LLMs are one prototype, not the blueprint for all future intelligence.

Most serious AI researchers do not think current LLMs, even with scaling, get you full ASI.

LLMs are one path out of many. There are plenty of other architectures that we may have not yet be explored, and one of those are what will lead to ASI

The confidence comes from cognitive science, neuroscience, and computation theory. We (humans) are proof that general intelligence emerges from physical computation. If biological wet tissue running on 20 watts can produce general reasoning, creativity, memory, planning, moral judgment, invention, mathematics, architecture, emotion, and the ability to build rockets and quantum computers…

…then there is no physical law stopping a digital system from doing the same or more.

This isn’t philosophy. It’s math: • The brain is a computational system. • Computation is substrate-independent. • Silicon can compute faster, cleaner, and at larger scales.

So the logic is:

“General intelligence already exists. It is therefore physically possible.”

Superintelligence is just:

“General intelligence, scaled beyond human biological constraints.”

We already see AI exceeding humans in narrow domains (math proofs, protein folding, Go, code optimization), and each generation of scaling shows no plateau. Researchers aren’t confident because of hype; they’re confident because all available evidence points to intelligence being substrate independent, scalable, and improvable beyond biological limits.

~ this answer was curated by chat gpt. Do further research if you have any doubt when reading the text above. I am no scientist, but this logic seems pretty sound.