All intelligence is fundamentally search over Turing machines, and anything that happens can be described by Turing machines of various lengths; learning and scaling involve searching over more and longer Turing machines and applying them to specific cases.

Large neural network models can always be decomposed into small specialized models, each solving specific tasks equally well, because the large model is itself just a gigantic ensemble of small task-tailored models addressing an escalating number of tiny problems.

There is no General Master algorithm and no special intelligence fluid; intelligence is instead composed of a tremendous number of special cases that organisms learn and then code into their brains.

No localized 'IQ gland' or anatomical structure exists in the brain that, if removed or damaged, would eliminate fluid intelligence, because fluid intelligence is not a unified property but rather the recombination of many learned specialized solutions.

Intelligence took a long time to evolve in humans and remains rare because direct genetic encoding of solutions is always more efficient and adaptive than general-purpose search processes, which are expensive, unreliable, and slow (taking whole days to learn).

For many organisms and ecological niches, general-purpose intelligence is not adaptive because it does not pay to be intelligent; there are more direct, efficient solutions to environmental problems than general-purpose learning mechanisms.

In ecological niches where the environment is static, where intelligence would be prohibitively expensive, or where organisms are short-lived, it is evolutionarily very difficult to evolve general-purpose learning mechanisms; specialized solutions tailored to specific problems are strongly favored instead.

Variation in human intelligence reflects differences in compute capacity; individuals with more intelligence have more compute in order to do search over more Turing machines for longer.

There is no general Master algorithm and no special intelligence fluid; instead there are a tremendous number of special cases that we learn and then code into our brains.

Variation in human intelligence reflects differences in computational capacity for search over Turing machines; more intelligent people simply have more compute to search over more Turing machines for longer.

The brain is a gigantic ensemble of small models tailored to solve the ever-escalating number of tiny problems encountered; large brain functions can be decomposed into these specialized models, just as large neural networks contain extractable small models for specific tasks.

The grand parsimonious theory of intelligence is that all intelligence is search over Turing machines of various lengths, and learning and scaling involve searching over more and longer Turing machines and applying them to specific cases.

Human-level intelligence is rare and took a long time to evolve because small Turing machines could always be encoded more directly and efficiently by genes with sufficient evolution, making specialized hardcoded solutions superior to the costly, slow, unreliable search process that intelligence requires.

For many creatures, it does not pay to be intelligent because general-purpose intelligence is not actually adaptive; in static niches, in environments where intelligence is expensive, or for short-lived organisms, evolving a specialized tailor-made solution is superior to evolving a general-purpose learning mechanism.

No IQ gland exists in the brain because there is nowhere that eliminates fluid intelligence when damaged; fluid intelligence emerges from the brain's learning of individual specialized problems and their recombination rather than from a dedicated neural substrate.