The Product Manager's Essential Reading list for 2026
Thinking beyond features: strategy, systems, and leadership for product leaders.
Previous reading lists: 2024, 2023, 2018, 2016 pt1, 2016 pt2
I’ve written some version of this post on and off since 2016. It’s become a useful forcing function for me: to step back, reflect on how the role of product changing, and recalibrate what’s worth paying attention to next.
As product managers become more senior, the work shifts in nature. Less feature definition. More decision-making under uncertainty. More power, more trade-offs, more organisational complexity — and, increasingly, more AI.
This year’s list tries to avoid classic “how to do product management” books. Instead, it focuses on systems, strategy, incentives, leadership, AI, and judgment.
Each of these books has been recommended to me by a friend: be that a human one or an AI one ;-)
Here’s my reading list for 2026.
1. Supremacy — Parmy Olson
This book tells the story of the modern AI race — OpenAI, DeepMind, and Big Tech — but it’s not a technical book. It’s about ambition, incentives, speed, ego, and organisational structure.
What I found most compelling is how often the outcomes hinge on leadership decisions rather than breakthroughs. Governance choices, funding models, narrative control, and who gets to ship first all matter enormously.
If you’re a product leader thinking “AI strategy” is mostly about models or features, this book is a sharp corrective.
2. The Unaccountability Machine — Dan Davies
This book gives language to things I’ve seen this first hand in some form at every company, and in every org I’ve ever worked in - but struggled to articulate cleanly.
Davies explains why complex systems can make consistently bad decisions even when they’re staffed by smart, well-meaning people.
The problem isn’t incompetence — it’s diffusion of responsibility, feedback loops, incentives, and procedural fog. Decisions get made, but no one quite owns them.
For senior PMs, this is an incredibly useful framing. Many product failures are not execution problems or talent problems — they’re systemic. This book helps you spot when the system itself is working against good outcomes.
As they say, step 1 in solving a problem is to understand that you have one.
3. The Thinking Machine — Stephen Witt
A fascinating account of NVIDIA’s decades-long transformation into one of the most strategically important companies in tech. What makes this book stand out is its long time horizon — this isn’t a story about reacting quickly, but about patiently compounding the right bets.
As a product leader, it’s a powerful reminder that platform strategy, ecosystem thinking, and long-term conviction often matter more than short-term optimisation. NVIDIA’s success didn’t come from chasing trends — it came from quietly positioning itself underneath them.
If you’re responsible for big product bets with uncertain payoffs, this book is deeply reassuring.
4. Impact-First Product Teams — Matt LeMay
This is one of the most useful “anti-product-process” books I’ve read. LeMay takes aim at the way product management has become overloaded with rituals, artefacts, and ceremony — often at the expense of real impact.
What I appreciated most is that this isn’t anti-discipline or anti-rigour. It’s anti-theatre. It challenges leaders to ask whether their roadmaps, OKRs, and frameworks are genuinely helping teams make better decisions — or simply creating the appearance of control.
If your organisation feels busy but not effective, this book will land hard.
5. The AI Product Playbook — Marily Nika & Diego Granados
One of the first genuinely practical books I’ve seen on building AI-powered products without falling into hype or hand-waving. This isn’t about prompts or demos — it’s about discovery, delivery, risk, and iteration when the underlying system is probabilistic and evolving.
The authors are refreshingly honest about where classic PM tools break down in AI contexts, and how to adapt rather than blindly reuse them.
If you’re leading teams building AI features (or entire AI products), this book helps ground strategy in reality rather than excitement.
6. Reimagined — Shyvee Shi, Caitlin Cai & Yiwen Rong
This book focuses on something many AI discussions miss: experience design. Rather than obsessing over models, it explores how generative AI changes workflows, interfaces, and user expectations.
I found it particularly useful as a way to reframe product thinking away from “features powered by AI” towards entirely new interaction models. The examples are concrete, practical, and grounded in real product work.
For product leaders, the key insight is clear: generative AI’s biggest impact is experiential. Treating it as just another backend capability is a mistake.
7. The Seven Rules of Trust — Jimmy Wales
Jimmy Wales is the founded of Wikipedia so he knows a thing or two about this subject.
