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The nonprofit\u00a0ARC Prize Foundation<\/a>\u00a0on May 1, 2026, released the\u00a0results of a new benchmark<\/a>: a test of an AI system\u2019s ability to solve a game. The results were striking \u2013 humans scored 100%, while the most advanced AI systems scored under 1%.<\/p>\n At first glance, this may be surprising to users of AI who are impressed by its polished essays, codebases and multistep projects generated in seconds. How can these brilliant AI systems struggle with these simple Tetris-shape puzzles?<\/p>\n That confusion points to a risk: AI is becoming integrated into everyday life faster than people can make sense of it.<\/p>\n We are\u00a0cognitive psychologists<\/a>\u00a0who study<\/a>\u00a0how to teach difficult concepts. To recognize the limits and risks of today\u2019s AI agent systems, it\u2019s important for people to grasp that the systems can both accomplish superhuman feats and make mistakes few humans would. To that end, we propose a new way to think about AIs: as button-pushing explorers.<\/p>\n We teach college students, a group rapidly incorporating AI tools into their daily routines. That gives us regular opportunities to ask what they think is going on with AI. The answers vary widely. One student said that someone at OpenAI or Anthropic is reading and approving every response the system generates. Another, more succinctly, said, \u201cIt\u2019s magic.\u201d<\/p>\n These responses illustrate two tempting ways of making sense of AI. At one extreme, AI is treated as an inscrutable black box \u2013 a powerful but ultimately mysterious force. At another, people explain it using the same assumptions they use to understand other humans: that its outputs reflect reasoning or judgment.<\/p>\n The worry is that these misinterpretations don\u2019t go away as users gain more experience interacting with AI, and they\u00a0might get reinforced<\/a>. When AI performs well, its output can feel like evidence of understanding or confirmation that it really is something like magic. That apparent success makes it harder to question what the system is actually doing. Biases can seem logical or inevitable; harmful behavior can look like a deliberate choice or even fate, as if it could not have gone any other way.Mental models for AI<\/strong><\/h4>\n
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