AI Data Centres Are Driving Up Smartphone Component Costs
Rising AI smartphone costs are not just about flashy new features; they start deep in the supply chain. The same high-performance memory chips that power flagship phones are now heavily used in AI data centres, where training large models demands vast amounts of fast storage. As demand for these components surges, smartphone makers face far higher bills for core hardware. Akis Evangelidis explains that memory which once cost around USD 20 (approx. RM92) per device has in some cases climbed to USD 100–120 (approx. RM460–RM552). For manufacturers working with strict component budgets, this inflation squeezes how much they can spend on cameras, batteries, and displays without raising retail prices. The result is a structural upward push on flagship phone pricing, driven less by optional luxuries and more by the basic smartphone hardware requirements needed to support modern AI workloads.
On-Device AI Processing Demands More Powerful Chipsets
Modern on-device AI processing—like live translation, generative photo editing, or personal voice assistants—relies on increasingly advanced chipsets. These processors need powerful CPU cores, robust GPUs, and often dedicated AI accelerators to run models locally instead of in the cloud. That hardware adds complexity and cost to every flagship design. To stay competitive, brands are compelled to include these capabilities, even if many users only notice incremental benefits in daily use. Yet skipping them risks a phone being perceived as outdated. This arms race means AI-ready silicon becomes a baseline expectation, turning into a hidden cost driver. As AI workloads grow, manufacturers also need more memory bandwidth and storage, tightening the link between AI performance and component budgets. Ultimately, the promise of smoother, privacy-preserving on-device AI comes with a tangible impact on how much flagship devices cost at launch.
AI Features Are Becoming a Justification for Premium Pricing
AI is rapidly becoming the new marketing anchor for flagship phone pricing. Brands highlight AI cameras, smarter assistants, and personalized interfaces as reasons to justify higher price tags, even when everyday improvements over last year’s models are modest. With memory prices ballooning and chip costs rising, manufacturers lean on AI narratives to explain why discounts are smaller or delayed. Evangelidis even suggests that the familiar pattern of rapid post-launch price drops may no longer hold, as AI-driven demand for memory stays high. Instead of cutting prices, some brands may adjust them upward over a product’s life cycle. For consumers, this means AI is both a genuine innovation and a convenient cover for structural cost increases. The gap widens between marketing promises and practical benefits, creating a landscape where paying more increasingly buys incremental AI refinements rather than transformative new experiences.
Older Flagships Risk Losing Access to New AI Tools
As AI capabilities increasingly depend on specific hardware, older flagship phones are being left behind sooner. On-device AI processing often requires newer chip architectures and larger, faster memory, so many cutting-edge AI features are restricted to the latest generation. This fragments user access: two phones running the same software platform can offer very different AI experiences based solely on their hardware. Over time, older devices may miss out on new AI cameras, productivity tools, or assistants that require more advanced silicon. For users, this creates a subtle but powerful upgrade pressure. Even if their phone still performs well for everyday tasks, missing AI features can make it feel obsolete. Combined with component prices that are unlikely to fall quickly, this dynamic nudges consumers into shorter upgrade cycles—reinforcing a loop where AI both enhances smartphones and accelerates their replacement.