AMD’s argument is a forecast, not a proven outcome: as NPU-equipped PCs become common and AMD makes its Ryzen AI software tools easier to use, more software developers may add features that run locally on the NPU. The hardware category is real, but broad application adoption still depends on useful features, compatible software, privacy requirements, performance and cost.
What AMD’s David McAfee was arguing
In a CRN interview, AMD client-channel executive David McAfee presented a two-part ecosystem theory. First, PC makers would ship enough systems with neural processing units (NPUs) to create a meaningful installed base. Second, that installed base would give independent software vendors (ISVs) a reason to optimize applications for the NPU. AMD’s Ryzen AI software toolchain was intended to reduce the engineering work involved.
The important qualification is timing. The interview described a strategy and a prediction, not evidence that ISVs had already adopted NPUs across the software market. A large installed base can attract developers, but only when applications deliver a benefit users notice.
What an NPU does in an AI PC
An NPU is a processor specialized for neural-network and other AI calculations. In a suitable workload, it can operate alongside the CPU and GPU rather than forcing either general-purpose processor to handle every operation. AMD’s rationale is that sustained, repetitive AI tasks may run more efficiently on the NPU, potentially reducing power use.
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That is a design goal, not a blanket performance or battery guarantee. The result depends on the model, software implementation, drivers, operating-system support, memory movement and whether the workload is actually supported by the NPU. A program that has not been optimized for the NPU may continue to use the CPU, GPU or a cloud service.
CPU, GPU and NPU roles
| Processor | Typical strength | Why an application might use it |
|---|---|---|
| CPU | General-purpose, sequential and control-heavy work | Runs the operating system, application logic and tasks without specialized acceleration |
| GPU | Highly parallel graphics and compute | Handles graphics and AI workloads that benefit from substantial parallel throughput |
| NPU | Specialized, often sustained neural-network inference | Can handle supported local AI operations with an efficiency-oriented design |
What qualifies as an AI PC?
AMD’s March 2025 article reproduced an IDC definition: “a system that combines a CPU, a graphics processing unit (GPU) and an NPU on the same physical processor.” That definition identifies the components; it does not mean every application will use the NPU.
The same AMD article said PCs with NPUs rated at 40 trillion operations per second (TOPS) or more can qualify as Microsoft Copilot+ PCs. Copilot+ eligibility is a separate Microsoft platform and feature context, so buyers should verify the current requirements for the specific Windows edition and model rather than treating 40 TOPS as a universal definition of an AI PC. Systems below that level can still support AI features in other applications.
Why hardware scale could attract ISVs
Developers normally prioritize platforms that offer a large potential audience. McAfee’s thesis was that once NPU-equipped laptops reached sufficient volume, an ISV could justify adding an NPU execution path, testing it and supporting it. AMD also positioned its Ryzen AI toolchain as a way to help developers port or optimize models instead of building every low-level component themselves.
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The original interview referred to more than 50 laptop designs using first-generation Ryzen AI chips at that time. That was a historical count, not a current catalog total, and it should not be used to estimate today’s selection.
What must happen for the prediction to work
- Reach: enough compatible PCs must be in customers’ hands.
- Tooling: developers need stable libraries, model support, documentation and predictable drivers.
- Visible benefit: local execution must improve latency, privacy, cost, battery life or another user outcome.
- Compatibility: the same feature must behave acceptably across operating systems and processor generations.
- Demand: businesses and consumers must value the feature enough to influence software and PC purchases.
Has software already flocked to the NPU?
Not on the evidence available here. An April 2026 IDC paper sponsored by AMD said most AI workloads still run in the cloud, while operating systems and business applications are beginning to unlock local NPU use. That describes a developing transition, not a completed migration.
AMD has documented concrete collaborations that show what NPU use can look like:
BUFFERZONE NoCloud AI
AMD describes BUFFERZONE NoCloud AI as a Chrome or Edge browser extension that performs local phishing detection. The example illustrates a security use case in which processing on the PC can reduce dependence on sending content to a remote service. The description is AMD’s account, not an independent comparative test.
McAfee Scam Detector
AMD says McAfee Scam Detector uses Ryzen AI NPU support to help identify AI-manipulated audio in video streams. Whether a user sees a benefit depends on the application’s implementation, supported hardware and the specific stream being analyzed.
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Styrk AI
AMD describes Styrk AI as running a local large or small language model on AMD NPU hardware while connecting to its cloud portal for prompt protection. This is a hybrid design: some inference is local, while cloud services remain part of the workflow.
These named products demonstrate targeted partnerships, not proof that ISVs generally have adopted NPUs or that the products outperform cloud or CPU alternatives in every situation.
Why run AI locally at all?
Local inference can provide a shorter response path, keep some data on the device and reduce the amount of information sent to a provider. It may also lower recurring cloud-compute use for repeated workloads. Those advantages matter most for tasks that are small enough to run on the device and sensitive, interactive or frequent enough to justify local processing.
Cloud processing remains useful for larger models, centralized administration, rapid model updates and workloads that exceed local memory or performance limits. The practical future is therefore likely to be mixed: an operating system or application chooses local NPU, CPU, GPU or cloud execution according to the task and policy.
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What the market forecasts actually say
AMD’s March 2025 article attributed two figures to IDC research in an AMD-sponsored white paper:
| Figure | What it measures | How to interpret it |
|---|---|---|
| 93.9% by 2028 | Forecast share of commercial PC shipments described as AI PCs | An IDC forecast, not a measured current share; the white paper was sponsored by AMD |
| 95% | Surveyed respondents reporting AI use in some capacity | Respondents were manager level or above at companies with at least 1,000 employees and responsibility or input in PC purchasing |
| 97% | Surveyed respondents favoring broad AI deployment in their organizations | A survey result from that defined sample, not a population-wide adoption rate |
Forecasts and executive surveys help explain why AMD is investing in the category, but neither establishes that a particular NPU feature is useful, available on every model or widely deployed today.
Do you need an NPU?
Only if a feature or workload you care about actually supports it. An NPU-capable system can provide future options, but the presence of the hardware alone does not make an application faster, private or offline.
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Check these questions before buying
- Which applications? Confirm that the operating-system features and programs you use support local NPU execution.
- Which threshold? If Copilot+ features matter, check the current NPU requirement and the exact model’s eligibility.
- What workload? Distinguish small, repeated inference from large-model work that will remain cloud-based.
- What data policy? Determine whether local, cloud or hybrid processing satisfies your organization’s privacy and governance rules.
- What trade-off? Compare total system performance, battery behavior, memory, software compatibility and price—not TOPS alone.
No source cited here establishes one universally best Ryzen AI laptop, and model capabilities vary. Treat NPU support as one part of a workload and software decision.
The realistic outlook for AMD’s thesis
AMD is right that an installed base and developer tooling can reinforce each other: more compatible PCs can improve the business case for optimization, and better applications can make those PCs more attractive. But the “flock to the NPU” outcome is conditional. Developers will continue to choose the execution path that delivers the best combination of user value, reliability, compatibility and operating cost.
For now, the evidence supports a growing set of local and hybrid examples alongside a market in which most AI work still runs in the cloud. AI PCs are an established product direction; universal NPU application support is still an open ecosystem question.
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