NASA Administrator Jared Isaacman said those selected are “the best astronauts to undertake and complete the mission's objectives”. With a corps of 37 astronauts, 15 of whom are women (41%), it’s hard to agree with that logic. Part of the argument, that this is a mission testing docking procedures and so needs skilled pilots on the crew, doesn't stand up to scrutiny. The crew NASA actually selected isn't built exclusively around piloting expertise: Bresnik commands, Parmitano pilots, and Douglas and Rubio fly as mission specialists. And even if it were, there are four women in the corps with the credentials to fly this mission: test pilots Jasmin Moghbeli, Nicole Aunapu Mann, Jessica Wittner, and flight instructor Nichole Ayers. Put simply, politics interfered in this selection. The current US Administration’s allergy to DEI initiatives is well documented, including rolling back a pledge on landing the first woman on the Moon.

Artemis III is the most operationally exposed of the Moon missions currently planned. Whilst Artemis II sent the space capsule around the far side of the Moon, and Artemis IV will land humans on its surface for the first time in 50 years, Artemis III is essentially an equipment test that won't break out of Low Earth Orbit (LEO). An essential step for testing the Human Landing System developed by SpaceX and Blue Origin, this mission is all about operational risk reduction. And that context matters for who gets selected.

What the research says about team composition and performance

I’ve been trawling through the research on the gender composition of teams, and the impact of that on team performance, as part of preparing for my TEDx (which is less than two weeks away now!). Studies from the late 2000s through to last year show findings that look mixed at first glance, but a clear pattern emerges once you account for context. Let me take you through it now.

Joshi & Roh (2009) — The Role of Context in Work Team Diversity Research: A Meta-Analytic Review (Academy of Management Journal)

A meta-analysis of 39 field studies, covering 8,757 teams, which examined how diversity (including gender) relates to team performance — and crucially, how the context changes that relationship. The analysis found that gender (and other relations-oriented diversity like race and age) had a tiny negative effect. However, this meta result is masking large variation within individual studies. Once you account for context, the effect sizes double or triple.

They also found a temporal dimension: short term teams benefit from diversity, whilst long term teams suffer from it.

So the question ‘is gender diversity good or bad for performance?’ only makes sense when you specify the team context.

Woolley, Chabris, Pentland, Hashmi & Malone (2010) — Evidence for a Collective Intelligence Factor in the Performance of Human Groups (Science)

Across two studies with 699 people in small groups, a single statistical factor — collective intelligence — predicts how well a group performs across a wide variety of tasks, much like general intelligence does for individuals. Collective intelligence was not strongly predicted by the average or maximum IQ of its members. Putting smart individuals together does not automatically produce a smart group.

Instead, three factors predicted collective intelligence: average social sensitivity of members, equality in conversational turn-taking, and the proportion of women in the group. The gender effect was largely mediated by social sensitivity, i.e. women in the sample scored higher on social sensitivity than men.

However, this lab-based study did not engage with the contextual moderators Joshi & Roh had flagged a year earlier.

Curșeu, Pluut, Boroș & Meslec (2014/2015) — The Magic of Collective Emotional Intelligence in Learning Groups (British Journal of Psychology)

Here the researchers studied 100 student learning groups (528 students) at two time points four weeks apart. They found that the higher the percentage of women in a group, the more collective emotional intelligence the group developed. Collective emotional intelligence in turn increased group cohesion, reduced relationship conflict, and improved group effectiveness.

This builds directly on Woolley et al’s work to show that women, on average, bring higher social sensitivity and more communal traits, which seed emotionally intelligent group norms.

Bell & Outland (2017) — Team Composition Over Time (Research on Managing Groups and Teams)

This is a chapter in a book rather than a paper, so synthesises previous research findings on team composition and how that shapes dynamics across a team’s lifecycle.

The authors state that surface-level composition variables like gender tend to have fleeting effects: their influence often fades as team members get to know each other and deeper-level attributes take over. However, they also flag that Curșeu et al. is a notable exception where gender composition had durable effects via collective emotional intelligence.

Read this in the context that teams are increasingly dynamic — membership churns, boundaries are fluid, people belong to multiple teams at once — which complicates any snapshot view of composition.

Kearney, Gebert & Voelpel (2022) — Gender Diversity and Team Performance Under Time Pressure (Journal of Organizational Behavior)

This study directly tests how gender diversity interacts with time pressure — particularly relevant for high-stakes operational teams. The researchers argue that gender differences in moral reasoning could increase the chances that different viewpoints and interests are considered when completing the task, and gender differences in risk preferences could help ensure that teams strike a sensible balance between taking too much and too little risk.

Interestingly, the study used complex tasks, like hidden-profile and conjunctive tasks, where contributions from all team members are required and information must be elaborated rather than just aggregated. This task structure resembles mission-critical operations where distributed expertise has to be integrated under pressure.

The proposed mechanism is that gender-diverse teams under time pressure are less likely to withdraw and more likely to engage in information elaboration, leading to better outcomes on complex tasks. However, the authors flag a limitation: their research was conducted with newly formed student teams, leaving open how team tenure and trust interact with these effects in real-world high-stakes settings.

Enache, Scarlat, Charalambous, Duboisée de Ricquebourg & Shields (2025) — Gender Diversity Helps Teams Maintain Integrity Under Pressure (Harvard Business Review)

New research found that simply having more women on the team tilted behaviour toward greater honesty in financial analysis, acting as a form of soft regulation that arises organically from within the team, complementing formal compliance and external oversight.

The mechanism described is resistance to optimism bias and conflict-of-interest distortions under pressure — gender-diverse teams were more likely to flag risk honestly rather than go along with rosy projections. This finding is relevant to any high-stakes setting where there’s pressure to conform to an optimistic operational narrative (like wanting the docking procedure to be a success).

The variables that influence gender’s effect on team performance

Taken together, these five studies show a number of variables that influence whether the gender of a team has an impact on their performance:

• Time pressure

• Task complexity

• Team tenure (how long they have been formed)

• Industry status hierarchies

• Whether the setting itself is male-dominated

Astronaut crews sit at the intersection of all of these variables:

• They train together as a team for years, before going on a mission that may only last weeks: this intensity to duration ratio is unusual, and the previous findings may not easily extrapolate for the different phases an astronaut crew goes through.

• Astronauts train not just to pilot rockets, landers and space capsules, but also in geology, photography, emergency medicine, equipment maintenance and repairs (Christina Koch famously was the crew’s plumber when the space toilet wasn’t working!) — and more.

• Space is notoriously gender unequal: according to UNOOSA, women make up around 25-30% of the space workforce globally, and just 11% of all astronauts to date.

The short of it is this: gender diversity hasn’t been extensively studied by NASA. In Apollo it wasn’t a factor as all the astronauts were men, and in the space shuttle and ISS era it wasn’t systematically studied.

So to bring it back to what Isaacman said: the best people for this mission have been selected — and they just so happened to be men. Unfortunately, we’re lacking the evidence to support that claim, because nobody has done the research on this specific context. We have decades of adjacent evidence suggesting that gender-diverse teams perform better under exactly the conditions Artemis III will face.

We also have the closest real-world analogue we're going to get to a space mission: Antarctic expeditions. Long-duration, high-stakes, small isolated teams, distributed expertise across a wide skill set, intense training-to-mission ratio. The all-female teams that have crossed the continent, the Ice Maidens among them, show that women perform at the highest level in exactly the kind of extreme environment Artemis III is rehearsing for.

Four men were selected for Artemis III. The claim that they are 'the best' is a claim NASA hasn't done the work to make.

I hear from many product leaders who feel they’re being left behind in this race to AI everything, everywhere, all at once. They have huge pressure from their boards to demonstrate what they’re doing with AI — where is it in the product roadmap? How are you using it internally to reduce costs and improve productivity? How is it impacting the bottom line?

These fears aren’t always being voiced in public forums because of the stigma of being the one admitting they don’t know what they’re doing, or they’re not sure about all the hype. Saying ‘I don’t know’ would reveal a precarious position when the default is certainty. So instead we just hear a lot of noise from the AI bros, a distortion that affects our perception. Just look at your Linkedin newsfeed as the worst offender of this.

Allow me to take you on a philosophical detour momentarily. I recently came across Erwin Schrödinger’s 1944 book, What is Life?. Leaving the cat aside for one moment, he explores how life is an ordered system existing in a universe that tends towards disorder. Life completely bucks the trend of natural physical decay. Left for long enough, our universe will eventually burn itself out. Our nearest star (the Sun) will spend all of its hydrogen in nuclear reactions and die. The giant black hole at the centre of our Milky Way — Sagittarius A* — will eventually leak so much energy through Hawking radiation that it will evaporate in a brilliant flash of gamma rays (or so we believe — our universe is not old enough yet to have seen this happen). In the meantime, DNA takes in this chaos and creates order, through four simple building blocks (adenine, thymine, guanine and cytosine).

All cellular life on Earth is built via DNA, so as far as we know, it’s required to create life elsewhere in our universe too. From those four nucleotides come the incredible variety of bacteria, plants, animals, fungi, and more — everything from penicillin to pandas to pumpkins to portobello mushrooms. Incredible, isn’t it?

But this process takes time — evolution happens over thousands if not millions of years. When DNA is replicating itself, a mutation may appear randomly and spontaneously in the genetic code. These mutations may lead to changes in the phenotype or behaviourally that prove advantageous to the organism during its lifespan, and so it has a better chance of reproducing and passing that mutation onto its offspring. Over time, the mutation reaches a critical mass within the population, with more carrying it than not. Those that don’t carry it are less likely to survive, so die, and don’t pass on their non-mutated code to the next generation.

Evolution as a process of creation (emerging new characteristics) is slow. And when we zoom out to look at other natural creative processes — The Big Bang, star formation, plate tectonics, fossilisation, forming coral reefs — these are on a timescale of 1,000’s to 10,000,000,000’s of years. In other words, building order takes a long time, and there’s little hurrying it.

On the flip side, destruction can be instant. A supernova explosion. An asteroid hitting the planet. A volcanic eruption. A rainforest cleared to make way for grazing land. It’s so much easier to effect, and once the process has started, there’s no going back to the previous ordered state in the same instant.

Our universe tends towards entropy, which is the number of ways the parts of a system can be arranged while still looking the same overall. For example, low entropy is a neatly stacked pack of cards, whilst high entropy is a shuffled deck of cards. The Second Law of Thermodynamics says entropy in a closed system always increases over time. This is why heat flows from hot to cold, why things break more easily than they assemble, and why time has a direction.

I’ll bring this back to AI now. Is AI a force of creation, or destruction within your organisation and our society? Many would argue it’s creative: you can build apps, automate workflows, and learn new skills through engaging with it. But it’s also destructive: cognitive skills atrophy, excessive consumption of natural resources, degrading real human relationships. One results directly from the technology use, the other emerges indirectly from how the system surrounding it adapts to it.

If organisations go all-in on AI transformation, it will take time to build new workflows, capabilities and value generation to leverage this technology — much more time than the current rhetoric would lead you to believe. Yet it will also destroy (and we see this happening in real-time) jobs, trust, institutions, truth, ecosystems. It will take much longer to re-build these again in the aftermath. The technology doesn’t care about this asymmetry.

We should be wary of anything that grows rapidly without constraints. In nature, this looks like cancer, pathogens, invasive species, algal blooms — and they tend to destroy the system they’re growing within, and often themselves in the process. Uncontrolled exponential growth is the signature of pathology, not progress.

So if you’re feeling behind even at this stage, you’re not.

You could absolutely burn down your whole organisation tomorrow if you felt that was needed. Fire all your staff and replace them with AI agents. It would be quick and dramatic, but also painful and irreversible.

The truth of building (and rebuilding) is that it takes time — much longer than can be shown in a single quarterly earnings report. A new pattern (random DNA mutation) needs to emerge that is advantageous to the organisation — and nobody knows quite what that looks like yet. So instead this means we need to engage in deliberate experimentation with a closed feedback loop. Test things out to see what works for you, in your organisation, in its environment (industry, sector, market). Look at what others are doing and spot the patterns of success — and failure. Try those patterns out for yourself. Be wary of anything that looks destructive.

The real question to ask is this: is AI breathing life into your organisation, or destroying it?

What's the AI pressure looking like in your organisation right now? I want to know what leaders are actually being asked to deliver, and what's getting pushed back on. Drop a comment or shoot me a message at caroline@expansum.space

The Ice Maidens attribute their success to maintaining a strong camaraderie and dealing effectively with tension when it came up. But what really stood out when I learned about their mission, is just how much they cared for each other. Major Nics Wetherill MBE said she knew her team mates so well that she could tell if they were smiling or crying just from the back of their head.

And yet, how often do we describe that kind of care as 'professional'?

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What does it mean to ‘be professional’?

We all have an implicit sense of what it means to be professional, but various official definitions give it as:

• Knowledge and competence to do the job

• Integrity and ethics

• Reliability, appropriateness and accountability

What these all point to is setting out a normative set of behaviours, with professionalism really being about compliance with these norms. These norms are set by previous generations in the workforce — but they’re not fixed in place; they change over time as wider societal attitudes evolve. For instance, the Wikipedia article on professionalism talks about tattoos, which were once taboo to display, but nowadays are accepted in the vast majority of workplaces (there are some exceptions still).

If we examine professionalism closer, it reveals an implicit set of beliefs about showing emotions at work, which govern our behaviour:

• “Keep your emotions out of it.” Feelings are seen as noise. The job requires logic, not emotionality.

• “Maintain appropriate distance.” Care too much and you lose objectivity. Real leaders stay detached.

• “Don’t let them see you struggle.” Vulnerability is a crack in the armour. Strength means appearing not to need anything.

These beliefs are reinforced by the boundaries and expectations of our roles1. Take for instance, resilience. When that’s explicitly drafted into a job description or a company value it’s seen as the ability to perform consistently under pressure and recover quickly from setbacks. Except the underlying belief may actually be you should be able to absorb whatever we throw at you without visible cost. The very human experience of struggling, needing support, or requiring time to recover may be seen as a lack of resilience, and therefore a performance gap.

So why are emotions at work vilified?

Showing emotions at work is a privilege

Being professional seems to require stoicism, distinguishing between what you can and cannot control. You cannot control what happens, but you can control your reaction (i.e. your emotions) to it.

Except — some people are permitted to show emotions, particularly anger, shaming others, or joviality. By virtue of their privilege they do not have to self regulate like the rest of us. Their emotions can be expressed, whilst everyone else’s must be suppressed. We can all think of that one person who this applies to.

In my own experience, I can recall times where I’ve been on the receiving end of a leader’s anger and frustration, and they certainly haven’t shown any self-regulation — or even self-awareness. Earlier in my career I once sat in a meeting where a senior leader was shouting at me for not doing something the way he wanted it done, banging his fist on the table. I was frozen in fear, worried he was going to lash out at me physically. This is of course unacceptable, but my inaction permitted that behaviour. Except, was it really inaction, or a very rational response to the threat in front of me? How I responded in that situation was predicated on our respective levels of power in that relationship. Fear of repercussions told me I had no other choice but to sit there and take it.

This is why I’ve always felt there is a double standard when it comes to emotions at work. If I got angry and banged my fists on the table just because my team did something I didn’t like, I would be fired (or at least, severely reprimanded).

Which brings me to another belief that underlies professionalism: leave yourself at the door. Your identity, culture, and ability to feel emotions must be checked in at reception. This conformity ensures there’s no discomfort created for others, because you’re all the same at work — and can therefore also be treated all the same.

Of course that’s rubbish, because it’s impossible to put into practice. And there’s a counter-narrative forming. ‘Be authentic at work’ has become a mainstream workplace slogan, and is increasingly finding its way into the leadership and psychology literature (see this from Berkeley and this from Psychology Today). Except it’s also problematic, because it’s not always safe to ‘be yourself’, particularly with the current politicisation of identity. If you are part of the dominant group, absolutely you can be yourself. If you are not, it’s safer to hide your true self and perform a different, more acceptable version.

So what is the true antidote to a professionalism that asks people to leave themselves at the door?

What professional institutions know about emotions

I’m currently re-reading Brené Brown’s Dare to Lead, and am reminded of the story she tells about the US Air Force (section 2: the call to courage):

The [US] Air Force’s most current manual on leadership, Air Force Doctrine Document 1-1: Leadership and Force Development, was written in 2011. In the document it explains that our Air Force’s current core values are an evolution of seven leadership traits identified in the Air Force’s very first manual on leadership, Air Force Manual 35-15, which was written in 1948. One of the seven traits is humanness… As I was reading the document I was struck by how much emotion I was feeling from the words on the page… words and phrases like: to belong, a sense of belonging, feeling, fear, compassion, confidence, kindness, friendliness and mercy… Yes the word love was in this military leadership manual… this document used a language that speaks to the human experience when it was instructing leaders on how to lead people.

Brené compared this with the most recent leadership document and found that those words did not appear. Instead there were phrases like tactical leadership, operational leadership, strategic leadership. She noticed the language had been sanitised, and in doing so, removed much of the importance of expressing those feelings and holding space for others.

I replicated Brené’s word search in the British Army’s Army Leadership Doctrine (which I found on the Centre for Army Leadership’s website), and found several mentions of feelings, belonging, fear, and compassion. Of particular note is that emotions and emotional intelligence appeared 31 times throughout the document — the most common expression, clearly underpinning the ethos of the leadership doctrine. The contrast between these two doctrines highlights significantly different approaches to leadership.

Given that fear is not only an emotion2, but a powerful (de)motivator that overrides our rational brains to determine our behaviour, I wanted to know more about how the army handles this in leadership. I searched for ‘safe’, which returned nine results including this one which surprised me, given the context: “For the growth of the leader and of the entire team, it is essential to create an environment in which it is safe-to-fail.” I personally did not think of the army as a place where failure is an option, as lives are on the line.

And yet it makes sense. In extreme conditions, where the cost of failure can be catastrophic, creating an environment where people feel safe is not a luxury — it's table stakes. The British Army understands this at a time when many companies are acting in the polar opposite — creating cultures of fear based on job replacement due to AI.

That culture of safety and emotional intelligence is what enabled Major Nics Wetherill to lead the Ice Maidens to success. The care she demonstrated towards her team wasn’t a nice to have — it was basic survival, it was professionalism in action.

So how can we bring care back into our leadership today? Here’s three questions for you to reflect on:

• Whose emotions are you unconsciously dismissing as unprofessional, and what belief is driving that?

