Edtech Is Still Mailing DVDs

When I went to college, Netflix was a company that sent DVDs through the mail. You made a list of everything you wanted to watch, which Netflix called your “queue”. Its standard subscription plan in 2002 allowed you to have three DVDs out at the same time, with no due dates or late fees, and the company also offered other plans with different service levels. When you returned one, Netflix sent another available title from your queue.

At the time, it was brilliant. You did not need to drive to Blockbuster. There were no late fees. The catalogue was enormous compared with what a physical store could stock, and Netflix could learn what you liked and recommend what you should watch next. But there was one enormous limitation Netflix could not optimise its way out of: you still had to wait for the next DVD to arrive.

When Netflix launched streaming in 2007, it began removing the physical delivery altogether and subsequently expanded internet delivery beyond the PC to other devices. The customer relationship remained, the recommendation system remained, and much of what created value remained. The red delivery envelope and the wait did not.

That distinction is increasingly how I think about edtech. I have argued in my first essay that as much as 90% of today's edtech will disappear, consolidate, become irrelevant or be absorbed into something else over the next few years. I do not believe that because education is becoming less important. I believe it because learning is having its Netflix moment.

People are still searching for information. They are still trying to understand difficult concepts, practising skills, preparing for exams, learning languages, changing careers, solving problems and asking for explanations. What has changed dramatically is how they do it.

They open ChatGPT, Claude, Gemini or Perplexity. They ask the question directly and then ask another one. They upload the document they are struggling with. They ask for an explanation at the level of a 12-year-old, then at the level of a graduate student. They ask for examples and practice questions, argue with the answer, ask the system to quiz them and ask it to explain where they went wrong.

This is no longer hypothetical consumer behaviour. OpenAI reported in 2025 that more than one-third of college-aged young adults in the United States used ChatGPT and that approximately one-quarter of their messages were related to learning or schoolwork. UNESCO reported that more than two-thirds of secondary-school pupils in high-income countries were already using generative AI, while Anthropic's 2026 usage data placed educational instruction and library work as the second-largest category of Claude.ai usage.

The learning did not disappear. The mode of consumption changed. And much of edtech is still mailing DVDs.

The uncomfortable history of edtech

The obvious explanation for why so much of edtech now looks vulnerable is generative AI. It would be convenient to say that ChatGPT arrived and suddenly made a healthy industry obsolete. Unfortunately, the chronology does not support that explanation.

The industry had serious structural problems long before ChatGPT appeared. If you look at the past 10 or 15 years of edtech, you can almost see a succession of attempts to solve the problems created by the previous generation. The early enterprise systems were often horrible. They were difficult to buy, difficult to configure and difficult to use. Implementing one could require consultants, integrations, training, migration projects and months of work before a learner saw anything at all.

Then SaaS arrived and appeared to solve the problem. Products became easier to buy, easier to implement and better designed. They were hosted in the cloud and subscription-based, with no giant IT project required. This was genuine progress, but somewhere along the way the industry began confusing the removal of implementation friction with the creation of value.

We became extraordinarily good at making software: a course platform, a quiz platform, a tutoring platform, a teacher platform, an engagement platform, a language app, a wellbeing app, a financial-learning app, a professional-development app, a skills platform. Another login, another dashboard and another subscription. Because SaaS made software easier to sell, an enormous amount of software was sold. That did not mean anyone used it.

UNESCO's 2023 Global Education Monitoring Report found that an average of 67% of education-software licences in the United States were unused and 98% were not used intensively. The EdTech Genome Project cited in the report looked at roughly 7,000 pedagogical tools representing $13 billion of spending and found that 85% were either a poor fit or implemented incorrectly. That is an astonishing number, and I think the industry has been too comfortable calling this an implementation problem.

Of course implementation matters. Schools fail to train teachers. Leaders buy software without changing behaviour. Products integrate badly. Teachers do not have time. Procurement and usage are disconnected. But from the perspective of a business model, those explanations only get you so far.

If I build a product that produces value only when the customer trains everyone correctly, integrates it perfectly, continually champions usage and somehow persuades thousands of busy humans to develop a new habit, those dependencies are part of my product economics. I cannot simply declare them to be the customer's problem.

