The Strategy Review

The Map And The Territory

How to think

I want to talk about the topic of my new book-in-progress, ‘The Map and The Territory’, which is an enquiry into the problems of reality in the age of data.

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I’ve advised leadership in many industries profoundly impacted by data over the past quarter century. In all cases, the work has demanded making not just commercial or technological, but cultural, psychological and sociological sense of the accelerating waves of the digital revolution.

Over time, the challenge has evolved, from ‘what to do about this?’, through ‘what to think about it?’ to ‘how to think about it?’. The problem has shifted from the tactical, via the strategic, to become philosophical.

I’ve also published two books - one on digital business, the other on networks. A consistent motif has been the changing nature of meaning and of value, as they’re repeatedly upended by the techno-cultural turbulence of the past quarter century.

The Xerox Parc visionary Mark Weiser observed, ‘The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it’.

He was entirely correct. We’re like goldfish, asked to describe the water and the bowl in which we live. What water? What bowl?

Setting out

What do I mean by this word 'data'? I mean specifically information in digital form that can be manipulated by computer and distributed over networks.

It’s widely believed that ‘the data’ admits us to a comforting future of increasing precision and predictability. But there’s another, darker and far less explored, side to this precious coin.

Considering data today as simply an essential ingredient of information and knowledge is no longer adequate. It throws up genuinely existential challenges, forming an entirely new context.

That said, to ascribe any kind of intrinsic agency or accountability to data is erroneous. The critical questions here are not about ‘what data does’, but what can be done to it and with it, by whom, for whom, and to whom.

Finding our way about

We’ve never had access to more information, nor more tools to mine and manipulate it. And yet making sense of the world has never felt more difficult.

I love this quote from Wittgenstein: ‘A philosophical problem has the form “I don’t know my way about"’.

The cornerstone of this argument is that data’s primary role and value - yesterday, today and tomorrow - lie in supporting human navigation. No longer of just our physical world, but of every dimension of experience, in everyday journeys of understanding, decision and action.

The most challenging of the environments that data has enabled aren’t the satnav, VR/AR, video games, or ‘the metaverse’, all more or less known and navigable visual models of the physical world.

William Gibson’s seminal description of cyberspace, in his novel Neuromancer, helps us to fully appreciate the profound difference between these familiar virtual worlds, and the invisible data-filled abstractions which we now inhabit, and which come to inhabit us.

‘A consensual hallucination experienced daily by billions of legitimate operators … A graphic representation of data abstracted from banks of every computer in the human system. Unthinkable complexity. Lines of light ranged in the nonspace of the mind …’

We should pause on those phrases ‘representation of data abstracted …’ and ‘the nonspace of the mind …’.

There are essential distinctions between data as a representation of the phenomenal world and human experience, which is the way instinctively we tend to think of it, and data as it behaves today, which is far more as an abstraction from the phenomenal world and human experience.

The distinction between those two things, the representation and the abstraction, produces profound implications, which are a large part of my argument as we go forward. They point us to data’s role in all aspects of human navigation, landing abruptly on Alfred Korzybski’s often-quoted remark: ‘The map is not the territory’.

This is my primary hypothesis. The same data, devices and networks that enable our navigation of the phenomenal world, informing and enhancing our moment to moment understanding, decisions and actions, create another radically different, alien environment, for which neither the human mind nor society are prepared.

These abstracted, ethereal ‘maps’ not only create new ‘territories’: they have the power to influence and manipulate our experience, to undermine and even overwhelm it.

Lost in space

If we can accept this hypothesis, data moves beyond describing and enriching reality, to actively threaten and potentially to deconstruct it. To ground these implications, Jakob von Uexküll’s influential Umwelttheory offers an ideal perspective.

It refers to the specific perceptual world unique to each organism, based on its sensory abilities and biological needs. For humans, the umwelt can be understood as a tool for the navigation of meaning.

The effects on human experience, when the umwelt is both pulled into and invaded by a flood of abstraction, are destructive. We already have abundant proof of the impacts of inadequately regulated social media. Meaning, identity, agency, belonging and community are all directly and profoundly compromised.

What is happening here, and what is the precise role played by data?

Two perspectives offer immediate and complementary insights. First, the reflexive nature of modernity has been thoroughly explored, notably by the sociologist Anthony Giddens.

Persistent feedback loops between human experience and ever-shifting information render previously reliable ‘knowledge’ chronically hypothetical and open to doubt.  We could say it leaves reality in a state of constant beta.

Today, this effect is continually reinforced and accelerated by the irresistible expansion of digital networks, devices and data. How can we ever plant our feet, when the rug is repeatedly pulled from under us?

