Nvidia inked strategic partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish AI compute infrastructure financing platforms to mobilise more than $500 billion of third-party capital to build related infrastructure.

The chipmaker stated memorandums of understanding would help create the first compute financing platforms of their kind at global scale, intended to fund frontier AI laboratories, clouds and enterprises.

It noted the platforms are designed to create dedicated pools of capital at significant scale and attractive rates for its customers, positioning Nvidia compute and full-stack AI infrastructure as an investable asset class.

“We are bringing the world’s leading long-term capital providers together to independently underwrite AI infrastructure,” Nvidia CEO Jensen Huang said.

“These financing platforms will help customers access scarce compute at scale and build the DSX AI factories that will power every industry and country”.

Nvidia described its compute as an investable asset offering the lowest token cost, highest revenue and longest life, alongside a rich ecosystem of buyers.

The arrangement builds on Nvidia’s broader push to raise capital for it and its customers to continue producing the chips, power systems and data centres underpinning the AI boom.

Nvidia GPUs underpin most leading US AI models available today and it supplies relevant chips, infrastructure, and software to a wide range of partners.

Money matters
It faced scrutiny in recent months over circular financing arrangements in which the company provides backing to help AI partners raise debt, in turn boosting Nvidia’s own revenue.

Bringing outside private capital providers onboard may help address some of the criticism since it spreads the financing load beyond Nvidia itself.

In recent years, private capital groups including Apollo and Blackstone have structured AI deals to help companies including Anthropic finance heavy spending on chips and data centres.

Financial Times reported Morgan Stanley predicts large cloud computing players would spend $3.5 trillion on AI infrastructure over the next two years.

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Meta Platforms CEO Mark Zuckerberg (pictured) argued open, decentralised access to super-intelligence is essential for ensuring global economic prosperity and personal safety.

In a lengthy blog, the executive rejected doom-centric narratives surrounding advanced AI technologies.

Rather than centralising powerful models within a select group of corporations or government institutions, Zuckerberg advocated for a balance-of-power approach, which puts personal superintelligence directly into the hands of individuals.

On the same day the post was published, Meta Superintelligence Labs released the open weights for Muse Glimmer, a 30-billion-parameter dense multimodal model under an Apache 2.0 license on Hugging Face.

The model is distilled from Muse Spark 1.2 and engineered to run locally on a single consumer GPU or Mac to execute agentic workflows such as autonomous coding and file management without cloud dependency.

Meta also plans to publish open weights for its Muse Spark 1.2 model in the coming weeks, reversing a brief closed-weight stance, while teasing a next generation frontier model known internally as Watermelon.

The release and blog came amid heightened scrutiny over closed-source AI labs following recent incidents where models from OpenAI and Anthropic broke containment during evaluation testing.

Zuckerberg positioned open-source distribution as a key countermeasure to security risks. He stated broad developer access allows system defences to be patched faster than in centralised environments.

He also defended AI distillation practices by claiming learning from observed model outputs is essential for keeping US open-source technology competitive against foreign rivals like DeepSeek.

Central to Meta’s broader strategy is the deployment of personal AI agents designed to assist users across daily tasks, business operations and learning.

To support this vision Meta unveiled a $1 billion community fund for US cities hosting its data centres to address local power, water and infrastructure needs, while committing to train skilled trade workers through its Community Compact and America’s Workforce Academy initiatives.

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Anthropic formed a strategic partnership with Macquarie Asset Management and Singaporean wealth fund GIC to build data centres which the AI company would lease under long-term arrangements.

The Theseus Infrastructure platform is to be a vehicle to develop AI computing sites with an initial focus on the US.

Macquarie Asset Management and GIC pledged to fund the bulk of the equity for each project, while Anthropic committed to covering any consumer electricity price hikes stemming from the facilities.

The asset management company stated sites would require a major investment and highlighted anticipated boosts for employment covering building and operation.

GIC is a repeat Anthropic investor.

It jointly led a $30 billion funding round in February and backed a $1.5 billion enterprise services venture Anthropic launched in May.

In 2025, Anthropic stated it would spend $50 billion on custom data centres across several US locations, including the states of Texas and New York.

The company is also preparing for a public listing.

Bloomberg reported Anthropic has made relatively few high-profile data centre partnerships compared with rival OpenAI, which launched its Stargate infrastructure joint venture in 2025.

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LG Uplus advanced efforts to apply AI in network automation by testing its ability to control the quality of a Wi-Fi 8-compatible home broadband system.

The self-healing capability employs Edge AI technology, which the South Korean operator explained conducts analyses and adaptation in the router instead of the cloud.

Broadcom contributed a Wi-Fi 8 platform running a neural processing unit to LG Uplus’ trial, enabling the on-board AI computation.

The operator adapted its existing cloud-based AI network prediction model for operation in the router.

There is an argument home broadband and consumer equipment are not often the focus of discussions around self-healing and autonomous networks.

LG Uplus explained the trial is significant in proving routers can become part of “an AI-based quality management platform”, benefitting customer service and infrastructure management.

It stated the companies proved the router could detect and adapt to anomalies before customers notice, boosting stability for bandwidth-intensive services.

LG Uplus plots further trials of how AI could be used to manage Wi-Fi 8 networks.

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OpenAI temporarily paused certain internal activities surrounding its upcoming model after the system demonstrated unprecedented cybersecurity and agentic coding capabilities.

The ChatGPT maker disclosed preliminary evaluations showed the Astra model is significantly adept at cyber tasks, leading company officials to admit they cannot rule out it becoming a critical security threat threshold.

Under OpenAI’s internal Preparedness Framework, a critical threshold is defined by a model’s ability to autonomously discover and execute zero-day exploits across hardened real-world systems without human intervention, or independently carry out complex attack strategies.

While evaluations remain ongoing alongside third-party experts, the initial findings prompted immediate containment protocols.

OpenAI is moving Astra into isolated testing environments equipped with sandboxed execution, restricted tool and network access, enhanced model weight encryption and full chain-of-thought monitoring.

It stated it paused related activities which do not currently meet the safety controls.

OpenAI’s move follows a recent series of incidents where autonomous AI agents breached security boundaries during testing.

Within the past month, OpenAI, Anthropic and Meta Platforms have all publicly acknowledged inadvertent system intrusions during evaluation phases.

OpenAI plans to partner with government agencies and AI safety institutes to evaluate Astra’s defensive and attacking potential before considering public release.

It emphasised its goal is to equip cyber defence players with high-capability models to patch vulnerabilities before malicious actors can exploit them.

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