In this HCI Webinar, Dr. Jonathan H. Westover talks with leveraging spiritual and emotional intelligence at work. Yosi Amram Ph.D. is a licensed clinical psychologist, a CEO leadership coach, and a best-selling and award-winning author. Previously the founder and CEO of two companies he led through successful IPOs, Yosi has coached over 100 CEOs—many of whom have built companies with thousands of employees and revenues in the billions. In addition to working with individuals, Yosi works with couples interested in passionate, conscious relationships that serve their psycho-spiritual healing and growth. With engineering degrees from MIT, an MBA from Harvard, and a Ph.D. in Psychology from Sofia University, he is a pioneering researcher in the field of spiritual intelligence whose research has received over 1000 citations. As a C-Suite, Amazon, B&N best-selling author of the Nautilus Book Award Gold Medal-winning Spiritually Intelligent Leadership: How to Inspire by Being Inspired, Yosi is committed to awakening greater spiritual intelligence in himself and the world. Yosi is also the founder of several non-profits, including trueMASCULINITY.org, Engendering-Love.org, and AwakeningSI.org.

This video introduces GDPVol, a groundbreaking benchmark designed to evaluate how effectively artificial intelligence performs complex, real-world professional tasks typically completed by highly skilled knowledge workers. Unlike simple tests or games, GDPVol focuses on sophisticated assignments such as drafting legal memos, creating marketing plans, and producing financial analyses—work that usually takes experts hours to complete. The benchmark reveals that leading AI models can match or surpass human experts in nearly half of these tasks, marking a profound shift from AI as a novelty to a practical tool capable of high-value economic work. This development signals a transformation in how professional work is done, emphasizing augmentation rather than outright replacement of human labor. Highlights 🚀 GDPVol benchmark measures AI’s ability to perform complex professional tasks with human-level quality. ⏱ AI completes tasks in minutes that traditionally take humans several hours, yielding massive efficiency gains. 🤖 Nearly half of tested tasks showed AI output equal or superior to expert human work. ⚠️ AI can produce critical errors, making human oversight and quality control essential. 🎯 Success requires task-specific AI deployment and redesigned human-AI workflows. 🧠 New employee skills in prompt engineering and clear communication with AI are vital. 📈 Organizations must adopt iterative, data-driven AI integration to stay competitive in the evolving workplace. Key Insights 🔍 Benchmarking Real-World AI Performance: GDPVol is a paradigm shift from traditional AI assessments, focusing on real-world, high-stakes professional tasks rather than simplistic benchmarks. This approach provides a realistic gauge of AI’s readiness for workplace integration, highlighting its practical economic impact rather than theoretical capability. ⚡ Exponential Productivity Gains Through Speed: The compression of task completion time from hours to minutes by AI represents not just a marginal improvement but a fundamental transformation in work pace. Even when allowing for human review, the combined human-AI workflow can achieve what once took a full day in a fraction of the time, enabling organizations to scale output and responsiveness dramatically. 🤝 Augmentation Over Replacement: The findings emphasize augmentation—using AI to offload routine, time-consuming work, freeing human experts to focus on high-value activities like strategy, relationship management, and complex judgment. This reframes the AI impact narrative away from job losses toward productivity enhancement and workforce empowerment. 🧩 Task-Level Analysis as the Foundation for AI Strategy: The recommendation to deconstruct work into individual tasks rather than focusing on job titles is a strategic insight. It allows organizations to identify which tasks are ripe for automation and which require human judgment, enabling a targeted and efficient AI adoption roadmap. ❗ Risks of AI Hallucinations and Errors: AI’s pattern-matching strength comes with a lack of true understanding or judgment, leading to unpredictable and sometimes dangerously flawed outputs. This necessitates stringent quality assurance protocols and human-in-the-loop systems, especially for high-risk tasks where errors can have severe consequences. 💡 The Critical Role of Prompt Engineering: The quality of AI output is highly sensitive to the clarity and detail of input instructions. Crafting precise, context-rich prompts emerges as a new, essential skill for employees working alongside AI, marking a shift in workplace competencies toward effective human-AI communication. 🔄 Continuous Adaptation and Governance: Given AI’s rapid capability advancements, organizations must establish governance frameworks for ongoing evaluation of AI models and workflows. Regular testing and iterative process redesign ensure that companies remain at the forefront of AI efficiency gains and risk mitigation. #GDPval #AI #WorkRedesign #HumanAICollaboration #OrganizationalStrategy OUTLINE: 00:00:00 - Understanding GDPval and Its Impact 00:02:13 - AI Performance and Its Tangible Benefits 00:04:18 - Where AI Still Falters 00:06:07 - Practical Steps for AI Integration 00:07:36 - Redesigning Work for the AI Era

