Every few years, the technology industry rallies around a new buzzword that promises to fundamentally reshape the way we interact with our devices. In 2024, that label is firmly affixed to “AI PCs”—a new breed of laptops and desktops sporting neural processing units (NPUs) designed to handle artificial intelligence workloads locally. The marketing push from hardware giants is strong, but for many everyday users, the question isn’t whether these machines represent the future, but whether there’s any good reason to care about them right now.
Manufacturers like Intel, AMD, Qualcomm, and Apple (with its “Neural Engine”) have spent the past year touting AI PCs as a step-change in computing. These computers are meant to bring cloud-level AI smarts directly to our laps, enabling instantaneous transcription, photo enhancement, real-time video effects, and a raft of other machine learning-powered features—all running without the need for an internet connection. In theory, this blend of privacy, speed, and intelligence should spark a new era of productivity and creativity.
But as MakeUseOf and other outlets have pointed out, the current landscape suggests that, for average users, the AI PC’s promise is more sizzle than steak. Let’s explore four key reasons why so many remain unconvinced by the current AI PC revolution.
According to XDA Developers, Intel’s Michelle Johnston Holthaus acknowledged that demand for newer, pricier “Lunar Lake” and “Meteor Lake” PC models is far outpaced by that for older, less expensive “Raptor Lake” machines. She stated, “What we're really seeing is much greater demand from our customers for n-1 and n-2 products so that they can continue to deliver system price points that consumers are really demanding ... Meteor Lake and Lunar Lake are great, but come with a much higher cost structure.”
This sentiment tracks with broader PC market trends. Most buyers, especially in a challenging macroeconomic context, stick with tried-and-true value over bleeding-edge tech. While some early adopters are willing to invest in AI PCs, mainstream adoption will remain limited as long as prices are elevated without clear, must-have upgrades.
Critical Analysis: The price premium for AI PCs is clear and well-documented. However, it’s worth noting that as with most new hardware technologies, costs are likely to fall if adoption increases and manufacturing matures. If future software creates an undeniable demand for local AI acceleration, this calculus could change. For now, however, the price-to-benefit ratio remains unconvincing for many.
A significant portion of these functions can also be performed using existing hardware, often via cloud-based services. Software giants have been quick to offer AI features on non-AI PCs, leveraging the cloud to bring advanced capabilities to millions of older machines. For example, Microsoft has extended Copilot+ features to additional Windows 11 devices, but requires an internet connection, ensuring much of the actual processing still happens in the cloud.
A test cited by the Just Josh YouTube channel demonstrates that even AI-branded laptops like the ASUS ProArt PX13 do not necessarily funnel their AI workloads through their NPU. Instead, tasks like running the Omni AI assistant default to the integrated GPU or the cloud, contradicting marketing claims about local AI acceleration.
Critical Analysis: While some niche workflows—especially those requiring privacy or real-time processing without connectivity—could benefit from on-device AI, the everyday user is unlikely to notice much of a difference at present. Unless developers begin building truly exclusive features that leverage NPUs in ways that can’t be matched by cloud or traditional processors, the real-world significance of AI PCs will continue to lag behind their branding.
As a result, many of the headline AI features on these devices continue to rely on cloud computing. Pressing the Copilot key on a new Surface or other AI laptop simply launches a chatbot that queries OpenAI’s models over the internet. Text and images are sent off-device, processed on powerful remote servers, and results returned—precisely the same workflow familiar to anyone using ChatGPT, Gemini, or similar AI tools on a regular PC, phone, or tablet.
Microsoft’s announcement to broaden AI Copilot+ support in Windows 11 required an internet connection across eligible devices as of April 2025—a tacit admission that local processing power remains limited.
Critical Analysis: While companies highlight local privacy and speed for edge AI tasks, the real-world constraints show this is presently the exception rather than the rule. Until NPUs in consumer hardware can match a meaningful fraction of what’s possible in the cloud, the rationale for splurging on an AI PC is weak—especially when users can already run top-tier AI workloads remotely from devices as old as a several-year-old smartphone.
Security Angle: It is true that local AI can theoretically improve privacy by keeping data on-device. Yet, this benefit only applies to workflows that actually use the on-device NPU. Reports suggest that, unless clearly specified, many current AI PC features are cloud-bound, so privacy gains are more theoretical than practical at this stage.
Windows Copilot, Microsoft’s flagship AI assistant, is functionally close to a glorified chatbot. It replicates much of what users already experience in ChatGPT or Bing Chat, occasionally spiced with some Windows-specific commands or file search abilities. Features that feel truly “baked in”—automated file management, context-sensitive help, or hands-off maintenance—are few and far between. Most of what makes up the “AI” in AI PCs is bolted on, not woven into the OS fabric.
