For decades, chess was one of the most important testing grounds for artificial intelligence. It offered researchers something almost uniquely valuable: a complete strategic environment governed by explicit rules, where machines could learn to search possibilities, evaluate positions, anticipate opponents, and make decisions across long sequences of actions.

Chess was, in many ways, a laboratory for machine strategy.

The earliest chess programs were crude. But the underlying problem was extraordinarily important: how can a machine reason about the future?

A chess position can be represented as a state. Every legal move produces another state. Every state creates new possibilities. Those possibilities can be searched, evaluated, compared, and searched again.

Position → Possibilities → Consequences → Evaluation → Decision

This architecture became a foundational environment for studying machine reasoning.

And eventually, the machines won.

In 1997, IBM’s Deep Blue defeated reigning World Chess Champion Garry Kasparov. Deep Blue combined specialized hardware, sophisticated search, evaluation functions, chess databases, and extensive human chess knowledge. At its peak, it could examine roughly 200 million positions per second.

The machine had surpassed the world’s greatest chess players at their own game.

But this was not the end of the relationship between chess and AI.

It was the beginning of a new one.

The circle has come full circle

Early AI needed chess grandmasters to help teach machines how to think strategically.

Today, we face a remarkable reversal.

AI systems are becoming capable of reasoning, planning, coding, researching, using tools, adapting to new environments, and solving problems that once required highly specialized human intelligence.

The machine is no longer merely learning chess.

It is learning to operate in the world.

And as AI becomes more capable, the strategic questions become more consequential:

  • What happens several steps from now?
  • What threats are invisible from the current position?
  • What strategy would an intelligent adversary use?
  • Which apparently beneficial action creates a dangerous position later?
  • What happens when the objective being optimized is not exactly the objective we intended?
  • Can we recognize a problem before the system has already committed to it?

These are not merely engineering questions.

They are questions of strategy.

And there are few people on Earth who have spent more time mastering strategy than the world’s greatest chess grandmasters.

That is why we believe they have a role to play in AI safety.

Why chess?

Chess is a remarkably pure strategic environment.

It strips away many of the complexities of the real world and leaves behind something fundamental: two intelligent agents making decisions, anticipating one another, exploiting weaknesses, managing uncertainty, and attempting to achieve an objective over a long horizon.

The board is finite. The rules are explicit. The consequences of decisions can be studied. And mistakes can be reconstructed.

This made chess an ideal environment for early AI research.

Researchers could take the problem of intelligence and turn it into something that could be represented computationally:

  • Here is the current state.
  • Here are the possible actions.
  • Here are the resulting states.
  • Which path produces the strongest outcome?

Brute-force calculation was an important part of the answer.

But chess also revealed something equally important:

Calculation alone is not enough.

There are too many possible positions to examine exhaustively. Strong play requires knowing which possibilities matter, which threats are real, which variations deserve attention, and which apparently attractive paths are strategically unsound.

That is where human expertise became essential.

And it remains essential today.

Why grandmasters?

A grandmaster is not simply someone who can calculate many moves.

At the highest level, chess becomes an extraordinary discipline of strategic thought.

Grandmasters develop an ability to recognize danger before it becomes visible, to compare possible futures, to understand an opponent’s incentives, and to distinguish between a move that looks good now and a position that will remain good later.

They spend decades learning to ask:

What am I missing?

That may be one of the most valuable questions in AI safety.

Grandmasters are exceptional at:

  • Long-horizon planning — thinking beyond the immediate consequence of a decision.
  • Threat detection — recognizing vulnerabilities before they become obvious.
  • Adversarial reasoning — actively searching for how an intelligent opponent could defeat a strategy.
  • Uncertainty management — making strong decisions without knowing exactly what the future holds.
  • Counterfactual reasoning — exploring alternative futures and comparing their consequences.
  • Pattern recognition — recognizing strategically meaningful structures without calculating everything from scratch.
  • Strategic deception — understanding that what appears to be an objective or intention may not reveal the underlying strategy.
  • Failure analysis — identifying the precise point at which a successful-looking strategy went wrong.
  • Position evaluation — understanding the difference between short-term advantage and long-term strategic strength.

These are not merely chess skills.

They are forms of reasoning.

And increasingly capable AI systems are themselves becoming strategic actors.

The problem of the closed loop

There is another reason independent human expertise matters.

AI is increasingly being used to develop AI.

Models write code for models. Models generate training data. Models evaluate outputs. Models propose experiments. Models critique other models. Increasingly capable systems may eventually participate directly in designing and improving their successors.

This creates enormous opportunities.

But it also creates a potential blind spot.

If one generation of AI is used to evaluate, train, improve, and validate the next, some assumptions may become self-reinforcing.

A system may inherit not only the capabilities of its predecessor, but some of its conceptual blind spots.

This does not mean AI-assisted research is inherently unreliable.

It means that independent intelligence becomes more valuable as the AI development loop becomes more powerful.

A chess analogy makes this intuitive.

If one grandmaster analyzes a position and declares it safe, asking the same grandmaster to check the analysis again is useful.

But asking another elite grandmaster to independently attack the position may reveal something the first player never considered.

The second player brings a different search process, different intuitions, different patterns, and different assumptions.

They may see the weakness precisely because they are not trapped inside the first player’s analysis.

AI safety needs this kind of independence.

Why humans?

Human beings are not unbiased. Researchers are not unbiased. Grandmasters are not unbiased.

No individual or institution can eliminate bias.

The objective is different:

Create enough diversity of reasoning that one system’s blind spots can be exposed by another.

The most capable AI systems should therefore not be evaluated exclusively by systems that resemble them.

They should be challenged by independent forms of intelligence.

That includes mathematicians, engineers, scientists, psychologists, philosophers, security researchers—and, we believe, elite strategic thinkers.

Chess grandmasters offer something particularly unusual.

They have spent tens of thousands of hours inside a closed, adversarial decision environment where strategic mistakes are ruthlessly exposed.

They know what it feels like to have a strategy that appears sound collapse several moves later.

They know how to search for the move an opponent hopes you will overlook.

They know that the most dangerous threat is often the one that does not look dangerous yet.

And they have learned to distrust attractive answers when the position has not been fully understood.

What we do

We bring the world’s strongest chess strategists into the AI safety process.

Our grandmasters work alongside AI researchers, engineers, and safety teams to provide an independent strategic perspective on increasingly capable AI systems.

We ask them to do something very specific:

Think several moves ahead.

They analyze potential failure modes, search for hidden threats, construct adversarial scenarios, examine long-horizon consequences, challenge assumptions, and identify strategic weaknesses that may be difficult to detect through conventional evaluation alone.

They do not replace AI researchers. They challenge them.

They do not replace AI systems. They provide an independent intelligence against which those systems can be tested.

The goal is not to make AI think like a chess player.

The goal is to use people who have become extraordinarily good at thinking strategically to help us understand machines that are becoming extraordinarily good at it themselves.

The strategists we need

The development of advanced AI may be one of the most consequential strategic challenges humanity has ever faced.

We should use every advantage available to us.

For decades, chess grandmasters stood on one side of the relationship with AI: they were the humans whose strategic abilities machines were trying to replicate and surpass.

Now the relationship has changed.

The machines are becoming more capable. The stakes are becoming larger. And the people who spent their lives mastering strategy may have an entirely new role to play.

Not as opponents of artificial intelligence. Not as replacements for scientists or engineers.

But as its strategic challengers.

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

Now we believe the world’s greatest strategists should help us understand what happens when that intelligence leaves the board.

The circle has come full circle.