You can't trust one token number across your tools

Substack · Read

• Token counts are meaningless without outcome context — the same token spend might be productive investment, learning overhead, or pure waste depending on what was accomplished.
• Guides building a dashboard that tracks token spend across Codex, Claude, and ChatGPT alongside work completed, enabling teams to classify spend into three buckets: productive, exploratory, and waste. The classification enables budget governance without cutting productive AI usage.
• Takeaway: the monitoring infrastructure for AI-as-labour requires the same outcome tracking you’d apply to any other operational cost centre.