See Also - Related Audio Books and Courses

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For  me, it’s because in a busy day, it’s often hard to find time to  read.  However, for those of us that commute or travel or workout or do  chores  or yard-work where our minds can absorb audio, there is a unique   opportunity to expand our horizons. With time, it’s also possible to   consume them at higher speeds now that they’ve gotten better at   compressing audio. Reading at 3x to 4x speeds on an hour’s commute each   way provides for the equivalent of 6-8 hours of learning each workday.

• Artificial Intelligence: A Guide for Thinking Humans — Melanie Mitchell
  A clear-eyed tour of how modern AI actually works (and where it fails),  helping leaders set realistic expectations before committing budget or  headcount. Great for aligning your team on limits, risks, and true  capability.
• Prediction Machines — Ajay Agrawal, Joshua Gans, Avi Goldfarb
  Reframes AI as cheap prediction, giving you a simple lens to spot  high-ROI use cases and redesign workflows. Ideal for sizing  opportunities and deciding build vs. buy.
• AI Superpowers — Kai-Fu Lee
  Puts AI progress in strategic and geopolitical context so engineering  plans align with market reality and competitive pressure. Useful  backdrop for multi-year roadmaps.
• Harvard Business School – AI for Leaders (HBS Online)
  Four-module, ~20-hour certificate that frames what AI can/can’t do, scaling responsibly, and how leaders drive adoption.
• MIT Sloan/CSAIL – Artificial Intelligence: Implications for Business Strategy
  A management-first view of AI’s capabilities, limits, and org implications; long-running gold standard for leaders.
• Coursera (DeepLearning.AI) – AI For Everyone (Andrew Ng)
  Non-technical fundamentals; shared language for execs, PMs, and engineers on what AI is and where it fits.

• AI Engineering: Building Applications with Foundation Models — Chip Huyen
  A practitioner’s guide to scoping, evaluating, deploying, and operating  foundation-model apps—turns “we should use LLMs” into production  architecture.
• Artificial Intelligence Bible (3-in-1): AI Agents, Prompt Engineering & Generative AI — AI Labs Institute
  Beginner-friendly coverage of agents, prompt patterns, and application ideas; good for quick pilots and internal demos.
• AI Engineering Bible — Thomas R. Caldwell
  A comprehensive overview of production-grade AI architectures, MLOps,  and lifecycle management. Ideal for engineers building or maintaining  large-scale intelligent systems.
• Microsoft Learn – Explore the business value of generative AI (Learning Path)
  For leaders who need quick, practical adoption frames tied to Copilot/Azure OpenAI.
• Microsoft Learn – Foundations of Generative AI for Business Leaders (Module)
  Orients non-technical leaders to GenAI concepts and opportunity framing.

• Intelligent Automation — Pascal Bornet, Ian Barkin, Jochen Wirtz
  Connects AI, RPA, and process orchestration into repeatable operating  models—great for moving from isolated wins to enterprise scale.
• The AI Engineering Bible — Thomas R. Caldwell
  End-to-end playbook for production-ready AI: architecture, governance,  MLOps, SLOs—useful when you’re graduating from pilots to platform.
• Kellogg Exec Ed – AI Strategies & Applications for Leaders
  How to leverage GenAI for CX, productivity, and new products; strong on scaling patterns and value creation.

• Generative AI for Leaders — Amir Husain
  Strategy-first guidance: where to place bets, how to staff, and how to train the org—concise and executive-friendly.
• An Elegant Puzzle: Systems of Engineering Management — Will Larson
  Not AI-specific, but essential scaffolding for org design, staff levels,  and technical strategy—the foundation you’ll overlay with AI  initiatives.
• Stanford GSB – Harnessing AI for Breakthrough Innovation & Strategic Impact
  Executive program on where AI creates strategic advantage and how leaders organize for it.
• Stanford HAI – Generative AI: Technology, Business & Society (Professional Education)
  People-first orientation across tech, business, and societal implications; good for leadership teams.

• Generative AI for Leaders — Amir Husain
  Concrete adoption patterns and training approaches for non-specialist  stakeholders; helpful for building broad alignment and traction.
• An Elegant Puzzle — Will Larson
  Offers practical mechanisms—team sizing, ownership, tech debt—that directly impact how smoothly AI changes take root.
• HBS Online – AI for Leaders
  Includes modules on scaling AI responsibly and organization-wide adoption—useful playbooks for change leads.
• Kellogg Exec Ed – Portfolio of AI Programs (incl. senior mgmt track)
  Multi-month options aimed at leading digital & AI transformation across the enterprise.

• Leading Effective Engineering Teams — Addy Osmani
  Lessons from a decade at Google on trust, decision velocity, and systems  thinking—useful as AI shifts definitions of “done” and review cycles.
• An Elegant Puzzle — Will Larson
  Systems-thinking for roles, ownership, and interfaces; helps teams adapt  their collaboration grammar in an AI-augmented environment.
• Wharton – Artificial Intelligence for Business (Online)
  Certificate oriented to cross-functional professionals; helps teams speak the same language and frame use cases.
• Wharton AI at Work (courses for professionals hub)
  Central page for professional offerings; useful when rolling training across multiple roles.