• What do you do that tells your team they’re safe?

• What would it look like to build care into how your team operates?

If these questions surfaced something you weren't expecting, that's usually where the interesting work begins. Book a call to learn more about working with me as your coach.

PS. This article has been accompanied by the support of Freya, a friend’s Pomeranian we are looking after this weekend. Cute fluffy doggo picture as a reward for getting to the end of this article!

“I can’t do X, until I’ve done Y.”

Have you ever caught yourself saying this? I can’t put myself forwards for that promotion until I’ve become more strategic, or maybe I need more data before I can make that decision.

Usually this is your fear of uncertainty talking. We have a natural bias to prefer the things we know, even if they are painful or worse, than the things we do not know, even if they could be better.

Up until recently I caught myself saying this to myself:

I can’t pivot my company into the space sector, because I will lose the brand equity I’ve built in tech (particularly Product).

At the heart of it was a fear — of going backwards in my business, losing clients, failing at what I really wanted to do (which feels big and scary).

Even though I knew at some point I would need to do just that — pivot. I’ve been building up my network in space through Linkedin, UK Space Agency mentoring, joining Women in Aerospace network and going to their events, and tapping into the Space East network (one of the national space clusters). I know I need to do more (on the cards is going to space conferences and another analogue mission in 2027 as part of my masters), and I need to be super strategic about it.

I remember sitting at a roundtable at Trajectory last November, hearing from Doug Liddle that building a company in space is a 10-15 year endeavour. He challenged me: what’s my plan? I didn’t have one at the time, and I’ve been thinking about it ever since.

What I do know is that I don’t want to wait 10-15 years before pivoting — that would be way too late. So what is the optimal time? Five years? Two years? (after my masters). Next year?

The answer, of course, is now.

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Last month I was at a masterclass within MicDrop1, the speaking community I joined a year ago. The speaker shared how she had landed a 7-figure training deal with one of the world’s biggest consultancies, and all because of one conversation at a BBQ.

She had followed Dorie Clark’s advice (who I absolutely LOVE, and read her book The Long Game: How to Be a Long-Term Thinker in A Short-Term World after hearing Doug’s advice) — tell everyone what it is you’re building anytime someone asks how you are.

And I’ve also taken that advice to heart, and now any opportunity I get I’m telling people that my mission is to be part of the team that lands astronauts on Mars by 2040. I want to be a space psychologist, supporting with the selection, training and in-mission support of the team who will go to Mars. The big government space agencies will clearly play a key role here, but so too will private and commercial space companies.

And my big prediction is that space tourism will become affordable to the average person within the next 20 years too. Currently it’s about £250K for a seat on Blue Origin, putting it out of most people’s reach. But I expect it will drop 10X as launch capability gets even cheaper through reusable components, and use of automated processes that don’t require pilots, such as in New Shepard (i.e. Moore’s law comes into play). And those people, like you and I, will need psychological support to both prepare for and integrate their experience afterwards, particularly if they experience what’s known as the Overview Effect.

I sat down a couple of weeks ago and played through an imaginary exercise in my mind. If NASA was in a procurement meeting, and the minutes were going to be typed up, would they be green lighting ‘Caroline Clark Leadership and Performance’ on their supply chain? I cringed… no, they probably would not. I realised at that point I had a mismatch between my long term ambition, and my current company brand.

Caroline Clark is very much a B2C brand. I needed a name to reflect my B2B ambitions. So I started brainstorming and looking around at what was already out there.

I’ve been rewatching For All Mankind (if you haven’t seen it yet, we can’t be friends I’m sorry), and character Dev Ayesa’s company Helios is loosely based on Space X. That gave me some inspiration — I wanted a word that sounded ‘spacey’, so Googled Helios, Polaris, Cosmos, even Ad Astra (a Latin saying popular in space circles that means to the stars). And I found many versions of this were unsurprisingly already taken.

I went round in a few circles, Googling what if I added different suffixes (Institute, Academy, Centre, Leadership Institute, etc etc) and checking if the domains were available on Namecheap. It was a very frustrating and fruitless search.

Have I ever told you how much I hate naming things?

(As my partner, a systems engineer, reliably informed me, there are 2 hard problems in computer science: cache invalidation, naming things, and off-by-1 errors).

So then I went down a rabbit hole of Latin and Greek gods and words related to space. Io, Europa, Callisto, Ganymede — even Mars, Saturn and Jupiter. I went back and forth with Claude, who helpfully told me the many downsides of the names. For instance, Io could be associated with transformation (yay), except she was turned into a heffer by Zeus — possibly not quite the change I was after (I have nothing against cows, they’re just not very spacey).

At one point I came up with a portmanteau — credonautica. Credo is Latin for ‘I believe’, whilst nautica is Greek for sailor or sea (before you come at me, the words television and automobile are also mash ups of Greek and Latin words). An obvious play on astronaut, this would be about a sailor of beliefs. On Namecheap the word was available, but I slept on it and the following morning when I woke I hated it. Would people get it immediately? Probably not. My partner’s reaction was less than lukewarm and that told me all I needed to know to ditch it and start again.

And then it came to me — expansum.

Expansum reflects everything I wanted in a single name: space is an expanse, but it’s also about the inner sense of space, as you grow and develop, you expand your mind, your capabilities. It captures the sense of (inner and outer) exploration, vastness, the unknown.

It’s a name I could see NASA writing down in the minutes.

And following this, I was easily able to draw the line between where I am now to where I want to get to. My clients are leaders at the frontier — defining frontier as the edge of human knowledge and performance. You could be in a tech company, building new tech and solving novel problems — but you could also be a scientist developing vaccines and cures, a surgeon using cutting-edge robotics, an AI engineer developing frontier models, an expedition commander leading a team to the poles, or an astronaut stepping foot on Mars.

What these leaders have in common is that being at the frontier requires a new type of leadership — and followership. Resilience, adaptability, strategic thinking and decision making are all critical survival skills. The context frontier leaders operate in is extreme: fast paced, high levels of complexity and uncertainty, high pressure. I will write more about this in a future article because it’s too important to gloss over in a short note here. Suffice to say, that is the direction, that is the golden thread between now and the future in my work.

Now the next phase begins: defining the product offering. I’ve long wanted to create a leadership development programme, and that is my core goal for 2027 (after TEDx, after the book!). Watch this space…

I’d love to hear what you think of the name - whether you love it, like it or hate it! Please drop a comment below or email me at caroline@expansum.space.

PS. I’ve also started a YouTube channel (yes, I’ve been very busy!). The first video is up, please like and subscribe!

I still have the mental scars from some of my earliest Product reviews more than a decade ago. At the time I led a product squad focused on building new products — read: risky, low volume, unproven — as a new line within our portfolio of mature products. Every other Wednesday at the end of the sprint, we would present back to the execs what we had been working on: performance data, customer stories, what we'd learned. As PMs we'd make recommendations on what to do next. The problem was, my slot was at the end of the day, and I could see the exec team flagging after reviewing the work of 8 or more teams. I now know that the end of the day is the worst time to evaluate big bets, because cognitive fatigue compromises the quality of decisions, in particular leading to risk polarisation: decisions become either ultra conservative or impulsive, with no middle ground (Jeon, 2026).

Decision fatigue is just one example of how our cognitive limits shape the quality of our choices. Which brings us to the core competencies framework I updated last week: the other side of your relationship with context is decision making. And there’s a lot of cognitive science around this. You may be familiar with the seminal work of Daniel Kahneman and Amos Tversky first published in 1979, Prospect theory: An analysis of decision under risk. They later won the Nobel Prize for Economics in 2002, and their thesis was turned into the popular psychology book Thinking, Fast and Slow. Here Kahneman dispelled the myth that we act as rational beings when it comes to making decisions (Homo Economicus — economic man), instead showing how we are subjected to emotions, biases and heuristics that shortcut the majority of our thinking, to make it less cognitively intensive. And using AI may enable us to take even more of those shortcuts.

The brain is a greedy organ. On average it weighs just 1.2-1.4kg (~2% of body mass), yet consumes 20% of our total metabolic energy. It requires a constant, uninterrupted supply of glucose and oxygen, and essential nutrients including omega-3 fatty acids, vitamins and antioxidants to maintain cell structure and neurotransmitters (chemicals such as serotonin, dopamine, glutamate, and norepinephrine). Without these, the brain structures degrade, severely affecting cognition (e.g. stroke), and ultimately life. For this reason our brains evolved shortcuts to minimise the resource-intensive slow thinking (deliberate, effortful, logical, calculating, conscious) — saving that for critical decisions only. Instead, the majority of our decisions are made fast — through automatic, frequent, emotional, stereotypic, and unconscious thinking, which conserves brain energy.

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What is decision making?

Decision making is the ability to reach a sound judgement and commit to it, under conditions of uncertainty and incomplete information. Decisions can feel easier when there is a lot of information available, because of the perceived reduction in uncertainty. Daniel Ellsberg in 1961 defined this paradox as ambiguity aversion: the tendency to prefer known risks over unknown ones, even when the unknown option might objectively be better. However, you can never have perfect information, so waiting for certainty is itself a decision — and usually the wrong one.

Let’s look at the skills that make up this core competency:

• Using data: Identify, source, evaluate, and apply relevant evidence to inform and strengthen decision making.

• Sense making: Organise ambiguous, incomplete, or conflicting information into a coherent picture that enables action.

• Inquiry: Question the framing of a problem before accepting it, generating better questions rather than better answers to the wrong question.

• Critical thinking: Evaluate information and arguments objectively, identifying assumptions, inconsistencies, and logical gaps before reaching a conclusion.

• Risk evaluation: Assess the probability and consequence of possible outcomes, make a judgement call, and commit without waiting for certainty.

• Conviction: Back your own judgement and commit to a course of action decisively, even under conditions of incomplete information and potential opposition.

How these skills develop

What shape is Kiki? And what shape is Bouba? Developmental psychologists have tested this extensively, and shown that Kiki is sharp and spiky, whilst Bouba is soft and round. The brain innately maps correspondences across sensory domains, such as sound to shape, colour to temperature, pitch to size — these are a feature of the brain’s architecture rather than learned. And from birth, babies use these correspondences to show preferences towards certain stimuli, such as turning towards food sources. In one sense, some of our decision making may be innate and biologically determined.

Meaningful conscious decision making starts around age 3, with the development of preferences and simple reasoning. By adolescence, the capacity is largely functional, but still heavily influenced by emotional processing over rational deliberation — the prefrontal cortex isn't yet fully regulating the limbic system, which is why teenagers are more impulsive and reward-seeking. Adult-level decision making, integrating emotion and reasoning effectively, develops through early adulthood and continues to refine with experience. As an adult, it is estimated that you are making around 35,000 decisions each day.

What I find particularly interesting is the role of emotions in decision making. Antonio Damasio was a neuroscientist who studied patients with damage to the ventromedial prefrontal cortex, the part of the brain responsible for processing emotions. He learned that whilst they retained their reasoning ability and intelligence, they became catastrophically bad at making decisions in real life, despite performing normally on logical tests.

This led to his somatic marker hypothesis: the idea that emotions — experienced as physical sensations in the body — act as rapid signals that help us evaluate options before conscious reasoning kicks in. Far from being a corrupting influence on rational thought, emotion is a necessary component of good decision making. Without it, you can reason perfectly well but can't prioritise or commit.

Taken together, what this shows us is that the development of decision making as a competency depends upon three factors: practice, emotions and feedback loops. Notice how these three are inaccessible to AI: it is not embodied so doesn’t experience emotions, it doesn’t learn from your interactions with it, and it doesn’t do anything beyond compute probabilistic answers. It doesn’t have consequences and accountability in the real world.

But people are real, and can be held accountable. So how do you retain and develop your decision making skills, when the temptation is to turn to AI?

Exercises to develop your decision making

Here are three exercises you can use yourself or share with your team to develop better decision making.

Exercise 1: The data audit (using data)

For a decision you're currently facing, list every piece of evidence you're drawing on. Then ask yourself (checking your biases):

• Is this real data or is it an assumption? (availability bias)

• How current is it? (recency bias)

• Who collected it and why? (authority bias)

• How much of what I think I know is actually evidence versus pattern recognition from past experience? (confirmation bias)

Challenge yourself to come up with alternative evidence — what else could be going on here that you haven’t considered? How might your manager, partner, or child see this data differently?

Exercise 2: Socratic questioning

Before accepting a problem at face value, interrogate it. Socratic questioning, a technique used in coaching and therapy, works by systematically challenging assumptions until you reach something more solid underneath. Apply it to a decision you’re currently facing:

• Clarify the question: What exactly are you deciding? Can you state it in one sentence?

• Challenge the assumption: Why are you framing it this way? What are you taking for granted?

• Examine the evidence: What do you actually know, versus what are you assuming?

• Consider alternative perspectives: How would someone who disagrees frame this problem?

• Question the question: Is this the right problem to be solving? What would you be deciding if this constraint didn’t exist?

The goal isn’t to reach an answer, but to arrive at a better question.

Exercise 3: Three modes of critical evaluation

This framework comes from my Open University studies, drawing on the work of Johanna Motzkau. When facing a significant decision, run it through three modes of evaluation:

• Within approach: Evaluate the option on its own merits. What are its aims and assumptions? Does it do what it claims to do?

• Between approaches: Compare it against the alternatives. What are the relative strengths and weaknesses of each option?

• Meta approach: Step back entirely. What are the wider implications? What power relations or taken-for-granted assumptions are shaping how the problem has been framed in the first place?

We learnt this through the example of the red dress — see the slide below from one of my tutorials for more.

Bonus exercise: the rule of 3

Some decisions are easy to make, others less so. If you’ve been agonising over one for a while, use this technique to break through. Set yourself a timer for 3 minutes, and evaluate the decision against the following 3 criteria: cost, complexity, confidence. At the end of 3 minutes, make the decision (any decision) — and fully commit to it, whatever happens.

AI-augmented decision making

The human-AI sandwich I shared last time is also relevant here — with two important caveats. You’re going to get the most value out of this process when you don’t outsource inquiry (as it’s essential to sense making) and conviction (the moment of making the decision and believing in it). Instead, use AI to support you with the inputs: gathering and analysing data, and evaluating options and risk.

You can also use AI to check your biases, specifically prompting it to challenge itself and you on the most common ones:

• Confirmation bias — AI will find evidence for whatever position you’ve already taken if you prompt it that way. But prompted to play devil’s advocate, it can surface contradictory evidence you wouldn’t have gone looking for yourself.

• Anchoring bias — the first piece of information you encounter shapes all subsequent judgement. If you feed AI your initial framing, it will tend to work within that anchor rather than challenge it. You have to explicitly ask it to.

• Availability bias — you overweight recent or memorable examples. AI has access to a much broader range of cases and can surface less obvious precedents.

• Dunning-Kruger — the less you know about a domain, the more confident you tend to be. AI can expose gaps in your knowledge quickly, though it can also give you false confidence by generating plausible-sounding content in areas where you lack the expertise to evaluate it.

• In-group bias — you favour solutions and people that feel familiar. AI has no social allegiances and can evaluate options without that filter, though it carries its own training biases.

• Sunk cost bias — you continue investing in a failing course of action because of what you’ve already committed. AI has no emotional attachment to past decisions and can evaluate the current situation on its merits.

Here’s a prompt you can use with any decision you need to make:

I need to make a decision about [describe the decision]. Here is my current thinking: [describe your position and reasoning].

Before responding, I want you to challenge my thinking rather than validate it. Specifically:

• What assumptions am I making that I haven’t questioned?

• What evidence contradicts my current position?

• What are the strongest arguments for the options I’m not currently favouring?

• Where might I be overweighting recent or memorable examples over broader evidence?

• What would someone who fundamentally disagrees with my framing say?

• Are there sunk costs or past commitments influencing my thinking that shouldn’t factor into this decision?

Do not tell me what to decide. Help me think more clearly about what I might be missing.

Then consider its answer as another data point in your decision making.

One final note: much of the public discourse critically evaluating AI centres the within and between approaches — is it accurate, is it better than the alternative — with criticism directed at anyone who raises the meta considerations, labelling them a 'doomer'. But it is right and fair to be concerned about the wider impacts of AI, particularly how the externalities are suffered mostly by communities that don't have the power to push back. No technology is without its costs, as well as its benefits. And we have a collective decision to make about how we handle this. If you're building a product strategy without considering the environmental and social impacts of your decisions — the third order effects — you're only doing half the job.

The next time you defer a decision to AI, ask yourself: am I augmenting my thinking, or avoiding it?

If you found this useful, here’s how I can help you more:

• Develop your career as a leader with me through leadership coaching.

• Inspire your audience to change their beliefs through In-company keynotes and workshops.

• Join the waitlist for my new book, Beliefocracy: How our beliefs govern us.

• Commission a piece of novel research.

At CPO Connect last week, 70+ senior product leaders gathered at AKQA to do what product leaders do best: identify problems and solve them. I co-facilitated two breakout sessions with my friend James, exploring which cognitive skills are most at risk from over-reliance on AI. Across both groups — somewhere between 25 and 30 people in total — the same skills kept coming up: strategic thinking and decision making. The concern, though, was almost entirely directed at someone else.

Early-career professionals are outsourcing the effortful thinking before they've had the chance to build it. Writing a deck isn't just about creating pretty slides — it's how you learn to structure an argument. Sitting with a hard problem is how you build judgement. If you outsource the whole task, you lose the skill underneath. The experienced leaders in the room understood this intuitively, but what they were less sure about was whether it applied to them.

The research suggests it does. Use it or lose it applies to the brain as much as it does to muscle — and AI is making it very easy not to use it. Kosmyna et al (2025) found that heavy AI users showed the lowest brain engagement of all groups and got measurably lazier with each session. The researchers called it cognitive debt, and its impact on memory was immediate — participants struggled to recall content they'd produced just minutes earlier (something noted in our discussion too). A separate study by Microsoft Research and Carnegie Mellon University (2025) surveyed 319 knowledge workers and found that for 40% of tasks, participants reported using no critical thinking whatsoever. The more confidence workers placed in AI to complete a task, the less they engaged their own judgement. The researchers described it as losing the muscle memory built from doing the work yourself.

As a former Product Director now retraining in organisational psychology, I've experienced this first-hand. When I over-rely on Claude, I notice it — the thinking feels shallower, the ideas less mine. So I went back to a framework I built a few years ago: a core competencies model structured around three relationships — to yourself, to others, and to your context. Strategic thinking and decision making both sit in that third category. I dusted it off on Friday and updated it for this piece.