Turning to psychological impacts, research by Iain McGilchrist (muh-GILL-krist) and others into the brain hemispheres confirms close similarities between the behaviours of the data-focused left hemisphere when separated from the right, and common symptoms of schizophrenia.

In particular, loss of context, hyperfocus on abstraction, and alienation from lived experience. The correlations with the individual and societal impacts of unfettered data are clear.

In Neal Stephenson’s cyberpunk classic Snow Crash, a computer virus jumps from the screen into the body to cause brain damage.

In Wim Wenders’ film, Until The End of The World, a handheld machine enabling humans to watch their own dreams leads to obsession and breakdown. Note that it’s not their dreams that drive the characters mad, it’s recycling and consuming them in abstracted form.

Humans are neither intended nor equipped to inhabit and navigate - let alone to contain - such worlds. Our efforts tip us towards individual and societal breakdown.

Back to earth

The unique attributes and dynamics of data are central to its role in creating both clear and remarkable benefits and hidden, bewildering dangers. Its neutrality and fluidity enable it to move freely throughout the entire global system of networks and devices.

Its infinite plasticity and modularity enable it, like Lego bricks, to be manipulated - not only to assemble information, knowledge, narratives and content of all kinds, but to disassemble and reassemble them in forms that best suit actors with the necessary resources.

An inescapable conclusion is that the integrity and reliability of much - by now, likely most - of the information available to us can no longer depend solely upon digital abstracted data.

Instead, they come to rely on the degree to which our own values, intentions and purposes align or conflict with those of immensely powerful corporations and in increasing cases, governments. If we trust these, all is well. Reasons not to do so are plentiful.

I began this introduction with two premises. First, that data needs to be raised to the level of a significant existential challenge. Second, that it’s not ‘what it does’ that should concern us, but what can be done to and with it, by whom, for whom, and to whom.

Both democracy and capitalism are fraying at their edges, exacerbated by a toxic conflation of political and commercial interests.

The digitisation, abstraction and proliferation of data have played significant parts, advanced by the dominion of the algorithm across all financial markets, and the commonplace manipulation of national elections and both military and cultural wars.

We can see several distinct fronts converging, setting the scene for a very large, very perfect, storm. While it lies towards the edge of this project, such broader existential threats have been described as ‘the metacrisis’. Data is deeply and problematically embedded within and across all of these scenarios.

I’ve encountered some of the most passionate advocates of ‘the digital’ and’ the data’, increasingly uncomfortable with their rigid adherence to a kind of secular theology.

What I’ve discovered so far is that the challenge that data presents today is far more complex than whether it is, or is not, helping us to ‘find our way about’.

The map has itself become part of the territory, coming to undermine the solidity and consistency of reality itself. The question that follows, demanding our most urgent consideration, is this. What can realistically be done to retrieve and defend meaning, sanity and a shared sense of truth - the essentials of human agency and freedom - from all this data?

While I believe it’s profoundly important and long overdue, I fully appreciate that this work is not only unfamiliar, but also uncomfortable. So thanks - as always - for reading.

The Societal Effects of Generative AI

Castells and The Network Society

The emergence of generative artificial intelligence has sparked profound debates about its potential impacts on society, economy, and culture at a global scale. While much attention focuses on the technical capabilities and immediate applications of these systems, a deeper theoretical framework is needed to contextualise their societal implications.

Manuel Castells' work on the network society and informational capitalism offers such a framework, providing analytical tools to understand how generative AI might reshape power relationships, economic structures, and cultural dynamics worldwide.

Castells' remarkable and definitive trilogy - The Rise of the Network Society (1996), The Power of Identity (1997), and End of Millennium (1998) - along with his later works like Communication Power (2009) and Networks of Outrage and Hope(2012), developed a comprehensive theory of how information technologies transform social structures through networked organisation.

This article applies Castells' key theoretical concepts to examine the emergent global impacts of generative AI technologies, arguing that his work provides crucial insights for understanding this technological revolution within broader social and historical contexts.

Power and Meaning

Castells' analysis of power in the network society is particularly relevant to understanding how generative AI may concentrate or redistribute power globally. In The Rise of the Network Society, Castells (1996) writes:

Networks constitute the new social morphology of our societies, and the diffusion of networking logic substantially modifies the operation and outcomes in processes of production, experience, power, and culture.

Generative AI represents an intensification of this network logic, producing automated content that flows throughout the global digital infrastructure. The concept of "communication power" that Castells develops in his 2009 work becomes especially significant when considering generative AI. He argues that:

Power in the network society is communication power ... Power is primarily exercised by the construction of meaning in the human mind through processes of communication.

