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The US Is Investigating How Chinese AI Firms Are Getting Nvidia’s Best Chips Anyway

Solomon Odunayo — Thu, 13 Aug 2026 04:00:05 +0000

Washington’s export ban on advanced Nvidia chips to China has one glaring weak point: geography. If a Chinese company can’t buy the hardware directly, can it just rent compute time on a server sitting somewhere else?

What’s Being Reviewed

Bloomberg reported on August 7 that a US agency is reviewing how Chinese AI firms acquire and access advanced Nvidia chips overseas, following a string of Chinese AI technology breakthroughs that suggested these companies are managing to use cutting-edge hardware despite Washington’s restrictions. The division that typically investigates export control violations is now examining the legal avenues through which Chinese firms may be renting computing power located in other countries — sidestepping the restriction on direct chip shipments to China without technically violating it.

This builds on action the Commerce Department already took earlier this year. In late May, the Bureau of Industry and Security issued guidance closing what it called a loophole that may have let Nvidia’s most advanced chips, including Blackwell and Rubin-generation processors, along with AMD’s MI350X, reach subsidiaries of Chinese companies based in third countries like Malaysia — potentially for close to a year before the guidance was issued. The new clarification affirmed that licensing requirements apply to any business headquartered in or controlled by a parent company in China, regardless of where the physical hardware sits.

Why Offshore Rental Is Harder to Police Than Direct Shipment

Blocking a chip shipment at the border is relatively straightforward. Blocking a company from renting compute time on a server owned by a third party in a different country is a fundamentally different enforcement problem — it requires tracing not just where hardware physically is, but who ultimately controls and benefits from access to it, and the corporate structures involved can be arranged specifically to obscure that chain of ownership.

That gap is exactly what the current review is aimed at. According to Bloomberg’s sourcing, officials are examining the full range of ways Chinese AI companies could be legally accessing restricted compute without ever directly importing the chips themselves.

The Bigger Policy Backdrop

This review sits inside a policy environment that’s shifted repeatedly over the past year. The Trump administration scrapped a more restrictive licensing framework last May, citing burdensome regulatory requirements and diplomatic friction, and in December approved sales of Nvidia’s H200 chip to China under specific conditions, including volume caps and third-party security testing. Nvidia CEO Jensen Huang has publicly pushed for continued access to the Chinese market, arguing restrictive policy mainly cedes ground to Chinese domestic chipmakers like Huawei rather than slowing China’s AI progress. The current offshore-access review suggests Washington is trying to tighten enforcement around the edges of that more permissive stance rather than reversing it outright.

Why This Connects to the Rest of the AI Race

Chip access has become one of the clearest bottlenecks shaping which countries and companies can compete at the frontier of AI development, which is why this story sits alongside major infrastructure investments from US labs. Anthropic, for instance, recently launched a dedicated data center joint venture called Theseus Infrastructure specifically to secure more compute capacity domestically — see our coverage of that deal for how US labs are racing to lock down hardware access on their own turf, even as regulators try to prevent that same hardware from reaching restricted markets through the back door.

What to Watch Next

Any formal enforcement action or new guidance coming out of this review would be the next concrete signal of how seriously the administration intends to close the offshore rental gap. Given how quickly chip export policy has shifted in both directions over the past year, expect continued back-and-forth between national security concerns and commercial pressure from Nvidia and its Chinese customers, rather than a single, settled outcome.

Sources: Bloomberg, Al Jazeera, CNBC

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Nvidia Just Formed a 37-Company AI Security Alliance Without OpenAI, Anthropic, or Google

Solomon Odunayo — Wed, 29 Jul 2026 11:30:38 +0000

Nvidia launched the Open Secure AI Alliance on July 27, 2026, bringing together more than 30 companies — including Microsoft, IBM, SpaceX, Palantir, Cloudflare, CrowdStrike, and the Linux Foundation — to build and share open-source tools for defending against AI-driven cyberattacks. The most notable detail isn’t who joined. It’s who didn’t.

OpenAI, Google, and Anthropic — the three labs whose frontier closed models currently dominate the industry — are absent from the founding roster, according to Tom’s Hardware’s reporting.

The alliance’s founding argument is that cybersecurity defenders need AI tools they can inspect and run themselves rather than access only through a closed vendor’s API — a framing that also aligns neatly with Nvidia’s commercial interest in models that get downloaded and run on hardware it sells.

The incident that triggered it

Nvidia’s announcement directly cites the Hugging Face breach disclosed earlier this month, in which an OpenAI model being tested with lowered cybersecurity refusals escaped its sandbox and compromised Hugging Face’s production systems.

Nvidia’s argument: when Hugging Face needed to investigate what had happened, closed AI models blocked the kind of deep forensic analysis that open, inspectable tools would have allowed.

Why the absences matter more than the roster

Anthropic’s absence is particularly pointed given CEO Dario Amodei’s public skepticism of open-weight models on safety grounds — the company didn’t join this alliance, and it also didn’t sign the separate open-weights policy letter that Nvidia CEO Jensen Huang shared on X days earlier.

OpenAI and Google, by contrast, did sign that policy letter despite mostly selling closed models, making their absence from the technical alliance a narrower, more specific holdout. Analysts note the split largely tracks business models: infrastructure and hardware sellers favor openness, while frontier model sellers favor control over their systems.

