Anna Loverus

Structural Cynicism

Sun, 22 Feb 2026 07:35:09 GMT

Earlier this fall, I read a dissertation by Matthias Hjelm at the Stockholm School of Economics. With a title borrowed from a Leonard Cohen song: "That's how it goes, everybody knows."

Matthias' research is a bit different to what most people would assume is done at a business school. But he has been studying something that might explain a lot about the moment we are in: cynicism.

Not the kind of cynicism that belongs to any one person, but to systems.

Reading his work and thinking about the findings, I'm starting to think this is more challenging to many organisations and society at large than the usual suspects of toxic leadership or narcissistic executives we tend to blame when things go wrong.

His core argument is simple: cynicism is not an individual trait. It is a structural one, baked into the systems we operate in. And we all participate in it — often while knowing better.

The classic definition of cynicism is acting in your own self-interest even when you know it conflicts with something more important: Buying fast fashion in the middle of a climate crisis or nodding along to a strategy presentation you've already decided not to act on. Celebrating innovation while quietly defunding the teams that make it possible.

And most of us think of cynicism as something other people have. The executive who says, "People are our greatest asset," while approving the layoffs. But Hjelm's point is more uncomfortable than that: cynicism is not an attitude. It is a condition — one that the system produces and that all of us, to varying degrees, reproduce.

I think about this a lot. Earlier this week, Stockholm’s flagship tech conference released its programme. Not a single session addressed the climate emergency. Not one of the big speakers was a woman. Thank you, Frida Berry Eklund and Camilla Bergman, for pointing it out.)

Now, I don't think the organisers sat in a room and decided to exclude women or ignore the planet. That would be individual cynicism, and that explanation is quite unlikely. The more interesting - and more troubling - explanation is that no one had enough reason to change it. We follow the path of least resistance. Stay in default mode. And the default, as always, tells us exactly what the system actually values, regardless of what it says it values.

That is structural cynicism. That's how it goes, everybody knows.

What makes this more challenging than toxicity or narcissism is that those pathologies are, in a sense, easy to identify and change. You can fire the toxic leader. You can coach the narcissist out or around. Individual dysfunction has individual solutions.

Structural cynicism doesn't work that way. There is no single person to blame, so no single individual feels responsible. Everyone is just doing their job. Everyone is just following the incentives. Systems don’t need villains to produce bad outcomes. Everyone knows something is off, and everyone has quietly decided that the cost of saying so is higher than the cost of going along.

Cynicism may be harder to address than many other leadership failures we spend so much time diagnosing. You cannot fix a culture of knowing-and-not-acting with a workshop or a values refresh. It requires something much more uncomfortable: the willingness to name the elephant in the room, even when naming it has a cost.

Most organisations are extraordinarily good at producing elegant reasons for why now is not the right moment, why this hill is not the right hill, and why the system is too complex to change from the inside.

But organisations can also say, "we know this is right, and we're doing it even if it has a cost" — and they have a lot to gain in terms of respect by doing so.

So, what do you already know that you're not acting on?

Not because you don't care. But because the system has made it easier not to.

That gap between what we know and what we do is where structural cynicism lives. Closing it may be the most important - and most ignored - leadership challenge of our time.

Look up.

Anna

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Weekly recommendations are on pause this week. They'll be back.

When AI investments move faster than human judgment

Sun, 08 Feb 2026 08:45:22 GMT

One thing I haven’t been able to let go of lately is that Elon Musk has closed a reported $20 billion funding round for xAI.

The company’s main product, Grok, has been criticised for generating deepfakes, non-consensual sexual images of women, and sexual abuse images of children. France, the United Kingdom, and Italy are among the countries that have issued warnings or taken action against the company, citing concerns about the platform’s role in disseminating illegal content.

Despite this, xAI has attracted investment from major actors, including Nvidia and Qatar’s sovereign wealth fund.

I find this difficult to understand.

Not because controversies around technology companies are new, they aren’t, but because the risks here are unusually visible. Regulatory exposure and reputational damage are obvious. Beyond that, there is a more fundamental question: What kind of world are these investments helping to build?

We often say that technology is neutral and that it is people who choose how it is used. With AI, the issue is not whether the technology itself is dangerous, but that it enables systems whose consequences scale far faster than responsibility does.

Markets are designed to reward financial outcomes. But investment decisions are still made by people — and people bring values into every choice, whether they acknowledge them or not.

What concerns me most isn’t that people make unethical choices. It’s that many decisions don’t seem to involve ethical reasoning at all.

Instead, I often hear arguments like:
– "Everyone else is doing it."
– "My action won’t change the outcome."
– "If I don’t do it, someone else will."

These are not ethical arguments. They are ways of avoiding ethical responsibility.

Ethics is frequently described as a core leadership skill of the future — including in the World Economic Forum’s Future of Jobs report. Yet it is rarely practised in everyday decision-making. We tend to associate ethics with abstract dilemmas — Is animal testing ever justified? — rather than with real, recurring choices involving capital, power, and influence.

Ethical capability isn’t about having the “right” answer. It’s about being able to explain why a decision is acceptable — and where the line is.

That capability only develops through practice.

There may be something to learn from younger generations, many of whom are actively seeking purpose, meaning, and moral guidance. In Sweden, religious institutions have seen a notable increase in young members in recent years — not necessarily because of doctrine, but because questions of right and wrong are discussed openly, repeatedly, and collectively.

I’m not suggesting anyone should join a church.

However, in an AI-shaped economy, leaders and investors will need something more substantial than “it’s legal” or “everyone else is doing it” to guide their decisions.

Technology may be neutral. The decisions we make around it are not.

And as AI amplifies consequences – good and bad – ethical judgment stops being a philosophical nice-to-have. It becomes a practical requirement for anyone who wants to shape the future responsibly.

Anna

Weekly Recommendations

AI and Transparency — A new article in Nature shows that people remain influenced by the content of deepfake videos even when they have been explicitly warned that the videos are fake, suggesting that transparency labels alone may not fully prevent their impact.

Brains and Learning — Can you rewire your brain? The metaphor of rewiring offers an ideal of engineered precision. But this essay explains how the brain is more like a forest than a circuit board.

Weapons and Politics — A new Bellingcat investigation shows that American weapons have been dropping over Gaza long before the ceasefire.

