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The article describes a fundamental shift in AI from chatbots to autonomous AI workers that deliver outcomes, transforming hardware, software, developer roles, product management, search, and pricing models.
A practitioner shares hard-won lessons on pricing AI agents for small businesses, arguing that framing them as 'AI employees' with salary-like monthly fees works better than per-seat or cost-plus pricing, and that trust and security concerns must be addressed before price.
ServiceNow's customer chief calls 'tokenmaxxing' an AI hype cycle, criticizing the focus on token volume as a metric, while Salesforce shifts to $2 per resolved issue pricing, emphasizing outcome-based measurement.
Salesforce announced a $2 per resolved AI agent issue pricing model, defining 'resolved' as completion without human escalation or customer abandonment. This outcome-based pricing contrasts with token metering and may set a precedent for enterprise AI costs.
A tweet discussing how AI pricing should be aligned with value delivered, contrasting outcome-based and usage-based models, with insights from Zack at Sierra Platform.
The article discusses how enterprises are becoming more efficient with AI usage, leading to a shift away from token-based pricing models toward outcome-based pricing, which could break many current AI product pricing strategies.
The author argues that the real opportunity in AI is not flashy apps but automating boring, repetitive workflows in specific industries, advising founders to start by manually doing the work and building small, targeted solutions.