The adult human brain contains roughly 86 billion neurons (Azevedo et al., Journal of Comparative Neurology, 2009) and runs on about 20 watts - roughly the power of a dim lightbulb. A frontier large language model can have hundreds of billions of parameters but requires megawatts of data-center power to train and orders of magnitude more energy per inference than a single human thought.
Where AI clearly wins: pattern recognition over enormous datasets, exhaustive search, recall of indexed information, and statistical consistency. Where humans still dominate: causal reasoning about novel situations, embodied common sense, robust moral judgment, learning a new skill from a handful of examples (few-shot learning in the truest sense), and genuine creative leaps. MIT CSAIL and Stanford HAI publish annual benchmarks tracking exactly which tasks have crossed the human-parity line and which remain open.
The Stanford AI Index Report (2024) shows AI now outperforms humans on image classification, basic reading comprehension, and visual reasoning, while humans still lead on competition-level mathematics, multi-step planning, and complex visual common-sense. Critically, AI systems can be confidently wrong (hallucination), and uncertainty calibration remains an unsolved problem.
Neuroscientists at the Allen Institute for Brain Science and DeepMind have noted that human brains learn from sparse, multi-modal, embodied experience - exactly the regime where current AI is weakest. This is why your two-year-old can generalize 'cup' after seeing three of them, while a vision model may need millions of labeled examples.