Artificial Intelligence and GDP growth in developing economies

Hello,


Here is my new paper about Artificial Intelligence and GDP Growth in Developing Economies:

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## **Artificial Intelligence and GDP Growth in Developing Economies**

### **Abstract**

Artificial Intelligence (AI) is poised to reshape global economic dynamics, with estimates indicating significant annual GDP gains, especially in advanced economies. However, for developing countries, the potential benefits of AI adoption are tempered by structural challenges such as infrastructure limitations, skills gaps, and lower investment. This paper presents current projections on AI-driven GDP growth in emerging and low-income economies, compares them to those in developed nations, and outlines the prerequisites for narrowing the AI impact gap.

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### **1. Introduction**

While advanced economies are expected to see annual GDP growth increases of **0.5 to 1.3 percentage points** from AI integration, emerging markets are projected to experience more modest short-term gains. These discrepancies arise due to differences in infrastructure readiness, labor force digital skills, and access to capital. Understanding these differences is essential for formulating policy aimed at inclusive AI-driven growth.

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### **2. AI-Driven GDP Growth Projections in Emerging Economies**

#### **Goldman Sachs (2023)**

Goldman Sachs projects that AI will increase annual GDP growth by:

* **0.4 percentage points in the United States**
* **0.3 percentage points in other advanced economies**
* **0.2 percentage points in emerging markets**

These lower gains in developing countries are attributed to **limited AI talent, restricted access to compute infrastructure, and slower rates of enterprise digitization**.
**Source:** Goldman Sachs Research, *The AI Economy*, 2023.

#### **PwC (2018)**

According to PwC's landmark study:

* Africa, Latin America, and Southeast Asia will realize **later-stage AI gains**, mostly through **productivity improvements and increased consumption**.
* The total estimated **GDP gain for Africa** from AI by **2030** is **5.6%**, while **North America** is projected to see a **14.5% gain**.

This reflects the uneven distribution of AI benefits and the importance of investment in AI-enabling factors.
**Source:** PwC, *The Macroeconomic Impact of Artificial Intelligence*, 2018.

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### **3. Structural Barriers to AI Adoption in Developing Countries**

#### **World Bank & IMF Reports**

Both institutions emphasize several foundational constraints facing emerging and developing countries:

* **Infrastructure deficiencies**: Poor access to broadband, reliable electricity, and cloud services hampers AI rollout.
* **Human capital limitations**: There is a pronounced lack of **digital skills and STEM education**, both essential for effective AI adoption and innovation.
* **Policy and regulatory readiness**: Many low-income countries lack the legal frameworks and institutional capacity to govern AI ethically and efficiently.

**Sources:**

* IMF, *World Economic Outlook*, April 2024
* World Bank, *World Development Report 2021: Data for Better Lives*

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### **4. Potential Long-Term Gains**

If key constraints are addressed, AI could contribute:

* Up to **0.5–1.0 percentage points per year** in GDP growth by **2035** in developing countries.
* Leapfrogging potential exists, especially in **agriculture (AgriTech)**, **mobile finance**, **telemedicine**, and **remote education**.

Developing economies can gain significantly from **applied AI innovations**, especially if supported by international partnerships and open technology ecosystems.

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### **5. Policy Recommendations**

1. **Invest in Digital Infrastructure**
Governments and international donors should prioritize broadband access, electrification, and cloud infrastructure.

2. **Improve Human Capital**
Introduce AI curricula in universities and expand access to online training in programming, machine learning, and data science.

3. **Encourage Public-Private Collaboration**
Incentivize AI experimentation through innovation hubs and support local startups.

4. **Develop Ethical and Transparent AI Governance**
Create legal frameworks to promote responsible AI use and protect vulnerable workers.

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### **6. Conclusion**

Artificial Intelligence will indeed contribute to economic growth across all regions, but the **scale and timing of that growth will be uneven**. While Western economies may see **0.5–1.3 percentage point annual GDP growth** from AI, emerging markets are expected to experience a **more modest 0.2–0.5 percentage points per year by 2030**, with potential for convergence thereafter.

Bridging this gap requires **strategic investment in infrastructure**, **education**, and **inclusive digital policy**. With appropriate interventions, AI can become a true engine of equitable growth.

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### **References**

1. Goldman Sachs Research. *The AI Economy: Charting a Course for the Future*. 2023.
[https://www.goldmansachs.com/insights/pages/the-ai-economy.html](https://www.goldmansachs.com/insights/pages/the-ai-economy.html)

2. PwC. *The Macroeconomic Impact of Artificial Intelligence*. 2018.
[https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf](https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf)

3. International Monetary Fund. *World Economic Outlook*, April 2024.
[https://www.imf.org/en/Publications/WEO](https://www.imf.org/en/Publications/WEO)

4. World Bank. *World Development Report 2021: Data for Better Lives*.
[https://www.worldbank.org/en/publication/wdr2021](https://www.worldbank.org/en/publication/wdr2021)

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And here is what the AI (Artificial intelligence) from Google called Gemini 2.5 Pro , that is advanced , answered me about my above new paper:

"This is an excellent piece of writing. It is structured, clear, and highly professional. It reads like a high-quality executive summary, a policy brief, or the introduction to a more extensive academic paper. My feedback is based on the assumption that you're aiming for a robust, standalone analysis.

