Glyman argues that attempting to minimize raw token pricing is pointless without measuring specific business output. Demonstrating true return on intelligence requires directly integrating model consumption data into general ledgers to see whether increased AI spend directly generates faster revenue growth and higher sales velocity.

Top engineers are already spending over $100k annually on AI tokens. Clay Bavor predicts this will become standard, with CFOs allocating token budgets alongside salaries. He estimates this could reach 20% of a developer's total compensation, a far cry from current single-digit percentages.

True ROI of AI isn't found in usage metrics like token counts. It's measured by identifying entire, expensive projects (e.g., a $4M manual document conversion) and using AI to make the problem 'vanish,' completing the work in hours instead of months.

An anecdote about an engineer spending $100M in a month on AI tokens reveals a core enterprise issue. For Lenovo's CFO, the problem isn't the amount but its lack of planning and clear ROI. This signals a shift from predictable software subscriptions to volatile, usage-based AI compute costs.

The shift to AI-driven development introduces a wildly unpredictable cost: token consumption. This expense could range from a minor line item to exceeding the entire engineering payroll, creating an unprecedented budgeting challenge for CFOs and threatening companies' profitability if not managed correctly.

Heavy users of AI development tools are skeptical of the astronomical token spending figures reported by some companies. This suggests many are either using the technology inefficiently or exaggerating their usage, raising questions about the true cost of AI development.

A forward-looking business metric is emerging where capital allocation shifts from human labor to AI agent labor, measured in 'token spend.' Some tech-forward companies already have token budgets 20-50% higher than their human payrolls, signaling a fundamental change in how businesses will operate and measure productivity.

Marketing leaders should treat unpredictable AI token costs like a headcount expense rather than a fixed software cost. This mental model better reflects its variable nature, where spend is tied to work output, similar to an employee or contractor.

Giving teams a 'token budget' is flawed because it incentivizes generating low-value output to hit a quota, similar to bad hiring quotas. Instead, companies must tie token consumption directly to business KPIs. This reframes AI spend as a value-creating investment, not a cost to be managed.