Notion

Liquid AI

Notion

Notion uses freemium pricing. The free plan is generous — unlimited pages and blocks for individuals.

The Plus plan is $10/user/month for small teams. Business is $18/user/month.

Enterprise is custom-priced. The free tier is intentionally powerful enough that individuals and small teams can use it forever without paying.

The revenue comes when those individuals bring Notion to their companies and entire organizations adopt it.

Notion also has an AI add-on at $10/member/month that adds AI writing assistance, summarization, and Q&A across your workspace.

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.

Notion

Ivan Zhao studied cognitive science at the University of British Columbia, obsessed with the idea that computers should be creative tools, not just consumption devices. He was inspired by pioneers like Alan Kay and Douglas Engelbart who envisioned computers as tools for thought.

In 2013, he started Notion with the idea of building a tool that combined documents, databases, and project management into one flexible workspace.

The first version of Notion launched in 2015 and basically nobody cared. The product was too abstract.

People didn't understand what it was or why they needed it. The company burned through its initial funding.

By 2015, Notion was nearly dead — Zhao had to lay off almost the entire team.

With the last of his money, Zhao kept just one engineer — Simon Last — and moved to Kyoto, Japan, where the cost of living was a fraction of San Francisco. For over a year, Zhao and Last rebuilt Notion from scratch in a tiny apartment.

They rewrote the entire product, making it simpler and more intuitive. The 2.0 version launched in March 2018.

This time, it clicked. Product Hunt loved it.

Twitter loved it. Within months, Notion was growing 50% month over month.

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.

Notion

Notion grew almost entirely through organic love. The product was so flexible that users built incredible things with it and shared them on Twitter, YouTube, and Reddit.

Template creators built entire businesses around selling Notion templates. YouTubers made "my Notion setup" videos that got millions of views.

Notion didn't need a marketing team because its users were the marketing team.

The template ecosystem was a massive growth driver. Notion made it easy to duplicate and share templates.

Productivity influencers and creators built elaborate systems — life dashboards, second brains, CRM systems, content calendars — and shared them with their audiences. Every shared template was a free advertisement for Notion.

The community strategy was deliberate. Notion hired community managers early and empowered super-users to become Notion ambassadors.

There are Notion meetups in cities worldwide. Notion Campus Leaders run groups at universities.

The company understood that for a tool this flexible, community was the best way to teach people what's possible.

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.

Notion

The "too flexible" problem. Notion can do almost anything, which means new users often stare at a blank page and have no idea where to start.

The learning curve isn't steep, but it's wide. People give up before discovering the product's power.

Every Notion user remembers the moment it finally "clicked" — but many potential users never reach that moment.

Enterprise adoption has been slower than expected. Notion is beloved by startups and small teams, but large enterprises need things like advanced permissions, audit logs, compliance certifications, and admin controls.

Building enterprise features while keeping the product simple for individuals is a constant tension. Companies like Confluence (Atlassian) have decades-long enterprise relationships that are hard to displace.

Performance at scale has been a persistent complaint. Notion pages with hundreds of blocks or large databases can become sluggish.

The app has improved significantly but power users with massive workspaces still hit performance walls. For a tool that promises to replace multiple apps, it needs to be as fast as each individual app it replaces.

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.

Notion

Notion is a workspace that combines notes, docs, wikis, project management, and databases into one tool. Pages can contain any mix of text, tables, kanban boards, calendars, galleries, and embedded content.

Notion Databases are the power feature — structured data that can be viewed as tables, boards, timelines, or calendars. Templates let users start with pre-built setups for everything from CRM to habit tracking.

Notion AI adds writing assistance, summarization, and the ability to ask questions about your workspace content. Notion Sites turns any Notion page into a published website.

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.

Notion

Index Ventures, Sequoia Capital, Coatue Management, Dragoneer Investment Group

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.