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.

Many buyers of AI solutions are sophisticated and understand that the underlying technology is often simple, leading them to question the true costs of running these systems.

Pricing strategies for AI agents can vary; if the service is used by humans, it may follow a per-user pricing model, while machine-to-machine services might adopt usage-based pricing.

Blending usage-based pricing with subscription pricing can provide predictability while allowing for growth, especially for AI startups.

Many potential AI projects exist, but outside of consumer software, few have the scale to apply a single piece of software to a billion users, making it challenging to generate similar financial value.

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.

I think the world needs more AI infrastructure, and as we improve our technology and reduce costs, demand for AI will increase significantly.

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

As AI technology evolves, the availability of powerful models may increase, potentially making them accessible for free in the future.