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What if tracking how much AI your team uses tells you more than tracking their hours?

In this episode of KP Unpacked, KP Reddy and Nick reveal a controversial management shift happening at Zero RFI: KP monitors enterprise Claude analytics and reaches out to employees with low token usage, not high spenders. The new performance metric isn't billable hours or output volume. It's curiosity, commitment to learning, and willingness to experiment. Someone burning through credits is building, iterating, testing limits. Someone avoiding the tools is resisting change. And if the CEO isn't in the top third of token usage on their team, they're failing at leadership.

The conversation unpacks Zero RFI's first internal hackathon: seven hours, cross-functional teams pulled out of silos, non-engineers shipping production code by end of day. One team built a preventative maintenance prediction system for a business they knew nothing about. Another deployed a Slack-to-Notion content aggregation engine an hour after presenting. The philosophy? More is better until better is better. Give people space, support, and freedom to build. Then track whether they're actually using it. Nick raises the scar tissue transfer problem: how do senior execs pass decades of decision-making lessons to junior associates without endless meetings? The answer lives in skills files, transcribed Notion calls, and treating Claude as a training partner, not just a task executor.

Key questions answered:

• Should you track employee token usage as the new performance metric?
• What happens when you reach out to low token users instead of high spenders?
• How did Zero RFI's internal hackathon work, and what did people build?
• Why is $30K/month in token spend an easy ROI decision for some CEOs?
• How do you transfer decades of institutional knowledge without one-on-one mentorship?
• What's the difference between using Claude for deliverables vs. training?
• Why are skills files the solution to IP leaving the building when employees quit?
• Should seed-stage CEOs be coding alongside their CTO or delegating?
• Why did PE firms decide San Francisco proximity matters more than New York headquarters?
• How do you codify scar tissue and lessons learned into persistent company memory?
• What should CEOs do if they're in the bottom third of their team's token usage?

If you're managing a team wondering whether to limit AI spend or incentivize experimentation, trying to scale institutional knowledge beyond senior leadership, or questioning what productivity measurement looks like when timesheets become irrelevant, this episode will reframe how you think about performance in an AI-first organization.

Listen now.