Perplexity, reportedly valued at $20B, is paying Snap—valued at half that—$400M for distribution. This inverted dynamic, where the less mature company pays for access, highlights how AI-related market caps are often detached from fundamental business performance like revenue and user base.
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A market bifurcation is underway where investors prioritize AI startups with extreme growth rates over traditional SaaS companies. This creates a "changing of the guard," forcing established SaaS players to adopt AI aggressively or risk being devalued as legacy assets, while AI-native firms command premium valuations.
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.
Snap's $400M deal with Perplexity, paid largely in stock, pioneers a new strategy for consumer platforms. They can leverage their massive user bases as a capital asset, trading distribution for significant equity stakes in capital-rich AI startups that desperately need user growth.
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.
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 current market is unique in that a handful of private AI companies like OpenAI have an outsized, direct impact on the valuations of many public companies. This makes it essential for public market investors to deeply understand private market developments to make informed decisions.
Perplexity's CEO argues that building foundational models is not necessary for success. By focusing on the end-to-end consumer experience and leveraging increasingly commoditized models, startups can build a highly valuable business without needing billions in funding for model training.
Recent acquisitions of slow-growth public SaaS companies are not just value grabs but turnaround plays. Acquirers believe these companies' distribution can be revitalized by injecting AI-native products, creating a path back to high growth and higher multiples.
Large tech firms invest in AI startups who then agree to spend that money on the investor's services. This creates a "circular" flow of cash that boosts the startup's perceived revenue and the tech giant's AI-related sales, creating questionable accounting.
The AI infrastructure boom is a potential house of cards. A single dollar of end-user revenue paid to a company like OpenAI can become $8 of "seeming revenue" as it cascades through the value chain to Microsoft, CoreWeave, and NVIDIA, supporting an unsustainable $100 of equity market value.