The evidence problem was just as uncomfortable. UNESCO found that only 7% of the UK edtech companies it reviewed had conducted a randomized controlled trial. Twelve per cent had used third-party certification, and 18% had participated in academic studies. In a survey across 17 American states, only 11% of teachers and administrators requested peer-reviewed evidence before adopting education technology.

So we created a peculiar market. Companies frequently did not prove that their products improved learning, buyers frequently did not demand proof before purchasing them, and the industry became extremely sophisticated at measuring whatever software happened to make measurable: registrations, logins, minutes watched, questions answered, courses completed and certificates generated.

Those things tell you what happened inside the software. They do not necessarily tell you that somebody learned, and they certainly do not tell you that the customer received enough value to keep paying.

COVID temporarily made almost every DVD look useful

Then COVID arrived. Global edtech venture funding was approximately $7 billion in 2019. It jumped to $16.1 billion in 2020 and $20.8 billion in 2021. By 2024 it had collapsed to approximately $2.4 billion, before moving only slightly upward to $2.6 billion in 2025.

The usual interpretation is that investors went insane during the pandemic and then came back to their senses. There is some truth in that, but COVID also created an extraordinary product-market-fit illusion because it removed the alternative.

Schools did not voluntarily select online education over fully functioning classrooms. Universities could not wait until the software improved. Employers could not simply cancel every training program indefinitely, and parents could not decide that their children had enough screen time and send them back to school. There was nowhere else to go.

I experienced this directly. Before the pandemic, companies like mine could spend the first half of a sales conversation explaining why learning should happen online in the first place. Suddenly we did not have to. The world had made the argument for us.

Usage exploded, but emergency adoption proves that a product can function when people have no alternative. It does not prove that they will continue choosing it when the alternative returns. The industry treated emergency traffic as though an entirely new behavioral pattern had been permanently established. Then the world reopened, and customers got their choice back.

If COVID temporarily disguised structural weaknesses that were already there, those weaknesses should have started showing up before generative AI became a serious substitute. The obvious place to look for that evidence is the public market.

The market broke before ChatGPT

I wanted to know whether the weaknesses that COVID had temporarily hidden had started showing up before generative AI became a serious substitute. Not because share prices can tell us whether people wanted to learn, they cannot, but because they can tell us something much narrower and still very important: what happened to the money investors put into the companies that were supposed to benefit from the digitisation of education.

So I went back to the end of 2020 and took the 20 largest pure-play listed education companies by market value at the time. They were TAL Education Group, Offcn Education, New Oriental, Chegg, Gaotu Techedu, Bright Horizons, Pearson, Kahoot!, Grand Canyon Education, China Education Group, China East Education, IDP Education, East Buy Holding, China Yuhua Education, Youdao, Laureate Education, Pluralsight, 2U, Cornerstone OnDemand and John Wiley & Sons.

Then I ran a deliberately simple experiment. Imagine that on 31 December 2020 you had $1 million and decided that edtech was where you wanted to put it. What would have happened to that money?

There are obvious limitations to what this exercise can prove. A share price is not a measure of pedagogy, learner demand or product quality. A company can grow revenue while destroying shareholder value because investors originally paid too much for it. Another can build an excellent product and still produce a mediocre return. Share prices are affected by interest rates, execution, competition, regulation, geography and the expectations already embedded in the starting valuation.

So I am not using the stock market as evidence that people stopped wanting education. I am using it to ask a much narrower question: were investors actually rewarded for allocating capital to the companies that represented the category?

There was also one enormous complication in the original 20. Nine of the companies were Chinese, and China's 2021 tutoring restrictions fundamentally changed the economics of large parts of the private education market. Those losses are completely real if you were an investor, but they are less useful if the question is whether the underlying business models were already weakening independently of an extraordinary regulatory event. So I calculated both.

The full 20-company portfolio tells us what actually happened if an investor bought the category as it existed at the end of 2020, including China. For the central comparison, I then removed the nine Chinese companies and looked at the remaining 11: Chegg, Bright Horizons, Pearson, Kahoot!, Grand Canyon Education, IDP Education, Laureate Education, Pluralsight, 2U, Cornerstone OnDemand and John Wiley & Sons.

