Xbox’s AI initiative with Muse is an attempt to read the tea leaves, not the room

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Xbox’s AI initiative with Muse is an attempt to read the tea leaves, not the room

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Earlier today, the heads of Microsoft’s Xbox division revealed Muse, a generative AI model that intends to create both visuals and gameplay for games. The model, which was trained on the largely forgotten Ninja Theory multiplayer […]

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Earlier today, the heads of Microsoft’s Xbox division revealed Muse, a generative AI model that intends to create both visuals and gameplay for games.

The model, which was trained on the largely forgotten Ninja Theory multiplayer game Bleeding Edge, is not a shocking leap for Microsoft’s Xbox division. The company as a whole, from CEO Satya Nadella down, has wholeheartedly embraced generative AI in all its initiatives. It was only a matter of time until the Xbox division would have to follow suit.

But it does come after a recent game developer survey of 1500 developers in a State of the Industry report statin that 30% of developers have a negative opinion of generative AI. It can be argued back and forth whether generative AI is here to stay or not, the developers making games seem to have an increasingly hostile reactions to generative AI replacing ideation processes.

But Microsoft is not alone in pursuing this, either. Capcom recently spoke about using generative AI for the idea phase of game development, arguing that the thousands of small decisions that go into games can be automated so as to phase out the tedium of game development and free up time and bandwidth to focus on the creativity of it. It does stand to reason that a model like Muse, which absorbed hundreds of hours of a game created by people, still needs people to create those ideas for anything to work at all.

It also begs the question: should Microsoft even care what developers think right now? As both a platform holder and arguably the largest third-party publisher in the industry, tackling modern design complexity and trying — like Capcom — to make it both cheaper and more efficient is likely of paramount importance for them in the coming years. As an example, the latest Fable project from Xbox Games Studio supposedly began work in 2018 and, as of writing, still does not have an announced date.

Would generative AI fix that or make it faster? Unknown. But games are taking longer and longer to develop and there is only so long that can be tenable for Xbox head Phil Spencer.

For Xbox’s part, “preservation” is a key part of their argument for Muse. In the initial statement, Fatima Kardar, corporate vice president of gaming AI at Microsoft, argued that Muse could make older games accessible to modern audiences without much work regardless of hardware advancements. This would, under Kardar’s reasoning, make the opportunity cost spent on remasters and backwards compatibility engineering moot. But nothing Xbox showed regarding Muse today bore that out.

In coming out publicly with such a big marketing push for Muse, it seems Xbox is trying to serve the two masters of shareholders and futurists. That’s all well and good, but it is important to remember that Muse had to train off hundreds of hours of a game created by people in Cambridge that the audience mostly did not connect with. If all of the elements in that equation are not happy, what can the end product be called except a compromise?

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And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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