When Satya Nadella strode onto the stage at Microsoft Build 2025, it became immediately clear that the company aims to do for the “Agentic Web” what Windows did for personal computing: provide the foundational platform, tools, and systemic vision upon which an ecosystem can flourish. Developers—which Microsoft has always valued as its bedrock—found themselves pitched to on nearly every front: from local AI model optimization to cross-platform capability, and from developer-friendly command-line tools to massive, cloud-scale AI infrastructure. Yet behind the display of progress and technical prowess, deeper themes undergird Microsoft’s strategy: openness, unification, and a relentless focus on optimizing the complete AI stack for performance, affordability, and real-world impact.
Unlike previous iterations of Windows machine learning libraries or the on-again, off-again support for cross-platform development tools, Foundry Local promises a high-performance runtime, a robust command-line interface, and agents-as-a-service. Developers on both Windows and macOS gain access to unified SDKs—meaning the notorious “works on my machine” dilemma is finally being addressed at scale. The fact that Microsoft built features like Recall and Click to Do for Copilot+ PCs using this same runtime underscores the company’s growing internal dogfooding: the tools Microsoft touts orbit their own engineering priorities.
While OpenAI’s o1 and DeepSeek marked early innovations in cloud-scale inference, Microsoft claims Phi Silica will “completely revolutionize” inference compute on PCs. This is a bold assertion, and while early technical benchmarks of SLMs like Phi Silica do indicate superior efficiency at smaller scales, real-world impact will ultimately depend on how robustly these models can be tailored to varied domains, and how easily developers can integrate them into production Windows applications. Nevertheless, support for LoRA out of the box marks a major democratization of AI customization.
This democratization of reasoning models is potentially transformative: it lowers entry barriers for small developers, challenges the primacy of web aggregators, and could—if widely adopted—shift power toward more open, user-controlled digital ecosystems. However, successful realization will depend on robust open standards, seamless developer tools, and careful navigation of privacy and trust issues. While Microsoft says “something big is going to shift,” the task of building durable, interoperable platforms for agentic web experiences remains a major engineering and governance challenge.
This close integration has immediate implications for next-generation AI apps, especially those relying on Retrieval-Augmented Generation (RAG). With Cosmos DB now available natively within Foundry and soon inside Fabric, developers can store and retrieve conversational histories, augment data with rich context, and mix natural language with SQL in real time—a true blend of developer productivity and AI innovation. The ability to run LLM (Large Language Model) queries directly from within PostgreSQL, and visualize or chat with semantic data via Copilot in Power BI, creates a seamless user experience for both technical and business stakeholders.
Nevertheless, the practical value of such tools depends on depth of integration, governance, and scalability. Competitors like Google and AWS also tout unified data analytics solutions; Microsoft’s advantage may lie in its deep integration with ubiquitous business platforms like Microsoft 365, Teams, and Power BI, giving it an edge in ecosystem cohesion.
To achieve this, Microsoft has embarked on a systems-level optimization mission, building what Nadella calls a “complete AI system.” This runs the gamut from silicon (such as the Maia and Cobalt chips) and supercooled data centers to the network (the new AI WAN 400-terabyte backbone) and app server stack. The presence of over 70 data center regions (with 10 opened in the last three months alone) positions Azure as the largest player by region count, and the claim that the “largest GB200-based supercomputer is going to be Azure” supports the company’s ambition to serve the most demanding AI workloads at scale.
On the connectivity front, the AI WAN and increased fiber optics (more added in the latest centers than in all previous Azure deployments combined) are essential for latency-sensitive distributed AI applications. By tying together a global mesh of state-of-the-art facilities, Microsoft is making a bid not just for scale but for unifying developer experience across public cloud, edge, and desktop.
The focus on developer tooling, particularly through Windows AI Foundry and the promise of pre-optimized local models, brings meaningful differentiation. The ability to customize models using LoRA, with a clear onramp from local to cloud and back, gives developers unprecedented flexibility.
Moreover, the arms race in AI hardware is intensifying. While Microsoft touts leadership in data center expansion, renewable cooling, and proprietary silicon, Google, Amazon, and Nvidia are all making similar claims. Independent benchmarking, transparency around energy and performance metrics, and openness regarding environmental impacts will become increasingly necessary as regulatory and public scrutiny continues to grow.