This lack of deep, systemic integration is not limited to Microsoft. Other PC makers, including ASUS, Acer, and Lenovo, offer their own AI features and assistants, but these typically provide lightweight value-adds. In some cases, the AI “assistant” doesn’t even use the advertised NPU, instead relying on traditional CPU, GPU, or (again) the cloud.
Vendors, in rushing to ride the AI PC marketing wave, have created a fragmented ecosystem. With little standardization and few killer apps, it’s no wonder many users report indifference or even skepticism toward AI-branded computers.
Critical Analysis: True step-change moments in computing occur when new technology is so deeply embedded in the operating system and software that it becomes invisible and essential. For now, that bar remains much higher than any current AI PC has reached.
- Real, Exclusive Local AI Features
AI PCs will become essential only if developers create transformative, on-device applications that change users’ day-to-day workflows in ways not possible with cloud or traditional hardware. This could mean lightning-fast, private voice assistants; ultra-responsive creative tools; or novel accessibility features that simply aren’t feasible today.
- Improved NPU Capabilities
Hardware advancements are needed. The AI workload gap between consumer NPUs and server-grade hardware remains vast. For local AI to match the capabilities of the cloud for real-world tasks, NPUs will need significant performance gains without draining battery or inflating prices.
- Deeper OS and App Integration
Incremental rollouts of AI features within the operating system are not enough. For the AI PC concept to deliver, AI must become an intelligent, context-aware layer woven deeply into how we interact with files, troubleshoot issues, automate routine tasks, and create or collaborate.
- Transparent, Standardized Marketing
Too much of the current AI PC conversation is marketing-driven, leading to confusion and disappointment. Setting clear expectations about which features genuinely run locally, what NPUs can and can’t do, and how privacy is handled will help rebuild trust and excitement.
- Battery Life Gains:
By offloading certain lightweight AI workloads from the CPU/GPU to the ultra-efficient NPU, devices may experience better battery life for supported tasks. Early benchmarks for select models suggest this is plausible for specific use cases.
- Local, Private AI Processing:
For applications that do leverage the NPU, data can stay on the device, minimizing privacy risks associated with cloud processing. This is especially important for sensitive fields like healthcare and law, where client confidentiality is paramount.
- Foundation for the Future:
History shows that hardware advances sometimes precede the software that takes advantage of them. Widespread deployment of NPUs may give rise to innovative applications a few years down the line—possibly in ways we can’t yet predict.
Overselling AI PC capabilities risks alienating consumers. If branding runs ahead of practical value, users who invest early may feel misled or let down.
- Security Considerations:
While local processing offers privacy benefits, it also shifts attack vectors. NPUs, OS components, and bundled AI features could create new vulnerabilities. Continued auditing and patching will be key.
- Environmental Cost:
If the race to equip every computer with an NPU leads to premature device replacement and higher resource usage without corresponding real-world gains, the environmental impact could mount.
Industry data backs this up. Intel and Microsoft both report that demand remains strongest for slightly older, non-AI models at accessible prices, with buyers showing little appetite for premium AI devices unless their advantages are both obvious and exclusive.
Those enrolled in specific fields—perhaps professional creators, privacy-conscious organizations, or developers on the cutting edge—may find it worthwhile as an early investment in a future-defined workflow. Mainstream users, however, are justified in caring little about AI PCs for now. The best advice is to keep an eye on the space: when hardware, software, and deep AI integration finally align, the difference will be both impossible to ignore and, critically, impossible to do without. Until then, focus on the essentials and purchase based on what genuinely matters to your workflow today.
This phase is well captured by the sentiment echoed across user forums and expert reviews: AI PCs may be the future, but for now, most people simply don’t care—and there’s little reason they should.
Source: MakeUseOf 4 Reasons I Simply Don't Care About AI PCs
Manufacturers like Intel, AMD, Qualcomm, and Apple (with its “Neural Engine”) have spent the past year touting AI PCs as a step-change in computing. These computers are meant to bring cloud-level AI smarts directly to our laps, enabling instantaneous transcription, photo enhancement, real-time video effects, and a raft of other machine learning-powered features—all running without the need for an internet connection. In theory, this blend of privacy, speed, and intelligence should spark a new era of productivity and creativity.But as MakeUseOf and other outlets have pointed out, the current landscape suggests that, for average users, the AI PC’s promise is more sizzle than steak. Let’s explore four key reasons why so many remain unconvinced by the current AI PC revolution.
