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This paper models how firms decide to allocate work between AI and human workers when AI may fail, considering the effect on skill investment and worker mobility. It finds that mobility can shift engagement from least-skilled to most-skilled workers below the AI benchmark.
A discussion on how AI has not yet solved fundamental career problems like identifying skill gaps or understanding rejection reasons, despite advances in other areas.
A reflection on Satya Nadella's idea that AI systems, not tools, drive efficiency, emphasizing the importance of human direction and system structure over individual tools.
This paper develops a formal theory of cognitive debt, where using AI as a substitute for first-principles reasoning builds up unverified obligations that lead to systemic fragility and a cognitive Minsky moment, showing that decentralized equilibrium over-adopts substitutive AI without accounting for externalities.
Satya Nadella argues that companies must build a learning loop combining human capital and token capital to retain control and avoid value being captured by a few AI models. He emphasizes the need for a frontier ecosystem rather than just a frontier model.
Microsoft CEO Satya Nadella argues that in the AI-driven economy, firms must build both human capital and token capital (AI capabilities) in a compounding learning loop, emphasizing that human agency remains crucial and that companies must retain control over their IP to avoid value being captured by a few frontier models.
Microsoft CEO Satya Nadella proposed the concept of 'Token capital', arguing that in the AI era, enterprises need to simultaneously manage human capital and Token capital, and build a learning loop to accumulate proprietary AI capabilities, to prevent a few models from monopolizing value.