Co-Intelligence
An accessible shared vocabulary for working with generative AI right now.
A curated reading list for people building what comes next
Nine books
for what’s next.
A practical, provocative shortlist about artificial intelligence, better organizations, ambitious execution, learning, and the systems that turn ideas into impact.
9books
6 lenses
1 next read
6 lenses
1 next read
Selection brief
Not nine versions of the same idea.
The strongest book clubs create useful disagreement. This list balances AI enthusiasm with AI skepticism, pairs bold strategy with execution discipline, and connects private-sector innovation to public-sector delivery and workforce learning.
- TimelyRelevant to decisions teams are making now.
- DiscussableA clear argument people can challenge.
- ApplicableIdeas that can move from conversation to practice.
Where to start
Three strong ways
into the conversation.
AI Snake Oil
A disciplined test of where AI works, where it fails, and how claims outrun evidence.
Recoding America
A bridge between technology, policy, public service, and implementation.
The full shortlist
Choose your lens.
Select a topic to narrow the list. Tap the bookmark on any title to build a browser-only shortlist.
01
Co-Intelligence
Mollick treats generative AI as a new kind of collaborator and gives readers practical ways to experiment without surrendering judgment. The book’s strength is its balance of immediacy, optimism, and clear operating principles.
Why it fits
It would give the club a common language for discussing how AI changes everyday knowledge work—not someday, but now.
Opening question: Which tasks should we always invite AI into, and which should remain distinctly human?
02
AI Snake Oil
The authors separate advances in generative AI from unreliable predictive systems and inflated marketing claims. Their central demand is simple: evaluate different kinds of AI by evidence, not by the aura surrounding the technology.
Why it fits
This is the shortlist’s essential counterweight—a practical foundation for responsible evaluation, procurement, and innovation claims.
Opening question: What evidence should an organization require before trusting an AI system?
03
The Friction Project
Not all friction is bad. Sutton and Rao show how organizations can remove needless obstacles while adding productive pauses that prevent reckless or low-quality work. The result is a useful lens for redesigning how work actually moves.
Why it fits
Every participant can bring a real process to diagnose, making the discussion immediately concrete and actionable.
Opening question: Where should we make work easier—and where should we deliberately slow it down?
04
How Big Things Get Done
Drawing on a large database of major projects, the authors explain why so many ambitious efforts run late and over budget. They advocate planning slowly, executing quickly, learning from comparable projects, and building through repeatable modules.
Why it fits
It moves innovation beyond ideation into estimation, delivery, risk, and the disciplined design of large initiatives.
Opening question: What would “think slow, act fast” change about one of our current projects?
05
Recoding America
Pahlka examines why sound public policy so often becomes frustrating service delivery. She argues that implementation expertise, empowered teams, user-centered design, and better technology practices must sit closer to policymaking.
Why it fits
For Eccalon, this may offer the richest connection between innovation, government systems, technology, and measurable public impact.
Opening question: What changes when implementation is treated as policy rather than an afterthought?
06
The Geek Way
McAfee looks at companies that organize around rapid learning, evidence, ownership, and productive challenge rather than hierarchy alone. He presents culture as an operating system that can accelerate adaptation.
Why it fits
It creates room to examine whether “innovative culture” is a slogan or a set of observable, designable behaviors.
Opening question: Which norm—science, ownership, speed, or openness—would most improve our work?
07
Pattern Breakers
The authors argue that breakout ventures begin with an inflection and a non-consensus insight—not a slightly improved product. They focus attention on future conditions that make previously impossible ideas possible.
Why it fits
It would sharpen how the club distinguishes genuine transformation from incremental improvement dressed as disruption.
Opening question: What recent inflection has created an opportunity that most organizations still cannot see?
08
Brave New Words
Khan explores how AI tutors and assistants could personalize education, support teachers, and extend high-quality learning. He also addresses the safeguards needed to use these systems responsibly.
Why it fits
It connects AI innovation directly to learning design, workforce development, access, and the changing role of human expertise.
Opening question: If everyone had a capable AI tutor, what should human educators do more of?
09
Abundance
Klein and Thompson ask why institutions that can imagine ambitious futures often struggle to build them. Their answer centers on capacity: rules, incentives, supply constraints, and systems that determine whether good intentions become tangible outcomes.
Why it fits
Its broad thesis invites a systems-level discussion about innovation, institutional capability, and the difference between announcing goals and delivering results.
Opening question: What do we say we want but have designed our systems not to produce?
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Editorial recommendation
Start with
Co-Intelligence.
It is concise, current, accessible across roles, and immediately applicable. Pair it with selected chapters or a follow-up session from AI Snake Oil to keep the conversation grounded and constructively skeptical.
Accessibility5 / 5
Timeliness5 / 5
Discussion value4.5 / 5
Eccalon relevance4.5 / 5