AI Agents
AI agents are software systems that can perceive their environment, make decisions, and take actions autonomously. They range from simple chatbots to fully autonomous systems. Understanding the spectrum of agent capabilities — and their limitations — is essential for leaders evaluating agentic AI for their organizations.
What is AI theater?
AI theater is the performance of AI adoption without the substance behind it: the grand keynote, the flashy demo, the pilot that never reaches production, the usage leaderboard that signals progress while producing little real value.
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.
AI for deep research: what the Stanford Virtual Lab still teaches us in 2026
In November 2024 a Stanford group spun up a Virtual Lab of AI agents using GPT-4o and ended up with experimentally validated nanobodies that bind a new SARS-CoV-2 variant. Eighteen months later the model looks weak and the design choices look stronger than ever. Four moves are worth pulling out: the AI picked its own team, five parallel meetings replaced individual judgement, the agents had hands, and compute time replaced calendar time.
SciSpace: AI-powered literature research for the rest of us
The Stanford Virtual Lab sits at the ambitious end of AI in science. Most working researchers and most executives trying to understand their R&D function will meet AI through something much more modest first. SciSpace is a good example: 280M papers, chat with your library, ~550 prebuilt task agents, inline citations as you write. The leverage is in pointing it at your own corpus first.
What is a self-driving lab?
A self-driving lab is a research operating model in which AI proposes the next experiment, robotic instruments run it, ML models read the result, and the loop runs again with light human supervision. The term has been in scientific use since 2018. What changed by 2026 is that the lab-in-a-box version has moved from research-paper claim to routine operating mode, and now interlocks with multi-agent systems in a way that was not possible five years ago.
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.
What is AI Spring?
AI Spring is the post-transformer renaissance in artificial intelligence, the seasonal opposite of historical AI Winters. The 2026 data point: Swedish AI startups raised nearly €1B in 2025, triple the prior year, with the highest unicorn density outside Silicon Valley. Spring happens in places where leaders choose to light something up.
HR's dual mandate in the age of AI
Most AI-in-HR coverage flattens the work into one story: automate recruiting, save HR people time. That undersells the opportunity by half. There are two distinct mandates HR has to lead, on different rhythms, with different team postures. Miss either one and the transformation fails.
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.
What is a skills-based organization (SBO)?
A skills-based organization (SBO) is the structure of work, mobility, and decisions organized around discrete skills rather than jobs and ladders. In 2026 the concept matters twice over: skills are the foundation for AI agents as much as for humans. Skills work pays twice.
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.
What is a Second Brain (and Personal Context Management)?
A Second Brain is an external system that remembers for you. In 2026, Tiago Forte renamed the discipline Personal Context Management — because the bottleneck has moved from capture to context, and AI quality is now a context-choice problem.
Company as intelligence: from AI overlay to AI-native
Jack Dorsey's Block laid off 40% of its workforce and the stock went up 26%. His manifesto argues that AI breaks a 2,000-year-old hierarchy problem. Three structural layers, three surviving roles, and one diagnostic question your leadership team should be asking.
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.
What is jagged intelligence?
Frontier AI models pass the bar exam and miscount letters in 'barrier' on the next tab. Andrej Karpathy's term 'jagged intelligence' explains why 95% of enterprise AI projects produce no measurable P&L impact, and why average benchmarks hide the problem.
PIEES data flywheels: closing the loop between collection and value
96% of organizations investing in AI report productivity gains. Only 5% capture value at scale. The gap is a loop problem. Turning the PIEES framework into five compounding data flywheels is how you close it.
What is AI memory?
AI memory is the emerging set of techniques that let AI systems remember who you are, what you've told them, and how you work, across conversations, sessions, and tools. Most organizations are only using two of its five layers.
