Yi Shi is the founder of FlashLabs, pioneering AI agent software. A computer science expert & e/acc proponent shaping transformative tech.
For the past three years, the central question in enterprise AI has been capability: Can the model do the task at all? In 2026, that question is largely settled. Frontier models write production code, parse legal contracts and run multistep agentic workflows.
The strategic question has shifted: How cheaply, how reliably and at what latency can your organization push billions of tokens through these models every week?
This matters because while training costs were the headline number through 2024, inference now dominates operational AI budgets. While inference costs have dropped 280-fold over the last couple of years, according to Deloitte, the increase in AI usage has meant they are still rising dramatically.
From my work building AI infrastructure, I repeatedly see the same handful of mistakes in managing inference costs. Here is what leaders can do differently:
