There’s a Person
Behind the Work.

Abigail Cember joins Az and César to explore unreliable LLMs, scientific discovery and authentic communication, asking what we should give to machines and what we must keep for ourselves.

Abigail Cember joins Az and César to discuss rebellious LLMs, accelerating science and the human skills at stake when we trust AI, from authentic communication to independent thought.

Az explains how he built Azbro with Contentstack: a conversational digital shadow combining reviewed sources, native AI, editorial control and a clear route back to the human behind it.

Az describes using Claude for a complex website migration while retaining human control of content strategy, business context and architectural decisions informed by years of expertise.

Az reflects on building complete products with AI as a solo builder, explaining how business context and engineering judgment shape delivery speed, quality and the changing role of leaders.

Daniel van der Merwe joins Az and César to explore how AI convenience affects independent thought, family attention and human connection, and why deliberately adding friction can help.

Daniel van der Merwe joins Az and César to explore how AI convenience affects independent thought, family attention and human connection, and why deliberately adding friction can help.

Daniel van der Merwe joins Az and Cés to revisit their questions about AI, work and automation, exploring how to use technology while protecting human connection, purpose and attention.

Az revisits the first four MACHINE ACTOR conversations with César Ramírez, showing how questions about AI became reflections on fear, trust, identity and the human response to change.

This episode guide maps a conversation about AI and the future of work, education and care, asking how expertise changes when machines provide answers and what should remain human.

This episode guide maps a conversation about AI and the future of work, education and care, asking how expertise changes when machines provide answers and what should remain human.

Az and Ces reflect on their fathers’ experiences to explore work, education, automation and empathy, asking what makes people valuable when machines become better at doing the work.

Auto-generated captions continue the discussion of family expectations, human value, autonomous services and time beyond work. Caption accuracy and speaker identities need review.

Auto-generated captions cover AI, expertise, education, work and care, asking which responsibilities should remain human as automation grows. Words and speaker identities need review.

This guide follows Az and Cesar through the human barriers to AI adoption, from shared language and organizational culture to empathy, leadership and the experience of joining a team.

This guide follows Az and Cesar through the human barriers to AI adoption, from shared language and organizational culture to empathy, leadership and the experience of joining a team.

Az reflects on the time and opportunities his parents gave him, asking how people should use the time AI saves and what choices will give the next generation a better future.

Az and Ces explore how people, culture and systems shape resistance to AI, drawing on product design, regulation and IKEA’s transformation to examine new ways of thinking and working.

Auto-generated captions explore resistance to AI through culture, leadership and empathy, including how people adapt to changing work. Caption accuracy and speaker identities need review.

Az uses IKEA’s customer service transformation to argue that automation should create new opportunities for people, preserving the judgment, empathy and customer knowledge they bring.

This guide explores how people build trust in AI through memory, companionship and everyday use, while examining privacy, emotional attachment and the boundary between understanding and simulation.

This guide explores how people build trust in AI through memory, companionship and everyday use, while examining privacy, emotional attachment and the boundary between understanding and simulation.

Az argues for shared platforms and governance in life sciences, replacing repeated infrastructure work with reusable foundations so teams can focus on useful, compliant customer experiences.

Az and Cés explore why people begin to trust machines, from thanking ChatGPT to AI companions, robotics and persistent memory, asking how technology is reshaping human connection.

Auto-generated captions explore trust in AI, companions, privacy and persistent memory, asking how machines may become part of personal life. Caption accuracy and speaker identities need review.

This guide revisits questions about AI uncertainty, bias and dependence, including why machines can feel trustworthy and how children may grow up relating to technology as a companion.

This guide revisits questions about AI uncertainty, bias and dependence, including why machines can feel trustworthy and how children may grow up relating to technology as a companion.

Az Zaver and César Ramírez introduce a record of living through AI’s arrival, tracing fear, trust, human resistance and the choices that shape how people and machines build a future together.

After recording MACHINE ACTOR, Az asks how human instincts for trust respond to AI that behaves like a person, and what this could mean for children growing up in conversation with machines.

Az Zaver and César Ramírez introduce a record of living through AI’s arrival, tracing fear, trust, human resistance and the choices that shape how people and machines build a future together.

Auto-generated captions record a conversation about AI, fear, identity and trust, including dependence on instant answers and the changing cost of tools. Words and speaker identities need review.

Az and Ces explore the fears beneath conversations about AI, from job security and identity to trust and the future of work, asking what those emotions reveal about people themselves.

Az proposes an ephemeral experience platform in which AI generates content and interfaces for a specific moment, using durable, governed knowledge to respond to individual context and intent.

Az compares the instant gratification of AI tools to an unlimited snack cupboard, questioning whether constant building creates value and arguing for more mindful, efficient use of tokens.

Az reflects on rebuilding Takeda.com and leading a global digital transformation, explaining how influence, shared purpose and collaboration helped align people around better outcomes.

Az questions how the selective written record used to train AI shapes bias and agreeable answers, arguing that quality depends on human questions and judgment rather than content volume.

Az explores how AI adoption affects confidence, pressure and professional identity, arguing that leaders should build psychological safety and literacy before measuring employee usage.

Az compares building software with AI to riding a bike, arguing that speed and creativity still depend on engineering standards, teamwork, judgment and ownership of what developers release.

Az argues that intelligent systems need human context as well as technical capability, asking how architecture can help people feel understood, empowered and successful when working with AI.

Az reflects on twenty years since his first job, emphasizing kindness, realistic expectations, continuous learning and a shared mission as foundations for good colleagues and meaningful work.

Explore Az’s work across digital marketing, software engineering, enterprise architecture and platform leadership, including delivery for Takeda, Novartis and other global organisations.

Explore the ideas behind Az’s work, his approach to connecting human needs with technology, and the technical skills and educational background documented in his professional profile.

Follow Az’s career from digital account work and product management to technical leadership, with experience spanning enterprise platforms, Workforce AI and Agentic MarTech at Takeda.