When evaluating AI companies, focus on customer love (gross retention) and efficient acquisition over gross margins. High margins are less critical initially, as the 99%+ decline in model input costs suggests a clear path to future profitability if the core product is sticky.

In this massive wealth-unlocking era of AI, worrying about moats or defensibility in the near term is a mistake. Founders and investors should reject zero-sum thinking and instead focus on identifying what is strategically important in the new world being created, as value is currently accruing across the entire stack.

Unlike in traditional SaaS, low gross margins in an AI company can be a positive indicator. They often reflect high inference costs, which directly correlates with strong user engagement with core AI features. High margins might suggest the AI is not the main product driver.

Redpoint Ventures' Erica Brescia states the current investment thesis for AI application-layer companies: disregard margins entirely for now. The focus should be on aggressive growth, raising capital, and building a brand to be seen as the category winner, even if the product is still early and unprofitable. This is a "play to win" strategy.

Established metrics for evaluating software (high gross margins, capital-light) are obsolete in the AI paradigm. Top AI companies often exhibit opposite traits, like low margins due to inference costs, signaling the "death of spreadsheet investing."

During major technology shifts like the move to cloud or AI, the best companies (e.g., hyperscalers, Snowflake) often have terrible early margins. In AI, inference costs are falling so rapidly that a company's margin profile can improve dramatically. Judging an early AI company on SaaS-era margin expectations is a mistake.

When investing in AI, the focus should be on companies building durable, multi-purpose infrastructure or solving real-world problems with a sustainable data flywheel. This approach is superior to backing firms with impressive tech demonstrations that lack a clear, defensible business model.

The initial AI investment phase, focused on infrastructure providers, is ending. The market now demands proof of ROI from AI adoption. Companies that can translate AI into measurable improvements in productivity, margins, and free cash flow are the new leaders, shifting focus from abstract potential to tangible evidence.

Unlike SaaS, where high gross margins are key, an AI company with very high margins likely isn't seeing significant use of its core AI features. Low margins signal that customers are actively using compute-intensive products, a positive early indicator.