As AI models grow ever larger and more powerful, the conversation often focuses on new model architectures, parameter counts, and dataset sizes. But another equally critical part of the AI revolution is happening at a lower level — in the hardware.
AI hardware is the backbone of modern AI. Without it, we wouldn't be able to train the massive foundation models making headlines today. But more importantly, it's becoming increasingly clear that simply scaling models with existing infrastructure isn't sustainable. Smarter, more efficient AI hardware will define the next frontier in artificial intelligence — not just more of the same.
Why AI hardware matters
A growing number of experts believe the future of AI depends not only on model size, but also on hardware innovation. As Forbes recently highlighted in their May 2025 article, "Why AI Hardware — Not Just Bigger Models — Will Define the Future of AI", performance bottlenecks, rising energy consumption, and physical limitations of current technologies are forcing the industry to rethink its approach.
Here are a few reasons why AI hardware is now centre stage:
-
Energy efficiency is critical
Training a single large AI model can consume millions of kilowatt-hours, comparable to the energy use of hundreds of homes over a year. As demand scales, AI’s energy footprint is growing exponentially, threatening both the environment and the economics of deployment. Innovation in hardware offers a route to greater efficiency without compromising performance.
-
The limits of scaling
The traditional approach - throwing more GPUs at a problem - is hitting practical and financial limits. AI hardware innovation allows us to extract more performance per watt, per dollar, and per square meter, making progress more sustainable and accessible.
-
Unlocking new use cases
AI hardware on edge devices can enable real-time inference in low-latency or low-power environments, from autonomous vehicles to wearable health monitors. This will open up entirely new markets and applications, far beyond what’s possible with cloud-only solutions.
The UK’s AI opportunity action plan
Earlier this year, the UK government released its AI Opportunities Action Plan, recognizing that hardware is a vital enabler of AI innovation and competitiveness. The plan highlights the strategic importance of AI infrastructure — including hardware — in securing the UK’s role as a global AI leader.
The UK also recently signed a deal with the EU to allow UK-based researchers access to EuroHPC’s world-class supercomputers, showing that they recognise the importance that hardware has in enabling innovation in AI.
AI hardware is not just a tech issue — it's a policy, economic, and strategic one. Governments and enterprises alike are beginning to recognize this and invest accordingly. The UK government appears to be serious in this regard promising £1 billion in funding to boost the UK’s AI compute power and a further £750 million for a new supercomputer at the University of Edinburgh in the most recent spending review.
Protecting innovation: the role of patents
With AI hardware innovations accelerating, from novel chip architectures to efficient memory systems, intellectual property will play a pivotal role in safeguarding competitive advantages.
Firms building or investing in AI hardware should carefully consider their patenting strategies, especially as the industry becomes more crowded and IP disputes more common. Patents can protect key advances in hardware design, manufacturing processes, energy-efficient algorithms implemented at the semiconductor level, and more.
Companies should also consider how geopolitical tensions can affect AI hardware supply chains. Many companies are seeking to diversify their supply chains and looking to new territories and companies should be mindful that patenting strategies may need to be adapted accordingly to protect inventions across diversified supply chains.
Stay tuned: our upcoming blog series on AI hardware
This blog kicks off our new series on AI hardware, where we’ll dive into:
-
What is AI hardware? – the challenges of training vs inference
-
The economics of AI hardware – costs, energy, and ROI
-
The sustainability challenge – how hardware can mitigate AI’s environmental impact
-
Edge AI and real-world deployment
-
When and how to patent your AI hardware innovations
-
And much more...
We have a core team of attorneys with a genuine interest and a deep technical background in AI hardware. Whether you're a startup building new chips or an enterprise deploying hardware for AI at scale, we're here to help you navigate this evolving landscape.
Stay tuned for our next blog — and join us as we explore the future of AI, from semiconductors to systems.
Rebecca Frith
Rebecca is a patent attorney working in our engineering team at Mewburn Ellis. She has a first-class MEng degree in General Engineering from Durham University where she specialised in electronics. After graduating, she worked for three years at a technology consulting firm as an electronics and firmware engineer. As a technology consultant Rebecca dealt with a variety of research and development projects for the defence and aerospace industries, including projects in computer vision, data security in machine learning, sensing devices, radar modelling, radio communications and safety assured electronics design.
Email: rebecca.frith@mewburn.com
Henry Suen
Henry is a trainee patent attorney in the Engineering practice group at Mewburn Ellis. His area of expertise consists of Artificial Intelligence (AI) Software, Networks, Distributed Systems, Data Storage Devices. Henry graduated with a first- class degree in Computer Science with Artificial Intelligence from the University of Leeds. His final year project looked at methods of reconstructing audio from spectrogram images using algorithmic and machine-learning approaches and proposed it as an alternative form of audio compression.
Email: henry.suen@mewburn.com
