Do we even have time for stories? Or maybe we're too busy playing a mythical RPG called Life in the 21st Century to tell stories ourselves?

I'd love to hear your stories and ideas, take the short 3-question survey here. Thank you!

Welcome back, it's been a hot minute. Everyone I talked to this past month has had some sort of frenzy or chaos. #blessedButStressed was a common denominator in many conversations I shared. And yes, I've been in a similar situation. September was incredibly busy with work projects, the AIP published an article I co-authored with Dr Paul Taylor-Pitt about the opportunities of blended organisational development, exploring the intersection of Generative AI and Appreciative Inquiry, and less than a fortnight ago, I had the privilege to start my studies at Birkbeck, University of London, to pursue an MSc in Organizational Psychology. I'm incredibly excited to be spending the next year with a wide range of inspiring academics and practitioners and deepen my critical inquiry into how technology influences organisational culture and change.

The myths of intelligence

The dominant story I've heard and lived this past month was one of being busy, excited, and hustling. And somewhere in all of that, I managed to take a few timeouts. A coffee with a fellow somatic practitioner, a coaching session, a weekend celebrating my parents' retirement. And on the flight to see my parents, I got hooked on a different story. A friend shared this podcast with me, The Emerald, which explores currents and trends through a mythical lens. Think well-researched academic papers meet the best storytelling podcast you know, and sprinkle a bit of much-needed somatic awareness and humility over it. The AI Episode reminded me of our exploration of different ways of seeing intelligence from January, taken to a deeper level.

One of the myths Joshua Michael Schrei explores in the episode is one I've been teaching in the AI Essentials course with General Purpose: Humankind has thought about artificial intelligence for a really long time. We're obsessed with the idea of creating something outside of us that's as intelligent as us, or more. As humans, we dream of creating something that has the power to annihilate us, and we know how to make it sexy.

And in a way, the stories we re-tell shape the stories in our history books. Or at the very least the stories we live. This gets exacerbated by our use of AI in our creation process since AI is creating new ideas from the data we feed it during training. Try asking ChatGPT to make you an image about a scenario involving AI. Chances are extremely high that you get bright blue holographic displays and humanoid robots all over – retelling a myth we created through decades of filmmaking.

The myths of equality

The idea of myths resurfaces in some of the readings for my MSc. Amis et al (2020) explore how three consistent myths are at the core of The Organisational Reproduction of Inequality.

1. The Myth of Efficiency: This myth refers to the false premise that adopting efficiency-enhancing practices is what leads to organizational success. It assumes that pursuing efficiency will automatically iron out inequalities when in reality, practices justified by efficiency often perpetuate or exacerbate existing inequalities.

2. The Myth of Meritocracy: This myth promotes the belief that advancement and rewards in organizations are based solely on an individual's capabilities and performance, rather than factors such as family background, race, gender, or class. Despite the widespread belief in meritocracy, the article argues that many organizational practices remain fundamentally non-meritocratic.

3. The Myth of Positive Globalization: This myth suggests that globalization is broadly beneficial for everyone - "a tide that lifts all boats." It obscures the ways in which globalization can create new inequalities or reinforce existing ones, particularly in the context of global production networks and multinational corporations.

These myths work together to create a framework of established ways of operating that often go unchallenged, despite evidence of their role in reproducing inequality. The authors argue that these myths help explain why inequality-producing practices persist in organizations across different domains and activities.

What are the myths we're ascribing to AI? The myths of its intelligence, its ability to do things we would otherwise do, that it makes the same conclusions we would make, the myths that we can delegate or download our knowledge into a model or system that can then make sense for us based on our instructions and ideas?

The myths about control and individuality

In his book Small is Beautiful, E.F. Schumacher's shares the idea that we can only think because our minds already hold a multitude of concepts. In this space filled with collected and memorised concepts, we can generate new thoughts. We can't think in a vacuum; our neurons connect, bringing individual concepts together in various combinations and rhythms.

He explains that as children, we absorb many concepts without realizing it. We mentally collect everything around us, and especially the ideas we collect early on can become dominant. As we create new thoughts and concepts, we view them through these early "filters", or beliefs and ways of understanding the world. And because a lot of these "filters" operate on a subconscious level, we might not even realize we're looking at the world through tinted glasses. We "just know" or "don't trust this". It's an interesting way to look at our imprint of values, ideas, and beliefs as a self-reinforcing system. We make meaning with the concepts we know, and to create new concepts, we must first make new meaning. In the same fashion, we create culture in the organizations we work in, blending and reinforcing the concepts, beliefs and values we hold individually and as a group.

In technical discussions about AI, it's often said that large language models can't think like humans. They don't understand meaning. However, AI operates similarly to human thinking by recombining existing concepts to create new meaning. Only it does this by representing abstract concepts as arrays of numbers and combining them with basic math. To a certain extent, AI gives the impression that it has intelligence, that it can act and think like us, and that it can speak like us. It speaks more and more naturally, and very convincingly. Something I got to research in-depth last month when I was tasked to find the best platform for AI-generated avatars. It's weird to see an almost-perfect clone of yourself speak words you've never said. Words that have been written by a different AI, maybe.

AI has learned from the best. The internet is full of words from tricksters, cheaters, marketers and politicians, which have been digested into large language models that can now speak like those tricksters. We now have an artificial trickster that can convince us its ideas are, in fact, our ideas. Burtell & Woodside (2023) put it a bit more bluntly:

If AI persuasion is left unchecked, more and more persuasive power in our society will shift towards opaque systems we do not fully understand and cannot fully control, which could contribute to humans losing some of the control of our own future that we have enjoyed in modern times. – Burtell & Woodside (2023)

How do we confront the myth of unbiased, controllable, intelligent technology? AI might not have an intention it creates itself, but it surely has some intention, a blueprint inherited from the information it's been given in its training. As we adopt these technologies, we might inadvertently deepen chasms and reinforce stereotypes or inequalities that have existed for a long time.

When we think about opportunities for people to progress in their careers, to learn new skills, to shape their careers through learning and lateral shifts in an organization, and when we use technological sense-making to replace parts of what humans did before, we relegate ourselves to smaller confines of thinking, decision-making, and creation. We give up other parts to our AI companions, and with that, give over part of the meaning-making to a story, a myth that we do not understand because we have not written it ourselves.

Suddenly the "beliefs" that inform our decision-making don't solely come from the people we employ, but also from a probabilistic amalgamation of terabytes of data hoovered up by the companies who created the foundation models for our AIs. The values of our work suddenly don't come from a culture we hold in our organization, or one made up of individual experiences of the people in the organization; In some ways, by adopting AI, we subscribe to a global story or a value system based on those global models that influence our decision-making.

How does it change the character of an organization? Of our societies? How might we create structures that moderate that effect so we can set our own intentions, goals, and approaches, and have those intentions operationalized with the use of technology and AI, but not give over our intellectual control to an entity whose sense-making and decision-making processes we don't understand?

How do we want to shape the myths of our time?

While we chase the myth of efficiency by automating more and more parts of our work, we might ask ourselves: Are we regressing as a species or evolving into something else? Is this a forward movement, a backward movement, or something sideways?

I'm inviting you to take part in shaping the myths and stories we're telling about this next chapter.

1. What are the stories you're hearing about working with AI?
2. What stories would you like us to tell? What is missing in the dominant stories?
3. How do you see yourself in this wave of change? Are you already part of it? What role do you want to play?

I would love to hear from you. Let's discuss in the comments below, or answer my short, 3-question survey. By doing so, you'll even help me refine the research questions for my master's thesis. Thank you!

Let's shape these stories together.