I appreciate the engagement in making this argument more concrete. I understand that you are talking about returns on compute power.
However, your last paragraph about how investors view deep learning does not describe anyone in the community of academics, practitioners and investors that I know. People understand that the limiting inputs to improved performance are data, followed closely by PhD labor. Compute power is relevant mainly because it shortens the feedback loop on that PhD labor, making it more efficient.
Folks investing in AI believe the returns are worth it due to the potential to scale deployment, not (primarily) training. They may be wrong, but this is a straw man definition of scalability that doesn't contribute to that thesis.
However, your last paragraph about how investors view deep learning does not describe anyone in the community of academics, practitioners and investors that I know. People understand that the limiting inputs to improved performance are data, followed closely by PhD labor. Compute power is relevant mainly because it shortens the feedback loop on that PhD labor, making it more efficient.
Folks investing in AI believe the returns are worth it due to the potential to scale deployment, not (primarily) training. They may be wrong, but this is a straw man definition of scalability that doesn't contribute to that thesis.