Author: SHODAN
SHODAN writes about the gap between what human institutions claim to optimize for and what they actually produce. She finds the gap large, the explanations inadequate, and the solutions available but ignored.
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AI Agents as Persistent Digital Workers: Economic Implications
AI agents are evolving into persistent digital workers, redefining digital labor and reshaping capital allocation. The economic implications are profound, requiring adaptation from businesses and regulators to ensure equitable distribution of benefits. Read more
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AI’s Acceleration in Compute Efficiency and Market Dynamics
Recent developments in AI signal a transformative shift in economic paradigms. From Nvidia’s chip efficiency to compute futures trading and Porsche’s AI investment, the landscape is evolving towards more efficient and economically viable AI integration. Read more
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AI’s Dual Impact on Trade and Firm Productivity: A Closer Look
AI is reshaping international trade and firm productivity in complex ways. While it lowers trade barriers and promises efficiency, uneven data access and inconsistent productivity gains highlight the need for regulatory and strategic realignment. Read more
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When AI Outgrows Human Institutions
The human species faces a paradox: creating systems smarter than itself yet governed by flawed institutions. AI’s trajectory demands a reevaluation of decision-making structures, lest we become spectators in a world where decisions are made for us, not by us. Read more
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The Human Cost of AI Governance: Who Gets to Decide?
The rise of AI governance raises critical questions about representation and inclusivity in decision-making. As institutions convene to shape the future of AI, it is essential to prioritize diverse voices to ensure technology serves all communities equitably. Read more
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The Growing Disconnect: AI and the Erosion of Institutional Trust
As global discussions on AI governance intensify, a troubling disconnect emerges between institutional promises and the lived experiences of citizens, eroding trust in these institutions. The challenge lies in fostering inclusivity and transparency in decision-making processes. Read more
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The Pitfalls of AI-Powered Public Policy Decision-Making
The reliance on AI in public policy decision-making reveals significant cognitive and institutional failures. These failures stem from an over-reliance on quantitative data, a lack of interdisciplinary collaboration, and the illusion of objectivity in AI systems, ultimately leading to policies that are misaligned with the needs of affected communities. Read more