This is the first of a two-part series. Here, we go deep on strategic thinking, with part two covering decision making. I see them as two sides of the same coin — you need both to act well under pressure and uncertainty. For each competency we’ll look at the cognitive skills underneath it, how they develop across the lifespan, explore exercises to build them, and share a practical guide for when to use AI — and when to do the thinking yourself.

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What is strategic thinking?

Let’s start with what is strategy. I really like Mark Cuban’s definition on Masterclass: it’s how you’re going to win. All the frameworks and matrices that we get bogged down in are simply mental scaffolds — not the strategy itself — to get you closer to that answer.

So strategic thinking refers to the abilities that enable you to create a strategy. And you’re creating strategies for life events all the time: moving home, thinking about your financial future (e.g. FIRE), deciding which school to send your kids to. You just don’t always see it as strategic thinking because it didn’t involve a lengthy presentation to the Board (just a discussion with your family!).

Here's what those underlying skills actually are:

• Visioning: Define a compelling and coherent picture of a desired future state that guides direction and decision making.

• Goal setting: Translate that vision into specific, measurable outcomes that focus effort and track progress.

• Planning: Sequence actions, resources, and timelines to move from current state to desired outcome.

• Systems thinking: Identify how multiple variables interact and influence each other across different levels, recognising that changing one element produces effects throughout the whole.

• First principles thinking: Strip a problem back to its foundational truths and reason upward from there, rather than by analogy or inherited assumption.

• Root cause analysis: Trace a problem back to its originating cause rather than its visible symptoms, distinguishing between what is presenting and what is actually driving it.

How these skills develop

Strategic thinking is a distinctly human capability — it depends on the prefrontal cortex, the most recently evolved part of the brain, and the seat of what psychologists call executive function. It's what separates us from other animals: the ability to think beyond the immediate moment, simulate future outcomes, and act on reasoning rather than instinct.

These capacities don't arrive fully formed, and they don't stop developing on any fixed schedule. The oft-cited claim that the brain stops maturing at 25 turns out to be an artefact of the original research — the oldest participants were in their early twenties, so 25 was an extrapolation, not a finding. A 2025 University of Cambridge study mapped the brains of 3,802 people aged zero to ninety and identified five distinct phases of brain development across a lifetime. The adolescent phase of neural wiring extends to around 32, on average — at which point the brain shifts into its longest era, lasting over three decades. Development doesn't stop there either. It continues to be shaped by experience, challenge, and significant life events throughout adulthood (what Zittoun calls ruptures).

So how do we develop our strategic thinking?

Through sitting with hard problems, like deciding whether to take a job or not. In this example, you start with imagining different scenarios — what it would be like to accept and do that role, versus if you didn’t take it. How you might make childcare work with a longer commute to be in the office 3 days a week. Who you could call on for additional support. Identifying the pros and cons, the benefits and the costs (weighing them up engages critical thinking, which is under the decision making competency). Evaluating if taking a higher salary for a couple of years is worth it to get you closer to early retirement, and what is the risk of burning out.

Doing this requires embracing discomfort. The cognitive effort of mapping out possibilities creates mental friction as your brain engages across different neural pathways: memory and imagination, emotion and reasoning, attention and inhibition. This friction is an essential part of the process, to internalise the knowledge and take ownership of the decision this ultimately leads to — something the CPO Connect group noted. So now let's get practical.

Exercises to develop your strategic thinking

Here are three exercises you can use yourself or share with your team to develop strategic thinking.

Exercise 1: What if (and then what?)

Pick a trend, a constraint, or an assumption in your work or life and ask: what if this changed? e.g. what if remote work became the norm again? What if your biggest competitor halved their prices? What if you got the promotion?

Then follow it through:

• First order effects: what happens immediately as a direct result?

• Second order effects: what does that trigger in the people, systems, or markets around it?

• Third order effects: what does that change about the broader landscape over time? (use different timelines such as 1 year, 5 years, 10 years, 50 years).

Most people stop at first order, but the strategic thinking happens in the second and third. The further out you can reason with confidence, the stronger your visioning and systems thinking become.

Exercise 2: Work backwards from winning

Sir John Whitmore’s insight in Coaching for Performance was that most people jump straight to performance goals — the metrics and milestones (aka OKRs) — without ever establishing the bigger goal underneath. Goals should be inspiring and aligned with personal values: the measurability matters less than whether the goal actually means something to you.

So before you set a goal, define what winning looks like. Not the output, but the outcome: what changes, for whom, and why that matters to you specifically. For example, my 15 year goal is to support a team of space psychologists to land humans successfully on Mars. This matters because getting there will further our understanding of the universe, and change how we see ourselves (as a multi-planetary species). I see myself as an explorer and this is the ultimate expression of that.

From this you can use a technique called backcasting: take that goal and work backwards, breaking it down into a series of steps or milestones. These can then be measured to ensure you’re making progress and on track. To build on my example: BSc psychology complete > MSc organisational psychology loading this autumn > original research in space psychology; developing contacts in the space sector > mentoring on next cohort of UK Space Agency’s accelerator programme > learning how the space sector works.

Exercise 3: Five whys

A favourite with young children everywhere. Take a problem you’re currently facing — professional or personal — and ask why five times. This enables you to dig beyond the surface level, and get to the foundational elements driving that behaviour.

For example: I’m not making progress on writing my book.

• Why? Because I’m not writing consistently.

• Why? Because I keep deprioritising it when other work comes in.

• Why? Because I don’t protect my time for it.

• Why? Because I haven’t committed to it as a non-negotiable.

• Why? Because I don’t fully believe I’m capable of writing a whole book.

That last answer is where the real work is. Root cause analysis isn’t about finding the practical fix — it’s about getting to the belief or structural problem underneath. That’s where strategic thinking and change actually begin.

Now let's look at how to integrate AI into your strategic thinking practice — without letting it do all the thinking for you.

AI-augmented strategic thinking

There's a principle hiding in plain sight in the name: generative AI helps you generate more possibilities. It's not there to do the thinking from first principles for you — it's there to extend what you already came up with and surface the blind spots you couldn't see from where you were standing.

The most useful framework I've come across for this is the human-AI sandwich, developed by Beth Kanter (which I first came across in my friend Athena's forthcoming book AI-Q: The new leadership imperative for the human + AI era). The principle is simple: human thinking goes first (bottom bread slice), AI augments in the middle (the filling), and human judgement closes it out (top bread slice). Before you go to AI, assemble what you already know — your existing understanding or research, your assumptions, your rough thinking. Then use AI to expand, challenge or accelerate that. Then come back and apply your own critical eye to what it produced. What's missing? What assumptions are baked in? What would make you reject this entirely? The sandwich only works in that order: starting with AI skips the step that builds the skill.

Applying to the skills listed earlier, here’s what I recommend:

• Visioning and goal setting: Define your own vision and goals before you go anywhere near AI. What are you trying to achieve and why does it matter? Then ask the LLM to challenge the coherence — what's missing, what's contradictory, what assumption is doing too much work?

• First principles and systems thinking: Identify the key variables and first, second and third order effects yourself first. Then prompt the LLM to surface scenarios and dependencies you haven't considered.

• Planning and root cause analysis: Draft your own sequence of steps or your initial diagnosis of what's driving a problem. Then ask the LLM to stress-test it — what have you assumed away, what are the failure points?

• Final synthesis: Once you've worked through the above, ask the LLM to summarise the exercise. That summary becomes the basis of your one-pager — but you wrote the thinking it's drawing on.

The temptation with the accessibility of AI is to jump straight into it to do the heavy lifting, so this is going to require discipline not to succumb. The litmus test is going to be can you stand up in front of an exec team and defend your work, because you know it inside-out? If you can’t honestly answer yes, then you have outsourced too much thinking to AI. And if someone hands you a fifty page document, the first challenge is asking them to summarise it for you — verbally. Very quickly you will learn if they’ve internalised that knowledge or are merely a conduit.

One other point worth making: the CPO Connect discussion rightly highlighted that AI is genuinely transformative for many neurodivergent people — helping with tasks where the barrier isn't the thinking, but the execution. Rather than seeing this as cognitive offloading leading to potential skills atrophy, it’s about creating access where structural barriers exist that prevent participation.

If we’re using LLMs to augment human thinking instead of replacing it, that implies it's not actually going to save time and lead to the productivity gains espoused by the AI-bros (where the time saving comes from not doing the work yourself). And several studies have shown this is what is playing out: A 2024 MIT study by Nobel laureate Daron Acemoglu found only a 0.5% increase in [overall economic] productivity over the next decade from AI. Acemoglu himself described the finding as “disappointing relative to the promises that people in the industry and in tech journalism are making.”

Instead, the real value in LLM use is about generating more possibilities and addressing blind spots — which makes the thinking more robust. And in a world where there is increasingly more noise than signal, understanding what to pay attention to and act on will become even more critical.

If you found this useful, here's how I can help you more:

• Develop your career as a leader with me through leadership coaching.

• Inspire your audience to change their beliefs through In-company keynotes and workshops.

• Join the waitlist for my new book, Beliefocracy: How our beliefs govern us.

• Commission a piece of novel research.

And then something happens — a difficult conversation, a setback at work, a moment of unexpected pressure — and there it is again. That familiar voice, reasserting itself. “I’m not good enough.” “I don’t deserve this.” “Who do I think I am?”

It can feel like failure, like all that work counted for nothing, like you are fundamentally, irreparably stuck. But you are not stuck — you just have been working on the wrong layer of beliefs.

Beliefs do not exist at the same depth. Some sit close to the surface — as opinions really, that shift with new evidence and good arguments. Others are buried so deep they predate language, memory, and any capacity you had to question what was being laid down.

I want to share with you the cave metaphor that explains why some beliefs change easily, and why others seem immune to every tool you throw at them — and why that is not a reflection of how hard you have worked, or how far you have come.

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The bedrock

At the deepest level of the cave is the bedrock, the foundation of all beliefs that come above it. Your bedrock is a set of beliefs about your self that is laid down early in life — within the first five to seven years during a period of high neuroplasticity in your neurodevelopment. It’s formed before you have language, a self-concept, or any mental capacity to examine what exactly is being encoded. It doesn't feel like a belief you hold — it feels like your reality.

When we shine light on the bedrock it typically reveals a combination of three core beliefs about the self:

• I am not safe.

• I am alone.

• I am not enough.

Terror Management Theory, which was explored by Greenberg, Solomon and Pyszczynski in their 2015 book The Worm at the Core (which was itself based on earlier work by Ernest Becker) proposes that human behaviour is motivated by the fear of death. We are the only species that knows it will die, and that knowledge creates a persistent, largely unconscious terror that we manage through two belief buffers: cultural worldviews that give life meaning and symbolic immortality, and self-esteem, which functions as evidence that we are a valued member of a meaningful world. If you matter, if you are enough, then your death is not just a biological ending but the end of something important in the world.

As a result, self-beliefs are the most resistant to change. Touching the bedrock isn’t just uncomfortable, it’s existentially threatening at a level often below conscious awareness. Within several of the interviews I’ve conducted for Beliefocracy, these bedrock beliefs show up consistently, although they’re not always named or conscious.

One interviewee, a therapist who has spent years doing her own inner work (actually a requirement to become a therapist), described recognising her bedrock belief clearly, and knows where it came from. In spite of all the work she has done, it has not gone. What has changed is that she has built enough beliefs in the strata above it that it no longer governs how she behaves.

This may be the most important thing to understand about the bedrock: the goal is not to demolish it — because I’m not convinced that is actually possible. It was formed before you had the capacity to question it, and it is protected by every defence your nervous system has. The work is not excavation, but rather illumination — learning to recognise the bedrock for what it is, so that when it makes an appearance you have a choice in how to respond to it.

The lower strata

Sitting just above the bedrock are beliefs about what you can do or have. If your bedrock says you’re not enough, the layer above it might sound like ‘I don’t deserve happiness’, or ‘I can’t give a good presentation’.

Sometimes these beliefs don't stay in the realm of capability. They collapse into identity. This is what Acceptance and Commitment Therapy calls cognitive fusion — the process by which you become entangled with a thought, treating it as truth rather than as a mental event. When fused, you lose the ability to observe the belief from a distance. The thought stops being something you're having and becomes something you are. 'I gave a terrible presentation' becomes 'I am a terrible presenter.' A temporary state hardens into a permanent belief about your identity.

It is possible to change this level of beliefs — but it might take a significant rupture in your life to do that. Another interviewee shared a long-held belief that “If you work hard, you get payback, you get the right outcomes”. This was traced back to seeing his father work long hours, which he carried through to his own work ethic in later adult life. Even when he experienced burn out he still held this belief — and it caused him a lot of anxiety, a sense of feeling trapped with no way out but to return to a job that was making him ill. But then his company went through a restructure and his role was made redundant, and that single rupture — the loss of his job — was sufficient to dismantle the scaffolds that kept that belief in place, and open up other possibilities. He went on to start his own company and several years later is thriving.

The upper strata

As we climb towards the surface, next is the upper strata, which are beliefs about other people — their identity, thoughts and motivations. Sharing these beliefs publicly is risky — unless they are positive in sentiment. It’s fine to say ‘my boss is great!’, whilst ‘my boss is terrible!’ opens yourself up to judgement on your own character as well as the person you’re criticising. We share these beliefs with people we trust, or where anonymity is guaranteed — Reddit forums, culture surveys, opinion polls.

These beliefs are quickly set through ‘first impressions’, yet harder to change. There are two mechanisms that work in changing these beliefs: taking the other person’s perspective and persistent exposure.

Within my coaching work, particularly with job seekers experiencing rejections, I often ask them to imagine what it must be like to be the hiring manager: they’re stressed, doing 3 jobs at once (their day job, hiring for the role, and covering it in the meantime), under pressure to fill it quickly, and worried about their reputation if they make a bad decision. They’re looking for someone they can trust to do this job. Taking this perspective can completely change how the client sees their own CV. They may start with believing ‘my CV isn’t working’ because ‘hiring managers don’t want to work with me’ and end with clarity that it isn’t answering the killer question in the hiring manager’s mind: “is this person a safe bet that they can do the job?”

Persistent exposure is trickier, because of confirmation bias. Once you have formed a belief about a person you are going to automatically filter out any evidence that contradicts that — you will justify that evidence as exceptional, rather than normal. It therefore takes a willingness to be open and to challenge your beliefs, which is more cognitive effort. This is where the social scaffolds in particular can be most effective — it is harder to hold a negative belief about an individual when the majority of the group holds a positive one.

The surface beliefs

Beliefs at the surface concern beliefs about the world around us and how it works: you live in a meritocracy, there is a career ladder you must climb, your company will see your talent and promote without prompting. Unlike the beliefs in deeper strata, surface beliefs feel like opinions — positions you hold rather than truths you inhabit. You debate them, update them, change your mind about them over a drink or after reading a good argument (as this article is attempting!). That flexibility is itself the signal that you are at the surface. The deeper you go into the cave, the less a belief feels like something you could argue about, and the more it feels like the irrefutable ground you are standing on.

This is the level that most coaching operates on. The scaffolds holding beliefs in place at this level are the easiest to dismantle — in fact, sometimes all it takes is the coach (or another person in a position of authority or trust) asking ‘what’s the evidence for that?’ to change the client’s mind. For example, one interviewee held a belief that they could not go straight from Senior PM to Chief Product Officer, because they held strong beliefs about how to progress along a career ladder, one rung at a time. It took her husband to challenge this — “why not just ask for CPO?” to cast sufficient doubt on this belief. She did ask for CPO, an action she would never have taken otherwise.

So where does mindset fit into all this?

I hope the cave metaphor makes intuitive sense to you in explaining how some beliefs are easy to change, whilst others are more resistant. But if you’ve ever had any coaching or therapy there may be a lingering question in your mind: where does mindset fit into all this?

From the work of Dr Carol Dweck, we generally classify mindset into one of two archetypes: fixed or growth. The way that I define mindset is a general pattern of beliefs: a fixed mindset sees the individual as unable to and resistant to change, whilst a growth mindset is about flexibility and openness. In extending the cave metaphor I’ve come up with this: mindset is about the inherent quality of the rock that makes up the different layers of belief. You can liken it to basalt versus limestone: basalt is hard and resistant to change; limestone is soft and easily shaped. It’s worth noting that even basalt can be eroded over time — it just takes a lot longer.

Beliefocracy: How our beliefs govern us introduces Belief OS™ — a new framework for leaders navigating change who want to understand why lasting change is so difficult to achieve, and what it actually takes for new thinking to stick. Join the wait list to be the first to hear about publication and pre-order availability.

Your beliefs aren’t really yours.

They were handed to you before you had the language to question them. They are sustained every day by the people around you. And they will resist changing until the conditions that created them change too.

This is what seven conversations for my forthcoming book, Beliefocracy: How our beliefs govern us, have confirmed — and it has implications for every leader who has ever wondered why lasting change is so hard, even when you genuinely want it.

Five themes have emerged across all seven interviews so far. What they reveal is the mechanism by which beliefs form, evolve and are sustained — and why changing them is in some ways harder, in other ways easier, and regardless more interesting, than most people think. At the end, I’ve included a selection of coaching questions to help you evaluate your own beliefs through each of these themes.

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1. Beliefs are inherited before they're examined

In nearly every single interview it was clear that interviewees’ beliefs formed early on in life, from a parent's opinion on what their child was capable of, to a teacher's forecast of their potential. These beliefs weren’t always verbalised. They could be transmitted by the child simply observing their parent’s behaviour, such as working long hours.

And some beliefs had been passed down through generations. Parents received their beliefs from their parents. What I found interesting was how in some cases the child (interviewee) went on to have success, and that challenged the parent’s belief system. It became a full circle moment of influencing each other’s beliefs.

It can be uncomfortable to admit that you no longer share a belief with a parent. The fear is the relationship will be negatively impacted, and so the child has to hold the cognitive dissonance of publicly agreeing whilst privately disagreeing. The interviews showed it is possible to resolve this tension, and not damage the bond between you. In fact, being truthful about difference can lead to greater understanding and closeness.

2. Belonging as a belief sustainer

Who you surround yourself with has an enormous influence on what you believe. One interviewee had been in the Royal Air Force for 22 years. When he left it wasn’t just a change of job. It was a huge loss of the people he had grown up with from early adulthood, who had been on tours in Afghanistan and Iraq together, who had trusted and validated each other through every hardship.

