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OpenAI Names GPT-5.6 Preferred Model for Microsoft 365 Copilot, Signaling Partnership Remains Intact

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OpenAI has sought to dispel growing speculation that its relationship with Microsoft is weakening, announcing that its newly launched GPT-5.6 model will serve as the “preferred model” for Microsoft 365 Copilot, the AI assistant embedded across Microsoft’s productivity software.

The announcement came during OpenAI’s launch event for GPT-5.6 on Thursday and follows recent reports suggesting Microsoft has been relying more heavily on its own artificial intelligence models to reduce costs, prompting questions about the future of one of the technology industry’s most influential partnerships.

In a blog post accompanying the launch, OpenAI said GPT-5.6 will power AI capabilities across Microsoft’s suite of productivity applications, including Word, Excel, PowerPoint and Cowork, reinforcing the companies’ continued collaboration despite mounting speculation about diverging AI strategies.

“Our partnership with Microsoft has always been about bringing the benefits of advanced AI to more individuals and organizations, and we’re excited to continue building on that shared commitment,” OpenAI said.

The announcement appears intended to reassure customers and investors that OpenAI remains central to Microsoft’s flagship AI offerings.

However, the practical implications of the designation remain unclear.

OpenAI did not specify what the ” preferred model being” means from a technical or commercial standpoint, nor did it indicate whether Microsoft will continue deploying its own proprietary AI models alongside GPT-5.6.

The development follows a Bloomberg report earlier this week that said Microsoft has increasingly begun replacing some OpenAI technology with internally developed artificial intelligence models known as MAI.

According to the report, Microsoft’s MAI models are already being used to power features in applications such as Word and Excel as the company seeks to reduce the substantial costs associated with operating large language models.

The report fueled renewed speculation that Microsoft and OpenAI, whose close alliance has reshaped the artificial intelligence industry over the past several years, may gradually be pursuing more independent strategies.

Since Microsoft’s multibillion-dollar investment in OpenAI, the two companies have worked closely to integrate OpenAI’s technology across Microsoft’s products, making services such as Microsoft 365 Copilot among the most prominent commercial deployments of OpenAI’s models.

More recently, however, Microsoft has significantly expanded its own AI research and development efforts. The company has introduced its MAI family of models while also developing specialized AI systems designed to reduce dependence on third-party providers and lower computing costs.

At the same time, OpenAI has increasingly broadened its commercial relationships beyond Microsoft, partnering with additional cloud providers, enterprise customers and hardware companies as it scales its own business. Those developments have led industry observers to question whether the partnership is evolving from an exclusive strategic alliance into a more conventional commercial relationship between two increasingly independent AI companies.

OpenAI’s latest announcement suggests that, at least for now, its models will continue to play a prominent role within Microsoft’s productivity ecosystem.

But the company’s statement does not directly contradict reports that Microsoft is incorporating more of its own AI technology into products where it believes doing so offers technical or economic advantages. Rather than indicating an either-or choice between OpenAI and Microsoft’s in-house models, the latest developments suggest Microsoft may increasingly adopt a hybrid approach, using OpenAI’s frontier models where they deliver the strongest performance while deploying internally developed models for specific applications or cost-sensitive workloads.

Such a strategy would allow Microsoft to balance access to OpenAI’s latest advances with greater control over operating expenses and product development.

Maintaining a leading role in Microsoft 365 Copilot remains strategically significant for OpenAI. Microsoft’s productivity applications serve hundreds of millions of users worldwide, making the software suite one of the largest enterprise distribution channels for generative AI.

Keeping GPT-5.6 at the center of those products helps preserve OpenAI’s visibility and bolsters its position in the highly competitive market for enterprise AI services.

While the “preferred model” designation signals that the partnership remains active, it leaves unanswered broader questions about how responsibilities and technologies will be divided as both companies continue investing heavily in their own AI capabilities. The announcement therefore appears less like a reversal of reports about Microsoft’s growing use of proprietary models and more like confirmation that the two companies continue to collaborate even as each pursues increasingly independent AI strategies.

Europe Pushes for Greater Transparency on Private Credit as U.S. Resists Data Sharing

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A growing dispute has emerged between European and U.S. financial regulators over access to data on the rapidly expanding private credit market, exposing a widening transatlantic divide over how to monitor risks in one of the fastest-growing corners of global finance.