Then I asked what happened to the $1 million.

In the ex-China group, $1 million invested in the median company became approximately $1.07 million. If the money had been divided equally across all 11 companies, it would have become approximately $1.31 million, largely because a small number of strong performers pulled the portfolio upward. If instead the $1 million had been allocated according to the companies' market values at the beginning of the period, effectively following the hierarchy investors had already assigned to the category, it would have become approximately $1.02 million.

Over the same period, $1 million invested in SPY became approximately $2.10 million. In QQQ, it became approximately $2.18 million. That comparison is difficult to make flattering.

The specialist edtech investor had to understand school procurement, pedagogy, implementation, course completion, regulation, institutional budgets, B2B sales cycles and the difference between an LMS, an OPM, a tutoring company and a university operator. They had to decide which companies deserved capital, which valuations were justified and which business models would survive. The passive investor could buy SPY and go to lunch.

Following the original market hierarchy of the ex-China education companies left the specialist investor with approximately $1.02 million. The same starting amount in SPY produced approximately $2.10 million. More than $1 million in additional value came from choosing the passive alternative.

That does not prove that edtech was useless, nor does it prove that there was no demand for digital education. It proves something much more uncomfortable for an investor: as a group, the largest listed education companies did not reward the additional specialization, concentration and risk required to invest in them.

But even that conclusion is too broad, because the companies did not perform remotely alike.

One million dollars invested in Laureate Education became approximately $5.67 million. In Pearson, approximately $2.21 million. Grand Canyon Education became approximately $1.71 million, Wiley approximately $1.45 million and Cornerstone OnDemand approximately $1.31 million.

At the other end, $1 million invested in Chegg fell to approximately $12,000. In 2U, it went to zero. IDP Education fell to approximately $122,000, Kahoot! to approximately $368,000 and Bright Horizons to approximately $478,000.

Those are not minor differences around a common sector result. One company multiplied the original investment more than five times while another effectively erased it.

That matters because it rules out the simplest conclusion. The market was not deciding that education itself had become worthless. It was distinguishing between very different kinds of businesses that happened to have been placed under the same edtech label.

In fact, that may be part of the problem with the category itself. We call an enormous range of fundamentally different businesses “edtech” because they all touch education. But a university operator, a publishing company, an assessment provider, an LMS, a tutoring platform and a consumer learning app do not necessarily share the same economics simply because their customers are learning something.

Some of the stronger businesses were attached to institutions, credentials, assessment, research, publishing, regulated operations or infrastructure deeply embedded inside customers. Others depended much more heavily on people repeatedly choosing to visit a separate digital destination for content, courses, activities or answers. And that second group contained some of the most spectacular destruction.

Which creates the obvious next question. If those businesses were especially vulnerable because users had to keep coming back to a separate platform for something they could increasingly obtain elsewhere, was ChatGPT the thing that broke them?

The chronology says no.

The first collapse happened before AI. Then came a second one.

Chegg is the most violent example. At its peak in 2021, the company was worth approximately $14.7 billion. Today its market value is approximately $100 million. More than 99% of the value disappeared.

It is extremely tempting to look at that destruction and tell a simple story: ChatGPT arrived, students stopped paying for answers, and an entire category broke. The problem is that the first break happened too early.

I separated the listed education companies according to whether general-purpose AI could reproduce a central outcome customers paid them to provide. This was not a classification of whether the companies themselves happened to use AI. It was a measure of how easily a general-purpose AI system could substitute for the outcome the customer had previously needed that company to obtain.

From February 2020 through November 2022, $1 million invested in the median higher-exposure company fell to approximately $550,000. Over the same period, $1 million invested in the median lower-exposure education business became approximately $1.39 million.

That happened before ChatGPT had any meaningful opportunity to cause the collapse. Something was already wrong.

Then the pattern did something important that is easy to miss if you simply compare the beginning and the end: it stabilised. From November 2022 through December 2023, $1 million invested in the median higher-exposure company became approximately $1.04 million, while the equivalent investment in the lower-exposure group became approximately $1.06 million. For roughly a year, the gap stopped widening. The vulnerable group had already been severely repriced, but it was no longer collapsing relative to the rest of the sector.

