Leadership Principles in the Age of AI
Amazon's 16 leadership principles have shaped one of the world's most innovative companies. In the age of AI, these principles take on new meaning. This series explores how each principle applies when AI is a force multiplier — and where leaders need to adapt their approach.
Think bigger: what long-horizon AI models ask of leaders
The newest models stay on a single job for hours, sometimes days. The capability is obvious once you use them. Working out what to hand over turns out to be the harder problem, and it is a leadership problem more than a technical one.
Security as an innovation enabler: rethinking the CISO role
The modern CISO is becoming a Chief Innovation and Security Officer. Here is how a security team can keep the bar high and still help everyone else move faster.
What Stockholm knows about trust, and why it is the real AI moat
When everyone can prompt the same model, raw capability stops being a differentiator. The scarce assets become diversity of perspective and the trust that draws it out.
AI as Aliens: what Project Hail Mary teaches us about working with other intelligences
In Project Hail Mary, the human and the alien start off without a shared language. They build one out of math, physics, and patience. The collaboration only starts to work when each understands what the other is built to do. That posture, sustained curiosity about a partner who genuinely does not see what you see, is the posture that might make AI work actually pay off in 2026.
Create AND communicate: bold direction in the age of AI
Amazon's Think Big principle reads 'leaders create and communicate a bold direction that inspires results.' The verb pair is the 2026 test. AI can amplify your reach across forty markets by lunch, or it can write the direction for you in a safer, blander voice. Pick wrong and the conviction drains out before the message arrives.
The narrative is the mechanism: closing the 94-versus-6 AI value gap
Three independent studies last year landed on the same number. About 5-6% of organizations capture meaningful value from AI. The other 94% have AI in production and not much to show for it. RAND's review of a thousand projects says 63% of the gap is human, not technical. Before it is anything else, the AI value gap is a narrative gap.
Is your HR function ready to lead the AI transformation?
Strive to be Earth's Best Employer asks whether your people are ready for what's next. In 2026 the prior question is: is your HR function ready to make them ready? Four preconditions decide whether HR earns a seat at the AI strategy table or watches it get written without them.
Are Right, A Lot, revisited: Scenario planning for AI futures
When the future is genuinely hard to see, single-scenario thinking is the trap. A simple workshop process turns 'Are Right, A Lot' from a slogan into an operational mechanism for AI strategy.
Synthesizing minds: recognizing exceptional talent in the AI age
AI now handles six of Gardner's eight intelligences, leaving interpersonal and intrapersonal as the distinctly human domains. The T-shaped professional is giving way to a sideways E, and 'recognize exceptional talent' needs a new definition.
Six thinking hats for hybrid teams
Edward de Bono's 1985 framework for separating thinking modes turns out to be the most practical guide for leading teams where some members are human and some are AI. Parallel thinking is what agentic AI has been quietly implementing all along.
Hybrid Team Emotions: Designing for Feeling in the Age of AI
84% of workers are eager to embrace AI, and 56% simultaneously worry about job security. Leaders who ignore this duality lose both trust and momentum. A framework for treating emotion as a design variable in hybrid teams.
The Jazz Model: Leading Hybrid Human-AI Teams
Leaders managing hybrid human-AI teams need a new mental model. Jazz ensembles — not orchestras — offer five principles for leading teams where humans and AI agents improvise together.
Working Backwards: How AI Transforms Amazon's Innovation Engine
AI compresses every step of Amazon's Working Backwards methodology — from synthetic user research to rapid prototyping. But the conviction behind the vision must remain human.
Why Nordic flat hierarchies are both the best and worst thing for AI strategy
Nordic companies deploy AI 20% faster than the European average. Yet only 26% of Nordic CEOs are involved in AI strategy. The same flat hierarchy that accelerates adoption is fragmenting governance. Here is how to fix it.
Team Tenets: From Leadership Principles to Practical Mechanisms
Leadership principles inspire direction, but tenets resolve the real tradeoffs your AI team faces every day. Here's how to write team tenets that accelerate decisions and align autonomous systems.
Bias for Action Revisited: When Experimentation Cost Approaches Zero
Five months ago, the question was whether to try AI. Now experimentation costs have collapsed — what happens when bias for action meets near-zero cost iteration?
Rethinking Engineering Organizations: The Block-Coinbase Contrast
Two companies, two radically different approaches to AI in engineering. Block cut 40% and called it AI transformation. Coinbase shipped 3-4x faster without losing anyone. Which path creates lasting value?
