AI, Taiwan, and the Chip Choke Point Problem

A single point on the map has become a single point of failure for the AI era: Taiwan’s advanced semiconductor production. As AI models get larger, more power-hungry, and more central to military advantage, the strategic value of leading-edge chips has climbed from “important supply chain topic” to “core national security risk.”

That’s why recent discussions in Washington about the pace of AI progress—up to and including predictions of human-level AI within the next few years—keep snapping back to the same uncomfortable question: what happens if China decides it can’t wait on Taiwan?

In this entry in our AI in Defense & National Security series, I’m going to be blunt: most national security organizations still talk about AI as software. The reality is that AI advantage is a stack—data, models, compute, chips, power, networks, people, and policy. Taiwan sits in the stack like a keystone. If it moves, everything shifts.

Why the AI race makes Taiwan more strategically valuable

The fastest way to understand the Taiwan-China-AI connection is simple: AI capability scales with access to compute, and compute scales with access to advanced chips.

For years, Taiwan’s semiconductor leadership was “mostly” an economic story. Now it’s also a deterrence story, because whoever controls the flow of advanced chips influences:

• How quickly cutting-edge AI systems can be trained
• How widely advanced AI can be deployed into real-world systems (factories, drones, cyber tools, intelligence pipelines)
• How resilient a country’s defense industrial base is under pressure

This is why policymakers increasingly frame a Taiwan contingency as more than a regional crisis. It’s a global compute shock.

The point people miss: AI isn’t scarce—compute is

AI research talent is widely distributed. Data is abundant. Algorithms spread fast.

Compute is different. High-end GPUs and leading-edge manufacturing capacity are capital-intensive, geographically concentrated, and slow to expand. When an AI breakthrough happens, the countries that can industrialize it first—turning models into operational systems at scale—get the real advantage.

That’s the strategic fear showing up in U.S. hearings and internal debates: a scenario where China doesn’t just “catch up” in AI research, but wins the deployment race by controlling the supply of the most capable chips.

Chips as a deterrence issue, not just an economics issue

If you work in defense, it’s tempting to treat semiconductors as a logistics problem—something acquisition teams, supply chain offices, or industry handle. That’s outdated.

Semiconductors are now a first-order deterrence variable. They shape how credible a nation’s:

• precision strike complex is
• ISR (intelligence, surveillance, reconnaissance) pipeline becomes
• cyber offense and defense capabilities scale
• autonomous systems mature

Here’s a sentence worth repeating in planning rooms: “Deterrence now includes compute denial and compute resilience.”

Compute denial: the quiet logic behind export controls

Export controls on advanced chips and manufacturing equipment are often described as economic competition. In practice, they function as capability throttles on the pace of AI scaling.

If you can slow an adversary’s access to the most advanced compute, you slow:

• frontier-model training cycles
• large-scale simulation for weapons and electronic warfare
• automated vulnerability discovery and exploitation tooling
• sensor-fusion performance at the edge

But export controls have a hard limit: they’re only as effective as enforcement and allies’ alignment—and they can be undermined by political tradeoffs.

Compute resilience: the missing half of the deterrence equation

The other side is resilience. Deterrence fails if your own access to compute can be cut off faster than you can replace it.

Compute resilience is built through:

1. Domestic manufacturing capacity (long timeline, expensive, politically complex)
2. Allied diversification (friend-shoring that actually results in production, not just MOUs)
3. Stockpiles and surge options for critical chips and components
4. Designing defense AI to degrade gracefully when compute is constrained

Most organizations do the first two (slowly) and ignore the last two (until it’s too late).

What AI changes inside military strategy: surveillance, decision speed, and deception

The China-Taiwan context matters because it’s one of the few places where you can see how AI impacts the full kill chain: sensing, targeting, decision-making, and execution—plus the adversary’s attempts to blind or confuse each step.

AI doesn’t just make systems “smarter.” It changes tempo.

Surveillance and ISR: more sensors, fewer humans in the loop

AI-enabled ISR is about triage. When you have floods of imagery, signals, tracks, and open-source data, the bottleneck is human attention.

AI helps by:

• prioritizing alerts (“this matters now”)
• correlating weak signals across sources
• detecting anomalies that don’t match expected patterns

In a Taiwan contingency, that can translate to faster recognition of mobilization indicators, maritime movements, missile unit activity, and logistics patterns.

But the hard truth is this: AI-driven ISR also increases the pressure to automate decisions. If your system flags a threat in seconds, leadership expects decisions in seconds. That’s not always compatible with escalation control.

