To add a bit more background:

We already had two major AI winters: https://en.wikipedia.org/wiki/AI_winter

More related articles:

• Deep Learning’s Diminishing Returns: The cost of improvement is becoming unsustainable (from 2021)
• Paper: Will we run out of data? Limits of LLM scaling based on human-generated data
• Can AI Scaling Continue Through 2030?

My opinion: We're facing a lot of issues, energy, training data is finite, it might be likely that the current architecture of AI models will hit a ceiling. We already pump in lots of compute for ever diminishing returns. I'm pretty sure that approach won't scale towards AGI.

But it doesn't need to keep growing exponentially to be useful. AI is hyped to no end. And it's a real revenue driver for companies. I'd say the bubble is over-inflated. Some people are bound to get disappointed. And in my eyes it's very likely that it won't keep growing at the current pace of the last two years. And ultimately we'd need to come up with some new inventions if we want AGI. As far as I know that's still utter sci-fi. Nobody knows how to revolutionize AI so it'll suddenly become 100x more intelligent. And it's unlikely that our current approach will get us there. But on the other hand no-one ruled out there is a possibility to do it with a more clever approach.