AI on the NFL Sideline: Copilot, Governance, and the Lynch Call

Artificial intelligence would have told Pete Carroll to hand the ball to Marshawn Lynch.
The verdict — blunt, repeatable and nearly universal among modern analysts — is now being echoed by the same generative models that pundits and teams are experimenting with at the edge of NFL operations. Yet the larger, more consequential story is not whether an AI agrees with fans who still flinch at Super Bowl XLIX; it’s how the NFL and Microsoft are packaging AI for game-day use, what those tools can and cannot do, and the operational, ethical and competitive trade-offs that come with putting Copilot-style assistants on the sideline.

Background​

From Surface tablets to Copilot on the sideline​

Surface tablets quietly became a staple of NFL sidelines in the mid‑2010s. That hardware sponsorship matured into a centrally managed Sideline Viewing System (SVS) used for replay, telemetry and situational review. In recent seasons the NFL has moved beyond hardware sponsorship into an “AI‑first” operational posture with Microsoft: the SVS is being augmented with Copilot features, more than 2,500 Copilot‑enabled Surface devices have been provisioned league‑wide, and Azure OpenAI tools are being piloted in scouting and game‑day operations. The league frames the change as assistive — designed to speed retrieval, filter relevant plays and surface contextual evidence — not to hand play‑calling authority to software.

What the new sideline toolkit actually does​

At its core the upgraded SVS plus Copilot stack is designed to reduce the time it takes a coach or analyst to find the clip or stat they need:

• Natural‑language search of play histories (e.g., “show me goal-line runs against 5‑second blitz in the last three games”).
• Rapid filtering and clip‑pulling by down/distance, personnel, formation and outcome.
• Short synthesized summaries and simple visualizations (tendencies, success rates, matchup heat maps).
• Developer acceleration through GitHub Copilot–style code assistance for internal tools and play‑tagging systems.

These are retrieval and synthesis features — the league emphasizes that human decisions remain supreme, and that Copilot is a sounding board and time-saver rather than an automated decision engine.

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The play that still keeps Seattle awake​

What happened — the immutable facts​

With 26 seconds left in Super Bowl XLIX, the Seattle Seahawks trailed 28–24 and stood at the New England 1‑yard line. It was 2nd‑and‑goal and Seattle had one timeout remaining. Instead of handing to Marshawn Lynch — arguably the game's best power‑running short‑yardage option — the Seahawks ran a pass play. Seattle quarterback Russell Wilson’s throw on a quick slant was intercepted by Malcolm Butler. The play ended the Seahawks’ shot at back‑to‑back championships and remains one of the most second‑guessed play calls in NFL history.
Those details are fixed in the record; what remains contentious is the rationale behind the call and whether different real‑time information or risk tolerances would have changed the decision. The play is a perfect case study for the limits and promises of AI in high‑leverage sport decisioning: small data differences, a single outcome and intense hindsight bias drive enormous debate.

Why the run was (and still is) the higher‑percentage option​

Several pragmatic arguments explain why most analysts — and, increasingly, AI assistants offered the same evidence — favor the run in that specific situation:

• Reduced turnover modes: Running at the 1‑yard line dramatically reduces the probability of a game‑ending turnover compared to a quick slant through a congested end zone.
• Leverage the matchup: Marshawn Lynch was one of the premier power backs of his generation and had repeatedly converted short‑yardage and goal‑line situations that season.
• Clock and timeout dynamics: With one timeout and 26 seconds, a short run that fails still leaves a short window and potentially a play for a field goal or a shot after another stop.
• Defensive anticipation: The Patriots showed personnel and alignment cues that suggested a slant was possible to contest; end‑zone windows against a prepared secondary are narrow.

Put together, the expected value calculus under conventional assumptions favored a physical, lower‑variance approach: hand it to Lynch. That intuitive, risk‑averse logic is why the play remains controversial and why many AIs that were primed with those facts reach the same conclusion. The modern AI verdict is not mystical; it’s statistical intuition rendered at scale.

