Last year, I made a bet that changed how I think about ai in education is about to change forever:. Here's what happened.
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 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 market doesn't care about your roadmap 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 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 customer feedback is the only metric that matters. 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 your team matters more than your technology. Once we made the switch, everything changed.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to ai in education is about to change forever: are fundamentally flawed. Not slightly off. Fundamentally flawed.
The root cause is usually one of three things:
- Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
- Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
- Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
Real Talk: What Actually Matters
I'm going to cut through the noise and tell you what actually matters when it comes to ai in education is about to change forever:.
First, execution speed beats perfection. Every time. I've never seen a company fail because they moved too fast on ai in education is about to change forever:. I've seen plenty fail because they moved too slow.
Second, measure everything. If you can't measure it, you can't improve it. Set up tracking from day one, even if it's basic.
Third, talk to your users. This sounds obvious but you'd be amazed how many founders build their ai in education is about to change forever: strategy in a vacuum. Get out of the building. Talk to real people.
This connects to broader themes around predictions, ai trends, personalized learning, future of edtech that I've been thinking about a lot lately.
The Bottom Line
Look, ai in education is about to change forever: isn't rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at ai in education is about to change forever: aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
I've been doing this for over a decade. The patterns are clear. The companies that take ai in education is about to change forever: seriously outperform the ones that don't. Every single time.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
Frequently Asked Questions
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.
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 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.
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.