While AI makes product development cheaper, the most promising AI startups raise more capital, not less. This is driven by high ongoing costs from using the latest models and investors' desire to pour capital into potential category winners to secure market dominance quickly.

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 application-layer companies are knowingly accepting negative gross margins by reselling expensive model inference. Their strategy is to first lock in users with a superior UX, then solve the cost problem later through vertical integration or cheaper models.

Unprofitable AI models mirror Uber's early strategy. By subsidizing services, they integrate into workflows and create dependency. Once users rely on the tool (e.g., a law firm replacing an associate), prices can be increased dramatically to reflect the massive value created, ultimately achieving profitability.

Unlike traditional software's zero marginal costs, AI-powered apps incur significant inference expenses that scale with users. One founder estimated needing $25M just for 100k monthly actives, challenging the classic VC model for consumer startups.

AI companies operate under the assumption that LLM prices will trend towards zero. This strategic bet means they intentionally de-prioritize heavy investment in cost optimization today, focusing instead on capturing the market and building features, confident that future, cheaper models will solve their margin problems for them.

Current unprofitability in some AI applications, like subsidizing tokens for coding, is a deliberate strategy. Similar to Uber's early city-by-city expansion, AI labs are subsidizing usage to rapidly gain market share, gather data, and build a powerful flywheel effect that will serve as a long-term competitive moat.

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.

AI companies like OpenAI are losing money on their popular subscription plans. The computational cost (inference) to serve a user, especially a power user, often exceeds the subscription fee. This subsidized model is propped up by venture capital and is not sustainable long-term.