AI, Chips, and Taiwan: Defense Planning Under Pressure

A single constraint is starting to show up in every serious defense AI conversation: compute. Not “cloud,” not “data,” not even “talent.” Compute—meaning advanced chips, energy, cooling, and the manufacturing capacity behind them.

That’s why a 2025 congressional hearing about the future of artificial intelligence ended up circling Taiwan. The logic is blunt: if AI capability is rising fast, then whoever controls the supply of leading-edge semiconductors controls the pace of modern military power—from intelligence processing to autonomous systems to cyber operations.

This post is part of our AI in Defense & National Security series, and I’m going to be direct: most national security AI strategies are still written as if compute supply is guaranteed. It isn’t. The China–Taiwan scenario makes that painfully obvious—and gives planners a practical case study for how to build resilience into AI-enabled defense.

Taiwan matters because AI runs on chip supply

Answer first: Taiwan matters because the most advanced AI systems depend on high-end semiconductors, and Taiwan’s chip ecosystem—especially leading-edge manufacturing—remains uniquely strategic.

The hearing referenced a growing belief in Washington that AI progress is accelerating toward human-level capability on surprisingly short timelines. Whether you buy “AGI by 2029” or not, the underlying driver is real: model capability scales with compute (chips), data, and engineering. When compute becomes scarce—or controlled by an adversary—military and economic power doesn’t just slow down. It shifts.

Here’s the uncomfortable defense-planning implication: the chip supply chain is now a readiness issue. Not “industrial policy.” Readiness.

If a crisis disrupts chip flows—or if Beijing can throttle access via coercion or control—then:

• ISR processing (imagery, signals, radar, acoustic) bottlenecks quickly
• autonomous platforms lose upgrade cadence and retraining cycles
• cyber defense loses the ability to run high-throughput detection and sandboxing
• command-and-control analytics degrade as compute budgets get rationed

The hearing also raised an economic shock scenario (phrased as a “global depression” risk in the event of a Taiwan takeover). For defense leaders, the economic framing matters because defense AI isn’t insulated from the commercial chip market. The same fabs and packaging lines that feed consumer tech also feed defense primes and government integrators.

Compute is the new “oil,” but harder to stockpile

Oil can be stored. Chips can’t be stockpiled at the same scale without obsolescence, security risks, and lifecycle headaches. Advanced chips also depend on a web of upstream constraints—lithography tooling, specialty chemicals, packaging, and test—that can’t be wished into existence during a contingency.

That’s why “domestic manufacturing” is only half the story. The other half is how defense programs architect for compute constraints.

AI changes the China–Taiwan threat picture in concrete ways

Answer first: Rapid AI progress increases the value of Taiwan’s chip leverage and strengthens incentives for coercion, while also improving both sides’ ability to sense, decide, and strike in a contested region.

The original article tied three threads together: fast AI progress, China’s ambition to dominate AI, and China’s more threatening posture toward Taiwan. The defense angle is that AI compresses timelines.

In a Taiwan scenario, the operational tempo is already brutal—dense air and maritime traffic, long-range fires, contested comms, and deception everywhere. AI intensifies that environment in three specific ways.

1) The “invisible battlefield” becomes decisive: sensing and fusion

Modern conflicts are increasingly won by finding things and tracking them under deception, not just by having exquisite platforms.

AI helps by:

• fusing multi-source intelligence faster (EO/IR, SAR, SIGINT, AIS, cyber telemetry)
• prioritizing targets under uncertainty
• detecting subtle anomalies (spoofing patterns, decoy signatures, supply movement)

But it cuts both ways. If both sides have strong models, the advantage goes to the side with:

• better data pipelines
• more resilient networks
• higher-quality training data from the theater
• sustained compute for retraining and adaptation

That last point loops back to chips. The side that can keep training, fine-tuning, and deploying at speed will adapt faster than the side that can only run yesterday’s models.

2) Autonomy scales mass—if you can keep it updated

Autonomous systems in contested waters and airspace aren’t science fiction. The hard part isn’t making a drone fly. It’s making autonomy reliable under jamming, spoofing, and partial comms, and updating behaviors as the adversary changes tactics.

In practice, autonomy depends on:

• edge compute (on-platform inference)
• robust perception in bad conditions
• frequent model updates based on new data

If a Taiwan contingency disrupts high-end chip availability, autonomy programs face a hidden failure mode: model stagnation. Your fleet still exists, but your software stops improving. That’s how you lose the adaptation race.

3) Cyber operations accelerate into machine-speed hunting

The hearing touched policy proposals to restrict advanced chip access to China. That’s partly about limiting AI-enabled military development—but it’s also about cyber.

AI-enabled cyber defense is moving toward:

• automated triage of alerts
• behavior-based detection at scale
• rapid malware variant analysis
• “hunt loops” that reduce dwell time

Cyber teams increasingly need compute to keep up with machine-generated threats. If compute becomes constrained, defenders get forced back into slower, signature-heavy approaches—exactly what you don’t want during a regional crisis.

