Stephen Brozovich is an Executive in Residence—People and Culture—at Amazon Web Services. He joined Amazon in 1999, long before it became the organization we know today, and has spent more than two decades working across technology, product development, talent management, leadership development, and organizational culture. Today, as part of AWS’s Executives in Residence team, Stephen helps senior leaders navigate large-scale transformation—work that, as he points out, often has less to do with technology than with human and organizational patterns.In this episode, Stephen and Subbu explore how Amazon translates leadership principles into everyday decisions—and what organizations must rethink as agentic AI transforms talent, team structures, governance, and the very nature of work.We explore:Stephen’s unconventional journey from studying music to web development, technology leadership, talent management, and organizational cultureWhy learning and pattern recognition have been the connecting threads across his careerHow mental models, behaviours, and artifacts come together to shape organizational cultureWhy what leaders do matters far more than the values written on a pageHow Amazon’s leadership principles were developed—and how they enable decisions to be made across a rapidly growing organizationHow principles such as “Dive Deep” and “Bias for Action” create productive tension and a common language for constructive debateHow customer obsession and Amazon’s Working Backwards process turn customer needs into new products and innovationsWhy Amazon reads written narratives during meetings rather than sending them as pre-reads—and how this creates shared context and equalizes voices in the roomHow ownership is reinforced through decision-making authority, compensation, and equityWhat “Strive to be Earth’s Best Employer” means—and why the best leaders are also teachersWhy replacing junior employees with AI may create a serious judgment and leadership gap six to ten years from nowHow organizations can experiment with new workforce shapes while continuing to build their next generation of senior talentWhy traditional, siloed operating models are giving way to multidisciplinary, outcome-based teams supported by specialized AI agentsWhat Singapore’s AI governance framework teaches us about access, human accountability, technical controls, and transparencyWhy greater autonomy for AI agents requires clearer guardrails—and why “the AI made the decision” can never be an acceptable answerWhy Stephen remains optimistic that AI will expand what domain experts can build while making distinctly human capabilities even more important

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