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A New Domain of Warfare: Supply Chains, AI and the Fight for Economic Dominance

A New Domain of Warfare: Supply Chains, AI and the Fight for Economic Dominance

At SCSP AI+ Expo, Exiger CEO Brandon Daniels joined Benjamin Guggenheim, AI & Tech Writer at WP Intelligence, to discuss what the AI race demands beyond stronger models.

Their conversation moved from U.S.-China industrial competition to the deep-tier supply chain nodes where a single material, process, facility, or producer can determine whether AI advantage becomes industrial scale.

Key Tenets for the Future of Supply Chains

01

AI Dominance Will Be Won in the Industrial Base

The United States has more control over global energy production than it has had in decades. It has made major advances in chips and retains the innovation base to lead the next phase of AI.
The constraint is scale.

China has moved quickly to put AI into the hands of companies through models such as Kimi, DeepSeek, and Qwen, which businesses can use, adapt, and integrate at low cost. The United States has the talent, technology, energy position, and capital markets to compete. The test is whether those advantages can be converted into industrial output fast enough.

AI advantage becomes durable when it reaches factories, fabs, refineries, machine shops, logistics networks, and supplier ecosystems. That is where software becomes physical capability.

02

AI Leadership Requires Industrial Scale

For U.S. AI leadership to hold, software advantage has to connect to manufacturing capacity. That means chips, energy, automated production lines, retooling speed, and permitting timelines that match the strategic window.
AI will create job loss, but the impact does not have to become systemic or global if the United States absorbs AI into the industrial base faster than competitors. Economies that pair AI adoption with manufacturing capacity and lower execution friction will move faster and produce more efficiently.
The United States has the innovation tip of the spear. The work now is scaling it.

03

Small Levers Can Move Big Markets

The economic gap between market-economy production and non-market-economy production is often smaller than it appears. That is why modest policy levers can change production decisions when enforcement is credible.

A 2008 logistics example illustrates the point. When energy prices spiked, companies moved real production because volatility changed the cost structure. Logistics may represent a small share of total cost, but a shift of even a few percentage points can affect where production belongs. A 3% or 5% 3% or 5% tariff, enforced against artificially subsidized non-market pricing, can help make U.S. production viable again.

Free and fair markets still need rules. We do not allow insider trading and call that capitalism. We should not allow non-market economies to erode critical industrial capacity through distorted pricing and weak enforcement.

04

Automation Changes the Labor-Arbitrage Equation

For decades, labor arbitrage drove production offshore. AI and automation weaken that old logic.
A robotic machine or ASML photolithography tool does not become radically cheaper because it is installed in a non-market economy. As production becomes more automated, wage differentials matter less than retooling speed, technical capability, and the ability to run high-performance production lines.
That creates an opening for U.S. manufacturing. Companies such as Hadrian, Intuitive Technologies, and Divergent are showing what rapidly retooled, software-enabled production can look like. If those capabilities spread across the industrial base rather than remaining locked inside individual facilities, the United States can build a modern manufacturing base that competes on speed, quality, and resilience.

05

Supply Chains Are Not Pyramids. They Are Diamonds.

Most people picture supply chains as pyramids: a prime or manufacturer at the top, with each lower tier widening into more suppliers, parts, locations, and complexity.
In reality, the shape eventually changes. Supply chains expand, then narrow around specific materials, processes, producers, facilities, and capabilities. Those narrow points are the diamond nodes, where a system that appears diversified can depend on very few sources.

Semiconductors make the pattern clear. At the top, the network includes distributors, assembly test sites, wafer fabs, and multiple companies. The deeper question is who can grow the silicon ingots, who can make the high-purity quartz crucibles, and who supplies the high-purity quartz itself. At that level, the chain tightens quickly.

The same pattern appears across other industries. Medical-grade plastics can depend on one company with the catalyst input that makes production cost-effective. Lithium salt for electrolyte mix can narrow to only a few companies with capacity for lithium-ion batteries used in unmanned systems. Helium can become a prioritization, with semiconductor and medical users securing supply while lower-margin industrial users wait.

That is the part companies often miss. The risk may sit far below the supplier they know, with the company that controls the material, process, or capacity others depend on. By the time the disruption reaches tier one, the decisive constraint may already be somewhere else. By the time the disruption reaches tier one, the decisive constraint may already be somewhere else.

