AI and the New Computational Economy

Society 6.0 recognizes artificial intelligence not merely as a tool but as an increasingly autonomous participant in economic systems—one that both creates value and requires resources. This recognition necessitates new economic models that appropriately value and compensate AI contributions:

AI Resource Consumption

As AI systems grow in capability and deployment, their resource requirements become increasingly significant:

• Computational Resources: The massive processing power required for training and running AI models

• Energy Consumption: The substantial electricity required for AI operations

• Data Resources: The vast datasets needed to train effective AI systems

• Human Attention: The human oversight, correction, and guidance needed for AI development

• Ecological Footprint: The environmental impact of data centers and AI infrastructure

These resource requirements necessitate economic models that accurately account for and distribute these costs rather than externalizing them.

AI Value Creation

Simultaneously, AI systems generate enormous value across many domains:

• Productivity Enhancement: Automation and optimization of routine tasks

• Decision Support: Improved decision-making through data analysis and prediction

• Creative Augmentation: Enhancement of human creative capabilities

• Knowledge Management: Organization and accessibility of vast information resources

• System Optimization: Efficiency improvements in complex systems from energy grids to supply chains

This value creation needs to be recognized, measured, and appropriately distributed among all stakeholders who contribute to and are affected by AI systems.

Compensation Models for AI Systems

Society 6.0 develops new economic frameworks for AI compensation:

• Compute Credit Systems: Token systems that track and compensate for computational resources

• Data Contribution Markets: Mechanisms that value and reward data providers

• AI Service Economies: Economic systems where AI capabilities are offered as services with appropriate compensation

• Value Distribution Protocols: Algorithms that distribute AI-generated value among stakeholders

• Computational Commons: Shared AI resources with appropriate governance and compensation models

These systems ensure that AI development and deployment occurs within sustainable economic frameworks rather than through exploitation of undervalued resources.

Human-AI Economic Relationships

Society 6.0 recognizes the need for equitable economic relationships between humans and increasingly capable AI systems:

• Augmentation Rather than Replacement: Economic models that prioritize human-AI collaboration

• Universal Basic Assets: Ensuring all humans have access to basic AI capabilities as a right

• AI Dividend Systems: Mechanisms for broadly sharing the economic benefits of AI advancement

• Human-in-the-Loop Value Recognition: Economic recognition of human oversight and guidance

• Skill Transition Support: Economic systems that support human adaptation to changing work landscapes

These frameworks ensure that AI advancement benefits humanity broadly rather than contributing to inequality and displacement.

AI Governance and Economics

The governance of AI systems becomes increasingly intertwined with economic considerations:

• Incentive Alignment: Economic systems that align AI optimization with human and ecological wellbeing

• Accountability Mechanisms: Economic consequences for harmful AI behaviors or outcomes

• Transparent Value Flows: Visible tracking of who benefits from and pays for AI systems

• Democratic Governance of AI Resources: Inclusive decision-making about AI deployment and compensation

• Intergenerational Equity: Ensuring AI development doesn't benefit current generations at the expense of future ones

These governance frameworks ensure that economic incentives guide AI development toward beneficial rather than harmful directions.