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Summary for Investors
What’s happening: The AI industry is expanding beyond NVIDIA GPUs, with hyperscalers and specialised chipmakers now developing custom chips.
Why it matters: Custom chips can make large, repetitive tasks more efficient, lower costs, and help companies avoid relying on a single supplier.
What the market is missing: The real value isn’t just in the chip’s computing power. Networking, packaging, and system design are also key to keeping large AI clusters running smoothly.
Key risk to watch: NVIDIA’s software remains widely used, so even as custom chips grow quickly, GPUs may not be replaced as quickly as some headlines suggest.
Investor lens: Keep an eye on this. NVIDIA remains at the centre, but Broadcom and Marvell are becoming increasingly important in building AI infrastructure.
For a time, investing in AI seemed straightforward: buy the company selling the tools. In this case, the tool was a GPU, an expensive one, with even pricier software to go with it.
That approach paid off. NVIDIA became the clear winner by providing the hardware everyone needed, especially when demand was high, and alternatives were limited. When people rush to buy computing power, the top supplier usually benefits.
NVIDIA’s performance since 2023
Eventually, the biggest cloud companies stopped asking how to get more GPUs and started wondering why they were paying for this setup in the long term.
That’s where custom chips come into play.
The next stage of AI infrastructure isn’t about getting rid of GPUs completely. Instead, it’s about moving repetitive, predictable, and costly tasks to custom chips that handle them better. Training advanced models still relies on GPUs, but tasks like inference, recommendations, and certain cloud services are shifting to specialised chips.
This shift is important because large cloud companies operate at such a big scale that even small improvements add up fast. Cutting power use, latency, or costs by a little can save billions. In AI, saving 20% isn’t just a technical win; it can change the whole business model.
Why custom chips are gaining ground
General-purpose GPUs are great because they’re flexible. They can handle many different tasks, and when things are changing quickly, that flexibility is very valuable.
But flexibility comes at a cost.
GPUs need to handle many tasks, so they have extra features and complexity. This is helpful when workloads are unpredictable, but less appealing when a cloud provider knows exactly what it needs the chip to do at a large scale every day.
A custom chip can be designed specifically for that job.
This usually leads to less wasted chip space, better power efficiency, improved use, and lower overall costs. It also gives companies more control, since they don’t have to wait in line with everyone else to buy the same chips from the same supplier.
This part often gets overlooked. Custom chips aren’t just about better performance; they also give companies more leverage.
If the AI boom is like a land rush, then having your own custom chips is like owning the factory instead of just buying tools from a store.
Four companies to watch besides NVIDIA
Many companies are part of this shift, but the four most important right now are Google, AWS, Broadcom, and Marvell.
Two of these are large cloud providers making their own chips to improve their business. The other two are chip companies helping make these systems work at scale.
They each play a different role, which makes this trend interesting.
1. Google: the long game in AI silicon
Google has been working on custom AI chips for longer than most people realise. Its TPU programme is not a side project. It is a serious attempt to reshape the economics of machine learning inside Google and across Google Cloud.
Google’s TPU programme from 2015
The architectural idea is simple enough to explain and hard enough to execute. TPUs are built around systolic arrays, which are highly specialised for the matrix operations that dominate many AI workloads. Instead of trying to be broadly programmable like a GPU, the chip focuses on moving data through a tightly structured compute fabric very efficiently.
This makes TPUs less flexible. It also makes them very good at the jobs they are built for.
That trade-off is the whole point. Google controls not only the chip, but also much of the surrounding software stack through TensorFlow, JAX and compiler tooling. That means it can tune the entire path from model to machine. In AI infrastructure, owning the chip is useful. Owning the way workloads are shaped to fit the chip is better.
What Google has really built is not just a processor. It is a system. And systems tend to be stickier than components.
If a customer optimises around TPU instances, Google is no longer merely renting compute. It is selling an environment.