US-China Trade War 2026: Chips, Rare Earths, and AI
The US-China trade war 2026 is not about soybeans, Boeing aircraft, or even tariffs. It is about something far more fundamental: who controls the physical infrastructure of artificial intelligence. As Trump prepares to land in Beijing and shake hands with Xi Jinping, negotiators are arguing over two things that will define the next decade of global power — semiconductor manufacturing and rare earth minerals. The rest is theater.
While European leaders debate green transition subsidies and GDPR compliance, the two superpowers are quietly drawing the map of the AI economy. The map does not include Europe.
Why the US-China Trade War 2026 Is Really a War Over AI Infrastructure
Here is the number that should keep every CEO awake at night: AI-related trade grew 40% in 2025 against a global trade average of 6.5%. Everything else in the world economy is growing at pedestrian pace. The AI economy is exploding. And the raw materials that make that explosion possible — advanced chips and the rare earth minerals that go into them — are almost entirely controlled by two countries. One is the United States. One is China.
This is not a trade war in any traditional sense. It is an infrastructure war. The chips and minerals at stake are the oil wells and pipelines of the 21st century. Whoever controls them controls the economics of AI for the next generation.
The Trump-Xi summit scheduled for late March and early April 2026, with preparatory talks already underway in Paris as of mid-March, is the negotiation that will set the terms of that control. And unlike oil negotiations from the 1970s, this one is happening in real time, at technological speeds, with consequences that cascade through every industry on the planet.
Understanding what is actually at stake — and what it means for your business — requires cutting through a lot of diplomatic noise.
The Asymmetric Power Structure: Who Controls What
To understand the US-China trade war 2026 dynamics, start with the power map. It is not symmetric, and that asymmetry is precisely what makes this negotiation so volatile.
The United States controls the compute stack. American firms — NVIDIA, Intel, Qualcomm, TSMC (through American-controlled supply chains), ASML (Dutch but dependent on US technology) — together represent approximately 92% of overall semiconductor supply chain value, according to analysis from the Center for Strategic and International Studies. More specifically, US and allied firms control roughly 90% of global semiconductor manufacturing equipment. You cannot build an advanced chip without American machines. You cannot run frontier AI without American-designed processors.
This is an extraordinary structural advantage. Every Chinese AI company — Baidu, Alibaba, ByteDance, Huawei — needs to either buy American chips or find a way around American export controls. The American export controls keep tightening. The most recent round, imposed in September 2025, extended controls to foreign affiliates, closing loopholes that had allowed chips to reach China through third countries.
China controls the materials stack. But here is where it gets complicated. Beijing controls 85 to 90% of global rare earth processing capacity, according to the IEA's analysis on critical mineral supply concentration. These are not exotic materials only relevant to science fiction: they are essential ingredients in every piece of modern electronics, including the AI chips that American firms design and manufacture. Neodymium goes into the permanent magnets in chip manufacturing equipment. Dysprosium and terbium go into components throughout the AI hardware supply chain. Without Chinese rare earths, TSMC slows down. Without TSMC, NVIDIA has no H100s to sell.
China exercised this leverage in two waves during 2025. In April 2025, Beijing imposed the first set of export controls on rare earth elements. In October 2025, it escalated dramatically — extending controls even to internationally manufactured products containing Chinese-sourced rare earth materials, regardless of where final assembly happened. The price response was immediate and brutal: dysprosium oxide prices tripled, terbium oxide more than doubled by May 2025. European automotive production lines ran out of rare earth supplies. TSMC started doing emergency inventory calculations.
This is the negotiating table. The United States holds the chip weapon. China holds the mineral weapon. Neither can fully defeat the other without destroying the AI economy they both want to dominate.
The Paris Talks and What Was Actually Negotiated
Before the main event in Beijing, senior negotiators from both sides met in Paris in mid-March 2026 to narrow the agenda. Bloomberg reported that the Paris talks focused specifically on technology trade — not the agricultural purchases or energy deals that will be announced publicly as deliverables from the summit.
What is on the table in Paris, and what will land in Beijing, reflects the real stakes.
On the American side, the core ask is predictability. US tech companies — and their investors — need to know where the floor is. How many Chinese customers can NVIDIA still sell to? Which categories of AI accelerators remain tradeable? The Trump administration has been aggressive on export controls, but the private sector has been pushing back. Revenue pressure from chip companies is real. Every Nvidia GPU that cannot be sold to a Chinese hyperscaler is revenue the company loses in the most lucrative market on earth.