European supervisors are seeking more detailed information about banks’ exposure to private credit investments, arguing that limited disclosure and increasingly complex financing structures make it difficult to assess whether risks could spread through the broader financial system.

U.S. Treasury officials, however, have pushed back against broader information-sharing, saying much of the requested data is confidential and warning that additional reporting requirements would impose unnecessary compliance costs on financial firms, according to several officials familiar with the discussions, who spoke to Reuters.

The disagreement comes as private credit has grown into an industry worth an estimated $2 trillion, with a significant portion of that market concentrated in the United States.

Private credit refers to loans made by non-bank lenders, such as investment funds and private asset managers, directly to companies rather than through traditional banks. The sector has expanded rapidly over the past decade as tighter banking regulations encouraged businesses to seek financing outside the conventional banking system.

The market has attracted investors seeking higher returns, but its rapid growth has also prompted regulators to question whether risks are becoming more difficult to identify because many private credit transactions occur outside public markets and are subject to less disclosure than traditional bank lending.

Those concerns have intensified following recent market strains, including redemption restrictions imposed by some investment funds and a series of high-profile corporate defaults that have raised questions about asset valuations and liquidity across the sector.

European authorities now want a much clearer understanding of the assets underlying private credit investments.

In particular, regulators are seeking detailed information about borrowers, loan valuations, collateral arrangements, and guarantees supporting those investments so they can determine where financial risks ultimately reside.

“We feel some resistance from some supervisors around the world,” Bundesbank Executive Board member Michael Theurer told Reuters.

“There are arguments that they are not allowed to share — they have legal restrictions. And then there is the general criticism that these are new reporting requirements, a new bureaucratic burden.”

According to officials, the discussions have taken place through international regulatory forums, including the Financial Stability Board (FSB), which coordinates financial stability efforts among major economies.

An FSB spokesperson acknowledged that inconsistent reporting standards and differing definitions across jurisdictions make it difficult to compare private credit risks internationally, reinforcing the need for more comprehensive disclosure and common reporting frameworks.

The dispute reflects broader differences between European and U.S. regulators over financial oversight. European authorities have generally favored more detailed supervisory reporting following the global financial crisis, while U.S. regulators have often expressed greater concern about expanding regulatory burdens on financial institutions.

The disagreement over private credit also comes against the backdrop of wider policy differences between Europe and the United States on issues ranging from financial regulation and technology to climate policy, trade, and international security.

For European regulators, the central concern is what supervisors describe as insufficient “look-through” visibility into private credit investment structures. Although existing data provides estimates of overall exposures, officials argue that aggregate figures fail to reveal where underlying risks are concentrated or how losses could spread through interconnected financial institutions.

Recent analysis by the European Central Bank (ECB) suggests that direct exposure to private credit remains relatively limited across the euro area. According to the ECB, euro zone banks hold an estimated €62.5 billion ($71.46 billion) in global private credit exposure, representing only 0.2% of total banking assets.

European insurers are estimated to hold approximately €211 billion, while pension funds account for another €52 billion in exposure. Those holdings are concentrated primarily among a relatively small number of large financial institutions in Germany, France, and the Netherlands.

Despite the modest aggregate figures, regulators say headline numbers no longer provide sufficient insight into potential vulnerabilities.

Officials are now concerned that private credit assets are being repackaged into increasingly complex investment structures, making it difficult to identify where risks ultimately sit within the financial system. Private credit loans can be bundled into collateralized loan obligations (CLOs), combined with leveraged loans, or incorporated into insurance-related investment structures before being sold to banks, insurers, pension funds, and other institutional investors.

As those assets move through multiple layers of the financial system, tracing the ultimate holder of the underlying risk becomes increasingly challenging.

“There are cascades of different investment layers — collateralized loan obligations, leveraged lending, asset-intensive reinsurances — and it is possible to combine all of them,” Theurer said.

“That makes the underlying risks opaque.”

The ECB recently conducted stress tests examining the impact of a severe downturn in global private credit markets. The analysis found that direct losses from private credit investments would likely remain manageable for banks and institutional investors.

However, the exercise also revealed a potentially more significant concern.

The greatest financial damage would likely arise not from defaults on private credit loans themselves but from broader market reactions, including falling asset prices and valuation losses spreading across interconnected financial institutions.

That finding has reinforced regulators’ belief that focusing solely on aggregate exposures may understate systemic risks.