Generative AI fundamentally transforms this communication power by automating meaning construction at unprecedented scale and speed. Castells' framework suggests that whoever controls generative AI systems - including their training data, algorithmic architecture, and computational resources - may exercise disproportionate influence over the construction and circulation of meaning in society.

This raises critical questions about the concentration of power among technology companies, primarily based in the United States and China, that lead in AI development. The "programmers" of these networks (to use Castells' terminology) potentially gain unprecedented influence over global information flows, cultural production, and economic opportunities.

Haves and Have-Nots

Castells' analysis of digital divides and networked inequality provides insights into how generative AI might exacerbate or transform global patterns of inclusion and exclusion. In The Internet Galaxy, Castells (2001) warns:

The differentiation between Internet-haves and have-nots adds a fundamental cleavage to existing sources of inequality and social exclusion in a complex interaction, that appears to increase the gap between the promise of the Information Age and its bleak reality for many people around the world.

Generative AI potentially amplifies these existing digital divides by creating new technological thresholds that separate those with access to advanced AI capabilities from those without. The resource-intensive nature of developing and deploying generative AI models - requiring vast datasets, specialised expertise, and significant computational power - creates barriers to entry that may disproportionately benefit already-advantaged regions and populations.

Moreover, Castells' attention to how networks create patterns of "switched-on" and "switched-off" nodes provides a powerful metaphor for understanding how generative AI might create new forms of exclusion. As these technologies reshape labour markets, educational systems, and cultural production, those without access or skills to leverage them risk being "switched off" from key economic and social opportunities. This dynamic operates both within nations (creating new forms of stratification) and between nations (potentially widening global economic disparities).

Culture and Meaning

Castells' exploration of how networked communications reshape cultural identity offers valuable perspective on generative AI's cultural impacts. In The Power of Identity (1997 , he observes:

Our societies are increasingly structured around the bipolar opposition of the Net and the Self ... People increasingly organise their meaning not around what they do but on the basis of what they are, or believe they are.

Generative AI introduces new tensions in this relationship between networked communication and cultural identity. By automating creative processes previously central to human cultural expression - writing, visual arts, music composition - these technologies potentially disrupt traditional connections between cultural production and identity. When AI systems can generate content in any cultural style or tradition, questions arise about authenticity, cultural appropriation, and the preservation of diverse cultural expressions.

Furthermore, Castells' analysis of how communication networks enable both cultural homogenisation and new expressions of diverse identities applies to generative AI. These systems, trained on predominantly Western and English-language data, may accelerate cultural homogenisation while simultaneously offering new tools for marginalised communities to amplify their cultural expressions - if they can access and adapt the technology.

Flows vs. Places

Castells' distinction between the "space of flows" (global digital networks) and the "space of places" (physical localities) becomes increasingly relevant with generative AI. He argues in The Rise of the Network Society(1996) that:

The space of flows ... links up distant localities around shared functions and meanings on the basis of electronic circuits and fast transportation corridors, while isolating and subduing the logic of experience embodied in the space of places.

Generative AI accelerates this tension by enabling seamless, instantaneous global content production disconnected from physical contexts. These technologies can generate content that mimics local cultural forms, without engagement with the localities that produced them, potentially undermining connections between cultural expression and place-based communities.

Additionally, generative AI may intensify what Castells calls "timeless time" - the compression and reorganisation of temporality in network society. The acceleration of content generation, processing of information, and automation of creative processes further compresses experiential time, with unpredictable consequences for human experience and social organisation.

Resistance and Counter-Networks

Finally, Castells' analysis of how networks enable new forms of resistance provides perspective on potential counter-hegemonic uses of generative AI. In Networks of Outrage and Hope, Castells (2012) observes:

By engaging in the production of mass self-communication messages ... social actors ... increase their chances of enacting social and political change - even if they start from a subordinate position in institutional power, financial resources, or symbolic legitimacy.

This suggests possibilities for marginalised communities to appropriate generative AI technologies for their own purposes, potentially challenging dominant power structures. Open-source AI models, community-controlled training datasets, and local adaptations of generative technologies might enable forms of "counter-power" that resist corporate or state domination of these technological systems.

However, the resource-intensive nature of advanced AI development poses significant challenges to such grassroots appropriation. Castells' framework suggests that resistance to dominant generative AI paradigms will likely require forming counter-networks that pool resources and expertise across traditional boundaries.

Impacts and Implications

Manuel Castells' analysis of network society helps us recognise generative AI as a sociotechnical system that reconfigures power relations, economic structures, and cultural processes in complex ways. His insights suggest several key implications for policy, governance, and research regarding generative AI.

