Arm Exec Warns AI Demands Threaten Tech’s Trickle-Down Economics

For decades, the semiconductor industry has operated on a simple principle: today’s cutting-edge technology becomes tomorrow’s affordable mainstream. That economic model, often called the “waterfall effect,” has delivered everything from powerful processors to advanced cameras into budget-friendly devices. But according to Chris Bergey, Arm’s Senior VP and GM of the Client Line of Business, that longstanding dynamic is facing unprecedented pressure.

In recent discussions with industry analysts, Bergey revealed that the surging complexity and costs at the premium end are creating a widening gap between flagship and mainstream chips. The result? Simply repackaging last year’s high-end silicon for cheaper devices “may not work” anymore. This represents a potential breaking point for an industry that has long relied on trickle-down economics to spread innovation across price segments.

The Economics of Bleeding-Edge Silicon

What’s driving this shift? Manufacturing costs have skyrocketed to almost unbelievable levels. Industry reports indicate that developing and manufacturing a flagship processor on the latest process node now costs “hundreds of millions of dollars.” To put that in perspective, Qualcomm’s latest Snapdragon 8 Elite Gen 5 is rumored to cost upwards of $200 per chip – meaning a single premium smartphone SoC can cost as much as an entire budget device.

The numbers behind this cost explosion are staggering. According to manufacturing analysis, wafer prices at leading foundries like TSMC have jumped dramatically over the past decade. Where Apple’s A7 chip used wafers costing around $5,000 back in 2013, today’s A17/A18 Pro processors using 3nm-class TSMC processes see wafer prices around $18,000. That’s a three-and-a-half-times increase in just over ten years.

Meanwhile, the physical size of these chips isn’t shrinking as much as you might expect. Despite moving to more advanced nodes, designers are trading theoretical density gains for additional capabilities. “You get the performance uplift; you’re not necessarily getting shrinkage,” Bergey noted in his analysis. The result is that flagship SoCs often stay between 100mm² and 300mm², but manufactured on processes that cost far more per square millimeter.

AI’s Double-Edged Sword

Artificial intelligence represents both the biggest driver of this cost explosion and a potential justification for it. Modern smartphone and PC SoCs aren’t just CPUs and GPUs anymore – they now integrate dedicated AI accelerators (NPUs), advanced image processors, 5G modems, and other specialized blocks. Industry observers note that accommodating “three computing elements – CPU, GPU, and NPU – and those are all growing” to meet AI demands has fundamentally changed chip economics.

The transistor count tells the story clearly. Apple’s A-series chips went from about 1 billion transistors on the A7 to 20 billion on the A18 Pro. Other chipmakers are seeing similar expansion as they race to deliver the double-digit performance gains consumers expect each generation.

Memory requirements are also climbing as a side-effect of AI. Where once operating systems and applications drove memory needs, it’s now on-device AI models that dictate those requirements. Premium smartphones and laptops increasingly ship with 12 to 16 GB of RAM to accommodate these hungry AI workloads. “The other thing that AI really stresses is memory bandwidth, but even especially memory size,” Bergey explained in his assessment.

New Strategies for a New Era

Faced with these economic realities, companies are shifting their strategies. Rather than relying solely on binning processes – where chips are categorized after production based on performance – manufacturers are designing specifically for new segments that don’t quite meet the high-end but are better than classic mid-range.

The Arm C1-Premium core serves as a prime example. Reportedly about 35% smaller in area than Arm’s top-end C1-Ultra core, it achieves similar performance on many key benchmarks. This kind of targeted design allows for cost-efficient chips focused on the mid-to-high-end market without simply recycling last year’s technology.

Even at the low end, we’re seeing technology steps rather than smooth cascades. In cellular connectivity, for instance, 5G modems are finally reaching entry-level devices, but not through last year’s expensive chips. Industry sources indicate that 5G chipsets still command about a 15% premium over 4G equivalents, though the original technology cost has dropped significantly since its introduction.

The Cloud Economics Question

One might wonder why companies are pushing expensive on-device AI when cloud processing exists. Interestingly, Bergey suggests that even cloud providers see the economic imperative for local processing. “Most of those players that own the cloud that are doing that are like, we need to get this stuff on devices,” he noted in his discussions.

The economics of cloud AI are indeed “cloudy at best,” as analysis of AI profitability has shown. While API costs for services like OpenAI might seem cheap to consumers, the underlying infrastructure costs are substantial. Offloading inference to devices represents a potential win-win: companies save on cloud infrastructure while consumers get faster, more private AI experiences.

An Industry at a Turning Point

There’s “tension” in the system right now, according to Bergey’s assessment, and AI is the primary driver. The industry is approaching a pivotal moment where the old playbook of steadily cascading technology needs an overhaul.

Some technology costs “could be exceeding some of the supply demand curve or the price sensitivity curve,” Bergey suggested. Yet he also acknowledges the industry’s history of adapting to economic pressures. “It’s not that many years ago that people said nobody would ever buy a phone for $1,000 and Apple came out with $1,000 phone and now we think that’s normal.”

The breaking of the old waterfall model isn’t necessarily a failure but rather an evolution. Innovation continues at a breathtaking pace, largely driven by AI and novel computing needs. High-end devices will keep stretching capabilities in ways that would have seemed unbelievable a decade ago. The challenge – and opportunity – for the industry lies in finding new ways to bring those advancements downmarket in economically viable forms.

For consumers, the outcome might still be positive, even if different. Devices are becoming “more important and more valuable” to our daily lives, potentially justifying higher costs. The waterfall may not flow as freely as before, but it’s being redirected into new channels that could ultimately deliver even more value – just through different economic models than we’ve known for the past several decades.