Historically, payroll has dominated corporate expenses. As AI automates knowledge work previously done by humans, a significant portion of the budget will shift. Spend on SaaS, APIs, and model usage will grow from a small percentage to a major line item, displacing traditional labor costs.

Building software traditionally required minimal capital. However, advanced AI development introduces high compute costs, with users reporting spending hundreds on a single project. This trend could re-erect financial barriers to entry in software, making it a capital-intensive endeavor similar to hardware.

Don't view AI through a cost-cutting lens. If AI makes a single software developer 10x more productive—generating $5M in value instead of $500k—the rational business decision is to hire more developers to scale that value creation, not fewer.

As AI agents reduce the number of human "seats" required to use software, vendors are accelerating their move from seat-based licenses to usage-based models. The revenue lost from fewer users is expected to be offset by higher consumption, as automated workflows interact with platforms far more intensively than human employees.

Historically, labor costs dwarfed software spending. As AI automates tasks, software budgets will balloon, turning into a primary corporate expense. This forces CFOs to scrutinize software ROI with the same rigor they once applied only to their workforce.

While AI companies are structurally lower gross margin due to cloud and LLM costs, this may be offset by significantly lower operating expenses. AI tools can make engineering, sales, and legal teams more efficient, potentially leading to a higher terminal operating margin than traditional SaaS businesses, which is what ultimately matters.

Software has long commanded premium valuations due to near-zero marginal distribution costs. AI breaks this model. The significant, variable cost of inference means expenses scale with usage, fundamentally altering software's economic profile and forcing valuations down toward those of traditional industries.

AI tools aren't just making employees more efficient; they are replacing human labor. This allows software companies to move from cheap per-seat pricing to a new model based on outcomes, like charging per support ticket resolved, capturing a much larger share of the value.

Cost savings from AI-driven productivity are not just boosting profits or going to shareholders. Companies are redirecting that capital to buy their own GPUs and TPUs, vertically integrating their tech stacks. This trend represents a major capital rotation from software and headcount into owning the underlying hardware infrastructure.