It’s not over. After GPUs and RAM, the AI boom is about to make computers even more expensive.

Disclaimer: Unless otherwise stated, any opinions expressed below belong solely to the author.

Just last month, Apple, the last holdout in the personal computing market, was forced to hike prices of its computers and tablets within the range of 10 to 30%, depending on the type and model.

The giant from Cupertino was able to wait out the AI-induced inflation thanks to its long-term contracts on memory chips and the TSMC manufacturing capacity it had booked for its Apple silicon processors well in advance.

The fact that it has long enjoyed some of the highest margins in the industry must have also helped, providing a buffer that allowed it to absorb some of the costs seeping through in other areas.

Windows PC buyers have had a much worse time for the past year, as the AI revolution hit their devices first. Laptops may still be attainable, but building a new desktop PC is currently nearly impossible for regular consumers after RAM prices exploded by several hundred per cent in late 2025.

This came on top of inflated prices of graphics cards, which AI came for first, as hyperscalers like OpenAI, Anthropic or Google needed to secure millions of them to train their artificial intelligence models.

That said, amid the surge hitting Nvidia and AMD cards, RAM sticks and SSD storage, one component remained unaffected: the central processing unit (CPU).

Unfortunately, it is about to change.

AI does not run on GPUs alone

GPUs are excellent at performing large numbers of relatively similar calculations simultaneously. This makes them indispensable for training artificial intelligence models, which is why they were essential in the early years of the AI boom.

To build your own AI model, you need GPUs—and A LOT of them.

But they do not operate independently.

CPUs still have to prepare and feed data to accelerators, manage memory, handle networking, launch tasks and coordinate all the other processes taking place around the model.

This becomes particularly important during inference—the stage when a trained AI model responds to users.

Training may take place once or periodically, but inference occurs every time somebody asks ChatGPT a question, generates an image, writes code with Claude or tells an AI agent to complete a task.

As the number of AI users and applications grows, inference demand grows with it.

On top of that, the emerging generation of AI agents is particularly hungry for general-purpose computing.

Unlike a chatbot that produces one answer and stops, an agent may browse files, call external tools, execute code, check results and repeat the process multiple times. Each of these actions creates work that CPUs are much better suited to handle.

Until recently, AI companies needed roughly one CPU for eight GPUs, but that ratio is expected to shrink rapidly and may approach 1:1 parity by 2029.

Consider the millions of GPUs that were sold in the past two to three years. Now, their deployments may need three, four, maybe even eight times as many CPUs. And there are new data centres being built as we speak.

So, not only is the new approach going to have to fill existing gaps, but also respond to the future demand.

Intel is already running short

This would not be a problem if chipmakers had large amounts of spare capacity. But they don’t.

Intel acknowledged that demand was already outpacing supply in late 2025 and warned that shortages would persist into 2026. Its data-centre business was unable to fully meet customer demand because of limited wafer capacity at its own factories.

The company is now prioritising the production of server chips, including its more lucrative Xeon processors, as AI demand grows.

This makes commercial sense. One high-end server processor can cost thousands of dollars, while the CPU in an ordinary laptop may cost the manufacturer a fraction of that.

But Intel cannot simply create more factory capacity overnight. If it produces more Xeons using constrained manufacturing lines, something else may have to give.

And that something could be consumer processors.

Industry reports suggest server CPU prices have already risen by as much as 20% since Mar, while consumer models have reportedly become 5 to 10% more expensive in some channels.

Additional increases may follow later this year.

AMD is benefiting too. Its data-centre revenue rose 57% year-on-year to US$5.8 billion in the first quarter of 2026, driven partly by strong demand for its EPYC server processors.

Unlike Intel, however, AMD does not own its leading-edge factories. It relies primarily on TSMC, which is also producing chips for Nvidia, Apple and numerous other companies competing for limited advanced capacity.

So, while Intel has to choose what to produce in its own factories, AMD has to compete for space at somebody else’s, which is the same Taiwanese company everybody already relies on.

The bottleneck is getting tighter.

Ordinary buyers will end up paying too

While server and consumer CPUs are not always manufactured on the same processes, and chip companies cannot freely convert every production line from one product to another, there are several ways the pressure can still reach consumers.

Manufacturers may prioritise their limited capacity, engineering resources and components for more lucrative enterprise products. Computer makers may pay more for chips under their supply agreements. Shortages of resources and input components can also raise the cost of the entire system.

And many high-end consumer processors can be used directly in enterprise settings. While they may not perform the most important and valuable tasks, they can serve in support roles to help save the precious server models for where they are needed most.

That, in turn, would hoover them up from the consumer market and drag the prices of all processors with them, as consumers turn to the next best option.

There is always another bottleneck

The AI boom began with the impression that the industry merely needed more GPUs. It quickly became clear that it also needed memory, storage and things outside of technical components, like electricity, building materials or qualified construction labour.

Now, CPUs are joining the list.

This is what happens when hundreds of billions of dollars are invested in one industry at the same time. Solving one shortage merely exposes the next bottleneck after that.

For consumers, the frustrating part is that they are competing with some of the richest companies in history, many backed by tacit or direct government support, as entire nations see harnessing AI as a strategic interest.

Hardware manufacturers will naturally sell their limited capacity where it generates the highest returns. At the moment, it is increasingly inside AI data centres rather than the computers sitting on our desks.

That’s why, unfortunately, if you were waiting for GPUs and RAM to become cheaper before buying your next PC, there may soon be another item to worry about. And there is no end in sight.

• Read other articles we’ve written on the artificial intelligence boom here.

Also Read: AI salaries in S’pore rose 5x faster than overall wages, with fresh grads landing S$90K AI jobs

Featured Image Credit: Shutterstock