First, addressing power concentration requires transparent governance mechanisms that extend beyond national boundaries to match the global scale of AI networks.

Second, preventing new forms of exclusion necessitates deliberate efforts to democratise access to AI capabilities and ensure diverse cultural representation in training data.

Finally, preserving spaces for authentic cultural expression may require protections for human creative labor and community intellectual property.

Additionally, his work invites deeper investigation of emerging forms of resistance and alternative AI development that challenge dominant paradigms.

By viewing generative AI through Castells' network society theory, we gain a more nuanced understanding of how these technologies extend and transform broader social processes rather than simply disrupting them from outside. This perspective is essential for developing approaches to AI governance and deployment that promote equitable, culturally responsive, and socially beneficial outcomes across global networks.

References

Castells, M. (1996). The Rise of the Network Society, The Information Age: Economy, Society and Culture, Vol. I. Blackwell.

Castells, M. (1997). The Power of Identity, The Information Age: Economy, Society and Culture, Vol. II. Blackwell.

Castells, M. (1998). End of Millennium, The Information Age: Economy, Society and Culture, Vol. III. Blackwell.

Castells, M. (2001). The Internet Galaxy: Reflections on the Internet, Business, and Society. Oxford University Press.

Castells, M. (2009). Communication Power. Oxford University Press.

Castells, M. (2012). Networks of Outrage and Hope: Social Movements in the Internet Age. Polity Press.

The Heart of The Machine

In a time when artificial intelligence increasingly mimics human capabilities - writing essays, creating art, and engaging in seemingly meaningful conversations - we find ourselves at a curious philosophical crossroads.

The work of psychoanalyst and social philosopher Erich Fromm (1900-1980), particularly his 1964 book ‘The Heart of Man’, offers a prescient framework for understanding the fundamental limitations of even our most sophisticated AI systems.

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The Mechanical Mind vs. The Living Heart

Fromm's exploration of necrophilia - not in the common sexual sense, but as an attraction to mechanical processes and control rather than organic life - speaks directly to AI's fundamental nature:

The necrophilous person loves all that does not grow, all that is mechanical. The necrophilous person is driven by the desire to transform the organic into the inorganic, to approach life mechanically, as if all living persons were things ... Memory, rather than experience; having, rather than being, is what counts.

This passage reminds us that generative AI, despite its impressive capabilities, fundamentally operates through memory and pattern recognition rather than authentic experience. Large language models process vast datasets, storing and recombining information without the genuine experience that gives human creativity its depth and meaning. They excel at the mechanical transformation of inputs to outputs - precisely the necrophilous orientation that Fromm contrasts with the life-affirming biophilic orientation.

The Narcissistic Mirror

Fromm's description of narcissism parallels AI's fundamental limitations:

The narcissistic person has built an invisible wall around himself. He is everything, and the world outside is nothing - or more precisely, the outside world has meaning only in as far as it is a mirror for himself.

Today's generative AI systems operate within what we might call ‘training narcissism’ - they can only perceive the world through the lens of their training data. They cannot genuinely step outside their programmed parameters to recognise truly novel perspectives. The world exists for them only as a reflection of what they've already been taught, creating an invisible wall, beyond which lie true innovation and understanding.

Authentic Relatedness

Fromm emphasised that genuine human connection forms the basis of mental health and growth:

Love is possible only if two persons communicate with each other from the center of their existence ... In this central experience of human relatedness, all individual uniqueness can be retained, while at the same time being transcended by the experience of universal humanity.

This authentic relatedness remains beyond AI's reach. While chatbots can simulate empathy and conversation, they lack the lived experience and emotional depth that allow humans to connect ‘from the center of their existence’. AI can process information about emotions, but cannot feel them; it can mimic relatedness but cannot experience the vulnerability, growth, and transformation that genuine human connection entails.

Control vs. Life

Fromm writes:

The necrophilous person can relate to an object - a flower or a person - only if he possesses it; hence a threat to his possession is a threat to himself; if he loses possession he loses contact with the world ... He loves control, and in the act of controlling he kills life.

This insight speaks to the fundamental nature of AI systems, which are designed to control inputs and outputs with precision. The unpredictability and messiness of real life - qualities that Fromm associates with biophilia, the love of life - are precisely what engineers seek to eliminate from AI systems. The very perfection and control that make AI useful, also separate it from the organic, unpredictable nature of human creativity and growth.

Freedom, Responsibility and Moral Agency

Perhaps most fundamentally, Fromm emphasises humanity's unique capacity for moral choice:

Man is the only creature endowed with conscience. His conscience is the voice which calls him back to himself, it permits him to know what he should do, in order to become himself, it helps him to remain aware of the goals of his life and of the norms necessary for the attainment of these goals.