1. High Cost with Insufficient Value
Perhaps the most immediate obstacle is simply financial. As of mid-2024, AI-branded PCs routinely cost hundreds of dollars more than comparably equipped models from previous generations. The inclusion of a dedicated NPU inflates the bill of materials, and manufacturers pass this cost on to consumers.According to XDA Developers, Intel’s Michelle Johnston Holthaus acknowledged that demand for newer, pricier “Lunar Lake” and “Meteor Lake” PC models is far outpaced by that for older, less expensive “Raptor Lake” machines. She stated, “What we're really seeing is much greater demand from our customers for n-1 and n-2 products so that they can continue to deliver system price points that consumers are really demanding ... Meteor Lake and Lunar Lake are great, but come with a much higher cost structure.”
This sentiment tracks with broader PC market trends. Most buyers, especially in a challenging macroeconomic context, stick with tried-and-true value over bleeding-edge tech. While some early adopters are willing to invest in AI PCs, mainstream adoption will remain limited as long as prices are elevated without clear, must-have upgrades.
Critical Analysis: The price premium for AI PCs is clear and well-documented. However, it’s worth noting that as with most new hardware technologies, costs are likely to fall if adoption increases and manufacturing matures. If future software creates an undeniable demand for local AI acceleration, this calculus could change. For now, however, the price-to-benefit ratio remains unconvincing for many.
2. Limited Real-World Use Cases
Despite the marketing blitz, genuinely transformative use cases for AI PCs remain thin on the ground. Photo enhancement, live transcription, background blurring, and automatic summaries—these features may sound impressive, but in most cases, they are enhancements of existing workflows rather than fundamentally new capabilities.A significant portion of these functions can also be performed using existing hardware, often via cloud-based services. Software giants have been quick to offer AI features on non-AI PCs, leveraging the cloud to bring advanced capabilities to millions of older machines. For example, Microsoft has extended Copilot+ features to additional Windows 11 devices, but requires an internet connection, ensuring much of the actual processing still happens in the cloud.
A test cited by the Just Josh YouTube channel demonstrates that even AI-branded laptops like the ASUS ProArt PX13 do not necessarily funnel their AI workloads through their NPU. Instead, tasks like running the Omni AI assistant default to the integrated GPU or the cloud, contradicting marketing claims about local AI acceleration.
Critical Analysis: While some niche workflows—especially those requiring privacy or real-time processing without connectivity—could benefit from on-device AI, the everyday user is unlikely to notice much of a difference at present. Unless developers begin building truly exclusive features that leverage NPUs in ways that can’t be matched by cloud or traditional processors, the real-world significance of AI PCs will continue to lag behind their branding.
3. AI Tasks Remain More Capable in the Cloud
The core engineering challenge is that most NPUs in today’s AI PCs are not yet advanced enough to handle the most sophisticated machine learning workloads. Large language models, advanced image generation, and real-time translation are simply beyond the reach of current consumer-grade NPUs.As a result, many of the headline AI features on these devices continue to rely on cloud computing. Pressing the Copilot key on a new Surface or other AI laptop simply launches a chatbot that queries OpenAI’s models over the internet. Text and images are sent off-device, processed on powerful remote servers, and results returned—precisely the same workflow familiar to anyone using ChatGPT, Gemini, or similar AI tools on a regular PC, phone, or tablet.
Microsoft’s announcement to broaden AI Copilot+ support in Windows 11 required an internet connection across eligible devices as of April 2025—a tacit admission that local processing power remains limited.
Critical Analysis: While companies highlight local privacy and speed for edge AI tasks, the real-world constraints show this is presently the exception rather than the rule. Until NPUs in consumer hardware can match a meaningful fraction of what’s possible in the cloud, the rationale for splurging on an AI PC is weak—especially when users can already run top-tier AI workloads remotely from devices as old as a several-year-old smartphone.
Security Angle: It is true that local AI can theoretically improve privacy by keeping data on-device. Yet, this benefit only applies to workflows that actually use the on-device NPU. Reports suggest that, unless clearly specified, many current AI PC features are cloud-bound, so privacy gains are more theoretical than practical at this stage.
4. Shallow and Fragmented Integration
The term “AI PC” conjures visions of an operating system and set of applications transformed from the ground up by machine learning—from natural-language interfaces embedded deep in the workflow to proactive support and self-healing systems. In practice, as MakeUseOf and multiple independent reviews confirm, the integration remains surface-level at best.Windows Copilot, Microsoft’s flagship AI assistant, is functionally close to a glorified chatbot. It replicates much of what users already experience in ChatGPT or Bing Chat, occasionally spiced with some Windows-specific commands or file search abilities. Features that feel truly “baked in”—automated file management, context-sensitive help, or hands-off maintenance—are few and far between. Most of what makes up the “AI” in AI PCs is bolted on, not woven into the OS fabric.
This lack of deep, systemic integration is not limited to Microsoft. Other PC makers, including ASUS, Acer, and Lenovo, offer their own AI features and assistants, but these typically provide lightweight value-adds. In some cases, the AI “assistant” doesn’t even use the advertised NPU, instead relying on traditional CPU, GPU, or (again) the cloud.
