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Nexus Institute for Work and AI: Research Deep Dive

Nexus Institute for Work and AI: Research Deep Dive

Written by: WRKdefined Podcast Network
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Join us for in-depth conversations with leading researchers at the intersection of work and artificial intelligence. Our hosts explore cutting-edge research, practical applications, and the profound implications AI is having on the workplace. From automation and job transformation to ethical considerations and emerging opportunities, each episode unpacks complex topics through thoughtful dialogue with the experts shaping the future of work. Whether you're a researcher, practitioner, or simply curious about how AI is reshaping our professional lives, these deep dives offer valuable insights grounded in rigorous research and real-world experience.All rights reserved by WRKdefined Daily Economics
Episodes
  • A Conversation about the Time Dividend Trap

    A Conversation about the Time Dividend Trap
    Oct 14 2026
    Generative artificial intelligence is frequently advertised as a tool that returns hours to employees, but empirical evidence reveals that these efficiency gains rarely translate into meaningful relaxation or slack. Instead, organizations and workers quickly absorb the saved minutes into expanded task portfolios, accelerated pacing, and heightened output expectations, creating a phenomenon termed the time dividend trap. Because companies typically capture these tech-driven time savings as default volume rather than deliberate allocation, employees often experience increased job demands, boundary erosion, and professional burnout. To counter this upward ratchet in workplace effort, leaders must intentionally measure net efficiency, establish clear reinvestment rules, and direct recovered hours toward human recovery and continuous learning. Ultimately, escaping this productivity trap requires recognizing that technology alone does not dictate how efficiency benefits are distributed; human choices and strategic governance shape the sustainable future of modern work.
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    1 hr and 3 mins
  • A Conversation about Navigating the Shift from AI Users to Builders

    A Conversation about Navigating the Shift from AI Users to Builders
    Oct 12 2026
    Employer demand for artificial intelligence capabilities is rapidly evolving from basic prompting toward hands-on tool construction and workflow automation. Although a vast majority of employees recognize the specific competencies they need to succeed in this changing landscape, a persistent support gap remains because many organizations fail to provide adequate developmental resources. To address this mismatch, companies must implement comprehensive strategies such as mapping role-specific capability rungs, funding dedicated learning time, and encouraging practical project builds. Furthermore, fostering collaborative networks through champions and engaged managers helps circulate vital knowledge across teams. Ultimately, leaders must align performance expectations with measurable outcomes and strategic judgment rather than superficial usage metrics to ensure sustainable workforce readiness.
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    55 mins
  • A Conversation about Designing for Hybrid Intelligence

    A Conversation about Designing for Hybrid Intelligence
    Oct 10 2026
    Although organizations heavily invest in artificial intelligence, many fail to see meaningful returns because they mistakenly view technology as a human replacement rather than a complement. This research examine the concept of hybrid intelligence, which unites human and artificial intelligence to achieve outcomes superior to what either could accomplish independently. Successfully implementing this approach requires intentional task architecture, ensuring that humans and machines are assigned roles matching their respective strengths. Furthermore, systems must prioritize interpretability and transparency so that users can develop calibrated trust rather than falling into patterns of automation complacency. Ultimately, capturing the full economic and operational value of artificial intelligence depends on thoughtful workflow design and fostering a continuous co-learning process between people and machines.
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    1 hr and 5 mins
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