Policy tools are colliding with operational reality

Answer first: The U.S. is debating chip supply and export controls while simultaneously relying on commercial chip ecosystems for defense AI—creating strategic tension that shows up in procurement, alliances, and readiness.

The hearing highlighted proposals such as a “CHIPS Act 2.0” mindset—expand domestic manufacturing incentives—and mechanisms to prioritize U.S. access to advanced chips.

From a defense-and-leads perspective (for agencies and contractors), the practical question isn’t “Do we like industrial policy?” It’s:

What’s your plan when the supply chain becomes a contested domain?

Because in 2025, it already is.

What defense leaders should pressure-test now

If you’re building AI capabilities for intelligence, surveillance, autonomous systems, or cyber, these are the questions that surface quickly in tabletop exercises:

1. Compute continuity: What happens to your mission if your access to top-tier GPUs/accelerators drops by 30% for 12 months?
2. Model update cadence: Which models must be retrained monthly vs. quarterly vs. annually?
3. Edge fallback: If cloud or reachback compute is degraded, what’s your minimum viable on-edge inference?
4. Data sovereignty: Where is training data stored, labeled, and validated—and can you keep doing that under crisis?
5. Vendor concentration: How many single points of failure exist in chips, packaging, drivers, or CUDA-equivalents?

Most organizations have partial answers. Very few have end-to-end answers.

Building AI-ready defense systems that survive a chip shock

Answer first: The smartest path is designing defense AI for contested compute—through model efficiency, hybrid architectures, diversified supply, and operational governance.

This is where I’ll take a stance: If your AI program assumes unlimited compute, it’s not a defense program—it’s a demo. Defense AI has to work when things get scarce.

1) Architect for “good enough” models, not only giant models

Bigger models can perform better, but defense use cases often value:

• reliability under distribution shift
• explainability for commanders
• fast retraining on new threat patterns
• deployment to constrained edge hardware

That points to a portfolio approach:

• compact, specialized models for edge detection and cueing
• larger back-end models for fusion, analysis, and planning
• clear rules for when humans must approve actions

In practice, this reduces dependence on the newest chips for every workload.

2) Treat data pipelines as operational systems

A model’s battlefield value depends on its pipeline: collection → labeling → validation → deployment. If any step is brittle, the model becomes stale.

Actionable moves that help immediately:

• standardize data schemas across sensors
• version datasets like software (with audit trails)
• run red-team data poisoning exercises
• bake in provenance checks (what fed this model, when, and from whom)

This is where defense contractors can differentiate: not by promising magical AI, but by shipping pipeline resilience.

3) Build for degraded comms and contested cloud

Taiwan scenarios assume comms disruption. So AI deployments should assume:

• intermittent connectivity
• bandwidth rationing
• spoofed or delayed telemetry

A robust pattern is edge-first cueing (cheap local inference) with selective reachback (send only what matters to higher compute). It’s not glamorous, but it’s how you keep operating.

4) Governance: stop treating AI as “software only”

Compute scarcity forces prioritization. That’s a governance problem as much as a technical one.

Strong programs create:

• mission-tiered compute budgets (what gets priority when resources shrink)
• model performance SLAs tied to operational outcomes
• a clear authority chain for model updates during crises
• security controls for weights, prompts, and fine-tuning data

If you don’t set these rules now, you’ll invent them mid-crisis.

People also ask: what does “AGI by 2029” change for defense?

Answer first: Even if AGI timelines are wrong, the race dynamics are already changing: compute advantage, deployment speed, and integration into factories and arsenals matter more than philosophical definitions.

The hearing referenced the idea that human-level AI could arrive soon. Plenty of credible experts disagree on timelines. For defense planning, the exact year is less important than the behavior it triggers:

• nations invest as if it’s near
• companies prioritize AI hardware and data centers
• militaries demand faster AI integration cycles

That creates a reinforcing loop where chip supply and AI deployment become strategic competition, regardless of whether AGI shows up on schedule.

In other words: you don’t need to believe in AGI hype to recognize the Taiwan–chips–AI nexus as a real security driver.

What to do next if you own AI readiness (government or industry)

AI in defense & national security is now constrained by industrial realities. Taiwan sits at the intersection of those realities and the most dangerous regional flashpoint in the Indo-Pacific.

If you’re responsible for modernization, procurement, or mission engineering, the practical next step is to audit your compute dependency the way you’d audit fuel, munitions, or satellite bandwidth. Put numbers on it. Identify single points of failure. Then redesign for contested supply.

If you want a sharper conversation with your team or partners, start here: Which mission threads fail first if advanced chip supply is disrupted—and what would it cost to make them degrade gracefully instead?