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 progress in deep learning, from AlexNet's GPU leap to today's massive models, is best understood as a history of scaling compute. This scaling, resulting in a million-fold increase in power, enabled the transition from text to more data-intensive modalities like vision and spatial intelligence.

The progression from early neural networks to today's massive models is fundamentally driven by the exponential increase in available computational power, from the initial move to GPUs to today's million-fold increases in training capacity on a single model.

The AI era is not an unprecedented bubble but the next phase in a recurring pattern where each new computing cycle (mainframe, PC, internet) is roughly 10 times larger than the last. This historical context suggests the current massive investment is proportional and we are still in the early innings.

The "bitter lesson" in AI research posits that methods leveraging massive computation scale better and ultimately win out over approaches that rely on human-designed domain knowledge or clever shortcuts, favoring scale over ingenuity.

AI should be viewed not as a new technological wave, but as the final, mature stage of the 60-year computer revolution. This reframes investment strategy away from betting on a new paradigm and towards finding incumbents who can leverage the mature technology, much like containerization capped the mass production era.

Citing the president of the Santa Fe Institute, investor James Anderson argues that current AI is the "opposite of intelligence." It excels at looking up information from a vast library of data, but it cannot think through problems from first principles. True breakthroughs will require a different architecture and a longer time horizon.

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.

The recent AI breakthrough wasn't just a new algorithm. It was the result of combining two massive quantitative shifts: internet-scale training data and 80 years of Moore's Law culminating in GPU power. This sheer scale created a qualitative leap in capability.