As search engine incumbents — namely Google — amp up their platforms with GenAI tech, startups are looking to reinvent AI-powered search from the ground up. It might seem like a Sisyphean task, going up against competitors with billions upon billions of users. But this new breed of search upstarts believes it can carve out a niche, however small, by delivering a superior experience.

One among the cohort, Perplexity AI, this morning announced that it raised $73.6 million in a funding round led by IVP with additional investments from NEA, Databricks Ventures, former Twitter VP Elad Gil, Shopify CEO Tobi Lutke, ex-GitHub CEO Nat Friedman and Vercel founder Guillermo Rauch. Other participants in the round included Nvidia and — notably — Jeff Bezos. 

Sources familiar with the matter tell TechCrunch that the round values Perplexity at $520 million post-money. That’s chump change in the realm of GenAI startups. But, considering that Perplexity’s only been around since August 2022, it’s a nonetheless impressive climb.

Perplexity was founded by Aravind Srinivas, Denis Yarats, Johnny Ho and Andy Konwinski — engineers with backgrounds in AI, distributed systems, search engines and databases. Srinivas, Perplexity’s CEO, previously worked at OpenAI, where he researched language and GenAI models along the lines of Stable Diffusion and DALL-E 3.

Unlike traditional search engines, Perplexity offers a chatbot-like interface that allows users to ask questions in natural language (e.g. “Do we burn calories while sleeping?,” “What’s the least visited country?,” and so on). The platform’s AI responds with a summary containing source citations (mostly websites and articles), at which point users can ask follow-up questions to dive deeper into a particular subject.

“With Perplexity, users can get instant … answers to any question with full sources and citations included,” Srinivas said. “Perplexity is for anyone and everyone who uses technology to search for information.”

Underpinning the Perplexity platform is an array of GenAI models developed in-house and by third parties. Subscribers to Perplexity’s Pro plan ($20 per month) can switch models — Google’s Gemini, Mistra 7Bl, Anthropic’s Claude 2.1 and OpenAI’s GPT-4 are in the rotation presently — and unlock features like image generation; unlimited use of Perplexity’s Copilot, which considers personal preferences during searches; and file uploads, which allows users to upload documents including images and have models analyze the docs to formulate answers about them (e.g. “Summarize pages 2 and 4”).

If the experience sounds comparable to Google’s Bard, Microsoft’s Copilot and ChatGPT, you’re not wrong. Even Perplexity’s chat-forward UI is reminiscent of today’s most popular GenAI tools.

Beyond the obvious competitors, the search engine startup You.com offers similar AI-powered summarizing and source-citing tools, powered optionally by GPT-4.

Srinivas makes the case that Perplexity offers more robust search filtering and discovery options than most, for example letting users limit searches to academic papers or browse trending search topics submitted by other users on the platform. I’m not convinced that they’re so differentiated that they couldn’t be replicated — or haven’t already been replicated for that matter. But Perplexity has ambitions beyond search. It’s beginning to serve its own GenAI models, which leverage Perplexity’s search index and the public web for ostensibly improved performance, through an API available to Pro customers.

This reporter is skeptical about the longevity of GenAI search tools for a number of reasons, not least of which AI models are costly to run. At one point, OpenAI was spending approximately $700,000 per day to keep up with the demand for ChatGPT. Microsoft is reportedly losing an average of $20 per user per month on its AI code generator, meanwhile.

Sources familiar with the matter tell TechCrunch Perplexity’s annual recurring revenue is between $5 million and $10 million at the moment. That seems fairly healthy… until you factor in the millions of dollars it often costs to train GenAI models like Perplexity’s own.

Concerns around misuse and misinformation inevitably crop up around GenAI search tools like Perplexity, as well — as they well should. AI isn’t the best summarizer after all, sometimes missing key details, misconstruing and exaggerating language or otherwise inventing facts very authoritatively. And it’s prone to spewing bias and toxicity — as Perplexity’s own models recently demonstrated.

Yet another potential speed bump on Perplexity’s road to success is copyright. GenAI models “learn” from examples to craft essays, code, emails, articles and more, and many vendors — including Perplexity, presumably — scrape the web for millions to billions of these examples to add to their training datasets. Vendors argue fair use doctrine provides a blanket protection for their web-scraping practices, but artists, authors and other copyright holders disagree — and have filed lawsuits seeking compensation.

As a tangentially related aside, while an increasing number of GenAI vendors offer policies protecting customers from IP claims against them, Perplexity does not. According to the company’s terms of service, customers agree to “hold harmless” Perplexity from claims, damages and liabilities arising from the use of its services — meaning Perplexity’s off the hook where it concerns legal fees.

Some plaintiffs, like The New York Times, have argued GenAI search experiences siphon off publishers’ content, readers and ad revenue through anticompetitive means. “Anticompetitive” or no, the tech is certainly impacting traffic. A model from The Atlantic found that if a search engine like Google were to integrate AI into search, it’d answer a user’s query 75% of the time without requiring a click-through to its website. (Some vendors, such as OpenAI, have inked deals with certain news publishers, but most — including Perplexity — haven’t.)

Srinivas pitches this as a feature — not a bug.

“[With Perplexity, there’s] no need to click on different links, compare answers or endlessly dig for information,” he said. “The era of sifting through SEO spam, sponsored links and multiple sources will be replaced by a more efficient model of knowledge acquisition and sharing, propelling society into a new era of accelerated learning and research.”

The many uncertainties around Perplexity’s business model — and GenAI and consumer search at large — don’t appear to be deterring its investors. To date, the startup, which claims to have 10 million active monthly users, has raised over $100 million — much is which is being put toward expanding its 39-person team and building new product functionality, Srinivas says.

“Perplexity is intensely building a product capable of bringing the power of AI to billions,” Cack Wilhelm, a general partner at IVP, added via email. “Aravind possesses the unique ability to uphold a grand, long-term vision while shipping product relentlessly, requirements to tackle a problem as important and fundamental as search.”