Humans and AI systems are increasingly operating within the same social and economic contexts. Alignment—ensuring AI behaves according to human values—is widely regarded as the central challenge for making AI beneficial while avoiding harm. We argue that this goal is fundamentally flawed. First, alignment lacks a clear definition because it presumes a stable, identifiable set of human intentions or values. Second, even when a concrete notion of alignment can be articulated, there is no general solution method. Third, even if alignment were solvable in certain cases, solving it would not necessarily eliminate the negative consequences of AI. The core issue is that alignment is usually framed as a property of individual AI systems, whereas hybrid human‑machine societies are complex systems shaped by interactions and feedback loops. Emergent risks—rather than merely the hazards of misaligned individual agents—are likely to become the dominant source of harm. Consequently, AI safety should be viewed as an ongoing challenge, not a problem that can be solved once and for all by verifying a specific model property.
Review: The argument that alignment is both unsolvable and insufficient underscores the need for system‑level risk management rather than focusing solely on aligning individual models.