Pure value-based pricing (e.g., per seat) fails for AI products due to unpredictable token costs from power users. Vercel's SVP of Product advises a hybrid model: one metric aligned with value (like seats) and another aligned with cost (like token usage) to ensure profitability.

AI's hunger for context is making search a critical but expensive component. As illustrated by Turbo Puffer's origin, a single recommendation feature using vector embeddings can cost tens of thousands per month, forcing companies to find cheaper solutions to make AI features economically viable at scale.

Unlike traditional SaaS, achieving product-market fit in AI is not enough for survival. The high and variable costs of model inference mean that as usage grows, companies can scale directly into unprofitability. This makes developing cost-efficient infrastructure a critical moat and survival strategy, not just an optimization.

AI is creating a fork in marketing strategy. It disrupts traditional demand acquisition channels like search, making it harder and more expensive to get measurable traffic. Simultaneously, it provides powerful new tools to monetize existing demand more effectively. This forces a strategic shift from a volume-based to a value-extraction model.

Unlike high-margin SaaS, AI agents operate on thin 30-40% gross margins. This financial reality makes traditional seat-based pricing obsolete. To build a viable business, companies must create new systems to capture more revenue and manage agent costs effectively, ensuring profitability and growth from day one.

The long-term monetization model for consumer LLMs is unlikely to be paid subscriptions. Instead, the market will probably shift toward free, ad- and commerce-supported models. OpenAI's challenge is to build these complex new revenue streams before its current subscription growth inevitably slows.

Mature B2B SaaS companies, after achieving profitability, now face a new crisis: funding expensive AI agents to stay competitive. They must spend millions on inference to match venture-backed startups, creating a dilemma that could lead to their demise despite having a solid underlying business.

Beyond upfront pricing, sophisticated enterprise customers now demand cost certainty for consumption-based AI. They require vendors to provide transparent cost structures and protections for when usage inevitably scales, asking, 'What does the world look like when the flywheel actually spins?'

Publishers are enthusiastic about marketplaces from AWS and Microsoft because they offer a path to usage-based revenue. This model is seen as more sustainable than the current one-off, flat-fee licensing deals with AI companies, potentially replicating the scalable monetization of digital advertising.