• Prediction Machines — Ajay Agrawal, Joshua Gans, Avi Goldfarb
  A crisp economic lens for prioritizing AI investments and redesigning processes around prediction.
• AI Superpowers — Kai-Fu Lee
  Global market insight that sharpens timing, partnerships, and competitive positioning for AI programs.
• MIT Sloan/CSAIL – Artificial Intelligence: Implications for Business Strategy
  Explicitly about strategy, economics, and operating model shifts (great pairing with Prediction Machines).
• Wharton – AI for Business (Specialization on Coursera)
  Strategy-first curriculum across use cases, data, and deployment.

• Artificial Intelligence: A Guide for Thinking Humans — Melanie Mitchell
  Sharpens literacy on failure modes, bias, and evaluation—great source material for internal guardrails and review boards.
• AI Superpowers — Kai-Fu Lee
  Adds societal and workforce implications to your governance lens—useful context for “responsible use” policies.
• Google Cloud – Generative AI Leader (path includes Responsible AI content)
  Builds shared literacy around responsible AI principles as part of business-level certification.
• MIT Sloan – Artificial Intelligence: Implications for Business Strategy
  Treats governance and organizational risk as first-class strategy issues.

• AI Engineering — Chip Huyen
  Concrete practices for data, evaluation, deployment, and monitoring—plug directly into SDLC checklists and runbooks.
• The AI Engineering Bible — Thomas R. Caldwell
  Practical guidance for scaling, reliability, and governance of AI systems as they move into production.
• Accelerate — Nicole Forsgren, Jez Humble, Gene Kim
  Research-backed DevOps metrics and practices to keep AI delivery fast, safe, and learn-oriented—DORA meets LLMs.
• Microsoft Learn – Business Value & Foundations Paths (pair)
  Use these to seed rituals around copilots, prompt patterns, and measurement within engineering/IT.
• Stanford GSB – Harnessing AI for Breakthrough Innovation & Strategic Impact
  Executive-level grounding for steering roadmaps and metrics as teams integrate AI into the product/SDLC.

• Futureproof: 9 Rules for Humans in the Age of Automation — Kevin Roose
  Practical guidance on cultivating distinctly human  advantages—creativity, empathy, and judgment—so your career grows as  automation expands.
• Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will — Geoff Colvin
  Makes the case that relationship skills and collaborative problem-solving become more valuable in an AI era—and shows how to develop them.
• Human Compatible: Artificial Intelligence and the Problem of Control — Stuart Russell
  A leading AI researcher on aligning advanced systems with human  goals—essential context for leaders shaping responsible careers and  organizations.
• Life 3.0: Being Human in the Age of Artificial Intelligence — Max Tegmark
  Big-picture scenarios for how AI may transform work, meaning, and society—useful for stress-testing personal and org futures.
• An Elegant Puzzle — Will Larson
  Guides leaders and senior ICs on evolving scope and judgment as automation grows—useful for ladders and upskilling plans.
• Coursera/DeepLearning.AI – AI For Everyone
  Helps non-technical leaders and technical ICs align on roles, responsibilities, and human strengths in an AI era.
• Stanford HAI – Generative AI: Technology, Business & Society
  Good venue for reflective discussion about human work and societal implications, not just tooling.List

• Learned Resilience: Cultivating Strength Through Struggle.
  Explores  a systematic loop for metabolizing the adversity and  challenges that  come with adapting to new paradigms like AI.  This  framework provides  the “how” for navigating the constant disequilibrium  of the digital age.
• Atomic Rituals: The Pathway to Transformation.
  Adopting  AI effectively requires changing daily habits.  This explores  how  structured, intentional practices and small, repeatable actions are  the  pathway to embedding transformation into an engineering culture.
• Edge of Chaos: Where Transformation Thrives A look at the dynamic threshold between stability and disorder where   innovation and transformation most readily occur.  Human Transformation   thrives at this edge, where the co-evolution of human creativity and  AI  capability is heightened.
• The Power of Believing You Can Improve by Carol Dweck.
  The  foundational TED Talk by Carol Dweck explaining the core concepts  of  the Growth Mindset.  She illustrates how our beliefs about  intelligence  and ability can dramatically impact our success in the  face of  challenges.
• Software 2.0 by Andrej Karpathy.
  A  seminal essay on the paradigm shift from traditional, human-written   code (“Software 1.0”) to code written by optimizing neural networks   based on data (“Software 2.0”). This provides essential context for the   fundamental changes AI brings to software development.
• How Generative AI is Changing How Developers Work –Harvard Business Review
  An  analysis of the practical impacts of generative AI on engineering   teams, focusing on productivity, skill shifts, and the evolving role of   senior engineers. This resource offers a valuable business and   leadership perspective on the transformation.List