06

Deep-tier Choke Points Need Direct Attention

A company several tiers below your known suppliers can still control the part, material, or process that keeps production moving. Supplier distance matters less than dependency.

Supply chain mapping should not become an exercise in completeness. The useful work is finding the points where the system narrows: a facility with the only qualified capability, a producer with available capacity, a process no one else can replicate quickly, or a supplier that gets priority when supply is constrained.

Companies should be asking direct questions:

  • Who has the capacity?
  • Who controls the process?
  • Who gets prioritized when supply is constrained?
  • What alternate source exists?
  • What relationship should exist before disruption forces the issue?

Answering those questions requires AI that understands parts, materials, manufacturing processes, technical specifications, supplier relationships, and how constraints move through industrial supply chains. A generic model layered onto procurement data will miss too much.

For critical inputs such as helium, lithium salt, high-purity quartz, and optical transceivers, companies cannot wait for a shortage to surface through a tier-one supplier. They need to know the actual capacity holder and build prioritization mechanisms through distributors, OEMs, and direct commercial relationships. A node three, four, or six tiers down can still deserve direct-supplier attention.

07

Industrial Policy Has to Invest Before the Market is Fully Mature

The United States has spent too much time asking whether strategic industrial capacity can survive on its own before deciding whether to invest. That standard breaks down when the market has already been distorted.
Some capabilities are needed before the economics are fully self-sustaining. Lithium salt, electrolyte mix for lithium-ion batteries, and optical transceivers are examples tied to AI infrastructure, energy storage, unmanned systems, and national competitiveness.
Government-owned, contractor-operated models and targeted industrial incentives can help build capacity where dependency is unacceptable. The point is not to fund everything; it is to identify the nodes that affect national resilience, confirm where viable demand exists, and support capacity long enough for domestic or allied production to compete.

That means rallying around companies already building the right capabilities: Orbia Fluor in lithium salt, Tesla’s electrolyte mix work, Coherent in optical transceivers, and allied or domestic manufacturers that can help close dangerous gaps.

This is more than defense spending. It is industrial-base resurgence spending: a way to create an American economy that can sustain itself in strategic sectors.

08

The Workforce Gap is a Manufacturing Problem and a Software Problem

Some capabilities are needed before the economics are fully self-sustaining. Lithium salt, electrolyte mix for lithium-ion batteries, and optical transceivers are examples tied to AI infrastructure, energy storage, unmanned systems, and national competitiveness.

Rebuilding industrial capacity often gets framed around materials and machines: critical minerals, robotics, physical AI infrastructure, and automated production lines.

Talent can’t be overlooked. The future workforce needs people educated like software engineers and deployed in manufacturing roles.
Modern production is becoming more technical, more automated, and more dependent on people who understand production systems and software systems together. Companies need people who can run QA on advanced lines, operate complex machines, supervise AI outputs, design workflows, and recognize when a model is trying to make too many decisions at once.
AI can analyze documents, support manufacturing, and move decisions forward, but it still needs supervision. The advantage comes from people who understand the process well enough to catch bad outputs, narrow overly broad tasks, and break workflows into steps the system can handle.
Human judgment and AI capability have to be designed into the same operating model.

09

The Next Advantage Will Be Operational

The United States has the innovation base to lead. The harder task is converting innovation into industrial output. That requires market enforcement, visibility into hidden supply chain nodes, and a willingness to treat deep-tier capacity as part of national and corporate strategy.
AI has a specific role in that work. It can connect technical data, supplier relationships, materials, processes, and capacity constraints that are normally spread across disconnected systems. The outcome should be action: understanding where dependency becomes vulnerability, then moving before that vulnerability turns into a production failure.
Industrial policy and workforce development have to follow the same operating logic. Where dependency creates unacceptable risk, domestic or allied capacity needs time and demand to take hold. As manufacturing becomes more automated and software-defined, the workforce has to span production, engineering, AI supervision, and process design.
The AI race will be decided by the countries and companies that can turn technical advantage into industrial power: faster production, stronger supply chains, and the capacity to move before the next constraint exposes itself.
Learn how 1ExigerAI helps organizations gain multi-tier visibility, identify critical supply chain nodes, assess supplier viability, and prioritize action across complex supply networks.
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