Sovereign AI, Reshoring, and the New Economic Order

How reshored automation, sovereign AIs, and tariff-driven policy are set to fracture the global economy

Tariffs + AI-driven automation = price stability with domestic resilience

The world is shifting from a highly interdependent global order into a fragmented, multipolar reality where each major power increasingly defines its own economic trajectory.

This is more than geopolitics. It’s about the rise of sovereign artificial intelligence, the reconfiguration of supply chains through automated reshoring, and a rewriting of economic strategy from first principles.

The Three Legged Stool

Treasury Secretary Scott Bessent has articulated a plan he calls the three legged stool for how her plans to reduce the deficit, cut spending, and maintain a healthy consumer economy.

1. Tariffs – to redirect value creation inward, creating economic incentives for domestic production and supply chain resilience.

2. Spending Cuts + Anti-Fraud – to reduce the inflationary drag caused by excessive federal expenditures and fiscal leakage.

3. Tax Reductions – to preserve consumer purchasing power and private sector liquidity, preventing a demand collapse as domestic costs recalibrate.

But this isn’t protectionism in the 20th-century sense. It's not about propping up dying industries or shielding inefficiency.

Instead, this is post-labor industrial policy, a strategy that assumes reshored production won’t be done by blue-collar laborers, but by automated systems, AI-driven processes, and robotics.

It’s industrial policy designed for a world where human labor is still a critical input, but is no longer the production bottleneck.

Not Your Grandfather’s Tariff Regime

The first leg: In the 1970s–90s, tariffs typically caused inflation because they raised prices on consumer goods without any domestic capacity to replace the imports.

In that era, reshoring was unrealistic, labor was expensive, unions were strong, and offshoring was the only way to stay price-competitive.

Today, that equation flips:

• Automation reduces marginal labor costs: A robotic assembly line doesn’t demand wages, healthcare, or time off. The upfront CapEx is high, but the long-term OpEx is minimal.

• AI flattens organizational overhead: Tasks that once required teams of mid-level managers and supply chain specialists can now be orchestrated by Large Language models and ERP-integrated digital agents.

• Energy and data are the new constraints, not labor or materials. The U.S. excels in both but need more. Especially energy.

So tariffs now serve a different function: they buy time.

Time for domestic companies to stand up automated operations and AI-native supply chains. This is about temporarily insulating the system so a new, non-labor-intensive industrial base can take root.

Spending Cuts Aren’t Austerity

Second leg: Bessent’s emphasis on reducing government spending and eliminating fraud is often misinterpreted as crude austerity. But the logic is more surgical.

In a high-liquidity environment, pouring trillions of federal dollars into an economy already constrained by supply (e.g., port bottlenecks, chip shortages) fuels demand-side inflation without increasing productive capacity.

By pulling back federal spending, and especially cracking down on fraudulent claims in Medicare, unemployment, and procurement, Besant’s team is attempting to shrink unproductive demand without touching productive investment.

This is a counter-Keynesian stance, not in the ideological sense, but in the technical sense. It presumes that inflation is not just a monetary phenomenon, but a fiscal and behavioral one.

Liquidity Without Inflation

The third leg, cutting taxes, serves to counterbalance the deflationary effects of spending cuts. Besant is betting that consumer spending and business investment can be sustained if you remove government-induced inflation while also freeing up private capital.

This is the fiscal equivalent of a barbell strategy: deflationary cuts on one end, pro-growth tax relief on the other.

The result is an economy where the state constrains demand to fight inflation while the private sector accelerates capital deployment into AI, automation, and local production.

What Makes This Radical

This is not Reaganomics 2.0. It’s not about shrinking government for ideological purposes. It’s about:

• Using tariffs to build time buffers for automation to scale.

• Using spending cuts to kill demand-pull inflation without crushing investment.

• Using tax cuts to sustain private liquidity and velocity during the transition.

In short: a macroeconomic scaffold for post-labor industrial policy.

The risks? If automation deployment stalls or if reshoring is slower than expected, you risk higher prices with no new capacity.

If sovereign AIs don't reduce costs fast enough, the inflation may still bite. But if it works, this becomes a blueprint for post-globalization economics. N

In the US, low unemployment and aging demographics make labor-intensive reshoring a nonstarter. The new model is low marginal labor manufacturing:

• Digital labor agents replacing customer service and admin tasks

• Fully robotic factories producing precision components

• AI-optimized logistics replacing just-in-time, fragile supply chains

• Reduced inflationary pressure over 3–5 years

• Enhanced national security via domestic production of semiconductors, defense inputs, and energy tech

• Decline of legacy export economies that depend on U.S. demand

So what is Sovereign AI?

Most people are familiar with ChatGPT, but Sovereign AI refers to something much bigger. AI that is developed, regulated, and strategically aligned with the interests of a nation-state.