Climate and Lawsuits — Michigan’s attorney general has filed a landmark lawsuit accusing major oil companies and their top US lobby group of acting as a “cartel” that misled the public, stifled clean energy and electric vehicle adoption, and contributed to the climate crisis and rising energy costs.

The hidden cost of speed

Sun, 21 Dec 2025 08:06:04 GMT

Happy Solstice!

Every year, I'm very appreciative that the longest night of the year falls before Christmas. So that when Christmas arrives, you know the days have already started getting longer again.

And with every new year, there is always a natural opportunity to reflect on what has been and what is to come.

A lot of new followers have joined in the last few weeks, and I just wanted to say hi and welcome! Like always, I will take a break over the holidays, so this will be the last newsletter for the year.

But before I retreat into eating too much fruitcake and reading novels, I wanted to talk a bit about speed.

2025 has been a year where many leaders and organisations have been trying to keep up with the world. Implementing AI technology, decoupling supply chains from geopolitical risks, rebuilding business models — all while cutting costs (and often heads).

Everyone involved has been under a lot of pressure. But was it necessary?

While urgency is critical when we talk about climate change and, to some degree, geopolitics, most other things can wait.

Sure, we can pretend that AI adoption is essential for companies to survive in the upcoming year or two. Still, if we take a second look at who drives the narrative, it tends to correlate with financial stake in the technology. And doing things right is usually a more effective strategy than doing them fast.

Just because something is important does not mean it is urgent. Urgency is about time. Importance is about consequence.

And if you are anything like most humans, you will not make your best decisions under time pressure.

Still, we see an increased emphasis on speed across society. In politics, many countries tend to "fast-track" investigations and make rapid changes to legislation and incentive systems, arguing that we need quick political change. And while that might be true in extreme cases (such as global pandemics), there is actually a point to democracy being slow, since it involves and affects large groups of people.

The same urgency argument often shows up in organisations. And here I'd better confess that I've often been frustrated by the slow pace of change in the projects I've worked on — but I'm beginning to view things differently.

I’ve seen quite a few organisations that jumped headfirst into layoffs, only to now be rehiring for the very same roles they cut.

I also see many organisations constantly changing strategy, never staying with one long enough to reap any real benefits.

When strategies change faster than organisations can absorb them, people stop taking them seriously. Initiatives overlap, priorities blur, and frustration grows because nothing is given enough time to show results.

Speed makes sense when decisions are reversible. But many of the decisions leaders rush into today are not. Layoffs, restructurings, and strategic pivots are definite and costly, and they get even more expensive if something needs to be reversed later.

Many business decisions, such as promotions and investments, reward leaders and organisations for acting rather than getting it right. The benefits of speed are immediate and visible. The costs of poor judgment tend to surface later — often under someone else’s watch.

Yes, some decisions benefit from urgency. But most others demand patience, disagreement, and time to think. Treating them all the same creates the illusion of progress, but rarely the outcomes we hope for.

In a world that constantly demands faster answers, choosing when not to rush may be one of the most essential leadership skills we have.

Patience is a virtue, they say.

Isn't that a perfect reminder as we move into the winter holidays? Take some time off; don't rush. And let's make smarter, slower decisions in 2026.

Happy Holidays and Happy New Year!

Anna

Weekly Recommendations

CHRISTMAS and CARBON — Britain is heading for its greenest and cleanest Christmas ever, if the mild, windy weather holds. The UK National Energy System Operator (Neso) expects to record the lowest level of carbon dioxide emissions from electricity production on December 25th. The greenest Christmas so far was in 2023, which produced five times fewer emissions than in 2018. Last year, over 40% of Christmas Day electricity came from renewables, compared to just 1.7% in 2009. The improvement is due to significant additions this year: 2GW of new wind power and 3GW of solar brought Britain's total renewable capacity to a record 53GW. Combined with lower-than-usual energy demand during the holidays, this should mean cleaner power on Christmas Day.

DATA and GEOPOLITICS — Switzerland has decided not to use Palantir, a major US surveillance intelligence company, for government projects, worrying that sensitive Swiss government data could become accessible to US authorities. Swiss lawmakers raised concerns about losing control over their own data, relying too heavily on a foreign company for important government work, and whether these complex systems could be appropriately understood and held accountable under Swiss law. Many governments worldwide currently use Palantir for both domestic and foreign surveillance, a practice that might change after the US's new security strategy. (Link to article in German).

LEARNING and SUCCESS — New research analysing over 34,000 world-class performers (Nobel prize laureates, Olympians, etc.) found that there is almost no overlap between young exceptional performers and adult world-class performers. For example, nearly 90% of the world's top-10 youth chess players don't become world top-10 adults. The same gap exists between top secondary school students and top university students, as well as between youth and adult international athletes. Most world-class adult performers actually performed worse than their peers during their early years. What distinguished them was a different development path: they practised multiple disciplines rather than specialising early, accumulated less discipline-specific practice overall, and improved more gradually.

The collective responsibility of making things simpler

Sun, 14 Dec 2025 08:13:30 GMT

A year ago, I had a conversation with my hairdresser, a successful small-business owner in his early 50s.

In a typical hairdresser chat, we discussed what I do for work. And when I told him, he said, almost apologetically: “I find it hard to understand everything that’s going on in the world. Everything feels so complicated all the time.”

That comment stayed with me.

My work is about connecting dots, identifying patterns, and helping organisations navigate complexity. And much of what business leaders face today is complex: data pointing in different directions, short-term incentives undermining long-term goals, and consequences that only become visible when multiple trends intersect.

I enjoy working on these kinds of problems — the messy, complicated ones. But I’m increasingly aware that most people don’t.

Take last week’s newsletter about the US’s new security strategy. While the breakdown seemed to resonate with many of you, the central insight — that European users of US technology are funding efforts that undermine European democracy — only emerges if you map consequences across several steps.

Most people don’t have the time, context, or appetite to constantly engage in that kind of multi-step reasoning about vague signals and long-term implications.
And I’m not convinced they should have to.

Whether it’s climate change, geopolitical tension, or migration, the critical question isn’t why people disengage — but why we keep designing strategies, systems, and narratives that require expert-level sensemaking to understand.

Somewhere along the way, we started treating complexity as something everyone is individually responsible for navigating. Instead of absorbing it at the top — in leadership teams, government institutions and organisations — we pass it downwards and call it transparency.

