Yes, I made my comment with the presumption that we are talking about AGI, not just a smart calculator bot making a slightly faster calculator bot. We have created some multi-modal AI that can accomplish different tasks, but the model itself is computationally inefficient and predictive rather than true learning (just like GPT-3 doesn't actually think logically, it's just a really advanced language prediction model).

As far as I am concerned, the difference between consciousness and AI is that an AI is an advanced look-up table using only simple logic while consciousness involves processing stored information for semantic meaning rather than adhering to an algorithmic process for syntactic meaning. See: Chinese translation room thought experiment.

AI today uses low-level logic en masse to produce high-level (relatively) thinking. With the addition of increasingly advanced neural networks, image-generation AI has utilized increasingly complex network structures, such as defining edges, shapes, complex shapes, and fuzziness around these levels. If we extend this notion to account for an AI that is capable of taking simple features such as moving shapes and we allow the AI to predict the shapes' locations, we may be able to reapply this scalable logic until the AI is able to understand complex ideas given sufficient inputs and sufficient training data. This is far-fetched from a modern technological standpoint, but not unbelievably far-fetched given how quickly we are advancing our AI.

If the human brain is made up of computations, then an elaborate series of computations is by definition what must define our consciousness, and thus it can be created with sufficient AI models. Switching to amplitude computers for computational efficiency or compressed memory models (current memory cell models scale linearly with space instead of logarithmically) may allow us to break through this barrier.

edit: sorry for the ramble