“What kind of assets are underneath and how are they valued?” one European policymaker asked.

“Where is the money? Where is the risk?”

U.S. regulators have generally expressed greater confidence that the banking system remains resilient. In May, Federal Reserve Vice Chair for Supervision Michelle Bowman said default rates among non-bank lenders would need to become “abnormally high” before posing a significant threat to banks.

She also noted that loans provided by banks to private credit firms appear to be well collateralized, reducing the likelihood of substantial losses under current conditions.

At the same time, Bowman said the Federal Reserve is requiring banks to provide more detailed information about lending to non-bank financial institutions, allowing supervisors to better assess concentration risks within the financial system.

The statement is understood to mean that while U.S. regulators acknowledge the need for closer monitoring, they remain less convinced than their European counterparts that the current level of systemic risk warrants broader international data-sharing requirements.

For European supervisors, however, the absence of more granular information could eventually have regulatory consequences. Several officials have warned that if regulators cannot accurately determine where private credit risks reside, they may have little choice but to require banks under their supervision to hold additional capital against potential losses.

Such a move would increase the cost of financing for banks with private credit exposure and could reshape how European financial institutions participate in one of the world’s fastest-growing asset classes.

Apple Sues OpenAI, Accusing AI Firm of Stealing Trade Secrets to Build Consumer Hardware

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Apple has launched a high-stakes legal battle against OpenAI, accusing the artificial intelligence company of systematically stealing confidential technology and trade secrets to accelerate its ambitions in consumer hardware, marking a dramatic collapse in what was once one of Silicon Valley’s most closely watched AI partnerships.

The lawsuit, filed on Friday in the U.S. District Court for the Northern District of California, alleges that OpenAI misappropriated Apple’s proprietary information through former employees and used the confidential knowledge to advance its own hardware development efforts.

The legal action represents a sharp reversal for two companies that only two years ago were publicly celebrating a strategic alliance that brought ChatGPT to the iPhone.

In the court filing, Apple accused OpenAI of orchestrating a broad effort to obtain its intellectual property.

“This much is clear, however: at every level, from members of its Technical Staff to its Chief Hardware Officer, and in coordination with business partners, OpenAI has been stealing Apple’s trade secrets and confidential information,” Apple said in its complaint.

The lawsuit centers on allegations of trade secret theft, one of the most serious forms of intellectual property litigation in the technology industry. Such claims generally accuse a company of acquiring or using confidential business information belonging to another company without authorization.

According to Apple, the alleged misconduct was not limited to isolated incidents but involved employees across multiple levels of OpenAI’s organization.

A significant portion of Apple’s allegations reportedly focuses on former Apple employees who either interviewed with or later joined OpenAI. The company contends that confidential information moved with those employees and ultimately benefited OpenAI’s hardware development efforts.

The filing signals that Apple believes OpenAI’s expansion beyond software into consumer devices has crossed legal boundaries.

The dispute comes against the backdrop of rapidly changing relations between the two companies. In 2024, Apple and OpenAI unveiled a landmark partnership that integrated ChatGPT into Apple’s operating system as part of its broader artificial intelligence strategy.

The announcement was viewed as one of the most important collaborations in the AI industry, with OpenAI Chief Executive Sam Altman appearing at Apple’s headquarters during the launch event. At the time, the partnership positioned ChatGPT as a key component of Apple’s effort to bring advanced generative AI capabilities to iPhone, iPad, and Mac users.

That relationship has steadily deteriorated, however, as OpenAI expanded its ambitions beyond AI software. Tensions increased significantly after OpenAI announced last year that it would enter the consumer hardware market through its $6.4 billion acquisition of IO Products, the startup founded by former Apple design chief Jony Ive.

The acquisition immediately intensified competition between the companies. Rather than remaining primarily a software provider supplying AI models to hardware manufacturers, OpenAI signaled its intention to build its own AI-powered consumer devices, placing it in more direct competition with Apple.

Industry observers viewed the deal as one of the clearest indications that OpenAI intended to challenge established consumer electronics companies by combining artificial intelligence with purpose-built hardware.

The growing rivalry has also become evident in Apple’s evolving AI strategy. Apple’s next-generation version of Siri, scheduled for release this fall, will rely on Google’s Gemini artificial intelligence models instead of ChatGPT.