Trust is one of those concepts everyone agrees is important, yet few organisations treat systematically. This book stands out by being evidence-based and pragmatic rather than fuzzy or moralistic.
The book shows how trust is built (and destroyed) through structures, incentives, and repeated interactions — not slogans or values posters. That makes it particularly relevant for product leaders designing platforms, ecosystems, or cross-functional organisations.
I came away more aware of how easy it is for product decisions — even sensible ones — to quietly erode trust over time if you’re not paying attention.
8. Power and Prediction — Ajay Agrawal, Joshua Gans & Avi Goldfarb
This is a short, sharp book that looks at AI through an economic lens rather than a technical one. Its central argument is that as prediction becomes cheap, the real value shifts to judgment, data, and organisational power.
For product leaders, this is incredibly useful framing. It helps explain why AI adoption often changes who gets to decide, not just how fast things happen.
If you’re thinking about AI strategy at a portfolio or company level, this book provides a clean mental model for where value (and disruption) actually emerges.
9. Working Backwards — Colin Bryar & Bill Carr
An inside look at how Amazon actually operates, written by long-time insiders. This isn’t a manifesto — it’s a description of mechanisms, habits, and trade-offs.
I don’t think most companies should try to copy Amazon wholesale. But as a case study in embedding strategy into process — especially through writing, decision discipline, and customer focus — it’s extremely instructive.
For senior PMs, the value here is in understanding how operating models shape product behaviour at scale, for better and worse.
10. High Output Management — Andy Grove
I can’t believe I’ve not included this before.
Despite being decades old, this remains one of the clearest explanations of what management actually is. Grove’s focus on leverage, outputs, and systems feels uncannily modern.
For product leaders, this book is a reminder that your job is not to do more work — it’s to create conditions where others can be more effective. That applies just as much to product organisations as it does to factories.
I re-read this every few years and find something new each time, especially as scope and responsibility increase.
11. The Power Broker — Robert A. Caro
This is a long, demanding book — but also one of the most important I’ve ever read. It’s a biography of Robert Moses, but really it’s a study of power, systems, and unintended consequences.
What makes it relevant to product leaders is the reminder that infrastructure decisions — often made far from users — can shape lives for generations. Product work is rarely neutral.
This book permanently changed how I think about scale, responsibility, and the long tail of design decisions.
12. Seeing Like a State — James C. Scott
A classic in systems thinking, this book explores why top-down simplification so often fails when applied to complex human systems.
For product leaders, the parallels are obvious: over-reliance on metrics, abstraction, and centralised control can destroy the very value you’re trying to create.
This book sharpened my scepticism of one-size-fits-all solutions and reinforced the importance of local context, feedback, and humility in large-scale product design.
13. Thinking in Bets — Annie Duke
I’ve been using the term “bet” to describe the initiatives within my org for some time now. While some things are more risky than others, every investment you make as a product leader or even as an IC PM at the feature level, is a “bet” - it’s a stake of your capacity on something that may or may not pay off.
A bet!
This book is about improving decision quality rather than chasing good outcomes. Duke borrows concepts from poker — probability, uncertainty, and expected value — and applies them to real-world decisions.
For PMs and product leaders, this framing is invaluable. So many product decisions are made under uncertainty, yet we judge them purely by outcomes after the fact.
This book helps separate luck from skill, and encourages healthier post-mortems and decision reviews.
14. Range — David Epstein
A compelling argument for breadth over early specialisation. Epstein shows how many high performers succeed not despite being generalists, but because of it.
This resonates strongly with senior product leadership, where success increasingly depends on synthesis: connecting technology, design, business, and human behaviour.
If you’ve ever worried that you’re “not technical enough” or “not specialised enough” for senior roles, this book is both reassuring and motivating.
15. The Goal — Eliyahu M. Goldratt
Written as a novel, this is an accessible introduction to systems thinking, constraints, and flow. Despite its age, it remains startlingly relevant.
For product organisations, the lessons about bottlenecks, local optimisation, and throughput map cleanly onto modern delivery systems.
If you’ve ever seen teams optimise themselves into worse outcomes, this book gives you a simple, memorable way to explain why — and how to fix it.
So there you have it. 15 more awesome books to add to your reading list for 2026.
If you have other recommendations - please let me know in the comments below.