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AI PCs: When Hype Meets Reality

AI PCs are selling in large and rapidly growing volumes, but much of today’s demand is being pulled forward by a looming Windows 10 deadline and vendor marketing rather than by clear, proven AI value for most buyers. The next three years will determine whether AI PCs become a durable platform shift or another over-specified category in search of a problem.

The Numbers Look Spectacular

The headline figures are impressive. Gartner projects that AI PCs will account for 31% of global PC sales by the end of 2025, with around 77.8 million units shipped. By 2026, shipments are forecast to jump to 143 million units, representing roughly 55% of the overall PC market. By 2029, Gartner expects AI PCs to “become the norm.”

Futurum Research, looking at revenues rather than units, estimates the global AI PC market will climb from near-zero in early 2024 to $25 billion in 2025, with a projected compound annual growth rate of about 38% through 2030 and a base-case size of $124 billion by decade’s end.

But for technology and business leaders, the real story is not that AI PCs are growing quickly. The more important question is why they are growing, who is buying them, and whether that demand is sustainable once two powerful artificial boosters fade: the end of Windows 10 support and the novelty of AI branding.

A Market Surge Powered by Windows 10, Not Just AI

The first inconvenient truth of the AI PC boom is that much of the 2025-2026 spike is less about AI and more about Microsoft’s Windows 10 end-of-support deadline in October 2025. Enterprises with fleets of aging laptops and desktops cannot ignore that date; they must refresh devices or face escalating security and compliance risks.

Futurum’s analysis is blunt: 2025’s AI PC revenue surge—up to $25 billion—is being driven primarily by enterprise refresh cycles ahead of the Windows 10 deadline. IDC’s work with IT decision makers points in the same direction, finding that eight in ten IT leaders plan to invest in AI PCs as part of those refreshes, with AI-capable systems rapidly becoming the de facto choice when organizations upgrade.

In other words, AI isn’t necessarily closing the deal; it is riding along on a renewal cycle that was going to happen anyway. AI is the “plus” on top of a non-negotiable requirement to modernize.

This explains two otherwise contradictory trends:

• AI PCs appear to be on track to dominate shipments within a few years.

• Analysts already expect enterprise AI PC adoption growth to decelerate after 2026 once the Windows 10 wave has passed and buying patterns normalize.

After that normalization, vendors and CIOs alike will confront a more sobering question: absent a forced migration, do the on-device AI capabilities of an “AI PC” justify their premium in price, power envelope, and complexity?

The Split Reality: Enterprise vs. Consumer Adoption

The early AI PC market is essentially bifurcated between enterprises and consumers with fundamentally different motivations and architectural preferences.

From a hardware and OS standpoint, this split shows up clearly. Arm-based laptops are expected to dominate the consumer AI segment, while x86 on Windows will command about 71% of AI business laptop sales in 2025. Arm’s appeal in the consumer space comes from battery life and tight system-on-chip integration, while enterprises stick to x86 for compatibility with Windows, line-of-business apps, and existing management tools.

Beneath that architectural divergence lies a deeper difference in motivations:

Enterprise buyers care about fleet-wide productivity, security, manageability, and total cost of ownership. They are willing to bet on AI if it can demonstrably automate repetitive work, assist knowledge workers, or improve endpoint security at scale. Analyst surveys show strong optimism here: about eight in ten IT leaders believe AI PCs will boost employee efficiency, particularly through on-device assistants that help with summarization, translation, and routine workflows.

Consumers, by contrast, are being asked to pay more for capabilities they only dimly understand. Research indicates that over 60% of U.S. consumers want AI PC prices to drop before buying, and half say they do not understand why they need an AI PC at all. 61% do not feel they use AI enough to justify the purchase. In 2025, consumer AI PCs are projected to account for only about 26% of AI PC revenue, with enterprises generating the remaining 74%.

This mismatch is at the heart of the AI PC hype problem: the segment looks like a runaway success on paper, but it is disproportionately dependent on enterprise upgrades that may not repeat, and on buyers who are not primarily motivated by AI capabilities.

When “AI PC” Is a Feature, Not a Strategy

The way AI PCs have been marketed to date has largely focused on hardware attributes: NPUs (neural processing units), TOPS (tera operations per second), and copious system memory. For many buyers, that looks like a familiar—and tired—story: faster chips, more cores, better benchmarks.

Gartner and Futurum both suggest that this hardware-first framing is already reaching its limits. The AI PC is not a category that will be sustained by silicon alone. Both analysts and OEM executives are converging on a similar insight:

The long-term success of AI PCs will depend less on NPU specs and more on software ecosystems, role-specific applications, and tightly integrated use cases that matter to actual users.

By the end of 2026, Gartner expects 40% of software vendors to prioritize AI capabilities on devices, a colossal jump from just 2% in 2024. This is the real inflection point: as more independent software vendors build small language models (SLMs) and AI features that run locally—taking advantage of the NPU, GPU, and CPU in concert—the AI PC stops being a “thing you buy” and becomes a “thing you experience” in specific workflows.

