What NFL Street Taught Me About the Future of Education

I still remember playing NFL Street on the PS2 when I was eight years old.

There was a create-a-player mode where you could add points to different attributes. Speed. Catching. Strength. Whatever made the player better.

I remember looking at that screen and thinking:

How does that actually work?

Not in a polished computer science way. I did not know what a variable was. I did not know what an object was. I did not know what game logic was. I just remember being fascinated that a number on a screen could change how a player moved in the game.

That was probably one of the earliest moments where software felt real to me.

It was not just a game anymore. It was a system.

The question underneath the game

I did not know how to answer that question at the time.

I was eight. I barely understood what Google was. I definitely did not know how to search for “how do video game stats work” or “how does a game engine translate attributes into player behavior.”

So the thought just stayed there.

But I think about that memory a lot now, especially after building Gridiron Rumble. It is not hard to see the line between an eight-year-old staring at NFL Street attributes and an adult building an arcade football game with AI tools.

Maybe that is part of why I wanted to build it.

Some childhood curiosity was still sitting there, waiting for better tools.

But I do not think the real story is only that I got into code because I liked video games. That is true, but it is not the interesting part anymore.

The interesting part is what would happen if an eight-year-old had that same question today.

What if the answer was right there?

If I were eight years old now, I could ask an AI assistant:

How does NFL Street know that one player is faster than another?

And instead of that question disappearing, I could get an answer at my level.

Not a lecture.

Not a textbook chapter.

An explanation I could actually understand.

Then I could ask a follow-up. Then another. Then I could say, “Can you help me make a tiny version of that?” Maybe it would generate a little demo where one player has speed 5 and another has speed 10. Maybe it would show me the code. Maybe it would turn the whole thing into a mini lesson about variables, movement, physics, or probability.

That is the part that feels profound to me.

AI can turn curiosity into a learning path before the kid even knows what subject they are asking about.

The kid thinks they are asking about a football game.

They are really asking about math, systems, design, simulation, and software engineering.

That is a very different starting point than memorizing facts for a quiz.

Every kid should have a learning path

I think every student is going to have some kind of personalized learning plan.

That does not mean every kid sits alone with a chatbot all day. I actually think that would be a mistake. Kids need other kids. They need social pressure, collaboration, play, friendship, conflict, teamwork, boredom, jokes, and all the messy human stuff that school provides.

That is why I have always been skeptical of any education model that removes the social layer too aggressively.

But the instruction layer can change.

Maybe the school day eventually looks less like six or seven hours of mostly passive classroom time. Maybe the focused academic work is two or three strong hours, personalized around what each student needs and what each student is curious about.

Then the rest of the day can be social, physical, creative, collaborative, and hands-on.

That might sound strange if you measure school by seat time. But if the point is learning, not compliance, it starts to make more sense.

A kid who is curious about football games can learn math through player ratings, acceleration, angles, and probability.

A kid who wants to be a doctor can learn biology through patient scenarios and diagnostic reasoning.

A kid who wants to be a lawyer can learn reading, writing, argument, evidence, and ethics through cases.

A kid who wants to be a writer can get feedback, examples, structure, and revision practice immediately.

A kid who wants to be an engineer can start building small systems before they even know the formal names for the concepts.

That is what excites me.

Not replacing ambition with a screen.

Giving ambition a path.

Big goals are good

When I was in high school, I wanted to be a doctor, a lawyer, a writer, and an engineer at different points.

That was not a problem.

That was healthy.

Kids should have big goals. They should try on different futures. They should be able to ask what a doctor does, what a lawyer does, what an engineer does, what a writer does, and then actually explore those paths in a real way.

Most of us do not know what we like until we try a bunch of things.

The current education system does not make that easy enough. It often asks kids to sit still, memorize information, pass a quiz, move to the next unit, and eventually make major life decisions with limited exposure to what the work actually feels like.

That model creates compliance.

It does not reliably create curiosity.

And curiosity is the whole thing.

Math, reading, and writing will change

I do not know exactly what the future classroom looks like.

I do not know the right balance between AI tutoring, teachers, group work, projects, tests, grades, public school, private school, homeschooling, and everything else.

But I feel pretty confident that math, reading, and writing need to be taught in fundamentally different ways.

Math cannot just be worksheets and formulas detached from the reason anyone would care.

Reading cannot just be comprehension questions after a passage nobody wanted to read.

Writing cannot just be five-paragraph essays optimized for a rubric.

Those skills still matter. Maybe more than ever.

But the way we teach them has to meet the world kids are entering.

If AI can answer factual questions instantly, then memorizing facts for a quiz becomes a weaker model. Facts still matter. Knowledge still matters. But the value shifts toward asking better questions, checking answers, building judgment, making arguments, understanding systems, and applying knowledge to real problems.

That is a much harder kind of education.

It is also a much better one.

College will change too

I feel this in software engineering especially.

My computer science degree taught me critical thinking. It taught me problem-solving. It gave me a foundation. I do not want to dismiss that.

But I use almost none of the specific coursework directly in my job today.

My job is shipping things.

It is understanding messy requirements, making tradeoffs, debugging weird systems, communicating clearly, protecting production, and getting useful software into people’s hands.

College gave me theory. It did not teach me how to ship.

That gap existed before AI. AI just makes it impossible to ignore.

If a student can build working software much earlier, then education has to decide what it is really for. Is it gatekeeping? Is it credentialing? Is it theory? Is it practice? Is it social development? Is it helping students discover what they care about and become useful in the world?

I hope it becomes more of the last one.

The hard part is managing it

I am more excited than scared about all of this.

But I am definitely a little scared.

Giving every kid access to an always-available assistant is powerful. It raises real questions about attention, safety, dependency, misinformation, motivation, privacy, and what happens when a child starts trusting a machine too much.

We should take that seriously.

But I do not think the answer is to pretend the tools are not here.

We are in 2026. These tools are already good. Imagine where they will be in a year.

The better question is how we manage them well.

How do teachers use AI without being replaced by it?

How do students use AI without losing the ability to think for themselves?

How do schools preserve the social parts that matter while rebuilding the instructional parts that are not working?

How do we turn curiosity into capability faster without turning childhood into productivity software?

I do not have all the answers.

But I keep going back to that eight-year-old version of me looking at NFL Street and wondering how the numbers worked.

That question had nowhere to go.

A kid today might be able to follow it.

That feels like the future of education to me.

Not everyone learning the same thing at the same pace for the same quiz.

More kids finding the thing that makes them ask: