Position: AI Safety should prioritize the Future of Work

Current efforts in AI safety prioritize filtering harmful content, preventing manipulation of human behavior, and eliminating existential risks in cybersecurity or biosecurity. While pressing, this narrow focus overlooks critical human-centric considerations that shape the long-term trajectory of a society. In this position paper, we identify the risks of overlooking the impact of AI on the future of work and recommend comprehensive transition support towards the evolution of meaningful labor with human agency. Through the lens of economic theories, we highlight the intertemporal impacts of AI on human livelihood and the structural changes in labor markets that exacerbate income inequality. Additionally, the closed-source approach of major stakeholders in AI development resembles rent-seeking behavior through exploiting resources, breeding mediocrity in creative labor, and monopolizing innovation. To address this, we argue in favor of a robust international copyright anatomy supported by implementing collective licensing that ensures fair compensation mechanisms for using data to train AI models. We strongly recommend a pro-worker framework of global AI governance to enhance shared prosperity and economic justice while reducing technical debt.

Current AI safety research focuses mainly on preventing misuse, harmful outputs, or hypothetical future risks like rogue AI. It overlooks a more immediate and pressing issue: how generative AI disrupts the job market and undermines meaningful human labor. Automation driven by AI is accelerating job loss, disproportionately affecting lower-income and less-resourced communities. We propose that AI safety should prioritize the future of work by integrating labor market concerns into its core agenda. We highlight a policy framework to support displaced workers, promote equitable access to AI resources, and implement fair remuneration for creators whose data trains AI models. We also recommend technical safeguards, such as AI-generated content detection and watermarking, to preserve trust in widespread AI use across critical domains.