Beyond Human Before Human-like

People understand Artificial General Intelligence (AGI) wrong. AI will absolutely solve problems humans could not solve. It already started. Anthropic’s Mythos reportedly found zero-day vulnerabilities in major operating systems and browsers, including bugs that survived ten or twenty years of human review, automated testing, and pentesting. One was a now-patched 27-year-old bug in OpenBSD — an operating system known for security.

But it still does not mean AI is a human mind. Look at the caption image generated by AI. You will notice immediately that something is off with the “superhuman” shoulder and the banner. Your brain spots it naturally because it reconstructs 3D structure from a flat 2D image. But for AI noticing an "AI slop" shoulder in a picture may be almost as hard as finding a rare bug in OpenBSD for you.

Evolution optimises for survival, and in humans that did not mean simply being stronger or faster. It meant self-awareness, critical reasoning, long-term planning, reading other people’s intentions, detecting lies, building trust, forming groups, and surviving inside those groups. Not good at it? You die, with your whole group. That is how survival of the fittest works. Human cooperation and social cognition are our AGI, and they are much more complicated than finding bugs in old software. We never evolved to be coders. Our brains are bad at it, and GitHub is the evidence.

Take chess. There is no human alive who can beat a top chess engine. But this does not mean chess engines are “smarter” in the human sense. It means humans were not evolved for chess.

Imagine a dystopian world where people were only allowed to have children if they were in the top 20% of chess players. Keep this show running for 1000 generations. All kids would become chess masters at kindergarden. This is not magic. This is selection. We did similar things with domestic animals. Wolves did not start as natural sheep managers, but selective breeding produced dogs that can herd, guard, retrieve, track, and read human signals in ways wolves do not.

But we never bred humans for chess. Neither for finding bugs, coding, doing materials science, drug discovery, engineering design... That is why we are naturally bad at it.

Modern AI is loosely inspired by biological neural systems. Very loosely. Like saying an aeroplane is inspired by a bird. True, but please do not expect it to lay eggs.

Human brains learn continuously. Our neural network changes all the time. That is neuroplasticity. Long-term memory is not stored as notes attached to a prompt. It is embedded into a living, changing system. LLMs are different. A trained model is mostly frozen. It does not learn from every conversation the way a human does. “Memory” in current AI systems is usually external: conversation history, saved notes, markdown files, vector databases, retrieval systems, tool logs, user profiles, or some other data pulled back into the prompt.

Imagine driving this way. Every time you approach a turn, someone gives you the road rules, your car manual, a map, your old driving notes since high school, and a few memories of accidents you avoided (or not). Then you infer what to do next - turn or not.

And that is just one example of how the human brain and AI are different.

Will AI be smarter than humans in specific domains? Yes. In many places, it already is. Will that automatically become AGI? No.

A machine can be superhuman at chess, hacking, coding, scientific research, while still being fragile with memory, context, grounding, and judgement.

The human brain is not just neural paths in the cortex. It is a significantly more complicated organ. There are some processes we still don't fully understand. This matters because LLMs were inspired by pieces of biological intelligence, and that worked incredibly well. So the most likely path to AGI may be more of the same: keep copying, abstracting, and engineering more functions from the brain and nervous system. We just started.