Mo’ Compute Mo’ Problems (by Silly Rabbit)

The investing world has split into two opposing camps arguing past each other about AI. One side believes superhuman machines are inevitable and imminent. The other rejects AI entirely. Both operate from the same flawed assumption: that intelligence exists on a single axis and can be ranked simply as "better" or "worse" than humans.

• The boundary between human and machine capability is fuzzier than anyone admits. We don't know in advance which problems machines will eventually dominate, and the gap between "nowhere close" and "truly dominant" can close faster than anyone expects.

• Current AI dominance shows a specific pattern. Machines win reliably only at tasks that are simple, discrete, repetitive, involve massive data, require extreme speed, or specifically degrade when humans are involved. Most real work doesn't fit this profile.

• The two sides aren't just disagreeing. They're using incompatible definitions of "intelligence." One treats it as a single dimension. One treats it as a multidimensional possibility space with multiple types of cognition operating simultaneously. These aren't the same argument.

• The practical consequence is being ignored. Investors and firms are either all-in on automation or rejecting it entirely, when the actual opportunity sits in the middle ground neither side is fully utilizing.

• This polarization is costing real money. If the question isn't "will machines beat humans at everything" but rather "which specific human capabilities are machines actually better at in your domain," then both camps are building strategy on the wrong question entirely.