AI Could Have Helped the Spurs Win the NBA Finals

What if experience became scaleable?

The San Antonio Spurs didn’t lose the NBA Finals because they weren’t talented enough.

That’s what makes a hindsight look at the series so fascinating.

They had the best player on the floor.

They had youth.

They had athleticism.

They had leads… big leads (The Ringer, 2026).

In fact, the Spurs led in all five Finals games and built double-digit advantages multiple times throughout the series. Yet the Knicks repeatedly found ways to come back and ultimately won the championship in five games.

This wasn’t something mystical. No one had the “clutch gene.” Josh Hart bricked an open fast break layup with a chance to take the lead late in that historic game 4. Jalen Brunson’s go ahead 3 in the same game? Also a brick.

So, how did they win? It was experience. But not in the way you may think.

De’Aaron Fox is an experienced veteran and yet was easily the most ill-prepared to play in this series. He made indescribably bad plays that seemed entirely disconnected from the moment, whereas the rookie Dylan Harper, was neck and neck with Wemby (no pun intended) with effectiveness on the floor.

They are proof that experience isn’t just time you spend doing something. It’s really a form of pattern recognition.

It’s seeing a situation and instinctively knowing what tends to happen next.

The Knicks had it.

The Spurs didn’t.

Which got me thinking:

What if AI could accelerate experience? Not by calling plays or replacing coaches. Not by telling Victor Wembanyama to stand in the dunker spot.

But by helping teams recognize patterns before they become lessons learned.

The Hidden Difference Between Good Teams and Championship Teams

The Spurs are young.

Extraordinarily young.

Victor Wembanyama—the undisputed leader of the team and future face of the league—is only 22.

Stephon Castle is barely old enough to rent a car.

Dylan Harper is still closer to high school than his prime. (This should strike fear in the hearts of the rest of the league, by the way).

Their Finals run wasn’t supposed to happen this quickly. The Thunder were heavily favored in the conference finals, even without their second-leading scorer Jalen Williams.

And yet they were suddenly playing on basketball’s biggest stage against a Knicks team built around battle-tested veterans who had spent years accumulating playoff scars. In fact, the Knicks didn’t to the Spurs, what the Pacers did to the Knicks in their run to the finals just last year.

That experience showed up everywhere.

When momentum shifted.

When the crowd got loud.

When every possession suddenly felt twice as important.

The Knicks seemed comfortable operating in the space where every moment could be the moment. This is because they recognized that the exact same pattern that emerged in their conference finals loss against the Pacers was emerging against the Spurs—only this time, the Knicks were the beneficiaries.

So, the Knicks weren’t seeing the future. It wasn’t some “championship DNA” that made them victorious.

They were recognizing the present.

They had seen versions of these moments before.

The Spurs looked like a team learning these patterns in real time.

Which is exactly what they were doing.

AI as a Championship Simulator

One of the most interesting applications of AI in sports isn’t prediction.

It’s simulation.

Imagine a system trained on decades of playoff basketball.

Not to predict winners.

To identify situations and patterns.

The AI notices:

“Teams with a 12-point lead entering the fourth quarter against high-pressure defenses tend to become overly isolation-heavy.”

Or:

“When this lineup begins missing consecutive corner threes, opponents increase transition opportunities by 18% over the next five possessions.”

Or:

“When Wembanyama is forced to initiate offense late in games, defensive pressure increases dramatically and ball movement decreases.”

These aren’t play calls. These are patterns that weren’t possible for Spurs head coach Mitch Johnson to learn in real-time.

Experience compressed into information.

Historically, teams gained this knowledge through years of winning and losing.

Through blown leads.

Through playoff collapses.

Through painful lessons that only reveal themselves after the fact.

The Wemby-led Spurs are now on this list.

AI offers a different possibility:

What if teams could learn from experiences they never personally played through?

The Real Value Isn’t Strategy

Most people assume AI’s value in sports is tactical.

Better rotations.

Better matchups.

Better scouting.

And those things do matter.

But I suspect the biggest opportunity is actually cognitive.

The Finals exposed how difficult decision-making becomes under pressure.

Players become tired.

Coaches miss information and hyperfocus on execution.

Crowds create noise.

Momentum alters perception.

Humans stop operating under ideal conditions.

The challenge isn’t intelligence or ability.

The challenge is judgment.

And judgment deteriorates when stress rises.

This is true in basketball. It’s true in aviation. It’s true in security operations. It’s true almost everywhere humans make high-stakes decisions.

The most interesting AI systems of the future may not be the ones that make decisions for us.

They may be the ones that help us make better decisions ourselves.

Human in the Loop

This is where I think surface-level conversations around AI often go wrong.

The goal isn’t to replace human expertise.

The goal is to augment it.

The world doesn’t need AI coaches, referees, engineers, pilots, surgeons, or architects.

We need faster access to lessons that currently take years to learn.

Because what ultimately defeated San Antonio wasn’t talent.

It’s time.

The Knicks had accumulated more playoff experience.

More late-game reps.

More failures.

More opportunities to learn what pressure feels like.

The Spurs gained some of that experience this series. And yes, that’s part of what will make them so dangerous moving forward.

But AI raises an interesting possibility:

What if experience itself becomes scalable?

What if a young coach could benefit from the collective lessons of thousands of playoff games?

What if a doctor didn’t have to cycle through multiple medications before recognizing a treatment pattern that an AI had already seen a million times?

What if a security analyst could identify an attack because an intelligent system recognized echoes of incidents that occurred years earlier on the other side of the world?

For most of human history, expertise has been constrained by a simple reality:

We can only learn from the experiences we’ve lived through.

AI may be the first technology capable of changing that and in real time.

The future of AI won’t be about replacing the humans who make our organizations, professions, and institutions possible.

It will be about helping humans recognize the moment they’re in before it slips away.

In the NBA, that might be the difference between a generational talent becoming the undisputed GOAT or ending up as little more than a trivia answer.

Elsewhere, it could be the difference between a breach and a near miss.

A successful surgery and a failed one.

Life and death.