Established SaaS firms avoid AI-native products because they operate at lower gross margins (e.g., 40%) compared to traditional software (80%+). This parallels brick-and-mortar retail's fatal hesitation with e-commerce, creating an opportunity for AI-native startups to capture the market by embracing different unit economics.

The compute-heavy nature of AI makes traditional 80%+ SaaS gross margins impossible. Companies should embrace lower margins as proof of user adoption and value delivery. This strategy mirrors the successful on-premise to cloud transition, which ultimately drove massive growth for companies like Microsoft.

AI companies can accelerate enterprise adoption by focusing on workflows already outsourced to BPOs. This provides pre-codified standard operating procedures (SOPs), existing QA processes, and simpler change management, as replacing a vendor is easier than displacing an internal team.

VCs have traditionally ignored the massive $16T services sector due to its low margins. AI automation can fundamentally change this by eliminating repetitive tasks, allowing these companies to achieve margin profiles similar to software businesses, thus making the sector newly viable for venture investment.

Unlike high-margin SaaS, AI agents operate on thin 30-40% gross margins. This financial reality makes traditional seat-based pricing obsolete. To build a viable business, companies must create new systems to capture more revenue and manage agent costs effectively, ensuring profitability and growth from day one.

OpenAI Chair Bret Taylor argues that the biggest hurdle for established software companies isn't adopting AI technology, but disrupting their own business models. Moving from per-seat licenses to the outcome-based pricing that agents enable is a more profound and difficult challenge.

Traditional SaaS metrics like 80%+ gross margins are misleading for AI companies. High inference costs lower margins, but if the absolute gross profit per customer is multiples higher than a SaaS equivalent, it's a superior business. The focus should shift from margin percentages to absolute gross profit dollars and multiples.

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.

While AI can reduce labor costs, the most powerful value proposition is generating significantly more revenue. The AI company Salient found success not by pitching savings on call center staff, but by proving its AI could increase debt collection rates by 50%—a far more compelling outcome for clients.