Imagine if the U.S. dollar, the military, and the legal system were all controlled by Microsoft. That’s the risk if foundational AI systems, like language models, image generators, or national security decision engines, are owned entirely by private companies or foreign states.

Sovereign AI is the response to that. A push by governments to build or license AI technology to serve the interests of their constituents.

How Different Regions Are Approaching Sovereign AI

🇺🇸 United States: Emergent, Capital-Driven

• Approach: The U.S. hasn’t declared an official Sovereign AI framework (the Stargate Program comes close), but its model is emerging organically through the private sector.

  • OpenAI, Google, xAI, Microsoft, Amazon, Anthropic, and Meta are developing powerful models, largely funded by venture capital and monetized via commercial APIs.

• Implications:

  • Pros: Insanely fast innovation; diverse ecosystem; market-driven solutions.

  • Cons: Fragmented alignment; risk of centralization, profit driven motives that could exploit users.

  • Reality: Washington is now playing catch-up, trying to graft national security policy onto tech it doesn’t fully understand.

🇨🇳 China: State-Integrated AI

• Approach: China’s Sovereign AI effort is tightly integrated with the state. The CCP sets strategic objectives, and Chinese tech firms are effectively state-licensed actors. Every large model must comply with speech controls, data localization, and political filters.

• Implications:

  • Pros: Unified vision; ability to deploy AI at national scale; low friction between government and tech.

  • Cons: Limited open research; suppressed innovation in politically sensitive areas; potential export barriers.

  • Reality: China may dominate applied AI (e.g., surveillance, logistics, industrial automation), but lags in foundational breakthroughs.

🇮🇳 India: Hybrid Democratic-Tech Strategy

• Approach: India is not yet a top-tier player in foundational AI, but its government is crafting a unique middle path: democratized access to AI tools (e.g., IndiaStack, Aadhaar integration), public-private partnerships, and a push to incubate homegrown LLMs.

• Implications:

  • Pros: Huge upside due to English fluency and massive developer base.

  • Cons: Weak infrastructure, especially in Tier 2/3 regions; uneven regulation; data privacy concerns.

  • Reality: India could be the “sleeper cell” of global AI, especially in multilingual models and software tooling.

🇷🇺 Russia: Strategic Coercion Model

• Approach: Russia views AI through the lens of national defense and information warfare. Its Sovereign AI ambition is less about economic growth and more about state control, cyber dominance, and narrative shaping.

• Implications:

  • Pros: Highly centralized control over model output and deployment.

  • Cons: Low private sector innovation; sanctions limit compute access; brain drain.

  • Reality: Russia will likely field sovereign AI optimized for military and propaganda use cases, not for global commercial relevance.

🇪🇺 European Union: Ethical Regulator and Standards Setter

• Approach: The EU is less interested in building the biggest models than in defining the rules of the game. Its focus is on regulating AI via frameworks like the EU AI Act, demanding transparency, fairness, and alignment with democratic values.

• Implications:

  • Pros: May set global regulatory standards (as it did with GDPR)

  • Cons: Lacks scale in foundational models; regulatory burden may stifle domestic players like Mistral.

  • Reality: The EU is a moral and legal power in AI, not a technological one. The downside is that bleeding edge players may decide to not do business in European markets in order to not deal with burdensome regulation.

AI is no longer just a tool, it’s infrastructure. The nations that control its development, deployment, and alignment will wield disproportionate cultural and economic power.

• The U.S. may win the race to AGI but may end up with centralized companies that exploit users (similar to algorithms in the attention economy).

• China may dominate industrial AI but struggle with trust and interoperability.

• India could leapfrog in applied AI for real-world problems, if it scales fast enough and overcomes bureaucratic corruption.

• Russia is likely to focus AI on coercive power before productive output.

• The EU may shape the global moral boundaries of AI, but risk irrelevance in the arms race.

We're talking about AI as the new atomic unlock, the new currency standard, the new internet, and every sovereign actor is now racing to build or align their own.

Post-Globalization = Localized Abundance

What replaces globalization isn’t isolation, it’s modular self-sufficiency

• National AI stacks from model to hardware

• Regional food, energy, and defense verticals

• Reduced intellectual property flow across borders

• Talent friction: brain drain, tighter immigration controls

The new currency isn’t just GDP.

It’s code sovereignty, data control, and autonomy in the production stack.

• Reshoring is real, but it’s robotic

• Sovereign AI is policy infrastructure, not just tech

• Globalization is fragmenting into competing tech blocs

• India could leapfrog China if infrastructure accelerates

• The U.S. will lead in monetization if coherence is maintained

• China will dominate scale, but risks brittleness as it struggles to find where the value will accrue

The next ten years will determine who runs the software layer of civilization.