On the Chinese side, the core ask is exactly symmetric: how high is the fence, and how big is the yard? Beijing accepts that some level of American technology restriction is permanent. What they need is a stable operating environment — the ability to plan R&D roadmaps, manufacturing investments, and commercial strategies without wondering if the rules change every six months.
The rare earth lever is China's negotiating tool to extract that predictability. It is not a weapon they want to fire permanently — China's own manufacturing sector needs functioning global supply chains — but it is a credible threat that creates leverage.
What is actually unlikely to happen in Beijing: any grand bargain. The more realistic outcome, as analysts at the Atlantic Council and US-China Business Council have noted, is a narrowing of deliverables to manageable commercial agreements — more soybean purchases, more Boeing orders — while the tech track gets kicked into a longer conversation across the rest of 2026.
The critical question for European businesses is what happens during that "longer conversation."
Europe Is Not at the Table
This is the part that should alarm any European CEO, startup founder, or board member reading this.
The US-China trade war 2026 is being negotiated bilaterally. Europe is not a party to it. European businesses will receive its outcome as a fact of life, with no input into its terms.
This is not new. What is new is the stakes. When the US and China negotiated Phase 1 trade deals in 2019-2020, the primary economic damage to Europe was competitive — Chinese firms got preferential terms for American agricultural imports that European farmers didn't get. Irritating, but manageable.
The current negotiation is about the physical substrate of the AI economy. If the US and China reach an arrangement — explicit or implicit — that carves up the advanced chip market, the rare earth supply chain, and the AI compute stack between their two ecosystems, European companies will find themselves on the outside of both.
Consider what this means practically. European AI startups that are building on American cloud infrastructure (AWS, Azure, Google Cloud) are operationally inside the American tech perimeter. They benefit from American chip supply. But they are also subject to American export controls — if those controls expand, European startups using US-origin chips in applications involving Chinese customers or partners will face compliance exposure they currently do not account for.
European industrial companies — Volkswagen, Airbus, Siemens, the entire automotive supply chain — are structurally dependent on Chinese rare earths. The European Parliament published analysis in late 2025 confirming that China's export controls have hit EU industries disproportionately hard. The European Association of Automotive Suppliers reported production line halts in mid-2025 after rare earth supply dried up. If the US-China negotiation results in a bilateral managed trade arrangement that prioritizes American rare earth access over European, European manufacturers lose.
The EU Critical Raw Materials Act, announced with much fanfare, sets a target of extracting 10% of critical minerals domestically and processing 40% within Europe by 2030. These are good targets. They are also four years away, and the timeline of the current trade war is measured in months, not years.
What the AI Supply Chain Actually Looks Like
To understand why this matters for your business specifically, it helps to understand the physical chain that connects Chinese rare earth mines to AI applications running in the cloud.
Layer 1: Mining. Rare earth elements — neodymium, dysprosium, terbium, praseodymium, and about a dozen others — are mined primarily in China (dominant), with secondary sources in Australia, the United States (primarily the Mountain Pass mine in California), and a handful of other countries. The mining is concentrated, but not uniquely so: the US-China dominance here is strong but not absolute.
Layer 2: Processing and separation. This is where Chinese dominance becomes overwhelming. The chemical processes required to separate individual rare earth elements from ore are extraordinarily polluting, energy-intensive, and technically complex. China built this processing infrastructure over decades, accepting environmental costs that Western democracies would not accept politically. The result is that 85-90% of all rare earth processing globally happens in China. Even if you mine in Australia, you likely send ore to China for processing.
Layer 3: Materials fabrication. Processed rare earth oxides are converted into metals, alloys, and compound materials — permanent magnets, sputtering targets, phosphors. These go into manufacturing equipment for semiconductors, and into components throughout the electronics supply chain.
Layer 4: Semiconductor manufacturing. TSMC (Taiwan), Samsung (South Korea), and Intel (US) are the primary manufacturers of advanced chips. They are structurally dependent on Layer 3 materials. TSMC has been explicit about its rare earth dependency: without stable rare earth supply, its manufacturing timelines for AI chips extend significantly.
Layer 5: Chip design. NVIDIA, AMD, Google (TPUs), Amazon (Trainium), and increasingly Chinese companies like Huawei design the AI-specific accelerator chips that power modern machine learning. American chip designers dominate this layer. Chinese firms are several generations behind but closing.
