The venture market is bifurcated, with a small group of high-profile AI companies—a 'Private Mag 7'—commanding massive valuations based on narrative strength. This elite tier operates in a different reality from the rest of the startup market, which still functions under more normative conditions.

Unlike traditional SaaS where a bootstrapped company could eventually catch up to funded rivals, the AI landscape is different. The high, ongoing cost of talent and compute means an early capital advantage becomes a permanent, widening moat, making it nearly impossible for capital-light players to compete.

OpenAI is labeling its massive $100B+ funding round a "Series C," a term typically for much smaller raises. This highlights the unprecedented capital requirements of building foundational AI models, effectively creating a new category of venture financing that dwarfs traditional funding stages and signals a new era for capital-intensive startups.

Ben Horowitz argues that AI fundamentally changes a core tenet of startups. Previously, a small, fast team had a durable advantage against incumbents. Now, competitors with massive capital for data and GPUs, like Elon Musk's xAI, can catch up almost instantly, making moats less secure.

The AI fundraising environment is fueled by investors' personal use of the products. Unlike B2B SaaS where VCs rely on customer interviews, they directly experience the value of tools like Perplexity. This firsthand intuition creates strong conviction, contributing to a highly competitive investment landscape.

Aggregate venture capital investment figures are misleading. The market is becoming bimodal: a handful of elite AI companies absorb a disproportionate share of capital, while the vast majority of other startups, including 900+ unicorns, face a tougher fundraising and exit environment.

AI companies raise subsequent rounds so quickly that little is de-risked between seed and Series B, yet valuations skyrocket. This dynamic forces large funds, which traditionally wait for traction, to compete at the earliest inception stage to secure a stake before prices become untenable for the risk involved.

Contrary to common belief, the earliest AI startups often command higher relative valuations than established growth-stage AI companies, whose revenue multiples are becoming more rational and comparable to public market comps.

The CEO of Numeral notes that in the current fundraising climate, startups must heavily feature AI in their pitch to secure investor meetings. Furthermore, landing a major AI lab as a customer has become a key signal for VCs, leading to valuation multiples as high as 100-200x revenue for some companies.