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.

AI development history shows that complex, hard-coded approaches to intelligence are often superseded by more general, simpler methods that scale more effectively. This "bitter lesson" warns against building brittle solutions that will become obsolete as core models improve.

Today's AI boom is fueled by scaling computation, which is a known engineering challenge. The alternative, embedding nuanced, human-like inductive biases, is far harder as it requires a deep understanding of the problem space. This difficulty gap explains why massive models dominate AI development over more targeted, efficient ones—scaling is simply the more straightforward path.

The goal of AI development shouldn't be to perfectly replicate human cognition, a complex and perhaps unfalsifiable target. Instead, a more pragmatic approach is to draw high-level inspiration from nature to build novel forms of intelligence designed specifically to understand and serve human needs.

The popular concept of AGI as a static, all-knowing entity is flawed. A more realistic and powerful model is one analogous to a 'super intelligent 15-year-old'—a system with a foundational capacity for rapid, continual learning. Deployment would involve this AI learning on the job, not arriving with complete knowledge.

We don't fully understand how advanced AI models work. Creators don't program them with explicit knowledge but train them on vast datasets and then run experiments to discover their capabilities. This makes AI development more of a science—studying an unpredictable artifact—than traditional engineering, highlighting an inherent lack of control.

The training process of a large language model is not just "learning" in the human sense. It's a rapid recapitulation of evolution, where the system reverse-engineers cognitive functionalities that took nature millions of years to develop. This framing highlights the immense, untapped potential of the deep learning paradigm.

Philosophy should have been central to AI's creation, but its academic siloing led to inaction. Instead of engaging with technology and building, philosophers remained focused on isolated cogitation. AI emerged from engineers who asked "what can I make?" rather than just asking "what is a mind?".

While biology (birds) provides initial inspiration for flight, progress eventually requires engineering machine-specific solutions (jet engines). Similarly, AI learned foundational principles from human cognition, but its recent breakthroughs come from non-biological methods like massive scaling. The focus should be on universal "laws of thought," not just mimicking biological hardware.