Inside Perplexity AI’s Unicorn Journey: From AI Search Startup to $20B+ Answer Engine

Published: November 16, 2024
Last updated: July 8, 2026

From a roughly $500 million valuation in early 2024 to $9 billion by the end of that year – and now into the $20B range – Perplexity AI has become one of the most visible companies in the race to reinvent search. The next milestone is already being discussed: CEO Aravind Srinivas said Perplexity is planning for a 2028 IPO, regardless of what happens with OpenAI or Anthropic listings.

Today, in our GenAI Unicorn series, we explore Perplexity AI, an AI-powered answer engine that uses artificial intelligence, large language models, real-time web retrieval, and citations to give users direct answers instead of a page of links. The company’s AI-driven approach made it one of the most exciting challengers to Google. It also placed Perplexity in the middle of a growing fight over intellectual property, publisher rights, copyright, and the future economics of the open web.

That tension is no longer theoretical. In 2024, The New York Times sent Perplexity a cease-and-desist notice demanding that the company stop using Times content for generative AI purposes. In 2025, The New York Times sued Perplexity, alleging that the company copied, distributed, and displayed millions of Times articles without permission to power its generative AI products. Perplexity has argued that it is not training foundation models on publisher content, but indexing web pages and surfacing factual citations. The courts, naturally, get the fun part.
Perplexity AI’s journey is remarkable. In 2022, it secured initial funding through cold emails – before even having a product. Starting as a Twitter-based search tool, it soon pivoted to challenging the very foundation of online search. Since then, it has expanded into Pro search, enterprise, finance, shopping, APIs, and AI agents. In 2026, it is no longer just a fast-growing AI search startup. It is a company trying to become the answer layer of the internet.

How does it manage to thrive among controversies? Can it truly challenge Google? What happens as OpenAI, Google, and every browser suddenly want to own AI search too? And can Perplexity turn a beloved product into a durable public company by 2028? Let’s dive into the details and uncover the trends shaping this unique story. It’s a long read.

In today’s episode:

Aravind Srinivas – the co-founder and CEO of Perplexity – initially wanted to study Computer Science at Indian Institute of Technology (IIT) Madras but was admitted to Electrical Engineering. That didn’t stop his interest in algorithms programming.

“A friend mentioned a machine learning contest to me. At the time, I didn't even know what ML was. It turned out to be fun, and I ended up winning the contest without spending too much time on it – it just came naturally. That’s when I decided to dive deeper into it,” tells Aravind Srinivas his story.

The same 2017 when he earned his a Bachelor's and Master's degree in Electrical Engineering from IIT Madras, he went to Berkeley, to work on his Ph.D. in Computer Science. But the most brilliant part was his strategic internship.

Internship: 2018, May-Aug – OpenAI, Research on Policy Gradient Algorithms

“I came to Berkeley thinking I was definitely one of the top AI PhD students. Then I joined OpenAI, and it hit me hard – everyone was so much better than me. It was a big reality check.”

That summer of 2018, OpenAI published its first GPT (Generative Pre-trained Transformer) model.

"We realized there was a new way of learning – using all the internet data to learn from it – and I felt that was going to be crucial. I told my advisor in Berkeley, ‘This is the right direction; we should pursue it.’ Surprisingly, he was open-minded and said, ‘Alright, I’m not a specialist in this, but let’s give it a try.’ So we spent a lot of time – holidays, weekends – just learning, coding, and understanding everything we could. We did this for two years, which eventually led me to a new research focus: combining generative AI and reinforcement learning. This approach powers technologies like ChatGPT, which doesn't just predict the next word but ensures it knows how to communicate effectively with humans."

Internship: 2019, May-Sept – DeepMind, Large-Scale Contrastive Learning: CPCv2

In 2020, Aravind Srinivas met Denis Yarats over email after independently publishing similar research papers on AI training methods, each two days apart – Srinivas at UC Berkeley and Yarats at NYU. This shared academic interest sparked an ongoing dialogue between them on AI advancements.

Internship: 2020-2021, May-April – Google, Transformers for Vision: Bottleneck Transformers, HaloNet. SoTA Vision Models: Copy-Paste Augmentation, ResNet-RS.

While at Google, Srinivas started thinking: “How could we innovate in search with Google so dominant?” At the moment, he was also reading the book “In the Plex: How Google Thinks, Works, and Shapes Our Lives”, and was very inspired by that. Transformers seemed to hold massive potential for a breakthrough in search. Aravind even reached out to Ashish Vaswani, one of the creators of transformers at Google, saying, “I want to work on this with you – it’s the next big thing.”

