In a world where artificial intelligence is the new frontier, the microchips that power it have become instruments of national security and economic competition. Navigating this landscape requires a deep understanding of the complex web of regulations governing their sale and use. Today, we sit down with Matilda Bailey, a networking specialist with a keen focus on the technologies shaping our digital future. We’ll explore the intricate challenges posed by new US export controls, from the operational hurdles facing international data centers and the strategic pivot towards the Middle East, to the subtle but significant impact these rules have on global technological competition.
The proposed GAIN AI Act includes a 15-day priority window for US buyers. Beyond simple delays, what specific operational and financial challenges does this create for international data centers? Can you walk us through a scenario of how this could disrupt a major infrastructure project?
This 15-day window sounds short, but in the world of hyperscale data center construction, it’s an eternity that introduces a devastating level of uncertainty. These projects are planned years in advance, with billions of dollars in capital expenditure tied to strict timelines. Imagine a large cloud provider in Europe embarking on a major expansion to meet surging AI demand. They’ve secured land, power agreements, and have construction crews on a tight schedule. They place an order for tens of thousands of top-tier AI chips, which is the heart of the entire project. Suddenly, because of the GAIN AI Act, a public notice of this sale goes out. A US-based competitor, perhaps one that was slower to plan, sees this as a golden opportunity. They express interest, and because they are a US buyer, they get priority. The European project is now completely stalled, not for 15 days, but indefinitely, as they have no idea when they can secure a new allocation of chips. This triggers a catastrophic domino effect: penalty clauses with their own enterprise clients are activated, market share is lost to the very competitor who took their chips, and the entire financial model for the project collapses. It fundamentally turns long-term infrastructure planning into a high-stakes gamble.
The Affiliates Rule is suspended until late 2026. What specific due diligence steps should non-US operators take now regarding their ownership structures to prepare for its return? Can you provide concrete examples of “red flags” in investor profiles that would cause concern under this rule?
The suspension of the Affiliates Rule is best viewed as a temporary grace period, not a reversal of policy. This is the time for non-US operators to get their house in order. The most crucial step is to conduct a deep, forensic-level review of their entire ownership structure, peeling back every layer of corporate vehicles and investment funds. The rule extends restrictions to entities that are at least 50% owned, directly or indirectly, by a company on one of the US government’s restricted lists. That “indirect” part is the real minefield. A major red flag would be an investment from a private equity fund that has opaque beneficial ownership. If you can’t identify the ultimate source of the capital, you have a serious problem. For example, if a significant investor in your data center is a holding company registered in a neutral country, but a little digging reveals its capital originates from a Russian entity on the specially designated nationals list, you would be in violation once the rule is reinstated. Another red flag is any complex, non-transparent structure that seems designed to obscure the ultimate owners. Proactive diligence now means mapping every single investor and ensuring there are no connections, however distant, to entities in countries like China or Russia that are on those US lists.
US policy now encourages AI chip exports to the Middle East to counter Chinese influence. What specific safeguards are attached to these deals to prevent technology diversion, and what metrics would define success in building a “US-centric tech stack” in the region?
This policy shift is a fascinating geopolitical play. The US is essentially trying to build a technological sphere of influence in a strategically critical region before China does. The safeguards attached to these massive chip deals, like the ones powering the “AI factories” in Saudi Arabia and the UAE, are stringent. They are built around enhanced “Know Your Customer” and end-use monitoring requirements. The chip exporters are on the hook to ensure these tens of thousands of advanced chips are actually being used for their stated purpose—training large AI models for economic diversification—and not being funneled to US adversaries. This involves ongoing verification and potentially even location-based features within the hardware to prevent illegal diversion. Success for the US won’t just be measured in chip sales. The key metric will be the widespread adoption of the entire American AI ecosystem. This means seeing the region’s major AI initiatives built not just on US hardware, but also on US-developed models, software, and standards. True success would be when a company in Riyadh or Dubai defaults to a US-based cloud and AI platform, effectively locking out Chinese alternatives from the region’s critical digital infrastructure for a generation.
The “Know Your Customer” requirements force chip sellers to request sensitive data. What specific types of business information are operators asked to provide, and what’s a step-by-step process for sharing this data while protecting commercial secrets? Perhaps you could share an illustrative anecdote.
The “Know Your Customer” requests go far beyond a simple credit check. Operators are being asked for highly sensitive commercial information. This includes detailed diagrams of their corporate ownership structure, lists of their major clients or end-users, and precise descriptions of the intended applications for the AI chips. They need to prove the technology won’t be used for something like military intelligence. Sharing this information is a delicate dance. I worked with a client who was building a specialized AI cloud for financial modeling. The chip seller, as part of their due diligence, demanded a list of their key customers. My client was rightly terrified, as the chip seller’s parent company had a competing cloud division. Giving them that list felt like handing over their entire business plan. The solution was a multi-step, legally-bound process. We started by providing anonymized data and project descriptions. Then, under a very strict NDA, we established a “clean room” where a trusted, independent third-party auditor could review the sensitive customer data to verify the end-use for the chip seller, without ever revealing the specific customer names to the seller’s business divisions. It’s a cumbersome but necessary process to satisfy compliance without giving away the keys to your kingdom.
The deal allowing sales of chips like Nvidia’s H20 to China creates a clear technology tier. How does this affect the competitive landscape for global cloud providers, and what specific strategies or workarounds are Chinese firms using to mitigate the performance gap from restricted top-tier chips?
This policy intentionally creates a tiered system that directly impacts global competition. Cloud providers operating outside of these restrictions have access to the absolute best-in-class chips, like Nvidia’s H100 and A100 series. This gives them a massive performance-per-watt and speed advantage in training the most advanced AI models. Chinese firms, relegated to using less powerful, export-compliant chips like the H20, are at a distinct hardware disadvantage. To compensate, they’re becoming incredibly innovative with workarounds. Their primary strategy is a brute-force approach combined with software genius. They are clustering together immense numbers of these lower-tier chips, networking thousands of them to collectively achieve the performance of a smaller number of top-tier chips. This is far less efficient in terms of energy consumption and physical space, driving up their operational costs. The other key strategy is hyper-focusing on software optimization—writing incredibly clever code that can squeeze every last drop of performance out of the constrained hardware they’re allowed to buy. They are essentially trying to close a hardware gap with software and scale, a difficult but not impossible task.
What is your forecast for the future of AI chip export controls?
My forecast is for a future of escalating complexity and granularity. The current model of controlling specific hardware based on performance thresholds is just the beginning. I anticipate we will see a move towards more sophisticated, “full-stack” controls that target not just the chips, but the AI models themselves, the software used to train them, and even specific applications. The US will continue to use these regulations as a primary lever in its foreign and national security policy, creating a dynamic where the rules of engagement are constantly shifting. For data center owners and operators, this means compliance can no longer be a static checklist. It has to become a core business strategy, requiring constant vigilance, strategic infrastructure planning that accounts for geopolitical risk, and the agility to adapt to a landscape that is being redrawn in real-time. It’s no longer just about procuring technology; it’s about navigating a global technological cold war.
