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AI-as-a-Service (AIaaS) tools should move away from traditional SaaS seat-based pricing. Pricing should be based on metrics like volume of data transcribed, tokens used, value created, or costs saved, reflecting the actual utility and impact of the AI agent. This would align pricing with the economic benefits delivered by the AI.
I've been thinking about vertical SaaS lately. From 2018–21, only 24 % of 80 software IPOs were vertical SaaS. Why? Smaller customer pools and limited value capture kept the upside capped. AI changes the math. Instead of putting clipboards in the cloud, AI does the work itself—and that rewrites three fundamentals: 1. Value | Outputs, not clicks - Pre-AI apps sped up human workflows. - AI-native apps ship the deliverable—draft the brief, reconcile the invoice, triage the patient. When software does the work, it earns a bigger share of the value created. 2. Pricing | Usage, not seats - Seat licenses mapped to headcount. - AI teammates meter documents, calls, or tasks. 3. TAM | Core industry spend, not IT budget - Old ceilings: field-service software ≈ $5.5 B, restaurant POS ≈ $12 B, construction management ≈ $10 B. - New horizon: legal services alone top $1 T. When software augments the lawyer’s, nurse’s, or analyst’s job, it taps the services budget—not just the software line item. Takeaway: Bigger value → usage-based pricing → 100× larger markets. Bonus for founders: Many of these opportunities are untapped.