Humanity is currently engaged in a massive experiment involving the scaling of deep neural networks, particularly large language models. This undertaking involves substantial investments from private labs, potentially reaching hundreds of billions of dollars over the next few years. The core questions revolve around what happens when these networks are trained with vast amounts of data and computational resources. Despite the scale of this effort, the underlying theory of deep learning remains immature, leaving many uncertainties about how neural networks operate internally and what new capabilities may emerge from this scaling. The article introduces the "quanta" hypothesis, a framework proposed by Eric Michaud and colleagues that aims to explain how neural networks' performance and internal mechanisms evolve with increased scale. They highlight that certain scaling laws govern the predictability of network performance, showing that mean test loss decreases in a consistent manner as network parameters, training samples, and training time increase. This predictability mirrors phenomena in thermodynamics, suggesting an underlying order in the behavior of complex systems. However, the emergence of specific abilities in larger models—where smaller models fail to perform certain tasks—is more challenging to predict. Michaud's work also addresses the limitations of current theories and the need for a more unified understanding of deep learning. He points to existing studies on neural scaling that attempt to clarify how various engineering choices affect network learning. As the field progresses, the article raises critical questions about the future of pretraining, its potential plateauing, and what that means for AI capabilities. This inquiry into the scaling of neural networks reflects significant stakes, as the implications could redefine human interaction with technology and the nature of intelligence itself.