There were definitely people "at the top" who were essentially arguing that more scale would get you to AGI - Ilya Sutskever of OpenAI comes to mind (e.g. "next-token prediction is enough for AGI").

There were definitely many other prominent researchers who vehemently disagreed, e.g. Yann LeCun. But it's very hard for a layperson (or, for that matter, another expert) to determine who is or would be "right" in this situation - most of these people have strong personalities to put it mildly, and they often have vested interests in pushing their preferred approach and view of how AI does/should work.

People were originally very surprised that you could get so much functionality by just pumping more data and adding more parameters to models. What made OpenAI initially so successful is that they were the first company willing to make big bets on these huge training runs.

After their success, I definitely saw a ton of blog posts and general "AI chatter" that to get to AGI all you really needed to do (obviously I'm simplifying things a bit here) was get more data and add more parameters, more "experts", etc. Heck, OpenAI had to scale back it's pronouncements (GPT 5 essentially became 4.5) when they found that they weren't getting the performance/functionality advances they expected after massively scaling up their model.