The speaker argues that Small Language Models (SLMs) are rapidly improving in "intelligence density" and will be capable of handling 90% of common enterprise work tasks by 2030, at a dramatically lower cost than frontier LLMs. This enables massive cost savings (citing AT&T cutting costs by 90%) and could lead to "hyperdeflation" in AI inference pricing. It empowers small teams to serve niche markets profitably, potentially eroding the economic moat of frontier model providers. The entire technology services sector built on AI applications should be watched closely, as the underlying cost and accessibility of intelligence are shifting, enabling new business models and threatening incumbents reliant on expensive API calls. Frontier models continue to advance at a pace that maintains a significant capability gap for complex, novel tasks that SLMs cannot handle, preserving their premium pricing power.