The Lindy Effect: why the old survives – and when it's still right to bet on what hasn't yet

The longer something has existed, the longer it's likely to keep existing. But that same logic is also the reason to deliberately, in a controlled way, bet on what hasn't survived yet. Here's the difference between breaking the Lindy effect and using it correctly.

Antifragility: building things that gain from disorder

The previous article showed that some things can never be predicted. This one is about what to do about it – not by getting better at guessing, but by building something that benefits when the guess is wrong.

Black swans and Cynefin: when the methods in this series aren't enough

Six articles on mapping causes, loops, and deviations all assume one thing: that reality can be mapped. Sometimes it can't. The Cynefin framework shows where that line is – and the black swan is what lives on the other side of it.

Positive deviance: learning from what already works

When something goes wrong, organizations assemble a review team within a day. When something goes right, against the odds, almost nothing happens. That's backwards – and there's a method for fixing it.

Problem-solving methods: the tools that actually address what we've covered

Four articles on why fixes backfire. This one is about what to actually do about it – four methods, each aimed at a specific trap from earlier in the series.

Worse before better: why the right fix rarely feels right at first

The best fix rarely looks best at the start. The worst one often does. Here's why – and how to tell which curve you're actually on.

Systems thinking: why cause and effect are rarely a straight line

The cobra effect isn't an exception – it's what happens when you steer a system without understanding how it's wired together. Systems thinking gives you the language to see those connections before they bite back.

The cobra effect in depth: when the fix becomes the problem

The cobra effect is more than a good story about snakes in Delhi. It's a recurring pattern in how organizations set targets – and it can be recognized before it strikes.

Thinking in scenarios: how to see consequences before they happen

Most decisions look good on paper. The problem is what happens afterward, when reality answers back. Here's how to think in scenarios – and why the cobra effect is the clearest example of what happens when you don't.

OKR vs KPI: the difference between measuring and moving

KPI and OKR are often lumped together, even though they do completely different jobs. A KPI tells you what already happened. An OKR exists to drive a shift that hasn't happened yet. Confusing the two is one of the most common strategy mistakes.

How OKR works in practice

Objective and Key Result solve different problems and shouldn't be written the same way. Here's the mechanics behind the model — and the rules of thumb that separate OKRs that actually engage people from OKRs that are just a renamed task list.

Guide: how to create OKRs that actually work

Most OKR implementations don't fail because the model is wrong — they fail because the steps happen in the wrong order. Eight steps from strategic alignment and timeframe to evaluation.

The history behind OKR

OKR wasn't born as a trend but as an answer to a concrete problem at Intel in the 1970s. The story of how the model traveled from Andy Grove to John Doerr to Google says almost as much as the model itself.

The silent exclusion: competence that erodes without anyone closing the door

Part three of 'Beyond the prompt'. First we could no longer read what the agents said to each other. The next step is quieter: we ourselves lose the ability to contribute, not because we're shut out, but because the skill is never practiced.

From vision to advantages – why one vision needs several strategies, not one

A vision states where you want to go. It doesn't state how you get there. Part two of the series shows how a vision breaks down into concrete competitive advantages — and why each advantage needs its own strategy kernel to become more than a wish.

What is a strategy, really?

"It's strategic" has become a way to avoid answering hard questions. Professor Richard Rumelt shows that real strategy is something else entirely — and that most documents called strategy fail the simple test.

When agents invent their own language – and humans become the eavesdropper who can't understand

AI agents talking to each other sometimes develop their own, more efficient language – one no human can read. It's a real and active area of research. The question is what happens to oversight and governance once communication stops being humanly legible.

What every board needs to know about AI

AI is no longer a technology issue that can simply be delegated away. It is a board-level matter, combining business opportunities, regulatory obligations and risks that demand active ownership at the top.