Finally, the pitch for a distributed agentic web—where reasoning models cooperate across domains—may run afoul of entrenched commercial interests, regulatory frameworks, and the inertia of incumbent platforms. Microsoft will need to navigate standards bodies, privacy advocates, and—perhaps most consequentially—users themselves, who will decide if these new AI-powered platforms truly deliver more value than risks.
Yet, as promising as this vision may be, successful realization will depend on two things: execution and trust. Microsoft must deliver not just on ambitious technical roadmaps but also on transparency, interoperability, and genuine openness. Users and developers will demand not only new capabilities but proof that their privacy, security, and agency are preserved—even as agents become more powerful.
In the end, the future of the agentic web—and Microsoft’s place within it—will hinge on this delicate balance. For now, Build 2025 shows a company deeply aware that the stakes have never been higher, nor the opportunity greater, to redefine computing anew.
Source: CRN Magazine Microsoft Build 2025: CEO Nadella Takes Platform, Systems Approach To The ‘Agentic Web’
Rethinking Local AI Development: The Windows AI Foundry
An Open Platform Vision—Across Devices and Architectures
Microsoft’s unveiling of the Windows AI Foundry—and specifically its “Foundry Local” toolset—signals a tectonic shift in how local AI applications are built, tested, and deployed on personal devices. For years, the bottlenecks in AI adoption at the edge have stemmed from inconsistent runtime performance, device fragmentation, and high barriers of entry for both app developers and end-users. Nadella positions Windows as “the most open platform with massive scale,” emphasizing that its tools and models now work “across CPUs, GPUs, NPUs, and in the cloud.” This proposition is significant: More than a billion Windows users and devices translate into an enormous addressable market for locally powered AI experiences—a reality validated by Adobe, Zoom, and other developers already leveraging on-device AI features within the Windows ecosystem.Unlike previous iterations of Windows machine learning libraries or the on-again, off-again support for cross-platform development tools, Foundry Local promises a high-performance runtime, a robust command-line interface, and agents-as-a-service. Developers on both Windows and macOS gain access to unified SDKs—meaning the notorious “works on my machine” dilemma is finally being addressed at scale. The fact that Microsoft built features like Recall and Click to Do for Copilot+ PCs using this same runtime underscores the company’s growing internal dogfooding: the tools Microsoft touts orbit their own engineering priorities.
Lowering the AI Customization Bar: Phi Silica SLM and LoRA
Central to Foundry Local is a catalog of pre-optimized, open-source models that developers can deploy and tune locally. Of note is the built-in Phi Silica Small Language Model (SLM), which can be customized using LoRA (Low-Rank Adapters)—a technique growing in popularity for efficiently fine-tuning transformer-based models on limited hardware. This empowers developers to create lightweight, highly specific models for their apps without needing massive GPU clusters or specialized hardware.While OpenAI’s o1 and DeepSeek marked early innovations in cloud-scale inference, Microsoft claims Phi Silica will “completely revolutionize” inference compute on PCs. This is a bold assertion, and while early technical benchmarks of SLMs like Phi Silica do indicate superior efficiency at smaller scales, real-world impact will ultimately depend on how robustly these models can be tailored to varied domains, and how easily developers can integrate them into production Windows applications. Nevertheless, support for LoRA out of the box marks a major democratization of AI customization.
Unified Application Lifecycle, Secure MCP Services
One of the perennial pain points in modern development—whether cloud or edge—has been the lifecycle management of AI applications. By expanding Windows AI Foundry to cover the full development lifecycle and integrating with a native MCP (Model Composition Protocol) registry, Microsoft brings security, discoverability, and manageability to the AI pipeline. MCP-compatible clients can discover secure, vetted servers for functions like file systems, app actions, and user settings. This infrastructure is crucial not just for incremental innovation but for ensuring that rapid proliferation of local AI apps does not come at the expense of end-user privacy and device performance.The NLWeb Project: Toward a Truly Agentic Web
At Build 2025, Nadella introduced “NLWeb”—the Natural Language Web Project—as Microsoft’s vision for universalizing AI-driven reasoning and intelligence. The concept: any app, website, or data source can both contribute to and draw upon aggregated intelligence using natural language interfaces. The ambition is clear: break down silos, enable compositional reasoning across distributed sources, and invert the power dynamic of centralized, aggregator-driven Web platforms.Compositional Intelligence and Reasoning Models
Unlike traditional search and feed paradigms that rely on aggregate data and opaque recommendation engines, NLWeb enables a distributed model of intelligence composition. Developers can plug reasoning models into NLWeb, allowing user intents—expressed in natural language—to trigger complex, cross-app synthesis and automation. In effect, the “feed” or “search” of tomorrow is not a destination but a dynamic composition of intent-aware, distributed intelligence.This democratization of reasoning models is potentially transformative: it lowers entry barriers for small developers, challenges the primacy of web aggregators, and could—if widely adopted—shift power toward more open, user-controlled digital ecosystems. However, successful realization will depend on robust open standards, seamless developer tools, and careful navigation of privacy and trust issues. While Microsoft says “something big is going to shift,” the task of building durable, interoperable platforms for agentic web experiences remains a major engineering and governance challenge.