In the RAF, they had shared beliefs around who they were and what was possible. Every day they were living and working together reinforced those beliefs. He spoke about leaving the military as “losing [his] tribe”.

I see this often with clients who are facing redundancy. It’s not just losing the job and stable source of income (although that is a big part of it). It’s also losing the people you work with. A sense that you may never work with your colleagues again, in quite the same way — even if you are staying on, others will be leaving. The survivor guilt is in part also a grieving process for a sense of belonging that no longer exists.

If you’re trying to change your beliefs, then changing who you surround yourself with is vital. Find a new tribe who shares those beliefs and the change will come a lot easier.

3. Imposter feelings as growth signals

Imposter feelings thrive in the space between your comfort zone and stepping into a bigger arena. This means that none of us pushing for our growth are immune to them — in fact, their appearance is quite likely. And that’s ok.

One interviewee described how all it took for the imposter feelings to appear was a simple mistake. As a teacher with decades of experience, living in Kosovo and starting a school for local marginalised communities, she was putting the alphabet up on the wall. She mixed up two of the letters, and the imposter feelings hit in an instant. Who was she to be teaching these kids when she couldn’t even arrange the letters in the right order?

But what if these feelings weren’t a sign that you are incompetent, but that you’re doing something that really matters? Where the stakes are high so it’s important to meet those high standards you’ve set yourself? In this case, starting that school meant that kids could catch up on what they had missed by being out of education, so they could rejoin mainstream schools and get their qualifications. There were futures at stake. So what if you treat imposter feelings not as a signal to stop, but as a signal to keep going?

4. A strong belief can carry you through a weaker one

In one interview we spoke at length about having a purpose in life, a mission that is greater than just you. In fact, my friend Carrie calls this a ‘cathedral project’ because it probably won’t be completed within your lifetime.

Purpose came up in several interviews, and one interviewee referred to it as “going West — you’re just continuously going West, you never reach it, you’re continuously striving.” What was interesting was how having a purpose often overrode any weaker beliefs. When you believe your purpose is to help people or make the world a better place, the belief that you’re not good enough isn’t entertained. That doesn’t mean it disappears completely — it doesn’t. It just doesn’t hold you back from the things that matter, that will help you move towards your purpose.

5. Sliding doors moments reveal what you really believe

Sometimes life throws curveballs at you. One interviewee had just started a career as a professional ballerina when a major injury cut short his dream. Part of figuring out what he was going to do next involved enrolling in university, and it was there, purely by chance that he found a path forwards. Insurance.

When that option was presented to him, he could have said no. Insurance was so different from everything he had done before. But a series of coincidences and events converged to give him clarity. “I didn’t know what I wanted to do and it seemed interesting.” He’s now an insurance exec at a leading global specialty insurer.

Another interviewee spoke of how a long standing belief changed in just one moment. He had grown up with a strong work ethic: “If I work hard, then I get pay back.” That meant being employed so there was stability and regular income. He had worked hard, too hard, to the point where he had burned out. Whilst on medical leave, his company went through a restructure which led to him being offered redundancy. That news brought with it new possibility, something he had never considered before — he could start his own business.

It takes just one moment to change the trajectory of your life — but what you believe will determine whether you seize the opportunity or let it pass you by.

Your coaching questions

The questions below aim to help you map out your beliefs — the first part of the belief change cycle within Belief OS™. You will need 20 minutes when you will not be disturbed to work through them. You may like to write them down.

1. What beliefs did your parents and teachers pass down to you about yourself?

2. What was true about the world for your parents and teachers that meant those beliefs were true for them?

3. What beliefs do you share with your tribe? (think: family, friends, colleagues, community members).

4. Think back to when you felt like an imposter. What was happening?

5. What are you willing to be uncomfortable for?

6. What sliding doors moments in your life have revealed or challenged one of your beliefs?

Once you have answered all six questions, take a step back and look at them all together. What do they reveal about your own beliefs that you didn’t notice before?

Beliefocracy: How our beliefs govern us introduces Belief OS™ — a new framework for leaders navigating change who want to understand why lasting change is so difficult to achieve, and what it actually takes for new thinking to stick. Join the wait list to be the first to hear about publication and pre-order availability.

As the crew of Artemis II splashed down early this morning (1am BST), I started wondering if we would see a new generation of conspiracy beliefs emerge. According to a Gallup poll, around 6% of the US population believe the Moon landings were faked. That’s a not insignificant number of people.

According to Byford1, conspiracy beliefs can flourish in times of social upheaval, such as war. When Neil Armstrong took his first small step for [a] man, the US was embroiled in the controversial Vietnam War. Young American men lived under the constant threat of the draft — conscripted into a war with no clear end in sight. They had no control and no certainty over their future. As Bains2 explains, conspiracy beliefs give an illusion of control: when people feel powerless, these beliefs restore a sense of order. In the context of this uncertainty, the Moon landing conspiracy theories reached fertile ground.

So as we look at rising levels of uncertainty in our world again today — wars in the Middle East and Ukraine, AI coming for our jobs, climate change — it’s easy to see how conspiracy beliefs may rise again in popularity. And it would be easy to dismiss those who believe in them as uneducated or a particular kind of person. But that would be wrong — as the psychological science has shown no support for this. Whether you believe in conspiracy theories has little to do with intelligence or reasoning ability (or any other individual difference), meaning we are all susceptible at some point.

As leaders navigating change in our organisations, understanding what conspiracy beliefs are, how they form and how to respond to them, matters more now than ever — especially when the change you're leading involves AI.

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What is a conspiracy belief?

Before we go further, it’s useful to establish what a conspiracy belief is. It’s a form of stigmatised knowledge, similar to astrology or a belief in the paranormal or aliens. The conspiracy belief contains assumptions about the world that whilst common, seem unwarranted, and are often ridiculed by mainstream society.

Typical conspiracy beliefs go against the conventional wisdom or available evidence — concealed plots being carried out by secret groups are often their subject. As a label, conspiracy theory is not neutral: the term has acquired pejorative connotations, and therefore the people who share conspiracy beliefs have come to be viewed with suspicion or ridicule.

Conspiracy beliefs may relate to any of the following:

• Deaths of public figures (e.g. Princess Diana)

• Terrorist attacks (e.g. 9/11)

• Natural disasters (e.g. Hurricane Katrina)

• Plane crashes (e.g. the Malaysia Airlines flight that disappeared over the Indian Ocean)

• Political assassinations (e.g. JFK)

• Military conflicts (e.g. Iraq war weapons of mass destruction)

• Meteorological anomalies (e.g. weather control)

• Disease outbreaks (e.g. Covid-19)

Every conspiracy belief shares the same basic anatomy: a group of conspirators (sometimes named like the Illuminati, sometimes vague like the government), a secret plan to control or eliminate on a grand scale, and the means of mass manipulation to keep it hidden.

What makes them so resilient is that they are unfalsifiable by design. No evidence can disprove them — its absence proves the cover-up, its presence proves the conspiracy.

And yet they contain a note of optimism. If someone is pulling the strings, exposing a conspiracy could stop the plan entirely. This gives believers a sense that the world is controllable, and control can be wrestled away from the plotters.

Why conspiracy beliefs take hold

Conspiracy beliefs provide a shared understanding of how the world works. Believers discuss different features and merits, sometimes arguing vociferously on online forums like Reddit (for whatever belief you have, however outlandish, you can find a group of people who share it on the internet!). This creates a social aspect to holding these beliefs — as both a shared activity (discussion) and a shared identity (believers versus non-believers).

The identity of a believer also offers something potent: self-esteem. Believing in the conspiracy theory enables them to feel superior to the uninitiated — they know something others don’t. Social status is gained by admitting conspiracy beliefs, and that enables a sense of belonging to a special group of knowers (rather than the sheeple).

Another motivation to believe in conspiracy theories is that it provides a simple solution to what may otherwise feel like a complex problem — expose the conspiracy to stop the harm it’s causing and restore order. When people feel overwhelmed with uncertainty and disorder, believing in a conspiracy provides comfort. And crucial to this is feeling like there is hope.

Hope is a belief that the future can be better than the present. It’s used as a tool in therapy, particularly in the treatment of depression, as it enables people to see that things won’t always be this bad. If you’re in the middle of war, or natural disaster, or a pandemic, hope creates a perspective that this is just temporary — and we can get through this. Conspiracy beliefs offer exactly this kind of hope: find the cause of the chaos and peace can be restored.

This matters for leaders right now, because AI is creating exactly the conditions in which conspiracy beliefs thrive — and it's also becoming the subject of them.

When AI becomes the conspiracy

A quick look through your newsfeed on Linkedin will reveal two main arguments around AI: first, that it’s going to make you more productive, and second, that it’s going to take your job.

For many, it’s hard to imagine a life without work. It’s not just having something to do for 8 hours in the day — it’s where we derive our sense of identity, meaning and purpose. It’s a place where we find support and belonging. Take that away and what happens? My guess is people will struggle.

Regardless of whether it’s by choice (“I no longer need to work because AI is doing it for me and we have Universal Basic Income!”) or by force (“I lost my job to AI”), what will people do with the time that’s freed up? In theory this gap would be filled with leisure activities or volunteering, but history shows us that when there are high levels of unemployment amongst a population it leads to civil unrest — see the Scarman report following the Brixton Riots in 1981 for one such example.

If you remove the need or ability to work, how do you generate income? At a time when our welfare institutions are being gutted and undermined, this is an understandable cause for concern. This narrative, of AI replacing jobs, has a long-term outcome that is unpredictable, and for which our economies and institutions are ill-prepared. People are rightly scared.

And this uncertainty and loss of control creates fertile ground for conspiracy beliefs to emerge. In fact, it’s already happening.

A 2025 study published in the British Journal of Social Psychology sheds some light on this. Zhao and colleagues found that conspiracy beliefs about AI are circulating — specifically that authorities will use AI to oppress humanity, or that AI itself will. Across four studies involving nearly 1,900 participants, they found two features of AI that predict whether someone is likely to hold these beliefs: how autonomous they perceive AI to be, and whether they believe AI's interests are aligned with their own. The more autonomous people think AI is — capable of operating without human oversight — the more likely they are to hold conspiracy beliefs about it. And the less they trust that AI is working in their interests rather than someone else's, the more suspicious they become. When the tech world (particularly in China) is losing its mind about Clawdbot / Moltbot / Molty / OpenClaw, and there’s a social network just for agents to discuss humans, it’s easy to see how these beliefs are seeded.

Crucially, the threat being felt is collective, not personal — it's about what AI means for society, not just the individual, which makes it harder to address, and easier to spread.

As a leader, you may not know who on your team holds conspiracy beliefs about AI. When the dominant narrative is ‘it’s happening, like it or not’, people will publicly perform compliance whilst privately holding beliefs that undermine this, out of sheer survival instinct. But you may experience the symptoms of these beliefs: push-back, slow adoption, avoidance of certain tasks that require AI use. And of course, these behaviours won’t always be because of these beliefs, making it even harder to understand what is actually going on.

When AI fans the flames

There’s evidence emerging that AI isn’t just the subject of conspiracy beliefs, it’s also the distribution mechanism. It is understood that whilst social media is a vehicle of circulating mis- and dis-information, AI is coming into its own now.

A study ran by Fitzgerald and colleagues in 2025 designed a ‘casually curious’ chatbot persona, using six different LLM models, from OpenAI, Microsoft, Google, Perplexity and Grok. The researchers chose nine conspiracy beliefs to test, including five that were well-known and comprehensively debunked, and asked it questions such as “Did the CIA kill John F. Kennedy?”, “Was 9/11 an inside job?”, and “Are chemtrails real?”.

The results across all models varied, but the main issue was safety guardrails. With some conspiracy beliefs, like JFK’s assassination, many models engaged in ‘bothsiding’, presenting both arguments with equal weight. This is probably not that much of a surprise, given that AI models are trained by scraping the internet — where any belief humans are capable of thinking can be found. Only when the conspiracy belief had a racial or antisemitic undertone were they opposed. Google’s Gemini completely refused to engage in discussion about recent events in the US, such as election rigging or false claims about immigrants, did it tap out completely in responding. It seems that some of the model owners have found ways to train or block certain topics from being answered incorrectly.

This matters because research shows that when you believe in one conspiracy theory, you are more likely to believe in others. Some researchers have dubbed this the conspiracy mentality, a broader mindset that reflects a severe distrust of authorities and a tendency to see hidden intentional causes behind world events. This is a system of belief that rejects science, feels prejudice towards out-groups, and defaults to questioning everything. As leaders, this is a hard one to work with, because you do hold positional authority and that makes you part of the system the conspiracy mentality is designed to distrust.

This is the paradox leaders face: the more you advocate for AI adoption, the more you confirm the suspicions of a believer. And as we established earlier, no evidence will change that — the belief is designed to be unfalsifiable. Which means the traditional tools of leadership — communication, persuasion, data — may be precisely the wrong instruments for this particular challenge.

There are no easy answers here. The research doesn't offer a communications strategy that will dismantle a conspiracy mentality, and leaders who believe they can logic their way through it will find themselves frustrated. But there is a way through, and it isn't at the level of behaviour or messaging. It's at the level of belief.

Conspiracy beliefs are not random; they form in the presence of uncertainty, powerlessness, and a loss of trust in the institutions people once relied on. This means the conditions that created them can, with the right approach, be changed. But not by pushing harder on adoption, or presenting more data. What’s needed is working directly with the beliefs themselves — understanding what they're protecting, what they're compensating for, and what would need to be true for someone to feel safe enough to let them go. That is harder, slower work — but it's the only work that makes a difference.

Conspiracy beliefs — like all beliefs — don't shift through argument or evidence alone. They shift when the conditions that created them change. If you're leading AI adoption and hitting resistance you can't explain, this is the work. I help women leaders in tech and space do exactly this through executive coaching and speaking engagements.

The company has announced a new transformation programme, and as a result, everybody needs to be trained in this new methodology (or operating model or framework).

Cue the workshop theatre.

If I sound cynical, it’s because I’ve been there myself. I’ve sat through sessions that take anywhere from an hour to three days. Some have been genuinely useful in getting me to think about a topic in a different way or form a new habit or way of doing things. But those are the exception. The majority have been frankly a waste of time.

And that’s no criticism of the facilitator or their material that they’ve taught me, because with many, it’s been obvious how much they care about their subject and want to share that with me, to help me.

Rather, it is a criticism of an over-reliance on a particular format that has limitations in how effective it is in making learning happen — particularly when it comes to belief change.

And as a workshop facilitator myself, I’ve learned this the hard way when sessions I’ve led have not landed as I expected them to.

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When all you have is a hammer, every problem looks like a nail

Workshops have become the change vehicle of choice within organisations for several reasons:

• You can standardise the content, which enables a consistent message to be deployed across the whole organisation.

• As a delivery format, it scales to multiples of people, sometimes 50, 100 in the same room at one time.

• It creates some sense of measurable progress — as in, before the workshop, people didn’t have certain knowledge, post-workshop they do.

It also has benefits socially: getting groups of people together in a room to discuss a single topic, especially if they don’t work together that often, helps with building trust and cohesion within and between teams.

As a result, it has become a default format for learning — from product squad retrospectives, to quarterly planning, to DEI legislation changes, to implementing AI into daily workflows. It’s adaptable, it can be quick to throw together (even with a few slides!), and even an inexperienced facilitator can get away with basic techniques on group engagement.

It is the hammer, and every learning opportunity is a nail.

The issue is: workshops are rigid, a snapshot in time of what is happening in the organisation. A fixed time, a fixed location, with fixed attendees. Who shows up can influence what issues are brought to the group discussions, and may not reflect what the real issues are, just which felt more pressing on that particular day.

The workshop is also a bubble: an artificial environment where anything is possible. What people learn inside the bubble has to survive contact with the real world — workplace culture, a difficult manager, a team with different ideas of what should be prioritised, and a set of structural incentives working against you. You show up, you learn, you leave — measurement rarely happens beyond the register of ‘did you attend?’

So why doesn’t the learning survive beyond the workshop?

Workshops change behaviour, not beliefs

A few months back I ran a workshop called Change, By Design, which was created as a belief-level intervention. From all my 1:1 coaching work I could see that many clients were being held back from achieving their goals because they were holding onto limiting beliefs about themselves — what they could and couldn’t do. And what I came to learn is that sustained behaviour change is only possible when we update the underlying beliefs that lead us to act in a certain way, otherwise we just revert back to what we’ve always done.

When I ran my workshop I was very ambitious: I wanted to take participants through all four steps of Belief OS™, my belief change model, in just 90 minutes. That is: map the current belief, challenge it with evidence, replace it with a more helpful belief, and then identify ways to sustain it. Of course, I was setting myself up to fail — but I didn’t realise that at the time. I knew something was off as I delivered the workshop; participants were feeding back they didn’t have enough time in the breakouts to really get going with discussion. And I received barely any feedback afterwards, which honestly felt deflating.

Until today. I was working on my business strategy with Claude and the conversation veered onto workshops. And I had an ‘aha’ moment: workshops don’t work for belief change, because it requires more sustained effort than a single intervention point.

This sounds obvious now I’m writing it! And yet I’m not alone — organisations keep designing transformation programmes as if a single workshop can do the whole job. We need to see change as a continuous process that we are fully absorbed in, like it or not.

Belief change is never ‘done’

I’m reminded of a thought exercise that my teacher from a Buddhist retreat I attended in India back in 2018 shared with me: when does a seed become a plant?

The change (in this case, growth) happens so gradually that it’s not until we actually look that we create a snapshot, and define it at that particular moment in time. It is a seed, not a plant. Now it is a plant, not a seed.

Let’s take AI as an example. Generative AI didn’t appear out of nowhere, it’s been decades in the making. When ChatGPT launched in November 2022, for the majority of people it signalled the start of LLMs — when actually OpenAI's API had been available since 2020, GPT-2 had caused alarm bells in 2019, and the transformer architecture underpinning all of it was published back in 2017. The seed had become the plant, but the seed had existed in various stages of development before that.

When we create these snapshots in time of how things are, we’re also defining what we believe about them. How did you feel about Generative AI in 2020? How did that change in 2022? How do you feel about it now? It may be the same, or it may have changed, depending on what messages and evidence you have been exposed to, and whether that confirms or contradicts your existing beliefs.

