LLMs predict the next token in a sequence. The brain's cortex may function as a general prediction engine capable of "omnidirectional inference"—predicting any missing information from any available subset of inputs, not just what comes next. This offers a more flexible and powerful form of reasoning.

The brain's hardware limitations, like slow and stochastic neurons, may actually be advantages. These properties seem perfectly suited for probabilistic inference algorithms that rely on sampling—a task that requires explicit, computationally-intensive random number generation in digital systems. Hardware and algorithm are likely co-designed.

To achieve 1000x efficiency, Unconventional AI is abandoning the digital abstraction (bits representing numbers) that has defined computing for 80 years. Instead, they are co-designing hardware and algorithms where the physics of the substrate itself defines the neural network, much like a biological brain.

The debate over whether "true" AGI will be a monolithic model or use external scaffolding is misguided. Our only existing proof of general intelligence—the human brain—is a complex, scaffolded system with specialized components. This suggests scaffolding is not a crutch for AI, but a natural feature of advanced intelligence.

While today's computers cannot achieve AGI, it is not theoretically impossible. Creating a generally intelligent system will require a new physical substrate—likely biological or chemical—that can replicate the brain's enormous, dynamic configurational space, which silicon architecture cannot.

The Fetus GPT experiment reveals that while its model struggles with just 15MB of text, a human child learns language and complex concepts from a similarly small dataset. This highlights the incredible data and energy efficiency of the human brain compared to large language models.

DeepMind's Shane Legg argues that human intelligence is not the upper limit because the brain is constrained by biology (20-watt power, slow electrochemical signals). Data centers have orders of magnitude advantages in power, bandwidth, and signal speed, making superhuman AI a physical certainty.

The computer industry originally chose a "hyper-literal mathematical machine" path over a "human brain model" based on neural networks, a theory that existed since the 1940s. The current AI wave represents the long-delayed success of that alternate, abandoned path.

AI models use simple, mathematically clean loss functions. The human brain's superior learning efficiency might stem from evolution hard-coding numerous, complex, and context-specific loss functions that activate at different developmental stages, creating a sophisticated learning curriculum.