Traditional SaaS is obsolete. According to Tan, companies must now adopt an "agentic" approach, using AI to radically compress decision-making and development cycles from months to hours. Those that fail to embrace this new paradigm will be outcompeted.

Unlike mobile or cloud, which were sustaining innovations that enhanced existing SaaS models, AI is a disruptive force. It fundamentally challenges seat-based pricing and requires a difficult, full-stack pivot of a company's business model, culture, and organizational structure.

Traditional SaaS companies charging on a per-seat basis are highly vulnerable to disruption. Paul Bricault warns that AI-native companies can offer superior functionality at lower costs, leading to a "rip and replace" cycle that will put immense pressure on incumbent, non-AI-native software businesses.

As AI makes the software itself easier to build and replicate, the durable value of a SaaS company is no longer the code. Instead, the moat lies in the customer relationship, the proprietary data, the system of record it represents, and the deep understanding of user workflows.

In categories like customer support, where AI can handle the vast majority of queries, charging per human agent ('per seat') no longer makes sense. The business model is shifting to be outcome-based, where customers pay for the value delivered, such as per ticket resolved or per successful interaction.

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 traditional per-seat SaaS model is losing relevance. As AI allows for the completion of discrete workflows, customers expect to pay for the outcome ('do this thing for me'), not for access. This per-task model is a significant competitive advantage against legacy players.

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.

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.