Layer 6: AI infrastructure. Cloud providers and hyperscalers — AWS, Azure, Google, and their Chinese counterparts Alibaba Cloud, Tencent Cloud, Huawei Cloud — purchase chips and build the data centers that run AI workloads.
Layer 7: Applications. Your business, using AI APIs, tools, and platforms built on Layer 6.
The US-China trade war 2026 is being fought primarily at Layers 2 through 5. But if you are operating at Layer 7 — which means essentially every business on earth — you are exposed to shocks that travel up from below.
The Four Scenarios for the Rest of 2026
Based on current trajectory and the dynamics around the Trump-Xi summit, four scenarios are plausible for the remainder of 2026.
Scenario 1: Managed Coexistence (Most Likely)
Both sides agree to a structured freeze — similar to the one-year suspension that held from November 2025 to November 2026. Export controls on chips remain but are calibrated to avoid triggering full retaliation. Rare earth export controls remain suspended or narrowly applied. Trade continues in a constrained but functional way. This scenario is the baseline expectation from most analysts following the Paris talks.
For European businesses: continued uncertainty, rising chip costs, but no acute crisis.
Scenario 2: Bilateral Grand Bargain (Possible but Unlikely)
Trump and Xi reach a broader technology trade deal — American chips flow more freely to Chinese buyers, China locks in rare earth supply for American manufacturers. This scenario is structurally appealing but politically difficult for both sides.
For European businesses: potentially locked out of preferred access terms on both chips and minerals as the deal prioritizes US-China bilateral flows.
Scenario 3: Escalation (Significant Tail Risk)
One side blinks wrong — either the US tightens export controls aggressively ahead of the summit as a negotiating move (which Trump has signaled with a new Section 301 investigation launched in March 2026), or China re-activates rare earth export controls. Prices spike again, supply chains seize up, and the Paris talks collapse.
For European businesses: acute supply chain crisis, cost spikes across manufacturing, potential production halts for companies with deep rare earth exposure.
Scenario 4: Decoupling Acceleration (Long-Term Risk)
The summit fails to produce any durable framework, and both sides accelerate parallel supply chain construction — the US investing in non-Chinese rare earth processing, China investing in domestic chip manufacturing at competitive scales. This is the multi-year trajectory regardless of what happens at the summit.
For European businesses: eventual bifurcation into two parallel tech ecosystems. Every European company will have to choose which ecosystem to operate in, or pay the premium to operate in both.
The Real Cost of the AI Supply Chain War on Your Business
I have worked with businesses across industries on AI implementation — hospitality, healthcare, sports retail, agriturismo, professional services — and the pattern I see consistently is that leadership underestimates infrastructure risk while overestimating application risk.
Everyone worries about whether their AI model will hallucinate. Almost nobody has done a supply chain exposure analysis on their AI infrastructure.
Here is what that analysis should cover in 2026.
Compute cost trajectory. AI inference and training costs have been falling rapidly thanks to chip efficiency gains. But if the US-China trade war disrupts chip supply chains, that deflationary trend reverses. NVIDIA's production costs rise if TSMC faces rare earth supply constraints. Those costs get passed to cloud providers, which pass them to API consumers. An aggressive escalation scenario in the US-China trade war 2026 could meaningfully raise AI compute costs for European businesses within 12-18 months.
Supply chain audits for hardware-dependent businesses. If your business manufactures anything — electronics, automotive components, industrial equipment, medical devices — you likely have rare earth exposure you have not fully mapped. The CLEPA data from 2025, showing European automotive production lines halting from rare earth shortages, was not a surprise to supply chain professionals who had done proper exposure analysis. It was a surprise to everyone else.
Regulatory compliance exposure. American export controls apply to American technology regardless of where the end user is located. European companies using American AI chips, American cloud services, or American AI models in applications that involve Chinese customers or partners may face compliance obligations they currently ignore. This is not theoretical: the US has already extended controls to foreign affiliates, and the direction of travel is more enforcement, not less.
Strategic dependency on American AI infrastructure. Europe's AI ecosystem runs almost entirely on American chips, American cloud infrastructure, and increasingly American AI models. This is a geopolitical dependency that European policymakers are aware of and attempting to address — but the AI strategy for the next decade must account for this structural reality.
McKinsey's Numbers and What They Actually Mean
McKinsey's semiconductor research tells a story that the trade war makes urgent. Their analysis projects the semiconductor market reaching $1.6 trillion by 2030, driven overwhelmingly by AI demand. Memory and logic device segments are growing at greater than 30% annually. The IDC revised semiconductor revenue growth rates upward from 15.5% to 17.6% in 2025 as AI infrastructure investment accelerated.
