为什么 AI Agents 按结果定价这么难？

Core Viewpoint - The concept of performance-based pricing for AI Agents is currently unattainable due to the lack of necessary technological, organizational, and cultural infrastructure [10][11][12]. Group 1: Attribution Challenges - Attribution of success in AI projects is complex, as multiple variables and human collaboration make it difficult to determine who deserves credit for outcomes [16]. - Establishing an attribution system requires advanced capabilities, including autonomous coding by AI Agents and well-defined product requirements [17][18]. - Tracking the value created by AI over time poses significant challenges, as existing infrastructure is inadequate for maintaining causal links [19]. Group 2: Measurement Feasibility - Even if attribution issues are resolved, measuring outcomes remains fundamentally challenging due to time delays in realizing benefits [20]. - Many valuable outcomes are subjective and difficult to quantify, leading to a focus on easily measurable but less significant results [21]. - Introducing performance-based pricing can alter team behavior, potentially leading to dysfunction similar to issues seen with KPIs and OKRs [22]. Group 3: Trust Deficit - Performance-based pricing necessitates unprecedented trust between suppliers and users, requiring transparency in sensitive business metrics [23]. - Suppliers need access to client systems for verification of claimed outcomes, raising significant security and privacy concerns [24]. - Disputes over outcomes and attribution lack a legal framework for resolution, complicating the implementation of performance-based agreements [25]. Group 4: Organizational Resistance - Most organizations are structurally unprepared for performance-based pricing due to procurement resistance and existing accounting practices [28][29]. - Financial teams may resist paying more for suppliers who create additional value, reflecting a zero-sum mindset deeply embedded in corporate culture [29]. Group 5: Market Structure Issues - The current AI market structure, dominated by a few suppliers, makes personalized performance agreements impractical [30]. - Standardizing outcome-based pricing across various use cases and industries is unfeasible, leading suppliers to default to usage-based pricing [32]. Group 6: Path Forward - A mixed pricing model that gradually incorporates performance elements is seen as a more realistic approach, despite its inherent complexities [36]. - Initial steps include starting with measurable agent metrics and building trust through data transparency [37][38]. - Over time, the proportion of performance-based pricing could increase as attribution systems mature, but this transition will require significant effort and investment [39]. Group 7: Key Insights - The vision for performance-based pricing in AI is valid, as it aligns incentives and fosters genuine value creation, but the path to realization is longer and more complex than anticipated [41].