Given the field's record of AI winters, it would be naive to think progress will certainly continue, but given the amount of progress that has been made as well as how it's being made, it would also be naive to think it will certainly not.
The advances that have come in the last few years have been driven first and foremost by compute and secondarily by methodology. The compute can continue to scale for another couple orders of magnitude. It's possible that we'll be bottlenecked by methodology; there are certain things that current networks are simply incapable of, like learning from instructions and incorporating that knowledge into their weights. That said, one of the amazing things about recent successes is that the precise methodology doesn't seem to matter so much. Diffusion is great, but autoregressive image generation models like Parti also generate nice images, albeit at a higher computational cost. RL from human feedback achieves impressive results, but chain of hindsight (supposedly) achieves similar results without RL. It's entirely plausible to me that the remaining challenges on the path to AGI can be solved by obvious ideas + engineering + scaling + data from the internet.
We've also gotten to the point where AI systems can make substantial contributions to engineering more powerful AI systems, and maybe soon, to ideation. We haven't yet figured out how to extract all of the productivity gains from the systems we already have, and next-generation systems will provide larger productivity gains, even if they are just scaled up versions of current-generation systems.