The "stranded asset" in AI pricing (arnon.dk)

Recent discussions around AI pricing models reveal a growing frustration among users due to the mismatch between consumption-based AI credits and traditional seat-based licensing. This hybrid model complicates user experiences by creating siloed entitlements, where organizations purchase large pools of AI credits that some users can’t access. As a result, many credits become "stranded assets," unused by casual users while heavy users hit their limits, leading to dissatisfaction and ineffective spending. This issue is significant for the AI/ML community as it highlights the urgent need for a rethink in billing strategies. Organizations are eager to leverage AI, but current pricing structures may stifle adoption and lead to churn as users recognize they are paying for unutilized credits. Experts suggest adopting a "Shared Corporate Wallet" to pool entitlements, ensuring that all credits are available to a team collectively rather than being tied to individual user accounts. As AI integration into business processes escalates, vendors must adapt their pricing to align better with customer needs or risk losing clients to more user-friendly models.