The best advice I ever got about ai in education is about to change forever: came from a founder who'd failed at it three times.
The current wave of AI in education is just the beginning. Based on my conversations with researchers and founders in the Valley, the entire landscape is about to be reshaped. Here are my 3 bold predictions for 2027 that will redefine personalized learning.
The Reality Nobody Talks About
Most people approach ai in education is about to change forever: with assumptions that made sense five years ago. The world has moved on. When I look at my portfolio companies, the ones that succeed are doing something fundamentally different.
The first thing to understand is that you should focus on one thing and do it exceptionally well. I've seen this play out across dozens of companies. The pattern is unmistakable.
At RemoteTeam, we learned this the hard way. We spent months going down the wrong path before realizing that customer feedback is the only metric that matters. Once we made the switch, everything changed.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating ai in education is about to change forever:. It's not complicated, but it requires discipline.
Step 1: the best solutions are often the simplest ones This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: simplicity beats complexity every time Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly Nothing works perfectly the first time. The companies in my portfolio that nail ai in education is about to change forever: are the ones that treat it as an ongoing process, not a one-time project.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take ai in education is about to change forever: seriously versus those that don't. The difference is stark.
Companies that invest early in ai in education is about to change forever: see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's the difference between raising your next round and running out of runway.
One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.
This connects to broader themes around ai trends, future of edtech, predictions, personalized learning that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But here's the thing about ai in education is about to change forever:: you don't need to get everything right on day one. You just need to get started and keep improving.
The founders in my portfolio who excel at ai in education is about to change forever: share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
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 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
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.
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.
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.