The decision represents a notable shift away from OpenAI and suggests Apple has largely diversified its AI partnerships while reducing reliance on the company.

The move also indicates that competition among leading AI developers is reshaping alliances across the technology industry. Companies that once collaborated to accelerate AI adoption are increasingly becoming direct competitors as artificial intelligence expands into smartphones, personal computing, and dedicated hardware devices.

Apple’s lawsuit raises broader questions about how companies recruit talent in an industry where experienced AI engineers are in exceptionally high demand. Legal disputes involving former employees often focus on whether individuals improperly transferred confidential information from previous employers or simply relied on general technical expertise acquired during their careers.

The distinction frequently becomes central in trade secret litigation, where courts must determine whether proprietary information was unlawfully used or whether employees merely applied skills and experience they were legally entitled to retain.

If Apple succeeds, the case could become one of the most consequential intellectual property disputes in the AI industry, particularly as technology companies compete to develop AI-powered consumer devices.

As of Friday, OpenAI had not publicly responded to Apple’s allegations.

The case is expected to draw close attention across the technology sector because of its potential implications for employee mobility and trade secrets.

OpenAI Launches Chatgpt Work To Bring AI Coding Capabilities To Business Users At Lower Cost

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OpenAI on Thursday unveiled ChatGPT Work, a new AI-powered productivity agent designed to help professionals create documents, presentations, websites and other work products using advanced coding capabilities without requiring programming expertise.

The launch marks OpenAI’s latest push into the fast-growing enterprise AI market, where technology companies are increasingly competing to build autonomous software agents capable of completing complex workplace tasks with minimal human supervision.

ChatGPT Work is powered by GPT-5.6, OpenAI’s newest and most advanced artificial intelligence model, which also made its public debut on Thursday after its release was delayed last month at the request of the U.S. government over national security concerns.

AI Agent Designed For Everyday Professionals

OpenAI said ChatGPT Work combines the conversational abilities of ChatGPT with Codex, the company’s AI coding technology, enabling users to generate websites, presentations, reports, and other digital content through natural language instructions.

The product is aimed at business users who want to benefit from sophisticated coding capabilities without learning programming languages or interacting directly with developer-focused software.

OpenAI hopes to make advanced AI tools accessible to a much broader range of professionals across industries such as finance, consulting, education, healthcare, marketing, and legal services.

“You can apply the model’s ability to code to solve problems across every industry,” said Ty Geri, Product Manager for ChatGPT Work.

Geri described GPT-5.6 as “competitive with models that are far, far more expensive at twice the speed and much, much cheaper.”

The introduction of ChatGPT Work comes as competition among leading AI developers shifts increasingly toward enterprise software, where customers typically generate more predictable and higher-margin revenue than consumer subscriptions.

The product is OpenAI’s direct response to Anthropic’s Claude Cowork, launched in January, which similarly focuses on helping professionals automate multi-step workplace tasks.

Both companies are investing heavily in what is known as agentic AI—systems capable of planning, reasoning and executing sequences of actions rather than simply responding to prompts. Unlike traditional chatbots that primarily answer questions or generate text, these AI agents can complete end-to-end assignments such as creating business reports, analyzing data, building websites or coordinating workflows across multiple software applications.

The competition has become increasingly important as both OpenAI and Anthropic prepare for potential public offerings, making enterprise adoption a key metric for future investors.

One of OpenAI’s primary selling points is affordability. The company introduced GPT-5.6 in three different model sizes, allowing customers to choose between performance and cost depending on their needs.

OpenAI said the approach makes advanced AI capabilities available to a wider range of businesses that may have found previous frontier models prohibitively expensive.

The focus on pricing underlines growing concerns among corporate customers about the cost of deploying large language models at scale. As businesses move beyond experimentation and begin integrating AI into everyday operations, inference costs have become an important consideration.

Max Weinbach, an analyst at Creative Strategies, said the smallest version of GPT-5.6 delivers performance comparable to much larger models while costing significantly less.

“This is the first time where I’ve seen the small models complete these kinds of tasks,” Weinbach said.

According to Weinbach, the smallest GPT-5.6 model can perform tasks at roughly the same level as the largest version while costing about one-fifth as much. That could make enterprise AI deployments substantially more economical, particularly for organizations processing large volumes of requests.

ChatGPT Work builds on OpenAI’s growing portfolio of autonomous AI products. The company previously introduced Operator, which enables AI to interact with websites on behalf of users, and Deep Research, which conducts multi-step online research and analysis.

