AI companies often struggle to define the value they generate, leading to uncertain pricing strategies as they experiment with different models.

Charging by activity and changing pricing based on workflows are effective strategies for AI companies, moving towards value-based pricing rather than cost-based pricing.

Successful startups frequently test their pricing, adjusting it as they learn more about their market.

AI companies should align their pricing with what matters to their customers after demonstrating value, rather than relying solely on fixed or consumption-based pricing models.

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.

Startups should experiment with pricing strategies, especially when they have a small customer base, as it's easier to adjust prices than to raise them later.

To succeed, AI companies need to understand their unit economics and how to price their services effectively based on the value they provide.

When launching an AI product, it's crucial to experiment extensively first, as AI results can be unpredictable and exceed expectations.

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