These are extraordinary growth numbers. They explain why both the US and China are willing to use economically disruptive tools — export controls, rare earth restrictions, tariffs — in a fight over market position. The prize is not the current market. The prize is controlling a trillion-dollar-plus market in a world where AI infrastructure is as fundamental as electricity.
The McKinsey analysis on AI's impact on the semiconductor industry is also sobering in a different way: while AI has driven explosive revenue growth industry-wide, only the top 5% of semiconductor companies have captured most of the upside. The rest of the industry — and the customers that depend on it — are absorbing higher costs without commensurate benefits.
For any business that is not in that top 5% — which means essentially every business that uses AI rather than builds it — the US-China trade war 2026 represents a cost risk, not a revenue opportunity.
The Huawei Signal: What China's Domestic Chip Push Tells Us
One data point deserves specific attention before we get to what smart CEOs are doing: Huawei's Mate 60 Pro.
In August 2023, Huawei quietly launched a smartphone powered by the Kirin 9000S — a 7-nanometer chip manufactured entirely within China by SMIC, without American manufacturing equipment. The intelligence community was surprised. The chip is not competitive with TSMC's best, but it exists. It works. It demonstrates that China has broken through the 7nm barrier despite export controls.
This matters for the US-China trade war 2026 calculations in a specific way. American export control strategy assumes that restricting access to advanced chip manufacturing equipment will permanently keep Chinese chip production at least two generations behind the frontier. The Huawei Mate 60 Pro proved that assumption needs revision. China's domestic chip capability is advancing faster than the export control framework anticipated.
The implication is not that American chip dominance is about to end — TSMC's 3nm and 2nm processes are still years ahead of anything China can manufacture domestically. The implication is that the window for using chip restrictions as a negotiating lever against China is narrower than policymakers assumed five years ago. This changes the calculus at the Trump-Xi summit. Beijing has more credibility when it argues that it can survive chip restrictions, even if at significant cost.
For CEOs thinking about AI strategy, the Huawei signal means that the bifurcation of AI ecosystems is not a distant hypothetical — it is already underway at the hardware level. The question is how quickly the split propagates up to the application layer.
What Smart CEOs Are Doing Right Now
Across the clients I work with, the ones navigating this environment most effectively share three characteristics.
First, they have mapped their AI infrastructure dependency. They know which cloud provider they are on, which chip generation powers that provider's AI infrastructure, and what their cost exposure would be if AI compute prices increased 20-30%. They have also mapped any hardware supply chains for physical products. This is not a 90-day exercise — it should have been done already — but if you have not done it, Q2 2026 is the time.
Second, they are building flexibility into their AI vendor relationships. Sole-source dependency on a single AI provider is a strategic liability in a US-China trade war environment. The businesses I see doing this well have multi-cloud strategies, are testing open-source model alternatives alongside proprietary APIs, and have negotiated flexibility clauses into enterprise AI contracts that allow them to migrate workloads.
Third, they are treating AI infrastructure as a board-level geopolitical issue, not a technology issue. The CEOs who are most exposed in 2026 are the ones who delegated AI strategy entirely to their CTO. Every CEO needs an AI strategy in 2026 — but that strategy must now incorporate geopolitical supply chain risk alongside capability assessment and ROI analysis.
Case Studies: How Supply Chain Risk Plays Out in Practice
Working with WSB Sport, an operator in sports retail, we implemented an AI-driven marketing system that delivered a 30% increase in sales. The infrastructure underneath that system — recommendation algorithms, dynamic pricing, customer segmentation — runs on American cloud AI. It is a Layer 7 application with full dependency on Layers 1 through 6 below it.
When we audited the supply chain risk exposure in late 2025, the finding was clear: if AI compute costs increase 25%, the ROI on the AI marketing system remains strongly positive, but the payback period extends by roughly 8 months. That is manageable. But if compute costs double — a scenario that is possible in the aggressive escalation case — the economics of certain AI applications that are currently compelling become questionable.
The response was not to abandon AI marketing. It was to document the cost sensitivity, build alternative scenarios into the planning model, and establish triggers for vendor switching if costs cross certain thresholds. That is what risk management looks like in the US-China trade war 2026 environment.