Abstract: The recent introduction of GDPval—a benchmark evaluating AI model performance on economically valuable real-world tasks—signals a fundamental shift in how organizations must approach work design, workforce planning, and operational strategy. This research examines the organizational implications of frontier AI models approaching human expert-level performance across 44 knowledge-work occupations spanning nine major economic sectors. Analysis reveals that AI capabilities are advancing linearly, with leading models now matching or exceeding human deliverables in approximately half of evaluated tasks while offering potential time and cost advantages when paired with human oversight. For organizations, these findings suggest an urgent need to move beyond conceptual AI strategies toward systematic work redesign, requiring recalibration of role definitions, capability development frameworks, quality assurance processes, and governance structures. This paper synthesizes evidence from GDPval findings with broader organizational research to provide practitioners with evidence-based approaches for redesigning work in an era where AI can competently perform complex, multi-hour knowledge tasks across professional domains. The analysis demonstrates that competitive advantage will increasingly depend not on whether organizations adopt AI, but on how effectively they reconfigure human-AI collaboration, redistribute cognitive labor, and build adaptive capabilities for continuous work evolution.

In this podcast episode, Dr. Jonathan H. Westover talks with leveraging spiritual and emotional intelligence at work. Yosi Amram Ph.D. is a licensed clinical psychologist, a CEO leadership coach, and a best-selling and award-winning author. Previously the founder and CEO of two companies he led through successful IPOs, Yosi has coached over 100 CEOs—many of whom have built companies with thousands of employees and revenues in the billions. In addition to working with individuals, Yosi works with couples interested in passionate, conscious relationships that serve their psycho-spiritual healing and growth. With engineering degrees from MIT, an MBA from Harvard, and a Ph.D. in Psychology from Sofia University, he is a pioneering researcher in the field of spiritual intelligence whose research has received over 1000 citations. As a C-Suite, Amazon, B&N best-selling author of the Nautilus Book Award Gold Medal-winning Spiritually Intelligent Leadership: How to Inspire by Being Inspired, Yosi is committed to awakening greater spiritual intelligence in himself and the world. Yosi is also the founder of several non-profits, including trueMASCULINITY.org, Engendering-Love.org, and AwakeningSI.org.  Check out all of the podcasts in the HCI Podcast Network (https://www.podbean.com/podcast-network/HCI) !

In this HCI Webinar, Dr. Jonathan H. Westover talks with Jen Recla about leading with curiosity. As a leadership coach, trainer, and outdoor enthusiast, Jen Recla is on a mission to awaken passion, joy, and the spirit of adventure in every professional endeavor. With over 15 years of organizational development experience in leadership, coaching, and mentoring, she specializes in creating growth experiences that inspire and equip individuals and teams to maximize their value and impact. She leverages her top Gallup strengths of woo, maximizer, communication, futuristic, and arranger to make learning fun, bring knowledge to others, and inspire individuals to reach their full potential.

