About

Liquid AI is an efficiency-first foundation model company.

Our mission is to build efficient general-purpose AI at every scale. This means Liquid products are highly capable, compute and cost optimized, and ready to bring intelligence on any device and medium of choice.

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Liquid Thesis

Intelligence has to be ubiquitous

Demand for capable and practical intelligence compounds exponentially. Yet we do not have the energy and capex infrastructure to serve it. Cloud-only serving is intractable on cost, power, and latency. With the rise of open source, enterprises have started realizing that they can own intelligence instead of renting it. We think enterprises should own their data, intelligence, and decision power. An untapped opportunity to complement cloud-based intelligence is the physical world: nearly 2.2 billion devices that can host a foundation model ship every year, across phones, PCs, wearables, and cars alone, before counting robots and machines. Serving powerful AI on these surfaces creates a large market opportunity, enabling us to deliver intelligence efficiently to all.

Co-design models with hardware, from first principles

To make intelligence ubiquitous, we must co-design models with hardware, from first principles. Hardware and use cases set hard limits on memory, battery, and latency, and devices are fragmented across CPUs, GPUs, and NPUs from many chip makers. Meeting these limits becomes even harder when frontier quality is required for models that must fit the constraints of the processor. Tailoring the model to each chip makes every device present as one substrate. Therefore, foundation model training technology must have the hardware in the loop. This way, the fragmentation of on-device processors and low-level software can be solved at once and agents can flourish on top of the local intelligence layer.

Intelligence should not be static

The world changes, new data arrives, and real-world signals are noisy, so deployed systems must learn what works for each user and improve safely after they ship. Ideally, AI systems in production should be lifelong learners. Adaptation gives rise to deep personalization. We have been pioneers in building modern recurrent machine learning systems that can also recursively improve their own performance, such as Liquid neural networks, STAR, and LFMs. Our thesis has always been about building efficient and adaptive learning systems, and today we are realizing this vision.

Team

We are researchers and business leaders shaping the next generation of AI.

Spun out of MIT CSAIL, four founders — Ramin Hasani (CEO), Mathias Lechner (CTO), Alexander Amini (CSO), and Daniela Rus — started Liquid AI with the mission of building state-of-the-art, general-purpose AI systems that are capable, efficient, highly aligned, and trustworthy.

Values

The core Liquid values

  • Everyone at Liquid is working on something that matters to the mission. We’re a team of people who care deeply about impact and are motivated by results, and we hold ourselves to a high bar because the work demands it. There is no room for ego or stubbornness: ideas are valued and incorporated based on their merit and evidence alone, not on their source, and everyone is expected to continuously fine-tune their skills for improved performance.

  • AI is a new frontier, and there is no playbook. We work from first principles, stay comfortable with ambiguity, and give our colleagues the autonomy to drive their own work. Leadership sets the destination, but the route is yours to discover and deliver. Always prioritize building over process-creation.

  • Because everyone at Liquid is an expert in their domain, there is a high level of trust given to our colleagues to execute autonomously and deliver something that works. If something isn’t working, call it out early and do whatever it takes to help fix it: Take ownership, wear different hats, and step outside your comfort zone where it makes sense. Otherwise, delegate, defer, and trust your colleagues to do the same. Seek feedback early and often, but don’t let it become a blocker for delivering.

  • Decisions at Liquid are documented, traceable, and open to scrutiny. Favor mechanisms whose behavior can be inspected over black-box shortcuts; when shipping a model, a process, or a change, the reasoning behind it should be legible to the next person who reads the code, the diff, or the post. Surface tradeoffs explicitly, write down what you tried, and prefer techniques that explain themselves over those that only work.

  • Our colleagues at Liquid prioritize the needs of the company, and the company prioritizes the needs and wellbeing of its people. We are a product-driven, customer-first business, and its challenges are everyone’s to own. Company goals should align with and support the personal, professional, and academic growth of everyone on the team. We respect each other’s time, care about each other as people, and treat each other as equals.

Partners

Together, we’re turning the promise of an AI-powered world into reality.

Our partners are collaborators, innovators, and champions of the future we’re building. We partner with the organizations defining the next generation of AI — from hardware and infrastructure to inference and real-world applications.