Ramin Hasani spent a decade studying a 2-millimeter worm before anyone outside academia knew his name. Then he solved an equation that had gone unsolved for over 100 years, published it in Nature Machine Intelligence, and woke up to an inbox full of term sheets he never asked for. That paper became Liquid AI.

While every other foundation model company was racing to build bigger transformers and consume more compute, Ramin went the other direction. Liquid foundation models cut the exponential energy cost of AI down to linear — same output quality, a fraction of the hardware. They run on phones, laptops, and cars. No data center required. Today his models are downloaded 1.4 million times per week on Hugging Face. Liquid AI raised $270M at a $2.2B valuation with 110 people — while the companies they compete with raised that in a single week.

In this ICONS episode, Ramin covers the full arc — from growing up in Iran with an anatomy professor uncle who made him study the human brain, to the two professors at Politecnico di Milano who introduced him to brain-inspired computing, to the PhD at Vienna and MIT where he discovered that a microscopic worm with 302 neurons could control 95 muscles better than any robot ever built. He explains the mathematics behind liquid neural networks, why the transformer architecture hits an inevitable energy wall, and what two years at Vanguard — the $8 trillion asset manager — taught him about building for real enterprise value. He also talks about closing Mercedes-Benz in four months, why he thinks 85% of AI token spend is waste, and what he believes the world looks like in 25 years when humans stop talking to each other entirely.

Topics Discussed

• Growing up in Iran, studying in Italy, completing his PhD across Vienna and MIT
• The C. Elegans worm: 302 neurons, 95 muscles, and four Nobel Prizes
• How modeling worm neuroscience led to the discovery of liquid neural networks
• Solving a 100-year-old differential equation — and why he thought he’d win a Nobel Prize
• The Hacker News moment: one paper, one morning, and an inbox full of Silicon Valley term sheets
• Transformer architecture explained — and why it becomes an energy crisis at scale
• Liquid foundation models: reducing exponential compute to linear without sacrificing quality
• Two years at Vanguard: what a $8 trillion business teaches you about enterprise and large numbers
• Why foundation model companies should be started by scientists, not operators
• Raising $270M while competitors raised $20B — and why that’s a feature, not a bug
• 1.4 million downloads per week: how a 110-person company became one third of Nvidia’s open source footprint
• Mercedes-Benz: closing an enterprise OEM deal in four months
• Shopify: foundation models powering product search and recommendation at scale
• Why they charge per request, not per token — and why contracts run 4 to 10 years
• The talent war: winning 3 out of 10 battles against Anthropic, xAI, Meta, and Cursor
• The taste test: why training a machine learning model is an art, not a recipe
• The next frontier: Bezos’s Prometheus, AI for science, and the world model thesis
• Why humans can’t grasp exponentials — and what the worm’s nervous system has to do with it
• 25 years from now: neural chips, parallel communication, and realities indistinguishable from the physical world
• The goal: Liquid foundation models running on 50% of all devices on the planet by 2029