Phil Spencer, CEO of Microsoft Gaming, is retiring, Satya Nadella has announced. Asha Sharma, the President of Microsoft's CoreAI division is taking over Spencer's role, while Sarah Bond, the current President of Xbox, is resigning."I am long on gaming and its role at the center of our consumer ambition, and as we look ahead, I’m excited to share that Asha Sharma will become Executive Vice President and CEO, Microsoft Gaming, reporting to me," Nadella says. "Over the last two years at Microsoft, and previously as Chief Operating Officer at Instacart and a Vice President at Meta, Asha has helped build and scale services that reach billions of people and support thriving consumer and developer ecosystems. She brings deep experience building and growing platforms, aligning business models to long-term value, and operating at global scale, which will be critical in leading our gaming business into its next era of growth."Alongside Sharma, Matt Booty, the current head of Xbox Game Studios, is getting promoted to Chief Content Officer, and will report to Sharma. Sarah Bond, who like Spencer served as a public face for the Xbox brand and was assumed to be his successor, is leaving Microsoft to "begin a new chapter."Spencer joined Microsoft in 1988, and has worked on Xbox since at least 2001. He assumed responsibility for Microsoft's gaming brand and its various studios and associated subscription products in 2013, before becoming an Executive VP of Gaming in 2017 and later CEO of Microsoft Gaming in 2022. Spencer's biggest impact on Xbox will likely be remembered as the creation of Game Pass, Microsoft's "Netflix for Games" and the wave of studio acquisitions Microsoft completed from 2018 to 2022, which included smaller studios like Double Fine and the massive $68.7 billion purchase of Activision Blizzard King.While Microsoft has plenty of developers and IP to fall back on, it's struggled to compete with the likes of Sony and Nintendo during the current console generation. Microsoft's gaming division has gone through widespread layoffs, its revenue continued to fall throughout 2025 and it raised the prices of both its consoles and Game Pass Ultimate, which likely won't help things going forward. Sharma is in many ways inheriting a broken-down car.As far as her plans go, Sharma’s email to staff that was included in Nadella's announcement is light on details. Sharma says she plans to continue developing "great games," wants to "recommit" to core Xbox fans and "invent new business models and new ways to play." Whether that is enough to turn Xbox's fortunes around remains to be seen.This article originally appeared on Engadget at https://www.engadget.com/gaming/xbox/xbox-head-phil-spencer-is-leaving-microsoft-212838419.html?src=rss

It’s the final of a world sporting competition. A lot is at stake, including the need for highly reliable and scalable connectivity to provide the most enjoyable experience for attendees and viewers watching on televisions and other devices.How do you ensure that you can deliver on that? Digital twins.As a real-time virtual replica of a physical environment, digital twins can provide the ultimate testing ground to iron out connectivity issues long before any actual event gets underway.Pre-event network planning and simulation, including crowd behavior, test connectivity scenarios, issue detection, and identifying ‘dead’ zones where no data can be up or downloaded, is possible by harnessing a digital twin that is connected to real data streams from sensors, networks, and devices.With such utility, digital twins are emerging as a key decision support system to guide autonomous networks, reduce the risk of failures and improve outcomes in service operations. As the industry group, TM Forum, said recently: “Digital twin networks can help CSPs better understand the timeframes, processes and resources required for autonomous network implementation, risk of service interruption and reduce much of the uncertainty about the impact of AN use cases on the network.” (TM Forum, 2025)

Introducing the service digital twin

Though network digital twins at the domain level have been around for a while, emerging standards are ushering in the exploration of service digital twins.These are cross-domain and provide holistic models for understanding impacts on service performance, identifying potential issues due to changes in network behavior, and optimizing efficiency in operations. They offer a simulation environment for planning and operating networks while having a comprehensive view of network entities and their states, traffic, and interactions.Digital twins also boost business agility and enhance customer engagement by enabling operators to be more agile in responding to market demands. They can also be used to optimize operational processes, thereby lowering costs and managing risk.

Increasing autonomy in operations – is there a catch?

Automation as a concept is not new in operations, but the evolution towards autonomous networks takes automation and AI to the next level.AI in operations is about learning by leveraging data from different sources, analyzing what happened in the past to predict what might happen in the future, and using this in a preventative way to ensure that services never fail. While AI can predict the network behavior, it doesn’t predict the impact it will have on operations.Another key element for autonomy is the use of GenAI, which brings in the capabilities to drive more system-led operations, like copilots and assistants, to make it easier to manage networks, auto-generating recommendations to aid decision-making and autonomously deriving resolution actions to fix issues.While these enable higher levels of autonomy, some big questions still need to be answered around the control of autonomous network operations.While humans are taken out of the loop to minimize errors, what is the risk of adding AI and GenAI into the loop? More reliance on intelligence coming from GenAI-powered agents carries the risk of potentially biased decisions, erroneous recommendations, or inconsistent outcomes.Another big issue is AI maturity and how to ensure that the AI recommendations do not trigger adverse impacts on the network. With all these questions still open, there is a need to have a validation, or “watchdog” element, for autonomous networks.

Digital twins offer the solution

The simple answer lies in finding a mechanism to check the validity of recommendations, assessing the impact of actions on the network without directly impacting the network itself, and understanding the “what if” when services are created or fail, new actions are performed, or certain changes take place.This is where digital twins come in by offering a simulation platform to accurately mimic the network, using intelligent models to assess the impact of network behavior, identify more efficient alternatives, and validate recommendations to achieve higher accuracy in network actions.The digital twin can simulate various situations, derive the impact, and analyze what might happen to complement the capabilities brought by AI and GenAI. It can help to assess network resilience in different scenarios to identify capacity bottlenecks and manage dynamic service level agreement (SLA) definitions by considering the network behavior patterns to improve efficiency and optimize service delivery.Most importantly, they offer a ‘production-network replica’ to validate insights, recommendations, and impacts on the network without having to use the network as the object of a trial-and-error experiment.

Real-world use cases of service digital twins

The service digital twin has several real-world applications, all of which contribute to achieving autonomous networks.The first is service planning. A significant challenge in delivering cross-domain services is checking the availability of diverse resources across multiple domains and technologies. What makes it more complex is that failure in one area can lead to having to roll back the entire service operations, which can be costly, in terms of effort and financially.Digital twins help predict service feasibility based on anticipated network behavior by leveraging AI-enabled prediction and service modeling capabilities.Digital twins can also assist with service maintenance. By testing the twin with different data inputs and simulating varying network states and conditions, an environment identical to the real network is formed. This environment can be used to assess the impacts of network changes and maintenance actions on network services and even the end-user experience.Finally, they can also assist with service operations.As service digital twins model the service itself and hold a real-time replica of the service state and performance, they can be tested with various inputs to optimize service delivery and performance. This also means that AI-driven recommendations can be validated first on the digital twin before being applied to the network to check for adverse impacts. This is referred to as a “twin-first” approach.

Solving the autonomous networks puzzle

Digital twins are a key piece of the jigsaw puzzle of autonomous networks, which aims to drive network monetization, optimize customer experience, and enhance efficiency while controlling costs.A “twin-first” approach allows operators to perform impact analysis and validate AI-driven actions before implementation, enhancing reliability and efficiency, and enabling zero-touch, zero-failure operations shaping the future of next-generation networks.

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