Remember Paul the Octopus? The German sea creature predicted World Cup winners in 2010. He chose food boxes draped in national flags. He got eight out of eight right.
Now he has digital successors — and they run on data, not seafood.
The 2026 FIFA World Cup is the first major tournament where widely available AI chatbots are making bold predictions. Fans, researchers, and banks are all asking the same question: which AI gets it right?
What Are the AI Chatbots Predicting?
The leading AI systems are not in agreement. Each model brings its own bias.
ChatGPT and Claude both back Spain as the 2026 World Cup winner. Their prediction reflects Spain’s recent form, squad depth, and tactical consistency.
Le Chat, the chatbot from France’s own AI company Mistral, naturally tips France. National pride aside, it points to France’s attacking firepower and squad experience.
DeepSeek and Qwen, both Chinese AI models, favour Argentina. They point to Argentina’s world-ranking status and Lautaro Martínez-led attack.
Tech news platform Tom’s Guide tested Google Gemini, ChatGPT, and Perplexity AI. All three picked Spain as champion. All three listed France as runners-up.
News outlet Decrypt found the same Spain-first trend among western chatbots. But it confirmed that DeepSeek and Qwen both broke the consensus by backing Argentina.
Why Does Each AI Pick a Different Winner?
Every AI model trains on different data. Western models consume more English-language football analysis, which leans heavily toward Spain and France. Chinese models absorb a different media diet — one that covered Argentina’s 2022 triumph extensively.
This creates genuine prediction divergence. It is not a bug. It reflects the model’s training sources, not a calculation error.
Who Is Running a Scientific Test?
Germany’s Ludwig Maximilian University (LMU) is running the most structured AI prediction study of the tournament. Their project is called LLM SoccerArena.
They publish each AI model’s game-by-game predictions on a public website. Fans can track accuracy in real time as matches conclude.
LMU researcher Stefan Feuerriegel explained the purpose clearly. He said the question of whether language models can support real decisions is critical. He also said benchmarks must test how models handle dynamic information, uncertainty, and real-world outcomes — not just abstract tasks.
The LMU team tests two types of prediction:
- Internal knowledge only — what the AI knows from training data alone.
- Web-augmented predictions — the AI searches for live injury news, squad selections, and betting market odds before answering.
The second method consistently produces sharper predictions. Real-time injury data matters enormously in football.
How Does This Compare to Paul the Octopus?
Paul the Octopus became a global sensation during the 2010 World Cup in South Africa. He correctly predicted the outcome of all seven Germany matches. He also called Spain as the tournament winner.
He died in October 2010, four months after his World Cup fame.
Paul relied on instinct — or appetite. AI relies on patterns across billions of data points.
The key difference: Paul gave one answer per match with zero explanation. AI systems give probabilistic reasoning with supporting evidence. That makes them harder to love, but easier to challenge.
Is AI Actually Helping Teams on the Pitch?
Predictions are just one slice of AI’s role at this World Cup.
Researchers from Australia’s University of the Sunshine Coast wrote for The Conversation that AI now supports coaches with tactical analysis, helps medical staff manage player recovery, assists referees with decision review, and even helps ticket security teams flag fraudulent sales.
Their verdict was direct. No AI agent will score a goal or coach a team at this tournament. But every winning team will have used AI somewhere along the way.
Bank of America Weighed In Too
Even Wall Street joined the prediction game. Bank of America analysts tested Microsoft Copilot on World Cup outcomes. The chatbot gave equal weight to Spain and France — mirroring roughly 40 percent of surveyed fans who also backed France.
When a major investment bank uses an AI chatbot to forecast football results, you know this trend has moved well beyond sports entertainment.
Quick-Reference: AI World Cup 2026 Predictions
| AI Model | Predicted Winner |
| ChatGPT (OpenAI) | Spain |
| Claude (Anthropic) | Spain |
| Le Chat (Mistral) | France |
| Gemini (Google) | Spain |
| Perplexity AI | Spain |
| DeepSeek (China) | Argentina |
| Qwen (Alibaba) | Argentina |
| Microsoft Copilot | Spain / France (equal) |
The Bigger Question: Can AI Actually Predict Football?
Football is beautifully unpredictable. That is why billions of people watch it.
AI models are highly accurate at identifying statistical probability. They are not good at predicting a goalkeeper’s inspired performance, a red card in the 30th minute, or a goal from a set piece in extra time.
LMU’s research framework addresses this gap directly. By testing AI against live results, match by match, they build an honest record of where machine reasoning holds — and where chaos wins.
The 2026 World Cup runs through July 19. By then, we will know whether Spain’s AI backers got it right, or whether Argentina or France rewrote the script.
Paul the Octopus had an 8-for-8 record. The AI leaderboard is just getting started.