This is not the new capability that LLMs have pioneered. It's true that it is difficult to sift out signal from the noise of a vast data trove, but it is diffic

This is not the new capability that LLMs have pioneered. It's true that it is difficult to sift out signal from the noise of a vast data trove, but it is difficult in a way that people have been getting extremely good at since the late 90s. What you're describing is a Google-level capability, and that's truly a very complex thing not to be downplayed. But it's a capability that we've had and been honing for decades now. I'm sure language models and transformer techniques will be (or more likely: already are) an important part of the contemporary systems that do this stuff. But I'm skeptical that they care much about GPT-4 itself (or other general models). I'm not skeptical about whether they think it is useful and an important capability to incorporate ML techniques into their systems, I'm unsure how much utility they see in general (the "G" in AGI) models.