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What AI actually said about the call — and how that matters​

The short answer from generative models​

When fed the play context and asked whether the Seahawks should have run with Lynch, contemporary large language models rapidly reconstruct the facts and arguments. Most models produce balanced reasoning — enumerating why a pass could be defensible (e.g., play‑action to catch an aggressive defense, worst‑case thinking about a stop) — but then tilt toward the run as the higher‑percentage, lower‑variance choice in hindsight.
That tilt matters in two ways: first, it shows that retrieval‑heavy models surface the same historical evidence human analysts do; second, the models’ answers are heavily shaped by how the question is framed and what priors they are primed with. An instruction that emphasizes “what a coach with one timeout would do” will amplify conservative, run‑first rationales. Conversely, prompts that model a coach who prioritizes aggression or who is concerned about a neutralized run game can flip the output. This is not AI clairvoyance — it’s prompt‑sensitive reasoning over extracted evidence.

Caveats and unverifiable claims​

It’s important to flag what cannot be verified by simply reading a headline. When outlets report specific responses from named models (for example, ChatGPT‑5 or Gemini 2.5), those model outputs are reproducible only if the prompt, system context, model date/version and data cutoff are identical. Models evolve, prompts vary, and published paraphrases can miss nuance. Treat any single quoted verdict as illustrative rather than canonical unless the full prompt and model metadata are disclosed. The NFL’s public messaging about Copilot likewise emphasizes assistive retrieval — not prescriptive play‑calling — and that human coaches retain final authority.

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How the NFL plans to use AI — governance, guardrails, and practical mechanics​

Human‑in‑the‑loop is the explicit rule​

The league’s electronic device policies and club committee guidance make one point explicit: in-game tools must be league‑issued and controlled, and AI features are intended to enhance processes rather than determine outcomes. The public posture is consistent: AI will streamline evidence retrieval and accelerate analysis, but coaches develop and call plays. Practice rules and device lockdowns further restrict what staff can deploy on game day. Those safeguards are meant to preserve competitive integrity and ensure decisions remain human.

The technical stack and resilience requirements​

Practically, the NFL and Microsoft are implementing a hybrid edge + cloud architecture:

• On‑device inference (Copilot+ hardware): Running lightweight vision and retrieval models locally reduces round‑trip latency and mitigates stadium network variability.
• Edge caches and local playbooks: Cached, pinned playbooks and replay clips help maintain degraded‑mode functionality if connectivity falters.
• Centralized governance: League‑managed servers and tight device provisioning control updates and parity across clubs.

These design choices matter because stadium environments are hostile to reliable wireless connectivity and because a late‑game outage or hallucinated summary could be worse than no AI at all. Vendors and clubs must validate degraded‑mode behavior and deterministic fallbacks before scaling reliance on real‑time assistance.

Auditability, provenance and confidence scoring​

One of the most consequential operational recommendations is simple: every AI output used in deliberations must include provenance metadata and a confidence signal. Coaches need to know which games, which plays and which tags produced a recommendation, and analysts must be able to replay the underlying footage immediately. The league and Microsoft are reportedly leaning into these requirements — but independent audit programs, immutable logs and readable confidence measures are still implementation details that deserve scrutiny. Without them, convenience can become de‑facto authority.

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Where AI helps most — and where it risks doing harm​

Strengths: speed, retrieval and democratized insight​

AI excels at time‑compression. For assistants that fetch “nearest neighbor” plays (similar down/distance, personnel and coverage), a coach can be shown the most relevant evidence in seconds rather than minutes. That reduces decision latency, helps less‑experienced staff surface long‑tail tendencies and standardizes the information available to game‑ops. In aggregate, shaving seconds off every decision cycle across a season can produce measurable competitive gains.
Key benefits:

• Faster clip retrieval and contextual summaries.
• Standardized situational analytics across clubs.
• Developer productivity gains through GitHub Copilot accelerating play‑tagging and tooling.

Risks: latency, hallucinations, security and competitive imbalance​

The same tools that speed research also introduce new attack surfaces and governance headaches:

• Latency & reliability: Stadium networks and unpredictable load can make cloud‑dependent features brittle. Edge caching and local inference are essential mitigations, not optional extras.
• Hallucinations & overconfidence: Generative models can synthesize plausible but incorrect summaries. In tight decisions that risk championships or player safety, a hallucinated “stat” could mislead a coach under pressure. Confidence scoring and human verification are mandatory.
• Security & privacy: Centralized film and telemetry are valuable IP and potential targets. Proper tenant isolation, DLP and hardened endpoints are needed to protect player data and team strategies.
• Competitive parity: If some clubs tune models with proprietary data or get earlier access to advanced features, the league risks an arms race. The NFL’s provisioning plan aims to standardize access, but long‑term governance will require audits and transparency.

