As a futurist, I sometimes get asked whether I am an optimist or a pessimist about the future. My answer is usually “neither.” Both options share the same underlying assumption: that the future is already decided.

The optimist does not look at a situation and weigh the possibilities. The optimist already knows it will work out. The pessimist does not assess the evidence and conclude that things look grim. The pessimist already knows it will go wrong. Neither of them is making a judgment. Their disposition determines the outcome in advance, regardless of what is actually happening. And that is the problem: if the future is already settled, then nothing you do can change it. Both positions are fundamentally deterministic. Both make action irrelevant.

But the future is not settled. Futures, as the German philosopher of technology Armin Grunwald has argued, are constructs that exist only in the present: linguistic expressions composed of knowledge, assumptions, values, and interests. They are ideas about what might come, shaped by what we believe, what we fear, and what we want. That means they are open. Contested. And, crucially, shapeable.

I have been thinking about this for a while, because the same logic applies to how we talk about utopia and dystopia. We tend to treat them as descriptions of the future: utopia is the good ending, dystopia is the bad one. But that is not what they were invented for. Utopias were always meant to be mirrors held up to the present, showing what’s missing, what could be better, what we should work toward. Dystopias were warnings: this is where we end up if we do not change course. Both are tools for action. Both exist to make us do something.

We have degraded them to forecasts. And that is the trap.

Currently, the dystopian forecast is winning. The collapse narrative has become the default assumption about where things are headed: not a scenario to be examined, but the background understanding of the West.1 Climate destabilization, democratic backsliding, technological disruption, rising inequality—the problems are real. The question is what we do with them. And increasingly, the answer seems to be, “Assume the worst and hope for the best.” Collapse narratives have become self-fulfilling in this way. The more people treat collapse as inevitable, the more they withdraw from the systems that could prevent it.

So what do we do? The standard response is predictable: we need more utopias. More positive visions. More hopeful stories about the future.

I understand the impulse. And I think it misses the point.

Why utopias will not save us

Three factors currently undermine the call for utopias.

First, the word itself has become an insult. Call an idea “utopian” and you have dismissed it. The great utopian projects of the 20th century, from Soviet communism to the neoliberal promise of universal prosperity through deregulation, did not end well. Utopia now means “disconnected from reality.” Hard to build a movement on a word that makes people roll their eyes.

Second, every utopia carries a shadow. Who is allowed to define the ideal society? What happens to those who disagree? Utopias tend to be exclusive (who belongs?), elitist (who decides?), and in their worst forms, totalitarian (what about dissenters?). One person’s utopia is, quite literally, another person’s dystopia.

Third, and this is the one that matters most: the problem right now is not that we lack analysis. We can all see what is wrong. We know about climate change, inequality, and the erosion of democratic institutions. We are not short on diagnosis, and we are not short on ideas for improvement. What we lack is the will to start. The gap is not between “what is” and “what could be.” It is between knowing and doing. Between understanding the problem and actually picking up a tool.

Utopias are effective at showing us what is missing. They are not particularly adept at getting us off the couch.

Resisting dystopia

Kim Stanley Robinson’s The Ministry for the Future begins with a wet-bulb heat event in India. Millions die.2 This is not a warning about a distant future. This is an opening scene set in the near present, in a world that looks a lot like ours. And what follows is not a descent into hell and not an ascent into paradise. What follows is a group of people trying to do something about it. Clumsily, contradictorily, with imperfect tools and competing interests and no guarantee that any of it will work.

The characters argue. They fail. They make deals with people they do not trust. They use methods that are morally questionable. The progress, when it comes, is messy and partial and constantly at risk of being undone. Robinson himself has described the book this way:

Resisted dystopia. Two words, and suddenly there is a third option.

Isabella Hermann, a political scientist and science fiction scholar based in Berlin, has given this third option a name and a framework. In her 2025 book Zukunft ohne Angst (“Future Without Fear,” currently only available in German, with an English edition in planning), she defines anti-dystopia through three characteristics.

It begins in the catastrophes of the Anthropocene. Anti-dystopia starts here, in a world of climate crisis, democratic erosion, and structural inequality. No sugarcoating.

It decouples catastrophe from resignation. This is the crucial move. Just because things are bad does not mean they must end in oppression or collapse. The values of justice, community, and transformation break the seemingly inevitable link between crisis and surrender. Catastrophe does not have to mean dystopia. That link is a choice, not a law of nature.

