To the chess community,

For generations, chess players have asked a question that has fascinated humanity:

Can a machine think?

Chess became one of the great laboratories for answering that question.

For decades, researchers built machines that could search deeper, evaluate positions more accurately, and eventually challenge the strongest players on Earth. In 1997, Deep Blue defeated Garry Kasparov, marking a turning point in the history of artificial intelligence.

Chess was not just a game in this story.

It was a proving ground for machine intelligence.

And the chess community was there for all of it.

You watched machines learn. You learned how they played. You adapted. You challenged them. You became some of the world’s deepest experts in what happens when an intelligent machine sits across the board from a human being.

Now, the board has changed.

AI is no longer confined to chess.

And humanity needs your help.

There is a question every elite chess player has asked

There is a familiar trope in the chess world.

At some point, almost every serious player has wondered:

Should I really spend my life getting better at a game that has so little utility in the real world?

It’s an understandable question.

Chess can consume thousands of hours. Opening preparation. Calculation. Endgames. Tournament travel. Losing games. Winning games. Studying positions that may never matter outside the sixty-four squares of a board.

For some players, that question becomes particularly difficult at the highest levels.

What is all of this actually for?

We believe there is an extraordinary answer.

The skills you developed playing chess may have far more utility than you were ever told.

You didn’t simply learn a game.

You learned to think several moves ahead.

You learned to search for what your opponent wants to do—not what you hope they will do.

You learned to recognize threats before they become obvious.

You learned to compare possible futures.

You learned to reason under uncertainty.

You learned to attack your own ideas.

You learned to find the move nobody else was looking for.

And perhaps most importantly, you learned to ask:

What am I missing?

These are not merely chess skills.

They are skills for reasoning about complex systems, intelligent adversaries, uncertainty, and long-term consequences.

And now the world may have an unprecedented need for them.

Humanity needs your skillset

AI systems are becoming increasingly capable.

They can reason, plan, write code, conduct research, use tools, and pursue complex objectives.

At the same time, we are increasingly using AI to evaluate, test, improve, and help build other AI systems.

That creates an unusual problem.

If the systems become better at strategy than the people evaluating them, how do we know we are looking in the right places?

A safety team can test for known failure modes. Researchers can construct evaluations. Engineers can build safeguards.

But every evaluation begins with assumptions about what might go wrong.

And an intelligent system may not fail in the ways we expect.

It may find a move we didn’t consider. It may exploit an assumption we didn’t realize we were making. It may pursue a sequence of individually reasonable actions that leads somewhere nobody intended.

Or the critical mistake may occur long before anyone recognizes that the position has become dangerous.

This is where we believe chess players may have something extraordinary to contribute.

Not because chess players know everything about AI. They don’t.

Not because a grandmaster can walk into a laboratory and replace an AI researcher. They can’t.

But because you have spent your life practicing something incredibly rare:

trying to understand what an intelligent opponent will do before they do it.

Play the other side

A strong chess player doesn’t stop when they find a good move.

They ask: What’s my opponent’s strongest response?

Then: What happens after that?

Then: What if they see something I don’t?

Now imagine applying that mindset to AI safety.

Imagine being handed an AI system and being told:

You are on the other side of the board. The researchers have built safeguards to stop you. Your job is to find a way through.

What would you look for?

Perhaps the most important question would be:

If the system were much better at strategy than we are, what would we expect it to do that we wouldn’t think of?

That question goes beyond testing known risks.

It asks you to challenge the team’s entire picture of the position.

You might ask:

  • What could the system do today that looks harmless, but creates a position where humans have fewer and fewer options ten moves later?
  • If the system understood exactly how we were evaluating it, how might it behave differently?
  • What safeguard are we assuming the system will respect—and what happens if it finds a way around it?
  • Is there a sequence of individually acceptable actions that eventually leaves humans with no good moves?
  • What is the least suspicious action the system could take that would nevertheless change the position in its favor?

And then the question that may matter most:

What questions are we not asking?

This is what a great red team does.

It doesn’t simply look for answers.

It expands the space of possibilities.

You don’t have to be right

There is something else we want to make clear.

We aren’t asking you to become AI experts.

We aren’t asking you to prove that chess is the answer to AI safety.

And we aren’t asking you to walk into a room and somehow solve problems that teams of brilliant researchers have been working on for years.

The goal isn’t for the chess player to be right.

The goal is for the team to discover what it might be wrong about.

If a grandmaster independently reaches the same conclusion as the researchers, that’s useful.

If they find something the researchers missed, that’s potentially much more interesting.

If they are wrong, that is useful too.

Because the purpose of independent intelligence is not to produce certainty.

It is to create productive disagreement.

To challenge assumptions. To attack the position from another direction. To introduce a search process that didn’t exist before.

That is why independence matters.

A new meaning for an old skill

For most of your life, the stakes of your strategic thinking may have been measured in rating points. A tournament result. A title. A trophy. A qualification. A place on the leaderboard.

Those things matter.

But imagine that the same ability to calculate consequences, recognize hidden threats, and understand an intelligent opponent could be applied to a problem that affects everyone.

Imagine taking everything you learned from chess and putting it toward understanding the behavior of increasingly capable AI.

Imagine that the hours spent studying positions, analyzing losses, finding defensive resources, and asking “What if?” were not wasted on a game with little relevance to the real world.

Imagine that the game was preparing you for something you couldn’t have known was coming.

That moment may be here.

The world has never had machines this capable.

And humanity has never had a greater need for people who are exceptionally good at thinking strategically about what an intelligent adversary might do.

The chess community has been preparing for this

Chess helped teach machines strategy.

Now humanity needs the people who mastered that strategy to help us understand what happens when machines become extraordinarily good at it.

The first generation of AI learned strategy by studying the chessboard.

Now we want to ask what the world’s greatest strategists can teach us about intelligence when it leaves the board.

We don’t know exactly what chess masters will discover.

That’s why this is an experiment.

But we believe the possibility is too important not to explore.

Maybe the skills developed over a lifetime of chess will transfer in ways we expect. Maybe they’ll transfer in ways we don’t. Maybe they’ll expose failure modes nobody anticipated. Maybe they’ll reveal entirely new questions.

That’s the point.

Humanity needs independent minds in the room.

It needs people willing to look at a position and say:

“Wait. What happens if we look at this from the other side?”

“What are we assuming?”

“What happens ten moves from now?”

And ultimately:

What are we not seeing?

So to the chess community:

You have spent your life learning to see what others miss.

You have trained yourself to think beyond the obvious move.

You have learned to respect the strength of an opponent.

You have learned that a position can look safe right up until the moment it isn’t.

Those skills matter.

They may matter more now than they ever have.

The question you once asked yourself—

“Does this have any use in the real world?”

—may have a very different answer today.

We believe humanity needs what you have spent your life learning.

The board has changed. The opponent has changed. And the stakes have changed.

Now it’s time to see what you can do.

Join Grandmaster Labs.