Microsoft is rolling a targeted overhaul of Windows Search that will use on‑device AI to deliver
semantic file search and a notable responsiveness uplift on Copilot+ PCs, ushering a practical performance boost for users with modern NPUs and changing how Windows 11 finds your files and photos.
Overview
Windows Search, the background indexing and query service that powers File Explorer, the taskbar search box, and Settings, is being augmented with a semantic indexing layer that runs on-device when hardware meets Microsoft’s Copilot+ PC requirements. That layer stores vector embeddings for documents and images and uses nearest-neighbor matching to return results that reflect meaning and intent rather than literal keyword matches. Microsoft has been previewing the capability through Windows Insider builds and is expanding the rollout to more Copilot+ hardware, with cloud-photo and OneDrive integration included in the improvements. These changes are notable because they shift a traditional lexical indexer—optimized for filenames and literal content matches—toward a hybrid model that blends lexical indexing with semantic similarity. For eligible devices this can translate into searches that find the right document even when users can’t recall exact filenames, and into substantially lower latency for certain queries by executing inference on a local Neural Processing Unit (NPU) rather than routing every request to the cloud.
Background: Copilot+ PCs, NPUs and Microsoft’s direction
What is a Copilot+ PC?
Copilot+ PCs are a Microsoft-defined hardware class that combines traditional CPU and GPU resources with a high‑performance NPU and a set of platform requirements intended to accelerate on-device AI. The platform guidelines emphasize a
40+ TOPS class NPU (trillions of operations per second), baseline memory and storage, and OEM-certified components to guarantee consistent AI experiences. Microsoft and partners position Copilot+ as the architecture that enables local generative and inference tasks while integrating tightly with cloud services when needed.
Why TOPS and on-device inference matter
TOPS is an industry shorthand for raw NPU throughput. Devices with 40+ TOPS can execute reasonably large neural models locally, enabling features that would otherwise require cloud calls. Running semantic indexing inference on these NPUs offers several practical advantages: lower latency, offline availability, reduced network traffic, and better privacy posture for routine queries because sensitive data need not leave the device. However, TOPS is only one axis—memory bandwidth, model quantization, and driver stack maturity also shape real-world performance.
What’s changing in Windows Search
From lexical indexing to a hybrid semantic model
Traditionally, Windows Search uses a lexical index: names, metadata, and literal content matches are pre-indexed so keyword searches are fast. Microsoft is adding a semantic layer that creates
vector embeddings for document text and image descriptors (including OCR text and object labels). When you submit a natural-language query like “photos from our Paris trip” or “spreadsheet with June travel budget,” Windows converts the query into an embedding and performs similarity matching against the stored vectors alongside the classic index. The result is search behavior that returns relevant items even when they don’t contain the exact search words.
Where this runs and when it’s available
The semantic layer is enabled on
Copilot+ PCs—initially on Snapdragon-based devices and rolling to qualifying AMD and Intel machines that meet Microsoft’s NPU and platform specifications. The feature surfaced in Windows Insider flights in early 2025 and is being rolled out gradually to Dev, Beta, and Release Preview channels; eligible Insiders have reported builds exposing the capability and Microsoft’s documentation lists the relevant builds by channel. Microsoft also added support for finding photos saved in the cloud (OneDrive) as part of the improved search experience.
Supported file types and languages
Early previews list supported document formats (.txt, .pdf, .docx, .pptx, .xls / .xlsx) and common image types (.jpg, .png, .gif, .bmp) for the semantic indexing experience. Microsoft also notes support for a handful of languages at launch (English, Chinese, French, German, Japanese and Spanish), with expansion expected over time. These format and language constraints reflect where Microsoft has validated its small models and vision pipelines for local inference.
The performance case: why this can be a “huge” boost
Lower latency through local inference
Offloading semantic model inference to the NPU removes cloud round-trips for common queries. For simple natural-language lookups and image‑based searches, on-device execution cuts network latency entirely and leverages hardware designed for massively parallel tensor math. The user-perceived improvement is shorter time-to-first-result, smoother interactive search, and offline functionality when network connectivity is limited or unavailable.
