How Startups Are Rethinking Value and Monetization [Testμ 2026]

At Testμ Conf 2026, Citi Ventures managing director Vibhor Rastogi argued that AI agents undermine traditional unlimited per-seat SaaS pricing because a single licensed user can deploy hundreds or thousands of agents, causing vendors’ token and infrastructure costs to grow without corresponding revenue. He expects an interim model of seat-based pricing with consumption limits, model-tier restrictions, and spending caps, while viewing outcome-based pricing as a longer-term goal complicated by measurement, attribution, data quality, and responsibility when agents fail. Rastogi emphasized that successful enterprise agent deployments require more than capable models, including proprietary context, evaluation systems, security guardrails, tools, knowledge bases, and operational harnesses, and he advised companies to retain ownership of their context, enrichment logic, harnesses, and evaluations while using external foundation models and specialized infrastructure. He identified evaluation infrastructure, AI and agent security, and knowledge graph or semantic ontology layers as promising startup categories, argued that coding agents have established deployment patterns likely to extend to other business functions, and said enterprises are adopting cautiously because of testing and risk concerns rather than lack of technical progress. He also maintained that AI value is being distributed across chips, models, applications, and infrastructure, while stressing that startups must understand token costs, align pricing with customer value rather than raw consumption, and prevent agents from receiving uncapped authority to spend money.

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