Current AI Pricing and Its Limits

Most companies are stuck in two traditional models:

• Usage-based: Charged per token or API call
• Seat-based: Charged per user or month

Neither directly connects to the business value AI actually creates.

"We're still pricing AI like traditional SaaS when we should be pricing on outcomes."

Outcome-Based Pricing

Instead of paying per token, pay per resolved support ticket ($1/ticket) or per converted qualified lead.

"What if you paid $1 per resolved customer support ticket? Or only for leads that actually converted?"

This model charges only for actual business contribution.

A Conceptual Challenge, Not Technical

"We're building AI that makes humans 2% more productive. But we should be building AI that replaces entire workflow chains."

AI Data Flywheel

Every AI application generates valuable data, which feeds better models. The most valuable dataset will ultimately be how humans interact with AI.

Who Wins: Value Chain Owners

"The most important lesson: companies that own the full value chain will win. Not because they have better models, but because they can price on outcomes."

Example: Instead of per-minute voice AI billing, take a percentage of revenue generated from bookings the AI made.

The Paradigm Shift

"The future belongs to those who can shift from 'what does this cost to run?' to 'how much value does this create?'"

Key Terms: AI Application Pricing, SaaS, Usage-Based / Seat-Based, Outcome-Based Pricing, Value Chain, Data Flywheel, Business Value Creation