Physical intelligencefrom first principles.
[ ∇E → 0 · settling into stable memories ]
We are building machines that understand the physical world the way humans do, by remembering what they have seen and reasoning about what comes next.
Status
Stealth
Location
Edinburgh
Launch
2026
Access
Waitlist open
See. Reason. Predict.
The loop that connects perception to action.
See · Reason · Predict
The loop that connects perception to action, built for machines that operate in the real world.
See
3D understanding from ordinary video.
Reason
Physical properties. Uncertainty. Context.
Predict
What the world does next, before it does it.
At the intersection of 3D perception, physical reasoning, and associative memory.
Built for robotics. Useful everywhere.
We are building at the intersection of perception, reasoning, and memory: the ingredients machines need to operate confidently in the physical world.
3D perception
Scene understanding from ordinary video: geometry, objects, and spatial layout.
Physical reasoning
Properties, forces, uncertainty, and context that govern how the world behaves.
Associative memory
Stable recall and retrieval, patterns that settle like Hopfield networks into memory.
Built for robotics · Useful everywhere
Why we are called Hopfield
Energy landscapes, stable memories, and the physics of learning.
In 1982, John J. Hopfield described a network whose dynamics flow downhill across an energy landscape, settling into stable states that behave like memories. Noisy input is drawn toward the nearest stored pattern. Associative memory as the relaxation of an energy function.
That picture of attractors, basins, and slow descent toward a minimum still echoes in modern attention and retrieval. We named the lab in his honour, and the energy field across this site is a small homage: systems settling, like memory, toward their stable state.
John J. Hopfield · Nobel Prize in Physics, 2024
What to expect
What does Hopfield Labs do?
We are building foundational AI infrastructure for machines that need to reason about the physical world. More soon.
Who is this for?
Researchers, engineers, and teams building systems that operate in the real world: robotics, simulation, perception, planning.
Why Hopfield?
John Hopfield's work on associative memory and energy-based learning is foundational to how we think about intelligence. We are building on those ideas for the physical world.
When do you launch?
We are in active development. Join the waitlist and you will be among the first to know.
Stay in touch
Join the waitlist or send us a message.
Waitlist
Be first when we launch
One email when we are ready. Early access for researchers and engineers.
- Early access when we launch
- Occasional research and product notes
- One email. No newsletter spam.
Have a question? Send a message
Contact
Send us a message
Tell us what you are working on. We read everything.
- Thoughtful replies, not auto-responses
- Researchers, builders, and teams welcome
- Typically hear back within a few days
Or email hello@hopfieldlabs.com