Data Fabric, Copilot, and Natural Language Analytics
Unified Data Access and AI Readiness
Underlying all of Microsoft’s agentic ambitions is the need for unified, readily accessible data. Here, the company’s investments in Azure Cosmos DB and Fabric come to the fore. Fabric, described as the heart of Microsoft’s analytics stack, now integrates Cosmos DB—which brings both structured and semi-structured data (text, images, audio) under a single, accessible roof. The claim: “AI apps need more than just structured data...you can unify your entire data estate and make it ready for AI.”This close integration has immediate implications for next-generation AI apps, especially those relying on Retrieval-Augmented Generation (RAG). With Cosmos DB now available natively within Foundry and soon inside Fabric, developers can store and retrieve conversational histories, augment data with rich context, and mix natural language with SQL in real time—a true blend of developer productivity and AI innovation. The ability to run LLM (Large Language Model) queries directly from within PostgreSQL, and visualize or chat with semantic data via Copilot in Power BI, creates a seamless user experience for both technical and business stakeholders.
No-Code Digital Twins and Analytics for All
Another compelling feature is Fabric’s integrated digital twin builder: a low-code/no-code environment for mapping real-world assets and systems to digital representations. This tool potentially lowers the expertise bar for businesses wanting to leverage digital twins for simulation, management, and analytics. When paired with Copilot’s conversational data navigation, Microsoft is empowering a much broader audience to analyze, visualize, and act on data—an important step toward democratizing both AI and analytics.Nevertheless, the practical value of such tools depends on depth of integration, governance, and scalability. Competitors like Google and AWS also tout unified data analytics solutions; Microsoft’s advantage may lie in its deep integration with ubiquitous business platforms like Microsoft 365, Teams, and Power BI, giving it an edge in ecosystem cohesion.
A Complete AI System: From Datacenter to Desktop
Optimizing for Tokens Per Watt Per Dollar
One of Satya Nadella’s most telling remarks at Build 2025 reframes the AI infrastructure conversation: “It all comes down to delivering the most tokens per watt per dollar.” In a world increasingly defined by the economics and environmental footprint of large-scale AI, this credo captures the central tension developers face—how to deliver cutting-edge experiences with ever-lower latency, ever-higher availability, and ever-reducing costs.To achieve this, Microsoft has embarked on a systems-level optimization mission, building what Nadella calls a “complete AI system.” This runs the gamut from silicon (such as the Maia and Cobalt chips) and supercooled data centers to the network (the new AI WAN 400-terabyte backbone) and app server stack. The presence of over 70 data center regions (with 10 opened in the last three months alone) positions Azure as the largest player by region count, and the claim that the “largest GB200-based supercomputer is going to be Azure” supports the company’s ambition to serve the most demanding AI workloads at scale.
Cooling, Sustainability, and the Closed-Loop Future
Microsoft is also innovating in sustainability. Maia’s closed-loop liquid cooling, which enables AI workloads with “zero water” consumption, addresses the astronomical environmental costs associated with traditional cloud cooling systems—an issue that has drawn growing scrutiny as generative AI has boomed. If realized at commercial scale, this could mark a substantial improvement in data center sustainability. However, third-party validation will be key, as cooling and energy savings claims are notoriously difficult to generalize across diverse real-world deployments.On the connectivity front, the AI WAN and increased fiber optics (more added in the latest centers than in all previous Azure deployments combined) are essential for latency-sensitive distributed AI applications. By tying together a global mesh of state-of-the-art facilities, Microsoft is making a bid not just for scale but for unifying developer experience across public cloud, edge, and desktop.
