Does treating AGI as a north star goal undermine research planning?

This position paper argues that making "artificial general intelligence" the "north-star goal" of AI research undermines the community's "ability to choose effective goals." The abstract names six traps, "obstacles to productive goal setting," that are "aggravated by AGI discourse": Illusion of Consensus, Supercharging Bad Science, Presuming Value-Neutrality, Goal Lottery, Generality Debt, and Normalized Exclusion. The introduction ties the pull of the goal to recent LLM advances and the aim of "achieving human-level 'intelligence,'" then observes that instead of converging the field around shared goals, AGI discourse "has mired it in controversies."

The argument runs through disagreement over what the goal even refers to. Researchers "diverge on what AGI is and assumptions about goals and risks," so a target called AGI cannot supply the common direction a north star is supposed to provide. The paper's remedy has three parts: prioritize specificity in scientific, engineering, and societal goals; center pluralism about "multiple worthwhile approaches to multiple valuable goals"; and foster innovation through greater inclusion of disciplines and communities.

The discussion passage adds a positive fallback. If the community still wants an overarching goal, it should be "the support and benefit of human beings," because evidence-based ways of asking whether technology meets people's needs are "well-established," while AGI-driven communities "often lose sight of the needs of people as a goal, in favor of focusing on just the technology." It also suggests that processes ensuring technology benefits humans could offer "collectively legitimate responses" to socially significant disagreements about goals, which links the human-benefit goal to the inclusion recommendation.

Most AGI notes in the library take the goal as given and work inside it. Does software intelligence exist independent of hardware and environment? criticizes one family of definitions for a specific flaw, whereas this paper's complaint is broader: the trouble is using a contested term to set the field's goals at all. What bottlenecks define the path from AGI to superintelligence? maps routes beyond AGI as an endpoint, and this paper asks whether AGI should be the endpoint that organizes research in the first place. The divergence it cites also bears on How soon do AI researchers expect artificial general intelligence?, since aggregate timelines are only as shared as the referent, though the excerpt does not itself discuss forecasting. Closest in spirit is Can human-AI research teams improve faster than autonomous AI systems?, which also puts human-centered aims ahead of autonomous capability.

The excerpt names the six traps but does not define them, and it gives no evidence for how each one operates. It offers no comparison showing that specific, pluralistic goals outperform an AGI framing, and it is a position paper, so what it supplies is argument rather than data. The introduction's "Reason 3" implies earlier reasons that the excerpt does not include. What can be carried forward at this strength is the framing claim, that a contested goal term is itself a governance problem for a research field. Any specific claim about a single trap would need the full paper.