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.

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."

The startup landscape now operates under two different sets of rules. Non-AI companies face intense scrutiny on traditional business fundamentals like profitability. In contrast, AI companies exist in a parallel reality of 'irrational exuberance,' where compelling narratives justify sky-high valuations.

The current wave of AI companies is growing at unprecedented rates, far outpacing the growth curves of the mobile, social, or SaaS eras. They are becoming larger and more consequential much faster, a phenomenon described as "speed running the process of company growth."

For a proven, hyper-growth AI company, traditional business risks (market, operational, tech) are minimal. The sole risk for a late-stage investor is overpaying for several years of future growth that may decelerate faster than anticipated.

Unlike traditional SaaS, achieving product-market fit in AI doesn't guarantee a viable business. The high cost of goods sold (COGS) from model inference can exceed revenue, causing companies to lose more money as they scale. This forces a focus on economical model deployment from day one.

Software has long commanded premium valuations due to near-zero marginal distribution costs. AI breaks this model. The significant, variable cost of inference means expenses scale with usage, fundamentally altering software's economic profile and forcing valuations down toward those of traditional industries.

The current AI investment climate feels as 'risk-free' as the 2021 bubble. Venture firms are likely using flawed loss-ratio models, underestimating how many AI 'unicorns' will fail to generate returns, just as they did with the B2B SaaS unicorns from the previous cycle.

Despite a $380 billion valuation, Anthropic's CEO admits that a single year of overinvesting in compute could lead to bankruptcy. This capital-intensive fragility is a significant, underpriced risk not present in traditional software giants at a similar scale.