Heterogeneous Compute: Arm, X86, and AI Chips
The rollout of Cobalt, Microsoft’s Arm-based virtual machines (VMs), signals a move toward a truly heterogeneous computing future. As Teams and Defender have been migrated to these VMs at “massive scale,” Microsoft demonstrates that even its most mission-critical, high-availability services can run on Arm. Coupled with support for specialized AI silicon and established x86 infrastructure, this multiplatform approach positions Microsoft as a rare player equally invested in cloud, edge, and device-native AI computing.Critical Analysis: Strengths, Challenges, and Open Questions
The Case for a Systems Approach
Microsoft’s systems-level perspective—integrating software, hardware, and cloud infrastructure under a single optimization mandate—remains one of its greatest strengths. Unlike cloud-only or device-centric players, Microsoft can control and tune the entire stack. This spells opportunity for developers who want to minimize latency, maximize performance, and flexibly deploy across any environment.The focus on developer tooling, particularly through Windows AI Foundry and the promise of pre-optimized local models, brings meaningful differentiation. The ability to customize models using LoRA, with a clear onramp from local to cloud and back, gives developers unprecedented flexibility.
Openness and Ecosystem Play
By positioning Windows as “the most open platform with massive scale,” Microsoft walks a delicate line between leveraging its incumbency and genuinely enabling third-party innovation. The integration of open source models and cross-platform tooling (including first-class Mac support) is a marked shift from the closed, proprietary strategies of tech’s earlier eras. If Microsoft can maintain genuine openness—backed by transparent standards, interoperable APIs, and true cross-vendor partnership—it could cement its stature as the agentic web platform of record.Privacy, Control, and Security
The proliferation of local AI agents and compositional intelligence comes with significant risks: data leakage, unauthorized automation, and model misuse. Microsoft’s investment in vetted, secure MCP registries and discoverable, controlled AI services is designed to mitigate these issues, but history has shown that security is never “solved”—it is ongoing. The industry will need to closely watch how Microsoft manages updates, vulnerabilities, and compliance across its new agent-based ecosystem.Challenges and Competitive Landscape
Despite Microsoft’s comprehensive vision, several challenges loom. Fabric and Cosmos may provide a unified data layer, but large enterprises often rely on multi-cloud and hybrid stacks. Interoperability with AWS and Google Cloud, as well as on-prem systems, will be essential for seamless developer and enterprise adoption.Moreover, the arms race in AI hardware is intensifying. While Microsoft touts leadership in data center expansion, renewable cooling, and proprietary silicon, Google, Amazon, and Nvidia are all making similar claims. Independent benchmarking, transparency around energy and performance metrics, and openness regarding environmental impacts will become increasingly necessary as regulatory and public scrutiny continues to grow.
Finally, the pitch for a distributed agentic web—where reasoning models cooperate across domains—may run afoul of entrenched commercial interests, regulatory frameworks, and the inertia of incumbent platforms. Microsoft will need to navigate standards bodies, privacy advocates, and—perhaps most consequentially—users themselves, who will decide if these new AI-powered platforms truly deliver more value than risks.
Conclusion: The Agentic Web and the Next Generation of Windows
Microsoft Build 2025 stands as a bold declaration that the next era of computing will be profoundly agentic: software entities empowered to reason, synthesize, and act across the vast digital landscape. With the Windows AI Foundry, Microsoft offers developers a powerful, open platform for building these agents at local scale. Through NLWeb, it proposes a way to democratize the very processes of reasoning, search, and automation. In Fabric and Cosmos, it provides the glue that holds together the data that fuels tomorrow’s apps. And at the infrastructure level, it promises an optimized, sustainable, global AI backbone with an eye always on cost, scale, and environmental impact.Yet, as promising as this vision may be, successful realization will depend on two things: execution and trust. Microsoft must deliver not just on ambitious technical roadmaps but also on transparency, interoperability, and genuine openness. Users and developers will demand not only new capabilities but proof that their privacy, security, and agency are preserved—even as agents become more powerful.
In the end, the future of the agentic web—and Microsoft’s place within it—will hinge on this delicate balance. For now, Build 2025 shows a company deeply aware that the stakes have never been higher, nor the opportunity greater, to redefine computing anew.
Source: CRN Magazine Microsoft Build 2025: CEO Nadella Takes Platform, Systems Approach To The ‘Agentic Web’
