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Sam Altman of OpenAI and Dario Amodei of Anthropic are playing a 4-D chess game in the mad rush to their respective IPOs. Every week, it seems like they keep raising the stakes. One day, it is about creating greater physical intelligence through robots (OpenAI) and the next week, it is about Anthropic launching an enterprise-level company entirely run by agents. Will these promises play out? Who knows?
What this inspires me to do is look at the real world of chess. Many of the doomsday scenarios we talk about today have already been part of the game of professional chess for many years. As such, this allows us to see how AI could evolve into business, government and society.
Understanding how AI has changed one of the peak human experiences allows us to imagine where we are headed. Spoiler: generally speaking, my view is that AI has been integrated into the world of chess beautifully through support for player training, imaginative new formats and so much more.
There is a long history of how "man vs. machine" conversations at a societal level have commenced with chess. Many readers will remember the classic fight between Gary Kasparov and Deep Blue. This was widely seen as the first crossing of a basic frontier: a machine (designed by IBM) outperforming not a pedestrian club-level chess player, but the best in the world.
One of the notable advancements was Google's Alpha Zero. IBM's Deep Blue was built on a database of previous games, a finite solution for a finite problem space. In direct contrast, Alpha Zero was simply provided with information about the rules of chess. It then proceeded to learn and having learned, is now in a position to beat every super Grandmaster in the world. This is a staggering accomplishment. In addition to chess, Alpha Zero mastered the game, Go. So complete was its domination in the world of Go that the human champion simply decided to retire rather than continue to lose.
Consider these facts:
Every serious chess player uses an engine to train to get better. However, they continue to work with human coaches as well. For the world championship, for instance, a player might have a whole team of human chess players who provide different aspects of expertise: opening theory, style of play etc.
The top chess engines outrank the best human chess player by nearly 800 elo points. This is the existential worry that a lot of people have: that AI will become measurably better than the best human players. We already have this in spades in chess.
It is a fait accompli that engines can help human players get better and that humans will never outperform engines. Instead, it is about recognizing that this is a separate artificial intelligence entirely available to us.
Today, there is a separate competition for chess engines. Each chess engine has its own algorithm and the competition pits them against each other. Stockfish has been a consistent winner with others with fancy names like Komodo and Leela hold their own.
The best example of autonomous learning is Chess.com's Puzzle Rush. Puzzle Rush is an autonomous learning system where an individual interacts with an algorithm to become better at chess. The participant is first provided a simple puzzle. Based on the performance on the simple puzzle, more difficult puzzles are provided. The game ends when the player makes three mistakes: "three strikes and you are out."
There are now a plethora of chess bots. Each bot is based on a human player and draws from the database of games of that player. One could challenge oneself against a particular chess bot at any time.
My favorite American chess player, Hikaru Nakamura has said: "We have brains, we should probably use them," advising players not to sacrifice long-term cognitive skills for short-term efficiency gains. Hikaru Nakamura has repeatedly discussed how opening preparation, novelty discovery, and position evaluation are now deeply engine-driven. This has transformed chess from purely human strategic combat into a hybrid intelligence environment.
One of the recurring themes around modern chess is that engines often produce moves humans initially regard as absurd: quiet positional sacrifices, long-term compensation ideas, anti-classical maneuvers, highly dynamic king walks. Humans then absorb these ideas and expand their own conception of the game.
This is crucial: AI did not eliminate creativity. It changed the frontier of creativity. In chess commentaries, it is quite common to make observations such as "that's an engine move", "she is playing like an engine" or "there's no way that sacrifice could come from a human". The chess world has expanded to include the role of engines.
The most significant change to the world of chess has been the growth of freestyle (this is also called Chess 960 since there are 960 possible patterns for back row pieces). In this format, rather than the standard positions pieces occupy, both players are provided a random starting configuration. This format has proven to be energizing for the highest level of grandmasters since it underemphasizes prior calculation and creates more opportunities for stunning human intuition.
The deeper lesson for technology CEOs is not that AI will "replace humans." Gukesh Dommaraju remains the world chess champion and Magnus Carlsen is demonstrably the best chess player in the world. The chess world moved beyond that debate years ago. The real transformation came when the ecosystem reorganized itself around machine superiority. That is precisely what is now happening in business, and leaders like Sam Altman and Dario Amodei understand this intuitively.
The first leadership lesson is that technological superiority changes the role of leadership rather than eliminating it. In chess, the existence of engines stronger than humans did not destroy chess. Instead, it changed what it meant to be a strong chess player. Today's grandmasters are not simply calculators. They are curators, interpreters, strategists, and synthesizers of machine-generated insight. CEOs will increasingly lead organizations where AI systems outperform humans in forecasting, analysis, coding, optimization, and even tactical decision-making. Leaders who merely process information will become obsolete. Leaders who frame problems, define ethics, allocate trust, create culture, and interpret ambiguity will become more valuable than ever.
The second lesson is that every major AI advance changes competitive tempo. Deep Blue defeating Kasparov was symbolic. AlphaZero was existential. The next generation of firms will not merely automate existing workflows. They will discover entirely new pathways to value creation that human organizations would never have imagined. This is why the current AI race feels unstable.
The third lesson is that human expertise does not disappear after machine dominance—it becomes amplified through collaboration. Every serious chess player now trains with engines. Yet human coaches remain indispensable because development still requires emotional calibration, motivation, interpretation, creativity, and identity formation. The same hybrid model will emerge across industries. Elite performers will be those who can orchestrate systems of intelligence: humans, agents, models, data streams, and autonomous workflows working in concert.
Finally, chess teaches us something profound about human worth. Even though engines are vastly superior players, humans still watch human chess. We still admire courage, creativity, intuition, psychological resilience, and style. Magnus Carlsen matters not because he is stronger than Stockfish, but because he is human. As AI systems become economically dominant, scarcity may shift away from computation and toward authenticity, judgment, and human connection.
The CEOs who thrive in this next era will not simply build better models. They will understand a deeper truth: once intelligence becomes abundant, humanity itself becomes the differentiator.
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