AI companies are no longer just hiring smarter people. They are buying bigger brains.
Constructed from GPUs, data centres, and billions invested in compute, this industrial shift reveals a simple truth observed by Anthropic co-founder Jack Clark: AI progress comes from training at scale.
Bigger models trained with more compute consistently become better models. The expected wall turned out to be a ramp.
This changes the economics of intelligence. Training is no longer mere research; it is infrastructure and geopolitics.
For the School of Thinking, the lesson is profound. Human brains improve through training x10. Training at scale. Artificial brains do, too.
OpenAI claim to have spent USD19 billion last year on training. Training at scale. More than wages. More than marketing. More than wages and marketing combined.
Yet Australian companies spend a mere fraction of wages and marketing on training.
It validates our foundational principle: Train Brain Daily!
Escaping your Current View of Situation (CVS) to reach a Better View of the Situation (BVS) can be a trained cognitive reflex. If machines achieve x10 leaps through scaled training, biological brains can apply the same x10 strategy.
This demands a shift in human infrastructure: Should children be taught neuroscience in primary school?
Why wait?
Teaching neuroscience to 10-year-olds is the biological equivalent of scaling compute.
The universal law of intelligence is:. You get better by training—whether your brain is made of GPUs, or human synapses.
Jack Clark, Anthropic co-founder and author of Import AI, has argued that the story of modern AI is largely the story of training at scale.
