AI Shockwave Reshapes Global Economy, Compute Power Becomes The New Oil

*Source: https://themiilk.com/reports/203?utm_source=Viewsletter&utm_campaign=37d83803de-EMAIL_CAMPAIGN_2025_08_05_08_52_COPY_01&utm_medium=email&utm_term=0_-f7bc1a2247-385751177

● Global Economy Shockwaves AI’s Unseen Impact

Global Economy and AI Trends: Essential Insights for the Next 12 Months, 3 Years, & 10 Years

Let’s get started right away.
This article includes the following important content.
1) Global economic outlook and investment/policy impacts for short-term (12 months), medium-term (1-3 years), and long-term (3-10 years) scenarios.
2) ‘Invisible’ impacts of AI developments on inflation, labor market, and supply chain — key points not well covered in other news.
3) Practical opportunities and risks of AI applications by industry, including big tech, semiconductors, cloud, and healthcare.
4) A checklist of investment and corporate strategies resulting from changes in national policies (interest rates, exchange rates, industry protection).
5) Actionable plans and recommended portfolios/business strategies.

The following content is organized in chronological order (short-term→medium-term→long-term), providing key messages, specific evidence, and action points for each item.
SEO keywords (global economy, AI, economic trends, technological innovation, artificial intelligence) are naturally incorporated.

1. Short-Term Outlook (0–12 Months): Economic, Interest Rate, and AI Investment Trends

Summary of Global Macroeconomic Situation.
Post-interest rate normalization, there are significant regional divergences in the global economy.
In the U.S., a mix of slowing employment and consumer spending may lead to the Fed maintaining a cautious rate stance.
The Eurozone is likely to maintain low growth rates due to weak energy and demand.
China’s fiscal and real estate easing has limited effects, leading to sluggish growth recovery.
Key Points Not Commonly Discussed.
The initial cost burden of AI adoption does not immediately translate into reductions in facilities and labor costs.
However, AI is beginning to rapidly lower the unit costs of certain services (finance, legal, accounting) within the ‘service inflation’ composition.
This could lead to structural changes in the service price index within the CPI, potentially altering central banks’ policy criteria.
Industry-Specific Short-Term Impacts (Investment & Business).
Big Tech & Cloud: Cloud and GPU demand may see limited strength, aligning with commercial AI model adoption.
Semiconductors: Orders for high-performance AI chips (for servers) continue, but price pressures coexist due to eased supply chain bottlenecks.
Finance: AI-driven risk model improvements are rapidly introduced, creating initial investment demand in trading and risk management.
Action Points.
Portfolio: Maintain a defensive position while diversifying purchases of AI infrastructure-related stocks (cloud, data centers, GPU supply chains).
Corporate Strategy: Redesign business models to prepare for ‘service cost reductions’ through internal R&D and external AI partnerships.

2. Medium-Term Outlook (1–3 Years): Productivity Transition and Industry Restructuring

Macroeconomic Scenario.
With the full-fledged commercialization of AI, productivity indicators (labor productivity) gradually improve.
However, productivity gains vary significantly by industry, and labor market realignment intensifies.
The technology hegemony competition between China and the U.S. accelerates investment and supply chain restructuring.
Insights Not Widely Covered Elsewhere.
“Computing power/electricity” becomes a new real asset (class of commodity).
Elements like data center power, GPU availability, and cooling infrastructure emerge as regional competitive factors.
This means investment in electricity and power networks combines with existing semiconductor and software competition.
Medium-Term Industry Changes.
Manufacturing: Smart factories and AI quality control significantly reduce defect rates and inventory costs.
Healthcare: AI-based diagnosis and drug discovery compress R&D cycles, reshaping the bio-investment roadmap.
Retail/Commerce: Personalized automation reconstructs marketing and logistics cost structures, altering retail margins.
Regulatory and Policy Risks.
Enhanced demands for data governance and AI regulation (explainability and safety) directly impact national competitiveness.
Businesses must consider regulatory adaptation as a cost and recognize potential costs from strategies to circumvent regulations (localization).
Action Points.
Investment: Expand medium-term positions in AI-leading companies, power infrastructure, data center REITs, semiconductor materials, and equipment.
Corporate: Establish data governance systems, plan for regulatory scenario responses, and introduce employee reskilling programs.

3. Long-Term Outlook (3–10 Years): Structural Transformation and New Economic Paradigm

Macro Structural Changes.
As AI automates parts of service and knowledge labor, the global production structure reshapes.
Technological innovation applies upward pressure on long-term growth rates but may exacerbate disparities in distribution and gaps between regions and industries.
Detailed Key Points (Unique Perspective).
A market for AI-created ‘quantified labor’ (micro-tasks) develops, reshaping wage structures and freelance ecosystems.
Additionally, AI models become key ‘intangible assets,’ leading to competition in intangible capital among nations.
National competitiveness is redefined as a combination of ‘quality of model training data + compute accessibility + regulatory friendliness,’ rather than simple manufacturing capabilities.
Long-Term Industry Implications.
Energy: Data center power demand reshapes energy policy and grid investment priorities.
Education & Labor Market: Public and private investment in lifelong learning and retraining infrastructure expands.
Finance & Capital Markets: AI-related intellectual property (IP) and models become central elements of value assessment.
Action Points.
Policy: National strategies must balance the ‘data-compute-regulation’ axis.
Corporate: Invest in long-term R&D and data accumulation strategies and establish legal and technical safeguards for model and data protection.

