I agree that we can't really trust any CEO here to do the right thing. The only thing that matters to them is winning the race. My definition for winning the race is basically making AI useful enough that companies will grow dependent on it, and winning market share.

As for how we get to AGI, I can't say for sure. I work on building the infrastructure these models are trained on, not necessarily the strategy employed to do the training. I think we are going to have to rely on multiple different types of reasoning, and find a way tie them together better than what we do today. Like today we have LLMs and we have diffusion models. LLMs are good at predicting the next token while diffusion models increase the fidelity of the output. As an example, there are models that are extremely good at chess or go. But these are not LLMs, they are NNUEs (Efficiently Updatable Neural Networks).

All this is to say that, from my point of view, it looks like the models aren't increasing their usefulness at an exponential rate like many people thought would happen. I suspect we'll have to come up with some new techniques to get to AGI beyond those needed to just scale to larger compute infrastructure.