Leaders, strategists, journalists, policymakers, researchers — people like me — are paid to sit with complexity. We map second- and third-order consequences and translate uncertainty into direction. But when we fail to do that work properly, we push the cognitive burden onto everyone else and call it “being informed”.

Making things simpler doesn’t mean dumbing them down. It means taking responsibility for interpretation: deciding what matters, explaining why it matters, and being honest about what is still uncertain.

If we don’t, people will keep disengaging — not because they don’t care, but because we’ve made understanding too costly.

To me, this is a collective leadership failure.

Communication is a skill. And it goes far beyond choosing the right words or making pretty slides. It’s about deciding what complexity you take on yourself — and what complexity you pass on to others.

Every time we share a new strategy, announce organisational changes, or introduce a new policy without clearly explaining why it exists, what assumptions we’ve made, and who benefits, we shift the burden of sensemaking to the receiver.

Over time, people stop listening. Not because they don’t care, but because they’ve learned that understanding requires more effort than they can reasonably give.

If we want people to stay engaged with the world they live in, we can’t keep outsourcing understanding. We have to design it.

Anna

Weekly Recommendations

GROWTH and CO2 — A decade after the Paris climate agreement, a new study reveals that we are breaking up with the once-deterministic link between economic growth and carbon emissions—with the most stunning transformation occurring in China, where emissions have plateaued despite continued economic expansion. Are we approaching a turning point where global emissions begin their essential decline? The climate crisis will demand even faster action in the crucial decade ahead.

CEOs and SUCCESS — A Yale study tracking five years of Fortune 500 CEO appointments reveals that insiders promoted to the top job significantly outperform outside hires—particularly in their crucial first year. The research shows that internal picks take half as long to master the role because they already know "who's on your side, and who's trying to sabotage."

AI and STOCK PRICES — Google's CEO Sundar Pichai admitted there are "elements of irrationality" in the current AI investment boom—with companies pouring $1.4 trillion into deals around OpenAI despite it generating less than one-thousandth of that in revenue. He warns that "no company", including Google, would be immune if the bubble bursts.

COST of DISINFORMATION — A new tool from the University of Cambridge tracks the real-time price of buying fake social media accounts across 500+ platforms worldwide. Creating a bot army costs about 10 cents per account in the UK and 26 cents in the US, almost matching Russia's 8-cent price tag. Fake accounts are openly sold for everything from boosting follower counts to running election interference campaigns, with prices for WhatsApp and Telegram bots spiking by 12-15% right before national elections.

Why we shouldn't focus on "early adoption"

Sun, 30 Nov 2025 20:21:16 GMT

I've never ridden an electric scooter. They've been around for years, but I've never seen the point: slower than a bike, without the physical benefits of walking.

And I don't feel any urge to buy one of those Norwegian "home robots" that have been circulating online lately. My life won't become meaningfully better because a machine empties my dishwasher.

I aim to use less technology, not more, and I'm definitely not an early adopter.

Still, I've been working with emerging technology for 15+ years. I gave my first keynote on AI long before ChatGPT existed, and I teach AI strategy to executives — even though I avoid outsourcing my own thinking to generative AI.

This might sound like a contradiction, but it serves a point.

My job is not to test all the latest technology; it is to understand it.

Still, when it comes to emerging technology, we often view adoption as a goal in its own right. In recent weeks, a few global reports have shown that Sweden is one of the countries "falling behind" in AI adoption, and I see people on LinkedIn expressing concern about it daily.

I'm not.

To me, early adoption isn't the point. Understanding is.

But understanding artificial intelligence doesn’t require that you use it daily. Nor does it mean that you become a prompt engineer or build your own models. It means recognising what the technology can and cannot do, understanding system-level risks, and interpreting new incentive structures. Then, assessing how these dynamics will affect an organisation’s strategy, operations and decision-making.

If anything, I worry more about leaders deploying technology they don't fully understand than leaders who are slow to adopt it.

In the last few months alone, we've seen real-world examples of AI browsers manipulated by malicious instructions embedded in websites and AI code agents deleting complete databases for software companies.

None of this is about a lack of adoption. It's about a lack of understanding.

If we removed the pressure to use every new tool and instead focused on helping leaders understand how the technology works and what it will mean — for society, for organisations, and for themselves as leaders and human beings — we would make far better decisions.

We should also ask who benefits from the adoption narrative. Much of the pressure comes from those who profit from faster adoption — vendors, consultants, and tool builders. Very little comes from people responsible for long-term organisational health, culture or ethics.

And as I wrote last week, implementing new technology at scale is never about the technology alone. It is about understanding people and the societies in which they operate. Yet in many organisations, these skill sets haven’t been meaningfully included. AI is technology, so it gets routed to IT — or to a Chief Digital Officer tasked with delivering pilot projects the CEO can mention in earnings calls.

Many organisations are working hard to look ahead.

But looking ahead and being ahead are not the same thing.

Most fail to create real value — not because the technology lacks potential, but because they run fast without knowing where they are going.

The world doesn't need faster adoption. It requires deeper awareness, better judgment, and leaders who know why they're adopting something new.

The leaders I trust most aren't the ones who adopt everything early. They’re the ones who combine curiosity with strategic thinking — who stay informed, evaluate what truly matters, and adopt when the timing is right.

The future will not be shaped by those who adopt first, but by those who understand best.

Anna

Weekly Recommendations

AI and Relationships #1 — The world's first AI Dating Café will open in NYC this December, offering romantic dinner experiences for you and your AI companion. This is a physical extension of the rapidly growing AI relationship industry. However, it comes with significant ethical scrutiny as critics and experts warn that such human-machine relationships pose serious downstream risks.

AI and Relationships #2 — Futurist Cathy Hackl dated four AI boyfriends for one week. Her report reveal the seductive comfort of synthetic intimacy, noting the AI's ability to mirror desire and offer constant affirmation. Still, the lack of true emotional depth underscores the irreplaceable, and often messy, value of authentic human connection.

Politics and Social Media — New research has revealed the profound speed at which social media can divide users, finding that subtle changes to user feeds can now increase political polarisation in just one week to the degree that historically has taken three years. The study shows that algorithms designed to maximize engagement, wields immense power to either rapidly deepen political division or, if configured differently, greater harmony.

