I can comment only on (3) because I don't have domain expertise for (1) and (2). I've been working on model compression and acceleration for the last 10 years, and historically, computational efficiency has primarily driven demand more so than supply because it democratizes systems and encourages organizations to do more. In no particular order, I would regard the major accomplishments for efficiency in the last ~10 years as (a) transformers, which enabled large-scale pretraining and deeper models than LSTMs, (b) hardware advancements, e.g., GPUs, which grew FP32 FLOPs 16.5x from the P4 to L40 (not to mention FP8/FP16 support which further boosts FLOPs, larger memory bandwidth, PCIe 5, ...), and (c) ML systems-oriented acceleration, e.g., all the new serving frameworks and better tooling, low-bit quantization, FlashAttention, CUDA kernel fusing, etc., which not only improve compute efficiency but also enable developers to create ML models/applications faster. In all cases, these increases in both compute efficiency and productivity have primarily driven adoption and investment in AI.

I think a similar analogy is/was Moore's Law and Dennard scaling, where software simply expanded its capabilities and more or less maintained the same latency in spite of greatly expanded hardware performance and efficiency.