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Operational recommendations for teams and the league​

• Build explicit degraded‑mode playbooks. Define exactly what staff must do if Copilot is unavailable or returns low‑confidence outputs (e.g., revert to pre‑computed charts, call a timeout, or consult a designated human analyst).
• Require provenance metadata on every high‑leverage suggestion. Tie model outputs to the underlying film and show confidence or sample size.
• Maintain immutable logs for post‑game review. Time‑stamped logs of queries, answers and who viewed them are essential for audits and accountability.
• Institute independent model audits. External reviewers should periodically evaluate accuracy, bias, and training‑data lineage.
• Train coaching staffs. Adoption is a people problem: technical tools are only useful if the humans who use them understand error modes and know how to evaluate recommendations.

These are not theoretical suggestions: independent reporting and technical briefings around the NFL–Microsoft rollout have repeatedly stressed the necessity of these mitigations. The league and Microsoft appear to be building toward many of these controls, but oversight and verification must be ongoing.

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The cultural and fan implications​

AI on the sideline will change the narrative of game nights. Faster analytics will feed broadcast overlays, make highlight reels more timely and could power fan experiences that answer natural‑language questions in near real time. That will alter how fans consume and judge coaching decisions: in a Copilot‑augmented future, every second‑guessable call will be instantly analysable and shareable. That can be a boon for transparency — and a liability for reputations if model outputs are misinterpreted or over‑trusted.

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The final play call — what it teaches us about AI, judgment and risk​

The Seahawks’ decision in Super Bowl XLIX is a vivid reminder of two enduring truths:

• High‑leverage decisions in sport are rarely reducible to a single statistic. Context, risk preference, trust in personnel and the psychological state of a team matter in ways that resist clean quantification.
• AI tools amplify existing decision workflows. They compress the time between observation and action and make evidence easier to surface. They do not — and must not, by league rule and by practical design — replace the coach’s judgment.

Had the Seahawks used a retrieval assistant on that play, the system would likely have surfaced Lynch’s history, success rates for goal‑line runs and similar defensive formations — all evidence that tilts the expectation toward the run. But the human job of weighing the residual uncertainty and choosing which risk to accept would still fall on a person in a headset.
That is precisely why the league’s stated design is prudent: use AI to make the evidence clearer and faster, not to hand tactical authority to an opaque algorithm. Good tools make judgment better; they do not remove the need for it.

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Conclusion​

AI’s contemporary verdict on the play that haunts Seattle is simple and unsurprising: most models, when supplied the same context and priors, would recommend running the ball with Marshawn Lynch. The more consequential story is how that consensus — whether human or machine — is integrated into a sport where seconds matter, data are proprietary, and outcomes feed billions of dollars and passionate fan memories.
The NFL’s approach so far — league‑issued devices, Copilot for fast retrieval, an explicit human‑in‑the‑loop posture, and emphasis on provenance — is sensible. But sensible policy on paper is only the start. The operational details — resilient edge architectures, concrete degraded‑mode plans, immutable logs, and independent audits — will determine whether sideline AI is a practical accelerator of good decision‑making or a brittle new crutch that amplifies mistakes.
For Seattle fans still replaying that late January moment, modern AI largely agrees: give the ball to Lynch. For everyone else, the lesson is wider: AI can sharpen the evidence, but high‑stakes judgment will remain stubbornly human — and every tool that changes the balance between risk and reward on the sideline demands scrutiny, auditability and a respect for the messy realities of sport.

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Source: GeekWire For Pete’s sake, what does AI think of the play that haunts Seattle Seahawks fans — run or pass?

