Balancing AI Existential Risks and Near-Term Harms

This article explores how technical governance researchers navigate the complex divide between existential risks and immediate societal harms in artificial intelligence. It examines the quantitative and qualitative frameworks used to evaluate these differing threats, how these assessments directly influence funding allocations and regulatory priorities, and the emerging consensus on dual-purpose interventions that address both current and future AI challenges simultaneously.

Technical governance researchers categorize AI risks into two primary buckets: near-term societal harms and long-term existential risks (x-risk). Near-term harms include algorithmic bias, misinformation, copyright infringement, and automated cyberattacks. These issues are already occurring and have measurable impacts. Conversely, existential risks involve hypothetical future scenarios where advanced AI systems surpass human control, potentially leading to catastrophic outcomes or human extinction. Evaluating these against one another requires comparing high-probability, lower-impact events with low-probability, infinite-impact events.

To prioritize funding, researchers rely on distinct decision-making frameworks. Those focusing on existential risk often use expected value theory. Because the negative value of human extinction is astronomical, even a fraction of a percent chance of it occurring mathematically justifies massive investment in preventative alignment research. Philanthropic organizations operating under this framework allocate hundreds of millions of dollars toward interpretability, control mechanisms, and theoretical alignment. Conversely, public sector funders and traditional grantmakers prioritize empirical evidence. They direct resources toward near-term harms where data exists to prove both the problem's severity and the efficacy of proposed technical solutions, such as differential privacy and bias auditing tools.

When setting regulatory priorities, researchers utilize a risk-tiering approach based on computational thresholds and model capabilities. Instead of choosing between near-term and long-term safety, modern governance frameworks separate regulations by the scale of the AI system. For widely deployed, smaller-scale models, researchers advocate for sector-specific rules focusing on transparency, data privacy, and consumer protection. For "frontier" models—those trained with massive amounts of compute—researchers push for stringent pre-deployment testing, mandatory red-teaming, and cybersecurity standards designed to prevent catastrophic misuse or loss of control.

Increasingly, technical governance researchers are moving away from treating these two categories as mutually exclusive. The current priority is identifying convergent interventions—technical and policy mechanisms that mitigate both immediate and existential threats. For example, research into mechanistic interpretability helps developers understand how an AI reaches a decision. In the near term, this removes biased outputs; in the long term, it allows researchers to detect if an advanced system is developing deceptive or misaligned goals. Similarly, establishing robust model evaluation institutions builds the regulatory infrastructure needed to police current disinformation while preparing oversight bodies to manage superintelligent systems in the future.