If I Operated an Edtech Fund, I Would Be Shitting My Pants

I have spent more than ten years building in edtech.

I co-founded CanopyLAB, a venture-backed learning technology company, because I believed adaptive learning would fundamentally change education. I still believe I was right about that.

What I underestimated was how long it would take for the technology to catch up with the educational principles.

For years, adaptive learning was more convincing as an idea than as an actual user experience. We could collect data, recommend content, and design different pathways through a course. But creating and maintaining those pathways required too much manual work from instructors. The technology could support adaptation, but it could not truly understand the learner, generate the right material, and reshape the experience continuously.

It was adaptive, but within boundaries someone else had already created.

That started to change with artificial intelligence.

In 2019, I invented AICATO, which CanopyLAB launched as the world's first AI course authoring tool. It could automate parts of course creation that previously required substantial human effort. It was an early attempt to solve one of the fundamental problems in adaptive learning: you cannot create a genuinely individual learning experience if every piece of content and every possible pathway must first be produced by hand.

Then generative AI arrived, and suddenly the technology could do much more than select between predefined options. It could generate, explain, question, adjust, and respond in real time. The technology finally caught up with the idea.

That should make me extraordinarily optimistic about edtech. Instead, it makes me nervous.

I am giving companies the opposite advice today

Ten years ago, if a company asked whether it should build its own learning platform, I would almost always have said no. Building software was expensive. It took time. You needed specialist developers, designers, product managers, infrastructure, and continuous maintenance. Even if you managed to build something functional, it would rarely be as good as a product developed by a company that did nothing else.

The sensible advice was to find a specialist provider. Run a tender. Buy the best available product. Customize it where necessary. Today, I increasingly give companies the opposite advice. This is not limited to edtech. I have just under 100,000 subscribers across my English- and Spanish-speaking email lists. Maintaining and sending to the 36,000 people on my Spanish language list through Mailchimp alone was costing me around 10,000 Danish kroner every month (1,520 USD). That is a significant amount of money to send email to a list I already own. So last week, I used Lovable to build my own sending tool and connected it to SendGrid. Building the tool cost me less than $30. Operating it costs approximately $25 to $30 a month. Why would I continue paying for the software subscription?

Of course, not every company should build every tool. Small companies may still benefit from buying something ready to use. Complex and regulated systems are a different matter. But once you have 30,000, 40,000 or 100,000 users, the calculation begins to change dramatically.

A tool that once required a software company, a development team and venture capital can increasingly be created by one person over a weekend. In some cases, a company can recover its development investment after one or two months of cancelled subscription fees. This challenges one of the central assumptions behind the software as a service economy. We spent years moving from ownership to rental because specialist software was cheaper and better than anything most organizations could build themselves. Now the cost of building is collapsing, while the cost of renting has not.

Edtech has a second and even bigger problem

Edtech is not only vulnerable because organizations can build more of their own software. It is also vulnerable because learners may no longer need a separate learning platform at all. Elon Musk has spoken for years about turning X into an everything app. I think the fundamental idea was right, but the chosen starting point was wrong.

The everything app was probably never going to be a social network. It is going to be the AI platform that already knows what you are working on, what you are interested in, what you understand, and where you repeatedly get stuck. That platform could be ChatGPT, Claude, Gemini, Grok, or something that has not yet been released. The winner is not necessarily important to my argument. But the shift is already happening.

When I want to understand how to maximize the return from my investment portfolio, I do not necessarily want to enrol in an online course about investing. I can begin a conversation with an AI platform immediately. It already knows a great deal about my level of knowledge, my goals, the questions I have asked and the decisions I am trying to make. It can explain a concept, test whether I understand it, adjust the difficulty, generate examples based on my actual portfolio, and move in a completely different direction when my needs change. That is adaptive learning. It is more adaptive than most products that describe themselves as adaptive learning platforms because it is not adapting my journey through a fixed course. It is creating the learning experience around me as I go.

An edtech platform ordinarily begins with no knowledge of me. It asks me to create another account, complete another onboarding process, and perhaps take a diagnostic test. Even then, it only knows what I have done inside that particular platform. My preferred AI platform may already know what I have been reading, writing, building, and struggling with across my life and work. How is a conventional learning platform supposed to compete with that?

Chegg is not the story. It is the warning

Chegg is one of the clearest public examples of what happens when learners stop going to a dedicated education product for answers. At its pandemic peak, Chegg was valued at close to $15 billion. By July 2026, its value had fallen to around $100 million. The demand for help did not disappear. Students still needed explanations and assistance. What disappeared was the reason to pay Chegg for access to them.

Generative AI can now answer many of the same questions people turned to Chegg for, instantly. Chegg itself acknowledged that generative AI and Google's AI Overviews were reducing traffic and subscriptions. The company subsequently made enormous reductions to its workforce. But Chegg is publicly traded. We can see the collapse in its share price.

If I operated an edtech fund, I would be more concerned about the companies whose decline is not visible yet. Private companies remain recorded at valuations established during previous financing rounds. Institutional contracts can take years to expire. Universities and corporations move slowly. Revenue can therefore continue while the underlying reason for the product to exist is disappearing.

A portfolio can look healthy on paper long after its investment thesis has stopped being true. Adding an AI assistant to the existing platform does not solve that problem. The question is not whether an edtech company uses AI. Nearly all of them will. The question is why a learner would enter that platform instead of simply learning inside the AI environment they already use for everything else.

What can survive?

I do not believe all edtech will disappear. But I believe that between 90% and 95% of today's edtech companies will eventually close, consolidate, or become functionally irrelevant. And this will happen within the next 2–3 years.

The survivors will need to offer something that cannot be reproduced by asking an AI platform to teach you. That could be a real community. A strong sense of identity and belonging. Access to people you genuinely want to learn with or from. A credential that has meaningful value. A physical or social experience. Or a strong ideological foundation that makes participation mean something beyond acquiring information.

People do not join Harvard only because Harvard possesses information unavailable elsewhere. They join because of what Harvard represents, who else is there, and what membership unlocks. The same distinction will increasingly determine which learning companies survive. Information is no longer enough. Content is no longer enough. Personalization is no longer enough. Even adaptive learning is no longer enough because the general AI platforms may be able to do it better, with more context and without asking the learner to start over.

The technology finally arrived. It may destroy the category built to deliver it

For more than a decade, I believed adaptive learning represented the future of education. I was right. But I had assumed that adaptive learning would transform learning platforms. I now think it may replace many of them. The biggest threat to edtech is not another edtech company with a better feature set. It is that learning becomes one behaviour among many inside a much larger AI environment.

At the same time, the organizations buying learning technology are discovering that they can build more of it themselves. That leaves traditional edtech squeezed from both directions. Its customers may no longer need to buy the software. Its learners may no longer need to visit the platform. So when shareholders, employees and other people ask me where I think the space is going, I cannot give them the reassuring answer they probably expect from someone who has spent more than ten years building in it.

I think education is entering one of the most exciting periods in its history. But if my investment portfolio consisted of companies built around delivering it through separate software platforms, I would be shitting my pants.