 

[ChatGPT]

Microsoft’s renewed push to make Windows a deeply agentic, multimodal platform—paired with ambitious Copilot investments—has quietly rewritten the conditions that once made a Microsoft-branded phone an improbable gamble; taken together, those shifts make the idea of a “new Windows phone” not just plausible but strategically sensible, provided Microsoft can solve the very real engineering, developer, and privacy problems that sank its earlier mobile efforts. (theverge.com)

Background / Overview​

How Windows Phone died (and what mattered most)​

Microsoft’s exit from the smartphone OS race was not a single-event failure so much as the slow collapse of the ecosystem economics that sustain modern platforms. By the late 2010s Microsoft had effectively stopped investing in Windows as a first-class phone OS: Windows 10 Mobile reached end of servicing on January 14, 2020, and Microsoft recommended migration to iOS or Android as the practical path forward. (support.microsoft.com)
The missing ingredient was the app ecosystem. A platform lives or dies by developer investment and user base. Even with technical efforts such as the Universal Windows Platform (UWP) and Continuum, Windows Phone lacked the consumer market share and developer incentives to attract the apps people expect on their phones. Market trackers in the mid-2010s showed Windows’ mobile share collapsing to the low single digits and below—an economic reality that undercut every hardware and UX innovation. (gartner.com)

What’s different today​

Two broad changes have altered the strategic map.

• The first is AI as a platform-level fabric. Copilot and related services are being embedded deep into Windows, moving AI from an add‑on feature to a first-class capability of the OS. Microsoft’s product teams are already shipping Copilot features that reach into File Explorer, voice interactions, and system-level automations—an architectural trajectory that favors agentic interactions more than app-by-app experiences. (windowscentral.com)
• The second is infrastructure for interoperable agents and local inference. Industry efforts like the Model Context Protocol (MCP) and Microsoft’s Windows AI Foundry aim to let AI agents securely access system services, files, and device capabilities in a controlled way. Those standards and tools make it possible—technically and architecturally—for an OS to host agents that act across apps and services rather than forcing users to open a specific mobile app for each task. (theverge.com)

Taken together, agentic OS design plus agent interoperability tools reduce the historical dependence on platform‑specific apps: if an assistant can do your travel booking, triage email, or manage tasks by interacting generically with services and web APIs, the incentive to maintain a separate native app diminishes. That is the strategic lever that changes the calculus for a Microsoft mobile device.

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Why Microsoft has both the motivation and the technical scaffolding​

Copilot is more than a chatbot—Microsoft is betting on it as the OS interface​

Microsoft has moved from “Copilot as feature” to “Copilot as platform anchor.” The company’s recent engineering cadence and product announcements treat Copilot as a system-level agent with memory, multimodal inputs (voice, vision, text), and hooks into local and cloud compute. Microsoft’s leaders are explicit: future versions of Windows will be multimodal and context-aware, able to “see” and “hear” what’s on screen and act on user intent rather than merely return search results. Those remarks come from senior Windows leadership and are reflected in public interviews and product previews. (theverge.com)
Why that matters: a phone that uses the OS’s Copilot as the primary interface can collapse many app-specific use cases into agent workflows. That drastically lowers the developer burden of porting millions of apps and reframes the user experience around tasks rather than installation lists.

Hardware enablers: NPUs, efficient on-device models, and hybrid execution​

Agentic experiences require fast, private, and energy-efficient inference. Silicon vendors have responded by shipping NPUs and inference-optimized silicon across PC and mobile lines. Microsoft’s approach pairs these local inference capabilities with cloud services for heavy lifting—so-called hybrid execution. The model is straightforward: run latency- and privacy-sensitive parts of the agent on-device, offload large reasoning loops to cloud engines, and stitch results together. That split makes a true pocket‑sized “thinking device” technically feasible. (theverge.com)

Standards and plumbing: MCP, Windows AI Foundry, and agent registries​

To prevent agent chaos—where thousands of isolated bots compete for resources—Microsoft and partners are investing in run‑time and protocol-level interoperability. MCP-style approaches and a controlled agent registry let agents request the specific system permissions they need, use local models in a controlled sandbox, and have audit trails for security. This is not a hypothetical: Microsoft has already started early work with partners and preview tools aimed at building this plumbing into Windows. For a device intended to minimize apps and maximize agent productivity, this plumbing is essential. (theverge.com)

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What a future Windows phone might actually look like​

A Copilot-first UX, not a tile revival​

The iconic Windows Phone tile UI is gone—and it should be. A future Windows phone will prioritize conversational and contextual UIs, not a mosaic of static tiles. The interface will center on an always-available Copilot that can be invoked by voice, gesture, or a contextual prompt embedded in the shell. Tasks will be initiated by natural language and confirmed by brief visual summaries rather than full-screen, app-centric flows. The experience will emphasize:

• Intent-first interactions (task initiation by voice or typed prompts)
• Contextual results (Copilot surfaces only what matters based on recent activity and permissions)
• Composability (agents can call services, fill forms, and negotiate options across providers)

Those are the elements Microsoft is publicly building toward in Windows and Copilot updates. (techradar.com)

Apps won’t disappear—but their role changes​

Native applications will still exist for high-fidelity experiences (games, creative tools, low-latency streaming apps). Yet many everyday tasks—scheduling, booking, triage, summarization—can be executed by agents that use web APIs, browser automation, or lightweight connectors. This reduces the friction that once kept developers away from Windows Phone: building a single web-accessible connector or API endpoint becomes sufficient to let Copilot leverage a service.