Reduced CPU load and energy efficiency
When NPUs handle AI work, the CPU and GPU are freed for other tasks or can idle more often. Microsoft’s Copilot+ messaging emphasizes that NPU-accelerated workloads are both faster and more power-efficient for AI tasks compared with running inference on a CPU. That implies improved battery life during sustained AI-driven activity and less thermally induced throttling in thin-and-light laptops. Real-world gains depend on the NPU design and the device’s overall thermal envelope.
Indexing trade-offs and initial cost
Semantic indexing requires building vector representations for the files you choose to index. That initial indexing pass may be I/O and CPU/NPU intensive—and Microsoft explicitly recommends plugging in Copilot+ PCs for the first full indexing to avoid battery or performance impacts. After initial indexing, queries should respond much faster and with higher relevance for semantically complex searches. The net effect for everyday desktop discovery tasks can feel dramatic, but the one-time indexing cost is real.
What to expect in practice — measurable and unverifiable points
- Expect faster, more helpful search results when your device is a Copilot+ PC and you enable “Enhanced” indexing.
- Expect the search box to return items based on meaning, not just literal word matches.
- Expect offline search for semantic queries on-device when the NPU is available.
- Do not expect guaranteed numeric speedups from Microsoft yet—publicized benchmarks demonstrating “X× faster” for Windows Search have not been released; the “huge performance boost” is a qualitative summary of better relevance and lower latency, not a quantified throughput metric. Microsoft’s previews and OEM materials cite the 40+ TOPS NPU target but do not publish standardized search latency or throughput numbers for broad comparison. Readers should treat “huge” as context-dependent until independent benchmarking is available.
Security, privacy and manageability
On-device AI reduces cloud exposure but doesn’t eliminate telemetry
Running semantic inference locally narrows the surface area for data leaving the device, which is a meaningful privacy win for many sensitive searches. Microsoft’s Copilot+ architecture explicitly targets on-device inference for routine queries. That said, some experiences—especially cloud integrations like OneDrive photo search and deeper Copilot features—will still involve cloud services. Administrators and privacy-conscious users should verify behavior for their account types (personal Microsoft Account vs. Entra/Work accounts) because Microsoft’s previews already show differences in what is surfaced from personal versus organizational OneDrive content.
Index scope, exclusions and enterprise controls
Windows 11 exposes controls to limit which folders are indexed and to exclude sensitive locations. The OS still supports Classic vs. Enhanced indexing modes: Classic targets Documents, Pictures and Desktop by default; Enhanced indexes most of the PC if selected. Enterprises can exclude network shares, removable media, or specific folders, and administrators can script or manage settings centrally through MDM or policy (PowerShell and the Windows Search API remain usable for automation). For large-scale rollouts, IT teams should plan initial indexing windows and storage considerations for the index database.
Data residency and compliance
Organizations with strict compliance needs will want to validate whether any semantic processing or model telemetry is routed to Microsoft cloud endpoints for hybrid features. Microsoft’s Copilot+ messaging emphasizes on-device capability, but cloud-assisted scenarios and logging can still occur depending on feature set and tenant configuration. Until Microsoft publishes enterprise‑grade compliance guidance specific to semantic indexing, cautious organizations should pilot the feature in controlled environments and consult their Microsoft account teams.
Compatibility, rollout and the practical upgrade path
Who gets it and when
The semantic search preview has been visible in Windows Insider builds in 2025 and is being rolled out gradually across the Dev, Beta and Release Preview channels for qualifying Copilot+ hardware. Microsoft’s messaging shows initial availability on Snapdragon-powered Copilot+ PCs and an expansion to eligible AMD and Intel devices "soon"—the company has included the feature in multiple preview builds across channels as it validates broader hardware support. Users on older or non‑Copilot+ hardware will not see the on-device semantic experience until their hardware meets the NPU/firmware requirements or Microsoft provides alternative implementations.
How to try it now
- Join the Windows Insider program in a supported channel where the feature is rolling out.
- Ensure your device qualifies as Copilot+ (OEM marketing, system page, or Microsoft compatibility listings).
- Update Windows and install the preview build that exposes the semantic search feature.
- Go to Settings > Privacy & security > Searching Windows and enable Enhanced indexing if you want the broadest coverage (plugging in your device during initial indexing is recommended).
