AI enables a fundamental shift in business models away from selling access (per seat) or usage (per token) towards selling results. For example, customer support AI will be priced per resolved ticket. This outcome-based model will become the standard as AI's capabilities for completing specific, measurable tasks improve.

Contrary to popular belief, advertising is the smallest part of Stack Overflow's business (20% of revenue). The company's financial stability comes from its enterprise SaaS product for internal knowledge management and a burgeoning data licensing business selling its curated Q&A data to AI labs.

LM Arena, known for its public AI model rankings, generates revenue by selling custom, private evaluation services to the same AI companies it ranks. This data helps labs improve their models before public release, but raises concerns about a "pay-to-play" dynamic that could influence public leaderboard performance.

Standard SaaS pricing fails for agentic products because high usage becomes a cost center. Avoid the trap of profiting from non-use. Instead, implement a hybrid model with a fixed base and usage-based overages, or, ideally, tie pricing directly to measurable outcomes generated by the AI.

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.

Stack Overflow structures its AI data licensing deals as recurring revenue streams, not one-time payments. AI labs pay for ongoing rights to train new models on the entire cumulative dataset, ensuring the corpus's value is monetized continuously as the AI industry evolves.

To avoid the trust erosion seen in traditional search ads, Perplexity places sponsored content in the 'suggested follow-up questions' area, *after* delivering an unbiased answer. This allows for monetization without compromising the integrity of the core user experience.

Perplexity achieves profitability on its paid subscribers, countering the narrative of unsustainable AI compute costs. Critically, the cost of servicing free users is categorized as a research and development expense, as their queries are used to train and improve the system. This accounting strategy presents a clearer path to sustainable unit economics for AI services.

In the age of AI, software is shifting from a tool that assists humans to an agent that completes tasks. The pricing model should reflect this. Instead of a subscription for access (a license), charge for the value created when the AI successfully achieves a business outcome.