TL;DR:
- Traditional software purchases rely on clear, line-item financial ROI calculations.
- AI is a general purpose technology that defies project-by-project financial modeling.
- Companies must fund AI as baseline workplace infrastructure.
- Technology companies sell model-based intelligence in token units, decoupling reasoning from the human brain.
Corporate budgeting requires predictable financial returns
Standard corporate finance evaluates software through line-item return on investment. A business case calculates labor hours saved, multiplies those hours by loaded wage rates, and verifies that cost reductions exceed software licensing fees within a set payback period. This model fits narrow automation tools like document parsing or optical character recognition, where inputs and outputs are static and measurable before deployment.
General purpose technology defies line-item ROI
General purpose technologies produce emergent returns that break traditional business cases. When companies adopted the internet in the nineties, early attempts to justify web access department by department failed. The internet functioned as foundational utility infrastructure that enabled new types of work across all departments. Requiring a financial payback calculation before giving an employee a web browser or email inbox reduced organizational velocity.
Capability expansion creates unscripted enterprise value
AI models shift technical capabilities directly to non-specialists. Financial controllers query complex datasets through conversation, operations managers generate infrastructure configurations without prior syntax knowledge, and team members analyze unfamiliar domain terms live during meetings. These micro-task efficiency gains occur thousands of times daily across business functions, making them impossible to quantify on an upfront spreadsheet.
AI must be funded as baseline workplace infrastructure
Enterprises that gate intelligence tools behind project-level ROI create a competitive disadvantage. AI requires funding as per-seat utility infrastructure alongside laptops, electricity, and network access. Narrow point-solutions remain subject to traditional financial scrutiny. Withholding intelligence tools from knowledge workers to save license fees sacrifices organizational bandwidth for short-term budget targets.
Where we are headed: raw intelligence is becoming a traded commodity
This development leads to the formation of an intelligence market. Technology companies sell intelligence as a commodity priced in token units across different performance tiers. This intelligence depends on AI models running inside large data centers, making reasoning independent of the human brain. Decoupling raw intelligence output does not replace human judgment or system design (Engineering is still hard). Corporate costs shift from payroll-bound reasoning to external compute subscriptions. As businesses absorb this utility into daily operations, economic leverage concentrates among model providers and hyperscalers who supply the underlying intelligence grid.