And it is inherently imperfect. There is no endpoint. No blueprint for the perfect society. No guarantee. Anti-dystopian action is contradictory, incomplete, and a continuous process of trying to make things somewhat better without knowing whether it will work. Hermann puts it bluntly: enduring these contradictions is itself anti-dystopian.

This is what convinced me. Anti-dystopia promises nothing. It does not say, “Do X, and everything will be fine.” It says something much more modest and much more honest: only if we do something is there even a possibility that things could get better.

Hermann has a line that stuck with me: if an era gets the stories it deserves, then the emergence of anti-dystopian fiction tells us something about a need in society to push back against a pessimistic view of the future. The appetite for something beyond the dystopia-or-utopia binary is already there. It just did not have a name.

What anti-dystopia looks like in practice

Anti-dystopia is more than a literary genre. It is a posture. And that posture has consequences for how we act.

Thousands of answers, not one. There is a moment in Octavia Butler’s Parable of the Sower where a student challenges the protagonist: “So what’s the answer?” She replies, “There isn’t one. I mean there’s no single answer that will solve all of our future problems. Instead, there are thousands of answers, at least. You can be one of them if you choose to be.”3

You can be one of the answers. Not find the answer, not wait for the answer. Be one. This operates at every scale. International climate policy, imperfect and contradictory as it is, counts. So does a housing cooperative in your neighborhood. The point is not to determine the right level of engagement. The point is to choose one and start.

Contradictions as feature, not bug. In Butler’s novel, Lauren Olamina founds a new belief system in the middle of societal collapse. She cannot fix the world around her. What she can do is build a small community within her means, one that allows its own premises to be questioned and challenged from within. That willingness to hold contradictions extends beyond community building. Take technology: unlike dystopias from 1984 to Black Mirror, anti-dystopia holds technology as a tool that can liberate or oppress, depending on how we choose to use it.

Acting without a guarantee. In Station Eleven, a traveling theater troupe performs Shakespeare in a world where civilization has collapsed. Their motto, painted on one of their wagons: “Survival is insufficient.” They do not perform because theater will rebuild civilization. They perform because a life worth living requires more than just staying alive.

That is anti-dystopian action at its core: doing what makes the world more human, even when you cannot prove it matters in the grand scheme. Kim Stanley Robinson put it differently when he corrected Martin Luther King’s famous line. The original says: “The arc of the moral universe is long, but it bends toward justice.” Robinson’s version: “The arc of history is long, but we can bend it toward justice.” The difference is two words, and it changes everything. We can. Agency identified. The arc does not bend on its own.4

A third movement

So here is where I end up. Dystopia or utopia. Pessimism or optimism. These feel like the only options, and they are both dead ends, because they are both fundamentally passive. They describe states of the world, and they ask you to predict which one is coming.

Anti-dystopia does something different. It is not a third state. It is a third movement.

Consider the alternative. A world in which everyone who has the capacity to shape things has already decided that shaping them is pointless. That alternative is not a scenario but a guarantee.

Isabella Hermann writes:

“Rebelling against the circumstances is always worthwhile. Because working for justice, community, and transformation, whether through small everyday actions or through the struggle for comprehensive social reform, is always better than doing nothing. And nobody needs to be afraid of doing the wrong thing, because anti-dystopian action is by definition an ongoing, contradictory process that has no perfect endpoint and offers no blueprint for ‘correct’ action.”5

I find that liberating. It removes the one excuse that keeps most of us stuck: the fear that whatever we do will not be enough. Of course it will not be enough. That is the point. Do it anyway.

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Further Reading

• Isabella Hermann: Zukunft ohne Angst (oekom, 2025). Currently in German only.

• Isabella Hermann: Die Anti-Dystopie als Widerstand gegen negative Zukünfte (re:publica 25 talk video, in German).

• My Garden Note on Anti-Dystopia for a longer exploration of the framework and its literary examples.

• We interviewed Isabella Hermann on the Kritische Zukunftsforschung Podcast (in German) about her work on science fiction and futures.

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I hear this question constantly. From dinner parties to client workshops. It has become the default ice-breaker for the anxious professional class. And I have come to believe that the question itself is the problem.

Not the answer. The question. There is nothing wrong with taking collapse seriously as a scenario. If anything, that is precisely what futures thinking should do: sit with uncomfortable possibilities. But notice what the question assumes about the response. Not, “What would we need to change?” Not, “How do we prevent it?” The only question that comes naturally is where to run.

That is the tell. The collapse is treated as something other than one scenario among many to be examined, prepared for, or maybe averted. It is treated as settled. And the only remaining variable is personal: where do you go?

