AI Code Blitz- 5-Year Takeover, Economy Fractured, Compute-Data Dominates

*Source: https://www.cnbc.com/2025/09/25/ai-billionaire-alex-wang-teens-should-spend-all-of-your-time-on-this.html

● AI Code Blitz 5-Year Takeover, Economy Fractured, Compute-Data Reigns

2025-2030 Global Economic and AI Trends: Key Outlook and Practical Strategies

Key Summary: Rapid replacement of AI (Artificial Intelligence) coding within 5 years, investment/job/policy risks and opportunities due to global economic restructuring, the three essential infrastructures companies must prepare now (data, compute, governance), the ‘prompt + domain’ composite capabilities individuals should build, and four hidden variables often not covered in the news (compute sovereignty, data royalty, code provenance certification/insurance, and wage structure polarization due to AI).

What you will gain from reading: 1) Economic impacts and investment points for short-term, mid-term, and long-term scenarios.

2) Execution roadmaps for companies (product, HR, regulatory response).

3) Career and learning roadmaps individuals should start immediately.

4) Potential regulations and response strategies at the policy, local government, and national levels.

5) Market structure changes and monetization models brought about by ‘data royalty’ and ‘compute sovereignty,’ which are rarely discussed in other media.

0–12 Months (Short-Term): Practical Application and Inflection Point Signals

Surging demand for AI infrastructure begins to shake global compute prices and investment cycles.

Competition for ‘securing compute’ intensifies among major cloud providers, chip manufacturers, and nations.

Companies prioritize investment in productivity tools (code generation, testing, deployment automation).

In the job market, a premium for individuals with AI capabilities quickly forms.

Investment Points: Cloud providers, GPU/AI chip supply chains, MLOps/data labeling platforms, code quality/security tools.

Company Action Guideline: Prioritize building core data pipelines and version control.

Risk Management: Immediately examine the legal liability and licensing issues of outputs generated by code generation models.

Policy Signals: Export controls, data mobility restrictions, and AI ethics guidelines are highly likely to emerge rapidly in the form of legislation or executive orders.

1–3 Years (Mid-Term): Code Automation, Labor Restructuring, and GDP Composition Changes

Many repetitive and standardized codes begin to be replaced by AI, leading to an increase in ‘labor productivity in the service sector’ in productivity statistics.

However, there is a risk of deepening income inequality as profits concentrate on capital (model ownership, compute).

Company Strategy: ‘Platformize’ products through AI, and package data, models, and APIs for monetization.

New Business Models: Data royalty, model fine-tuning agency services, code provenance certification/insurance services, and AI governance SaaS grow rapidly.

Labor Market Changes: Demand for ‘prompt engineers,’ ‘AI verification engineers,’ and ‘domain-prompt composite specialists’ explodes.

Investment Strategy: Diversify investments across infrastructure (data centers, low-latency networks), AI security/compliance solutions, edge computing, and education/retraining platforms.

Key Economic Indicators to Monitor: Wage spread for premium AI skills, compute prices ($/FLOP), and the ratio of software/AI CapEx to company R&D.

3–7 Years (Mid-to-Long Term): AI-Driven New Industrial Structure and Geopolitical Reorganization

Almost all standard code and repetitive software development reach a level where models can generate them.

‘Compute Sovereignty’ becomes the core of national competitiveness, and some countries move to strengthen control over their domestic servers and data.

Data becomes an asset, giving rise to a ‘data royalty’ market.

Economies of scale become stronger, increasing the likelihood of platform companies strengthening their market dominance.

Global supply chains are reorganized around semiconductors and specific AI services.

Macroeconomic Impact: While there will be an increase in productivity, consumer spending propensity may slow down due to distorted income distribution.

Long-Term Investment Themes: Human-machine interfaces, high-value domain-specific AI (medical, legal, finance), data trust/fiduciary structures, IP/liability insurance markets.

The 4 Most Important Things Rarely Discussed in Other Media (Exclusive Insights)

1) Compute sovereignty operates like ‘national strategic resources’ of the oil and gas era.

Nations that control access to compute and high-quality data will possess new technology leadership and investment incentives.

This is not merely a technological competition but a restructuring of diplomacy, trade, and investment economics.

2) A data royalty market emerges.