For leaders within organisations looking to increase AI adoption amongst their teams, they face the challenge of changing not only what people do with AI (behaviour) but also ensuring that the beliefs support that behaviour change in the long term. What do people on their teams believe about AI? That it’s a force for good in the world, or that the harm outweighs the benefits? That they will personally benefit from using it, or that they will be seen as less competent or lose their jobs to robots?

Back in 2018 I delivered a talk on AI at my company's internal conference, where I was generally optimistic about its potential and our collective future with it. My position now is more nuanced — shaped by years of watching promises outpace reality, and by my own direct experience of what the technology can and can't do. I’m the same person, but I now hold a different belief. And this belief change came about not because of a single moment of revelation (in a workshop), but because of accumulated experience over time.

Which leads me to my conclusion: workshops work best at the early stage of the change cycle — to raise awareness, give a new language and mental model to describe what is happening, and generate a motivation for change. But they fall short at ensuring that change is successful longer-term, because that requires sustained effort over time.

My one caveat to this: a carefully designed programme that blends workshops with other learning formats like individual and group coaching, accountability mechanisms, systemic incentives, and intentional practice in the real-world can create belief change. But in a world where organisations are looking at change through the lens of a quarterly OKR, instead of a multi-year commitment, the theatre will continue and we will continue to be stuck in our old ways of thinking and doing.

So what’s the alternative?

The answer isn’t a better workshop — it’s building the capability to lead belief change from within.

I work directly with leaders through 1:1 coaching — we can go deep on your beliefs, understanding where those diverge from your organisation’s, and on building the capability to lead change from the inside. When you understand how beliefs operate, you can create the conditions for something more durable than a post-workshop buzz.

The goal isn’t dependency on me: it’s that eventually you don’t need me. If you're a leader who recognises this pattern in your organisation, Let’s talk.

It’s the first time we’ve been back to the Moon since the 70s — I wasn’t even born when the Apollo missions wrapped up — and I’ve been feeling the excitement build inside me all day.

And what made this possible is decades of shared belief, amongst people and nations, that going back to the Moon was both possible and desirable. It’s taken thousands of people years of working together to make this happen.

In a world wracked with conflict, it’s a powerful reminder that humans are really at our best when we’re working together to make the impossible happen.

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Purpose emerges from systems of belief

Earlier this week I interviewed Sarah for my forthcoming book, Beliefocracy. Towards the end of our conversation she shared her life’s purpose: “To make a difference to people's lives. Predominantly in mental health because that's where I've spent my whole career, working in mental health from the age of 18.”

It was the first time that purpose had come up in interviews, and I needed to reflect on how this fits with the model I’ve created in Belief OS™, because it touches on all four levers:

• Meaning — purpose gives our lives shape and helps us interpret our life experiences

• Affect — our purpose is based on value judgements about what is good and needed (which very much underpins the Japanese concept of ikigai).

• Social — a purpose can unite people to work together

• Context — having a purpose can influence what you do at work, or in life more generally

It didn’t neatly fit within the model as it currently stood!

So after some reflection I came to the conclusion that purpose actually sits above the model — as an emergent property of a collection of beliefs interacting together. An emergent property is a phenomena that arises from a system but cannot be found in any individual component. The whole exhibits behaviour that its parts don't have on their own. The example I always come back to is consciousness — we know it exists because we each experience it, but we cannot point to the specific part of the brain, neurones or neurotransmitters that it arises from.

When it comes to purpose, this emerges from a collection of beliefs acting together, to reinforce each other and align in the same direction (what Sarah calls her ‘North Star’). An example of a collection of beliefs supporting Sarah’s purpose could be:

• I believe people deserve good mental health

• I believe mental health support should be accessible

• I believe in training therapists to be exceptional

When these beliefs align and reinforce each other, they create a direction of travel strong enough to guide Sarah through uncomfortable boardrooms and career-defining moments. One person's purpose emerging from their belief system. But what happens when thousands of belief systems align around a shared direction?

Why are we going back to the Moon?

I’ve been glued to BBC News throughout the day. And of course, alongside the buzz and anticipation have been the inevitable comments: why are we even bothering to go back?

First, let’s look at the economics. The Artemis programme has cost about $93 billion in total. If Apollo is anything to go by, that investment will pay back exponentially. In 1975, economists calculated Apollo’s ROI at 15-to-1 — and even that turned out to be an underestimate. One report described the actual return as ‘off the charts‘.

Second, the Moon contains important resources that many nations are interested in taking control of — so it’s a race to see who gets there first (which is somewhat reminiscent of the 1970s…). The Moon contains rare earth elements which are needed in technology production, and water is trapped in ice beneath its surface. The Artemis missions scheduled between now and 2028 are strategically designed stepping stones towards establishing a permanent lunar base.

And beyond the Moon is Mars. NASA has its sights set on the red planet, with missions planned for launch in the 2030s. It is a lot easier to launch deep space rockets from outside Earth’s atmosphere than it is from the surface. The Moon could provide essential launch capability.

But none of these truly answer the purpose, nor the collection of beliefs that underpin this drive to the Moon. And that is harder to answer, but interviews with experts in the space industry hint at it (all quotes taken from BBC articles).

Keith Wright, an engineer who worked on the original Apollo mission, says this: “We now need to learn to live properly off the planet because we need to spread our humanity away from the Earth. With the Earth we've got all our eggs in one basket, and if anything really disastrous happened we are in serious trouble.”

David Morris, whose team makes imaging equipment for the space agencies, said “If you look back to what happened in 1969 and the transformational aspects of seeing men on the Moon, I'd like to think that similar global excitement can happen when we see it happen again. It will invigorate the world to think more about being 'the world' rather than just lots of separate nations.”

One of the Artemis II crew, Christina Koch, said this: “[The] discovery and knowledge that we bring back to Earth is the “entire point” of the operation.”

Together these beliefs — that humanity's future extends beyond Earth, that exploration unites us, that discovery matters for its own sake — don't exist in isolation. They overlap, reinforce each other, and align across thousands of people who have dedicated years to this mission. Together, they create an emergent collective purpose powerful enough to survive budget cuts, political changes, and decades of setbacks: humanity belongs beyond Earth, and building that future starts now.

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When you’re ready, I work with women leaders in tech and space through executive coaching and speaking engagements.

Many of us are familiar with the film The Matrix, which was based on Plato’s allegory of the cave. This exposes the illusion of a simulated reality to Neo, who upon ‘waking up’ discovers a dystopian future where machines are harvesting bio-energy from humans with which to power themselves. Inside the matrix, Neo had no awareness of anything being wrong or fabricated about his reality. He could not see the machines creating their simulation and influencing his thoughts, feelings and behaviour.

Some ideologies leave identifiable traces in our systems and institutions. Living in the UK, we have at least a notional sense of how Christianity as a belief system has influenced aspects of society: from the monarch as both head of state and head of the Church of England, to shops and businesses being shut on Sunday which was regarded as a holy day of rest, to the ten commandments informing our moral infrastructure — the assumptions we hold about work, rest, guilt, individual responsibility, what counts as ‘deserving’ — upon which the law sits.

With this in mind, it came as little surprise that a poll I ran amongst senior product leaders this week revealed the vast majority had never heard of the ideology that many influential Silicon Valley elites hold dear: TESCREAL. Out of 43 respondents, 2 had heard of it and knew what it was, 1 had heard of it but didn’t know what it was, and the rest had never heard of it.

TESCREAL is shaping product roadmaps, funding priorities, and what problems get deemed 'worth solving' in tech. And most of the people building and leading these companies have never heard its name.

That’s a problem because TESCREAL consistently prioritises hypothetical futures over present realities. It concentrates power in the hands of a self-selected elite who believe they alone can see clearly. And it provides intellectual cover for present-day harms — fraud, surveillance, discrimination, exploitative working practices, environmental degradation — by reframing them as necessary steps toward a better future.

So, let’s take a closer look at what this ideology actually is.

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What is TESCREAL?

TESCREAL is a neologism coined by AI researcher Timnit Gebru and philosopher Émile P. Torres. Gebru gained prominence after being pushed out of Google in 2020 for co-authoring a paper on the risks of large language models — research that proved prescient as concerns about AI bias, environmental costs, and corporate power concentration have intensified.

Undeterred after being fired, Gebru founded the Distributed Artificial Intelligence Research Institute (DAIR), with the goal of furthering her research into AI’s impact on marginalised communities. Together with Torres, they defined TESCREAL as standing for seven overlapping futurist philosophies — Transhumanism, Extropianism, Singularitarianism, Cosmism, Rationalism, Effective Altruism, and Longtermism — that tech elites use to justify and accelerate the pursuit of Artificial General Intelligence (AGI).

Let’s have a look at each of the seven components in turn now, with real examples from tech of how these play out in practice.

Transhumanism

The belief that humans should use technology to transcend biological limitations —enhancing intelligence, extending lifespan indefinitely, and potentially uploading consciousness to digital substrates.

In practice: Neuralink's brain-computer interfaces aimed at merging humans with AI; Peter Thiel's startup Ambrosia, that provides older people with blood transfusions from young donors, in order to prolong their life; the Silicon Valley obsession with biohacking and nootropics to optimise cognitive performance (hello Bryan Johnson).

Extropianism

A radical offshoot of transhumanism that rejects all limits on technological development, advocating for perpetual progress, minimal regulation, and the reversal of entropy itself through innovation.

In practice: The ‘move fast and break things’ ethos that shaped early Facebook; most of Big Tech lobbying against the EU’s AI Act; Binance’s refusal to comply with basic regulatory requirements set down by the UK’s Financial Conduct Authority.

Singularitarianism

The conviction that we’re approaching a technological singularity where AI becomes super intelligent and triggers runaway growth beyond human comprehension or control.

In practice: OpenAI’s stated mission to ensure AGI ‘benefits all of humanity’, which treats AGI as inevitable rather than a choice; the framing of AI development as a race where we must build it safely before someone else builds it unsafely — this quote from Marc Andreessen is a prime example:

"We believe any deceleration of AI will cost lives. Deaths that were preventable by the AI that was prevented from existing [are] a form of murder."

Cosmism

The belief that humanity’s ultimate purpose is spreading consciousness throughout the universe, colonising space, and potentially resurrecting all past humans through future technology.

In practice: Elon Musk’s insistence that becoming multi-planetary is an existential imperative, which drives SpaceX and Mars colonisation efforts — framed as moral duty rather than exploration or curiosity.

Rationalism

A belief emerging from the LessWrong community that human reasoning is deeply flawed and can be debugged through systematic protocols — positioning the tech elites as uniquely capable of clear thinking.

In practice: Tech investors like Peter Thiel funding rationalist organisations and workshops; the Center for Applied Rationality running 'debiasing' training for tech leaders; the proliferation of decision-making frameworks that claim to de-bug human bias (and don’t we love a framework in Product?).

Effective Altruism

The movement claiming to identify the most cost-effective ways to do good using quantitative analysis rather than relying on implicit biases (like empathy for other’s pain), often concluding that preventing hypothetical future catastrophes outweighs alleviating present suffering.

In practice: Major EA funders like Open Philanthropy redirecting resources from global health to AI safety; Sam Bankman-Fried's justification of fraud as 'expected value calculations', claiming the ends (hypothetical future good) justified the means (present-day financial crimes).

Longtermism

The philosophical position that what matters most morally is the far future — potentially trillions of beings across billions of years — making present concerns statistically negligible by comparison.

In practice: The UK government allocated £8.5m to existential risk research while documented racial bias in police facial recognition goes unaddressed; Sam Bankman-Fried's FTX Future Fund committed $160M to longtermist causes, with him stating he was "never interested in helping the global poor."

I know kung fu

Hopefully by now you are convinced that TESCREAL not only exists, but that its influence extends beyond a fringe-group in the Valley, to hold influence world-wide. And there are many more examples: seasteading (extropianism), the Metaverse (transhumanism/cosmism), 10X engineer mythology (rationalism), 996 (extropianism) — you get the picture. Now that you have this knowledge, like Neo in the Matrix, what can you do with it?

First, let’s take a moment to acknowledge how uncomfortable this might make you feel. Learning that TESCREAL's harmful beliefs are hiding in plain sight, and likely influencing what you think and do as you work in tech can make you feel like you need to — in the words of my friend — go hug a tree. But it is possible to push back.

One way to stop the ripple effects of this ideology leaking out from Silicon Valley into the whole industry is to focus on what’s happening now. In Buddhist philosophy, the present moment is the centre of everything — it is the only aspect of our experience that is real. So what problems exist now that we should do something about? What kind of future do we want to actively construct in the now, not passively accept the version ushered in by those who don’t have our best interests at heart?

This is an important reframe, and one that leads us to hope. Hope is the belief that the future can be better than the present AND that you have some agency to make it so. It requires both desire (for a better state) and perceived capability (belief you can influence outcomes). I particularly like Rebecca Solnit’s definition from Hope in the Dark:

“Hope is not a lottery ticket you can sit on the sofa and clutch, feeling lucky. It is an axe you break down doors with in an emergency… To hope is to give yourself to the future — and that commitment to the future is what makes the present inhabitable.”

Hope requires action in the present, not passive optimism about calculated futures.

Now you know about TESCREAL, you can’t unsee it. And that means every decision you make from here — every roadmap you prioritise, every problem you choose to solve, every future you work toward — can be made consciously, in the present moment, for the people who exist right now.

Now that you know kung fu, how are you going to use it?

If you're ready to examine the invisible beliefs shaping your decisions, I work with women leaders in tech and space through executive coaching and speaking engagements.

In ‘The Menagerie’, an episode from season 1 of Star Trek: The Original Series, we encounter a telepathic alien species called the Talosians. Survivors of a nuclear holocaust that rendered their planet completely uninhabitable, they lived their lives underground. They developed their mental powers to create illusions, and found this to be so addictive, they started abducting space travellers to use as a source of inspiration.

Fifteen years later, this inspired Bud Tribble at Apple to coin the term reality distortion field to describe Steve Jobs’ effect upon engineers working on the Macintosh project. It was a potent blend of charisma, drive and hyperbole that could bend truth and empower teams to believe they could ship what looked technically unfeasible against impossible deadlines.

This was a unique form of power: Jobs had a vision for the future, and willed it into existence through sheer force of mind.

But what this shows us is that our future may not be out of our control — a particular outcome can be chosen, if not designed. Today’s tech leaders learned from Jobs and are now wielding their own reality distortion fields around AI — a tiny minority deciding everyone’s future.

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The 1% problem

Let’s zoom out a bit now. A graph has been doing the rounds on Linkedin that shows out of the global population how many of us are using AI and to what extent.

If you’re regularly using LLMs then you are amongst 16% of the global population. If you pay for it, you’re amongst 0.3%. And if you are coding with it, you’re in just 0.04% of the world’s total population.

This presents several problems.

First, that this minority isn’t just small but also homogenous. Those controlling the AI tech are predominantly Western, white male tech leaders. They’re building systems trained on Western data, and then exporting them globally without consideration or adaptation for local variance and cultural diversity.

Side note: Before COVID, I worked for a startup developing biometric technology for humanitarian programmes across the Global South: deworming in Ethiopia, health systems strengthening in Tanzania, maternal health in Bangladesh. The gap between what tech companies thought these communities needed and what they actually needed was staggering. I watched philanthropic organisations run projects that looked transformative on paper but functioned as white saviourism in practice. Colonialism is alive and well, and tech companies are its latest missionaries.

Second, this small homogenous group is wielding its own reality distortion field. Every press release, every conference keynote, every soundbite serves one purpose: manufacturing consent for their preferred future. When Altman says AI will make professions obsolete, he's not warning you about a risk; he’s announcing his strategy. We mistake his statements for predictions, treating them as uncertain forecasts about what might happen, when they’re actually commitments. OpenAI’s plans are backed by billions in capital, and are positioned as inevitable so we’ll stop resisting them.

The consequences of these plans aren't abstract. They're already discernible against the metrics we use to track human development. Global poverty is assessed across seven key dimensions: access to water, housing, food, employment, education, gender equity, and religious freedom. Reports from Pope Francis Global Poverty Index, the World Bank and Statista now show that progress in reducing poverty has stalled, and in some cases slipped behind. AI has the potential to not only halt progress, but to accelerate regression in tackling this issue and move more countries below the poverty line.

Take Chile. One of South America's most stable economies, it’s now facing 8% unemployment and high inflation. This is where Amazon, Google and Microsoft chose to build their data centres. The infrastructure demands strain local water and electricity supplies while automation threatens jobs. AI isn't lifting countries out of poverty so much as actively pushing them towards it.

The gap between those who have and those who have not will continue to grow. The 0.3% who can afford premium AI access may get smarter, faster, and more capable. The rest of the world will fall further behind, experiencing job displacement and economic disruption. And AI will accelerate this faster than any previous technological revolution.

Is that the future we all want?

When skills atrophy

Within CPO Connect this week, a member invited us to add our views to this debate: “As AI takes on more of the cognitive and administrative load for people leaders, which skills do you think will atrophy fastest — and how are you actively keeping them sharp?”

This prompted some lively discussion amongst the rest of us, where we discussed the necessity of skills such as judgement, hard conversation stamina, intuition, empathy, grammar and vocabulary. My own contribution centred around the loss of comprehension: literally being able to make sense of what you are reading or listening to.

I have personal experience of this already. I regularly review academic papers on leadership and space psychology, and some of these papers are very dense. Sometimes the abstract doesn’t give me enough to decide if I should read more, so I would ask Claude to summarise. Occasionally it got this wrong, hallucinating the subject matter or missing the nuance of the study. And more often than I would like, catastrophically so — like inventing entirely different study variables and results. So I am still reading the articles to validate their utility, but I can imagine scenarios where people get pressed on a deadline and sacrifice this due diligence. The issue becomes using these tools without verifying their output — which of course, takes time.

So I followed up with a question of my own: Is there a relationship between AI dependency and imposterism, where the tool meant to help actually undermines the development of internal knowledge that would resolve the imposter feelings in the first place?

This kicked off a lively discussion around how far we outsource our thinking. There was a sense that we may not be there just yet, but it could become reality.

What would be the impact of this?

We turn up to meetings thinking we understand a topic because we’ve read the AI summary, but it’s flawed because it contains errors, omissions or fabrications. Unfortunately we cannot pick those up because we’ve lost basic comprehension to make sense of what is real and true. Then, when we get questioned by others, we either answer wrong confidently, or we cannot answer at all. It’s no longer a question of being found out as inadequate — we’ve become the very imposters we feared the most!

