On one hand, institutions are loudly proclaiming the widespread adoption of AI PCs; on the other, buyers aren’t willing to pay extra for AI at all—this paradox paints the true picture of the personal computer market in 2027. According to Omdia’s latest forecast, AI PC shipments will climb from 33% in 2025 to 60% in 2027, reaching 79% by 2030. Yet the same survey shows that only 9% of respondents list AI capabilities as their main reason for upgrading, while 62% upgrade solely due to performance issues, hardware failure, or the desire for a newer model. Furthermore, 69% would pay a premium for a PC that runs AI locally—but when it comes time to open their wallets, AI remains far from being the central decision driver.
The upgrade cycle paves the way
If buyers aren’t actively seeking AI, how can penetration rates still soar? The answer lies in timing: whether users explicitly request AI or not, the groundwork for local AI adoption naturally emerges with each wave of device upgrades. A deeper push comes from the cloud—after leading vendors shift to token-based billing, task costs remain opaque until completion, and intelligent agents trigger long chains of per-use calls, making expenses increasingly difficult to manage. Meanwhile, purely cloud-based AI raises concerns around compliance, data sovereignty, and IT blind spots (78% of office workers use AI several times a week, but only 12% do so through their organization’s account). With diminishing open-source influence, shrinking gaps between cutting-edge models, quantization enabling models to fit within PC memory, and NPUs becoming standard, more and more workloads can now stay local.
Memory is the bottleneck to widespread adoption
The real risk lies in materials. Since 2025, memory has accounted for nearly 40% of PC material costs—more than doubling from its previous share of 15%—potentially forcing buyers to opt for lower-end configurations. Yet local AI happens to be even more memory-intensive. OEMs must carefully calculate whether the price difference between high-end specs and cost savings from reduced cloud usage can offset the trade-off, while ODMs and chipmakers need to determine how affordable memory prices should be to make such solutions accessible. Meanwhile, distributors and hosting providers must turn this vision into viable enterprise-level strategies. AI PCs won’t replace the cloud, but through ecosystem collaboration, they can help clarify this complex financial equation.