Can AI Play Bridge – Yet?

We’ve seen it happen in Chess. We watched it happen in Go. We even saw it in Poker. One by one, our most complex games have fallen to the cold, calculating power of Artificial Intelligence.

But there is one holdout. One game that experts whisper is too human, too subtle, and too communicative for a machine to truly master: Bridge.

Recently a post on Bridge Winners referenced The Nook Challenge – a high-profile event in Paris in 2022, where an AI named Nook (built by the French startup NukkAI) competed against eight world champion bridge players. Nook won this challenge with a victory rate of 83% (67 wins out of 80 deals).

At the time, headlines screamed that the barrier had been broken. Today, as we navigate through an era of highly advanced AI, it is worth asking: Did the machines actually conquer the final frontier of card games, or is there more to the story?

Let’s deal the cards.

The Machine Advantage: Flawless Execution

When the cards are face down, bridge becomes a game of “imperfect information.” You don’t know exactly where the Ace of Spades is, but you can guess. In the card play phase—where the goal is simply to take tricks—AI is a beast.

Rapid Calculation: Nook can simulate thousands of card distributions in the blink of an eye.
Probability Perfection: It calculates the statistical “optimal line of play” instantly.
Zero Fatigue: It never gets tired, never gets bored, and never forgets that the 7 of diamonds was played three tricks ago.

As AI researchers point out, in pure calculation and execution, the machines have officially arrived. They do not suffer from human lapses in concentration.

The Asterisk: The “Honest Robot” Exploit

Before we bow down to our new robot overlords, we need to look at how the famous Nook challenge was set up. Nook didn’t play a “real” full game of bridge. It only played the execution phase (the contracts were pre-determined).

Furthermore, world-renowned bridge expert and analyst Kit Woolsey dropped a bombshell on the results, pointing out a massive flaw in the tournament’s design: Nook and the humans weren’t playing against human defenders. They were playing against an older computer bot named Wbridge5.

Because Nook had trained extensively against Wbridge5, it learned a fatal flaw about its robotic opponent: it was too honest.

“The contest did prove that AI could exploit deficiencies in Wbridge5 better than human experts who had not properly trained in exploiting these deficiencies,” Woolsey explained.

Woolsey noted that Wbridge5 was programmed to always signal its card count honestly. If it played a specific card (like a 7), Nook mathematically knew exactly where the other cards were and would adjust its play perfectly. A human champion, however, would never assume a defender is that predictably honest, and would play the standard, statistically safer odds.

The human champions were playing “proper” bridge. Nook was playing “Exploit the Bot” bridge. As Woolsey pointed out:

“If the defenders had been human experts whose carding was not exploitable in this manner, I have no doubt that the expert declarers would have out-performed Nook.”

The Final Boss: Why Bidding Breaks AI

But as we all know, Bridge is actually two games in one: Bidding and Play. By skipping the bidding phase, the Nook challenge bypassed the very thing that makes bridge so difficult for computers.

Even today, the bidding phase remains one of the greatest unsolved puzzles in game-playing AI:

The Language Barrier: Bidding is a highly constrained, secret language spoken between two partners. An AI doesn’t just need to know its own cards; it needs to interpret the subtle, coded meaning behind its partner’s bids while hiding information from opponents.
Emergent Conventions: Human players use established conventions to ask specific questions about their partner’s hand. When forced to learn bidding from scratch, AIs struggle to invent and agree upon these complex communication systems naturally.
Improvisation and Deception: Human experts constantly improvise. They bluff, they disrupt, and they make bids designed purely to annoy and confuse the opponents. AI models are traditionally terrible at improvising in highly chaotic, psychological environments.

What is Happening Right Now (2025–2026)?

You might be wondering: with the recent explosion of Large Language Models and AI agents, haven’t we fixed this yet? Not quite, but the research has taken a fascinating turn.

Bridge as the Ultimate AI Benchmark: Today, universities and tech labs don’t just view bridge as a game to win; they view it as the ultimate test for Agentic AI (AI that can plan, reason, and collaborate). Recent papers published in the IEEE Xplore digital library use bridge bidding as the gold standard for teaching machines how to cooperate in multi-agent, imperfect-information environments. Researchers are actively publishing new models to prevent AIs from getting trapped in “local optima” when trying to guess a partner’s hand.
NukkAI’s Real-World Pivot: The creators of Nook haven’t just been sitting at the card table. NukkAI is currently using the exact lessons they learned from bridge—specifically how to handle incomplete information and teamwork—to build AI agents for massive real-world problems. They have recently showcased their technology at global conferences (like the World Aviation Festival), applying their “bridge brain” to complex B2B challenges like airline crew planning, crisis management, and logistics, where humans and AI must cooperate dynamically.
The “Neurosymbolic” Shift: To finally crack the bidding phase, modern researchers are moving away from pure brute-force calculation. As highlighted by DeepLearning.AI, the industry is actively embracing a “neurosymbolic” approach. This combines Deep Learning (pattern recognition) with Symbolic AI (hard-coded logic and rules) to help bots actually understand the context and intent behind a bid, rather than just crunching the odds. It also makes the AI “explainable,” which is crucial for a game built on trust.

The Verdict

So, can AI beat humans at bridge?

If you mean playing a specific hand of cards perfectly based on pure math? Yes, absolutely. But if you force an AI to sit across from a partner, read the room, communicate via an auction, and execute a full game of Bridge from start to finish? Human champions still win.

Bridge remains a fascinating benchmark for cognitive scientists exactly because it mirrors the messy, real world. Success isn’t just about raw computing power; it’s about cooperation, intuition, and reading between the lines.

Perhaps Kit Woolsey summed up the current state of AI and bridge best:

“I am not trying to downplay the results shown by AI in the contest. It was an extremely impressive display of the progress which has been made in this area. But as of today, human experts are still better declarers than any AI as far as I know.”

For now, the humans are safe.