Microsoft’s latest pitch for an “intelligent Windows” is being built around a tiny chip inside some new laptops — the Neural Processing Unit (NPU) — but the business case, user benefits, and long-term implications remain murky even as device makers rush to ship AI-capable hardware.
Microsoft has publicly positioned NPUs as the enabling silicon for a new class of devices it calls Copilot+ PCs. These machines pair the traditional CPU and GPU with an NPU rated at 40+ TOPS (trillions of operations per second), a floor Microsoft says is needed to run the first wave of on-device AI experiences. In Microsoft’s framing, NPUs allow small language models and other AI workloads to run locally with lower latency and power draw, enabling features such as real-time translation, image generation, live captions, Windows Studio Effects, and the controversial Recall feature.
At the same time, NPU-equipped machines are proliferating: industry trackers report that a growing slice of notebooks now includes dedicated NPUs, and vendors from Qualcomm, Intel and AMD are shipping chips that hit marketing TOPS figures. Yet for everyday users, the return on buying a Copilot+ PC today is far from obvious. Many of the experiences Microsoft highlights either require cloud components, exist as incremental productivity tweaks, or remain in preview. That mismatch — aggressive hardware marketing, modest immediate payoff — is fuelling healthy skepticism in IT teams, pundit circles, and among privacy advocates.
TOPS — tera operations per second — is the headline metric vendors use to advertise NPU performance. But TOPS is a blunt instrument: it measures theoretical peak arithmetic throughput under certain precisions and doesn’t translate directly to user experience. Memory bandwidth, latency, model architecture, quantization format (INT8, FP8, INT4 and so on), and software stacks all shape real-world performance. In short, two NPUs with similar TOPS ratings can behave very differently depending on system design and workload.
That number is a marketing and engineering compromise. It’s high enough to enable multi-modal tasks and low-latency inference, but not so high as to be exclusive to ultra-premium silicon. Several mainstream silicon families now tout TOPS figures in this ballpark, which helps Microsoft present Copilot+ as an attainable upgrade rather than a niche premium segment.
Importantly, Microsoft treats Copilot+ PCs as a distinct hardware category: Copilot+ devices must meet extra hardware criteria (NPU 40+ TOPS, 16GB RAM, 256GB storage) on top of standard Windows 11 minimums. The company markets this as an ecosystem move — a way for hardware partners to offer “AI-enabled” experiences that are tightly integrated with Windows.
Privacy tools, browsers, and messaging apps have already pushed back: developers have implemented DRM-like flags and other measures to prevent screenshots or recording. Privacy-minded browser vendors have even added default mitigations to block Recall-style capture. That community response highlights a core tension: local AI enables richer features, but those same primitives can inadvertently harvest sensitive data if controls and developer opt-outs are not granular and respected.
Within the broader set of “AI-capable” devices, the subset that meets Microsoft’s Copilot+ specification is still a minority but is growing. Vendors are pricing aggressively, offering device discounts and promotions to move inventory, and pushing Copilot+ branding to future-proof purchases.
Yet hardware without killer software remains an expensive promise. For many users, the current Copilot+ features are neat but not transformative. Privacy debates around Recall and early developer pushback highlight the need for more robust controls and clearer policies. Enterprises must weigh the upside of local AI against compatibility headaches, manageability concerns, and potential future pressure to refresh hardware.
The sensible approach for most organizations and consumers is cautious optimism: watch the ecosystem, validate real-world workloads, insist on strong privacy controls, and avoid buying into NPUs as insurance against an uncertain future. If and when small language models and practical on-device agents become demonstrably useful across everyday workflows, the NPU will look less like a marketing spec and more like a delivery of value. Until then, NPUs are an intriguing piece of silicon that still needs a compelling software story to earn their place in everyone’s next laptop.
Source: theregister.com Microsoft pushes NPUs as a way to an intelligent Windows
Microsoft has publicly positioned NPUs as the enabling silicon for a new class of devices it calls Copilot+ PCs. These machines pair the traditional CPU and GPU with an NPU rated at 40+ TOPS (trillions of operations per second), a floor Microsoft says is needed to run the first wave of on-device AI experiences. In Microsoft’s framing, NPUs allow small language models and other AI workloads to run locally with lower latency and power draw, enabling features such as real-time translation, image generation, live captions, Windows Studio Effects, and the controversial Recall feature.At the same time, NPU-equipped machines are proliferating: industry trackers report that a growing slice of notebooks now includes dedicated NPUs, and vendors from Qualcomm, Intel and AMD are shipping chips that hit marketing TOPS figures. Yet for everyday users, the return on buying a Copilot+ PC today is far from obvious. Many of the experiences Microsoft highlights either require cloud components, exist as incremental productivity tweaks, or remain in preview. That mismatch — aggressive hardware marketing, modest immediate payoff — is fuelling healthy skepticism in IT teams, pundit circles, and among privacy advocates.
What is an NPU and why 40+ TOPS?