Vendors, in rushing to ride the AI PC marketing wave, have created a fragmented ecosystem. With little standardization and few killer apps, it’s no wonder many users report indifference or even skepticism toward AI-branded computers.
Critical Analysis: True step-change moments in computing occur when new technology is so deeply embedded in the operating system and software that it becomes invisible and essential. For now, that bar remains much higher than any current AI PC has reached.
What Needs to Change for AI PCs to Matter?
If the current generation of AI PCs feels like a solution in search of a problem, what must change before they command genuine enthusiasm?- Real, Exclusive Local AI Features
AI PCs will become essential only if developers create transformative, on-device applications that change users’ day-to-day workflows in ways not possible with cloud or traditional hardware. This could mean lightning-fast, private voice assistants; ultra-responsive creative tools; or novel accessibility features that simply aren’t feasible today.
- Improved NPU Capabilities
Hardware advancements are needed. The AI workload gap between consumer NPUs and server-grade hardware remains vast. For local AI to match the capabilities of the cloud for real-world tasks, NPUs will need significant performance gains without draining battery or inflating prices.
- Deeper OS and App Integration
Incremental rollouts of AI features within the operating system are not enough. For the AI PC concept to deliver, AI must become an intelligent, context-aware layer woven deeply into how we interact with files, troubleshoot issues, automate routine tasks, and create or collaborate.
- Transparent, Standardized Marketing
Too much of the current AI PC conversation is marketing-driven, leading to confusion and disappointment. Setting clear expectations about which features genuinely run locally, what NPUs can and can’t do, and how privacy is handled will help rebuild trust and excitement.
Notable Strengths, and the Arguments in Support
While skepticism is warranted, AI PCs aren’t without merit. Some specific strengths are worth acknowledging:- Battery Life Gains:
By offloading certain lightweight AI workloads from the CPU/GPU to the ultra-efficient NPU, devices may experience better battery life for supported tasks. Early benchmarks for select models suggest this is plausible for specific use cases.
- Local, Private AI Processing:
For applications that do leverage the NPU, data can stay on the device, minimizing privacy risks associated with cloud processing. This is especially important for sensitive fields like healthcare and law, where client confidentiality is paramount.
- Foundation for the Future:
History shows that hardware advances sometimes precede the software that takes advantage of them. Widespread deployment of NPUs may give rise to innovative applications a few years down the line—possibly in ways we can’t yet predict.
Potential Risks and Pitfalls
- Overpromising and Underdelivering:Overselling AI PC capabilities risks alienating consumers. If branding runs ahead of practical value, users who invest early may feel misled or let down.
- Security Considerations:
While local processing offers privacy benefits, it also shifts attack vectors. NPUs, OS components, and bundled AI features could create new vulnerabilities. Continued auditing and patching will be key.
- Environmental Cost:
If the race to equip every computer with an NPU leads to premature device replacement and higher resource usage without corresponding real-world gains, the environmental impact could mount.
The Consumer Perspective: What Do Users Really Want?
When consumers shop for a new PC, their priorities remain remarkably consistent: battery life, CPU/GPU performance, memory, storage, display quality, keyboard and port selection. These essentials trump cutting-edge AI features for most people. Shallow or fragmented integration of AI capabilities is unlikely to sway purchasing decisions in the absence of clear, daily value.Industry data backs this up. Intel and Microsoft both report that demand remains strongest for slightly older, non-AI models at accessible prices, with buyers showing little appetite for premium AI devices unless their advantages are both obvious and exclusive.
Conclusion: The Wait-and-See Era of AI PCs
To summarize, the AI PC as marketed in 2024 stands at an inflection point. The hardware is impressive in its potential, but its real-world value remains limited by early-stage software, lackluster integration, and persistent cloud dependence for high-value features. For most users, there’s little compelling reason to pay a premium for an NPU-equipped device when the best AI features remain accessible on older hardware—often via the cloud, and with little practical downside.Those enrolled in specific fields—perhaps professional creators, privacy-conscious organizations, or developers on the cutting edge—may find it worthwhile as an early investment in a future-defined workflow. Mainstream users, however, are justified in caring little about AI PCs for now. The best advice is to keep an eye on the space: when hardware, software, and deep AI integration finally align, the difference will be both impossible to ignore and, critically, impossible to do without. Until then, focus on the essentials and purchase based on what genuinely matters to your workflow today.
This phase is well captured by the sentiment echoed across user forums and expert reviews: AI PCs may be the future, but for now, most people simply don’t care—and there’s little reason they should.
Source: MakeUseOf 4 Reasons I Simply Don't Care About AI PCs