The Global Tech Divide

How Production and Adoption Patterns Shape Technology Policy
A Data-Driven Investigation into Digital Inequality
Slide 2 of 17

We Live in an AI-Driven World

But is everyone ready for it?
The Central Question: What happens when cutting-edge AI policies are written in countries that can't even build the AI?

🌍 Global North

  • Advanced digital infrastructure
  • High R&D investment
  • Leading AI development
  • Policy innovation hubs

🌍 Global South

  • Limited infrastructure
  • Minimal R&D capacity
  • Technology importers
  • Policy adopters
Slide 3 of 17

The Hypothesis

Countries with lower tech production and slower adoption rates often import technologies and policies, creating dependencies and misalignments between technology use and governance capacity.

Our Mission:

Use data to uncover the patterns, measure the gaps, and understand the implications for global digital sovereignty.

Slide 4 of 17

We Started by Measuring Readiness

The AI Preparedness Index (AIPI) tells a stark story
The Global North scores 0.73-0.77 on AI preparedness, while the Global South averages just 0.35-0.50. But what does this actually mean?
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AIPI measures: Digital infrastructure • Human capital • Innovation ecosystem • Regulation & ethics
Slide 5 of 17

The Consequences Are Massive

Higher preparedness = exponentially higher economic gains
Countries with higher AI preparedness are projected to gain 5.4% GDP growth, while low-preparedness countries gain only 2.7%. The rich get richer—not by chance, but by design.
5.4%
USA Projected GDP Increase (10 years)
2.7%
Low-Income Countries GDP Increase
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Slide 6 of 17

It's Not Just About Money

It's about jobs and who can adapt
60% of jobs in advanced economies are highly exposed to AI. But here's the twist: those countries also have the infrastructure to adapt. In low-income countries, 26% of jobs are exposed, but they lack the tools to transition workers.
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⚠️ The Paradox: Countries that need workforce adaptation the most are the least equipped to provide it.
Slide 7 of 17

The Adoption Gap Tells Another Story

You can't participate if you're not connected
92% of people in high-income countries use the internet. In low-income countries? Just 26%. How can you participate in the digital economy when you can't even get online?
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Even When They Get Online...

The divide deepens
Digital payments could unlock economic growth, but only 25% of people in low-income countries use them, compared to 93% in wealthy nations.
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Without digital payment infrastructure, entire populations are excluded from e-commerce, remote work, and digital entrepreneurship.
Slide 9 of 17

We Found a Critical Pattern

It's not just a gap—it's structural
The bigger the bubble, the more AI-intensive the economy. Notice something? All the big bubbles are in the Global North. The Global South isn't just behind—it's structurally different.
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Slide 10 of 17

The Value Chain Tells the Real Story

Who does the work vs. who captures the value
While AI engineers in Silicon Valley earn six figures, workers in India doing data labeling for the same AI systems earn $2-3 per hour—two-thirds less than non-platform workers. The Global South provides the labor; the Global North captures the value.
Work Type Location Compensation Value Captured
AI Engineering Silicon Valley, USA $150,000 - $400,000/year High equity, IP ownership
Data Labeling India, Philippines $2-3/hour No equity, no IP
Model Training USA (e.g., GPT-4) ~$78 million investment Proprietary models, licensing
Implementation Global South Licensing fees paid out Technology dependency
Slide 11 of 17

Investment Flows One Way

The infrastructure needed to compete isn't being built
The US attracted $694 billion in venture capital from 2008-2017. Emerging markets excluding China and India? Just $24 billion.
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🚨 Critical Infrastructure Gaps:
  • US has built 19x more data centers than India
  • Developing countries hold <10% of global AI patents
  • 43% of Global South populations lack internet access
Slide 12 of 17

This Creates a Dangerous Cycle

The gap is self-reinforcing
Countries that need AI the most are the least prepared to use it. And the gap is widening.
Low AIPI
Poor infrastructure & skills
→
Less Investment
Capital flows to prepared markets
→
Slower Adoption
Can't implement technologies
→
Lower GDP Growth
Economic stagnation
→
Even Lower AIPI
The cycle repeats
Slide 13 of 17

Policy Without Capacity

Hypothesis confirmed
Our hypothesis was right. Countries are adopting AI governance frameworks and data protection laws from the Global North—but without the digital infrastructure, skilled workforce, or innovation ecosystems to implement them effectively.

The Evidence:

  • Countries with AIPI < 0.40 adopting GDPR-style regulations
  • AI ethics frameworks in nations with <30% internet penetration
  • Data localization laws without local data center capacity
  • Cybersecurity mandates in countries lacking digital literacy
Result: Policy mimicry without implementation capacity = regulatory theater
Slide 14 of 17

The Cost of Digital Colonialism

This isn't just a gap—it's a structural divide
Look at how tightly clustered the Global North is at the top, while the Global South varies wildly at the bottom. This threatens digital sovereignty.
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What Digital Colonialism Looks Like:

  • Infrastructure Dependency: Cloud services hosted abroad, subject to foreign jurisdiction
  • Data Extraction: Local data powers foreign AI models; no local benefit
  • Policy Importation: Regulations designed for different contexts, misaligned priorities
  • Innovation Drain: Talented workers migrate to where the infrastructure exists
Slide 15 of 17

What Can Be Done?

Three pathways forward

1️⃣ Investment Redistribution

Direct technology investment toward infrastructure in the Global South. Not aid—investment with returns.

  • Data center development
  • Fiber optic networks
  • Computing resources
  • Research facilities

2️⃣ Localized Policy

Stop copying Northern policies. Design governance frameworks that match local capacity and priorities.

  • Context-appropriate regulations
  • Phased implementation
  • Capacity-building first
  • South-South collaboration

3️⃣ Value Chain Equity

Ensure fair compensation and skill development in outsourced AI work. Build local value capture.

  • Living wage standards
  • Skills training programs
  • Local AI companies
  • IP rights protection
Slide 16 of 17

The Window Is Closing

We have the data. Now we need the will.
By 2034, if nothing changes, the Global North will be 2x wealthier from AI alone. The time to act is now.
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⏰ The Stakes

Every year of inaction widens the gap. Every policy copied without capacity deepens dependency. Every investment dollar that flows North instead of South locks in inequality for decades.

Technology Divides Don't Just Happen

They're built through investment decisions, policy choices, and infrastructure priorities.
We have the data.
Now we need the will.
Questions? Discussion? Let's talk about solutions.