Working with a medical center client — where AI-assisted scheduling and capacity management contributed to a 20% increase in usable capacity — the exposure was different. Healthcare AI is subject to regulatory oversight that makes quick infrastructure migration difficult. Compliance requirements effectively lock you into specific cloud providers and specific data handling architectures. In that environment, the risk management approach was different: focus on regulatory flexibility in contract terms, and invest in on-premise inference capability for critical applications where cloud dependency poses unacceptable continuity risk.
The hospitality project — revenue management AI that contributed to a hotel group growing from €9M to €10M in revenue — surfaced a third dimension: exposure through hardware vendors in property management systems. Hotel PMS systems contain chips, those chips contain rare-earth-derived components, and the US-China trade war 2026 escalation creates supply chain uncertainty for the physical infrastructure of hospitality technology. Not an acute crisis, but a factor in any technology refresh planning.
The Self-Assessment: Your US-China Trade War Exposure Score
Use this framework to assess your business's exposure to the US-China trade war 2026 dynamics. Assign 1 point for each "yes."
Compute exposure: - [ ] Your business uses AI APIs or cloud AI services as part of core operations - [ ] More than 20% of your IT budget runs on a single American cloud provider - [ ] You have not modeled the impact of a 25% increase in AI compute costs - [ ] You have no multi-cloud or open-source AI fallback strategy
Hardware/supply chain exposure: - [ ] Your business manufactures physical products containing electronic components - [ ] You have not conducted a rare earth exposure audit of your supply chain - [ ] Your hardware vendors have not provided supply chain risk disclosures for 2026 - [ ] You have single-source dependencies on rare-earth-dependent components
Regulatory/compliance exposure: - [ ] You have Chinese customers, partners, or operations - [ ] You use American AI technology in applications touching Chinese counterparties - [ ] Your legal team has not reviewed US export control applicability to your AI use cases - [ ] You are unaware of the current status of US-China technology export restrictions
Strategic exposure: - [ ] Your AI strategy was developed without input from your legal or compliance team - [ ] Your board has not discussed geopolitical technology risk in the last 6 months - [ ] You have no contingency plan for a 3-month disruption in AI service availability - [ ] Your AI vendor contracts do not include force majeure or service continuity provisions
Scoring: - 0-3: Low exposure. Maintain awareness, no urgent action required. - 4-7: Moderate exposure. Conduct supply chain audit and review vendor agreements in Q2 2026. - 8-11: High exposure. Initiate risk mitigation immediately. Engage external expertise on AI supply chain strategy. - 12+: Critical exposure. Your business has significant unmanaged geopolitical supply chain risk. This requires executive-level attention now.
Most of the European mid-market businesses I work with score between 6 and 10 on this framework. The average is higher than most CEOs expect when they first take the assessment.
The 30/60/90 Day Action Plan
Days 1-30: Map Your Exposure
The first priority is knowing what you do not know. Conduct a structured AI infrastructure audit covering:
- All AI and cloud services in use, mapped to their underlying chip infrastructure
- All hardware vendors, with explicit rare earth and semiconductor content documentation requested
- All Chinese counterparty relationships, reviewed for US export control applicability
- A spend analysis showing AI-related costs as a percentage of operating budget
This does not require external consultants for most businesses. A two-day internal workshop with your CTO, CFO, and general counsel will surface 80% of the material exposure. What you are looking for is concentration risk: single-provider dependencies, unhedged hardware supply chains, and undocumented compliance obligations.
Document findings in a geopolitical risk addendum to your existing risk register. Most risk registers do not have this category. Add it.
Days 31-60: Stress-Test and Prepare
With exposure mapped, run three scenarios against your operating model:
Scenario A (Baseline - Managed Coexistence): 10-15% increase in AI compute costs, stable hardware supply, continued compliance uncertainty. What does this do to your AI investment ROI? Which projects become marginal? Which remain strongly positive?
Scenario B (Moderate Escalation): 25-35% compute cost increase, 6-month rare earth supply disruption for hardware-dependent supply chains, expanded US export control enforcement against US-allied companies. Which operations face continuity risk? What vendor alternatives exist?
Scenario C (Severe Escalation): Temporary suspension of US-China technology trade, acute chip supply shortage, rare earth prices returning to or exceeding May 2025 peaks. What is the continuity plan? Which AI applications can you run on-premise or with open-source models? Which business processes can you operate manually for 90 days?
The outputs of this scenario analysis are: a prioritized list of risk mitigation actions, vendor diversification priorities, and contract terms to negotiate on renewal.