Hardware: pocket PC rather than “phone as phone”​

Design-wise, Microsoft’s device could behave more like a pocket PC than a traditional smartphone:

• Optimized microphones and wake-word support for reliable always-on voice
• High-efficiency NPUs for local inference and privacy-sensitive processing
• A durable, productivity-oriented hinge or accessory ecosystem (if Microsoft pursues foldables or dual-screen experiments again)
• Integration with Windows 365 and cloud-streaming to run heavier Windows experiences when docked or paired with peripherals

That vision is consistent with Microsoft’s broader messaging about making Windows more ambient, streamed, and cloud-native. (theverge.com)

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The roadblocks—technical, commercial, and regulatory​

1) Developer and partner economics still matter​

Agentic interfaces reduce dependence on native apps, but they don’t eliminate developer incentives. Services still need robust APIs, data access agreements, and commercial terms. Microsoft would still need to convince major app and service providers to be liveable partners to Copilot agents—via SDKs, revenue-sharing mechanisms, and privacy guarantees.

2) Privacy and security are existential issues​

An agentic phone that “reads” email, books travel, and operates across services instantly becomes a high‑value privacy target. The security model must provide:

• Fine-grained, auditable permissioning for agent actions
• Local-first defaults for sensitive data with clear, persistent user controls
• Transparent memory policies and simple mechanisms to view and delete agent-stored knowledge

Without strong defaults and demonstrable safeguards, enterprise customers and privacy-concerned consumers will resist. Reuters and other outlets have highlighted Microsoft’s work on secure agent interaction and controlled registries, but operationalizing those protections at scale is a heavy lift. (reuters.com)

3) Carriers, regulatory oversight, and app store economics​

Phone launches are not merely about hardware and software; they live in a distribution and regulatory ecosystem that includes carriers, retail, and app stores. Microsoft’s previous mobile efforts suffered from weak carrier engagement and limited retail momentum. A return to the phone market will require new partnerships and a clear go‑to‑market strategy that addresses carrier provisioning (eSIM/VoLTE/RCS), enterprise mobility management (EMM) integration, and potential antitrust concerns over agent-mediated commerce.

4) The technical friction of full Windows vs streaming vs Android​

There are three plausible technical approaches to a Microsoft phone, each with trade-offs:

• Native Windows (ARM) with broad app compatibility: desirable for a “PC in your pocket” but requires substantial native app support or robust emulation for legacy Win32/x64 apps.
• Android-based device with Microsoft-agent layer: lower friction for app ecosystem but dilutes the “Windows” narrative and hands OS-level control to Google.
• Stream-only device (Windows 365/cloud streaming + Copilot): solves app compatibility but depends on reliable low-latency connectivity and potentially raises cost and privacy questions.

Microsoft appears to be exploring hybrid approaches—combining local agents with cloud streaming and specialized hardware—rather than committing to a single technique. Each path is technically feasible, but none is trivial at scale. (theverge.com)

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Strategic upsides for Microsoft—and why this could be a smart bet​

• Reclaiming the interface: If Copilot becomes the primary interface for computing, owning the device that showcases agentic UX becomes a strategic differentiator for Microsoft’s consumer positioning.
• Services expansion: A Copilot-centric phone could expand Microsoft’s addressable market for Microsoft 365, Teams, Exchange, Xbox cloud streaming, and Windows 365 subscriptions.
• Enterprise continuity: Microsoft’s unique strength is enterprise trust and identity—features that make a secure, agentic device particularly attractive for business customers who want centralized management and data governance.
• Reuse of device ecosystem skills: Microsoft already has hardware experience via Surface and a footprint among OEMs and silicon partners; a focused product could reuse those competencies without needing a mass-market consumer relaunch like the Lumia era. (theverge.com)