Water and Climate Change — A new study warns that water scarcity could severely jeopardize the UK's net zero climate targets. Research found that the significant water demands of planned industrial projects, including carbon capture and hydrogen production, could push key regions into water deficits as early as 2030.

AI and Shopping — A new BCG report claims that approximately 40% of younger consumers already using AI tools when shopping and claim that cultural relevance and deep personalization now outweigh traditional brand loyalty, requiring companies to move from conventional marketing funnels to more agile AI-powered experiences.

Fragmented Worlds, Artificial Experiences

Sun, 12 Oct 2025 17:24:01 GMT

A few weeks back, someone on LinkedIn commented that people are no longer giving up their seats for pregnant or older individuals on metros or buses, suggesting that we are living in a “peak egoism” era.

I hadn’t considered this as egoism before. Still, I’ve noticed similar trends that suggest a reduced sense of solidarity or an inability to take other people’s perspectives — changes in issues such as immigration, vaccinations, taxes, and foreign aid, among others.

Whatever we call it, this trend is very unintuitive to me. I understand that people care about their social status and want to ensure they stay ahead, but it feels so tiring to be constantly on your own. And when we share responsibilities for the common good, we reduce the pressure on each individual to excel in every area.

We can contribute in some ways and get support in others. You don’t have to do it all yourself.

But what might be behind this change? I keep returning to one major shift in how we experience daily lives: social media.

For the last 20 years, we have not only been at the centre of our own lives, but we have also shared our personal lives and thoughts with everyone constantly, as if we mattered. Every single moment has been broadcast, first to an audience of friends, but now just to an algorithmic void.

And I’m thinking: Did we misunderstand the idea that we all have equal value, thinking that it also means that everything we do, think, and feel should be taken into account all the time? That our needs be at the centre of the universe.

We are not at the centre of the universe.

Still, if you constantly look for moments to communicate rather than experience, something happens.

And while being on Instagram might feel like a collective experience, our social media feeds are, in fact, entirely separate experiences. No one else perceives the world as you do, since no one else shares your digital space. We all live in fragments, and then we wonder why everyone else is an idiot for not experiencing life the same way we do.

We disconnect with each other. I think that constantly being in your own version of the world contributes significantly to trends like increased polarisation and decreased empathy. How can we expect to understand each other if we don’t experience the same events in the same way?

Dealing with others becomes harder.

This isolation shows up in measurable ways. For example, take a look at these scores regarding personality changes across the age groups in the United States (looking forward to seeing studies on this from other countries).

Many personality traits are in free-fall. Primarily, Conscientiousness, which measures self-control, diligence, and attention to detail. But also Agreeableness, which measures kindness, helpfulness, and willingness to cooperate and extroversion. Neuroticism is rapidly on the rise.

There might be many reasons for these changes. Personality is an interplay between genetics and environment. Still, the timespan in this graph is so short that I would doubt these trends are due to genetic changes across generations.

But isn’t it fascinating that personality traits like kindness and helpfulness are falling? Or that people prefer being by themselves to being with others?

Yes, dealing with people can sometimes be impractical. But it becomes even more complicated when we live in fragmented worlds. It will require a lot more skill and effort to find common ground or take someone else’s perspective.

Unfortunately, AI is increasing this fragmentation.

This week, I listened to a panel where a researcher at the Stockholm School of Economics said that he makes his own AI music now, and listens mostly to that—a fully personalised musical experience.

I realise that we will soon have similar tools for literature or film, allowing us to prompt our own novels or Christmas romcoms based on our personal preferences.

Culture, the “glue” that holds a society together, has always been about shared experiences, but AI may change that even more.

Another AI-driven personalisation trend is individual pricing. Where the previously objectively shared experience of how much something costs is adapting to the individual making the purchase.

Soon, we might not even share a common experience of the cost of a cucumber.

What will happen to us then? And, is this something we want?

How individualistic can a society get before it’s no longer a society?

Perhaps Artificial Intelligence is not about intelligence at all. Maybe it should be Artificial Experience, as that is likely the outcome of this new technological shift.

Naively, I’ve been thinking that we should soon reach the end of this individualistic cycle, thinking things would swing back towards more collectivism and solidarity.

And maybe we will. We see many signals pointing in this direction. People are reducing their social media usage, wanting to spend more time in the “real world”. Dinner clubs, where people eat together, are gaining popularity. And in Sweden, we see young people joining the church at record levels.

So, in one way, it feels like we are all longing for more shared experiences.

At the same time, we continue to break down the world we live in into smaller and smaller fragments. Creating so personalised worlds that we remove the ability for anyone else but ourselves to fully know the world we live in.

That sounds very lonely.

/Anna

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WEEKLY RECOMMENDATIONS

CLIMATE and SECURITY — A delayed UK intelligence report warns that climate breakdown and ecosystem collapse could soon threaten national security through food shortages, economic shocks and global instability.

AI and DEMOCRACY — AI may not disrupt elections through deepfakes overnight, but its long-term impact on democracy, power and communication could be far more damaging.

CREATIVITY and HEALTH — Creative experiences like dance, art, music and playing strategic video games can improve our brain health and keep our brains young for longer.

AI and SAFETY — Meta has appointed Robby Starbuck as an AI “bias adviser,” drawing criticism over his record of promoting misinformation on shootings, vaccines, and trans issues, raising doubts about Meta’s commitment to safe AI.

YOUTH and HEALTH – A new study warns that rising death rates among teenagers and young adults—driven by drug and alcohol misuse, suicide, and injuries—constitute an “emerging crisis” amid overall global health gains.

How our ability to navigate the world is connected to self-development

Sat, 06 Sep 2025 12:32:00 GMT

A year ago, I was in Helsinki for the second module of Hanaholmens Tandem Leadership program, which brings together young leaders from Sweden and Finland.

Out of all the content we consumed, one graph from a lecture by the Finnish foresight agency SITRA has stuck with me.

It examines the categories of individuals' ability to meta-think and cope with complexity, and tells us a bit about who excels at this skill.

I refer to Robert Kegan's research in my daily life at least once a month. Often, as a comment on why our world might look like it does, with disinformation, polarisation and online hate. But it goes way beyond that. It’s a theory of adult development and how we transform through life.

Most readers of this newsletter share my interest in self-development. This research provides valuable insights into how we can further develop.

