition fees, often exceeding US$50,000 per year, paid by full-fee students at leading American and British universities.

The technical side is relatively easy. High-quality material in mathematics and engineering is already available in textbooks, university notes, recorded lectures and open educational resources. Grok’s verbal and technical capabilities make it possible to train specialised tutors capable of explaining the material, answering questions and generating exercises.

The initial reception by public pundits is mixed. The Guardian runs the headline:

“Musk thinks he is a visionary by reinventing the failed MOOC experiment”

The criticism is not absurd. MOOCs (Massive Open Online Courses) had once been expected to revolutionise education by leveraging the possibilities offered by the internet to provide university content to a large proportion of the population. Yet, after the initial excitement, they failed to deliver the expected revolution in higher education. MOOCs survived and developed into a large market for online courses, professional certificates and degrees, but became only a relatively minor complement to conventional universities rather than substitutes for them.

The Guardian journalist is right that reinventing a past failure without accounting for the reasons for its failure is unlikely to work. But she could also have thought of an established technology subsequently displaced by a more intuitive one: the BlackBerry being upended by Steve Jobs’s iPhone. At first sight, the products were similar. The difference? Steve Jobs understood the behavioural keys to widespread appeal that the BlackBerry was missing. He retained portability and accessibility while making the iPhone so easy and intuitive to use that it massively expanded the number of people for whom the cognitive gains outweighed the cognitive costs of using it.

Similarly, Musk and his team of designers took stock of the limited success of MOOCs and identified two key behavioural barriers that had to be broken to provide a product with broad appeal: psychological motivation and economic returns.

Solving the psychological problem of motivation

Let’s face it: we all typically lack the motivation to engage in repeated, painstaking small steps for long-term rewards. Failed savings plans, failed diets, languishing gym memberships and unused subscriptions to scientific magazines are all examples. This is known as present bias: we tend to value present rewards more than future rewards and often find it hard to delay gratification. It is the cause of behavioural plagues such as procrastination and doom-scrolling past the time when we should go to bed.

In education, it means that students often struggle to maintain a steady pace of work over several months, let alone several years.

MOOCs failed partly because of this. Left, literally, to their own devices, students had too many reasons to do something else rather than look at another slide containing complex knowledge to acquire. Video games, friends and social media were simply too much competition. Students started to lag behind while telling themselves that they would catch up, until the prospect became so unrealistic that accepting reality and dropping the course was the only option. For every 100 people registered at the start of an early free MOOC, something like five to ten ended up finishing it. These figures partly reflected the fact that registration cost nothing and many people had never intended to complete the course, but the difficulty of sustaining motivation was real.

Universities beat this systematic procrastination, somewhat, by offering a rigid schedule of lessons and assessments leading to examinations on given dates. The smaller amount of freedom is what helps students resist their short-term temptations and complete their studies. This success through binding commitment has famously been described by Jon Elster through the story of Ulysses having himself tied to the mast to resist the appeal of the Sirens.

To be a compelling educational product, an AI suite therefore needs to beat this motivational problem. The Grok University team understands the challenge and relies on two complementary strategies.

The first is that the flexibility of AI allows it to do something a university course cannot: personalise the progress of each student. This has a direct consequence: it can lower the cost of learning.

The cognitive cost of learning comes from two polar situations. When the content is too easy, it is boring and one wants to do something else. When the content is too hard, one is lost in front of a wall and sees little point in continuing. Good teaching presents the material in steps that are neither too small and boring nor too large and inaccessible. The sweet spot of learning is where these steps continue to arouse interest while providing new answers that we can understand.

Unfortunately, in a lecture theatre, a university professor can provide only one explanation at one pace. For some students, it will be too slow; for others, it will be too fast. AI tutors can avoid this problem by calibrating the difficulty to the level of the student. This is one of the real innovations of Grok University. It learns to identify each student’s level and maps it to adapted steps in the presentation of knowledge.

This makes learning more natural and easier. In practice, it lowers the cognitive cost of learning and therefore makes it more appealing relative to the other activities students might consider. With the addition of gamification and social-interaction features, the AI tutor is a product with a totally different appeal from a MOOC.

But personalisation alone cannot eliminate procrastination. A student can still postpone working with a perfectly calibrated tutor. Grok University therefore retains some of the discipline imposed by conventional universities. Its courses are organised around specific examination dates booked in a given city. The AI tutor does not only help students learn; it divides the programme into weekly targets, monitors their progress and intervenes when they start to fall behind. Students can join cohorts and compare their progress with others. The programme remains flexible, but students cannot postpone everything indefinitely.

Failing to overcome the challenge of students’ sustained motivation was not the only reason for MOOCs’ limited success. They had clearly been missing something even more critical. They seemed to assume that people learned primarily to acquire knowledge. If that were the case, people everywhere would be happy to connect online and learn new things.

People clearly have some degree of curiosity, as the success of science channels on YouTube testifies. But it is well known in economics that acquiring knowledge and skills is only one of the benefits of education. The other is signalling. By going to university and receiving a degree, students signal to potential employers, peers and partners their likely skills, capacity to work, ability to engage in conceptual thinking and ability to write in a structured and reasonable way.

MOOCs were useful, but a stamp saying “has completed a MOOC in engineering” was a very unclear signal. How difficult had it been? How did it compare with a standard degree? Had the person identified on the certificate actually completed the work?

MOOCs faced the fundamental challenge of credentialisation. Successful credentialisation rests on a coordination of beliefs. Students care about a degree because employers care about it, and therefore they try hard to obtain it. Employers care because they know that students care and try hard. Creating a new qualification outside the established norm faces the problem that nobody knows what it is worth. Even where identity was verified, employers had little experience with the qualification and no clear understanding of what different results meant.

For Grok University to work, Musk and his team therefore need to nail the challenge of credentialisation. This leads to the key innovation. Grok University has no campuses, but it has physical testing centres around the world. As early as 2029, candidates can sit exams in major world cities: Los Angeles, New York, Rio de Janeiro, London, Paris, Berlin, Delhi, Beijing and Tokyo. The goal is to cover all cities with more than two million inhabitants by 2035.

These centres offer face-to-face examinations where the identity of the participants is verified. The examination is taken under standardised conditions, and thereby provides useful information because participants’ scores can be compared across all testing centres. The tests are furthermore better at precisely identifying the level of each participant. They are adaptive: their difficulty changes throughout the examination until the system has estimated each candidate’s level across several dimensions.

Nonetheless, these new tests can work only if a circle of shared expectations settles around them. Students must think the tests are worthwhile because employers care, while employers must care because serious students take them and the scores convey useful information.

The logic is like a nuclear reaction: it can either take off or fizzle out. To give it a good start, Musk leverages another card in his hand: his control of SpaceX and xAI and his considerable influence over Tesla. These companies announce that applicants scoring in the top 1 per cent of Grok University’s mathematics, computing or engineering exams are guaranteed a first-round interview for relevant positions. This simple fact gives Grok University some practical relevance right away. There is a pool of potential students who see right away a valuable outcome if they succeed