Despite clear ROI, Glean's founder argues current AI costs are "absurdly expensive," citing a single internal engineering triage agent that cost one million dollars per month. He believes this is a historical anomaly and predicts that competition and open source will force inference prices to drop by orders of magnitude.

As AI agents become more sophisticated, they will autonomously seek out and use the cheapest decentralized services for tasks like storage and processing. This creates a relentless, 24/7 market pressure that will continuously drive down the fundamental costs of computing for everyone.

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.

The era of using the most powerful AI model for every task is ending. Companies are now focused on the trade-off between quality, cost, and latency. The key question is no longer "Which model is best?" but "Which model is good enough for this task at the lowest price point?"

Current AI services are heavily subsidized. Founders must realize that if the AI funding bubble ends before the underlying cost of compute drops significantly, API prices could skyrocket to cover their true cost. This race will determine the future unit economics of AI-powered features.

The common practice of model distillation suggests that AI capabilities will eventually be commoditized. As smaller models can cheaply mimic larger ones, differentiation will shift away from raw performance to product integration and price, likely triggering a massive price war among providers.

The amount of compute spent on web search for an AI agent should be proportional to the cost of the LLM it's feeding. Expensive models warrant more pre-processing on the search side to optimize their costly context windows, while cheaper models do not.

Today's high AI inference costs feel prohibitive for consumer apps. However, this is a short-term challenge. The market is heading towards an intersection where dramatically cheaper models meet a consumer base increasingly willing to pay for valuable digital services, enabling sustainable business models.

The cost for a given level of AI capability has decreased by a factor of 100 in just one year. This radical deflation in the price of intelligence requires a complete rethinking of business models and future strategies, as intelligence becomes an abundant, cheap commodity.