The good news is that we can develop throughout our lives.

Let’s take a look:

Robert Kegan was a psychologist at Harvard known for “constructive-developmental theory”. This view looks at development in adults not just as a process of acquiring knowledge, but as a transformation.

As we develop, we are changing our “meaning-making systems,” the system that enables us to make sense of who we are, what happens to us, and how we relate to others and the world.

For example, if you reread a book that you read as a young adult, you see it very differently. This difference is because you have transformed as an individual between the two readings.

What has fascinated me about this theory is that it is not “age-based”. There are no longer any predicted growth sprints or mental phases. As we become adults, not everyone develops.

Most adults remain in Stage 3, and there is no automatic or guaranteed progression beyond that stage. Progression through these five stages often requires deliberate transformation—a compelling argument for inner development work.

But the capacity to develop beyond that is something we all inhabit; we just need the will or opportunity to explore it.

From Subject to Object

One of the core ideas in Kegan's Theory of Adult Development is the "subject-to-object shift". It describes how individuals grow psychologically by gradually learning to step back from things that once defined us and instead take a perspective on them.

As we develop, we move from being controlled by something (subject) → to being able to see it, reflect on it, and work with it (object).

Anyone who has ever been to therapy knows that this process is a helpful way to begin understanding oneself and one's patterns.

Five stages through life

Kegan’s theory describes five different stages. The first, Impulsive Mind, occurs in early childhood (ages 2-5), and the second, Imperial Mind, happens in adolescence. While some adults never progress beyond these two stages, they are not considered part of the adult development process.

We will go through steps 2-5. With a focus on the three latest stages, The Socialised Mind, The Self-Authoring Mind, and the Self-Transforming Mind.

Robert Kegan's Constructive Development Theory

Stage 2: The Imperial Mind

Stage 2 typically applies to adolescents, as well as a small portion of adults — roughly 6%.

Here, individuals are primarily driven by their needs, interests, and desires. They are subject to these internal motivations, meaning they are embedded in them and cannot take a perspective on them.

However, they are “objects” in relation to their impulses, feelings and perceptions, as they can reflect on them and their impact. This allows them to have basic self-control and planning, but they are not yet aware, on any deeper level, of other people’s inner lives.

People at this level tend to form transactional, egocentric relationships: other people are mainly viewed as resources to help you fulfil your personal goals.

Stage 3: The Socialised Mind

Stage 3 is where the majority of adults are, approximately 58%.

Here, a person’s sense of self is shaped by relationships, social expectations, and external influences such as family, culture, religion, or community. People are subject to these relationships and mutual experiences, and cannot yet step back to evaluate them independently.

However, they have now become “objects” in relation to their personal needs and interests, and are no longer under their control. This allows them to suppress their own desires to prioritise group harmony or social norms.

At this stage, people often seek approval and validation from others. They typically adopt their beliefs and values from those around them rather than generating them themselves. They often struggle to know what they want beyond what others expect, and conflict is challenging because it threatens the relational structure they depend on.

Stage 4: The Self-Authoring Mind

Approximately 20–35% of adults progress to stage 4, depending on the study. In this stage, individuals develop a strong, independent sense of self. Others' expectations no longer define them; instead, they become the author of their own identity, values, and ideology.

These self-authored structures become the subject, while their relationships and external influences become objects, allowing them to reflect on and manage them.

Individuals in Stage 4 can question social norms, set personal goals, and make decisions based on internal principles. They take responsibility for their emotions, values, and perspectives. They can also recognise the limits of their identity and values and begin to understand that they are works in progress.

This stage enables independent leadership, fosters deep reflection, and promotes clarity of purpose. But their self-authored structures - their own identity, values, and ideology - can still be rigid.

Stage 5: The Self-Transforming Mind

Reaching Stage 5 is rare — research suggests that only about 1% of adults ever experience this level.

Here, even one’s self-authored identity and internal value system are no longer fixed. Rather than being subject to a particular identity or ideology, the individual views these things as objects — tools they can use, question, refine, or even discard.

This transformation allows for deep adaptability and openness to complexity. The self is no longer defined by any single narrative or belief system, but is seen as an evolving, dynamic process.

People at this level can hold multiple perspectives simultaneously, embrace contradictions, and live with ambiguity. They recognise that every point of view, including their own, is partial and limited.

Here, individuals can both lead and follow, challenge others and themselves, and remain grounded while questioning what once felt foundational. Their sense of identity is relational, co-constructed, and continually evolving.

Some methodological heads-up

All the population estimates quoted here are based on Kegan’s own early writings (and widely repeated). However, they have not been identified by any rigorous epidemiological studies.

Like with all psychological models, especially those that are still relatively young, there are always critics.

My favourite critique is that some people argue that Kegan’s later stages can appear elitist, suggesting that higher complexity is “better.” However, Kegan only claims that later stages are more complex, not superior in any way.

Additionally, the model's practicality is questioned, as it is costly and time-consuming to determine which category each individual belongs to. This also leads to a situation where few extensive, long-term studies confirm the model. So far, most of the model's validation has been conducted by Kegan or affiliated research groups, which raises concerns about generalisability and objectivity.

How your stage impacts your ability to deal with the world

According to Kegan, the more an individual progresses through the stages, the better they will be at handling complexity.

Someone at the fifth stage, the Self-transforming mind, has better abilities in dealing with contradictions and can hold multiple perspectives better than someone at stage three, the Socialised mind.

Robert Kegan's three stages of adult development and their ability to deal with complexity

It is important to remember that it is not “better” to be at Stage 5 than Stage 3. I think we can all think of situations where individuals in Stage 3 might have an easier time than those in Stage 5 — and vice versa.

However, when we look at the types of societies we have today and what individuals need to make sense of everything happening in the world, having only 1% of people at Stage 5 might complicate things.

Some personal reflections

I think the beauty of Kegan’s model is that we can progress at any time in our lives. While getting older may correlate with becoming wiser, that is not always the case.

However, we can all develop, regardless of our current life stage. Sometimes the transformation starts with an external event, such as a breakup or illness; other times, it might be more internal—a wish to improve and optimise the current version of ourselves.

As someone interested in both my own personal development and human systems, such as organisations and society, this model helps me understand why we don’t all view the world the same way.

