Steering AI Beyond Automation

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• The dominant AI paradigm in Silicon Valley, focused on “pure automation” and AGI, is economically unpromising and misses AI’s potential; the authors advocate for an alternative vision centered on “human augmentation” and “human-AI collaboration.”
• A systematic research agenda for collaborative agents should tackle four key technical problems: developing “white-box agents” that reveal intermediate processes (requiring “process traces”), achieving “real-time interaction” for human “flow,” enabling “continual collaboration” beyond turn-based delegation, and creating new methods for “evaluating human-AI collaboration.”
• The current bias towards automation is driven by economic incentives (reducing labor costs), technical convenience (clean benchmarks for autonomous systems), and a deep “intellectual culture of CS” that views autonomous, “end-to-end” systems as an “aesthetic ideal” and human involvement as a “defect.”
• Steering AI towards augmentation requires a “bottom-up, market-driven approach” (e.g., new products, domain-specific firms), cultural shifts within computer science (elevating HCI, changing pedagogy), and targeted policies like research funding and mitigating tax biases that currently favor automation.