Unfortunately, the timing wasn’t right. So 2021, he joined OpenAI as a Research Scientist, focusing on language and diffusion generative models. Then GitHub Copilot – a tool enabling programmers to complete code as they write – achieved real adoption and profitability.

The timing was finally right to bring generative technology to market. In 2022, Aravind Srinivas, then at OpenAI, and Denis Yarats, at Meta AI, teamed up with Andy Konwinski, a co-founder of Databricks, and Johnny Ho, a former Quora engineer and Wall Street quant trader, to develop… just something new.

In August 2022, Perplexity was born. The four visionary founders had a mix of ideas about search, inspired by Google, Copilot moment, academic rigor with correct citation, transformer capabilities, manifested in brewing ChatGPT that Srinivas, working at OpenAI, most likely knew about.

“I thought the startup moment had arrived where you could actually translate it to products.”

Yet, they had no clear idea or product.

Nonetheless, Aravind reached out via cold emails to a few investors, like Nat Friedman and Elad Gil. Within a month of its inception, in September, with a strong belief in the transformative power of transformers (remember, this was before ChatGPT), they raised their seed round: $3.1 million. And the work began.

Their initial idea was to create a tool that translated natural language into SQL queries to interface with databases, initially focused on public Twitter data. The founding team built a demo that allowed users to ask questions about structured data, like querying Elon Musk’s follower metrics. It was called Bird SQL, text-to-SQL solution, and they’ve been working on it for at least four months.

Both Elad Gil and Nat Friedman were impressed by the speed at which the team iterated on ideas and delivered. “You’ll mention an idea to them, and then a day later, they’ll have something built,” said Elad Gil. “They were sending me demos twice a day,” added Nat Friedman.

And then, November 30 happened, ChatGPT was unleashed onto the world.

“I remember that day very clearly,” says Dennis Yarats. “I had just woken up and saw a lot happening on Twitter. We quickly recognized that this was groundbreaking – it wasn’t something that would just come and go. We immediately began considering what we had: a prototype addressing early feedback that ChatGPT was receiving, like concerns about hallucinations and lack of source transparency.

We decided to act on the opportunity. Within two days, we put together a simple website as a joke and posted it on Twitter, not expecting much attention. But to our surprise, it gained traction quickly, with people retweeting and praising it, despite its limitations – it was slow and didn’t work perfectly,”

Aravind: “It happened on December 7. It was a unique launch – we didn’t have a sign-up process or a waitlist, just a simple search bar. Michael Dell even sent me a LinkedIn message congratulating us. That was a huge moment for me.”

The holiday season approaching, and they were hesitant to continue, thinking interest might fade. But in January, the traffic hadn’t dropped – instead, it had grown. That was a clear sign they had something valuable. So, they unanimously decided to pause text-to-SQL and fully focus on general search, setting aside months of work – a choice that proved right.

“Every sentence that it says also has corresponding reference, or we call it a citation. This is all coming from our academic background. Like my co founder, Dennis, and I are PhDs, we figured that we would use this principle that everything in a paper that you write in academia, you have to back it up with reference from some other paper. And that's how Perplexity works,” explains Srinivas.

Soon they realized that user may start with curiosity without a clear idea of what they want. Rather than expecting them to be expert prompt engineers, how AI can assist users in refining their inquiries? The responsibility, the founders argued, lies with the AI to guide users toward effective prompts. To address this, they developed a "copilot" feature on their site, designed to ask clarifying questions as users type, helping to expand and refine the prompt interactively – similar to conversing with a friend while making decisions. That also makes communication more “human-like”. That’s how they came up with their proprietary intent-recognition algorithm. They call it Search Companion and it’s available for their Premium subscribers.

Citations are a cornerstone of Perplexity's strategy to build trust and transparency in AI-generated responses. By attributing information to reliable sources, Perplexity empowers users to validate content and make informed decisions. This focus on accountability sets it apart from traditional language models, which often lack the ability to provide verifiable references.

Beyond RAG and source citation, Perplexity's flexibility with multiple language models enhances its reliability, allowing users to cross-check outputs and identify potential hallucinations. Specialized searches in platforms like Reddit or Wolfram Alpha further refine its ability to address niche queries with precision.

Despite its advancements, Perplexity acknowledges the need for continuous refinement. The team actively works to address challenges such as outdated or inaccurate sources and the risk of “second-hand hallucinations” (occur when AI generates responses based on incorrect or hallucinated information from another AI model or source.)