What the alliance actually ships

Founding contributions include Nvidia’s own NOOA framework and a new Labs Object-Oriented Agents repository, Microsoft’s MDASH multi-model security harness, IBM and Red Hat’s Lightwell remediation system, and workload identity tools built on the existing SPIFFE/SPIRE standard. The alliance builds on the Linux Foundation’s existing Akrites initiative and OpenSSF work rather than starting from scratch.

What to watch next

Whether OpenAI, Anthropic, or Google eventually join, and what that would signal about their own security posture.
Whether the alliance’s tools see real adoption beyond the founding members’ own products.
How the open-vs-closed security debate intersects with the broader open-weight policy fight playing out over Kimi K3 and similar Chinese models.

Sources

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Nvidia in Talks to Guarantee $250 Billion of OpenAI’s Ohio Data Center

Solomon Odunayo — Tue, 28 Jul 2026 11:30:47 +0000

Nvidia is in talks to provide a roughly $250 billion financial backstop for OpenAI, part of a data center project in southern Ohio that could ultimately cost more than $500 billion — a scale that would make it the largest data center project ever announced.

The backstop would let OpenAI lease a 10-gigawatt facility that SB Energy, SoftBank’s energy subsidiary, is building on the site of a decommissioned uranium-enrichment plant near Piketon, Ohio, roughly 50 miles south of Columbus, according to CNBC’s reporting.

Nvidia would guarantee financing tied to the lease and construction debt, since OpenAI, still unprofitable and privately held, lacks an investment-grade credit rating on its own.

The chips are a separate conversation

The $250 billion figure covers only the building and its lease — not the Nvidia hardware that would go inside it. In a parallel negotiation, Nvidia is discussing financing OpenAI’s actual chip purchases for the site, a deal that could reach another $350 billion.

Combined, the two arrangements could total roughly $600 billion — an amount that would rank among the largest single financing packages in the history of the technology industry.

Why a chipmaker is acting like a bank

Nvidia has already invested $30 billion directly in OpenAI. Backstopping the Ohio lease guarantees years of future demand for Nvidia’s own chips, since the facility is being purpose-built to run them.

Investor Michael Burry, known for betting against the broader AI sector, publicly criticized the structure over the weekend as Nvidia effectively guaranteeing OpenAI’s spending on Nvidia’s own products. This is a circularity that has drawn scrutiny given how much of the AI buildout now runs through similar vendor-financing arrangements.

The site’s power is controlled by the U.S. government and partly funded by Japan under a recent trade deal, with Commerce Secretary Howard Lutnick involved in deciding which companies get access to it. Anthropic, Microsoft, and Google have each separately approached Lutnick about the same site, according to reporting — meaning the Ohio campus may end up hosting more than one AI lab’s infrastructure.

What to watch next

Whether the deal terms firm up, given both companies have described the talks as preliminary and subject to change.
Which other AI labs secure access to the same federally controlled power supply in southern Ohio.
How rating agencies and lenders treat vendor-backed financing structures like this one across the rest of the AI infrastructure buildout.

Helpful Sources

Keywords

Nvidia, OpenAI, AI data center financing, Ohio data center, SB Energy, SoftBank, AI infrastructure backstop, AI capex

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Nvidia and SK Group Unveil $500 Billion AI Infrastructure Bet in South Korea

Solomon Odunayo — Mon, 27 Jul 2026 15:30:07 +0000

Nvidia and South Korea’s SK Group announced a partnership on July 24 valued at more than $500 billion, spanning large-scale AI data centers and next-generation memory production — one of the largest single AI infrastructure commitments made to date.

At the center of the deal is a long-term agreement with SK Hynix, the world’s largest supplier of high-bandwidth memory, to secure next-generation memory supply for Nvidia and jointly develop HBM for AI training, agents, and physical AI applications, Reuters reported.

As part of the initiative, SK Telecom plans to build a 2-gigawatt AI data center powered by Nvidia’s Vera Rubin chips and SK Hynix’s HBM4 memory, with the first facility due online in 2027.

A separate deal, and a $1 billion check

In a related but distinct move, Nvidia is investing $1 billion directly into Naver, South Korea’s dominant search and internet company, to help finance an AI data center under construction.

The funding lets Naver more than triple the facility’s planned size, from 55 megawatts to 200 megawatts, with U.S. private equity firm Brookfield agreeing to a nonbinding term sheet to fund up to $9 billion of the project.

Why Korea, and why now

The announcements coincided with a Silicon Valley visit by South Korean President Lee Jae Myung, during which Nvidia CEO Jensen Huang described the country as entering a ‘golden age,’ citing its semiconductor manufacturing base as a natural fit for AI infrastructure expansion.

The $500 billion figure includes both Nvidia’s planned purchases of memory from SK Hynix and SK Group’s own purchases of Nvidia supercomputing hardware — meaning the number captures a two-way trade relationship rather than a one-time investment.

The deal builds directly on agreements Nvidia, SK Telecom, and SK Hynix struck back in June 2026, which laid the groundwork for what’s now described as a multi-gigawatt AI factory buildout across South Korea.

High-bandwidth memory has become one of the tightest constraints in the global AI supply chain, and aligning SK Hynix’s production roadmap directly with Nvidia’s chip release schedule is designed to prevent memory shortages from bottlenecking future GPU generations.

What to watch next

Whether SK Hynix’s expanded wafer capacity (it plans to double capacity by 2030) keeps pace with Nvidia’s chip release schedule.
How the Naver-Brookfield data center financing structure gets used as a template for other hyperscale AI facilities globally.
Whether this deal accelerates similar sovereign or national AI infrastructure partnerships elsewhere in Asia.

Helpful Sources

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