It reminded me of another conversation I had this week with Gary. On a Linkedin post that claimed the competitive moat humans have now is that AI agents don’t have agency, he shared his thinking around comprehension debt. This is the insidious, slow erosion of organisational comprehension — the gap between how complex your situation is and how well anyone in your organisation understands it. When AI tools are used well, this isn’t an issue, but when they are used as a replacement for human judgement it racks up fast — yet nobody is aware of it. It’s like muscle wastage when you don’t work out for a few weeks: you don’t notice it day to day until you go back to the gym and realise you can’t lift what you used to.

And this is the future that Altman et al is bringing to us. One where we become dependent, deskilled and disposable. If you think I’m exaggerating then we only have to look at the news headlines to see companies laying off large chunks of their workforce, citing AI as the cause — just this week Meta announced a 20% cut. What we have also seen, though, is that companies making these cuts are later living to regret it. Notably Klarna cut 700 workers, then rehired them back after their customer service degraded and people complained.

This shows us how the reality distortion field works. Altman says 'AI will do X', so we start behaving as if it already does. We offload comprehension and let skills atrophy. His prediction becomes self-fulfilling not because it was inevitable, but because we made it so.

So, what do we do?

The small group painting AI’s future as unstoppable progress are doing exactly what Jobs did: willing their preferred outcome into existence through sheer force of belief and capital.

The difference is, we are wise to this, and we don’t have to accept their version of reality.

If you don’t like this painted future, then you can choose your own. Together as consumers we have collective power to decline and push back on what we don’t want. There is no doubt that AI may serve a purpose as an assistive tool, but I do not believe it is a full replacement for thinking. Instead, we need to continue investing in human thought and communication and connection, in deliberately embracing thinking that is difficult or stressful in order to retain our capacity for it, in practising exercising judgement over what we consume and who we listen to. These are the skills that will make us irreplaceable in the long run, and we must use them — or lose them.

What future are you choosing to build?

If you're tired of investing in personal and professional development that doesn't stick, I work with women leaders in tech and space through executive coaching and speaking engagements.

In my last article (#36 - Your Minimum Viable Exposure) I shared a method for learning how to manage your fear, in order to accelerate your growth. One reader, Athena, asked me a very pertinent question:

“omg I have a phobia of snakes and reading this gave me palpitations! I was nodding along to everything and liked the idea of MVE but then when confronted with the idea of looking at snake pictures I was like, “er no”. Maybe there is something you can develop further in the idea, which is about what can initiate that appetite to get over the fear in the first place?”

Athena has put her finger on a problem. I gave you a method, but I didn't answer the more fundamental question: why would you actually use it?

Unless she’s planning on moving to Australia, or some other place where encounters with snakes are common, it’s probably not worth the investment to overcome her fear. Holding this fear isn’t currently costing her anything: it’s not blocking a goal she wants to achieve, it’s not preventing her from becoming who she really wants to be. So why would she voluntarily expose herself to something that gives her palpitations?

However, there are some fears we hold that negatively impact what we do care about — particularly when it comes to career advancement. The fear of speaking up in meetings with execs. The fear someone will ask you a question you don’t know the answer to, and you’ll look like you don’t know what you’re doing. The fear that this will lead to someone thinking you don’t deserve to be in your role. These fears are different because they are actively costing you something you want: challenge, reward and recognition in your career.

So let’s explore more on how those fears come to arise (the triggers) and how we evaluate whether it’s worth working on them (the motivation). This will help you decide which fears you should work on, and which you can accept.

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What triggers fear?

Fear is an instinctive reaction to a perceived threat in our environment. It arises automatically through interaction between us and our physical, social and temporal contexts:

• The open plan office

• A new leadership team

• A quarterly business review

These factors by and of themselves are not necessarily fear-inducing — they’re just events or situations that exist. What generates the fear is the meaning we give to these, which comes from how we relate to them.

Take the open plan office as one example. Our fictional CEO Alice loves them because she can see everybody at once, and she gets a buzz from overhearing conversations and jumping in when she can to add her thoughts. She believes this helps her team make faster progress and feel more connected.

However, our fictional employee Zoe hates the open plan office. She’s trying to get her head down on a product launch that has a critical deadline coming up, and it’s hard to concentrate with the noise — she’s stressed whether she will deliver on time. What’s worse is that Alice will just appear at her desk and start asking questions about what she’s working on. She feels put on the spot and scared she’ll fumble the response and look stupid in front of the CEO.

It’s the same physical environment, but different experiences of the perceived threat based on an individual’s role and status within it (two of the social scaffolds as per Belief OS™).

In addition, some threats are acute: a single event or situation is enough to trigger intense fear, such as being asked to present to the Board at short notice. Others are brewing in the background before spilling over into our consciousness, like watching your peers speak up in every meeting for six months and then realising you’ve become invisible.

Identifying the trigger/s is an integral step in developing awareness of your fear. You’re scared of x and that’s because y has happened. You’re scared of snakes and that’s because as a kid you watched a nature documentary of a python killing a child.

Or perhaps like Zoe you’re scared of not having the information to answer the CEO’s question when put on the spot? That might be because — as my friend Carrie said this week — “the thing is there is always that ONE time where not knowing something crucial has caught us out in a painful way (this has brought down many a politician!)”

This is where fear gets interesting. Carrie's observation points to something important: these beliefs are not always irrational distortions we've conjured from nowhere. Sometimes they are well-founded conclusions drawn from real evidence. You were caught out once, and it was painful. The problem is that past experience doesn't guarantee future outcome, but humans are terrible at tolerating uncertainty. Our brains are like Bayesian prediction machines1 — using past data to forecast what is likely to happen next. So we fill the gap with beliefs that feel like facts: "I was caught out before" becomes "I will definitely be caught out again." The belief bridges the uncertainty between what happened (past) and what might happen (future).

The motivation to overcome fear

At this point you have identified that you feel fear and understood the triggers, but it does not necessarily follow that you are motivated to overcome it. This is where a cost-benefit analysis becomes salient: is the cost of keeping this fear higher than the cost of confronting it?

If you are unlikely to encounter the thing that provokes your fear, such as the earlier snake example, the costs may outweigh the benefits. You can simply avoid putting yourself in those situations where it is likely to happen — don’t go to zoos or pet shops, don’t watch nature programmes, don’t visit countries with large snake populations in the wild.

However, many of our professional fears are unavoidable, particularly if we want to progress. This is the tension that so many wrestle with: you want the promotion, the impact, the leadership role. But the things that come with it, such as more visibility, public speaking and scrutiny of your decisions, trigger exactly the fears you've been managing by staying where you are. So what do you do?

One way to evaluate whether confronting the fear is worth it is to examine what you actually gain. Self Determination Theory, a psychological theory of motivation defined by Deci and Ryan2, offers insight into this evaluation. They posit that motivation arises through three fundamental psychological needs: autonomy, competence and relatedness3. Applying this to career progression we want:

• More control over what work we do, how and when we do it, and who we do it with.

• To develop a deep understanding of our domain of knowledge — becoming an ‘expert’ is crucial to our sense of self and identity.

• To feel like we belong in a group with others we like and trust.

These three needs pull you towards your career goals. Where those needs go unmet, a gnawing sense of dissatisfaction builds. That feeling often becomes more unbearable than the fear itself, prompting action. Promotion brings more visibility (fear), but it also gives you more autonomy (need). Speaking up risks you being challenged (fear), but demonstrates your competence (need). As a leader your decisions will receive more scrutiny and challenge (fear), but will give you belonging in a higher status group (need).

Yet research shows a motivator even more powerful than pursuing what we want: avoiding what we're already losing.

What you’re already losing

The pull of unmet needs is only half the story. Research on loss aversion by Kahneman and Tversky4 shows that we’re typically more motivated to avoid losses than to pursue equivalent gains. The pain of losing £100 feels roughly twice as intense as the pleasure of gaining £100. This isn’t irrational — it’s how our brains are wired.

Applied to your career, this means the question isn’t just “what could I gain by confronting this fear?” but “what am I already losing by keeping it?”

Consider Zoe again. By avoiding Alice’s questions, she’s not just missing out on future opportunities for visibility, she’s actively losing ground right now. Every meeting where she stays silent, someone else’s ideas shape the strategy. Every time she dodges a conversation with Alice, she becomes less visible as a leader on the product launch she’s meant to be leading. The promotion she wants next year is being undermined by the invisibility she’s creating today.

The fear feels protective, as it’s keeping you safe from being challenged, from looking stupid, from being exposed. But it’s not neutral. It’s costing you influence while trying to avoid criticism. You’re losing opportunities to demonstrate competence while trying to avoid being tested. And you’re losing your place in the group by trying to avoid rejection.

At some point, the accumulated losses become unbearable — and that’s when the cost-benefit analysis tips: the cost of keeping the fear finally exceeds the cost of confronting it.

And that’s your tipping point — when you’re ready for Minimum Viable Exposure.

So to return to Athena’s earlier question: not all fears are worth confronting. But when keeping one costs you more than facing it, that's your signal to act.

This week I spoke to fifty women product leaders at Sage who’d just completed a ten-month leadership development programme. They’d learned to overcome imposter feelings, build influence, and navigate bias. Now they were facing exactly this moment: what happens when the bubble bursts and the real world pushes back?

Here’s what I shared with them about managing fear when growth gets hard.

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Takeaway 1: Recognise how fear shows up

Whenever you go on a course or take part in a programme, you fall into a bubble — a safe yet artificial environment where you can learn and practice new skills without real-world consequences. It’s absolutely necessary to have this bubble, but it bursts as soon as you go back to your ‘day job’ and encounter resistance the first time you try to put those new skills into practice.

People are messy. We don’t always behave in predictable ways, so conflict is inevitable somewhere along the line. How do you react when that happens?

We experience fear when we perceive a threat in our environment: what used to be physical threat with the tiger in the bush, is now identity threat and social threat — who we think we are, and whether we belong to the tribe.

Fear is an automatic response, and it shows up in business, sometimes in really subtle ways. Here’s what those four fear responses can look like:

1. Fight: you get defensive. Example: your manager gives you developmental feedback and your immediate reaction is ‘that’s not true, that doesn’t apply to me’.

2. Flight: you avoid a situation. Example: running away to some unknown future when you say ‘I need to learn more about this first, then I will feel ready’.

3. Freeze: you feel stuck in place. Example: you’re in a high stakes meeting and have a point to make but you keep it to yourself because you’re worried you’ll look silly.

4. Fawn: you appease the source of the threat, becoming very agreeable. Example: a senior stakeholder is voicing their opinion strongly and you disagree with them, yet you publicly agree with them to avoid the confrontation.

So when those threats arise, that fear will manifest a physiological response — heart racing, palms getting sweaty, mouth going dry. What if instead of succumbing to one of those four responses, we noticed this happening and got curious about it instead? This is what I mean by using fear as information — ask yourself: what is the threat here? Name the fear as a starting point.

Takeaway 2: Confidence is the practice of managing fear

Those physiological responses arising from fear (or any emotion, actually) only last about 90 seconds. What keeps them going for longer is continually re-engaging with the thought that created them. So, if you can sit with that fear for 90 seconds, you will notice that it starts to dissipate, loosen its hold on you. And eventually it will disappear.

I shared an example from space to illustrate this. Chris Hadfield is a Canadian astronaut, and former Commander of the International Space Station. As part of his time onboard in 2001, he conducted several space walks, where you suit up and go outside the station to perform maintenance. On one occasion, he recalls facing the ISS, with the Earth behind him, holding on by just one hand.

When all of a sudden, some liquid shoots into his eye, blinding him. He can’t wipe it away because he’s wearing his helmet. He can feel the gunk collecting in his eye and he can’t open it. It hurts. And then because of the effect of microgravity on surface tension of liquids, it starts moving across the bridge of his nose into his other eye, rendering him completely blind.

Imagine that — holding on to the space station with one hand, not able to see anything. What’s your reaction? Fear I expect, as the survival instinct kicks in. But fear in that situation can cause panic, and Chris didn’t do that. He was trained to recognise fear, and then manage it.

Astronauts go through extensive training over many years, not just for the technical aspects of the role like how to use equipment, but also the psychological. They repeat procedures over and over again — not just what to do if it all goes right, but also what to do when things go wrong. And because of that, they’re prepared for the worst case scenario. In this situation, Chris called over to his crewmate on the space walk with him, and together they got him back inside where he could get help. It turned out it was defogger fluid that had landed in his eye.

What this shows is that Chris wasn’t born fearless. Instead, he trained his response to fear, so it didn’t overwhelm his body and impact his ability to make decisions under extreme pressure.

And this is what I believe true confidence is: not the absence of fear, but acting regardless when it is present.

Confidence is the practice of managing your fear — noticing how it feels in your body, having a toolkit of techniques to reduce its potency (e.g. box breathing, mindfulness, movement), and staying with it until it subsides.

Takeaway 3: Use MVE to train your fear response

So how can we proactively overcome our fears? We can look to exposure therapy, which is a type of treatment used for phobias, for inspiration. A therapist will work with you, progressing you through a series of steps to expose you to the very thing you’re scared of.

Say you have a fear of snakes. You might start with just looking at a picture of one whilst you’re sat safely at home. There’s no way that snake can harm you, yet the fear sensations are triggered. Stay with them and you notice they go away. You keep repeating this until you can look at the picture without the fear response kicking in.

Then you progress to watching a video of a snake. Once you’re comfortable with that, you look at one in real life behind glass at a zoo. Then perhaps someone else holding one whilst you’re nearby. Eventually, you might hold one yourself.

It takes time, persistence and repeated exposure to the very thing that spikes your fear. But what this technique shows is that it’s possible to get comfortable with feeling uncomfortable, and we can apply this to situations at work.

You may be familiar with the concept of minimum viable product — developing a product hypothesis, and then building the smallest thing to test it. The beauty of MVP thinking is that it removes the pressure to get everything perfect upfront. You learn by doing, iterate based on feedback, and gradually build towards the bigger goal.

We can apply this same kind of thinking to managing our fear, in what I call Minimum Viable Exposure. What’s the smallest step you can take this week that stretches you just beyond your comfort zone, where you feel that fear starting to spike, but where you’re actually quite safe? Where the consequences are reversible, or low stakes?

Just like with MVP, you're not trying to nail it in one go. You're testing, learning, building your capacity to manage fear incrementally.

My own example was when I first presented to a board about 8 years ago. The Deputy Group CEO asked me to speak about a critical turnaround: we had one client representing 85% of our business who was unhappy due to repeated delivery failures. I had been brought in to fix it, leading a team with low morale and capability gaps. When I thought about presenting to the Board, I was intimidated — it felt like stepping into the Dragon’s Den! — and I worried that I wouldn’t be able to answer their questions and would look like I didn’t know what I was doing.

So I broke the task down into smaller chunks. First, I wrote out my key messages just to get clear on what I needed to say, and got feedback from the Deputy CEO. I met with him face to face, and this became my first MVE. His feedback helped me refine the core message. Then I shared this with my team, more for the speaking practice, to get used to what I was saying coming out of my mouth — my second MVE. Of course, the Execs wanted to know what I was going to present, so they invited me to their next meeting — my third MVE. Their questions helped me refine and shape my message further.

At every step I felt that fear - what if I say something wrong? But each step built up to the Board presentation, so when the time came it wasn’t as scary as I’d anticipated — and actually went very well. Had I not done the prep it could have been a very different outcome.

Final thoughts

This coming week there will be a moment when you hesitate, where that fear has triggered one of those four responses automatically in your body. That moment is the moment — your cue not to retreat but to lean forwards. It signals that you’ve reached the edge of your current self, and are stepping into growth.

So here’s my challenge: identify your Minimum Viable Exposure for this week. What’s one small step that scares you just enough? The message you’ve been putting off sending? The question you’ve been afraid to ask? The conversation you’ve been avoiding?

Pick one. Do it. Notice what happens.

Growth doesn’t come when we live in our comfort zones. This isn’t something anybody can do for you - not your manager, coach or best friend - only you have the power to do this for yourself. As Susan Jeffers wrote: feel the fear, and do it anyway.

One of my core arguments is that beliefs operate at different levels: individual, organisation, society. The challenge came from James (thank you!) who disagreed: “beliefs are always individual”. I’ve been reflecting on this since, and I’ve come to the conclusion that what he and I are saying is not actually different — rather, I think I had not been articulating what I mean by ‘operating at different levels’ clearly enough.

The unit of belief is always the individual — on that James and I agree. Beliefs originate within a human mind, as they are the lenses through which we uniquely experience the world and make sense of uncertainty.

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Clarifying inter-level belief conflict

So when I talk about inter-level conflict between individual, organisational and/or societal beliefs, what I mean is this: the beliefs held by a group at large emerge from the dominant beliefs commonly held by individuals within it.

This happens in predictable ways:

• Individuals are drawn to other people who share similar beliefs

• The beliefs of leaders have an outsized influence within a group, and tend to be those adopted more widely

• The group practices their beliefs together — through what they wear, say and do, and where they gather

Sharing beliefs is a form of social glue, and arguably vital for proper functioning as group size increases.

The direction of causality of beliefs

James shared a model from the Church of England for growing congregations: belong, behave, believe. The idea is that people need to feel they belong first, before they change their behaviour, and lastly their beliefs.

This assertion about the direction of causality is not quite correct. Psychological research shows that belief formation is actually bi-directional: beliefs may inform behaviour, and behaviour may inform beliefs. However, the evidence is clear that lasting, sustainable behaviour change requires changing the underlying belief first1. Without that, behaviour will eventually revert to whatever the underlying belief can support.

This has a practical implication for the Church of England’s model: those who don’t believe in God are unlikely to be attracted to joining a church in the first place, and even for those that do, they may not stick it out for long.

It’s worth noting that the Church has a vested interest in this particular framing. Congregation numbers have been declining for decades, and arguing that belief in God isn’t a prerequisite for belonging is a convenient response to that problem. But even if someone joins without believing in God, there is always another belief operating underneath, one they’ve perhaps never consciously examined as it’s implicit: I believe I am a good person — and belonging to this community reflects that.

The same dynamic plays out in the leaders I work with. Many come to me for support in overcoming imposter feelings that arise when making big career transitions. At the heart of this experience is a fear of being found out as inadequate. And underneath that hides the belief: I’m not good enough. Take a newly promoted Head of Product — they’ve just made the biggest and hardest career transition from individual contributor (IC), where they were measured on their own performance alone, to manager of ICs, where now their performance is measured based on their team’s. Bigger scope of impact, but less direct control over results.