Profit sharing becomes possible for high-quality data generated by companies and individuals, creating legal and technical frameworks where data providers (individuals, companies) can demand royalties.

3) Code provenance certification and ‘AI output insurance’ become core businesses.

As the risk of legal disputes and security incidents increases, a market emerges for tracing the origin of code and transferring risk through insurance.

4) If AI widens the productivity gap, GDP growth will rise simultaneously with wage stagnation in the short term.

This creates new dilemmas for monetary and fiscal policies (trade-offs among growth, inflation, and distribution).

Individual Practical Roadmap (Youth, Developers, Non-Majors, Investors)

Youth/Beginner: We recommend ‘10,000 hours of building products’ with AI coding tools (e.g., Replit, Cursor).

Combine this with prompt design, testing/debugging loops, and simple fine-tuning exercises.

Intermediate Developers: Invest in model fine-tuning, MLOps, and data engineering.

Evaluate parts being replaced by automation, and develop system design, architecture, and safety verification skills.

Non-Majors: Build ‘prompt + domain’ combined skills based on domain expertise.

In highly regulated fields such as medical, legal, and finance, domain knowledge determines the value of prompts.

Investors: Include AI infrastructure (hardware, cloud), software (platforms, security), and education/retraining sectors in your portfolio.

Risk Management: Prepare hedging strategies for regulatory risks, concentration risks, and rising compute costs.

Company Execution 6-Step Checklist

1) Secure data infrastructure and governance.

Establish data catalogs, quality metrics, and access control systems.

2) Establish a compute strategy.

Review hybrid operations of on-premise and cloud, and contractual options for prioritized compute acquisition.

3) Model governance and legal review.

Clarify model licensing, provenance tracking, and liability sharing.

4) Employee retraining and job redesign.

Create ‘value-enhancement’ scenarios by combining core tasks with AI.

5) Security and quality verification.

Implement automated testing and security verification pipelines for AI outputs.

6) Experiment with new monetization models.

Rapidly launch pilot products such as data royalty, model API-fication, and fine-tuning consulting.

Policy Recommendations (National/Local Government Level)

Short-Term: Strategic stockpiling for AI/compute acquisition and establishment of international cooperation channels.

Mid-Term: Introduction of data ownership/royalty frameworks, and regulations for personal data trading/compensation.

Long-Term: Educational redesign (lifelong transition education, AI utilization capabilities), and income redistribution mechanisms (including retraining vouchers, universal basic income experiments).

Principles of Regulatory Design: Balance technological neutrality, innovation promotion, and risk minimization, while preparing to strengthen competition laws against compute centralization and platform monopolies.

Important Signals (Monitoring Indicators)

Trends in compute unit prices (GPU rental fees, cloud prices).

Proportion of AI-related skills and wage premium in job postings by role.

AI CapEx ratio of major companies and data assetization indicators.

Model release speed, open-source contributions, and patent application counts.

Regulatory trends: Legislation status of data mobility restrictions, AI liability laws, and export controls.

Specific Positioning from an Investor’s Perspective

Mix of safe and growth assets.

Short-Term (0–12 months): Emphasize cloud providers, chip manufacturers, and MLOps leaders.

Mid-Term (1–3 years): Domain-specific AI, security/verification startups, education platforms.

Long-Term (3–7 years): Data trust/infrastructure ownership, AI-interface companies.

Hedge: Prepare for semiconductor supply chain disruptions and regulatory shocks with options, short-term bonds, and cash.

Practical Checkpoints — What to Do Today

Office Workers: Map out the resources gained and risks associated with automating 30% of your team’s core tasks with AI.

Developers: Use code generation models to run a ‘fast prototype → assurance test’ loop more than 10 times.

Recruiters: Introduce a blended scorecard for AI capabilities and domain expertise.

Policymakers: Review compute demand forecasts and local government data center attraction policies.

< Summary >Core: Most standard code is highly likely to be generatable by AI within 5 years.

Result: Ownership of compute and data becomes the core of wealth and power, restructuring labor markets, investment, and policy.

Individual Strategy: Differentiate with ‘prompt + domain expertise’.

Company Strategy: Invest first in data, compute, and governance infrastructure, and establish verification and insurance structures for AI outputs.

Policy Proposal: Design new regulatory and redistribution mechanisms considering data royalty and compute sovereignty.