Days 61-90: Execute Priority Mitigations
Based on your stress-test outputs, focus Q3 2026 mitigation efforts on your three highest-exposure items. Common priority areas I see with clients:
Vendor diversification: Begin testing a secondary cloud AI provider, even at low volume. Establish a contractual basis with an open-source model host (Mistral, Cohere, or a self-hosted option) that could absorb workloads if your primary provider faces supply-driven price increases.
Hardware supply chain documentation: For any physical product line with potential rare earth exposure, obtain written supply chain risk disclosures from your tier-one vendors. Request that they map their own exposure to Chinese processing capacity. This documentation is valuable both for internal risk management and for any regulatory or insurance requirements that may emerge.
Legal review of AI contracts: Have your technology contracts reviewed specifically for export control applicability, data residency requirements that create US-China compliance exposure, and service continuity provisions in force majeure scenarios.
Board briefing: Prepare a one-page brief for your board on geopolitical AI supply chain risk. Include your exposure score, scenario analysis summary, and mitigation priorities. This is a governance issue, not just a technology issue. Building board awareness now positions you to get rapid approval for mitigations if the situation escalates.
The European Strategic Moment
There is an argument — optimistic but not unreasonable — that the US-China trade war 2026 is a strategic opening for European AI development.
If the two superpowers are increasingly treating AI infrastructure as a weapon in a bilateral contest, European businesses have incentive to develop alternatives that are not entangled in that contest. The EU's AI Act, for all its regulatory complexity, establishes a values-based framework that neither the US nor China is building. European cloud providers — Hetzner, OVH, Deutsche Telekom — are positioning on sovereignty grounds. The European Chips Act aims to build domestic semiconductor capacity, though the timelines are long.
For AI implementation in European businesses, sovereignty-focused architecture is increasingly worth the premium. Running AI workloads on European infrastructure, with European providers, using European-origin where possible, reduces geopolitical exposure even if it temporarily increases costs.
This is not an ideological argument. It is a risk management argument. If the US-China trade war 2026 produces a bifurcated technology landscape, being caught on the wrong side of a fence is existentially expensive. Building infrastructure that operates above both fences costs more in the short term but preserves strategic optionality.
What Comes After the Beijing Summit
Whether the Trump-Xi summit produces a deal or a stalemate, three dynamics will continue regardless.
American export controls will not loosen permanently. The bipartisan political consensus in Washington around technology competition with China is the strongest it has been since the Cold War. Regardless of what Trump announces in Beijing, the structural trajectory is toward continued — probably increased — restrictions on advanced AI chip exports to China over the medium term.
China's rare earth leverage will intensify, not diminish. China's processing dominance took decades to build. The US and European efforts to develop alternative processing capacity are real but slow. Until alternative processing scales — which agentic AI systems and advanced manufacturing can help accelerate but cannot shortcut — China retains structural leverage on rare earths. Beijing has demonstrated willingness to use it. That will not change.
The AI supply chain will bifurcate gradually. The scenario of a clean, sudden split into "American AI" and "Chinese AI" is unlikely. The more likely trajectory is a gradual, messy, expensive bifurcation — different chip ecosystems, different AI model families, different cloud infrastructure — that imposes increasing costs on businesses trying to operate in both markets. The question for every global business is not whether to prepare for bifurcation, but when and how.
Conclusion: The Deal You Are Not Invited To
The US-China trade war 2026 is the most consequential economic negotiation of this decade, and most businesses are approaching it as spectators. That is the wrong posture.
You are not a spectator. You are a participant in the AI supply chain that is being renegotiated in Beijing. Whether you know it or not, the outcome of those negotiations will affect your compute costs, your hardware supply chain, your regulatory obligations, and your strategic positioning in the AI economy.
The businesses that come out of this period strongest will be the ones that treated geopolitical technology risk as a core business issue in 2026 — not a specialized topic for government affairs departments and trade lawyers, but a fundamental input to strategy, operations, and investment decisions.
Trump and Xi are negotiating the infrastructure of your future. The question is whether you are ready for the outcome, whatever it turns out to be.
If you want to understand how this shifts your specific AI strategy — whether you are a manufacturer with rare earth exposure, a tech company with US export control questions, or a services business building on AI infrastructure — a structured review of your AI roadmap through a geopolitical lens is the right starting point.
The conversation is worth having now, before the Beijing summit produces outcomes that force the issue.