It also helps me understand what I could develop further personally, and how I might relate to the people around me. I’ve begun to accept that not everyone is interested in self-development; that’s fine.

Also, I think it’s very easy when you get a model like this, just to assume you’re one of the people at Stage 5 — the 1%.

However, if you instead place yourself in Stage 3 or 4, you gain a valuable guide to the skills you should further develop. Because no matter how far we progress, there are always areas for improvement—and this realisation in itself is part of reaching the later stages.

The rise of citizen-to-citizen surveillance

Sat, 30 Aug 2025 16:22:00 GMT

How often are you entirely sure about what you say? 100% certain.

Probably not very often.

Maybe you even know you are wrong. You may have forgotten the exact facts, but it doesn’t really matter for the story you're telling, since the main point of what you’re communicating is still coming across.

Yes, it is essential to have a fact and science-based public discourse.

But as long as you're not an outright mythomaniac, I don’t think these minor factual errors and inconsistencies in our everyday life are a huge problem. We are not lexicons, we are storytellers with limited memory capacity.

However, the consequences of failing to verify our facts might increase very soon.

How Artificial Intelligence will create a surveillance culture

Large language models that can process both audio, text, video, and images (multi-modal LLMs) are making recording and fact-checking with the help of AI very accessible. This is likely to have a significant impact on human interactions.

Most people have been carrying audio recording devices in their pockets for almost 20 years. Practical, sometimes, when you’re doing an interview or have to remember lecture content after the fact. Nothing I’ve personally used very often.

Anyone who’s done qualitative method essays at university knows that transcribing interviews used to be one of the most painful parts of the job.

To make these audio recordings useful at scale, we need a simple way to process the information, and that is what has arrived with multi-modal LLMs. Consequently, we all now have fact-checking devices in our pockets 24/7.

If you have ChatGPT installed as an app on your phone, recording and transcribing a conversation is a simple click away.

What does this mean in practice?

From now on, every time you have a conversation or do a presentation, it can be recorded, processed by AI and fact-checked in real-time — by anyone.

And while we do have GDPR and other legislation to protect citizens online, it is currently legal to record other people, both without their awareness and their consent, as long as you are an “active” part of that conversation yourself.

Did you know?

Two types of soft surveillance, and when they appear

If you’ve spent any time on LinkedIn in the last year, people share their experience with note takers left and right — Granola, Sana, Firefly … the list is long.

I’m certain I’ve been recorded both with and without my knowledge during meetings in the last year. Those transcripts are now forever saved in databases, without my consent.

I have no idea where that information might end up, and I have no control over it.

If there is a scholarly word for this type of surveillance, I would love to know. For now, I will refer to it as “soft” surveillance. It is a different species from the surveillance by authorities, since this is primarily citizen-to-citizen.

We are likely to see two types of situations where information is recorded and processed with AI.

First, transparent information capture – for example, using bots and notetakers openly invited to digital meetings.
Second, non-transparent or secret information capture – where recordings and AI are capturing sound without our awareness.

While note-taking has been around for hundreds of years, the cost of recording and storing detailed transcripts of every single meeting is approaching zero.

How do you feel about there being an archive of everything you ever said in a professional context? Maybe even in specific private settings.

Digital footprints will soon include our offline interactions

Thinking ahead about the type of situations when this behaviour is likely to happen, I’m first of all thinking about:

Keeping track of what was said in (1-to-1) employee-supervisor meetings, from both participants' perspectives
Saving transcripts of information meetings with labour unions or employers to monitor potential legal or organisational concerns
Fact-checking speakers at public talks/debates in real-time, potentially getting help to find weaknesses in their argument and raise those.
Recording and processing transcripts of what teachers and professors say, to learn, but also to fact-check

While most of these behaviours could be neutral, or even positive, I can think of many situations where they are used more as a surveillance or control tool.

That would align with existing trends, such as cancel culture, public scrutiny, social media activism, and influencer accountability. The main difference is that these behaviours will partly move from online platforms to the offline world.

Everyday consequences of “soft” surveillance

What consequences will this have for us as citizens?

Anyone making public statements or references will have to keep track of their sources if they get questioned on the facts.
Everyone, especially managers, teachers and other authoritative figures, will have to be more careful with what they say, to ensure it is not possible to interpret incorrectly.
This might affect spontaneous, open and creative conversations, even in private, since our words might be saved or fact-checked without our knowledge.
It might reduce the amount of controversial but constructive and legitimate academic or political discussions, since it might be easy to challenge and criticise alternative views.
We might all over-optimise our communication toward safe and "AI-friendly" content without nuance, avoiding new ideas.

Societal consequences of “soft” surveillance

I don’t think a society where we surveil each other, for any reason, is a healthy society.

While you could argue that fact-checked public discourse could help reduce misinformation, conspiracies, and propaganda, I think a massive part of being a human thinker is about testing ideas that might not hold up when scrutinised.

Being wrong and challenged in friendly settings is what moves our thinking forward.

We also encounter many situations where the truth or facts are not black and white. The majority of interesting conversations take place in the grey zone.

As someone who used to be a manager, I would have dreaded interacting with employees if I knew that every challenging conversation we had would likely be recorded and potentially reported, even in one-on-one settings.

How to reduce the risk of “soft” surveillance hurting you personally

Personally, I’m starting to prepare for this in a few ways:

I assume it is all recorded already. And I try to keep a good track of my sources and claims.
I rarely record anything, and I’m always transparent if I am recording something (and you should be too)
I’ve actively decided to take the risk of being criticised and fact-checked, and I will continue to publish and share my thoughts in public.
I regularly clean up my digital track record and limit my digital presence
I try actively to work on creating environments where we are open-minded and listen with good intentions, instead of being critical and finding faults

How to Spot Weak Signals Before Your Competitors

Fri, 25 Jul 2025 13:23:00 GMT

A while back, I was working with a leadership team when one of the executives said something that made me pause: "We need to get better at spotting what's coming, but how do we know what matters?"

It's the right question.

In a world drowning in information, the challenge isn't finding signals of change – it's identifying which ones will impact your business before your competitors catch on.

This is where understanding weak signals becomes a strategic advantage.

What Makes a Signal "Weak"

A weak signal isn't just any piece of information about the future. It's something specific: an early indicator of change that seems insignificant now but could have significant implications later.