Then came the second break.

During 2024 and 2025, as generative-AI adoption accelerated, $1 million invested in the median higher-exposure company fell to approximately $400,000. The lower-exposure equivalent became approximately $1.08 million.

That sequence is much more interesting than saying that AI killed edtech. First, the businesses most exposed to substitution fell sharply before ChatGPT. Then they stabilized. Then, once consumers had a radically easier way to obtain many of the same outcomes, the gap opened again.

That does not prove that AI caused every dollar of the second decline. Share prices are affected by interest rates, execution, competition, geography and starting valuation. But it establishes the order of events, and that order makes the comfortable explanation much harder to sustain.

If generative AI had suddenly broken an otherwise healthy industry, the first collapse should have followed the arrival of generative AI. It did not.

The more uncomfortable conclusion is that a meaningful part of edtech had already developed weak economics around products that were difficult to retain, poorly utilized or dependent on users repeatedly visiting separate destinations. The first market correction exposed those weaknesses. For a brief period, the damage stabilized. Then generative AI changed the way people could access many of the same outcomes, and the companies whose value propositions were easiest to reproduce started falling again.

That distinction matters enormously. If ChatGPT were the original problem, the obvious response might be to add AI to the existing product. But if the underlying need remains while the way people access it is changing, then the strategic question is much bigger: does the same value still need to be delivered through the same product at all?

What if the product is useful but the container is wrong?

Netflix became successful not because people suddenly wanted different movies, but because new technology allowed it to change the delivery mechanism and remove friction for the user. Today, the equivalent friction is rarely a physical barrier. It might be a payment model, a separate login, an app somebody has to remember to open, a course they have to commit to completing or a platform that requires people to change an existing habit.

Learning companies are now competing against learning that happens through TikTok, podcasts, YouTube and general-purpose AI. In a 2025 European study commissioned by YouTube, 74% of the young people surveyed said they had watched YouTube videos to learn something new for school. Sesame Street's official YouTube channels generated more than five billion views in the year before YouTube announced an expanded partnership that would make the platform the largest digital home for Sesame Street episodes from 2026.

Again, the demand for learning did not disappear. It moved. Picking the wrong container can therefore result in dramatic failure. A recent Danish example makes the distinction unusually concrete. MYiNNERME was a digital mental-health app developed by child psychologists for children, teenagers and their parents. It offered self-guided learning programs addressing anxiety, anger, shyness, grief, bullying, sleep and self-esteem, translating established psychological approaches into exercises and content families could use outside a traditional therapy session. The company behind MYiNNERME has now closed down, citing the inability to find a profitable business model.

The obvious conclusion would be that the product failed because there was insufficient demand for what it offered. I am not sure that is the right conclusion.

If I were approaching the same problem today, I would question whether an app should be the starting point at all. Imagine instead a faceless YouTube channel built around the company's characters. A child might encounter one of those characters in a video explaining anxiety, meet the same character in a story about bullying, use it for a breathing exercise and later recognize it in content about going to sleep or starting a new school.

Creating that universe no longer requires anything close to the production infrastructure it once would have. Characters can be created and animated with AI-assisted tools. Scripts, voices, translations and variations can be produced far more cheaply. Production and publishing can be automated. The same characters and psychological concepts can move from long-form YouTube into shorts, audio, printable exercises and material used by psychologists or schools.

They can also move outside the screen entirely. The same intellectual property could become stories, print-on-demand coloring books, activity books, cards or other physical products without the company needing to finance enormous inventories before discovering whether anyone wants them.

The point is not that MYiNNERME would necessarily have succeeded as a YouTube company. It is that the valuable asset was never necessarily the app. It was the expertise, the psychological frameworks, the exercises, the stories, the characters and the ability to translate difficult emotional concepts into something children could understand. An app was one way of wrapping that value. Today there are many more.

One particular company and one particular delivery model can therefore disappear while the underlying demand remains completely intact. In fact, that demand may now be served in more places than ever before: inside dedicated products, through creators, on YouTube, through AI, in schools, between therapy sessions and through physical products connected to digital worlds.