NPUs explained
An NPU (Neural Processing Unit) is a purpose-built accelerator designed to perform the matrix math common to machine learning inference more efficiently than a CPU or general-purpose GPU. NPUs use reduced-precision arithmetic and specialized datapaths to deliver high throughput at lower power, which is attractive for thin-and-light laptops and always-on scenarios.TOPS — tera operations per second — is the headline metric vendors use to advertise NPU performance. But TOPS is a blunt instrument: it measures theoretical peak arithmetic throughput under certain precisions and doesn’t translate directly to user experience. Memory bandwidth, latency, model architecture, quantization format (INT8, FP8, INT4 and so on), and software stacks all shape real-world performance. In short, two NPUs with similar TOPS ratings can behave very differently depending on system design and workload.
Why Microsoft picked 40+ TOPS
Microsoft’s Copilot+ PC messaging sets 40+ TOPS as the baseline for delivering its first wave of on-device experiences. The rationale is straightforward: run modest-sized language models, camera and audio processing, and other AI primitives locally without taxing battery life or requiring a cloud trip every time.That number is a marketing and engineering compromise. It’s high enough to enable multi-modal tasks and low-latency inference, but not so high as to be exclusive to ultra-premium silicon. Several mainstream silicon families now tout TOPS figures in this ballpark, which helps Microsoft present Copilot+ as an attainable upgrade rather than a niche premium segment.
Microsoft’s Copilot+ PC strategy: what it promises
The pitch
Microsoft’s proposition rests on three claims:- Better responsiveness: On-device inference reduces latency and should feel faster for tasks like image editing, transcription, and simple conversational agents.
- Privacy and offline capability: Keeping inference local avoids sending sensitive data to the cloud for every interaction.
- Battery efficiency: NPUs are optimized for the math of AI inference and can deliver better power-per-inference metrics than CPU or GPU alternatives.
The initial feature waves
Microsoft has organized Copilot+ experiences into staged “waves.” Early Wave 1 features include Recall (preview), Windows Studio Effects, Live Captions and simplified local generative tasks such as in-painting through bundled apps. Wave 2 and beyond extend into Click to Do, improved Semantic Search, and on-device agents that accept natural-language prompts inside Windows Settings and other places.Importantly, Microsoft treats Copilot+ PCs as a distinct hardware category: Copilot+ devices must meet extra hardware criteria (NPU 40+ TOPS, 16GB RAM, 256GB storage) on top of standard Windows 11 minimums. The company markets this as an ecosystem move — a way for hardware partners to offer “AI-enabled” experiences that are tightly integrated with Windows.
What’s actually usable today?
Useful — but niche — features
Some of the features Microsoft highlights genuinely benefit from local processing:- Real-time Windows Studio Effects (eye contact, auto-framing, background blur): these create smoother, lower-latency camera effects that are useful in meetings and content creation.
- Live Captions with translation: local models can reduce dependency on cloud translation and improve privacy for short snippets.
- Click to Do: when it works reliably, contextual actions on selected text or images can speed small tasks.
The privacy controversy: Recall
Recall, Microsoft’s opt-in screen- and context-capture feature, exemplifies both the promise and peril of on-device AI. Recall stores a searchable history of onscreen activity so users can later retrieve snippets of text, images, or video frames. It’s powerful in theory — but it also risks capturing passwords, payment details, and other sensitive content.Privacy tools, browsers, and messaging apps have already pushed back: developers have implemented DRM-like flags and other measures to prevent screenshots or recording. Privacy-minded browser vendors have even added default mitigations to block Recall-style capture. That community response highlights a core tension: local AI enables richer features, but those same primitives can inadvertently harvest sensitive data if controls and developer opt-outs are not granular and respected.
Market adoption: hype, hardware refresh cycles, and real demand
The current market picture
The hardware industry is shipping NPUs in increasing numbers. Analyst channel data show that a substantial portion of recent notebook shipments include an on-die NPU or NPU-capable SoC. That growth is driven as much by processor refresh cycles and OEM product roadmaps as by explicit customer demand for AI features.Within the broader set of “AI-capable” devices, the subset that meets Microsoft’s Copilot+ specification is still a minority but is growing. Vendors are pricing aggressively, offering device discounts and promotions to move inventory, and pushing Copilot+ branding to future-proof purchases.
Why enterprises are cautious
IT buyers are pragmatic. They weigh:- Compatibility: Enterprise software ecosystems still depend heavily on x86 compatibility. ARM-based Copilot+ offerings have encountered app-compatibility hiccups.
- Cost vs benefit: Premium pricing on early Copilot+ devices is a turn-off when the productivity upside is ambiguous.
- Manageability and security: New local AI layers introduce new attack surfaces and uncertainty in patch cycles and telemetry.
The strengths of Microsoft’s approach
- Platform coherence: Microsoft’s strategy links hardware, OS features, and developer tooling (e.g., Windows AI Foundry) into a coherent narrative. That unity lowers friction for OEMs and developers to target on-device capabilities.
- Battery-conscious AI: Offloading inference to NPUs can genuinely lower power consumption for many workloads, which matters in laptops and thin devices.
- Local resilience and latency: On-device models reduce dependency on network connectivity and cloud latency — key for remote or intermittent connectivity scenarios.
- Developer momentum: By setting a baseline (40+ TOPS) Microsoft gives developers a target for capability planning, which should encourage software investment over time.