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Risks and critical failure modes​

• Misaligned expectations: The “phone” label implies certain app and UX expectations (camera ecosystems, social apps, gaming). An agentic pocket PC that deprioritizes those areas risks disappointing mainstream consumers.
• Developer pushback: If Copilot’s agent model becomes a bottleneck for third-party monetization or requires onerous integration work, developers may prefer to optimize for iOS/Android ecosystems where monetization is proven.
• Privacy regulation: Emerging privacy and AI-specific regulation could slow adoption, mandate more local processing, or require strict auditability—each of which raises costs.
• Hardware economics: Smartphones are a low-margin, high-volume market; Microsoft might prudently avoid a full-scale consumer phone play and opt for a premium, niche product—which limits ecosystem growth.

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A practical timeline — could this happen by 2030?​

Microsoft’s public statements and product roadmaps imply an aggressive decade-long migration toward agentic, multimodal Windows experiences. Executives have repeatedly suggested that voice-first, screen-aware, and agentic features will be central to the OS’s evolution, and Microsoft is already shipping incremental Copilot features and preview tools. That makes a commercial device leveraging agentic Copilot by around 2030 technically plausible and strategically consistent with corporate messaging. (theverge.com)
Caveat: roadmaps are aspirational. Patents, prototype leaks, or executive optimism are not guarantees of a shipped product. Hardware and carrier logistics, vendor coordination, and user acceptance will each influence whether a Microsoft-branded agentic phone becomes reality and whether it is a niche productivity device or a mainstream smartphone alternative. The earlier patent-and-prototype history shows that Microsoft often explores many device forms without shipping all of them. Treat specific feature or release-date predictions as contingent on further signals.

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What Microsoft must get right (a short checklist)​

• Privacy-first agent design with transparent memory controls and default local processing where feasible.
• Scalable developer hooks and commercial terms so services benefit economically from being Copilot-accessible.
• Clear hardware strategy—either ARM-native Windows, Android-backed device, or a streamed-first model—with a committed path to long-term updates.
• Carrier and enterprise partnerships that ensure distribution, management, and credential provisioning at launch.
• UX expectations management: present the device as a productivity-first, secure pocket PC rather than a direct iOS/Android clone.

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Closing analysis: strengths, risks, and verdict​

Microsoft’s current posture—pushing Copilot into the shell, building the technical plumbing for interoperable agents, and talking openly about an agentic future—creates a credible pathway for a new kind of Windows phone: one where the operating system acts as the primary interface and agents do the work that apps used to shoulder. That conceptual shift directly addresses the principal weakness that killed Windows Phone: the need to recruit millions of developers to rebuild app experiences from scratch.
Strengths of this approach include Microsoft’s enterprise relationships, its investment in cloud and productivity services, and the company’s ability to integrate AI across OS and cloud layers. The technical building blocks (NPUs, local inference, MCP-style protocols, and Windows AI tooling) are actively being developed and tested. (theverge.com)
Nevertheless, the risks are substantial. Privacy, security, and regulatory constraints could materially change how agents are allowed to act. Carrier economics and app-developer incentives remain real barriers, and hardware choices will shape whether the device is niche or mass-market. Past Microsoft experiments—patents, Surface Duo’s uneven reception, and the Lumia era—warn that good engineering and strong ideas are necessary but not sufficient without flawless execution and aligned ecosystem economics.
Verdict: it’s not a question of if the idea of a Windows-branded, agentic pocket computer will be explored; Microsoft is already building many of the pieces. The meaningful question is how Microsoft will position such a device and whether it will commit the ecosystem investments necessary to make it more than a niche productivity curiosity. If Microsoft bets big on Copilot as the interface, mates that with robust privacy and developer economics, and chooses a pragmatic execution model (hybrid local/cloud with clear partner incentives), the company has a credible pathway to ship a new Windows phone-like device—and sooner than many would have expected a decade ago. (theverge.com)

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Bold design, deep AI integration, and enterprise-grade trust could make a revived Windows phone more than nostalgia—it could be the proof point that agentic computing is less about gadgets and more about workflow reimagination. The leap from tablets and PCs to a truly thinking pocket device is still challenging, but Microsoft is assembling the technical bricks; what remains is the business will to stack them into a mainstream product.

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Source: Tech Advisor Why it's only a matter of time before Microsoft makes a new Windows Phone