Quick Answer: The US leads in AI models, research, and capital, but its AI push depends heavily on Taiwan. Taiwan's TSMC manufactures most of the world's most advanced chips, including the GPUs that power US AI systems. Without Taiwan's fabrication capacity, packaging, and industrial ecosystem, American AI ambitions would stall at the chip layer.
Most AI coverage fixates on the model: which lab has the biggest benchmark score this quarter. The bottleneck that actually decides who wins sits lower in the stack. A data center full of the latest accelerators is useless if the chips can't be made at scale, packaged correctly, or powered reliably. As Foreign Affairs argues, that physical reality ties the US AI race directly to Taiwan.
The Five Layers Washington Has to Win
Nvidia CEO Jensen Huang describes the AI industry as a five-layer cake. Energy sits at the base, followed by chips, infrastructure, models, and applications. Each layer depends on the one below it, so a weakness anywhere limits the whole stack.
Here's the practical reading. A frontier model is only as good as the compute it trains on. That compute runs on accelerators, which are built in fabs, packaged, and installed in servers. Those servers need power and cooling. Only then does the application reach a user.
The source's core argument is that the US needs an advantage at every layer to win against China. Taiwan touches several of them at once, and that's what makes it hard to route around.
Why TSMC Sits at the Center of the Stack
Taiwan Semiconductor Manufacturing Company (TSMC) is the world's dominant contract chipmaker for leading-edge logic. Nvidia designs its flagship GPUs, but it doesn't own fabs. It relies on TSMC to manufacture them on advanced process nodes. Many other US chip designers rely on TSMC too.
Manufacturing isn't the only step. Advanced AI chips also depend on advanced packaging, such as TSMC's CoWoS (Chip-on-Wafer-on-Substrate) technology. Packaging combines logic dies with high-bandwidth memory into a single module. Memory makers like SK hynix, Samsung, and Micron supply that HBM, but the integration step has concentrated heavily in Taiwan.
Here's the part most guides skip. Designing a chip in California and fabricating it in Taiwan is only the visible layer. Equipment suppliers, materials vendors, test houses, and engineers cluster around the Hsinchu and Tainan science parks. Moving one fab doesn't move that ecosystem.
The Reshoring Gap: Why Building Fabs in America Isn't Enough
Washington has responded with industrial policy. The CHIPS and Science Act, passed in 2022, provides substantial subsidies and tax credits to encourage domestic semiconductor manufacturing. TSMC has committed to building fabs in Arizona, and Intel has pursued its own US capacity.
Those projects matter, but they don't solve the problem quickly. Leading-edge fabs take years to build and ramp, and new US sites have faced delays. Even after a fab is running, the most advanced nodes often still debut in Taiwan first, with the most experienced process engineers. Your yield problems get solved where the expertise lives.
That's the counter-intuitive finding. A US fab can produce chips without a Taiwan-based supply chain, but it can't easily reproduce the speed of learning that Taiwan's cluster provides. Dependence shrinks gradually. It doesn't disappear on a policy timeline.
The Energy Layer Nobody Talks About
The energy base of the cake matters more than most AI discussions admit. Data centers draw large, steady loads, and AI training clusters are among the most power-hungry workloads ever built. Utilities and grid operators are already reworking interconnection queues to handle them.
Taiwan has its own energy problem. The island imports the vast majority of its primary energy, mostly as liquefied natural gas and coal, and it has limited domestic generation. Fabs consume a lot of electricity and need uninterrupted supply. A blockade or supply disruption would hit chip output directly.
This is where the cause-and-effect chain becomes concrete. When an energy shock hits Taiwan's grid, fab output drops. When fab output drops, accelerator supply tightens. When accelerator supply tightens, US data center buildouts slow, and model training schedules slip. The energy layer and the chip layer are linked, and they share a single point of failure.
