Selling an efficiency-focused SaaS tool is harder than ever. CIOs are cutting classic SaaS tools while expanding their AI budget. Any remaining efficiency spend is being consumed by price hikes from giants like Salesforce, leaving no room for new, non-AI vendors.

For years, flat-rate AI subscriptions heavily subsidized power users, masking the true cost of token consumption. As providers shift to usage-based billing, this subsidy is ending. Enterprises now face "sticker shock" and must justify AI spend with clear ROI, moving from rampant experimentation to cost-conscious implementation.

As more companies integrate AI, their costs are tied to variable usage (e.g., tokens, inference). This is causing a profound, economy-wide transformation away from predictable seat-based subscriptions towards more dynamic usage-based models to align costs with revenue.

Usage-based pricing for AI faces strong customer resistance. Unlike cloud storage where usage is directly controlled, AI credit consumption can be driven by new vendor-pushed features. This lack of control and predictability leads to bill shock, making customers prefer the stability of per-seat models.

While overall enterprise software spending is hitting record highs, this growth is not a rising tide for all. Half the increase is consumed by existing vendors' price hikes and 30% is allocated to new AI initiatives, leaving minimal budget for traditional SaaS tools.

Enterprise software budgets are growing, but the money is being reallocated. CIOs are forced to cut functional, "good-to-have" apps to pay for price increases from core vendors and to fund new AI tools. This means even happy customers of non-mission-critical software may churn as budgets are redirected to top priorities.

A dominant market theme is that increased enterprise spending on AI is directly reducing budgets for other areas. This "crowding out" effect is impacting traditional software, IT services, and even non-AI hardware, creating a tough environment for incumbent vendors not central to the AI stack.

The move from pre-agentic to agentic AI workloads consumes massive resources. This has ended the 'AI subsidy era,' forcing companies like Walmart and Uber to implement usage-based models and strict caps on AI spending to control runaway costs and enforce discipline.

The initial explosion in AI spending was largely additive, not a replacement for existing budgets. Going forward, this will change. Companies will start substituting AI spend for traditional SaaS licenses and human capital as they rationalize operating expenses and seek higher ROI.