Strengths and opportunities
- Search that understands intent: Natural-language queries and image-descriptive searches make desktop discovery significantly easier for casual and power users alike.
- Offline usefulness and low latency: On-device semantic inference reduces reliance on cloud connectivity and delivers snappier responses for common queries.
- Integration across the OS: File Explorer, the taskbar search box, and Settings all benefit from the same semantic layer, delivering a uniform experience.
- Complement to cloud features: Built-in OneDrive photo integration blends local semantic results with cloud content for more comprehensive discovery.
These strengths are meaningful: they change the UX of finding content on a PC and align Windows with an industry push toward local AI acceleration.
Risks, limitations and open questions
- Heterogeneous hardware reality: Not all NPUs are equal. TOPS is a raw metric that doesn’t capture model memory footprint, driver quality, or thermal constraints; outcomes will vary between Qualcomm, AMD and Intel designs. Buyers should verify device NPU specifications and OEM validation before assuming parity.
- Initial indexing cost and storage footprint: The one-time index build may be I/O-heavy and time-consuming on large drives. Organizations need to plan for the indexing window and potential storage used by vector embeddings.
- Unquantified performance claims: Microsoft and press coverage describe major responsiveness improvements but haven’t published standard benchmarks for search latency, CPU/NPU utilization, or power consumption—so “huge” remains a qualitative claim until third-party benchmarks appear.
- Privacy nuance for cloud‑integrated features: While on-device inference reduces cloud exposure, OneDrive and other cloud integrations still surface content from the cloud; policy and account type differences already affect what gets returned for enterprise accounts. Administrators must validate behavior for their compliance needs.
- Compatibility and app ecosystem frictions: Copilot+ PCs initially skew toward specific ARM and specialized x86 SKUs; app compatibility, driver maturity, and tooling vary across the ecosystem and can complicate rollouts in mixed-fleet environments.
Recommendations for users and IT teams
Consumers and power users
- If you’re buying a new PC and AI-enhanced local search matters, look for Copilot+ certification and NPU specifications (40+ TOPS) in OEM materials.
- For existing Copilot+ hardware, enable Enhanced indexing in Settings > Privacy & security > Searching Windows and allow the initial index to complete while the device is plugged in.
- Keep expectations measured: the experience will feel much better for intent-based queries, but Microsoft hasn’t published numerical speedups.
IT administrators and enterprise teams
- Pilot the feature in a controlled lab with representative hardware and dataset sizes.
- Verify indexing exclusions, storage footprint, and initial indexing windows to avoid user disruption.
- Audit data flow and telemetry settings to confirm compliance posture for your organization.
- Work with OEM/partner channels to confirm Copilot+ device lists and driver maturity before broad deployment.
Looking ahead: what Microsoft and the industry must clarify
Semantic search on Windows is an important step toward integrating local AI pervasively in the OS, but the story is still maturing. Microsoft should publish:
- Clear, reproducible benchmarks that demonstrate typical latency and power improvements for representative search workloads.
- Enterprise-ready controls and documentation covering telemetry, logging, and data residency for semantic indexing.
- OEM certification details and a public list of qualifying Copilot+ SKUs with NPU, firmware and driver versions.
Until those areas are addressed, the feature remains a powerful preview of what on-device AI can deliver rather than a fully characterized replacement for existing search infrastructure.
Conclusion
Microsoft’s semantic indexing rollout for Windows Search marks a meaningful inflection point: search on Windows 11 is evolving from literal string matching to intent-driven discovery powered by local neural inference on Copilot+ hardware. For eligible users this represents a tangible usability and responsiveness improvement, particularly for natural‑language queries and photo discovery. The technical foundation—vector embeddings and on‑device NPU inference—promises lower latency and improved privacy for routine searches. However, the practical benefits will vary by device NPU, OEM implementation, and the size of indexed data, and Microsoft has not yet published standardized, third-party-verified performance figures. For now, the right course for enthusiasts and IT teams is to pilot the technology, verify device claims, and plan indexing strategy before a wider rollout.
Source: Neowin
https://www.neowin.net/news/a-key-component-in-windows-11-is-getting-a-huge-performance-boost-soon/