Most AI applications today, particularly those labeled as AI agents, often have low operational costs due to their reliance on software and LLMs, which are becoming cheaper over time.

Companies are moving away from renting AI solutions and prefer to own and control their models, seeking flexibility and the ability to implement complex systems.

The biggest concern for product managers regarding AI agents is cost, as many platforms price based on usage rather than value.

The pricing model for AI tools is shifting towards usage-based pricing, making it easier for companies to justify ROI and shorten sales cycles.

Many users are comfortable paying for multiple AI services, and while this may be sustainable in the short term, there could be a future desire for consolidation.

Many startups are focusing on selling tools for AI applications, but the reality is that many potential customers are still figuring out how to effectively use AI, leading to a disconnect between product offerings and actual market needs.

There's a lot of excitement around AI agents, but many people are unsure of their practical applications, often trying to apply them to problems that simpler solutions could address.

The pricing model for AI services is evolving; companies might pay for successful outcomes, qualified leads, or resource utilization, reflecting a shift in how value is perceived in AI-driven businesses.

Companies that sell AI solutions to enterprises can command higher budgets when they position themselves as replacements for traditional roles, like customer support or analytics teams.