Narayanan & Unger publish research agenda to steer AI beyond pure automation
random_walker · x · 2026-10-20
Princeton's Arvind Narayanan and Stanford economist Gabriel Unger published "Steering AI Beyond Automation," a research agenda tackling one of AI's most important questions: how to build technology that improves general welfare instead of relying on an "automate and compensate" bargain the public dislikes.
Key points:
- The dominant Silicon Valley program assumes AI's internal logic favors total automation of human labor, with AGI as its completion — a stance the authors call implicit technological determinism.
- Their alternative: AI, like any technology, can develop in multiple directions, including human augmentation and boosting the collaborative power of human-AI teams.
- They don't oppose automation per se, but argue many maximalist full-automation attempts are deeply unpromising technically and economically, and too much research is biased toward that agenda — partly from economic incentives, partly from computer science's deeper intellectual culture.
- Concrete proposals for steering away are scarce; this paper is their attempt at one.
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