Perplexity AI has faced significant legal and ethical challenges over content usage. The New York Times and News Corp accused the company of copyright violations, with lawsuits alleging unauthorized data scraping and verbatim reproduction of journalists' work. Additionally, Wired reported Perplexity’s disregard for robots.txt protocols, raising concerns about its web crawling practices. Accusations of plagiarism further spotlight ethical concerns, as Perplexity allegedly republished content without proper attribution. These controversies reflect broader tensions between AI companies and traditional media over content rights.

To address these issues, Perplexity launched a program sharing ad revenue with publishers like Time and Fortune. However, CEO Aravind Srinivas sparked further debate by arguing that facts should be universally accessible, rejecting ownership over knowledge. His controversial offer of technical support to The New York Times during a strike added to the company’s criticism, highlighting the complex relationship between AI innovation and media ethics.

Perplexity’s fundraising history now looks less like a normal startup curve and more like a chart of investor anxiety about search: $520M in early 2024, $9B by the end of that year, around $20B by 2025–2026, and now a planned 2028 IPO.

Perplexity’s fundraising history now looks less like a normal startup curve and more like a chart of investor anxiety about search: $520M in early 2024, $9B by the end of that year, around $20B by 2025–2026, and now a planned 2028 IPO.

As of the original version of this article, Perplexity was generating about $50 million in annual recurring revenue, a sharp jump from $2.5 million the previous year. By 2026, the company’s business model had broadened beyond Pro subscriptions into enterprise access, APIs, shopping, advertising experiments, and AI agent workflows. Even an AI compiuter. The bigger question is no longer only whether users like the product. They clearly do. The question is whether Perplexity can turn AI-powered search into a profitable business while compute costs, publisher disputes, and competition from Google and OpenAI keep rising.

Overall, the platform handles 100 million weekly queries and attracts major enterprises like Databricks and Zoom, contributing to its $50 million ARR as of October 2024.

In the interview to Lenny Rachitsky, Srinivas said: “Big challenges today revolve around scaling from our current size to the next level, both on the hiring side and in execution and planning. We don’t want to lose our core identity of working in a very flat and collaborative environment. Even small decisions, like how to organize Slack and Linear, can be tough to scale. Trying to stay transparent and scale the number of channels and projects without causing notifications to explode is something we’re currently trying to figure out.”

Srinivas once remarked, “Our competition isn’t even Google; it’s user awareness.” Small scale competitors face the same awareness problem:

Big guys:

The growing competition indicates that Perplexity’s success may hinge on how well it can either differentiate itself further or expand into niches that competitors have yet to explore.

In 2023, Perplexity received acquisition offers ranging from $150 million to $200 million from players such as X (formerly Twitter), OpenAI, Notion, and Microsoft. Elon Musk expressed interest in integrating Perplexity's technology to enhance X's search functionality. These discussions did not culminate in a deal back then. Now, with a valuation around $20B and a planned 2028 IPO, an acquisition would be much harder and would likely attract far more regulatory scrutiny.

Only X (formerly Twitter) might be bold enough to pursue this deal. Another appealing factor for Musk is how much traditional media hate Perplexity

Potential Synergy with Meta: As Meta develops its own AI-powered search engine to reduce reliance on Google and Bing, Perplexity emerges as a potential partner. Its proprietary search technology could accelerate Meta's initiatives, offering advanced web crawling and indexing solutions. However, Meta would need to assess the extent of Perplexity's proprietary technology versus third-party dependencies. A partnership or acquisition would hinge on Meta's confidence in Perplexity's consumer appeal and the justification of its valuation.

By 2026, Perplexity is no longer only an AI search company. It is pushing toward AI agents, browser-based workflows, enterprise research, shopping, finance, and developer infrastructure. That shift matters because the company’s ambition is no longer just to answer questions. It wants to become the layer where online tasks begin.

This also changes its infrastructure needs. In July 2026, Reuters reported that Perplexity plans to use Nvidia’s new Vera CPU for AI agent workloads, with Perplexity saying the chip performed AI agent coding tasks about 1.5 times faster than traditional CPUs. This is a small technical detail with a bigger meaning: if the future is always-on agents, not just search boxes, compute architecture becomes part of the product strategy.

The company’s biggest unresolved problem is still the same: Perplexity wants to organize the web into answers, while publishers want control over the work that makes those answers possible. That fight over intellectual property may shape AI search as much as model quality does.