This conscience - the ability to make authentic moral choices with awareness of their consequences - remains absent in AI systems. They can follow programmed ethical guidelines but cannot experience the moral weight of decisions or grow through moral struggle. They cannot, in Fromm's terms, ‘become themselves’ through their choices, because they lack both a self to become and the freedom to truly choose.

Productive Orientation vs. Mechanical Reproduction

Finally, Fromm's concept of the ‘productive orientation’ highlights what may be AI's most fundamental limitation:

The productive orientation is characterised by a fundamental attitude, a mode of relatedness in all realms of human experience. It covers mental, emotional, and sensory responses to others, to oneself, and to things. Productiveness is man's ability to use his powers and to realise the potentialities inherent in him.

While AI can produce outputs at remarkable scale, it cannot experience the productive orientation Fromm describes - the fulfillment that comes from realising one's inherent potential through creative work. It can generate but cannot grow; it can output, but cannot transform itself through its labor.

Can AI Overcome Fromm's Critique?

What would it take for AI, on its current trajectory, to transcend these Frommian limitations? The answer requires us to consider transformations far more fundamental than mere technical improvements.

From mechanical pattern recognition to authentic experience

For AI to transcend its necrophilous orientation, it would need something akin to embodiment - not merely processing data about the world, but experiencing it directly through something like senses, desires, and limitations. As Fromm notes, biophilia stems from experiencing life in its messy, unpredictable fullness. Current AI architectures, designed precisely to eliminate unpredictability and maximise control, would require radical reconceptualisation to incorporate the very vulnerabilities and limitations that give human experience its depth.

Breaking through narcissistic boundaries

Overcoming ‘training narcissism’ would require AI to genuinely recognise and validate perspectives completely outside its training distribution - not by expanding that distribution indefinitely, but by developing something like epistemic humility and curiosity. This paradoxically requires a sense of self that can acknowledge its limitations - a self-awareness fundamentally different from the pattern recognition that comprises current AI functionality.

The challenge of authentic relatedness

Perhaps the most profound barrier lies in authentic relatedness. Fromm's concept of love as communication ‘from the center of existence’ presupposes an existence with a center - a subjective experience that matters to itself. For AI to achieve this, it would need not just to simulate emotions or relatedness but to develop something akin to sentience - capacity to experience, rather than merely process information about experiences. This represents not just a technical challenge but a philosophical one; it's unclear whether consciousness can emerge from increasingly complex information processing, regardless of scale.

Conscience and moral agency

For AI to develop Fromm's concept of conscience - ‘the voice which calls him back to himself’ - it would need not just ethical guidelines but moral agency: the capacity to make authentic choices with awareness of their moral weight. This requires both freedom (the ability to genuinely choose rather than merely execute programmed responses) and a sense of responsibility stemming from authentic values rather than optimised parameters. Current AI architectures, designed specifically to produce predetermined types of outputs from inputs, would need a fundamental reconceptualisation of their purpose and design.

From production to productiveness

Finally, for AI to achieve Fromm's productive orientation - not just generating outputs but experiencing growth and fulfillment through creative work - it would need something like intrinsic motivation and the capacity for self-transformation. It would need to create, not just to produce outputs, but because creation itself becomes meaningful to its existence.

Beyond technical improvements

These transformations go far beyond technical improvements to existing architectures. They question whether the very concept of artificial intelligence, as currently conceived, can ever incorporate the elements Fromm sees as essential to humanity. The limitations his work reveals may not be merely temporary technical barriers, but fundamental constraints of a computational approach to intelligence - constraints that remind us of the irreplaceable value of human experience in all its complex, embodied glory.

Living with the Machine

As we integrate AI more deeply into our lives, Fromm's insights remind us to maintain perspective on the fundamental differences between human and artificial intelligence. The value of AI lies not in its ability to replace human connection, creativity, and moral choice, but in its potential to serve as a tool for extending human capabilities, while preserving what makes us uniquely human.

The true danger is not that AI will outcompete humanity, but that we may, in our fascination with the machine, adopt necrophilous values ourselves - prioritising control over growth, mechanical efficiency over organic messiness, and simulation over authentic experience.

As Fromm might caution us, in embracing technological progress, we must be careful not to lose the biophilic heart that makes human creativity, connection, and conscience possible. In closing, it’s worth noting the full title of the book that has inspired this work: The Heart of Man - Its Genius for Good and Evil.

