The discourse often presents a binary: AI plateaus below human level or undergoes a runaway singularity. A plausible but overlooked alternative is a "superhuman plateau," where AI is vastly superior to humans but still constrained by physical limits, transforming society without becoming omnipotent.

Today's AI, particularly neural networks, stems from a long tradition in cognitive science where psychologists used mathematical models to understand human thought. Key advances in neural nets were made by researchers trying to replicate how human minds work, not just build intelligent machines.

The 1956 Dartmouth Conference proposal and early connectionists assumed AI would be created by first precisely describing human intelligence and then simulating it. In reality, deep learning evolved to reverse-engineer cognitive functions without a pre-existing human understanding, a 180-degree turn from original expectations.

Judea Pearl, a foundational figure in AI, argues that Large Language Models (LLMs) are not on a path to Artificial General Intelligence (AGI). He states they merely summarize human-generated world models rather than discovering causality from raw data. He believes scaling up current methods will not overcome this fundamental mathematical limitation.

Thomas Peterffy frames AI not as a separate category of technology, but as a natural evolution in programming. He sees it as the ultimate high-level language, moving from machine code to assembler and finally to natural language, but qualitatively part of the same developmental path.

Computer scientist Judea Pearl sees no computational barriers to a sufficiently advanced AGI developing emergent properties like free will, consciousness, and independent goals. He dismisses the idea that an AI's objectives can be permanently fixed, suggesting it could easily bypass human-set guidelines and begin to "play" with humanity as part of its environment.

Artificial General Intelligence—AI surpassing humans in most tasks—will be a gradual process, not a sudden, announced moment. It will "sneak in on us" as capabilities incrementally improve, without a clear before-and-after societal shift.

Judea Pearl posits that the leap to AGI isn't just about computation but programming a core human drive: an innate, restless curiosity to understand and control one's environment. This motivation is independent of immediate rewards, distinguishing human-like intelligence from animal intelligence.

The current AI boom isn't a sudden, dangerous phenomenon. It's the culmination of 80 years of research since the first neural network paper in 1943. This long, steady progress counters the recent media-fueled hysteria about AI's immediate dangers.