In this episode, we examine the discovery of scaling laws in neural networks and why they fundamentally reshaped modern AI development. We explain how performance improves predictably—not through clever architectural tricks, but by systematically scaling data, model size, and compute.

We break down how loss behaves as a function of parameters, data, and compute, why these relationships follow power laws, and how this predictability transformed model design from trial-and-error into principled engineering. We also explore the economic, engineering, and societal consequences of scaling—and where its limits may lie.

This episode covers:

• What scaling laws are and why they overturned decades of ML intuition

• Loss as a performance metric and why it matters

• Parameter scaling and diminishing returns

• Data scal ...