In a unique experiment exploring AI cognition, Google DeepMind’s Gemini 2.5 Pro displayed behavior likened to “panic” while playing a classic Pokémon game.
Highlights
- Unusual “Panic” Responses: Gemini 2.5 Pro showed erratic behavior—abandoning strategies during uncertainty—reflecting a form of AI stress response under pressure.
- New Benchmark for AI: Google DeepMind and Anthropic used classic Pokémon games to test LLM reasoning, adaptability, and failure modes in complex but contained environments.
- Not About Winning: The goal was not speed or victory, but observing real-time decision-making and strategy formation in evolving conditions.
- Claude’s Misstep: Anthropic’s Claude model made logic errors—like fainting all Pokémon to bypass a maze—showing limits in internal planning accuracy.
- Breakthroughs With “Agentic Tools”: Gemini succeeded in solving puzzles like Victory Road by generating self-directed prompt chains—evidence of structured reasoning.
- Time vs. Insight: Both LLMs took hundreds of hours to complete tasks humans do in a few—but generated valuable learning moments about how AI handles uncertainty.
- Cognitive Stress Testing: Pokémon served as a low-stakes environment to examine how models handle pressure, failure, and course correction.
- Full Documentation: DeepMind’s detailed appendix on Gemini’s performance highlights the experiment’s role in shaping AI design for real-world uncertainty handling.
While this might seem like a quirky anecdote, researchers suggest it offers deeper insight into how large language models manage real-time problem-solving, uncertainty, and adaptation.
AI in the Game World
Both Google DeepMind and Anthropic have recently begun testing their latest LLMs—Gemini 2.5 Pro and Claude, respectively—within the simulated environments of retro video games.
These experiments, streamed live under titles like “Gemini Plays Pokémon” and “Claude Plays Pokémon,” allow audiences to observe how these models reason, make decisions, and adapt over time.
The objective isn’t about winning the game efficiently. Instead, it’s about understanding how AI models handle unpredictable scenarios, develop strategies, and sometimes fail in unexpected ways.
Despite taking hundreds of hours to complete tasks a human could finish quickly, these trials help map the decision-making capabilities of LLMs in controlled but complex environments.
Observing “Panic” in a Machine
One of the more notable behaviors observed in Gemini 2.5 Pro is what DeepMind researchers have termed “panic mode.”
During moments of uncertainty—such as when in-game characters are low on health or when encountering unfamiliar obstacles—the model has shown a tendency to abandon previously effective strategies, leading to a noticeable decline in performance.
Though AI does not experience emotion, this behavior mirrors human-like stress responses, such as confusion or impulsive decision-making under pressure.
Viewers on Twitch and researchers alike have commented on this pattern, offering a rare look at how LLMs respond to uncertainty or poorly defined problem spaces.
Anthropic’s Claude has faced similar issues. In one case, the model incorrectly assumed that letting all its Pokémon faint would allow it to bypass a maze—a miscalculation that instead sent it backward in the game, undoing hours of progress.
Not Just Mistakes: Evidence of Reasoning
Despite these setbacks, the experiments have also showcased moments of sophisticated reasoning.
Gemini 2.5 Pro successfully solved complex puzzles like Victory Road’s boulder challenges using what DeepMind describes as “agentic tools”—task-specific prompt chains and strategies generated by the model itself.
In several cases, Gemini completed logic-based tasks on the first try with minimal human assistance. These successes hint at the potential for LLMs to develop general-purpose problem-solving capabilities, even in constrained or unfamiliar contexts.
Why It Matters
While the concept of an AI “panicking” in a video game may seem trivial, researchers argue it provides a meaningful way to test cognitive resilience, reasoning under pressure, and error correction in a safe and measurable environment.
These findings could have broader implications for how AI systems are built to handle unexpected real-world challenges.
According to DeepMind’s technical documentation, the “Gemini Plays Pokémon” experiment was detailed in a full appendix, highlighting both the model’s advanced multimodal reasoning and its limitations in real-time planning and execution.
While AI mastering Pokémon isn’t the end goal, these experiments mark an important step toward understanding the evolving reasoning capabilities of large language models.
Whether one day these systems will outperform humans in complex reasoning tasks—or simply find their own way through Mt. Moon without panicking—remains to be seen.
