Why your brain is 3 milion more times efficient than GPT-4

Article quality and focus

• Many commenters found the title (“3 milion more times efficient…”) clickbaity and the spelling error distracting.
• Several felt the piece is a long, beginner-level “wall of text” heavy on basic computing (bits, ASCII) and light on the promised brain vs GPT-4 analysis.
• The vector database comparison is viewed by some as hand-wavy and even ad-like, lacking clear benchmarks, dataset descriptions, or rigorous methodology.

Energy efficiency: brain vs GPT-4

• Multiple comments argue the comparison mixes training energy for LLMs with inference energy for humans, which is not apples-to-apples.
• One rough recalculation (after correcting a kcal vs calorie error) suggests human brains may be only slightly more efficient than GPT-4 in “training” and less efficient during inference, under specific assumptions.
• Others note that if you include the energy cost of evolution, upbringing, education, or the infrastructure supporting humans, the accounting becomes extremely complex and somewhat arbitrary.
• There are nitpicks about misuse of units (e.g., “Watts per hour”) and simplistic analogies (one human vs entire GPT-4 data center).

Intelligence, understanding, and creativity

• Heated debate over whether LLMs “understand” language or merely do statistical prediction.
• Some insist only brains truly think, are original, and have qualia; LLMs are powerful “stochastic parrots.”
• Others argue humans are also pattern recognizers constrained by prior data, and that the distinction between memorization and understanding is blurry and methodologically unclear.
• Creativity is contested: one side claims humans can iteratively build genuinely novel concepts; the other says both humans and AIs just recombine existing patterns.

Practical value and limits of LLMs

• Several participants treat GPT-like systems as conversational search engines: good for summaries, code generation, and format transformations, but untrustworthy without verification.
• Some users report great success with programming help; others recount persistent hallucinations and factual errors (especially with certain models).

Brain vs computer models and hardware

• Commenters stress that brains and digital computers are fundamentally different physical systems; the “brain as computer” is a metaphor with limited reach.
• Points raised about the brain’s heavy “pretraining” via evolution and hardwired structure.
• Neuromorphic chips are mentioned as a promising direction for more brain-like, energy-efficient computation.