The collapse consensus

The climate is destabilizing. Authoritarian politics are gaining ground across democracies. None of this is news. What is relatively new is the degree to which collapse has become the default assumption about where things are headed.2

William Gibson gave it a name. In The Peripheral, he describes the Jackpot: not a single catastrophic event but a slow-motion pileup of crises, “more a climate than an event.” Gibson wrote fiction. But he always has been somewhat of a cultural empath, sensing where society is headed long before society knew itself. And the concept has become shorthand for how many people now experience the present. We are doomscrolling the Jackpot in real time.

In my master’s thesis, I worked on a concept for this kind of collective future expectation: Future Imaginaries. The idea is that certain images of the future become so dominant in a society’s discourse that they crowd out alternative ideas of the future entirely. People can no longer imagine that things could develop differently. The expectation becomes self-evident, ingrained in the background understanding, and taken for granted.

Here is a test for whether you are dealing with a Future Imaginary: Ask yourself how much internal and external resistance you would face if you tried to imagine a genuinely different future. Try telling someone at that dinner party that you think things might actually turn out okay. Watch their face. The condescension. The pity. As if you had not been paying attention.

The collapse narrative passes that test. It has become so prevalent that questioning it feels naive rather than critical. And that is precisely what makes it dangerous. Not because the underlying problems are not real. They are. But because when a single image of the future becomes this dominant, something else happens: it starts shaping behavior. And behavior, in turn, shapes reality.

The exit taxonomy

Albert Hirschman’s classic framework Exit, Voice, and Loyalty describes two responses to decline in organizations and states: you either leave (exit) or you speak up (voice). His key insight was that when exit becomes too easy, voice atrophies. Why invest in fixing something you can walk away from?

The collapse imaginary has made exit the default. And that exit takes many forms.

Geographic exit. Move somewhere safer. Portugal, New Zealand, rural Scandinavia. The calculus varies, but the logic is the same: this place is going down; find a better lifeboat. For the ultra-wealthy, this has become an industry like luxury bunkers in New Zealand, land purchases in Patagonia, and citizenship-by-investment programs marketed as insurance policies. For the rest of us, it is a fantasy that absorbs energy that could go elsewhere.3

Technological exit. Accelerate hard enough and technology will fix everything. The strongest version of this argument is the AGI bet: once we build superintelligent AI, it will fix climate change, cure disease, and optimize human civilization. This is an exit strategy dressed up as progress. It outsources agency to a machine that does not exist yet. It also absolves everyone of responsibility in the interim, as the machine is expected to solve the problem.

Psychological exit. Disengage. Stop reading the news. Focus on your garden, your family, your immediate circle. This one is harder to criticize because it is often a healthy response to information overload. But when it scales, when an entire educated class retreats into private life, the public sphere loses exactly the people who could shape it. The school board, the town hall, the local newspaper: they do not miss you when you leave. They just get worse.

Ideological exit. If democracy cannot handle the complexity, maybe we need something else. Techno-authoritarianism, seasteading, and post-democratic governance by the competent few. Peter Thiel’s famous line, “I no longer believe that freedom and democracy are compatible,” is the quiet part said aloud. When capital and ideology exit together, they do not just leave a gap. They fund the alternative.

Four different surfaces, one shared move: leaving rather than staying. Every hour spent on an exit plan is an hour not spent on engagement. And the people with the most resources and the most capacity to shape the systems they live in are the ones most likely to have exit plans.

Kim Stanley Robinson has a blunter term for it: “preemptive capitulation.” Giving up before the fight has started. That is what exit strategies amount to when the thing being exited is not a failing company but a shared civilization.

The assumptions behind the exit

The trouble with exit strategies is not that they are irrational. Given certain assumptions, they make perfect sense. But those assumptions deserve scrutiny.

The first assumption is that collapse is inevitable. This treats a Future Imaginary as a forecast. But no image of the future is neutral. Every claim about what “will” happen carries interests, values, and power dynamics. When tech executives declare that AI will eliminate most jobs, they are not reporting a weather forecast. They are constructing a narrative that serves specific interests. Framing a future as inevitable is itself a power move.

The second assumption is that individual influence is negligible. This confuses personal helplessness with political reality. No single person can stop climate change. But everything we have today, we have because people fought for it. People died so we can have weekends. The civil rights movement, the labor movement, the environmental movement: none of these were driven by people with exit strategies. We have become so accustomed to these achievements that we give up at the first sign of pushback.