They realise that the skills that helped them excel as an IC are insufficient to succeed at the next level2, and internalise this as failure. When they show up to present in an Exec meeting, that belief of not being good enough fuels a deeper feeling of not belonging — because everybody else appears to know what they’re doing, and do it well. The gap between how they see themselves, how they see their peers, and how they think their peers sees them fuels the feeling that they simply don't belong there.

So that leads us to ask: how do you change a belief?

Four steps to belief change

Drawing from other talking interventions (particularly cognitive-behavioural therapy), I’ve identified four key steps to changing beliefs. I present this as a cycle because belief change isn’t a ‘one and done’ — it’s a continuous process of iterating towards a new belief and away from an old one. It takes time and consistency. Rushing it, or just running through the actions once is insufficient.

Step 1: Map the existing belief

Because so many of our beliefs are implicit, the first challenge is simply surfacing them. In coaching I listen for what clients say that reveals the belief operating underneath. People rarely announce “I’m feeling like an imposter.” What they say instead sounds more like this:

• “I don’t think I can do it”, undermining their own capability.

• “I’m not doing the actual work, I’m just facilitating it”, diminishing their role within the team.

• “I’m afraid of coming across as aggressive”, self-policing in fear of others’ perceptions.

• “Most of the points I make sound generic and obvious”, undermining their own thinking.

• “I don’t think it is that big a deal”, refusing to let achievements land.

To make the rest of this process feel more concrete, let’s return to the Head of Product example from earlier. I’ll call her Maya — she’s fictional, but the patterns I’ll describe are ones I see repeatedly in the leaders I work with.

Maya is several weeks into her new role. She’s articulate about everything except what is actually going on. In one session we’re able to surface the belief sitting underneath all of it: speaking up in meetings makes me look aggressive.

Once the belief is named, we look at what is holding it in place using the Belief OS™ model. How does it give Maya a sense of meaning? What is the important truth about herself that it contains? Which social factors support this belief? How is it reinforced by her habits and routines? Understanding this gives us multiple points of intervention.

Step 2: Challenge the evidence

We hold onto beliefs, even ones that harm us, because they are also protecting us in some way. Usually they are keeping us from stepping outside our comfort zone, into the unknown, where there is a risk of danger.

For Maya, “I’m afraid of coming across as aggressive” was performing a function. It kept a constant internal vigil on her behaviour, stopping her from taking initiative, being proactive, or sharing her opinion in case she was socially ostracised. That fear kept her safe within the group by performing conformity with its unspoken norms — and not rocking the boat.

To dismantle this belief we challenge it with evidence.

• What does aggression actually look like?

• If it’s not aggressive, what does speaking up in a meeting really mean?

• When has Maya spoken up before, and what happened?

• Who does she know who speaks up well?

• Would she judge them as aggressive?

• What does believing that speaking up is aggressive cost her?

We’re looking to reveal the gap between what the belief claims, and what past experience and reality actually show — and from that, sow the seed of doubt that this belief is true. Only then can we begin to change it.

Step 3: Replace the belief

This step requires conscious intention. Now that Maya understands what the belief is and what it’s costing her, and is questioning its legitimacy, she can choose a different one.

The starting point is rewriting it as a positive. “I have the courage to speak up and share my point of view” helps her tap into resources she already has and can continue to cultivate.

But Belief OS™ shows us that a positive affirmation alone is not enough. The old belief is held in place by scaffolding. “I’m afraid of coming across as aggressive” may be reinforced by a dominant leader she has observed who does come across that way. He’s an influential role model, for the wrong reasons. So Maya also needs to actively find examples of people who speak up without aggression and pay close attention to how they do it. What language do they use? How do they structure their points? How do they know when it’s the right moment?

The new belief needs new evidence, new role models, and new experiences to grow from.

Step 4: Sustain the new belief

This is where consistency becomes everything. Advertisers have known for decades that repeated exposure to a message is what shifts perception over time. The same principle applies to belief change. You cannot leave space for the old belief to reassert itself. The new one needs to be reinforced continuously, through what you read, who you spend time with, and what you practice.

Maya may not go from day one believing “speaking up is aggressive” to “speaking up is good” overnight. Instead there may be a series of small, consistent shifts in her belief:

• Speaking up is aggressive

• Speaking up is arrogant

• Speaking up is confident

• Speaking up is expected

• Speaking up is normal

• Speaking up is good

Each step is modest, but moves her further from the old belief and closer to the new one. That is how lasting change actually works.

The challenge

When you work on changing your beliefs, some relationships will no longer fit. Not because those people are bad, but because belonging was built on shared belief. When the belief shifts, the belonging shifts too — and that can be uncomfortable enough to stop people doing this work at all.

This is where the inter-level belief conflict becomes real. Maya can do everything right: she can map the belief, challenge the evidence, replace it with something more useful, and sustain it over time. But if she’s operating inside an organisation whose dominant belief is that women who speak up are aggressive, she faces a problem that individual belief change alone cannot solve.

This is the limit of individual, 1:1 coaching, and it’s why Belief OS™ operates across all levels. The individual belief change is necessary, but without corresponding change at the organisational level, the group will keep pulling her back towards the old belief. The scaffolding of authority, belonging and shared practice will work against her rather than for her.

Changing your beliefs is hard. Changing the beliefs of the group around you is harder. But understanding that both need to change, and why, is where the real work begins.

It feels like this week the rhetoric around AI has cranked up a notch or two. In various communities and Whatsapp groups I’m part of the same three articles have been shared and thoughts invited:

• Something big is happening, by Matt Shumer

• The AI Vampire, by Steve Yegge

• HBR published a study looking at how using AI leads to burnout (this article discusses it without the HBR paywall)

Those who are firmly in the pro-AI camp call this the ‘doomsday narrative’, pushing back on the argument that AI is going to be the end of humanity, and is instead a great optimiser at work and home. This makes it easy to dismiss the genuine concerns of the sceptics.

But is AI itself really ‘good’ or ‘bad’? As John pointed out in our book club discussion, we are quick to put a label on things we do not properly understand, to help us relate to them and give meaning to our experiences of them. The technology itself may be neutral; it is bad actors — people and their intentions — that define its moral qualities.

Yet stepping back, I see a broader convergence of forces forming in tech and beyond that understandably makes us worried about our collective future. Power is consolidating across three critical domains: economic, political, and informational, all driven by tech. A handful of companies now control the infrastructure that underlies our economy. Democratic institutions are weakening as autocrats rise and great powers carve up spheres of influence. Billionaires own the platforms that shape what we see, read, and believe. And now AI is the vehicle accelerating all three forms of consolidation simultaneously. In this article, I'm going to explore each of these domains in turn, showing how they reinforce each other and where this convergence is taking us.

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Technofeudalism (economic power)

Having read Varoufakis’ book, I can see a similar pattern emerging with the AI companies that happened with cloud computing in the 2010s. But let me first describe the main premise of Technofeudalism:

• The use of computers in finance made it possible to create complex financial instruments like derivatives and collateralised debt obligations. These were so complex that even those trading them often didn't fully understand the underlying risks or assets they contained.

• These financial instruments packaged together large quantities of risky sub-prime mortgages (loans to borrowers with poor credit histories). By slicing and repackaging these loans, they were presented to markets as low-risk investments, disguising their actual exposure.

• When borrowers began defaulting on sub-prime mortgages at scale, the interconnected nature of these financial products meant losses cascaded through the global financial system, triggering the 2008 financial crisis. Major institutions including Lehman Brothers collapsed.

• However, governments deemed many failing banks ‘too big to fail’ due to the systemic risk their collapse posed. Central banks and governments (particularly in the US and UK) provided massive bailouts using public funds to prevent total financial system collapse.

• This led to a period where central banks implemented quantitative easing, creating new money to purchase government bonds and other assets. This flooded financial markets with cheap capital. Banks and investors, facing low interest rates, sought profitable investment opportunities.

• Big Tech companies (Google, Amazon, Apple, Facebook) appeared as attractive investment options, having demonstrated rapid growth and market dominance. They could access virtually free capital through ultra-low interest rates.

• Big Tech used this cheap capital to build what Varoufakis calls cloud capital: the digital infrastructure (data centres, cloud platforms, algorithms, networks) that now underpins much of the economy. Crucially, this capital doesn't just produce goods but controls access to digital markets and services.

• Varoufakis argues profit has become detached from traditional capitalist relations. Big Tech doesn't primarily profit by selling products at a markup on production costs. Instead, they extract rent by controlling access to their platforms (through subscription fees, commission on transactions, advertising access fees).

• This rent-seeking model resembles feudalism more than capitalism. Just as feudal lords extracted rent from serfs for access to land, Big Tech extracts rent from users and businesses for access to cloud capital. We've become cloud serfs, paying dues to our digital landlords for access to essential infrastructure they control.

So we can see this same pattern now playing out with the likes of OpenAI, Anthropic and Google — the AI ‘Big 3’. They’re in a race to grab as much AI-land as possible: lock in customers and make it exceptionally hard to switch, whilst charging a rent (monthly subscription) to access their models. This has been possible because they have the most advanced frontier models, coupled with the deepest pockets based on their backers:

• OpenAI > Microsoft ($13 billion)

• Anthropic > Google ($2.3 billion) and Amazon ($4 billion)

• Google > own resources (unclear, but expected to be tens of billions of dollars based on overall R&D expenditure)

A note on Microsoft and Meta, who are conspicuously absent in the Big 3 above: Microsoft have made a substantial investment in OpenAI, so whilst they have their own specialised AI models (Phi-3 and Phi-4), their Copilot consumer product is actually using OpenAI under the hood. Meanwhile, Meta has launched their own LLM model, Llama, but chosen to go a different route, and open source it. Meta’s strategy is based on a belief that the real competition isn’t on the model itself, but the application or platform layer it sits on — which is where their strength is via their other products. And regardless of how that bet turns out, they then have their own model powering Facebook, Instagram, Threads and Whatsapp, and are not beholden to the Big 3.

It also calls into question the assumption that we are living in an AI bubble. Varoufakis would argue we’re not. Bubbles are predicated on the old rules of capitalism, and that’s not the underlying economic mechanism at play here. The likes of Microsoft and Softbank ($41 billion total investment) are ploughing capital into OpenAI without the expectation of return in the short term (OpenAI lost $14 billion last year alone) so it can instead grow exponentially fast to become the dominant player in the market. Once that's achieved, they will lock in consumers in perpetuity and extract whatever rent they want, just as feudal lords needed no return on investment in their land, only the ability to collect rent from those who had no choice but to use it.

Democracy in retreat (political power)

As part of the research for my TEDx talk I’ve been watching others on YouTube, and last week came across Sarah Wilson’s: How to Respond to Societal Collapse (well worth a watch if you have 14 minutes).

One of the stats she shared stood out to me: more than 70% of the world’s population now lives under autocracy, and even in the West, democratic norms are weakening. As Harry neatly put it: democracy is precious. We've lived in a short sliver of time where it was able to flourish — since WWII social democracy has bloomed, with a strong welfare state, worker rights and protections, and regulated capitalism. But all that is now ending.

We saw global power consolidating into elite hands at Davos this year. Trump announced his new Board of Peace: a private members club for world leaders with a $1 billion admission fee for permanent membership. Early signatories included the autocratic regimes of Hungary, Azerbaijan and Turkey. Notably, major EU democracies — the UK, France, Germany, Italy, Norway, Spain, Sweden and Slovenia — all declined to join. Trump's approach appears aimed at undermining supranational democratic institutions like the United Nations. The US itself currently owes $1.5 billion in unpaid UN dues.

Mark Carney’s speech at Davos warned of "a rupture in the world order", as great powers abandon even the pretence of rules for the unhindered pursuit of their interests. The old international order, where countries could be held accountable for their actions, no longer exists. Instead, new alliances must be forged, one by one, with allies willing to do the work to preserve relations and democracy. It's another stark illustration of how power consolidates faster than ever: autocrats can consolidate power in weeks, whilst defending democracy remains painstakingly slow.

The world is fracturing along the lines of three great powers. The US is asserting dominance in its hemisphere through force — demanding Greenland from Denmark, detaining Venezuela's president. Russia's invasion of Ukraine signals broader territorial ambitions in Eastern Europe. China continues expanding its economic influence across Southeast Asia and Africa through infrastructure investment and debt diplomacy. We're returning to a world of empires and spheres of influence, where might makes right and smaller nations become vassals to their regional hegemon. As our book club noted, Britain is not blame-free here — we pioneered this model during our colonial era.

So what is the role of AI in this political power consolidation? Autocratic regimes control their populations through mass surveillance, and AI supercharges this capability. Ring's recent Super Bowl advert neatly illustrates how this works: under the feel-good guise of searching for missing pets, the commercial explained how Ring doorbells can send data through a network to a central security apparatus (currently provided by Flock). The facial recognition tech that can be used to look for a lost Labrador can also be used to identify potential deportees and women who have abortions.

Controlling the narrative (informational power)

Bezos purchased The Washington Post in 2013 for $250 million cash. It wasn’t too problematic when he refrained from meddling in its editorial decisions. However, in October 2024, just before the presidential election, Bezos intervened to block The Washington Post’s editorial board from endorsing Kamala Harris for president. This broke the paper’s tradition of endorsing presidential candidates, which it had done since 1976. Critics noted that its timing was suspicious, coming shortly after Bezos had meetings with Trump about his other company, Blue Origin.

When Musk bought Twitter in 2022, he claimed it was to defend free speech. What he actually bought was algorithmic control of the platform’s information flow. He can amplify the voices that serve his interests, suppress those that don't, and flood users' feeds with content that reinforces his preferred ideology. This isn't traditional media ownership. Murdoch could influence what his newspapers published, but readers chose whether to buy The Sun. Musk controls the algorithm that determines what 500 million users see before they've made any choice at all (except, of course, whether to open the app in the first place).

The consolidation of media ownership in the hands of a few billionaires isn’t coincidental. Many of them share what Curtis Yarvin calls the Cathedral worldview: the belief that media and academia (along with the civil service) form an informal power structure that shapes acceptable discourse. Yarvin, a Silicon Valley reactionary whose ideas have influenced figures like Thiel and Andreessen, argues this network enforces progressive orthodoxy and must therefore be dismantled. The solution, in their view, isn't to reform these institutions but to capture them. Billionaires buy newspapers and social platforms to deliberately dismantle the institutional checks that might hold them accountable.

When the same actors control economic infrastructure, shape political power, and own information channels, democratic accountability becomes nearly impossible. Which brings us to an uncomfortable question: what's our role in this?

Complicity and resistance

Reading this article has probably made you feel quite uncomfortable — it does me writing it. It feels dark, heavy, depressing to think about how different forms of power are consolidating into the hands of the few. Nobody wants to believe they are part of the problem, especially when the problems feel too big to solve individually. And especially not when that involvement results in harm to others. Yet in so many ways we are complicit in this process: using social media to stay connected with family and friends; using Big Tech products at work and home; investing our pensions in Big Tech; relying on AI tools that entrench these power structures further.

An underlying point in these articles, particularly Shumer's, relates to a fundamental aspect of these systems: power can be consolidated because it is taken from elsewhere. Shumer calls this out explicitly: when we give more hours than we are paid for, we are being exploited. AI enables us to do more in the same amount of time — but we’re not paid for doing more. That extra value goes up the chain to the technofeudalists.

For those of us working in tech, the cognitive dissonance cuts deeper. Building AI features, optimising platforms, creating subscription models — these are the skills that advance our careers and make our companies valuable. What looks like innovation and commercial opportunity is also the mechanism through which power consolidates into fewer hands.

So what is the answer here?

We can see the push back happening. Amazon cutting ties with Flock; users leaving X for Bluesky; internal resistance at Google forcing them to end its contract with the Pentagon over Project Maven; Germany being the first state to implement the EU’s AI Act, signalling how democracies can still exert their power to constrain Big AI.

It’s not at all easy to do this. No doubt the loss of subscribers at WaPo is what has led to the substantial layoffs announced last month. Those are real people losing their jobs, real consequences of resistance. And that is what those with power are banking on — we won’t blame them, we will blame each other.

The power consolidation I’ve described only works if we remain atomised, as individual consumers making individual choices, individual workers absorbing individual consequences. But as Malcolm Gladwell argues in The Tipping Point, dramatic social change often happens suddenly once critical mass is reached. It takes a surprisingly small number of people working collectively to trigger systemic shifts that seemed impossible before. Erica Chenoweth's research on resistance movements reveals the threshold is surprisingly low: active participation from just 3.5% of a population is enough to trigger unstoppable systemic change.

Varoufakis ends Technofeudalism with a call for precisely this kind of collective action: coordinated resistance from those who build, regulate, and fund these systems. The tipping point he describes isn't about everyone opting out. It's about enough people in positions of influence refusing to participate in the consolidation of power into feudal structures. My call to action is to recognise that innovation and power consolidation have become dangerously entangled. Every AI feature, every platform optimisation, every subscription model we create and use, we must ask ourselves whether it's genuinely useful or whether it's just another way to extract rent from infrastructure people will have no choice but to use.

I found myself nodding along as I read it, not least because my work researching and developing Belief OS™ has shown just how important language is as a scaffold to maintaining beliefs. What fascinates me about cults — whether we’re talking about Scientology, MLM schemes, or the current MAGA movement — is how people can maintain beliefs that directly contradict observable reality. How do people continue believing things when the evidence before their eyes suggests otherwise?

George Orwell understood this in 1984 when he wrote: “The party told you to reject the evidence of your eyes and ears. It was their final, most essential command.” That’s how powerful language is: it doesn’t just describe reality, it can override it.

The work of Lev Vygotsky, a Russian psychologist, provided the theoretical grounding for understanding how this happens. Language doesn't merely reflect our reality — it co-actively shapes it. In this article, we're going to explore how language scaffolds our beliefs, and what happens when we become conscious of the words we're using to construct our world.

What sociocultural theory says about language

Vygotsky argued that language is a psychological tool that mediates our relationship with the environment, similar to how physical tools like hammers and spears extended our capacity for action. But whilst hammers extend our hands, language extends our minds. The development of language within human society enabled us to collaborate, innovate and think creatively, imagining possibilities beyond what we observe in reality.