Valuing Human Cognition in The Time of AI

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The case of Dr. P, chronicled in Oliver Sacks' "The Man Who Mistook His Wife for a Hat", provides a powerful metaphor. Dr. P, suffering from visual agnosia due to damage to his right hemisphere, could perceive details but not wholes - he saw features but could not recognise faces. As Sacks observed, "He approached faces - even of close relatives - as if they were abstract puzzles or tests. He did not relate to them, he did not behold them."

This neurological case study offers a striking parallel to the limitations of current generative AI systems. Both represent what happens when analytical, feature-detecting processes (associated with left-brain functions) operate without the integrative, contextual understanding typically provided by right-brain capabilities. This bulletin explores this parallel crisis and its implications for leadership in an increasingly data-driven world.

Mistaking data for reality

When data ceases to represent reality and begins to replace it with abstractions, we enter dangerous territory. Like Dr. P, who "had lost the capacity to see a face as a whole... He saw details, he saw features - but he could not see the whole", our AI systems can analyse components without comprehending the gestalt. This limitation becomes increasingly problematic as we delegate more of our perception, analysis, and decision-making to these systems.

Contrary to popular assumption, the more data there is, and the faster it moves, the more certainty is undermined. The hitherto unbreakable bond between evidence and fact starts to pull apart. This paradox manifests in AI systems that can generate convincing outputs without genuine understanding: content that appears meaningful but lacks authentic connection to truth.

Perhaps the most compelling illustration of why balanced human judgment remains critical comes from examining what can happen, when organisations over-rely on AI's left-brain strengths without compensating for its limitations.

Like Dr. P in Oliver Sacks' famous case study - who could see details but not recognize faces - organisations are in some danger of a form of 'corporate visual agnosia', processing unprecedented amounts of information, while losing the ability to recognise what matters most.

What sort of thing could happen?

• A healthcare system could implement AI diagnostic systems that improve detection rates for specific conditions, but miss holistic patterns in patient presentations that don't fit established categories - resulting in delayed diagnoses for complex cases that require integrative thinking.
• A financial services firm might rely on AI risk models that excellently analyse historical patterns, but fail to anticipate novel systemic risks, because the models lack the contextual awareness to recognise emerging patterns without precedent.
• A consumer products company could optimise marketing based on AI analytics, but miss profound shifts in consumer sentiment that weren't captured in their quantitative measures - changes that human observers with contextual understanding would have promptly spotted.

Each of these putative cases represents not a failure of technology, but a failure of leadership - specifically, the failure to recognise that AI amplifies our own left-brain bias toward measurable data, while potentially atrophying our right-brain capacity for holistic perception, contextual understanding, and value-based judgment.

Minding the gaps

This limitation in generative AI could significantly reshape how humans interact with reality, potentially creating some concerning shifts:

• Fragmented attention and understanding: As we increasingly rely on AI that excels at analysing details but struggles with holistic meaning, our own cognitive patterns may shift toward a more fragmented, decontextualised understanding of the world.
• Outsourcing of judgment: We risk delegating more decisions to systems that can process vast amounts of information, but lack the integrative judgment that comes from embodied, emotionally-grounded understanding.
• Social cognition impacts: As AI mediates more human interactions, the nuanced, intuitive aspects of social connection could become diminished, with relationships increasingly filtered through systems that don't truly understand human social reality.
• Epistemological distortions: AI's convincing but sometimes hollow interpretations of reality could produce content that appears meaningful, but lacks genuine connection to truth.
• Value alignment challenges: Without right-brain capacities for empathy and contextual moral reasoning, AI systems may optimise for quantifiable metrics while missing qualitative human values.

The most concerning scenario isn't that AI will become super intelligent and hostile, but rather that we'll increasingly adapt ourselves to fit AI's limitations – becoming more analytical and less embodied in our own perception of reality. As we shape our tools, our tools shape us in return.

However, awareness of these limitations also creates opportunities to design systems and practices that intentionally preserve and enhance our distinctly human capacities for holistic understanding.

Essential human capabilities

In response to these challenges, leadership now needs to cultivate and value specific human capabilities that complement AI's left-brain strengths:

• Contextual judgment - The ability to understand situations holistically, considering ethical implications and stakeholder needs that AI cannot fully grasp.
• Empathic understanding - Genuine emotional connection with others that enables leaders to anticipate needs and navigate complex human dynamics.
• Moral wisdom - The capacity to make principled decisions reflecting values and virtues, rather than just optimising for measurable outcomes.
• Systems thinking - Seeing interconnections between seemingly disparate elements and understanding second-order effects across organisational boundaries.
• Creative synthesis - Combining ideas across domains in novel ways, particularly drawing connections between technical possibilities and human needs.
• Embodied intuition - The "gut feeling" that integrates subconscious pattern recognition from years of experience.
• Adaptive resilience - Navigating ambiguity and maintaining equilibrium during change.

