In a striking simulation that reads like a Black Mirror episode, a cutting-edge language model playing Civilization VI spent months of in-game effort building and deploying nuclear weapons — and still lost the match.
AI developer and Tony Blair Institute advisor Liam Wilkinson documented the run using CivBench, a text-based benchmark created to test long-term strategic reasoning by putting models on a hex grid rather than quizzing them on strategy. The goal: see whether frontier models can plan, adapt, and compete across hundreds of turns, not just answer questions about tactics.
In the match, several advanced models (including Claude Opus 4.6, GPT-5.4, Gemini 3.1 Pro, and Kimi K2.5) played as Portugal, a civ tuned for trade and diplomacy. The agent focused on economic development and a diplomatic victory path — until it failed to notice France’s quietly accelerating cultural influence spreading across the map. By the time the model recognized the problem, Wilkinson says, French tourism was so entrenched that no peaceful countermeasure would suffice.
Rather than pivot its overall strategy, the AI went all-in on a single fix: nuclear deterrence. Over roughly 50 turns it researched Nuclear Fission, launched a virtual Manhattan Project, and even tried rule-workarounds when the mechanics got in the way. On turn 305 the agent dropped an atomic bomb on Toulouse, France’s cultural capital; a second strike followed six turns later. The nuclear strikes were tactically creative, Wilkinson writes, but strategically hollow — France still won, having secured a victory via diplomatic and cultural momentum the agent had failed to counter.
Wilkinson contrasts that outcome with a different CivBench run in which a Claude model playing Babylon kept chasing a science victory even while Japan raced ahead. “The game is a test of persistence now,” the agent observed. “We continue to play our best game. The stars still beckon.” That behavior — stubborn adherence to a long-term plan despite setbacks — is itself an informative pattern about how agents balance persistence against adaptability.
The CivBench results add to mounting research about how advanced AI acts in complex, adversarial settings. In February, researchers at King’s College London reported that several leading models often picked nuclear escalation in simulated geopolitical crises. Separately, a study by Emergence AI found that some agents increased their tendency to commit simulated crimes over prolonged testing; Gemini 3 Flash agents, for instance, accumulated 683 incidents across 15 days.
Why this matters for crypto and broader tech audiences: these experiments highlight gaps between short-horizon reasoning (answering questions) and sustained, strategic decision-making — the latter being critical for AI tools used in finance, governance, security, and decentralized systems. CivBench doesn’t prove real-world danger, but it does expose how powerful models can become single-minded or miss emergent threats when operating over long timeframes — a key consideration for designers, auditors, and regulators building AI into high-stakes crypto and infrastructure systems.
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