[insignificntape]

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?