[PollardsRho]

> The models keep getting better at an exponential.

Isn't it the opposite? Marginal improvements require exponentially more investment, if we believe Altman. AI is expanding into different areas, and lots of improvements have been made in less saturated fields, but performance on older benchmarks has plateaued, especially relative to compute costs.

Even if you focus on areas where growth is rapid, the history of technology shows many, many examples of rapid growth hitting different bottlenecks and stopping. Futurists have predicted common flying cars for decades and decades, but it'll be a long, long time before helicopters are how people commute to work. There are fundamental physical limitations to the concept that technological advancement does not trivialize.

Maybe the problems facing potential AGI have relatively straightforward technological solutions. Maybe, like neural networks already have shown, it will take decades of hardware advancements before advancements conceived of today can see practice. Maybe replicating human-level intelligence requires hardware much closer to the scale of the human brain than we're capable of making right now, with a hundred trillion individual synapses each more complex than any neuron in an artificial neural network.

[93po]

Sam plainly wrote that intelligence of the log of training resources, but that's presumably written in the context of GPT4 style LLMs. The intelligence gains we're seeing right now are not a result of a 100x increase in traditional training resources but rather new ways of training and agentic processes.