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Many optimization patterns from the mainframe era are being revived in agentic AI systems. When a core resource is powerful but expensive and slow, we wrap it in caching, abstraction layers, routing, and edge computing. Today, that expensive core is LLM inference.
The familiar four moves are already visible:
Enterprise SaaS companies are reporting that AI is not disrupting their core businesses — it is dramatically expanding them.
Q1 2026 earnings calls revealed five consistent themes:
AI can fake effort. It cannot fake time.
Software features and intelligence can now be cloned in days. The enduring advantages are those accumulated slowly through real-world operation and physical reality.
Software companies spent twenty years avoiding the field. AI is forcing them back.
Enterprise AI is not plug and play. Deploying models across real data, systems, workflows, and operations requires deep understanding of how the company actually works.
Agents now read, write, and act directly on data through APIs, making the human-facing UI the weakest layer of enterprise software.
Traditional systems of record once derived defensibility from daily usage, muscle memory, undocumented workflows, and human-enforced data hygiene. Those advantages fade fast when agents become the primary users.