4. Detailed Regional Analysis: U.S., China, Europe, South Korea

United States.
Big tech’s AI advantage remains strong.
Rapid commercialization progresses in financial, health, and enterprise markets.
Policy risks include strengthened antitrust and AI safety regulations.
China.
A rapidly growing AI ecosystem centered on the domestic market.
Enhanced data localization and national security-based regulations alter global cooperation approaches.
Government support focuses on semiconductor self-sufficiency and expanding cloud infrastructure.
Europe.
Regulation-first approach (EU AI Act, etc.) forms a ‘safety and ethics’ based AI market.
Companies must pre-allocate costs for regulatory compliance.
South Korea.
Based on an export and manufacturing base + semiconductor capability, there are significant opportunities in ‘AI application demonstrations.’
However, securing data infrastructure and talent is crucial, and the speed of regulatory and industrial policy will determine competitiveness.

5. Industry-Specific AI Trends and Investment Points

Big Tech & Cloud.
Competition in large models (LLM) and custom model services intensifies.
Cloud companies accelerate business model transitions to ‘AI service platforms.’
Investment Points: Companies providing cloud negotiations and data mobility, model-serving infrastructure.
Semiconductors & Infrastructure.
Demand for AI-specific chips (accelerators), high-performance cooling, power solutions, and server/network equipment leads to long-term investment demand.
Investment Points: Companies in AI memory, high-bandwidth interconnects, and power-efficient solutions.
Healthcare & Bio.
AI accelerates drug candidate discovery, clinical design, and precision medicine.
Investment Points: AI-based drug platforms, digital diagnostics, data pipeline companies.
Retail & Logistics.
Cost structure changes through demand forecasting, inventory optimization, and robotics automation.
Investment Points: Warehouse automation/robot ventures, predictive analytics SaaS.

6. Risks and Pitfalls to Avoid

Excessive Expectations.
Immediate explosive productivity improvements do not occur across all industries.
Consider that over 70% of AI projects fail to expand beyond PoC.
Regulatory & Ethical Risks.
Risks of lawsuits, fines, and trust loss due to data usage and model bias issues.
Pre-regulation scenario planning and compliance systems are essential.
Geopolitical Risks.
Technology sanctions, export controls, and data localization policies may reconfigure supply chains and increase costs.

7. Checklist for Companies & Investors (Immediately Actionable Items)

Companies (Products & Strategy).
1) Create an AI application roadmap based on core business priorities.
2) Establish data governance and privacy frameworks.
3) Collaborate with regulatory response and legal teams to secure scenario-based response manuals.
4) Formulate talent retraining and recruitment plans and review external partnerships.
Investors (Portfolio).
1) Maintain a balance between defense and growth.
2) Diversify investments related to AI infrastructure, power, semiconductors, and cloud.
3) Consider a risk premium for sectors sensitive to regulation (e.g., healthcare).
4) Pre-design asset allocations for different scenarios (base, optimistic, pessimistic).

8. The ‘Most Important’ Hidden Keys

Computing power, electricity, and data become key components of future national competitiveness.
Countries and companies possessing all three simultaneously secure long-term dominance.
AI transforms the ‘service inflation’ structure.
The decrease in prices for certain high-cost services (legal, accounting, consulting) has significant impacts on corporate profitability, tax structures, and labor markets.
As models and data become IP, it becomes difficult to evaluate them using traditional financial statements.
Investors should incorporate ‘data quality, model maintenance costs, compute accessibility’ as separate items in valuation.

9. Conclusion — Summary Action Plan

Short-Term: Increase defensive and growth-oriented investments in AI infrastructure, data centers, and cloud.
Medium-Term: Focus on establishing regulatory and governance frameworks and employee retraining.
Long-Term: Strategically invest in securing data, models, and compute competitiveness, along with policy lobbying activities.

< Summary >The core is the ‘data-compute-regulation’ triangle.
In the short term, there are selective investment opportunities in AI infrastructure, cloud, and semiconductors.
In the medium term, power and data center competition, and regulatory adaptation determine corporate competitiveness.
In the long term, models and data are evaluated as intangible assets, becoming strategic advantages for countries and companies.
Points lacking in other analyses include the physical importance of ‘computing power and electricity’ and AI’s impact on service price structures.
Immediately actionable items are establishing data governance, preparing for regulatory scenarios, and AI infrastructure investment positioning.