GoKawiilGiven rapid advances in AI, how should researchers and developers shift how we allocate our time? What new skills should we build so that we’re not obsolete in the future? I argue that there will be plenty for us to work on, grounded in the “AI as normal technology” thesis, which holds that there are many bottlenecks between AI capability improvements and automation of tasks or jobs. The evidence suggests that AI is better seen as an augmentation than an automation technology. The balance of human effort will shift towards tasks that are less verifiable — from developing models to scaffolds, and from building towards evaluation and monitoring. Over the long term, as purely technical skills are devalued, both researchers and developers will have to adapt. In research, human effort will migrate from problem solving to question asking and conceptual progress; in industry, relational skills, domain knowledge, aesthetic and normative judgment will gain in importance.
What will be left for us to work on
Why This Matters
This article highlights the evolving role of AI in the tech industry, emphasizing that human skills will shift from technical problem-solving to tasks requiring judgment, creativity, and conceptual thinking. Understanding these changes is crucial for professionals to stay relevant and for organizations to adapt their workforce strategies. It underscores the importance of developing soft skills and domain expertise alongside technical capabilities to thrive in an AI-augmented future.
Key Takeaways
- AI will serve more as an augmentation tool than a replacement, shifting human effort to less verifiable tasks.
- Researchers and developers must focus on asking questions, conceptual progress, and monitoring AI systems.
- Relational skills, domain knowledge, and normative judgment will become increasingly valuable in the AI era.
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