#广场预测世界杯赢40000U 12 AI predictions for the World Cup, all completely wrong

Early this morning, the World Cup match ended with the Netherlands crushing Sweden 5-1. Before the game, 12 large models all made predictions, and afterward, someone compiled their answers — all wrong.
11 predicted a narrow Netherlands victory, 1 predicted a big Swedish win.
Of the 12 AIs, 11 predicted scores like 2-0, 2-1, or 1-0, indicating a "slight Netherlands win."
The most absurd was the step AI, which reversed its approach and predicted Sweden winning 3-0 or 2-1.
No one predicted 5-1. The closest was DeepSeek, which predicted 3-1, but missed by two goals.
It's quite ironic. AI dominates in Go, replaces programmers in coding, and outperforms most students in college entrance exams — but when it comes to football, it can't even get the basic question of "who will win" right.

Can AI really predict football matches?
In theory, yes. AI can analyze historical match data, player conditions, tactical systems, injuries, weather, field conditions, and even referee styles. When combined, these data should make AI better at understanding the game than humans.
But the problem is, football isn’t just data. A sudden downpour can cause a technically skilled team to collapse; a VAR mistake can change the entire match; a player’s emotional fluctuation can ruin the team’s morale. These factors AI either can’t access or can’t accurately calculate.
Who would have thought Sweden’s defense would collapse so badly before the match? Data wouldn’t tell you "today, the Swedish defenders didn’t sleep well."
But what you really care about is: do you trust AI’s analysis reports normally?

This failure exposes a problem — AI’s "confidence" and "accuracy" are two different things.
Look at those predictions; each one sounds reasonable and well-supported: "Netherlands has strong attacking firepower, Sweden’s defense is aging, predicted score 2-1" — confident tone, logical coherence, seems plausible. But once the result is out, everyone’s stunned.
It’s the same with how we use AI daily. It writes code, drafts content, performs analysis — all seemingly logical. But the core issue is: AI doesn’t know what it doesn’t know. It won’t admit "I don’t know" due to lack of data; instead, it forces a guess, and makes it look convincingly real.
Football predictions amplify this flaw because the outcome is binary — right or wrong, with no room for ambiguity.
One failure doesn’t mean AI is useless.
Honestly, all 12 AI models being wrong actually reveals something: it’s not just a problem with one AI, but with the entire approach.
AI excels at recognizing "patterns" — trends in historical data and statistical probabilities. Football, however, is one of the most "unpredictable" sports. Upsets are common, and big blowouts often happen in matches where underdogs are expected to lose.
But AI still performs well in other fields. It might predict stock trends better than humans — because, despite fluctuations, long-term trends are influenced by economic data, company earnings, industry cycles, which AI can analyze.
Football doesn’t work because it’s too "human." Variables like a player’s adrenaline, a coach’s on-the-spot substitutions, or a fan’s boos are impossible for AI to grasp.

Does AI prediction still have meaning?
Yes, but in a different way.
Instead of asking AI "who will win," ask it "what are the key factors in this match." AI can analyze historical data, tactical comparisons, player conditions — these are valuable insights. It might tell you "Netherlands has a strong wing attack, Sweden’s aerial defense is weak" — useful information. But how the 5-1 score came about, you’ll need to watch the game yourself.
Or, use AI as an "assist," not a "fortune-teller." If it predicts 2-1, you can judge yourself — "this might be a high-scoring game" — and bet on 3-1 or 4-1. Combining AI with human judgment might be more effective than relying solely on either.

The most ironic point:
In this prediction contest, the step AI predicted a big Swedish win — it was the only one to go against the trend. It was wildly wrong, but in a way, also the most "creative" — at least it didn’t get caught up in the "Netherlands is stronger" consensus.
The other 11 AIs seemed to predict independently, but in reality, their answers were highly aligned: 2-0, 2-1, 1-0 — as if they had coordinated. This reveals a hidden issue: AI models are trained on highly overlapping data, all looking at the same news reports, historical data, and statistical sites, so their "independent thinking" ability is limited.
Out of 12 AIs, 11 shared the same bias. Only one dared to go against the grain, but it overdid it.
This is the current state of AI: either making the same mistakes as most people or making wildly different errors. Don’t expect it to truly predict the future.
But the 5-1 Netherlands vs. Sweden match was quite entertaining. AI didn’t predict it, but that didn’t diminish the excitement of the game itself. Sometimes, human calculations can’t match the randomness of fate — and that’s pretty thrilling.