Traditional search is quickly becoming a relic of the past, and Perplexity embodies the shift toward something entirely new – a platform that prioritizes human-AI collaboration and transcends the boundaries of a mere search tool. By turning search into a conversation, Perplexity is redefining how we engage with information, emphasizing context and interactivity. This vision sets it apart. Could its approach be replicated? Likely – but Perplexity’s founders are moving fast, overcoming obstacles and shipping features aligned with their mission to build a dynamic knowledge hub where anyone can contribute and interact.

The company has already moved far beyond the $9 billion moment. By 2025–2026, Perplexity was being discussed around the $20B valuation range, and Srinivas has said the company is planning for a 2028 IPO. That changes the question. It is no longer just whether Perplexity can become a breakout AI search startup. It is whether it can become a durable public company while competing with Google, OpenAI, and the entire collapsing old bargain between search engines and publishers.

On the flip side, profitability looms as a significant hurdle. The coming year will be crucial in determining whether Perplexity can stand on its own or will be acquired – potentially bolstering a larger player in the race to redefine search.

Perplexity AI is an AI-powered answer engine that uses artificial intelligence, large language models, real-time web retrieval, and citations to answer user questions directly.

Perplexity is often described as an AI search engine, but the company frames itself more specifically as an answer engine. Traditional search returns links. Perplexity returns answers with cited sources.

Perplexity AI was founded in 2022 by Aravind Srinivas, Denis Yarats, Andy Konwinski, and Johnny Ho.

Perplexity was valued at about $20 billion by late 2025, according to Reuters reporting. Some private-market trackers and analysts describe the company’s 2026 valuation range as $20B+.

Yes, Perplexity is planning for a 2028 IPO. Reuters reported in June 2026 that CEO Aravind Srinivas said the company’s 2028 IPO timeline remained unchanged regardless of what happens with OpenAI or Anthropic listings.

The New York Times sent Perplexity a cease-and-desist notice in 2024 demanding that the company stop using Times content for generative AI purposes. The Times argued that Perplexity’s use of its content violated copyright law. Perplexity said it was indexing web pages and surfacing factual content as citations, not scraping data to build foundation models.

The New York Times sued Perplexity in December 2025, alleging that the company copied, distributed, and displayed millions of Times articles without permission to power its generative AI products. The lawsuit followed the earlier cease-and-desist notice.

Publishers argue that Perplexity’s answer engine can use their reporting to generate answers without sending enough traffic, payment, or credit back to the original source. The dispute is part of a broader intellectual property fight over how AI systems use copyrighted content.

Perplexity makes money through subscriptions, enterprise products, API access, shopping features, and emerging agent-based and usage-based pricing. It has also explored advertising.

Perplexity is better for some tasks, especially direct answers, research-style queries, citations, and conversational follow-ups. Google still has much broader distribution, index depth, maps, local search, shopping infrastructure, and default user behavior.

Not exactly. Perplexity AI and ChatGPT overlap, but they are built for different jobs. Perplexity is better when you want real-time search, direct answers, and citations you can check. ChatGPT is broader: writing, coding, reasoning, brainstorming, analysis, and long-form work. In simple terms, Perplexity is closer to an AI-powered answer engine; ChatGPT is a general-purpose AI assistant.

Perplexity AI is controversial because its answer engine summarizes information from across the web, including publisher content, and gives users direct answers instead of always sending traffic back to the original source. Publishers argue that this can weaken their business and misuse their intellectual property. The New York Times sent Perplexity a cease-and-desist notice in 2024 and sued the company in 2025 over alleged copyright infringement. Perplexity says it indexes web pages and cites sources rather than training foundation models on publisher content. This is now part of the larger fight over AI, copyright, and the future of the open web.

No, Perplexity AI is not simply “based on ChatGPT.” It launched after ChatGPT showed how powerful conversational AI could be, but Perplexity’s product is built around real-time web retrieval, citations, and answer generation. It has used different large language models, including third-party models and its own search-optimized models. The key difference is that Perplexity combines search with AI-generated answers, while ChatGPT started as a general chatbot and later added more web-connected features.

Perplexity AI is a privately held company. It was founded in 2022 by Aravind Srinivas, Denis Yarats, Andy Konwinski, and Johnny Ho. The company is owned by its founders, employees, and investors. Its backers have included major technology and venture investors such as Nvidia, Jeff Bezos, IVP, NEA, Bessemer Venture Partners, and SoftBank. Aravind Srinivas remains the public face of the company as co-founder and CEO.

The biggest risks are competition from Google and OpenAI, high AI infrastructure costs, legal disputes over intellectual property, and the challenge of turning AI search into a durable daily habit.

Bonus: Resources used to write this article

Thank you for reading and supporting Turing Post 🤍 We appreciate you