According to Apeira Capital's Natalie Hwang, the next stage of AI development will be shaped by the economics of running intelligence at scale. Companies are shifting from model size to focusing on inference costs, power efficiency, and system utilization. This transition is essential for mass-market deployment and sustainable growth.

Training costs have been the primary focus, but inference introduces ongoing expenses tied to usage. This forces a reassessment of how AI is scaled. Investors and industry leaders should be aware that companies relying on inefficient architectures may see operating expenses outpace revenue. This shift prioritizes unit economics and power consumption.

Power availability is also becoming a key factor in infrastructure decisions. Regions with limited power grids or high energy costs could face significant challenges. Infrastructure decisions will be shaped by energy availability and deployment feasibility. Companies that treat AI as an operating system are best positioned for this evolution.

Ultimately, the industrialization of AI will dictate capital allocation and competitive advantage. The focus will be on efficiency and predictability under real-world conditions. Many market expectations have yet to reflect these forces. Investors should watch how companies optimize for power, latency, and total cost of ownership to achieve sustainable scaling.