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Heltaher
Heltaher

The Intelligent Proxy: Ambition and Ambiguity in the Age of AI Co-Creation

Key Insights
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  • AI expands the creative search space by turning ideation into a dialogue between human intent and machine-generated variations.
  • The more we treat AI as a collaborator, the harder it becomes to assign responsibility when outcomes fail in the real world.
  • Optimization works best in bounded engineering problems; it breaks down on wicked, socio-technical systems where success is contested.
  • Meaningful human control requires calibrated trust and workflows that surface limitations instead of hiding them behind polished outputs.
  • The design challenge is increasingly about stewarding consequences, not just optimizing metrics.

References
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  3. Jiang, Q., Zhang, Y., Wei, W., & Gu, C. (2024). Evaluating technological and instructional factors influencing the acceptance of AIGC-assisted design courses. Computers and Education: Artificial Intelligence, 7.
  4. Zhou, C., Liu, X., Yu, C., Tao, Y., & Shao, Y. (2024). Trust in AI-augmented design: Applying structural equation modeling to AI-augmented design acceptance. Heliyon, 10.
  5. Van Quaquebeke, N., Tonidandel, S., & Banks, G. (2025). Beyond efficiency: How artificial intelligence (AI) will reshape scientific inquiry and the publication process. The Leadership Quarterly, 36.
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  8. Kandul, S., Micheli, V., Beck, J., Burri, T., Fleuret, F., Kneer, M., & Christen, M. (2023). Human control redressed: Comparing AI and human predictability in a real-effort task. Computers in Human Behavior Reports, 10.
  9. Weingarten, E., Meyer, M., Ashkenazi, A., & Amir, O. (2020). Human Experts Outperform Technology in Creative Markets. She Ji: The Journal of Design, Economics, and Innovation, 6.
  10. Sokol, K., Fackler, J., & Vogt, J. (2025). Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap. npj Digital Medicine, 8.