Comparing the Layers: Where the US Stands and Where Taiwan Matters
The table below summarizes how each layer of the stack depends on Taiwan, based on the framework in the source article and the public record of the supply chain.
| AI Stack Layer | Current US Position | Taiwan Dependence | Main Risk |
|---|---|---|---|
| Energy | Strong grid and gas base, but data center demand is outpacing new supply | Low direct role, but Taiwan's own grid affects fab output | Power shortages that constrain chip production |
| Chips (logic) | Leading chip designers (Nvidia, AMD) | High: TSMC fabricates most leading-edge designs | Concentration in one foundry and one island |
| Infrastructure (packaging, memory) | Strong in system integration and server design | High for advanced packaging like CoWoS | Packaging bottlenecks limit accelerator output |
| Models | Leading frontier labs and research base | Indirect, through the compute they rely on | Compute shortages slow training and deployment |
| Applications | Large software and cloud market | Indirect, through hardware availability | Cost and access tied to hardware supply |
The table simplifies things. The US leads in design, models, and applications. Taiwan's influence is concentrated in fabrication and packaging, which are the layers that are hardest to replace quickly.
Common Misconceptions About the US-Taiwan AI Link
The most common assumption is that the US can simply copy Taiwan's fabs and be done with it. The reality is more complicated. Even with subsidies, duplicating a mature ecosystem takes a decade or more, and the talent pool is the hardest piece to replicate.
Another misconception treats the link as purely a national security question. It is one, but it's also a commercial one. Taiwan's fabs serve customers across the world, and disruption would hit global prices and product timelines, not just US labs.
There's also nuance on the other side. Some analysts argue that heavy dependence is a deterrent: a war that disrupted chip supply would hurt everyone, including China. Others say that logic is shaky, because it assumes rational actors who weigh economic losses above strategic gains. Neither view is settled, and it's worth holding both.
What Practitioners Should Watch Next
If you work in AI infrastructure, procurement, or policy, the signals to track are specific. Watch advanced packaging capacity, not just wafer capacity, because packaging has been the tighter constraint for accelerators. Track how quickly new US fabs ramp to yields comparable to Taiwan's. And monitor energy contracts for fabs and data centers, since they shape the base layer of the cake.
Here are three things worth checking regularly:
- Packaging throughput at TSMC and its partners, since CoWoS capacity limits accelerator shipments.
- Yield and node timing for US fabs compared with the same nodes in Taiwan.
- Grid interconnection and power purchase agreements for data centers and fabs.
Each one reveals whether the dependence is loosening or hardening.
Frequently Asked Questions
Why does US AI depend on Taiwan?
Because Taiwan's TSMC manufactures most of the world's leading-edge chips, including the GPUs used to train and run US AI models. Advanced packaging and an experienced supplier ecosystem also cluster on the island. Designing chips in the US doesn't remove that dependence, because the physical production remains concentrated in Taiwan.
What is the Taiwan AI supply chain?
It's the network of fabs, packaging facilities, equipment and materials suppliers, and engineering talent that turns chip designs into finished AI hardware. TSMC anchors it, and it extends through memory suppliers and test houses. The chain matters because each stage depends on the others to hit yield and volume targets.
How much of US AI depends on Taiwan chips?
No single public figure captures the whole picture, because AI workloads span many chips and suppliers. What's clear is that most leading-edge AI accelerators are fabricated by TSMC in Taiwan. For US AI buildouts, that means the bulk of high-end compute depends on Taiwan-made silicon, even when the designs are American.
Can the US build enough chips without Taiwan?
Not quickly. Domestic fabs such as TSMC's Arizona sites will add capacity, but they take years to ramp and typically trail Taiwan's most advanced nodes. Experts generally expect partial reshoring to reduce dependence over time, not eliminate it on a short timeline.
When will the US stop relying on Taiwan for AI chips?
There's no firm date, and anyone offering one is guessing. Reducing dependence depends on fab construction timelines, workforce development, and sustained policy support. As of November 2026, the realistic expectation is a gradual diversification, with Taiwan remaining central for the most advanced nodes and packaging for years.
Don't let the AI model race hide the physical layers underneath it. The US advantage in AI depends on chips, packaging, and power, and Taiwan sits at the center of the first two. This week, map your own team's or company's AI hardware dependencies, from accelerators to packaging to data center power, and note where a single supplier or location creates risk. Pass this to someone managing AI infrastructure procurement who's been treating the chip supply as a solved problem.