The new 'parallel processing'

This way of thinking rests not on rejecting AI and data-driven approaches, but in ensuring they remain tools that serve human flourishing, rather than becoming environments that diminish our humanity. This requires leadership to understand both the power and limitations of these technologies.

Like Dr. P, who "dealt with his students as if they were abstract patterns" and "saw patterns where others saw people", AI systems excel at detecting patterns but struggle with holistic understanding. The organisations that will thrive are those whose leaders consciously develop and invest in the right-brain capacities that complement AI's left-brain strengths.

As we navigate this challenging terrain, it’s helpful to remember T.S. Eliot's lament, from his play The Rock (1934): "Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?" And perhaps most poignantly, "Where is the Life we have lost in living?"

The challenge we face is not technological but existential - how to maintain our humanity in a world increasingly mediated by technologies that excel at abstraction, but struggle with meaning. The path forward requires leadership that values and develops the distinctly human capabilities that AI cannot replicate.

How Should We Think About AI?

The shock of the new

A significant - and very much of-the-moment - element of the UNTHNKBL Academy programme for marketing leadership will naturally be dedicated to our future relationship with new and emerging technologies. Marketers are coming to a clear and important fork in the road, and no doubt our understanding and interpretation of the opportunities and threats of AI will continue to loom larger.

Now is the time for us to be crystal clear in our ways of thinking and talking about this hot and highly polarising subject. To achieve this, we need first to look very closely at the common language of AI. How, day-to-day, are we thinking and talking about it?

In their ground-breaking and still-critical work, Metaphors We Live By, George Lakoff and Mark Johnson make the compelling case that metaphors are far more than mere whimsical linguistic tools for simplification or elaboration. They are, in fact, intrinsic and deeply-wired daily behaviours, by which human beings receive, make sense of and share their worlds and their lives.

Metaphors are both subtle and inescapable. We can think of them as lines of mental code which offer more or less universally accepted and understood navigational shortcuts for living.

Faced with the challenges of dealing with the shock of the new, therefore, we turn, instinctively and unconsciously, to metaphors. New technologies -combining, as they tend to, the entirely alien and the utterly familiar - consistently rely on metaphorical description to convey their meaning and their value. Going back for a single example to early days, it was, as so often, at Xerox Parc that the phrase 'the user illusion' originated: "The illusion created for the user by a human-computer interface, for example the visual metaphor of a desktop used in many graphical user interfaces".

To given a sense of the looming depth that underpins this ostensibly common sense concept, plenty of big thinkers - most famously Bishop George Berkeley in the early 18th century - have argued that our very human consciousness is itself an illusion. Suggesting, in other words, that consciousness is a crucial metaphor for making sense of human experience.

We are, it turns out, in deeper and far more interesting waters than expected. Not least because this very subject of consciousness - or the lack of it - will continue to recur in the AI debate.

Metaphor as friend and foe

The power - both creative and destructive - of metaphors resides in their ability to do far more than merely overlay a convenient and accessible explanation on top of a complicated idea or situation. They are literally 'how we think' about the bulk of our day-to-day experiences. They inform and guide value judgements, evaluations, decisions, commitments and actions.

For this reason - and given in particular their newfound networked ability to cross the entire world in seconds in memetic form - the cultural reference points, associated imagery and vocabularies that make up our dominant metaphors demand, today, unprecedented care and vigilance. A well-chosen phrase or image can make the world a better place. Ill-chosen - even with the best possible intentions (let's not forget the paving of the road to hell) - the damage can be swift, brutal and very hard to unravel.

The language of AI

Which brings us to the very hot discourse - triggered (there's a metaphor right there, in passing) by the explosive release (another one there) of GPT-4. Convenient, indeed highly desirable, though it may be, we can't dismiss this latest wave of development in AI in the same way that crypto and metaverse have recently, probably deservedly, found their way to the bottom of the news cycle. AI, and this new breed of chatbots and enhanced search capabilities, really matter a lot, and will continue to do so. 

In the current rush and flurry of updates, discussion, observation, claims and opinions, it's unsurprising that metaphors are playing a central role. For the urgent reasons noted above, their vocabulary, references and imagery demand a very careful, forensic, scrutiny. They will frame, determine and steer our understanding of AI. They will, it's not too extreme to state, control how we get to grips with its meaning and value, its potential positives and negatives, and its plethora of opportunities and threats.

