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Economics of AI: Inversion of Software Economics
Historically, many SaaS businesses become cheaper as they grow, as fixed costs are spread across more users. AI products do the opposite when growth adds heavier workloads, more complex features, and more compute for each incremental customer.
Traditional SaaS
New AI Business Models
Each step down reflects product expansion into a more expensive operating mode: larger context, more calls, retries, routing, higher-cost models, or agentic workflows.
Illustrative ranges, not benchmarked targets.
- SaaS is built on spreading fixed costs, AI scales by stacking variable costs
In many software businesses, each additional user can be served at relatively low marginal cost. In AI, each new user generates incremental compute spend, so revenue and costs grow together. - More usage can hurt unit economics when compute grows faster than revenue
Feature expansion, coverage, and reliability add context, calls, retries, routing, or higher-cost models. The result is higher cost per user. - Early margin stability is a "false positive"
Early efficiency is misleading. The margin break happens after initial success, when growth forces a more expensive operating mode.
Growth is no longer a default path to profitability. In AI products, it exposes the variable costs hidden in deeper features, harder workloads, and reliability requirements.