Five years. That’s how long the founder in this composite case spent trying to force an idea to work. Five years of life, millions in funding, and countless sleepless nights, all culminating in a spectacular failure. The team was building an EdTech platform, and the founder was convinced that artificial intelligence was the key to unlocking personalized education for every student on the planet. The founder was wrong. Dead wrong.
Everyone talks about AI in education as the future, but nobody talks about the brutal realities of building it. This startup was a testament to that failure. Here’s what I’d want any founder to know before starting.
Lesson 1: AI Can't Fix a Broken Pedagogy
The grand vision was an AI tutor that adapted to every student's learning style in real-time. The team spent the first two years and over a million dollars building a recommendation engine from scratch. It was a technical marvel. It could track every click, every answer, every moment of hesitation, and serve up the ‘perfect’ next piece of content. They had data scientists, PhDs in machine learning, the whole nine yards.
But here was the problem: the content itself was based on a flawed teaching methodology. The team was essentially using a super-intelligent system to more efficiently deliver a bad lesson plan. They digitized standard, boring textbook content and expected the AI to magically make it engaging. It didn’t work.
Students would log in, get a few AI-powered recommendations, and then zone out. Engagement was abysmal. The platform had a 90% drop-off rate after the first week. The team tried tweaking the algorithm, adding more data points, and refining the models. Nothing helped. They were so focused on the ‘AI’ part of ‘AI in Education’ that they forgot the ‘Education’ part is what actually matters. A state-of-the-art engine in a car with no wheels goes nowhere. This pedagogy had no wheels.
Lesson 2: Data Is Useless Without the Right Context
As an engineer and data-driven founder, I share a common bias: more data is always better. This team had it too, and collected everything. I mean everything. They had terabytes of data on student performance. They could tell you that 73% of students in Ohio struggled with a specific type of algebra problem on Tuesday afternoons. Impressive, right?
It was also completely useless. The team was drowning in data but starved for wisdom. The numbers told them what was happening, but they gave zero insight into why. Why were students struggling? Were they tired? Was the content confusing? Was the user interface clunky? The dashboards were a sea of charts and graphs, but they were just vanity metrics.
In a pivotal board meeting, the founder proudly presented a deck showing a 15% improvement in ‘session duration’ after an algorithm update. One of the investors, a former teacher, asked a simple question: “Are the kids actually learning more, or are they just taking longer to get confused?” The founder had no answer. That question hit like a ton of bricks. The team had optimized for engagement metrics, not learning outcomes. They were tracking the wrong things.
Real context doesn’t come from a server log. It comes from talking to your users. It comes from sitting in a classroom and watching a teacher struggle with your software. It comes from seeing a student’s face light up when they finally understand a concept, not when your AI serves them another video.
Lesson 3: Teachers Are Your Customers, Not an Obstacle
This was the team's most arrogant and fatal mistake. They saw teachers as a hurdle to overcome. Their pitch was, “Let our AI do the teaching, freeing up teachers to be ‘facilitators’.” They fundamentally misunderstood the teachers' role. They were trying to replace teachers, and teachers knew it.
The team built this beautiful, complex product for students, assuming schools would just buy it and force teachers to use it. The reality was the opposite. Teachers are the gatekeepers, the champions, and the ultimate users of any tool in the classroom. If they don’t like it, it’s dead on arrival. And teachers hated the platform.
It was too complicated. It didn’t fit into their existing workflows. It generated reports they didn’t have time to read. The startup asked teachers to trust an algorithm they didn’t understand to make decisions about their students. It was asking them to give up control, and it offered them nothing but more work in return.
The founder spent months trying to sell to school districts, getting top-down mandates. The team even got a few pilot programs. But in every single one, teacher adoption was near zero. Teachers would log in once, get overwhelmed, and go right back to the tools they knew and trusted. The pilots all failed.
The Turnaround That Came From Failure
Shutting down a company like this is one of the hardest things a founder can do. It feels like a personal failure on every level. But lessons like these can be the most valuable education a founder ever receives. In this case, they became the foundation for the founder's next venture.
Instead of starting with AI, the team started with a problem. They spent 6 months just talking to teachers. They didn’t write a single line of code. They found a simple, painful, and universal problem: grading. Teachers were spending 10-15 hours a week on it.
The new product didn’t have a fancy AI engine. It was a simple tool that helped teachers create, distribute, and grade assignments faster. The team built it with teachers, getting feedback every single day. Once they solved that core problem and earned teachers' trust, they began to layer in intelligent features. Not a black-box AI, but simple, explainable tools that gave teachers superpowers—like automatically grouping similar mistakes so they could address them with the whole class at once.
This time, it worked. Teachers loved it because it was built for them. The team solved their problem first. The AI was in service of the user, not the other way around.
This journey in EdTech shows that AI is not a product. It’s a tool. And like any tool, it’s only as good as the person using it and the problem it’s meant to solve. Stop chasing the AI hype. Go find a real, human problem and solve that instead. That’s where you’ll find success.
Frequently Asked Questions
Q:How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Q:What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q:Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.