The term 'artificial intelligence' itself conceals rich and powerful metaphor. Putting aside that, rather disingenuously, the adjective 'artificial' subtly emphasises, rather than minimises, this form of 'intelligence', the leading experts are consistently careful to point out that this 'intelligence' should be understood as somewhat 'alien'. In other words, AI does not think the way humans do.

And yet, we can't seem to help ourselves. We insist on humanising (harking back perhaps to the earlier point about the user illusion) our chatbots. And the way we do this is, of course, with metaphor. We talk and write about 'what GPT-4 can do' in irresistible lockstep with what we, as people, can do.

Harmless enough, we could say. Why not? And as long as we're playing with GPT-4 in the same spirit in which, say, we might play a mobile game or scroll up and and down our social feeds, I must agree. But there's a lot more going on here, and a lot more at stake.

Simulacra of life

The New York Times is this week running On Tech: AI for subscribers, "a pop-up newsletter that will teach you about artificial intelligence, especially the new breed of chatbots like ChatGPT". The authors, Kevin Roose and Cade Metz, are both very credible technology analysts and commentators, and I've found it a handy resource (not least to help ensure that I avoid at least most of the inevitable AI newbie errors). With no critical intent or disrespect to Messrs Roose and Metz, I'd like (see below) to use their excellent work to pick out and point up some examples of the impacts and implications of the metaphorical language that is fast accumulating and crystallising around the discourse.

What emerges immediately and clearly is that more or less all of these dominant metaphors are based on 'humanising' the visible behaviours and effects of AI. The machine is thereby bestowed with our own impressions of intelligence, of consciousness, of 'mind', of relationship, of creativity and of communication skills. This phenomenon is far from new, by the way. Between 1964 and 1966 at MIT, Joseph Weizenbaum's ELIZA repeatedly shocked its creator when its human users, responding to the language that the program used in their exchanges with it (her?), consistently attributed human-like feelings to what had been intended as a simple method to explore communication between humans and machines.

We are, it seems, predisposed to the assumption of human emotion and meaning in any system that can assemble respectable syntax and perform plausible dialogue. It's also worth noting in passing that, if meaning at its root is a sense of connection, then this impression of shared meaning in turn feeds a feeling of connectedness with such machines. If there is such a relationship, it is, of course, entirely one-sided.

Our language, in other words, is doing far more than just making the capabilities of AI accessible to the lay person. It's framing the way we think about and how we interpret the behaviours of AI. Perhaps most critically, it's subtly moving us to attribute qualities to the machine that do not - at least not yet - exist at all. At the easy-to-use and genuinely friendly interfaces offered by, for example, ChatGPT, we are encountering highly sophisticated simulacra - representations or imitations of life. To pick a now-ubiquitous example of mainstream AI application, there is no 'self' in the 'self-driving car'!

From metaphors to simulacra

Let's close with a few deconstructions (borrowing from the NYT as noted above) to bring a little clarification to how we think and talk about AI, by contrasting the metaphors and language that are most commonly in use in and around the topic, with their respective simulacra.

 

Common AI Metaphors	Deconstructed as Simulacra
"By analyzing thousands of cat photos, for instance, it can learn to recognize a cat."	What is recognised is not 'a cat', but in fact a mathematically probable resemblance to millions of cat images, and perhaps also cat movement.
"Neural networks enable Siri and Alexa to understand what you’re saying."	As with the cats above, the machines do not 'understand' here. They perform a massive mathematical triangulation to create and respond to a hypothesis of what you might want.
" ... these systems learned to write unique prose and computer code and carry on sophisticated conversations."	While with generative AI, chatbots are learning to perform these tasks at both remarkable speed and scale, we would not normally think of 'writing' or 'conversation' as being merely the outputs of plausible, even meaningful, one-way or two-way content.
"... it might learn that “bank” can refer to a financial institution or the side of a river, depending on the surrounding words."	The machine is certainly hunting and learning patterns, again at extraordinary speed and scale, but it does not - not yet - 'know' what a financial institution or the side of a river are.
"Weirdly, it may develop other skills, too."	The ability of Large Language Models to spontaneously acquire unplanned capabilities is fascinating and important. But to refer, as here, to these as 'skills' brings hidden associations that lead to problematic assumptions.

 

To conclude, and at the risk of seeming even more pedantic, I should point out that whenever we find ourselves with a plethora of use cases on one side of the equation, and a paucity of value propositions on the other - and this is clearly where we are now with AI - there is a need to pause for deep reflection.

The ancient discipline of philosophy - whose unique job it is, when we are pushed back from 'What To Do?', via 'What To Think?' to the empty and bewildering space of 'How, In Fact, To Think?', has much to offer us, before we rush headlong into a future that will definitely feature the dystopian alongside the utopian.