But this creates another problem. If software becomes cheaper to build and content becomes cheaper to create, then simply owning good content may not be enough either.

The important question becomes: what remains scarce? It may be trusted brands. It may be credentials. It may be proprietary data, distribution, institutional relationships, community, regulatory approval, workflow integration, recognized assessment, intellectual property or demonstrated outcomes. The answer will differ by company, but the principle is the same. When both software production and content production become cheaper, investors have to identify the part of the business that cannot easily be reproduced. That is a much more useful distinction than asking whether an edtech company “uses AI”.

Edtech's DVD moment is also an investor problem

Generative AI has not only created new places for people to learn. It has also made it dramatically cheaper for companies to repackage the same intellectual property across many of those places.

But there is an important difference between MYiNNERME, the broader edtech industry and Netflix. Edtech is not entering this technological transition from a position of obvious economic strength. The financial problems and declines in value predate generative AI. Long before ChatGPT became a serious substitute, the sector had unused licenses, weak evidence of outcomes, difficult retention and business models that depended on users repeatedly returning to separate digital destinations. The public markets were already reflecting some of those weaknesses.

That is why I increasingly think the useful distinction is not simply between “AI companies” and “non-AI companies”. It may be between destinations and infrastructure. A destination requires the customer to decide to come to you. Open the app. Visit the platform. Start the course. Watch the lesson. Ask the tutor. Complete the activity. Return tomorrow.

Infrastructure is different. It sits inside a workflow, institution or system that already exists. Credentials, assessment, publishing, compliance, school administration, recognized qualifications, proprietary data and deeply embedded institutional tools can all possess forms of value that are harder for a general-purpose AI interface to intercept.

That does not make infrastructure businesses invulnerable, and it does not make destination businesses worthless. But the distinction matters because AI dramatically reduces the friction involved in obtaining information, explanation, practice and increasingly personalized instruction without visiting a specialized platform.

For an investor, the question is therefore not merely whether people will continue learning. They obviously will. The question is who captures the economics of that learning when the interface through which it occurs changes.

The public-market comparison makes that problem difficult to ignore. Following the original market hierarchy of the largest ex-China listed education companies turned $1 million into approximately $1.02 million. Over the same period, the same $1 million in SPY became approximately $2.10 million. The specialist investor had to understand pedagogy, procurement, implementation, regulation and a collection of highly specific business models, while the passive investor could effectively buy an index fund and go to lunch. The passive investor ended with more than twice as much money.

That is not an argument that education has no value. It is almost the opposite. People are still learning. Children still need help with anxiety. Adults still need to understand investing. Employees still need new skills. Students still need explanations. The demand is not disappearing. The uncomfortable question is why so many of the companies built to serve that demand have been so poor at turning it into shareholder value.

The MYiNNERME example makes that distinction unusually easy to see. The company can disappear while the knowledge it created, the problems it addressed and the people looking for those solutions remain. That value can now be wrapped in a YouTube universe, recurring characters, AI interactions, books, school programmes, tools for psychologists, physical products or combinations of all of them, often at production and distribution costs that would have been unrealistic only a few years ago.

The movie did not become worthless when the DVD did. But owning a DVD business after streaming arrived was not particularly helpful either.

That is the distinction I think matters for edtech investors now. Good content is not enough. Good pedagogy is not enough. Even enormous demand is not enough. If technology changes how people consume the underlying value, the company has to move with it, and it has to find a way to capture enough of that value to justify the capital invested.

Otherwise, why take the additional risk? If an investor can put $1 million into a passive index, do almost nothing and end with more than twice the money, specialist edtech capital has to earn its existence.

The next generation of winners therefore will not simply be the companies that use AI, the companies with the best courses or the companies that make today's apps marginally better. They will be the ones that understand what part of their product remains genuinely scarce and valuable, how new technology changes the cheapest and most natural way to deliver it, and where in that new system there is actually a business capable of capturing enough value to produce attractive returns.

That is why I think 90% of edtech can disappear without learning disappearing with it. We are not running out of things to learn. We may simply be running out of reasons to pay for them in the ways edtech spent the last decade building.