According to @godofprompt, enterprise software is undergoing a transformative shift driven by agentic AI adoption, with projections showing an increase from 5% to 40% penetration by end of 2026 (source: @godofprompt, Nov 17, 2025). This 8x growth, analyzed through the lens of game theory, highlights an industrial-scale prisoner's dilemma where software vendors must choose between maintaining traditional pricing models or defecting to AI-powered, outcome-based pricing. Early movers like Intercom and Salesforce, who abandoned per-seat pricing in favor of per-outcome or conversation-based models, have seen significant adoption and revenue growth, while laggards face margin erosion and higher churn. Stackelberg competition theory underscores the importance of credible commitment and proprietary data moats for sustaining first-mover advantage in a market expected to reach $450 billion by 2035. The rapid commoditization of AI models, platform-agnostic protocols like MCP, and the collapse of user interface advantages mean that only companies with unique data, regulatory barriers, or ecosystem control can avoid the race to the bottom. Over 83% of executives are increasing budgets for agentic AI, anticipating massive labor and software cost replacement, but 40% of projects are expected to fail by 2027 due to unclear value and risk controls. The market is fragmenting into three equilibria: winners with data moats and outcome pricing, survivors on hybrid models, and casualties stuck on legacy pricing. The analysis concludes that the Nash equilibrium of mutual defection is inevitable unless companies change the strategic game they're playing, emphasizing the urgent need for AI-first workflows and MCP compatibility to capture market share (source: @godofprompt, Nov 17, 2025).