People are becoming more and more reliant on AI and are eager to understand how it works. Besides having massive training data and exceptional logical reasoning abilities, AI has another impressive feature: parallel thinking. GPT or other tools are essentially language engines with a vast universe of knowledge that can perform logical reasoning at super high speeds. For regular humans, reasoning is linear, slow, and requires memory retrieval. AI, on the other hand, can perform parallel computations across thousands of reasoning paths simultaneously and then present you with the most likely useful one. It’s like when you ask a question, AI instantly calculates 200–2000 possible answers at the same time, scores them, ranks them, merges them, and outputs the optimal combination. GPT says: You ask a question, and I’ll generate a ‘universe of candidate answers,’ then automatically pick the one that seems most reasonable and most likely to please you. (Looks like it really does try to be likable.) The core of GPT is: Attention (self-attention) is a natural ‘parallel thinking’ structure. It allows the model to: focus on multiple parts of the input simultaneously, explore multiple reasoning paths at the same time, and generate multiple candidate computations. Attention heads are essentially ‘parallel thinking units.’ Parallel reasoning = parallelism of attention heads + parallelism of tree-like generation + parallelism of multi-version reasoning chains. These come from: Attention, the naturally parallel ‘multi-focus thinking.’ Beam/Tree-of-Thought Search, generating multiple candidate answer paths. Self-Consistency & Reasoning Engine, cross-verifying multi-version reasoning chains to select the best one.