ElevenLabs: The AI Voice Platform That Makes Emotion Audible
ElevenLabs delivers AI voice synthesis with emotional nuance that separates it from every alternative. With 3,000+ voices, 75ms latency, and expressive mode across 171+ languages, it is the infrastructure for voice-first AI experiences.
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 PIEES Framework: 5 Ways AI Creates New Customer Value
Most leaders understand AI can improve productivity. Fewer have a strategy for creating genuinely new value. The PIEES framework (Personalization, Interaction, Emotion, Experiences, Stories) breaks down the five dimensions where AI transforms what you offer customers.
What Is an AI Persona?
An AI persona is the deliberately designed personality and emotional character of an AI system. It is not cosmetic; it causally drives behavior and adoption.
Disruption by Non-Actors: When Your Customers Become Your Builders
The biggest AI disruption threat isn't from competitors — it's from customers gaining capabilities previously locked behind expertise and capital. An operations manager just replaced a $600K enterprise contract with an AI-built system.
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.
What Is Shadow AI?
Shadow AI is the unsanctioned use of AI tools by employees without IT approval or security review — the 2026 evolution of shadow IT that's faster, harder to detect, and significantly more dangerous.
Value Chain Compression: How AI Is Breaking Pricing Models and Unbundling Industries
Two trillion dollars in software market cap evaporated in 30 days. Insurance brokers lost 12% in a single day. The hourly billing model is dying. Here's the framework for understanding which value chain links are next.
What Is AI Fluency?
AI fluency goes beyond knowing what AI can do — it's the ability to independently apply AI tools to drive measurable results. Only 5% of workers have it. Here's why it matters.
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.
AI Disruption: Two Lenses for Seeing What's Coming
Disruption means questioning whether your industry's operating model will still make sense in three years. Two lenses — value chain compression and new actor emergence — help you see where it's heading.
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.
What Is an Agent Harness?
If the AI model is the brain, the harness is the body — the infrastructure layer that connects thinking to doing, and the make-or-break factor for agents in production.
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?
What Is Computer Use?
Computer Use is the AI capability to see, interpret, and interact with computer screens like a human — clicking buttons, filling forms, and navigating applications without needing APIs or custom integrations.
What Is Model Routing?
Model routing is the practice of directing different tasks to different AI models based on problem nature, required capability, cost, speed, and quality requirements.
"Which AI Should I Use?" Is the Wrong Question
Before selecting AI tools, define the problem being solved. Not all hard problems are the same kind of hard - and matching problem types to model strengths is the emerging leadership skill.
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?
From AI Activity to AI Value: Three Shifts
Most organizations I work with aren't lacking AI initiatives. They're running pilots, deploying copilots, building chatbots. What they're lacking is a clear line from all that activity to measurable business value.
Intent Engineering: The Missing Layer in Enterprise AI
We've taught AI what to know. We haven't taught it what to want. That gap is why most companies still see no tangible value from AI - and the fix starts with something Peter Drucker told us decades ago.
What Are Evals?
This Week's Term: Evals - structured tests that measure whether an AI system is performing to your standards, consistently and over time.
Building AI Agents That Work: 10 Design Principles for Business Leaders
Ten practical principles for leaders building or evaluating AI agent systems - whether for customer experience, internal operations, or any business function.
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.
Shape of AI: A Pattern Library for AI UX Design
In Section 2 I argued that the old design process isn't producing great AI experiences - and that teams need new principles for building products people actually love.
What Are MCP Apps?
In Issue #12, I introduced term MCP - the open standard that acts as a "USB-C port for AI," letting AI models connect to external tools and data sources through a universal interface. Since then, MCP has grown rapidly.
Agent Teams: The Next Frontier of AI Business Value
I ran two sessions this week with AI champions at a customer I'm working with. The topic: what agents actually mean, and what the role of humans becomes when working with them.
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.
What Is AI Orchestration?
This Week's Term: Orchestration - the coordination of multiple AI agents, tools, or capabilities to accomplish complex tasks that no single component could handle alone.