The weaknesses, risks, and unanswered questions
1. Hardware-first marketing, software-lag reality
NPUs are hardware looking for software. Building silicon before a clear set of compelling, widely demanded apps means many early Copilot+ benefits are speculative. Customers rarely buy hardware for marketing alone; they buy for apps and workflows they will actually use.2. TOPS is noisy and often misleading
Comparing NPUs by TOPS is like comparing engines by horsepower without considering gearing, aerodynamics, or fuel: it’s an incomplete metric. Differences in numerical precision, memory subsystem design, and runtime software mean TOPS figures rarely predict user experience cleanly.3. Privacy and data governance risks
Features that capture screen content, keystrokes, context or voice expand the attack surface and require rigorous consent, developer controls, and enterprise policy settings. The current ecosystem reaction — app-level DRM flags, browser blocks, and developer pushback — indicates Microsoft, OEMs, and ISVs will need far more mature privacy tooling and clear guardrails.4. Fragmentation and platform confusion
A two-tier Windows world could emerge: standard Windows 11 and Copilot+ Windows experiences. If Microsoft ties features to NPUs in exclusive ways, customers could face fragmentation where some apps behave differently across devices. This complicates IT procurement and software lifecycles.5. Upgrade cycles and compatibility pressure
Windows 11’s previous hardware shake-up (TPM, Secure Boot, CPU compatibility) forced many users and organizations to plan hardware refreshes. There is a legitimate worry that marketing and feature choices could create pressure for another mandatory hardware refresh if Microsoft ever decides to bake AI hardware into baseline compatibility lists. That would have major cost and environmental implications.6. Security and software supply chain risks
New silicon + firmware + AI stacks mean new firmware update flows, drivers, SDKs, and model distribution pipelines — each a potential vector for vulnerabilities. Enterprises will demand rigorous transparency and patch guarantees; the industry has not yet standardized against these new risk classes.Practical guidance for buyers and IT teams
- Define the use case first: Don’t buy NPUs because they’re trendy. Identify workflows where low-latency on-device inference yields measurable gains.
- Audit privacy controls: Look for granular controls, enterprise policy support, and the ability to opt-out of features that capture screen or audio data.
- Check software compatibility: For ARM-based Copilot+ devices, test the core enterprise apps and tooling thoroughly before mass deployment.
- Prioritize manageability: Ensure vendors offer centralized management, firmware update policies, and clear support SLAs for the new AI components.
- Consider lifecycle costs: Factor in potential retraining, driver updates, and the downstream effects of committing to a new hardware platform.
Developer and industry implications
Opportunity for app makers
Local inference unlocks new app patterns: offline assistants, real-time camera effects, and faster interactive UIs. Developers who optimize models for constrained targets, embrace quantization, and prioritize memory-efficient architectures will find appetite for differentiated apps on Copilot+ devices.Need for standards and transparency
The industry needs clearer benchmarks, standardized privacy flags, and transparent update models. Benchmarks must go beyond TOPS and include metrics like sustained inference throughput, latency, TOPS/W, and memory footprint for representative models.Model lifecycle and provenance
On-device AI introduces model supply-chain concerns. Enterprises will want attestable model provenance, signed model updates, and verifiable rollback mechanisms. These operational requirements will shape enterprise adoption.Where this goes next: two plausible paths
- Software-led maturation: Small language models, improved toolchains, and developer ecosystems produce clear, high-value apps that justify NPUs. In this path, Copilot+ becomes a productivity multiplier as local AI matures and integrates with cloud models for heavier lifting. NPUs remain optional for most users but attractive for power users and specific verticals (education, accessibility, content creation).
- Hardware mandate path: NPUs become a de facto requirement for certain Windows feature sets, either through marketing or through gradual feature gating. This creates stronger incentives for vendors to ship NPUs in mainstream SKUs and could pressure enterprises into refresh cycles. That path risks duplicate churn if features the market doesn’t value are used to lock out older hardware.
Conclusion
Microsoft’s push for NPUs and Copilot+ PCs is a logical next step in bringing AI closer to users — but the strategy is risky and incomplete. The company has correctly identified the technical advantages of local inference: lower latency, better battery efficiency, and privacy potential. It has also created a clear hardware target that galvanizes silicon vendors and OEMs.Yet hardware without killer software remains an expensive promise. For many users, the current Copilot+ features are neat but not transformative. Privacy debates around Recall and early developer pushback highlight the need for more robust controls and clearer policies. Enterprises must weigh the upside of local AI against compatibility headaches, manageability concerns, and potential future pressure to refresh hardware.
The sensible approach for most organizations and consumers is cautious optimism: watch the ecosystem, validate real-world workloads, insist on strong privacy controls, and avoid buying into NPUs as insurance against an uncertain future. If and when small language models and practical on-device agents become demonstrably useful across everyday workflows, the NPU will look less like a marketing spec and more like a delivery of value. Until then, NPUs are an intriguing piece of silicon that still needs a compelling software story to earn their place in everyone’s next laptop.
Source: theregister.com Microsoft pushes NPUs as a way to an intelligent Windows