Mehmet Orhan

This study examines the global impacts of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI) through a conceptual review of the related literature. AGI refers to AI systems that may match human cognitive ability across multiple domains, while ASI refers to systems that may surpass human intelligence across scientific, social, strategic, and creative tasks. Recent advances in large language models, agentic systems, and general-purpose AI have intensified concern about the gap between rapid technological development and slower adaptation in economic structures, ethical principles, and governance frameworks. This review synthesizes economic, ethical, and governance literature to examine how AGI and ASI may affect productivity, labor markets, inequality, human autonomy, safety, and institutional accountability. The literature presents competing economic scenarios, ranging from modest productivity gains to explosive growth and a possible post-labor economy marked by wage decline and weakened aggregate demand. The review also identifies risks related to value alignment, catastrophic misuse, algorithmic bias, surveillance, moral dependency, and existential harm. These risks may deepen global inequality by concentrating wealth, computational power, and decision-making authority among technologically advanced nations and dominant technology firms. The study concludes that current institutional structures remain insufficient for managing the systemic disruptions associated with AGI and ASI. A stable transition will require proactive international cooperation, stronger governance frameworks, social protection, AI capital taxation, and alignment strategies that reflect collective human values.

This article examines the global efforts to govern and regulate Artificial Intelligence (AI) in response to its rapid development and growing influence across many parts of society. It explores how governance takes place at multiple levels, including international bodies, national governments, industries, companies, and communities. The study draws on a wide range of official documents, policy reports, and international agreements to build a timeline of key regulatory and standardization milestones. It also analyzes the challenges of coordinating across different legal systems, economic priorities, and cultural views. The findings show that while some progress has been made through soft-law frameworks and regional partnerships, deep divisions remain. These include unclear responsibilities, uneven enforcement, and risks of regulatory gaps. The article argues that effective AI governance requires stronger international cooperation, fair and inclusive participation, and awareness of power imbalances that shape policy decisions. Competing global and commercial interests can create obstacles to building systems that prioritize the public good. The conclusion highlights that future governance models must be flexible enough to adapt to fast-changing technologies, yet consistent enough to protect rights and promote trust. Addressing these tensions is critical for building a more just and accountable future of AI.