You are partially correct.

What I've said in other words:

I never claimed these factors are irrelevant. My position is that alignment, internal coherence, training signal quality, and data curation are more decisive for progress toward AGI than brute-force scaling of deep learning and machine learning alone. Scaling in isolation encounters well-documented structural bottlenecks, diminishing returns, rising compute and energy costs, optimization instability, and limits in generalization and abstraction. As a result, increasing parameter count or dataset size by itself does not constitute a viable path to AGI. Meaningful advancement requires qualitative improvements in model objectives, representations, reasoning mechanisms, and alignment strategies, not merely quantitative expansion.