They tell you that AI is the silver bullet for education. That it will personalize learning, automate grading, and finally fix our broken school systems. I bought into that dream hook, line, and sinker. I spent five years of my life, and millions in venture capital, trying to build that dream. And I failed. Miserably.
My first EdTech company, a venture I poured my heart and soul into, went up in flames. It was a slow, painful burn. Five years of grinding, coding, and pitching, only to watch it all crumble. We had a team of brilliant engineers, a product that looked great in demos, and the unwavering belief that our AI was going to change the world. But it wasn’t enough. The market, it turned out, had other ideas.
Looking back, the failure was the best education I ever received. It taught me more than any business school ever could. It taught me that the hype around AI in education is just that—hype. The reality on the ground is far more complex, far more human, and far more challenging than any algorithm can account for. Here are the three biggest lessons I learned from my spectacular failure.
Lesson 1: Your “Perfect” AI is Useless Without Messy, Real-World Data
We were so proud of our AI. We had developed a sophisticated adaptive learning algorithm that could, in theory, create a unique educational path for every single student. It was a thing of beauty, a complex system of neural networks and decision trees that we’d spent years perfecting. We were convinced it was the most advanced piece of educational technology ever built. We were ready to revolutionize the classroom.
Then we tried to deploy it in a real school.
I’ll never forget our first pilot program. We’d partnered with a mid-sized high school in the Midwest. They were excited, we were excited. It was supposed to be our big break. The first week, our system crashed. Not once, but five times. The data the school was providing us was nothing like the clean, structured datasets we had used to train our models. It was a mess of inconsistent formatting, missing entries, and handwritten notes that had been scanned into PDFs. Our AI, the one that was supposed to be so intelligent, couldn’t make sense of any of it.
We spent the next six months just trying to clean up their data. We built custom parsers, wrote complex scripts, and even hired a team of data entry clerks to manually input information. It was a brutal, soul-crushing process. And all the while, our beautiful, sophisticated AI was sitting on the sidelines, completely useless. We had spent years building a race car, only to find out that the roads were unpaved, full of potholes, and covered in mud.
The lesson here is that the last mile of AI implementation is the hardest. It’s not about the elegance of your algorithm, but about your ability to grapple with the messy, chaotic reality of the real world. In education, this is doubly true. Schools are not tech companies. They don’t have APIs for everything. They have legacy systems, overworked teachers, and a million other priorities besides providing you with clean data. If you’re not prepared to get your hands dirty and deal with that mess, your AI will never leave the lab.
Lesson 2: Teachers Aren’t Cogs in Your Machine; They’re the Engine
In our quest to build a fully automated, personalized learning experience, we made a critical mistake: we forgot about the teachers. We saw them as a problem to be solved, a bottleneck to be automated away. Our platform was designed to be “teacher-proof.” We thought that if we could just create a system that was smart enough, we wouldn’t need teachers to do much of anything. They could just press a button and let the AI do the rest.
It was an arrogant, and ultimately fatal, assumption.
I remember one particularly painful meeting with a group of teachers from our pilot school. We were showing them our new and improved dashboard, a beautiful interface with all sorts of charts and graphs that tracked student progress in real-time. We were so proud of it. We thought they would be blown away. Instead, they were furious.
“You’re asking us to spend more time looking at a screen than looking at our students,” one teacher told me, her voice shaking with anger. “This isn’t helping me teach. This is just more work.”
She was right. We had built a system that was designed to serve our AI, not the people who were actually using it. We had created a tool that was more of a burden than a help. We had completely misunderstood the role of a teacher. They are not just content deliverers. They are mentors, motivators, and guides. They are the ones who build relationships with students, who inspire them to learn, and who help them navigate the challenges of growing up. No AI, no matter how sophisticated, can replace that.
The second lesson, then, is that AI in education should be designed to empower teachers, not replace them. It should be a tool that helps them do their jobs better, not a system that tries to do their jobs for them. It should automate the tedious, administrative tasks so that teachers can spend more time doing what they do best: teaching.
Lesson 3: The EdTech Sales Cycle is a Marathon, Not a Sprint
As a tech entrepreneur from Silicon Valley, I was used to moving fast. We built products, we shipped them, we iterated. We expected to see results in weeks, not years. That’s just how the tech world works. But the education world is a different beast entirely.
The sales cycle in EdTech is brutally long. It can take months, even years, to get a new product into a school. You have to deal with multiple layers of bureaucracy, from the district level all the way down to the individual classroom teacher. You have to navigate a complex web of regulations and procurement processes. And you have to do it all on a shoestring budget, because schools are notoriously underfunded.
We burned through our first round of funding just trying to get our foot in the door. We spent a fortune on marketing, on attending conferences, on hiring a sales team. But we were getting nowhere. We were stuck in a seemingly endless cycle of pilot programs and committee reviews. It was like trying to run a marathon at a sprinter’s pace. We were exhausted, and we were running out of money.
My final lesson is a harsh one: building a successful EdTech startup is as much about mastering the art of the long sale as it is about building a great product. You have to be patient. You have to be persistent. And you have to have a deep understanding of the unique challenges and constraints of the education market. You can’t just waltz in with a cool piece of tech and expect to be welcomed with open arms. You have to earn your place at the table.
The Road Ahead
My first EdTech startup may have been a failure, but it was not a waste. It was a five-year, multi-million dollar education in the realities of building technology for schools. It taught me that AI is not a magic wand. It’s a tool, and like any tool, it’s only as good as the person using it.
I’m now on my second EdTech venture, and we’re doing things differently this time. We’re starting with the teachers, not the technology. We’re building tools that are designed to solve their problems, not our own. And we’re taking the time to build real, lasting relationships with schools and districts. It’s a slower, more deliberate process. But I’m convinced it’s the only way to build something that will last.
The future of AI in education is not about replacing teachers, but about empowering them. It’s not about building a perfect, automated system, but about creating tools that can help us all become better learners, and better humans. And that’s a future I’m still willing to bet on.
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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 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
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