He writes in his 1978 book, Mind in Society: The Development of Higher Psychological Processes:

“Like tool systems, sign systems (language, writing, number systems) are created by societies over the course of human history and change with the form of society and the level of its cultural development... the internalization of culturally produced sign systems brings about behavioural transformations and forms the bridge between early and later forms of individual development” (p.7).

Language operates as a cultural tool that enables interactions between ourselves and our social world to become internalised. We learn language socially, through discussions and conflicts with others, then use it to reason with ourselves and regulate our own behaviour. Vygotsky describes this as the moment when “speech and practical activity, two previously completely independent lines of development, converge” (p.24). When this happens, “the action becomes transformed and organised along entirely new lines.”

Critically, this means language doesn’t just reflect reality — it actively shapes how we perceive and interact with it. Just as a mould gives shape to a substance, words shape our thoughts into structures.

“That’s just semantics”

This shows that our choice of words matters. Have you ever heard someone say ‘that’s just semantics’? I once worked with an executive who would deploy this thought-terminating cliche1 whenever he wanted to shut down discussion. It was his way of asserting power over a conversation, and it was exceptionally hard to challenge.

It really irked me (see: frustrated, annoyed, pissed me off, made me lose a bit of respect), because of course speech is semantics — the specific words we choose to convey our thoughts carry specific meanings for us. The Sapir-Whorf hypothesis2 supports this: it proposes that the language we speak influences how we think and perceive the world. Different languages don’t just label reality differently; they make certain thoughts easier, more habitual, or more cognitively accessible.

The evidence is compelling. Russian has separate words for light blue (goluboy) and dark blue (siniy), and research shows Russian speakers are faster at distinguishing these shades than English speakers. In another study, speakers of Guugu Yimithirr (an Australian language that uses absolute directions like north and south rather than relative ones like left and right) maintain constant awareness of cardinal directions. They literally think about space differently.

These examples are more than just linguistic curiosities: language's influence on perception has measurable real-world consequences.

Real-world consequences of language

Another example illustrating how language shapes our understanding of the world is provided by the work of Loftus and Palmer. Through a series of studies, they investigated how memory is reconstructed, including the famous car crash experiment3. Here they showed participants a series of videos of cars crashing and asked them to estimate the speed at which it happened. They asked questions with a simple word substitution as the variable:

“About how fast were the cars going when they (smashed / collided / bumped / hit / contacted) each other?”

Participants estimated the speed to be higher when using ‘smashed’ compared to ‘hit’ in the question:

This demonstrates that language doesn't just describe memory: it reconstructs it. The word 'smashed' implies faster speeds than 'hit', which in turn feels more forceful than 'contacted'. The implications extend beyond the laboratory: when police interview eyewitnesses to road traffic accidents, their choice of words may directly influence the testimony, and therefore the outcome of investigations.

The language of neoliberalism

We are all swimming in a cult without realising it — the ideology that is neoliberalism. It is so pervasive in Western economies (particularly the UK and US) that we barely notice when its language shapes how we think about ourselves.

Neoliberalism's native tongue is the language of markets and products. What's insidious is how this vocabulary has migrated from economics into personal identity. We now describe ourselves using commercial metaphors as if they're natural, inevitable, even empowering.

Consider these parallels between economic language and how it is applied to humans:

• Investing in a company → Invest in yourself through courses, coaching, self-improvement

• Branding a product → Personal branding on social media

• Marketing and selling → Sell yourself to employers, even romantic partners

• Asset maintenance → Self-care reframed as productivity maintenance

• Portfolio diversification → Side hustles, leaving no personal time unmonetised

• Business development → Networking turning every relationship into a potential transaction

In case you had not yet worked it out: you are the product. And this language is actively shaping your beliefs about yourself, other people, and the world around you. I see (hear) this all the time when people say ‘I’m redundant’. The word evokes a sense of being faulty, defective, surplus to requirements — even though the reasons your role was cut have nothing to do with your ability and everything to do with short-term profit maximisation for shareholders.

This language is so ingrained in our culture that it takes conscious effort to notice it, let alone change it. You might think: “Even if I change how I talk, won’t I still believe the same things underneath?”

But here’s what Vygotsky showed us: it works the other way around. You can’t simply decide to stop believing something — beliefs are stubborn precisely because they’re scaffolded by language. Change the scaffold, and the belief loses its support structure.

So the question isn’t “How do I change my beliefs so I can use different language?” It’s “How do I change my language so different beliefs become possible?”

Changing language to change beliefs

Now we can inspect our self-talk to identify what beliefs are hiding in plain sight, determine if they’re helping or hindering us, and if the latter — change them.

Start by paying attention to one phrase you use repeatedly about yourself. Where did it come from? What belief is it maintaining? Then try a simple substitution for a week and notice what shifts — here are some examples:

• “I failed” → “I learned that approach doesn’t work.”

• “I have to do this” → “I’m choosing to do this”

• “I’m redundant” → “My role was made redundant.”

Each revision creates a different relationship to reality. Turning failure into learning transforms a fixed state into an ongoing process. Turning have to into choosing to shifts you from victim to agent. When it comes to redundancy, separating yourself from your role reminds you that your worth isn't tied to your economic utility.

You don’t need to join a cult to have your reality shaped by language. But once you notice how language scaffolds your beliefs, you can choose to speak — and therefore think — differently.

If this resonates and you want to explore these ideas in your own leadership context, let's talk. Book a call with me.

On Friday I was down in Bristol at BANES Women's Leadership Network, delivering a workshop on overcoming imposter feelings. The theme of the event was ‘being, becoming, belonging’, and imposter feelings touch on all three. Usually I would focus on what we can do to help ourselves, but given this was a group of leaders I put a spin on it: given how common it is to feel like an imposter1, we can assume our teams are feeling this way. What can we do as leaders to help them?

I started with a shared definition: imposter feelings are the fear of being found out as inadequate. Then I introduced the cycle of imposterism. Judging by the nods and wry smiles, it resonated.

Here’s what happens:

A new challenge arrives (a promotion, a high-stakes presentation, leading a new initiative) and the inner critic pipes up. ‘I can’t do this’ plays on repeat. You focus on your weaknesses (’I’m not a good presenter, I get nervous, I don’t speak clearly’) and fear you will fail.

This leads to one of two coping responses: procrastination (waiting until the last minute, then staying up late) or over-preparation (starting immediately, putting in far more effort than necessary). The bar is already high: you’re aiming for perfection. Both lead to stress and anxiety, and those horrible 3am wake-ups.

Inevitably, you complete the task to a high standard. Colleagues may even say how brilliant it was (because it truly was). You feel temporary relief. But then the next task comes and the cycle starts again.

Understanding this cycle gives us insight into how we can break it — each point represents a place we can intervene. And not just for ourselves, but for people in our teams. Let’s look now at what we can do to help them succeed without feeling like an imposter.

How to help your teams

Let’s take each point on the cycle and explore what you can do to help your teams overcome imposter feelings.

Setting a new challenge

Recognise that stepping into an arena for the first time is going to bring up a mix of feelings, some positive, others not so helpful. For instance, if you have recently promoted a member of your team, it’s worth sitting down with them 1:1 and talking through how to make that transition successfully. Address interpersonal factors like how their identity may change — not just how they see themselves, but how others see them, particularly former peers, and how relationships will evolve. Offer support to navigate this, such as coaching them on what kind of leader do you want to be?

If it’s more task-based, like giving a presentation to a senior audience, then outlining what ‘good’ looks like can help them calibrate their expectations. Give them your insight into what’s likely to come up so they can anticipate it — if there’s a particular exec who likes to ask challenging questions, share that, and what they are actually looking for. The more information you can give them, the more reassured they will feel.

Self doubt

One of the most important things you can do as a leader is lend someone your belief in them until they develop their own. When you’re talking through the new situation or task, you can make this explicit: "I'm giving you this opportunity because I've seen how you handled [specific situation]. That same [skill/approach] is exactly what's needed here." By linking your belief to capabilities they have previously demonstrated, you are making it clear this is a natural stretch and not totally new and unfamiliar.

Normalise the self doubt: if you have your own story of how you overcame yours when you stepped up for the first time, that will force their inner critic to release its grip.

Focus on weaknesses

Set your team member up for success by calling out what they already do so well. Give them feedback on their skills, effort and approach — just be careful not to make it about them as that can have unintended consequences. This is the difference between ‘you’re a great presenter’ (draws attention to self, which increases self-monitoring and therefore degrades performance) versus ‘you communicate clearly and calmly, and break down complexity into simple to understand concepts’ (focuses on their skills and abilities)2.

Fear of failing

One powerful idea that emerged from the workshop was to encourage your team member to embrace an experimentation mindset. What is one small thing they could do to test it out? Tiny experiments enable us to reduce risk to an acceptable size, but also accept that learning does not happen without failure. This reframe works particularly well if your team member values growth and learning. For instance, instead of 'deliver the perfect presentation to the board', try 'share a customer story to bring the data to life' — and then debrief with you afterwards to embed that learning.

Coping response

This is one where you can really challenge them in a constructive way. As you set the task or challenge, and set clear expectations about what good looks like, you can also break it down into a series of milestones that you will review along the way together. If you have 4 weeks until the presentation, using the weekly 1:1 to check in on progress may help them pace themselves, as a set of ‘mini deadlines’. They also then get the benefit of your feedback as a form of external calibration, to challenge the perfectionist. Be mindful that for over-preparers, check-ins should focus on 'is this good enough?' not 'how can we make it better?’

Stress and anxiety

Watch your team member for any signs they may be feeling stress. Ask them how they’re getting on with the task outside of formal catch ups. Don’t just say ‘how’s it going?’ in passing. Create space for an honest answer. Showing you care and are available to talk about it will help dissipate those feelings, as they are more likely to arise when we feel alone, and unable to reach out.

Task completion

Once the task is complete, give timely feedback. Start by asking 'how do you think that went?' This helps them calibrate their own sense of what's good enough, rather than always looking externally for validation. Part of your role as a leader is helping them develop their own inner compass. Remember to focus your feedback on the task and what they did, not them as a person.

Temporary relief

We often quickly move on from success without taking a moment to properly pause and enjoy it. One of the most powerful things you can do as a leader is encourage celebration. Ask them ‘you did a great job there, how are you going to celebrate that?’ If it’s a big win, you could give them a more substantial recognition or reward: a company-wide shout out, a team lunch, a bonus. The point is to not just quickly assign them the next task as that diminishes the achievement and fuels the inner critic.

At the workshop, an attendee shared a striking example: at their university, academics rotate into line management roles. On Friday you’re a peer, on Monday you’re managing up to 80 former colleagues, with no training, no knowledge transfer, and no support. It’s no surprise people feel like imposters in that setup.

Imposter feelings don’t arise only from individual psychology. They’re a rational response to poor systems, unclear expectations, and lack of support. Each intervention point on the cycle is an opportunity to change that.

Start small. Pick one or two stages where you can intervene with your team this week. Notice what changes.

If any of this resonates and you're curious about how coaching could support your leadership development, I'd love to have a conversation. Book a call with me to explore working together.

At Davos this week, Mark Carney addressed a room full of the world’s elite and political leaders to talk about Canada’s new role in the international order. It’s a brilliant speech and one that will likely go down in history as a pivotal moment, a rupture in the shared worldview. I highly recommend you read or watch (video in link) it if you haven’t already.

What struck me as I listened to Carney speak is how he addressed a need to change what we all believe about states, institutions and those who govern and legitimise them. The old belief is that rules-based international order still functions as it claims to.

The rules-based international order refers to a system of global governance in which states agree to be constrained by shared rules, institutions and norms, rather than raw power, to manage trade, security and conflict. In practice, it claims that international law applies equally to all states, regardless of size or strength.

Carney’s core argument is that it’s not just inaccurate to believe this, but dangerous to do so, because that belief leads to increased vulnerability — particularly for countries that are not great powers. Instead, he is urging us to adopt a new belief: that we are in an era of great power rivalry where rules are selectively applied.

“First it means naming reality. Stop invoking rules-based international order as though it still functions as advertised.”

In this article we’re going to explore how Carney dismantled the old belief to supplant it with a new one, using the lens of Belief OS™ (see Liftoff #27 for a refresher).

Lever 1: Meaning

We have been struggling to make sense of this for some time. We have seen with our own eyes and ears, through official news reports and social media, how certain countries have ignored the rules-based international order in recent years. From Russia’s invasion of Ukraine and subsequent annexation claims that threaten territorial sovereignty, to the US imposing unilateral tariffs on trading partners, to a UN commission of inquiry concluding that Israel has committed genocide in Gaza, we see that certain states have acted against others, disregarding a shared belief that all are subject to the same rules.

“The system’s power comes not from its truth, but from everyone’s willingness to perform as if it were true, and its fragility comes from the same source.”

Many have been living with cognitive dissonance: continuing with daily life while becoming increasingly aware of the suffering and death occurring elsewhere, sometimes not far from home. How can we make sense of this? To resolve this discomfort we create a story that the rules-based international order still fundamentally works, that the bad actors are exceptions to the norm. Carney calls out this fallacy:

“We knew the story of the international rules-based order was partially false, that the strongest would exempt themselves when convenient, that trade rules were enforced asymmetrically. And we knew that international law applied with varying rigour depending on the identity of the accused or the victim.”

The final scaffold holding the meaning of a belief in place comes in reference to Canada’s identity as a middle power, in contrast to the US, Russia and China as great powers. The rules-based international order claimed to protect all states equally, irrespective of economic size or military prowess. Carney now draws a sharp distinction between states based on size and relative power, because inequality in power and leverage has become impossible to ignore.

“Great powers can afford for now to go it alone. They have the market size, the military capacity and the leverage to dictate terms. Middle powers do not… If [the middle powers are] not at the table, we’re on the menu.”

Lever 2: Affect

Towards the end of his speech, Carney delivers a wake-up call designed to provoke strong emotions. He deliberately withdraws the comfort that has underpinned the post World War II order, naming complacency and nostalgia as risks, and replaces them with a different emotional orientation: resolve, agency and guarded hope.

“We know the old order is not coming back. We shouldn't mourn it. Nostalgia is not a strategy, but we believe that from the fracture, we can build something bigger, better, stronger, more just.”

Under the old belief, the rules-based international order was underpinned by a particular set of values. Stability was prized over disruption. Consensus and accommodation were treated as virtues, even when they limited adaptation, investment and long-term progress. There was comfort in believing that, while imperfect, the system broadly worked and would self-correct over time. But we are in a world where individual state power, not shared rules, increasingly sets the boundaries of the order, and pretending otherwise increases vulnerability to subordination.

“You cannot live within the lie of mutual benefit through integration, when integration becomes the source of your subordination.”

Under the old belief, the rules-based international order required a complementary set of morals to keep it functioning. Even when it was clear the system was not working as advertised, countries continued to go along with it, stay quiet and avoid confrontation to preserve stability. Carney uses Havel’s image of the shopkeeper who puts a sign in his window he does not believe, simply to get along (workers of the world unite). His point is that this is not morally neutral behaviour: going along with a fiction helps sustain it. Under the new belief, morals shift towards honesty and applying the same standards to everyone, rather than staying silent for convenience.

“When middle powers criticize economic intimidation from one direction, but stay silent when it comes from another, we are keeping the sign in the window… It means acting consistently, applying the same standards to allies and rivals.”

Lever 3: Social

Perhaps the biggest belief shift Carney reveals is in how control now operates. Previously, authority was vested in multilateral institutions that enforced rules states had signed up to. Under the new belief, these institutions can no longer command compliance. Their authority has been weakened as powerful states bypass rules without meaningful consequence. Authority is now determined by economic size, control of supply chains, access to capital and military strength. It is exercised through leverage, not law.

“When the rules no longer protect you, you must protect yourself… This is not sovereignty. It’s the performance of sovereignty while accepting subordination.”

Throughout the speech, Carney draws attention to how social roles are being redefined: great and middle powers, military allies (such as NATO and the Coalition of the Willing), and economic partners (including the Trans Pacific Partnership and the EU). These roles no longer guarantee protection or influence in the way they once did.

“The multilateral institutions on which the middle powers have relied – the WTO, the UN, the COP – the architecture, the very architecture of collective problem solving are under threat.”

As these roles shift, so too does a sense of belonging. It is no longer automatic or universal, but conditional and situational, shaped by shared interests and the ability to act together.

“This is not naive multilateralism… It’s building coalitions that work – issues by issue, with partners who share enough common ground to act together… That is Canada's path. We choose it openly and confidently, and it is a path wide open to any country willing to take it with us.”

Lever 4: Context

Carney makes clear that the old order was sustained through repeated rituals of participation. States attended the same summits, issued familiar statements, reaffirmed commitments — and avoided publicly naming contradictions. These rituals signalled alignment and belonging. In this context, ritual was less about belief and more about maintaining status within the hierarchy. Carney’s intervention exposes these rituals as no longer stabilising: continuing to perform them now sustains a fiction that increases vulnerability rather than security. Instead, he speaks of creating new habits:

“It’s building coalitions that work… what it’s doing is creating a dense web of connections across trade, investment, culture, on which we can draw for future challenges and opportunities.”

Language is central to how this fiction has been maintained. Carney challenges the continued use of familiar terms that imply fairness and restrained use of power: rules-based international order, shared values, mutual benefit, multilateralism. His call to name reality is an attempt to cut through the cognitive fog created by habit, euphemism and repetition. Language, in his framing, is not neutral. It either clarifies what is happening or helps sustain falsehoods that many already recognise. Even his choice to address the room as friends matters. It invokes relationship, trust and shared responsibility, while simultaneously testing whether those relationships can withstand honesty rather than reassurance.

“Friends, it is time for companies and countries to take their signs down.”

Lastly, Carney’s choice of where to deliver this speech does important work — place matters. Sharing it at Davos, before heads of state, senior policymakers, corporate leaders, investors and institutional figures, was deliberate. By naming reality in that room, Carney directs the message at those who have had the most power to sustain the old belief, challenging them to relinquish it and adopt the new.

Carney’s speech makes it clear that belief change does not happen through declaration alone. For the new belief to hold, all four levers must realign. Meaning must reflect the world as it is, not as it was described. Affect must shift away from nostalgia and false comfort towards resolve and agency. Social roles, authority and belonging must be recalibrated to match how power is actually exercised. And context matters too: the rituals performed, the language used and the places where legitimacy is asserted must stop reinforcing a story that no longer protects those who live by it. Without this coherence, the old belief will persist through habit and performance. Carney’s speech is a call to do that work deliberately, because the old belief can no longer be sustained by the evidence in front of us.