The key to explosive AI revenue growth is shifting from per-seat SaaS models to monetizing inference. This "inference waterfall" creates a usage-based revenue stream that removes growth ceilings, enabling companies to scale at unprecedented rates by capturing value directly tied to AI consumption.

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.

Traditional SaaS models benefited from near-zero costs for new users. AI's high computational and token costs upend this, creating deeply unprofitable users and workflows unless firms carefully manage implementation and pricing.

SaaS pricing has always been determined by the value it delivers to customers, not its cost to build. While AI makes development cheaper and faster, it doesn't fundamentally change the value a product provides. Therefore, companies that solve important problems will maintain their pricing power and high margins.

AI is making core software functionality nearly free, creating an existential crisis for traditional SaaS companies. The old model of 90%+ gross margins is disappearing. The future will be dominated by a few large AI players with lower margins, alongside a strategic shift towards monetizing high-value services.

The dominant per-user-per-month SaaS business model is becoming obsolete for AI-native companies. The new standard is consumption or outcome-based pricing. Customers will pay for the specific task an AI completes or the value it generates, not for a seat license, fundamentally changing how software is sold.

The current model where users worry about the dollar cost of each AI-powered action is a temporary phase driven by high model costs. Descript's CEO believes the industry is moving toward outcome-based pricing, like charging per successful export, which better aligns value with cost.

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.

Drawing a parallel to AWS's history, AI inference costs are expected to continuously decrease over time. As usage skyrockets, providers will be incentivized to lower prices to capture market share, making fears of escalating costs for startups unlikely to materialize.