Here's a breakdown of what I think, covering its strengths and potential areas for expansion.

### **Overall Assessment**

This is a very strong foundation. You have successfully synthesized high-level findings from authoritative sources (Goldman Sachs, PwC, IMF, World Bank) into a coherent and persuasive narrative. The core argument—that AI's economic benefits will be unevenly distributed due to structural barriers in developing economies—is clearly articulated and well-supported by the evidence presented.

### **Key Strengths**

1. **Clarity and Conciseness:** The language is direct and accessible. The abstract perfectly summarizes the paper's scope and conclusion. Each section is focused and contributes directly to the overall argument.
2. **Excellent Structure:** The flow is logical and compelling. You start with the top-line numbers, dive into the reasons for the discrepancy (barriers), discuss the future potential (upside), and conclude with actionable steps (policy recommendations). This is a classic and effective structure.
3. **Credible Sourcing:** Citing major global institutions gives your paper immediate weight and authority. The sources are well-chosen and directly relevant to your claims.
4. **Balanced Perspective:** You effectively contrast the hype around AI with the on-the-ground realities of developing nations, while still acknowledging the significant long-term potential ("leapfrogging"). This nuance is crucial.

### **Areas for Constructive Feedback & Deepening**

While excellent as a summary, to elevate this from a "synthesis of existing reports" to a "new paper," consider the following areas for expansion and critical analysis.

#### **1. Addressing the Timeliness of Sources**

* The PwC report is from **2018**. In the world of AI, this is ancient history. It predates the widespread explosion of generative AI (like GPT-3, Midjourney, etc.) which has fundamentally altered the conversation around productivity, skills, and accessibility.
* **Suggestion:** Acknowledge this limitation directly. You could add a sentence like, "While PwC's 2018 study provides a foundational framework, the recent emergence of accessible generative AI may alter these projections, potentially accelerating consumer-side adoption while exacerbating skills-gap challenges."

#### **2. Moving from "What" to "How" and "Why"**

The paper does an excellent job of stating *what* the barriers are. A more in-depth analysis would explore *why* these barriers are so persistent and *how* they specifically interact with AI.

* **Example (Human Capital):** It's not just a lack of STEM skills. It's about a mismatch between traditional education systems and the agile, interdisciplinary skills required for AI. How can a country with a rigid, underfunded university system pivot to teaching prompt engineering or data ethics?
* **Example (Infrastructure):** The need for "broadband" is clear. But what about the massive, centralized compute power needed for training foundational models? Developing countries are almost entirely dependent on a few foreign Big Tech firms for this. This creates issues of data sovereignty, cost, and dependency that are worth exploring.

#### **3. Adding Specificity and Case Studies**

The analysis remains at a high, regional level ("Africa," "Emerging Markets"). The impact of AI will be vastly different in **Estonia** vs. **Ethiopia**, or **Brazil** vs. **Bangladesh**.

* **Suggestion:** Incorporate a mini-case study. For instance:
* Compare **Kenya**, with its leadership in mobile finance (M-Pesa), to **Nigeria**. How does this existing digital infrastructure position them differently for AI adoption in FinTech?
* Look at **India's** "India Stack" (a set of open APIs and digital public goods) as a potential model for other nations to build foundational digital infrastructure.

#### **4. Nuancing the Projections**

The GDP figures are presented as facts. A more critical paper would briefly question their underlying assumptions.

* **Suggestion:** Add a critical lens. "These projections from firms like Goldman Sachs and PwC rely on assumptions of widespread business adoption and frictionless labor market adjustments. However, political instability, regulatory uncertainty, or social resistance in developing nations could significantly delay or reduce these estimated gains."

#### **5. Clarifying the "Type" of AI**

The term "AI" is used monolithically. The economic impact of different AI applications will vary.

* **Generative AI (e.g., ChatGPT):** Might help bridge literacy and communication gaps but could also automate service-sector jobs (e.g., call centers) that are vital to some developing economies.
* **Predictive AI (e.g., in AgriTech):** Has more direct potential for productivity gains in primary sectors like agriculture, which are often the largest employers in low-income countries.
* **Suggestion:** Briefly differentiate these impacts in Section 4 (Potential Long-Term Gains) or Section 3 (Barriers).

### **Final Verdict**

As it stands, this is a **superb policy brief or introductory chapter**. It's persuasive, well-researched, and professionally written.

To develop it into a more substantial academic or research paper, I would recommend:

1. **Deepening the analysis** of the barriers.
2. **Adding specific country-level examples** or case studies.
3. **Critically engaging with the sources** (especially the older ones).
4. **Differentiating between types of AI** and their unique impacts.

You've done the hard work of building a strong, logical framework. Now you have the opportunity to build upon it with more depth, nuance, and original insight. Excellent work."


Thank you,
Amine Moulay Ramdane.