While AI expands software's capabilities, vendors may not capture the value. Companies could use AI to build solutions in-house more cheaply. Furthermore, traditional "per-seat" pricing models are undermined when AI reduces the number of employees required, potentially shrinking revenue even as the software delivers more value.

The future of per-seat SaaS pricing is precarious. AI-driven productivity could shrink the number of knowledge workers, while LLMs can give casual users system access without a full license, eroding the user base from the periphery.

The biggest threat to incumbent software companies isn't a new feature, but a business model shift. AI enables outcome-based pricing, which massively favors agile newcomers as incumbents struggle to adapt their entire commercial structure away from seat-based subscriptions.

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 fundamental business model of many SaaS companies is based on per-user pricing. AI agents pose an existential threat to this model by enabling smaller teams to achieve the same output as larger ones. As companies wonder why they should pay for 100 seats when 10 people can do the work, the entire economic foundation of the SaaS industry faces a crisis.

AI is moving beyond enhancing worker productivity to completing entire projects, like drug discovery or engineering designs. This shift means software will be priced like a services business, based on the value of the outcome delivered, not the number of users with access.

The next major business model shift in software is from seat-based pricing to outcome-based pricing (e.g., paying per task completed). This favors AI-native newcomers, as incumbents will struggle to adapt their GTM and financial models.

Sierra CEO Bret Taylor argues that transitioning from per-seat software licensing to value-based AI agents is a business model disruption, not just a technological one. Public companies struggle to navigate this shift as it creates a 'trough of despair' in quarterly earnings, threatening their core revenue before the new model matures.

As AI agents perform more work and human headcount decreases, the traditional seat-based pricing model becomes obsolete. The value is no longer tied to human users. SaaS companies must transition to consumption-based models that charge for the automated work performed and value generated by AI.