These SLMs are crucial for three reasons:

• They reduce latency, enabling real-time use cases like on-the-fly translation, transcription, or multimodal search.

• They improve privacy and compliance by keeping sensitive data on the device instead of shipping it to the cloud.

• They lower recurring cloud costs, particularly at scale in the enterprise context.

But those benefits only become obvious when they are surfaced through compelling software experiences in productivity tools, design apps, collaboration platforms, and vertical-specific systems. Without that software layer, an AI PC is simply an overpowered PC with theoretical potential.

This is why analysts emphasize that the future of AI PCs is in customization: devices tailored to specific roles, industries, and workflows, equipped with the right on-device models and apps out of the box. It is also why a “software-first” strategy is increasingly seen as non-negotiable for vendors and Microsoft’s PC partners.

Microsoft and Its OEM Partners: Opportunity and Pressure

No company is more central to the AI PC story than Microsoft, whose Windows ecosystem and Copilot offerings effectively define the category on the enterprise side.

Microsoft’s decision to tie Copilot+ branding to specific NPU performance thresholds and memory minimums (such as 16 GB RAM) has given OEMs a clear target. It has also, inadvertently, created significant bill-of-materials pressure: AI PCs tend to ship with more memory than their non-AI predecessors, and that memory is both vital for on-device models and exposed to broader component-market swings.

IDC warns that a global memory shortage—driven by demand for DRAM and NAND in both smartphones and PCs—could threaten the AI PC growth narrative by pushing prices higher or forcing OEMs to cut specs. Because AI PCs are defined at least in part by the presence of an NPU and higher memory configurations, this is not a problem vendors can simply engineer around without undermining the AI value proposition.

For Microsoft’s PC partners—Dell, HP, Lenovo, ASUS, and others—the combination of Windows 10 end-of-support, memory pricing uncertainty, and evolving AI expectations creates a precarious environment:

• In the short term, they enjoy a volume windfall as enterprises refresh systems ahead of the 2025 cutoff, often choosing AI-ready devices as the future-proof option.

• In the medium term, they face decelerating enterprise demand after the refresh cycle peaks in 2026, just as consumers are still cautious and price-sensitive.

• Strategically, they can no longer depend on CPU vendor roadmaps alone; they must differentiate on software integrations, management tools, and vertical-specific AI experiences that ride on top of Windows and Copilot rather than simply bundling them.

This is a fundamental shift in the PC business model. OEMs have historically relied on a narrow margin stack built around hardware differentiation—thinness, display quality, battery life, and modest amounts of bundled software. AI PCs demand that they become, to some degree, solution providers, with curated app stacks, pre-trained local models, and value propositions tuned to finance, healthcare, design, engineering, or frontline work.

If they fail to make that transition, AI PCs risk becoming commoditized even faster than traditional PCs, with the NPU reduced to another checklist spec.

The Architecture Battle: x86 vs. Arm in an AI-First World

Underneath the software and business-model questions is an architectural contest that will shape the AI PC market for the next decade: x86 vs. Arm.

According to Gartner:

• Arm-based laptops are poised to dominate the consumer AI PC segment, overcoming compatibility issues as more applications are ported or delivered via platform layers.

• x86 on Windows is projected to own about 71% of AI business laptop sales in 2025, reflecting entrenched enterprise standards and legacy application requirements.

From a business vantage point, this split suggests two diverging trajectories:

In the enterprise, x86 holds the high ground for now, especially with Intel and AMD integrating ever more capable NPUs and AI-optimized GPUs into their platforms. The message to CIOs is continuity plus AI: existing tools, same vendors, but with added AI acceleration and deeper integration with Microsoft 365 and Copilot.

In the consumer and prosumer sectors, Arm’s efficiency and integration—with potentially longer battery life and tighter OS-hardware co-optimization—could give OEMs more room to innovate on form factors and pricing once software maturity catches up. This is where sub-$1,000 “everyday” AI PCs are likely to emerge after 2027, catalyzing consumer adoption.

For Microsoft, walking this line means advancing Windows on Arm without undermining x86’s dominance in business. For OEMs and chipmakers, the AI PC is both a battlefield and a forcing function: whichever architecture can deliver the most compelling on-device AI experiences per watt and per dollar will have an outsized say in how the category evolves.

The Consumer Problem: Price, Purpose, and Perception

If the enterprise AI PC story is about timing and forced upgrades, the consumer AI PC story is about skepticism and stalled urgency.

Survey data highlights a stark set of perceptions:

• More than 60% of U.S. consumers want AI PC prices to fall before considering a purchase.

• 50% say they do not understand why they need an AI PC.

• 61% believe they do not use AI enough to justify the extra cost.

This is not primarily a hardware challenge; it is a narrative and value-communication challenge.

