What would your guess be for how much we need to scale for expensive agi? Are we talking 50x, 500x, 1000x, 5000x?

Feel free to ignore this rant (I mostly wrote it to get my own thoughts in order), but if you have time by all means correct me on anything I'm wrong about.

I know this is largely guesswork as to the needed model scale/compute power, but I'm a layman who generally disregarded the possibility of near-term agi... until I saw a few arguments showing the freakiness of the exponential curves at play. This has made me feel a bit existential to say the least. I know confirmation bias is a thing so I've sought out counterarguments. From what I've seen they mostly center around two points. The first is that long-term gains with our current pathway are untenable (our current ai lacks any sort of true intelligence and will quickly plateau), and that failed examples like self-driving cars are proof of how ill-equipped we are to deal with such complex problems.

To the dismay of my existentialism, neither of these arguments convince me. To the first point, the nay-sayers have a habit of bringing less data to their arguments than the near-termers. I don't doubt another ai winter is possible, but a stagnation first requires a slowing in progress. Exponential curves don't just slam to a halt, barring global catastrophe. The only place I see evidence of stagnation is in Moore's law, which is a pretty narrow measure. Even then, computing power by other measures is still expanding just as fast, and most engineers I've heard from still expect a few more doublings on the transistor counts. I don't doubt that just brute forcing agi by bringing more computing power to our current frameworks is unrealistic, but that's when I look at other measures of ai progress. GPUs produced, spending on ai, number of people working on ml; they're all expanding exponentially. People in the 60s famously predicted famine would engulf the rising population, but failed to anticipate that rising population would bring additional innovation. Hunger has only gone down at an exponential rate since then. Expecting engineers to suddenly stop looking for new ml frameworks is to ignore all historical trends.

To the second point, I agree that people have been claiming self-driving cars are two years away for the last ten years. This to me though seems more anecdotal than anything. Sure, a problem that optimists said would be solved in five to ten years is likely going to take twenty, but a ten year error is a blink of an eye all things considered. When you broaden the range of exponential technological progress to the start of the industrial revolution, this delay seems to be a molehill rather than a mountain. Regardless, we can find plenty of counter-examples of people underestimating the time it'd take for ai to do things like beat humans at Go. When both sides are just using anecdotes, only data can resolve whether we're systematically overestimating or underestimating progress. Unfortunately I've been unable to find anything on this, so for now I'm unconvinced by the argument we're too optimistic. I'm keeping an eye on prediction markets in the future though.