My point is that a bit of scaling (like 3x) doesn't matter, even though at the scale of GPT-4.5 or Grok 3 it requires building a $5bn training system, but a lot of scaling (like 2000x up from the original GPT-4) is still the most important thing impacting capabilities that will predictably happen soon. And it's going to arrive a little bit at a time, so won't be obviously impactful at any particular step, not doing anything to disrupt the rumors of no longer being important. It's a rising sea kind of thing (if you have the compute). Long reasoning traces were always necessary to start working at some point, and s1 paper illustrates that we don't really have evidence yet that R1-like training creates rather than elicits nontrivial capabilities (things that wouldn't be possible to transfer in mere 1000 traces). Amodei is suggesting that RL training can be scaled to billions of dollars, but unclear if this assumes that AIs will automate creation of verifiable tasks. If constructing such tasks (or very good reward models) is the bottleneck, this direction of scaling can't quickly get very far outside specialized domains like chess where a single verifiable task (winning a game) generates endless data. The quality data wall and flatlining benchmarks (with base model scaling) are about compute multipliers that depend on good data but don't scale very far. As opposed to scalable multipliers like high sparsity MoE. So I think these recent 4x a year compute multipliers mostly won't work above 1e27-1e28 FLOPs, which superficially looks bad for scaling of pretraining, but won't impact the less legible aspects of scaling token prediction (measured in perplexity on non-benchmark data) that are more important for general intelligence. There's also the hard data wall of literally running out of text data, but being less stringent on data quality and training for multiple epochs (giving up the ephemeral compute multipliers from data quality) should keep it at bay for now.

The example of self-driving cars is actually the biggest one that anchors me to timelines of decades or more. A lot of people's impression after the 2007 DARPA Grand Challenge seemed to be something like "oh, we seem to know how to solve the problem in principle, now we just need a bit more engineering work to make it reliable and agentic in the real world". Then actually getting things to be as reliable as required for real agents took a lot longer. So past experience would imply that going from "we know in principle how to make something act intelligently and agentically" to "this is actually a reliable real-world agent" can easily take over a decade. Another example is that going from the first in-principle demonstration of chain-of-thought to o1 took two years. That's much shorter than a decade but also a much simpler capability. For general AI, I would expect the "we know how to solve things in principle" stage to at least be something like "can solve easy puzzles that a normal human can that the AI hasn't been explicitly trained on". Whereas with AI,  we're not even there yet. E.g. I tried giving GPT-4.5, DeepSeek R1, o3-mini, and Claude 3.7 with extended thinking a simple sliding square problem, and they all committed an illegal move at one stage or another. And that's to say nothing about all the other capabilities that a truly general agent - say one capable of running a startup - would need, like better long-term memory, ability to formulate its own goals and prioritize between them in domains with no objective rules you could follow to guarantee success, etc.. Not only are we lacking convincing in-principle demonstrations of general intelligence within puzzle-like domains, we're also lacking in-principle demonstrations of these other key abilities.