Coming soon · Book Profile
Co-Intelligence: Living and Working with AI
A Wharton professor guides readers through the alien nature of Large Language Models, offering four principles for working alongside AI as a genuine co-intelligence rather than fearing or blindly trusting it.
A full profile of this book is on the way.
What it’s about
Co-Intelligence by Ethan Mollick is an essential, accessible guide for anyone trying to understand what generative AI actually is, what it can and cannot do, and how to live and work alongside it. Drawing on cutting-edge research, his own experiments, and real-world studies from consulting firms, law schools, and classrooms, Mollick explains how Large Language Models work, why they hallucinate, how they can be steered, and why their capabilities form a 'Jagged Frontier' that defies intuition. He lays out four practical principles for working with AI, then applies them across domains: AI as creative partner, coworker, tutor, and coach. Rather than offering apocalyptic warnings or naive techno-optimism, Mollick presents a grounded, research-backed framework for making meaningful choices about AI before those choices are made for us—arguing that the moment to engage seriously with this General Purpose Technology is right now.
The through-line
- Who it’s for
- A professional, educator, manager, student, or curious person who senses that AI is fundamentally important but feels overwhelmed, uncertain, and unsure how to engage with it productively or responsibly.
- The problem
- They lack a clear, practical framework for understanding what AI can and cannot do, how to integrate it into their work, and what choices they should be making right now. They feel anxious, left behind, or paralyzed—worried their job is threatened, their students are cheating, or that the technology is simply too alien and fast-moving to grasp.
- The plan
- Understand what LLMs actually are—token-prediction engines trained on human text—so you stop expecting them to behave like software and start working with their actual nature.
- Adopt the four principles: always invite AI in, be the human in the loop, treat AI like a person with a defined persona, and assume current AI is the worst you will ever use.
- Map the Jagged Frontier in your own domain by experimenting relentlessly to discover where AI excels and where it fails for your specific tasks.
- Classify your tasks into Just Me, Delegated, and Automated categories, and begin working as a Centaur or Cyborg to integrate AI into your workflow.
- Apply these principles across your key roles—as a creative, coworker, learner, and coach—using the research-backed examples the book provides.
- The payoff
- You become a skilled Cyborg or Centaur who uses AI to eliminate tedious work, boost creative output, and perform at a level previously beyond your reach. · You are able to identify the Jagged Frontier in your domain, catch AI hallucinations, and remain the indispensable human in the loop. · Your organization moves from fearful, shadow AI use to open, incentivized, democratized AI adoption that captures real productivity gains.
See our guide
Related profiles we’ve built
- The AI Marketing Canvas: A Five-Step AI Plan for Marketers →
- Artificial Intelligence →
- Artificial Intelligence - A Very Short Introduction →
- Effective Data Science Infrastructure →
- Human + Machine →
- Time Series Forecasting Using Foundation Models →
- Understanding Deep Learning →
- Big Data_ A Very Short Introduction (Very Short Introductions) →
Additional reading
- Designing Machine Learning Systems (DMLS) · Chip Huyen
The author explicitly positions 'AI Engineering' as a companion book to DMLS. DMLS covers the principles of traditional ML engineering (feature engineering, model training, tabular data), which this book builds upon by focusing on the new paradigm of foundation models.
- Artificial Intelligence: A Modern Approach · Stuart Russell and Peter Norvig
The book references this classic AI textbook for its definition of an 'agent' as a perceiving and acting entity, providing the foundational academic context for the 'Agents' section in Chapter 6.
- OpenAI Prompt Engineering Guide · OpenAI
Official documentation from the model provider on how to craft effective prompts, recommended by the author.
- Anthropic's Prompt Engineering Guide · Anthropic
Official guide from another major LLM provider, offering best practices for prompt design.
- dair-ai/Prompt-Engineering-Guide · DAIR.AI Community
A comprehensive, community-maintained GitHub repository covering a wide range of prompt engineering techniques.
- GPT Researcher · assafelovic
An open-source project that inspired the book's research summarization engine, recommended for further exploration of building production-grade LLM applications.
- Building Effective AI Agents · Anthropic
An article recommended by the author for a deeper understanding of the distinction between agentic workflows and autonomous agents.
- Model Context Protocol (MCP) Official Website · Anthropic and Community
The primary source for the MCP specification, tutorials, and ecosystem resources, essential for building or consuming MCP servers.
- FastMCP 2 Documentation · jlowin and community
The official documentation for the recommended Python SDK for building MCP servers, used in the book's example.
- Knowledge Graph Enhanced RAG · Tomaž Bratanič and Oskar Hane
Recommended by the author for readers interested in diving deeper into combining knowledge graphs with RAG.