From Hardware to Software and Back Again…
For the past two decades, the tech industry has been dominated by software and cloud computing, pushing hardware into the background, or data centre. However, the rapid rise of Artificial Intelligence (AI) is reversing this trend, making specialised AI hardware a critical focus for tech companies and bringing it back to the centre stage. From AI-optimised chips to high-performance computing clusters, businesses are once again prioritising hardware innovation to keep up with the massive processing demands of AI models. This marks a return to the era when tech firms designed custom silicon alongside their software, something we haven’t seen at this scale since the early days of computing. So, what’s driving this hardware renaissance and how is AI reshaping the industry?
AI Workloads Are Too Heavy for Traditional Hardware
AI models, particularly deep learning and generative AI, require an enormous amount of computing power. Traditional CPUs (Central Processing Units), which have been the backbone of computing for decades, are simply not efficient enough to process AI workloads at scale. This isn’t a regression back to hardware but an evolution, driven by the unique demands of AI applications, the quest for efficiency and the need for privacy and performance at the edge.
As a result, companies are turning to specialised AI hardware:
- GPUs – Graphics Processing Units
Originally designed for gaming, GPUs from NVIDIA (H100, H200) and AMD (Instinct MI300X) have become the gold standard for AI training and inference due to their ability to process vast amounts of data in parallel.
- TPUs – Tensor Processing Units
Google’s AI-optimised processors are designed specifically for machine learning workloads in Google Cloud AI services.
- NPUs – Neural Processing Units
Dedicated AI accelerators are being integrated into smartphones, laptops and edge devices to handle on-device AI tasks.
These innovations show that general-purpose computing is no longer sufficient, as AI needs dedicated hardware to function efficiently.
Specialised AI Chips
Specialised chips are a technological evolution, reshaping industries by enabling new levels of AI capability, efficiency and application. As the field progresses, the balance between performance, energy use and cost will continue to be pivotal, alongside the strategic importance of maintaining innovation and production capabilities in this critical technology.
Companies like NVIDIA, with their GPUs, Google with TPUs and startups like Graphcore with their IPUs (Intelligence Processing Units), are at the forefront, designing chips specifically for AI workloads. These chips can perform parallel processing at a scale that CPUs cannot, making them crucial for AI’s matrix operations.
Custom AI Chips
Some tech giants are no longer satisfied with relying on third-party chipmakers like NVIDIA, Intel and AMD. Instead, they’re investing in their custom AI chips, designed specifically for their software ecosystems.
Some of the most notable AI custom chip developments include:
- Apple M3/M4 Chips
Featuring a powerful neural engine that enhances AI performance in macOS and iOS. Apple owns the design and intellectual property of these chips, designing them in-house but contracts out the manufacturing to TSMC (Taiwan Semiconductor Manufacturing Company).
- Google Tensor G3
Optimised for AI-driven image processing, voice recognition and security on Pixel devices. Although Google designs the Tensor G3 chip in-house it partners with Samsung which assists with design and provides the manufacturing.
- Microsoft Maia AI Chip
Designed to reduce dependency on NVIDIA for AI workloads in Microsoft Azure Cloud. Microsoft designs the Maia AI chip in-house and contracts out the manufacturing to TSMC.
- Meta MTIA – Meta Training & Inference Accelerator
Custom-built to power Meta’s AI-driven social media algorithms and VR applications. Meta designs the MTIA in-house and contracts out the manufacturing to TSMC.
The move to custom AI chip development mirrors the early computing era when companies like Apple and IBM designed hardware specifically to complement their software. This approach faded during the rise of general-purpose processors.
AI is Moving to the Edge – Which Means More Hardware
For years, AI processing was almost exclusively handled in the cloud. However, there’s now a growing trend toward on-device AI, where AI computations run locally rather than relying on remote servers. This is where NPUs come into play, integrated into devices such as smartphones, laptops, IoT gadgets or wearables to handle AI tasks without cloud dependency. This is called edge AI, which helps to reduce latency, improve privacy and conserve bandwidth, requiring new hardware solutions to enable real-time processing.
Examples of edge AI include:
- Smartphones and Laptops
Devices now feature built-in NPUs to run AI features like live transcription, facial recognition and AI-powered photography without needing an internet connection.
- Autonomous Vehicles
AI chips, like Tesla’s Dojo Supercomputer, allow self-driving cars to process massive amounts of data in real-time.
- Industrial and IoT Devices
AI-powered security cameras, robotics and smart assistants require low-latency, high-efficiency AI chips to function reliably.
This transition reduces dependence on cloud computing, improving speed, security and power efficiency, but it also means companies must develop new, AI-optimised hardware to keep up.
AI’s Massive Power Consumption is Driving Hardware Innovation
AI isn’t just computationally demanding, it’s also incredibly power-hungry. Training a single large AI model can consume as much electricity as hundreds of households over an entire year. This has made energy efficiency a top priority for AI hardware designers.
New AI chip architectures are being developed to reduce power consumption while maintaining performance, including:
- AMD MI300X AI Accelerators
Designed for high-performance AI tasks while using less energy than NVIDIA’s GPUs.
- Intel Gaudi 3 AI Chips
Aiming to deliver cost-effective, energy-efficient AI processing.
- ARM-Based AI Chips
Companies like Apple and Qualcomm are shifting to ARM processors, which consume significantly less power than traditional x86 chips.
As AI models grow exponentially in size, designing more power-efficient hardware will be crucial in reducing operational costs and environmental impact.
The AI Hardware Race is Now a Geopolitical Issue
AI hardware development has become more than simple business strategy, now it’s a matter of national security. Governments around the world are recognising the strategic importance of AI chips and semiconductor manufacturing, leading to increased investment in domestic production.
Key AI government policy developments include:
- The U.S. Chips Act
A $52 billion government initiative to boost semiconductor production in America and reduce reliance on foreign manufacturing.
- China’s AI Chip Initiative
With U.S. sanctions restricting access to advanced NVIDIA GPUs, China is rapidly developing domestic AI chips to maintain competitiveness.
- The EU’s Semiconductor Strategy
Europe is investing heavily in AI chip production to secure its place in the global AI race.
With AI chips being essential for military, economic and technological dominance, countries are now competing fiercely to secure their supply chains, reinforcing the shift back to hardware.
The Revival of Hardware Innovation
AI is bringing the tech industry full circle, refocusing attention on hardware innovation after decades of software dominance. The demand for high-performance, AI-optimised hardware has led to the rise of custom AI chips, energy-efficient processors and edge AI solutions. As AI continues to evolve, companies that control both software and hardware will have a strategic advantage. Whether it’s NVIDIA leading the AI GPU market, Apple integrating AI into its silicon, or governments racing to secure AI chip production, one thing is for sure: AI looks to be making hardware the centrepiece of the next technological revolution.
You may want to read: “AI Hardware Innovations and the Future.”