Most consumers already experience AI as something ambient and cloud-based: better photos on their phones, more relevant recommendations in streaming services, smarter autocomplete in messaging, and perhaps chatbot usage in the browser. The push for on-device AI—as opposed to AI “in the cloud”—requires a distinct argument:

• Why does it matter that an assistant runs locally on a laptop instead of on a server?

• How much faster, more private, or more reliable does that make their day-to-day tasks?

• Why should they pay a premium for that difference?

So far, the industry has not answered these questions in a way that resonates with the average buyer. The early AI PC marketing cycle has leaned heavily on generic promises—”smarter,” “faster,” “more productive”—without tying those claims to concrete, relatable workflows in ways that consumers can test in a store or understand in a short demo.

Futurum’s projections suggest that consumers will eventually lean in: by 2030, consumer AI PCs are expected to account for around 38% of AI PC revenue, with a major accelerator being the availability of sub-$1,000 AI-capable systems after 2027. But to unlock that opportunity, vendors will need to do more than wait for prices to fall. They will need to:

• Package AI features into clear use cases—photo and video editing, creative projects, personal knowledge management, assistive technologies—that are obvious at first use.

• Offer tiered experiences, where even entry-level AI PCs deliver tangible advantages over non-AI models, not just a watered-down version of a flagship feature set.

• Leverage on-device privacy as a differentiator, particularly for students, families, and freelancers wary of sending sensitive content to the cloud.

Where the AI PC Truly Adds Business Value

For technology and business decision-makers, the practical question is straightforward: what can an AI PC do that a well-provisioned traditional PC, connected to cloud AI services, cannot?

Based on analyst research and early deployments, several categories stand out where on-device AI is not just nice to have, but strategically important:

Latency-sensitive, real-time assistance

Tasks like live transcription, simultaneous translation during meetings, real-time code assistance in local development environments, or multimodal note-taking can be dramatically smoother when inference happens locally. In high-stakes settings—executive briefings, courtrooms, medical consultations—depending solely on a cloud connection is often unacceptable.

Data residency and privacy

Regulated industries and jurisdictions with strict data sovereignty rules benefit when sensitive documents, voice data, or internal communications can be processed locally by SLMs that never send raw data off-device. This enables AI use cases that would otherwise fail policy or compliance reviews.

Cost optimization at scale

For large enterprises, running every AI interaction through cloud APIs can quickly become expensive and hard to forecast. Offloading frequent, lower-intensity tasks—summarization, basic analysis, document classification—to device-resident models can reduce cloud spend and smooth cost curves over time.

Resilience and offline capability

In frontline, mobile, or field-service roles, connectivity is intermittent or constrained. An AI PC that can function as an intelligent assistant even when offline changes what is possible for logistics, inspection, and maintenance teams.

These categories are where AI PCs move from being “nice-to-have” hardware upgrades to core enablers of digital transformation strategies. They also illustrate why a software-first approach is vital: if enterprises do not have the right applications, models, and workflows in place, they will not realize the advantages that justify the premium AI PCs currently command.

The Next Phase: From Hype to Habits

The AI PC market is entering a decisive phase. The initial excitement around Copilot+ devices, NPU benchmarks, and headline shipment numbers is giving way to a more pragmatic conversation:

• How much incremental productivity do AI PCs actually deliver, measured in hours saved or outcomes improved?

• Which job roles see the biggest gains—from sales and customer support to software development, marketing, and executive leadership?

• What is the total cost of ownership once training, change management, additional memory, and software licenses are factored in?

Analyst firms already anticipate that growth rates will normalize as the market matures, even as total revenue continues to expand through 2030. The critical transition is from deadline-driven purchases (the Windows 10 effect) to habit-driven usage (AI-infused workflows that employees and consumers rely on daily).

For Microsoft and its OEM ecosystem, that transition will demand:

• Deep integration of on-device AI with core productivity suites (Office, communications, collaboration), not as bolt-on features but as default behaviors.

• Investment in partner ecosystems—ISVs and vertical specialists—that can build domain-specific AI apps optimized for on-device models.

• Rethinking how PCs are positioned and sold, moving away from spec comparisons toward outcome-centric messaging that speaks to CFOs, CHROs, and line-of-business leaders, not just CIOs.

For enterprises, the strategic task is to treat AI PCs not as a one-time hardware refresh, but as part of a multi-year roadmap for intelligent endpoints: defining where local AI adds unique value, how it fits with cloud AI strategies, and how to measure impact.

And for consumers, the tipping point is likely to come when AI PCs quietly solve everyday problems—organizing digital lives, enhancing creativity, supporting accessibility—without fanfare or jargon.

The Paradox

The paradox of the AI PC market is that while it is undeniably booming, its long-term success depends on shifting the conversation away from “AI PCs” as a labeled category. When AI capabilities become simply how PCs work, integrated invisibly into the tools people already use, the hype will finally give way to habit—and the market’s explosive growth will be matched by equally durable demand.