On one side, institutions are loudly promoting the spread of AI PCs; on the other, buyers are not spending money on AI at all—and this contrast is painting the truest picture of the PC market in 2027. Omdia’s latest forecast says AI PCs will rise from 33% of shipments in 2025 to 60% in 2027, and reach 79% by 2030. But the same survey also shows that only 9% of respondents list AI features as their main reason for upgrading; 62% upgrade only because their machine has slowed down, broken, or they want a better model; and while 69% are willing to pay a premium for a computer that can "run AI locally," when it comes time to actually pay, AI is not the core decision factor.
The replacement cycle paves the way
If buyers are not driving it, why will adoption surge? The answer lies in timing: whether or not users specifically ask for AI, the installed base for local AI will arrive naturally with each replacement cycle. A deeper push comes from the cloud—after leading vendors shifted to token-based billing, task costs are only clear after the task runs, and agents trigger long chains of per-call charges, making spending harder and harder to manage. Pure cloud AI also raises compliance, data sovereignty, and IT blind-spot issues (78% of office workers use AI several times a week, but only 12% do so through a company account). Open-source weights are narrowing the gap with frontier models, quantization is squeezing models into PC memory, and NPUs are becoming standard, letting more and more work stay local.
Memory is the bottleneck to adoption
The real risk is in materials. Since 2025, memory has accounted for nearly 40% of PC bill-of-materials cost, more than double the previous 15%, potentially forcing buyers to choose lower configurations; yet local AI is exactly what consumes more memory. OEMs need to figure out whether the price premium for a stronger configuration can be recouped through cloud spending savings, ODMs and chipmakers must decide how cheap memory needs to be to stay affordable, and channel partners and hosting providers need to turn this proposition into solutions enterprises can actually deploy—AI PCs will not replace the cloud, but through ecosystem collaboration, they can make this muddled calculation clear.
