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.

> The models keep getting better at an exponential [sic].

We don't know if this is true. A lot of growth that appears exponential is often quadratic (https://longform.asmartbear.com/exponential-growth/) or follows a logistic function (e.g. Moore's law).

Additionally there's a LOT of benchmark gaming going on, and a lot of the benchmark solving is not down to having a process that actually solves the problems; it just turns out that the problems already kind of lie in the span of text on the internet.

For AGI hype, we need exactly one piece of evidence that machine AGI exists, or that we know how to build it. Otherwise, it's an exaggeration that AGI is imminent - otherwise known as hype. Or maybe it's hope, but sama suggests it should be an expectation.