Decision advantage isn’t just speed—it’s discipline

A lot of AI talk in national security obsesses over who can decide faster. Speed matters, but I’ve found the more decisive edge is often decision discipline:

• knowing what you’ll automate vs. what must stay human
• having pre-briefed thresholds for action
• rehearsing what you do when the model is wrong

If you don’t do that work, AI becomes a “false urgency generator,” pushing teams toward brittle choices.

Deception and counter-ISR: AI makes lying cheaper

AI strengthens ISR—and it strengthens deception.

Expect adversaries to use AI for:

• synthetic signatures and decoys that look “real enough” to classifiers
• rapid generation of disinformation tailored to units, regions, and languages
• automated probing of what your sensors and models “believe”

That raises a practical requirement for defense AI teams: build models that can express uncertainty and support red-teaming, not just output confidence scores.

The chip supply chain as a national security attack surface

The original RSS piece emphasizes the fear that control of Taiwanese chip output could trigger severe global economic effects. That’s real, but defense leaders should widen the lens.

Even without a full takeover scenario, the chip ecosystem creates multiple pressure points:

• Blockade risk: shipping disruption can choke high-value components quickly
• Coercion risk: threats to cut off supply can influence policy decisions
• Sabotage risk: manufacturing tools, firmware, and logistics systems are cyber targets
• Talent bottlenecks: advanced fabrication depends on specialized expertise that can’t be surged overnight

A useful planning frame is to treat semiconductors like energy security:

If your operational plan assumes unlimited compute, your operational plan is fantasy.

What “CHIPS Act 2.0” really needs to mean for defense

Calls for expanded domestic chip investment come up often. The trap is thinking it’s purely a bigger subsidy.

From a defense and national security perspective, “CHIPS Act 2.0” should prioritize:

• assured production for defense-relevant nodes (not only the most advanced consumer nodes)
• packaging and advanced substrates, which are major bottlenecks for high-performance compute
• secure-by-design fabrication pipelines (traceability, tamper-evidence, validated toolchains)
• workforce throughput (technicians and process engineers, not just PhDs)

If policy doesn’t address packaging, workforce, and assurance, you can spend billions and still end up with fragile capacity.

Practical guidance: building defense AI that survives a compute shock

If you’re leading AI programs in defense, intelligence, or the defense industrial base, the Taiwan compute risk isn’t abstract. It changes what “good architecture” looks like.

1) Design for “graceful degradation”

Assume that in a crisis you will have:

• less cloud access
• fewer high-end GPUs
• degraded bandwidth
• contested data flows

Then build systems that can fall back to:

• smaller models
• on-device inference
• caching, batching, and prioritization
• human-in-the-loop modes with clear playbooks

2) Treat model choice as a logistics decision

Teams pick models like it’s only about accuracy. In national security, model choice is also about:

• hardware availability
• inference cost per mission hour
• update cadence under disrupted supply

In other words: the “best” model is sometimes the one you can run reliably under stress.

3) Build a red-team program for deception, not just cyber

Most AI red-teaming focuses on jailbreaks and prompt abuse. That’s necessary, but defense AI needs a stronger emphasis on operational deception:

• adversarial examples for sensors and imagery
• decoy behavior in multi-sensor fusion
• data poisoning in contested collection environments

If your model has never been trained against an opponent trying to trick it, you haven’t tested it.

4) Measure what matters: time-to-detect and time-to-decide

Accuracy metrics alone don’t map to mission outcomes.

Better operational metrics include:

• time-to-detect relevant activity
• false alert burden per analyst hour
• time-to-decision with verification steps
• performance under degraded inputs (missing sensors, noisy comms, partial data)

These are the metrics that translate to deterrence and crisis stability.

What leaders should do in 2026 planning cycles

As budgets reset and programs get justified for the next cycle, compute security has to become normal, not exotic.

Here are moves that pay off quickly:

1. Map your mission-critical AI to hardware dependencies. Know which workflows break if GPU availability drops 30%.
2. Create a compute continuity plan. Include procurement, allocation rules, and “who decides” in a crisis.
3. Prioritize edge AI where it matters. Every inference you can do locally reduces reliance on centralized compute.
4. Run tabletop exercises that combine cyber, supply chain disruption, and AI deception. That combination is the real threat model.

If your organization can’t answer “what happens if advanced GPU supply is constrained for six months,” you’re not ready for the AI-security decade you’re already in.

Where this fits in the AI in Defense & National Security series

This post is a reminder that AI strategy is national security strategy—and that the hardware layer is now inseparable from deterrence, intelligence, and operational readiness.

If you’re responsible for AI in surveillance, intelligence analysis, mission planning, cybersecurity, or autonomous systems, the China–Taiwan–chips triangle should change how you architect programs and how you argue for investment.

The forward-looking question I keep coming back to is this: when the next crisis hits, will your AI capabilities be an accelerant—or will they be the first thing you lose because the compute pipeline snapped?