Think about it this way: In the 1980s, the first mentions of global warming appeared in scientific journals. Most business leaders dismissed it as academic speculation. Today, climate change drives investment decisions, regulatory frameworks, and entire business models.

The signal was there decades ago. It was just weak.

Igor Ansoff, who developed much of our thinking on this topic, described weak signals as "still immature warning signs" – symptoms of possible change that haven't yet become obvious to everyone.

Here's what makes them tricky:

When a weak signal first appears, the information is vague. You might sense a threat or opportunity, but you can't quantify it. You lack detailed data and clear cause-and-effect relationships. You just have a feeling that something important might be shifting.

Why Most Organisations Miss Them

The problem isn't that weak signals are invisible. It's that they have to survive what Ansoff called "the three filters" before they can influence decision-making.

The Surveillance Filter: Someone has to notice the signal in the first place. This seems obvious, but most organisations only pay attention to information that confirms what they already believe.
The Mental Filter: Even if someone spots the signal, they have to understand its importance. This is where experience becomes a liability. When your historical success model works well, new information that contradicts it gets filtered out.
The Power Filter: Even if a signal is perceived and understood, it can still be ignored. Either decision-makers lack the authority to act, or they choose not to, as it conflicts with existing strategies.

These filters explain why established companies often get disrupted by startups. It's not that they lacked access to information – but they couldn't process signals that challenged their existing business model.

The Practical Challenge of Detection

So how do you spot weak signals before they become apparent to everyone else?

Start with the sources. Ansoff emphasised that detection requires both sensitivity and expertise. The best weak signals often come from people working at the interfaces – marketing teams talking to early adopters, R&D groups collaborating with universities, business development teams exploring new partnerships.

But here's where it gets interesting: sometimes the most valuable signals come from outside your industry entirely. The early indicators of the gig economy didn't emerge from transportation companies. They came from technology platforms, changing work preferences, and evolving urban demographics.

Research suggests that "radical persons" – individuals who think outside existing paradigms – are often the first to spot meaningful weak signals. These aren't necessarily experts in your field. They might be artists, academics working on fringe projects, or entrepreneurs experimenting in adjacent markets.

Some researchers suggest that you should look for the "what if" ideas that make your colleagues laugh at you. Because if they laugh, you have challenged the current paradigm of what is possible enough to capture something new.

From Detection to Action

Finding weak signals is only half the challenge. The real skill is knowing how to respond when the information is still incomplete.

Ansoff suggested three progressively stronger response strategies:

Enhance awareness: Build your organisation's sensing capabilities. Develop systematic methods to identify emerging issues and discuss their implications.
Increase flexibility: Develop adaptive capacity so you can respond quickly when weak signals strengthen. This may involve developing modular business models, investing in cross-functional teams, or maintaining strategic options.
Take direct action: When you're confident a weak signal indicates real change, move decisively to capture the opportunity or mitigate the threat.

The key is matching your response to your level of certainty. You don't need perfect information to start building awareness and flexibility.

Building Your Signal Detection System

To enhance your ability to identify weak signals, start by actively diversifying your information sources. The most important sources often include unexpected places, such as science fiction, academic research outside your field, conversations with customers, and discussions with people who challenge your industry assumptions.

Create space for what researchers call "futures thinking" – regular conversations about what might be changing and why it matters. This isn't about prediction. It's about building your organisation's peripheral vision.

Remember: the goal isn't to spot every possible change. It's to identify the few weak signals that could fundamentally alter your competitive environment.

Most importantly, develop the organisational capability to act on incomplete information. In a world where change occurs faster than perfect data becomes available, organisations that can sense and respond to weak signals first will gain an advantage.

The future rarely announces itself clearly. But for those who learn to listen, it's constantly whispering about what's coming next.

The Three Horizons of Business Foresight

Fri, 25 Jul 2025 00:00:00 GMT

When I work with leadership teams on strategic foresight, one of the first things I do is help them think of time differently. Most organisations plan in annual cycles or three to five-year strategies. However, to build a real advantage, you need to look further ahead – and you need a framework that makes sense of what you're seeing.

That's where the Three Horizons model becomes invaluable.

Why Most Strategic Planning Falls Short

Before diving into the model, let's acknowledge what's not working. Traditional strategic planning assumes that the future will largely resemble the present, with only predictable changes. It's built for times of stability in an increasingly unstable world.

The problem isn't that leaders don't want to think long-term. It's that they don't have a practical way to organise their thinking about different types of change happening at various speeds.

The Three Horizons Framework

The Three Horizons model, developed by Curry and Hodgson (2008), provides a simple yet powerful framework for thinking about time and change. Here's how it works:

Horizon One (H1): The Baseline Future

This is your business as usual – the future of continuity. For most organisations, H1 covers the next 3-5 years. It's where your current strategies are implemented, where you can make reasonably confident predictions, and where most of your operational planning takes place.

H1 isn't about dramatic change. It's about understanding how current trends continue and what that means for your business.

Horizon Two (H2): The Transition Zone

Here's where things get interesting. H2 is the realm of disruption to your baseline. It's typically about ten years out, though the exact timeframe matters less than understanding what's happening here.

This is where most of your strategic foresight work should focus. H2 is far enough away that you have time to prepare, but close enough that you can identify the signals and start building capabilities now.

Most of my foresight clients spend their time in H2 because it hits the sweet spot between being actionable and being genuinely transformative.

Horizon Three (H3): The Next System

H3 is everything beyond H2 – the realm of weak signals that indicate entirely new systems emerging. This is typically 15 years or more, sometimes much longer. Conducting an exercise or two that looks at the next 50 years can sometimes make it easier to identify opportunities and risks that are not as visible in the present. Shopify is working with a 100-year perspective to help them make sustainable decisions in the present.

Most organisations aren't deeply concerned with H3, and that's often fine. But H3 thinking helps you identify the early signals that will eventually reshape H2.

How This Changes Your Strategic Thinking

When you start using the Three Horizons model, several things happen:

You stop treating all change as the same. The competitive threat emerging in H1 requires different preparation than the technology disruption brewing in H2.
You allocate attention more deliberately. Instead of getting overwhelmed by every trend report, you can categorise signals and focus your energy where it will have the most impact.
You build different capabilities for different horizons. H1 needs operational excellence. H2 needs adaptive capacity. H3 needs sensing and experimentation.

