The human genome is only 3 gigabytes and only 1–2% is protein-coding, which Dwarkesh argues is not enough space to store pre-trained neural network weights from evolution.
Where this was said
At 5:06 · chapter starts 3:11
Three objections addressed: (1) evolution pre-training humans — debunked via genome size; (2) multimodal sensory data — debunked via blind/deaf intelligence; and (3) scaling up models — addressed next. [1] — Dwarkesh Patel "The common objection — that billions of years of evolution pre-trained humans, making data comparisons unfair — doesn't hold up. The human …" 04:43 [2] — Dwarkesh Patel "If multimodal sensory data were the secret ingredient behind human intelligence, blind and deaf people would lack general intelligence. The…" 05:48
Frontier AI models are trained on tens to hundreds of trillions of tokens, versus roughly 200 million tokens a human sees from birth to adulthood — nearly a million-fold difference.
Frontier AI models are trained on tens to hundreds of trillions of tokens. A human sees about 200 million from birth to adulthood. That's close to a million-fold difference — and it reveals just how data-hungry these systems really are.
Humans can learn to teleoperate any humanoid or robot arm within hours, but AI systems require millions of hours of demonstrations and still can't perform complex open-ended tasks.
A teenager can learn to drive a car with about 20 hours of practice, yet self-driving car models from Waymo and Tesla require 3–4 orders of magnitude more data.
A teenager learns to drive in 20 hours. Even accounting for 16 years of growing up and building physical intuition, that's still 3–4 orders of magnitude less data than Waymo and Tesla use to train self-driving cars. This gap is the sample efficiency problem in concrete terms.
The common objection — that billions of years of evolution pre-trained humans, making data comparisons unfair — doesn't hold up. The human genome is only 3 GB, and 1–2% is protein-coding. That's nowhere near enough to store pre-trained neural network weights. Evolution found the right hyperparameters; it didn't train the weights.
If multimodal sensory data were the secret ingredient behind human intelligence, blind and deaf people would lack general intelligence. They don't. This suggests billions of sensory tokens aren't the key — and may mean the human-AI data gap is even larger than estimated.
Animals first sensed light 540 million years ago, triggering an evolutionary acceleration known as the Cambrian explosion within 10 million years.
It is estimated that half of all cortical activity in the human brain is involved in visual function, underscoring vision's central role in intelligence.
Cognitive neuroscience literature shows that by age 6, humans can recognize tens of thousands of different object categories — far more data than early AI systems were trained on.
Only about 3% of FDA-approved drugs have genuinely unclear mechanisms of action, making an unknown mechanism a meaningful early red flag for any compound.
Even compounds that clear preclinical testing often fail in humans: 30 to 50% of drugs entering phase 1 trials do not advance to phase 2, frequently because human behavior differs from animal models.
More than 80% of published BPC-157 research comes from a single academic group whose researchers have IP and commercial interests connected to the molecule, limiting independent replication.
Roughly 100 peptide drugs are already FDA-approved, about 150 more are in clinical trials, and 600–700 are in preclinical development, underscoring the legitimacy of peptide science broadly.
The scientist who discovered BPC-157 refused to disclose the screening method used to identify the compound and has never fully published the parent protein sequence — what Peter Attia calls 'scientific trust me, bro.'
The brain's visual, auditory, and motor cortices light up nearly identically whether an experience is real or imagined, making visualization a form of actual neural training.
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