That's not true. And trust me, dude, it scares the living ** out of me, so I wish you were right. Next-token prediction is the AI-equivalent of a baby flailing its arms around and learning basic concepts about the world around it. The AI learns to mimic human behavior and recognize patterns, but it doesn't learn how to leverage this behavior to achieve goals. The pre-training is simply giving the AI a baseline understanding of the world. Everything that's going on now, getting it to think (i.e. talking to itself to solve more complex tasks), or getting it do do maths or coding, is simply us directing that inherent knowledge it's gathered from its pre-training and teaching the AI how to use it.

Look at Claude Code. Unless they hacked into private GitHub/GitLab repos... (which, honestly, I wouldn't put beyond these tech CEO's, see what CloudFlare recently found out about Perplexity as an example), but unless they really did that, they trained Claude 4 on approximately the same data as Claude 3. Yet for some reason its agentic coding skills are stupidly enhanced when compared to previous iterations.

Data no longer seems to be the bottleneck. Which is understandable. At the end of the day, data is really just a way to get the AI to make a predicion and run gradient descent on it. If you can generate for example a bunch of unit tests, you can let the AI freewheel its way into getting them to pass. A kid learns to catch a baseball not by seeing a million examples of people catching balls, but instead by testing their skills in the real world, and gathering feedback from the real world on whether their attempt to catch the ball was successful. If an AI can try to achieve goals and assess whether or not its actions lead to a successful or a failed attempt, who needs more data?

I think the reason people feel it's plateauing is because the new improvements are less evident to the average person. When we saw GPT-4 I think we all had that "holy shit" moment. I'm talking to a computer, it understands what I'm saying, and responds eloquently. The Turing test, effectively. That's probably the most advanced benchmark humans can intuitively assess. Then there's abstract maths, which most people don't understand, or the fact that this entity that talks to me like an intelligent human being, when left to reason about something on its own devolves into hallucinations over time. All real issues, but much less tangible, since we can't relate it to behaviours we observe or recognize as meaningful in humans. We've never met a human that can write a snake game from memory in 20 seconds without errors, but can't think on its own for 5 minutes before breaking down into psychosis, which is effectively what GPT-4 was/is. After the release of GPT-4 we strayed well outside of the realm of what we can intuitively measure or reason about without the use of artificial benchmarks.

It's a self-evident truth. Even if today, at this very moment AI hits a hard plateau and there's nothing we can do to make AI better, ever, then this still holds true. It simply means we'll keep what we have right now. Any new model will be a step back and thus be discarded. So what we have today is the worst, and the best it will ever be. But barring that extremely unlikely scenario, like GPT-3 to GPT-4 and Claude 3 to Claude 4, we will see improvements (either incremental or abrupt) over the coming weeks/months/years. Any failed experiments will never see the light of day and the successful experiments will become Claude X or GPT X, etc.