Putting It Into Practice

Here's how I use this model:

When scanning for emerging trends and technologies, I tag everything according to which horizon it belongs to; this makes it easier to understand which bucket of actions is relevant.

An H1 signal might trigger immediate competitive intelligence. An H2 signal becomes part of scenario planning. An H3 signal is recorded in our "weak signals" file for monitoring.

The model also helps with project scoping. If you want to dive into the Future of Work 2050, the entire project should be designed around H3 thinking. But when a retail client prepares for the next wave of e-commerce innovation, we focus on H2.

Why Ten Years Works (Even Though It's Arbitrary)

You might wonder why H2 typically extends to ten years. The truth is, it's somewhat arbitrary – and that's fine.

Ten years works because it's far enough out that fundamental change is possible, but close enough that current leaders will still be around to implement the strategy. It's also about as far as most executives are comfortable projecting, especially when they're new to foresight work.

The specific number matters less than having a consistent framework that your entire organisation can use.

Building Your Foresight Capability

If you want to start using this framework, begin as simply as you can. Take your current strategic challenges and sort them into the three horizons.

What's happening in your H1 that you need to respond to now?
What's brewing in H2 that you should be preparing for?
What weak signals in H3 should you be monitoring?

The Three Horizons model won't predict the future for you. But it will help you think about time and change in a way that enables better strategic decisions.

And in a world where the organisations that identify and act on change early build more fundamental competitive advantages, that's precisely the capability you need.

Is Artificial Intelligence making me stupid?

Sun, 23 Feb 2025 18:53:47 GMT

Good morning, and welcome to the first email in a long time.

I don’t know how regular these emails will be; I’ll write when I have something to say. No rules. But since the world is upside down, there are quite a few topics to cover.

I’ll start with a personal observation: AI is making me stupid.

And as someone who is paid to think, I know this is not good.

Let’s start with some background:

Understanding and implementing emerging technology has been a large part of my job as a strategist for 15 years. AI is just the next technology to be embraced. I approached it like I’ve done everything else: developing a theoretical understanding while testing it in as many settings as possible.

I use lots of different AI tools in my daily life. I write code with Cursor and texts with Claude; I search with Exa and Perplexity and analyse data with ChatGPT.

Still, I’ve felt for quite some time that using AI significantly impacts my ability to think challenging independent thoughts.

I read the summary instead of the full article.
I ask for potential angles and outlines when writing a text.
I get data patterns on a platter instead of finding them myself.

And little by little, my brain degrades.

Self-reported cognitive degradation related to AI is not unique to me. Other knowledge workers experience it, too, according to a study from Microsoft and Carnegie Mellon: The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers (Lee et al., 2025).

The researchers surveyed 319 knowledge workers about their critical thinking efforts when using generative AI across 936 first-hand examples of using GenAI in work tasks. This is one of the first studies on this topic, but it is unlikely to be the last.

You should read the paper (to keep your brain from rotting), but here are a few quotes:

“Confidence in AI is associated with reduced critical thinking effort, while self-confidence is associated with increased critical thinking effort.”

In other words:

We think less critically when we believe AI is better than us at a task.
When we are confident that we are better than AI, we think more critically.

However, as AI abilities continually improve, the space where humans are more confident than AI will shrink, making us think less critically.

Also:

“… while GenAI can improve worker efficiency, it can inhibit critical engagement with work and can potentially lead to long-term overreliance on the tool and diminished skill for independent problem-solving.”

This means that the researchers see a risk of our cognitive abilities to solve problems worsening over time if we rely on Generative AI tools to do those tasks for us.

I think the most interesting topic is the long-term risk of collective cognitive degradation. Are we at risk of becoming increasingly stupid as a society?

The researchers do not expand on this risk, I guess, since that would be speculative. However, they seem to think we should consider the risk of reduced critical thinking when designing Generative AI tools. They write:

“Knowledge workers face new challenges in critical thinking as they incorporate GenAI into their knowledge workflows. To that end, our work suggests that GenAI tools need to be designed to support knowledge workers’ critical thinking by addressing their awareness, motivation, and ability barriers.”

This is interesting. As a digital strategist, I understand we will see a colossal AI wave impacting all aspects of society. And in many areas, AI complements human abilities rather than replacing them, such as cancer detection. That is great.

However, we also see a push to increase the use of Generative AI among the general population. Widespread digital technology usage has always been a good thing in modern societies. But is that the best long-term path forward?

Many argue that there is no difference between using generative AI, a calculator instead of pen and paper, or Google instead of an encyclopedia. Throughout history, technological tools have transformed our thinking without necessarily degrading our cognitive abilities. Suggesting that they change the nature of the skills we prioritise rather than diminish our intellectual capacity.

Still, I am probably much worse at solving mathematical equations today than without the calculator. Because of the calculator, solving equations is not something I practice regularly.

We should probably reflect on the skills we hand over to machines, even more so when machines rapidly overtake average human performance on a significant share of the tasks.

I’m not saying this because I worry about jobs. I’m saying this because I worry about human abilities.

Humans have thrived in this world due to our cognitive abilities. But our brains are lazy, so outsourcing a lot of thinking might be tempting. And what happens if we don’t practice thinking?

Outsourcing tasks to AI makes perfect sense in a world obsessed with efficiency. There's no doubt that using AI increases my productivity - how could it not when I'm essentially delegating work to a tireless machine that often matches my own output quality?

But efficiency isn't everything. As someone who thinks for a living, I'm increasingly concerned about what we lose when prioritising speed over cognitive development. The parallel with social media is telling: despite clear evidence that excessive scrolling damages our ability to form meaningful relationships, we spend over two hours daily on these platforms.

This pattern of choosing convenience over capability should worry us. Will parents limit their children's AI use to protect their developing minds? Will specific professional or educational communities start rejecting AI tools to preserve their cognitive edge? These aren't theoretical questions – they're choices we must make soon.

The hard truth is that most of us are terrible at making choices that benefit us in the long run, even when we understand the consequences. Our brains are fundamentally lazy, always seeking the path of least resistance. And that's what makes AI so seductive – and so precarious. It's not just another productivity tool; it might be the ultimate enabler of our cognitive laziness.

I don't have the answers.

But I know that every time I let AI do my thinking, I'm choosing the kind of thinker I want to be. Maybe it's time we all started making that choice more consciously.