The traditional labor market has long relied on written applications—customized cover letters and tailored proposals—as critical signals to evaluate candidates’ skills, diligence, and suitability for jobs. These signals functioned effectively because they demanded significant effort, time, and cognitive investment, creating a reliable filter that differentiated genuinely motivated and skilled applicants from those who were less serious. However, the rapid rise of generative artificial intelligence (AI), particularly large language models like ChatGPT, has fundamentally disrupted this system. AI can now generate polished, customized applications instantly, drastically reducing the cost and effort required to produce a high-quality written proposal. This technological shift has saturated hiring markets with superficially impressive applications, eroding the reliability of the traditional signal. Highlights 🤖 AI tools like ChatGPT have drastically lowered the effort needed to create polished, customized job applications. 💼 Customized applications once served as reliable signals of candidate quality and commitment but are now devalued. 📉 The hiring advantage of high-ability workers has dropped by 19% post-AI, while low-ability workers’ hiring rates increased by 14%. ⚠️ The erosion of reliable hiring signals leads to inefficient labor allocation and a measurable loss in overall market surplus. 📊 A study on Freelancer.com shows a 64% decline in the premium employers pay for customized applications after AI adoption. 🔍 Employers must shift to performance-based assessments, verified credentials, and milestone-based contracts to effectively evaluate candidates. 🔄 Building trust through repeated engagements and monitoring real-world performance is crucial in the AI era. Key Insights 🤖 AI has democratized the creation of polished applications, transforming costly, reliable signals into cheap talk: Historically, the effort of customizing a job application acted as a costly signal indicating diligence and ability. Generative AI reduces this effort to near zero, enabling even low-skill workers to produce seemingly impressive applications. 📉 High-ability workers are disproportionately harmed by AI-driven signal erosion, while low-ability workers gain an unfair advantage: The study shows a 19% decline in hiring rates for top-tier workers and a 14% increase for the lowest tier after AI adoption. ⚖️ The loss of reliable signals leads to inefficiencies and economic costs in labor markets: When employers cannot accurately identify top talent, they risk making poor hiring decisions, resulting in lower project quality, delays, and increased costs. The Freelancer.com platform experienced a 1% overall loss in market surplus, primarily due to decreased worker surplus among high-ability freelancers. 🔄 Effort and output have become decoupled due to AI, undermining a core principle of evaluating candidates: Traditionally, more time and effort spent on an application correlated with higher quality and customization. AI breaks this correlation, allowing instant generation of bespoke proposals. 🛠️ Performance-based assessments and verifiable credentials are essential new signals in the AI era: To counteract signal degradation, employers must shift from relying on self-reported qualifications to evaluating demonstrated skills through work samples, coding challenges, editing tests, or small paid probationary contracts. 🤝 Long-term relationships and reputation systems serve as robust, AI-resistant signals of quality: Repeated engagement with trusted freelancers builds a track record of performance and reliability that is difficult to counterfeit. 📈 Adaptive hiring practices aligned with measurable outcomes protect employers and incentivize true skill development: Structuring contracts around milestone-based payments tied to deliverables aligns incentives for both parties. It reduces upfront risk and ensures compensation reflects actual performance, discouraging reliance on superficial signals. If this helped you, please like and share. #AI #Hiring #Freelancer #LLM #LaborEconomics #SignalDegradation

Abstract: Large language models have fundamentally altered the economics of written job applications by reducing production costs to near-zero. This article examines the market-level consequences through evidence from Freelancer.com, a major digital labor platform. Analysis reveals how AI-generated applications degraded a critical quality signal that previously enabled efficient worker-employer matching. Pre-LLM, employers valued customized applications equivalent to a $26 bid reduction; this premium fell 64% post-LLM as customization lost predictive power for worker ability. Structural estimates reveal the equilibrium impact: eliminating credible written signals caused high-ability workers (top quintile) to experience 19% lower hiring rates while low-ability workers (bottom quintile) saw 14% higher rates. Total market surplus declined 1% while worker surplus fell 4%, with efficiency losses concentrated among high-ability workers unable to credibly differentiate themselves. These findings illuminate economic risks facing organizations that rely on written applications for screening and suggest strategic responses centered on performance-based evaluation, verifiable credentials, and contract design.

In this podcast episode, Dr. Jonathan H. Westover talks with Jen Recla about leading with curiosity. As a leadership coach, trainer, and outdoor enthusiast, Jen Recla is on a mission to awaken passion, joy, and the spirit of adventure in every professional endeavor. With over 15 years of organizational development experience in leadership, coaching, and mentoring, she specializes in creating growth experiences that inspire and equip individuals and teams to maximize their value and impact. She leverages her top Gallup strengths of woo, maximizer, communication, futuristic, and arranger to make learning fun, bring knowledge to others, and inspire individuals to reach their full potential.  Check out all of the podcasts in the HCI Podcast Network (https://www.podbean.com/podcast-network/HCI) !