27 February 2026
Developing a world-first thermodynamic computing chip
What if we could exploit principles found in natural systems to build dramatically more efficient computers? This question is at the heart of our Nature Computes Better opportunity space – and one that is only becoming more important as demand for computing power to train and use AI grows dramatically.
Normal Computing’s thermodynamic computing chip, CN101.
For decades, we have benefited from exponentially more computing power at lower cost. However, that trajectory is no longer a given; we have reached a point where we must fundamentally rethink our approach to computing.
“Modern day AI requires orders of magnitude more energy than the intelligence afforded to us through nature. We know highly efficient intelligence is possible, we just don’t know how to build it,” says Suraj Brahmavar, Programme Director.
The Thermodynamic Matrix Inversion project, funded through our Scaling Compute programme, sets out to tackle this challenge. The project is exploring how we can harness the laws of physics to create a new class of computer chips, so-called ‘thermodynamic’ computer chips, with the aim of making radically more efficient computers. It is led by Normal Computing, a startup founded by ex–Google Brain and Google X researchers behind early advances in physical-world AI and leading frameworks for probabilistic and quantum AI.
“Normal is questioning core principles of the digital paradigm to adhere more closely to modern AI algorithms. This project is the perfect embodiment of the Nature Computes Better opportunity space, exploring missing links between nature and modern-day computing, and trying to exploit these links to re-build our compute infrastructure with scalability top of mind from day one,” says Suraj.
Working with, not against, the laws of physics
Conventional computer chips (like CPUs or GPUs) are strictly digital, performing computations using billions of tiny switches that are either On (1) or Off (0). This requires the chips to be perfectionists – if there is even a tiny bit of ‘noise’, the chip can make a mistake. To maintain this precision, these chips have to expend a massive amount of energy fighting a constant war against the natural, chaotic vibration of atoms.
Normal’s approach tries to turn this on its head by actively working with this natural chaos. By leveraging physical fluctuations to perform computations, they aim to unlock a fundamentally new and radically more efficient architecture specifically designed for heavy AI workloads like image and video generation.
“Stochastic computing – instead of trying to be a tightly controlled, rigid, deterministic process – works with a modelling of the noise in silicon chips that we’re normally trying to suppress,” says Marc Bright, Silicon Team Lead at Normal. “That suppression is where a lot of the power cost comes from. We work with the randomness and that gives us an ability to reduce power density and consumption so that we can avoid hitting these maximum temperature limits.”
Since beginning their ARIA project in October 2024, Normal has moved with remarkable speed, launching and growing their London operations to a team of seven who have come together to reach a significant research milestone.
In June 2025, the team created CN101, the world’s first thermodynamic computing chip. Designed specifically for multi-modal diffusion GenAI model inference, this chip represents the first step on a roadmap targeting 1000x gains in energy efficiency and dramatically lower latency.
The real test
Creating a chip is an achievement in and of itself, but does the chip actually work? In an anxious but exciting moment for the whole team, in August 2025, the team ran the first chip’s first test.
“This particular chip was being brought up by one of our colleagues in LA, and he was looking at it at 8pm my time in the UK. I couldn’t bear to find out that it didn’t work, so I muted my notifications until the following morning,” says Marc.
“When I woke up, I saw that he was producing data out of the chip and that was a massive moment.”
The test was a success, a monumental first step in scaling this thermodynamic computing paradigm.
But this is only an early signal. It remains unclear how far this approach can scale, or whether the same behaviour will hold across different models and workloads. Can thermodynamic systems maintain useful signals as they grow in size? Where does noise enable computation, and where does it begin to degrade it? These are the questions that now define the next phase of the work.
“ARIA exists to fund the ideas where the potential impact is not marginal but transformational, even when the technical risk is high,” says Suraj. “Normal’s team has taken a fundamentally unconventional approach and delivered working silicon in CN101. That is an exceptionally rare outcome for work this ambitious and we are excited to witness this next phase of the journey.”
From strength to strength
The team’s technical success has also paved the way for significant commercial momentum. Normal recently announced $50 million in strategic funding led by the Samsung Catalyst Fund, bringing its total funding to more than $85 million.
New investors also include Galvanize, an investment firm focused on energy innovation founded by Tom Steyer and Katie Hall making its first semiconductor investment, and ArcTern, alongside existing investors Celesta, Drive Capital, Eric Schmidt’s First Spark Ventures, and Micron Ventures.
“CN101 proved that thermodynamic computing opens a fundamentally new path forward. We use our own AI to design these chips, with each generation improving the next. ARIA’s backing accelerates us toward multiple-order-of-magnitude efficiency gains for datacenter silicon,” says Faris Sbahi, Founder and CEO of Normal.
Bridging tech’s valley of death
Despite milestones like CN101 being reached, work like this also highlights a broader challenge. Startups developing novel AI hardware face a key challenge – they lack a “shop window” to showcase their innovations. They are forced to develop components in isolation, requiring huge capital investment and dependency on hyperscalers to access compute.
To address this, ARIA has committed an additional £50m to the Scaling Compute programme to launch the Scaling Inference Lab. This AI testbed will prioritise rapid iteration and open collaboration, ensuring that innovative, world-first technologies developed by startups like Normal have a clear path to deployment.
“We’re incredibly excited about what comes next,” says Craig Churchill, CBO of Normal. “Thermodynamic computing represents a fundamentally new approach to computation, and initiatives like the Scaling Inference Lab are critical to turning breakthroughs like CN101 into real-world impact at scale.”