Those capabilities were later consolidated into the ChatGPT Agent for individual users.

For corporate customers, OpenAI also offers Workspace Agents, which automate internal business workflows. ChatGPT Work represents the next stage in that strategy by combining conversational AI, coding assistance and productivity features into a single application aimed at knowledge workers.

New Productivity Features

Alongside ChatGPT Work, OpenAI announced several additional products intended to broaden the platform’s capabilities. The company introduced a new desktop application for ChatGPT, giving users a dedicated interface outside the web browser. It also launched a hosted websites feature that enables users to build and publish websites directly through ChatGPT Work without relying on external hosting services.

The additions indicate that OpenAI has the ambition to position ChatGPT as a comprehensive productivity platform rather than solely a conversational assistant.

ChatGPT Work will begin rolling out on Thursday through both web and mobile platforms. Initial access will be available to Pro, Enterprise, and Edu subscribers before expanding to Plus and Business users over the following days.

The staged rollout follows OpenAI’s broader strategy of introducing new capabilities first to higher-tier subscribers before making them available to a wider user base.

Meta’s AI Hardware Strategy and the Rise of Proprietary Silicon

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Meta has taken another major step in the artificial intelligence race with the unveiling of Muse Spark 1.1, a new agentic and coding-focused AI model, alongside the announcement of its in-house AI accelerator chip, codenamed Iris, which is expected to launch in September.

The dual announcement signals Meta’s growing ambition to compete directly with leading AI firms such as OpenAI, Google, and Anthropic, while also reducing its dependence on external hardware suppliers.

Muse Spark 1.1 represents Meta’s latest effort to push AI systems beyond simple conversational capabilities toward autonomous, task-oriented intelligence.

The model is designed with strong coding abilities and agentic functionality, enabling it to perform multi-step tasks, reason through complex workflows, and execute actions with minimal human intervention. This marks an important evolution in AI development as the industry increasingly shifts toward intelligent agents capable of handling real-world operations, software engineering tasks, research activities, and digital assistance.

One of the key highlights of Muse Spark 1.1 is its enhanced coding performance. The model reportedly demonstrates improved code generation, debugging, and software optimization capabilities, allowing developers to use it as a virtual engineering assistant.

As enterprises continue integrating AI into their software development pipelines, advanced coding models are becoming critical infrastructure for accelerating productivity and reducing development costs. The model’s agentic framework also positions it at the center of the next wave of AI innovation.

Agentic systems differ from traditional chatbots by possessing the ability to plan, make decisions, interact with external tools, and adapt to changing objectives. This capability opens the door for applications ranging from autonomous business assistants and customer service agents to research automation and intelligent workflow management systems.

Alongside the software announcement, Meta introduced its custom AI chip, Iris, which is scheduled for release in September.

The move reflects a broader industry trend in which major technology firms are increasingly designing proprietary hardware to support their growing AI ambitions. Companies such as Google with its Tensor Processing Units and Amazon with its Trainium chips have already demonstrated the strategic advantages of owning specialized AI infrastructure.

Meta’s Iris chip is expected to play a significant role in training and deploying future generations of AI models. By developing its own silicon, Meta can potentially reduce operational costs, optimize performance for its specific AI workloads, and lessen reliance on external suppliers like NVIDIA, whose GPUs currently dominate the AI hardware market.

As demand for AI computing power continues to surge, access to dedicated infrastructure has become a key competitive advantage. The announcement also highlights the intensifying competition in the AI industry.

Technology companies are increasingly realizing that success in artificial intelligence requires both advanced models and vertically integrated infrastructure. Software innovation alone is no longer sufficient; firms must also secure computing resources, data pipelines, and specialized chips capable of supporting increasingly sophisticated AI systems.

Meta’s strategy appears to be aimed at building a comprehensive AI ecosystem that combines powerful models with proprietary hardware. If Muse Spark 1.1 delivers strong performance and Iris proves capable of handling large-scale AI workloads efficiently, Meta could significantly strengthen its position in the rapidly evolving AI landscape.

As September approaches, industry observers will closely monitor how Meta’s new model and chip perform against competing offerings. Their success could reshape competitive dynamics in artificial intelligence and further accelerate the global race toward more capable autonomous AI systems and AI-native computing infrastructure.