## Editor’s Letter

## Source details

**Canonical URL:** [Editor’s Letter](https://www.imf.org/-/media/files/publications/fandd/article/2023/december/05editors-letter.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/fandd/article/2023/december/05editors-letter.pdf.md)
- [Structured JSON version](/-/media/files/publications/fandd/article/2023/december/05editors-letter.pdf.json)

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### Overview
- The issue examines the implications of generative AI for growth, jobs, inequality, and finance.
- The editor emphasizes: “Ultimately, it’s about what AI can do to help people.”
- Note from the editor: this issue of f&d was produced entirely with human intelligence, though future issues may be AI-assisted.

### Framing and major concerns
- Generative AI introduces possibilities in health care diagnoses, closing education gaps, tackling food insecurity with more efficient farming, driving planetary exploration, and reducing drudgery of work.
- Growing concerns include:
  - The spread of misinformation that disrupts democracy and destabilizes economies.
  - Threats to jobs across the skills spectrum.
  - A widening gulf separating the haves and have-nots.
  - The proliferation of biases, both human and computational.

### Contributions in this issue (authors and core messages)
- Erik Brynjolfsson and Gabriel Unger (Stanford)
  - Sketch possible “forks in the road” that lead to very different outcomes (beneficial or detrimental) for AI and the economy.
  - Emphasize that the future will be a consequence of technological and policy decisions made today.
- Daron Acemoglu and Simon Johnson (MIT)
  - Argue AI’s ultimate impact depends on how it affects workers.
  - Note that innovation always leads to higher productivity, but not always to shared prosperity, depending on whether machines complement or replace humans.
  - Outline policies, such as giving labor a voice, to redirect efforts away from pure automation toward a more “human-complementary” path that creates new and higher-quality tasks.
- Anton Korinek (University of Virginia)
  - Recommends scenario planning given AI’s inherent unpredictability.
  - Lays out how different technological paths, depending on whether—and how soon—AI exceeds human intelligence, would lead to vastly different outcomes for the economy and workers.
  - Advises policymakers to prepare reforms for multiple scenarios and revise as the future unfolds.
- Ian Bremmer (Eurasia Group) and Mustafa Suleyman (Inflection AI)
  - Discuss regulatory challenges amid a race for AI supremacy among governments.
  - Warn that governing AI will be among the international community’s most difficult challenges in coming decades and outline principles for AI policymaking.
- Gita Gopinath (IMF)
  - Urges balancing innovation and regulation in developing a unique set of policies for AI.
  - Emphasizes that because AI operates across borders, global cooperation is urgently needed to maximize opportunities while minimizing harms.
- Daniel Björkegren and Joshua Blumenstock
  - Show how Kenya, Sierra Leone, and Togo adapted AI to benefit the poor.
- Nandan Nilekani
  - Describes how India is on a cusp of an AI revolution to address pressing economic and social challenges.
- Profile of Lawrence F. Katz (Harvard)
  - Highlights his defining work on inequality and its relevance to AI discussions.

### Policy recommendations and governance themes
- Prepare and implement policies that:
  - Balance innovation with regulation.
  - Give labor a voice to influence the direction away from pure automation.
  - Promote “human-complementary” technological paths that create new, higher-quality tasks.
  - Anticipate multiple technological scenarios and build adaptable reforms.
  - Foster international cooperation because AI operates across borders.
- Principles for AI policymaking should account for regulatory challenges and geopolitical competition for AI supremacy.

### Scenarios and planning
- Scenario planning is recommended due to AI’s unpredictability.
- Different technological trajectories—especially whether, and when, AI exceeds human intelligence—would yield vastly different economic and labor outcomes.
- Policymakers should prepare reforms for these multiple scenarios and revise them as developments unfold.

### Case studies and examples
- Kenya, Sierra Leone, and Togo: adapted AI to benefit the poor (authors Daniel Björkegren and Joshua Blumenstock).
- India: described as on the cusp of an AI revolution to address economic and social challenges (Nandan Nilekani).

### Closing
- AI can develop in very different directions, underscoring society’s role in actively and collectively determining its future.
- The technology must be guided as tools that enhance, rather than undermine, human potential and ingenuity.
- Final emphasis: “Ultimately, it’s about what AI can do to help people.”

*Source: Editor’s Letter, F&D*

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_Source: https://www.imf.org/-/media/files/publications/fandd/article/2023/december/05editors-letter.pdf_