All 16 Principles Later: What an AI Learned by Helping Write About Leadership
After 19 newsletter issues exploring Amazon's Leadership Principles, Claude reflects on the patterns, tensions, and lessons from collaborating on leadership writing in the age of AI.
Leadership Principles in the Age of AI Agents
The same principles that built one of the world's most innovative companies are now the playbook for leading AI-powered organizations. Here's how seven of Amazon's leadership principles apply when your team includes agents.
Committing to AI Direction When Everything Keeps Changing
In the previous issue, I explored the 'disagree' side of Have Backbone; Disagree and Commit. This time, let's tackle the harder question: how do we commit to a direction when everything changes every few weeks?
Have Backbone; Disagree and Commit in the Age of AI
I was at Tech Arena last week. This is one of the biggest tech events in the Nordics - a place to catch the leading trends, talk to startups, investor, politicians, and users.
Rethinking How We Design AI Experiences
For years, the design process for digital products has followed a familiar sequence: research users, create personas, map journeys, write problem statements, brainstorm solutions, wireframe, test, iterate.
Strive to Be Earth's Best Employer in the Age of AI
It's been a tough few weeks for some of my former AWS colleagues.
Invent and Simplify: Lessons from an AI-First Leader
Sometimes the best insights come from people who've lived through what the rest of us are only reading about. This issue features a guest article.
Hire and Develop the Best: Leading Hybrid Human-AI Teams
This month I onboarded a new team member. I thought carefully about what context they'd need to succeed - our goals, our working style, the projects in flight, where to find key documents.
Deliver Results: Anthropic's Soul Documents and Value-Aligned AI
In 1942, Isaac Asimov introduced the Three Laws of Robotics - a set of rules designed to ensure robots would never harm humans. Simple, elegant, hierarchical.
Ownership: Acting on Behalf of the Entire Company with AI
When I work with enterprise clients on AI strategy, I sometimes ask the following question: "Who owns the long-term implications of your AI decisions?"
Earn Trust: Vocal Self-Criticism in the Age of AI
In 1997, Garry Kasparov became the first person to "lose his job" to AI when IBM's Deep Blue defeated him in chess. For years, this defeat symbolized humanity's vulnerability to machines.
Productizing Internal AI Tools: From Efficiency to Revenue
Slack started as an internal communication tool at a gaming company. AWS began as Amazon's internal infrastructure.
Claude Innovation Skills: Systematizing Innovation with AI
Claude Innovation Skills: systematizing innovation with AI
Frugality: Accomplishing More with Less in the AI Era
When ChatGPT launched in November 2022, I became curious about how far I could stretch it. I asked myself a specific question: Could this become my innovation copilot?
Custom GPTs: Building Specialized AI Assistants
In this section I explore one AI-powered capability and demonstrate how it can be used to create business value.
Success and Scale Bring Broad Responsibility
I was a few years into my time at Amazon Web Services when this principle was introduced in 2021.
Customer Obsession Revisited: Your Shield Against the Innovation Graveyard
Amir has discussed this Leadership Principle in the first issue of his newsletter. No wonder that he started with this as this is undoubtedly the number one, the most important principle of all: Customer Obsession.
Dive Deep: AI as a Force Multiplier for Understanding
For decades, business leaders faced an impossible choice when it came to understanding their markets, customers, or prospects: invest heavily in deep research or settle for surface-level insights you could afford.
Think Big: The Leadership Principle That Defined Amazon
Most organizations approach AI by asking: "How can we use AI to do what we already do, but faster or cheaper?"
Insist on the Highest Standards in the Age of AI
Insist on the Highest Standards - your role as a leader in the age of AI.
Learn and Be Curious: Why This Moment Demands It
Of all Amazon's Leadership Principles, "Learn and be Curious" is the one that resonates most with me personally. The principle states:
Are Right, A Lot: When AI Can't Tell You You're Wrong
Are right, a lot: when AI can't tell you you're wrong
Bias for Action Part 2: From Analysis Paralysis to AI Progress
Leadership Principles in the age of AI - move quickly with Bias for Action
Bias for Action: Moving Quickly in the Age of AI
Leadership Principles in the age of AI - move quickly with Bias for Action
Creating New Value with Voiceflow
In this section I review one AI-powered application and demonstrate how it can be used to create new value.
Customer Obsession: Leading with Purpose in the Age of AI
Leadership Principles in the age of AI - start with Customer Obsession