What will be left for us to work on?
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Princeton professor Arvind Narayanan's ICML 2026 keynote argues that AI should be understood as a transformative but 'normal' technology — comparable to electricity — that amplifies human potential rather than replacing workers. He presents three main arguments: the 'AI as Normal Technology' framework accurately describes AI's medium-term trajectory; recursive self-improvement won't suddenly eliminate jobs because AGI, ASI, and economic transformation are distinct dimensions of progress; and future jobs will shift dramatically from 'building' to 'evaluating and steering.' Drawing on research showing AI agents have improved in capability but not reliability, he explains why coding is not the bottleneck in software engineering and why Jevons' paradox suggests productivity gains historically increase employment. He advocates for human/AI 'co-superintelligence,' warns against black-box AI usage and the 'dependence spiral,' and calls for the AI research community to invest far more in evaluation over benchmark-driven building.
