Consumer electronics prices are shooting up. Energy prices are increasing fast. Even water bills are climbing. For a technology that promises “efficiency,” the ongoing AI gold rush seems to be taking things away, much like the proverbial gift that keeps on grabbing.
With hundreds of billions in AI investment already racked up for 2026, it’s important to remember the entire industry is currently built on a mountain of debt — and much of this borrowed money is being spent on data center capacity. That’s true, even though consumers would probably rather have a cheap Mac than spend money on an AI subscription service.
All this debt is being amassed because a small number of people at a very small number of firms have decided to make huge investments in the tech, which at present requires huge quantities of energy, memory and data center capacity to run.
But it won’t always be this way.
A mountain of debt, but we’re short of memory
Look, the industry as it is now just doesn’t seem sustainable. Trillions are being spent and memory vendors are shifting capacity to make the high-value, high-bandwidth memory these server farms require — at the expense of traditional consumer electronic suppliers.
The rapid rollout just creates AI tech will need to be replaced, likely at greater cost, in a few years’ time. In a nutshell, the industry is spending trillions to make billions; Sequoia’s David Cahn estimates the AI revenue gap between infrastructure expenditure and the revenue to justify it has already fallen $600 billion a year short.
At some point, the VC money will run dry, after which it is inevitable deployment will slow and demand for all the components — including memory used in these large language model (LLM) data centers will fall. Some analysts think capex growth in the sector could halt by mid-2027.
At that point, memory vendors will have expensive production facilities and extensive defaults on their order books. If the 2027 prediction is true, those vendors will feel this impact in the form of reduced forward orders by the end of 2026.
The problem is that the investments have become so vast that any slowdown will have consequential effects across all sections of the economy.
After the gold rush
Almost certainly, the technology will continue to improve, and the problems we’re looking to solve today might no longer be challenges once fresh innovation strikes. So, what happens next?
Let’s think about memory, the biggest pain point at the moment and where we will (hopefully) find future innovation. At present, some of the largest LLMs sit inside data centers supported by vast quantities of memory. These machines are built to handle really complex tasks, but most of the time are used to search the web, deliver writing assistance and summarize documents. Those frequently-transacted tasks barely stretch the capabilities of these services and Apple, and others have already figured out how to run such tasks on device.
That’s the first obvious space in which to innovate – to invest in 1-bit data LLM systems to miniaturize and distill models so they actually run on the device you’re using, rather than relying on all those remote servers.
The Apple shopping list
Apple’s interest in 1-bit data LLM pioneer PrismML speaks volumes about where the iPhone maker sees LLM development going, as did its acquisitions of Kuzu Inc., WhyLabs Inc, Pointable Inc., and Datakalab Inc. in recent years.
The beauty of PrismML’s tech is what it can do. It was recently used to compress Alibaba’s huge 27-billion-parameter Qwen 3.6 model from 54GB down to under 4GB, running with all 27 billion parameters active simultaneously — all without sacrificing benchmark performance.
The kicker? It managed to run that advanced, sophisticated AI model on an iPhone 17 Pro. My take? Just as music used to be captured on reel-to-reel tape and is now digitized and in the air, AI will move from the data center to the device, possibly faster than people expect.
Apple has three pillars for AI: On-device for most of what you need, on Private Cloud Compute servers for most of the rest, or via third-party server-based systems for the most demanding tasks. That’s a blueprint for how the industry will evolve as technologies represented by PrismML tend toward bringing more of that intelligence to the device. Over time, those local tasks will become more sophisticated, eroding the available market for today’s heavily-indebted AI incumbents.
Emerging priorities such as the need for privacy, data sovereignty, and trusted cloud will also spur the emergence of a multipolar AI future in which no one vendor dominates, further complicating their journey to profitability. It’s a model that favors the kind of service-agnostic, edgeAI approach Apple has taken.
EdgeAI for the rest of us
In the end, I don’t think there will be a need for much of the AI data center capacity now being built, because Apple and others will figure out how to use data minimization to transact sophisticated AI tasks on the device. For the most part, EdgeAI will deliver the consumer AI experience, while data centers cater to more sophisticated use. One day, after this gold rush has run its course, we’ll peer outside of our basements to see which of today’s AI firms actually are the chosen ones.
They may not be the ones you expect.
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