Perplexity AI

Liquid AI

Perplexity AI

Perplexity uses a freemium model. The free tier gives unlimited basic searches powered by a standard model.

Perplexity Pro costs $20/month and gives access to more powerful frontier models from OpenAI, Anthropic and Google, alongside Perplexity's own, unlimited Pro searches, file uploads, and image generation. Perplexity Enterprise Pro is designed for businesses with team management and API access.

The company also recently introduced advertising through "sponsored follow-up questions" — a controversial move that some see as inevitable and others see as the beginning of the end for ad-free search.

Liquid AI

Liquid AI builds foundation models, the big general-purpose brains that power chatbots and other AI tools. The difference is efficiency.

Their models, called Liquid Foundation Models, are designed to do more with far less memory and computing power. Companies pay to use them, either through a cloud API or by running the models on their own machines.

That second option matters. A bank or a hospital often cannot send private data to someone else's server.

Liquid AI lets them keep the model in-house. The pitch to a customer is basically this.

You get strong AI without renting a mountain of expensive chips.

Perplexity AI

Aravind Srinivas grew up in Chennai, India, got a PhD in AI from UC Berkeley, and worked as a research intern at OpenAI and a researcher at DeepMind. He watched the ChatGPT explosion in late 2022 and saw something everyone else missed: AI chatbots were interesting, but AI-powered search was potentially more valuable.

Google search hadn't fundamentally changed since 1998. It still showed you a list of links and made you do the work of reading and synthesizing information.

Srinivas co-founded Perplexity AI in August 2022 with Denis Yarats (ex-Meta AI), Johnny Ho (ex-Quora), and Andy Konwinski (co-creator of Apache Spark). Their idea was an "answer engine" — you ask a question, Perplexity searches the internet in real time, reads the sources, and gives you a synthesized answer with inline citations.

No ads. No SEO spam.

Just answers.

The product launched quietly and grew through word of mouth among researchers, developers, and knowledge workers who were frustrated with Google's increasingly ad-cluttered, SEO-gamed results. Within a year, Perplexity was handling millions of queries daily.

Liquid AI

Liquid AI is a spinout from MIT's Computer Science and Artificial Intelligence Laboratory, better known as CSAIL. The core science came from Ramin Hasani, who spent his PhD studying the nervous system of a tiny worm called C.

elegans. That worm has just 302 neurons and still manages to move, hunt, and react to its world.

Hasani and his collaborators asked a simple question. What if you built an AI that worked more like those neurons and less like a giant static network?

The answer was the liquid neural network, a model whose internal wiring keeps adjusting even after training is done. The team incorporated the company in 2023.

Daniela Rus, who runs CSAIL and is one of the most respected robotics researchers alive, came on as a co-founder. So did Mathias Lechner and Alexander Amini, both longtime research partners of Hasani.

Perplexity AI

Perplexity grew almost entirely through product quality and word of mouth. The first users were AI researchers and tech workers who shared it on Twitter.

Every time someone used Perplexity and got a better answer than Google, they posted about it. The product was its own marketing.

The mobile app was a growth accelerator. By launching on iOS and Android early, Perplexity captured users who wanted quick answers on their phones.

The app was faster than opening a browser and Googling — one tap, ask your question, get an answer. Mobile usage now exceeds desktop.

Strategic distribution deals expanded reach. Perplexity partnered with SoftBank (default AI assistant on SoftBank phones in Japan), Deutsche Telekom, and various hardware manufacturers to be pre-installed as the default search assistant.

These deals put Perplexity in front of millions of users who had never heard of it.

Liquid AI

The growth angle is size, or the lack of it. Most frontier models are so heavy they only run in data centers.

Liquid AI aims for models light enough to run on a phone, a car, or a factory robot. That opens doors the giants cannot easily reach.

The AMD partnership pushes the same idea. AMD makes chips, and it wants efficient models that show off its hardware.

So AMD led the Series A and became both an investor and a distribution partner. Liquid AI also releases open models to researchers, which builds a following before the sales team ever makes a call.

Perplexity AI

Google is the elephant in the room. Google controls 90%+ of the search market and generates $175 billion annually from search advertising.

Google has responded to Perplexity by adding AI Overviews to search results — essentially copying the answer engine concept. Google has unlimited data, unlimited compute, and the default position on every browser and phone.

Competing with Google for search is historically a death sentence.

The publisher problem is real and litigious. Perplexity synthesizes content from news articles and websites without driving traffic back to those publishers.

Multiple media companies have accused Perplexity of scraping their content and delivering it as answers, depriving them of page views and ad revenue. The New York Times, Forbes, and Condé Nast have all raised concerns.

If publishers successfully block Perplexity or win legal challenges, the product's utility decreases.

Monetization without ads is nearly impossible at scale. Perplexity initially positioned itself as the ad-free alternative to Google.

But $20/month subscriptions can't fund the compute costs of an answer engine serving hundreds of millions of queries. The introduction of sponsored questions signals that pure subscription revenue isn't enough.

Whether Perplexity can add ads without becoming what it set out to replace is the defining question.

Liquid AI

The honest problem is Goliath. OpenAI, Google, Anthropic, and Meta have raised tens of billions of dollars between them.

Liquid AI has raised under $300 million. The transformer architecture those giants use is battle-tested and improving fast.

Liquid AI has to prove its different approach not only works but keeps working as models get bigger. Betting against the most successful design in AI history is bold.

It is also the kind of bet that ends in either a great acquisition or a quiet shutdown, with not much room in between.

Perplexity AI

Perplexity Search is the core — ask any question and get an AI-synthesized answer with cited sources. Pro Search performs multi-step research, asking clarifying questions and digging deeper for complex queries.

Perplexity Pages lets users create shareable research articles from their queries. Collections organize saved searches and threads.

The Perplexity API lets developers integrate the answer engine into their own products. Perplexity recently added Spaces for team collaboration on research projects.

Liquid AI

The main products are the Liquid Foundation Models, a family that ships in several sizes so a customer can pick the smallest one that does the job. The second generation, LFM2, arrived in 2025, including LFM2-VL vision-language models built for phones, laptops, wearables and embedded chips.

The company has leaned hard into on-device AI rather than the cloud. The focus is always the same.

Match the quality of a much larger model while using a fraction of the memory and power.

Perplexity AI

Jeff Bezos, IVP, NEA, Databricks Ventures, Nvidia, SoftBank

Liquid AI

It started with a $37.5 million two-stage seed in December 2023 at a $303 million valuation, from OSS Capital, PagsGroup, Automattic (the company behind WordPress), Samsung Next and others. Angels included GitHub co-founder Tom Preston-Werner, Shopify's Tobi Lütke and Red Hat co-founder Bob Young.

Then AMD Ventures led the $250 million Series A in December 2024, which is unusual because AMD is a chipmaker, not a typical venture fund. That backing gave Liquid AI both money and a hardware ally.

When a major chip company writes the lead check on an efficiency-focused model maker, it signals a bet that the future of AI is not just bigger, it is leaner.