The rapid rise of compound AI systems (a.k.a., AI agents) is reshaping the labor market, raising concerns about job displacement, diminished human agency, and overreliance on automation. Yet, we lack a systematic understanding of the evolving landscape. In this paper, we address this gap by introducing a novel auditing framework to assess which occupational tasks workers want AI agents to automate or augment, and how those desires align with the current technological capabilities. Our framework features an audio-enhanced mini-interview to capture nuanced worker desires and introduces the Human Agency Scale (HAS) as a shared language to quantify the preferred level of human involvement. Using this framework, we construct the WORKBank database, building on the U.S. Department of Labor's O*NET database, to capture preferences from 1,500 domain workers and capability assessments from AI experts across over 844 tasks spanning 104 occupations. Jointly considering the desire and technological capability divides tasks in WORKBank into four zones: Automation "Green Light" Zone, Automation "Red Light" Zone, R&D Opportunity Zone, Low Priority Zone. This highlights critical mismatches and opportunities for AI agent development. Moving beyond a simple automate-or-not dichotomy, our results reveal diverse HAS profiles across occupations, reflecting heterogeneous expectations for human involvement. Moreover, our study offers early signals of how AI agent integration may reshape the core human competencies, shifting from information-focused skills to interpersonal ones. These findings underscore the importance of aligning AI agent development with human desires and preparing workers for evolving workplace dynamics.

Throughout the last few weeks, I have been trying to answer one burning question. Some of the answers that I have discovered were as a way to train artificial intelligence models for real-world use. Which is an interesting use case…but boring. At this moment, there is no clear winner of the Metaverse. What does that even mean?

As the startup market has matured over the years, so too have the models for company acceleration and growth. New industries have unfolded to nurture seedling companies and develop them from conception through to launch and eventual exit. These models include accelerators, incubators, VCs, angels, and now the startup studio model is unfolding as a new approach to accelerating innovation. Accelerators for startups come in all flavors from broad-stroke cohorts such as Techstars or 500 Startups to more niche accelerators focusing on specific verticals such as IndieBio. After a competitive application process, accelerators will select a cohort of startup teams (ranging from 10-20).