The "token maxing" phenomenon is reshaping how organizations think about AI budgets, but most companies are asking the wrong questions about AI spending.

In this episode, Kevin and Eli explore the reality behind engineers burning through massive token budgets - sometimes exceeding their own salaries - and what it means for practical AI adoption in mid-market companies.

From Stockholm engineers outspending their paychecks on Claude to Jensen Huang's $250K token requirements, we break down why most organizations need output-focused spending strategies, not ego-driven token consumption.

Key topics covered:

✅ The token maxing phenomenon and what's driving it

✅ Why most mid-market companies don't need massive AI budgets

✅ The difference between productive AI spending and token burning

✅ How to build sustainable AI strategies that survive subsidy endings

✅ Real-world examples of agents running amok overnight

✅ Microsoft's new agentic capabilities in Office suite

✅ Platform comparison: OpenAI vs Anthropic vs Google for different use cases

TIMESTAMPS:

00:00 — Intro and token maxing overview

02:30 — What token maxing actually means

05:45 — Jensen Huang's $250K token requirement