The third assumption, the unspoken one, is that “getting out” is even possible. The Jackpot, if Gibson’s model holds, is systemic. Portugal becomes hot too. New Zealand depends on global supply chains. The bunker needs a functioning civilization to be worth emerging from.4

The self-fulfilling trap

Here is where it gets circular. And this is the part that I think matters most.

The economic sociologist Jens Beckert has studied what happens when entire groups of people share the same image of the future. In Imagined Futures, he argues that under genuine uncertainty, people do not make rational calculations about what will happen. They rely on collective images of the future: what he calls “fictional expectations.” These shared expectations coordinate behavior. They allow people to act in a world they cannot predict.

Beckert’s key insight is this: when enough people adopt the same image of the future, that image starts to become true. The collective expectation becomes self-fulfilling. Not because anyone predicted correctly, but because coordinated action in the same direction narrows the space of possible outcomes. Or, to put it bluntly: if everyone expects the same future, the future is less open.

And this means that whoever shapes the collective expectation has power. Beckert calls it the “politics of expectations.” Much of business communication, from consulting reports to keynote presentations, can be understood as attempts to shape fictional expectations in someone’s favor.

Now apply this concept to collapse.

Collapse narratives produce exit behavior: capital flees and talent emigrates. Exit behavior weakens the institutions and systems that could prevent collapse. Weaker institutions confirm the collapse narrative. Which produces more exit behavior. The loop closes.

Consider what it means when ten thousand of a city’s best-connected, most engaged citizens are simultaneously researching visa requirements instead of running for school board, founding civic organizations, or showing up at town halls. The loss is not hypothetical. It is happening in real time, and it compounds. Each exit decision is individually rational. Taken together, they produce exactly the outcome everyone was trying to escape.

Florence Gaub has argued that fear of the future loses its grip once people believe they have the capability to meet it. Exit strategies do the opposite. They reinforce the belief that the situation is beyond anyone’s capability, which makes the fear self-sustaining.

The question behind the question

If the collapse narrative becomes self-fulfilling through the withdrawal of capable people, then every person who stays and builds makes the narrative a little less true, simply by continuing to engage.

When someone asks, “Where would you go if everything fell apart?” what they are really asking is, “Do you still believe this is worth fighting for?” The exit question is a test of faith in collective capacity. Faith that staying and building is not naive.

I think it is worth fighting for. Whether things work out, I cannot say. But the alternative, a world where everyone with the means to leave does, is guaranteed to be worse. How do you make staying feel like a strategy, not a sacrifice? I do not have a clean answer to that.

But I know this: the darkest futures are not the ones where things fall apart. They are the ones where everyone decided in advance that falling apart was inevitable and acted accordingly.

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Reading recommendations (German)

• Die unsichtbare Rolle der Zukunft in Organisationen: My new column for Changement Magazin on how organizations unconsciously assign roles to their future narratives and how that shapes their culture and decisions.

• Strategische Vorausschau in der deutschen Außenpolitik: My LinkedIn post on why German foreign policy keeps getting caught off guard: the difference between predicting the future and preparing for multiple futures.

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Amy Webb knows how to put on a show.

Her annual keynote at SxSW has become one of the most anticipated events in the futures industry. For 18 years, her Emerging Tech Trend Report has accompanied that keynote, and the report itself reaches a global audience well beyond the conference hall.

This year, she opened with a funeral. A custom-written song, a eulogy for the old Trend Report, complete with a marching band to welcome its successor: the Convergence Outlook.

I watched the full keynote. Not for the predictions. I wanted to understand something else: the way she invokes the future. How she uses language, the metaphors she chooses, and what assumptions she bakes into her framing. Because the implicit promise of foresight is to provide orientation: to surface hidden assumptions, open possibility spaces, and prepare for multiple futures. And I was curious whether the world's most prominent futurist delivers on that promise or whether she is doing something else.

The Storm Metaphor

The central metaphor of Webb’s 2026 keynote is the storm. She uses the word 24 times across 10,700 words. Capitalism is a “perpetual storm.” A convergence is “a storm system.” And the core message built around this metaphor is clear:

“This perpetual storm, it doesn’t care about you, which means that you have to care about the storm before it arrives.”

The metaphor is doing real work here. She arrives at it through Schumpeter’s concept of creative destruction, and the choice is precise. A storm is a destructive natural phenomenon. It tears down what stands, levels structures and leaves wreckage. “Each new wave wipes out the last one,” she says. New technologies “can make you irrelevant overnight.”

The future, in this framing, is something that destroys the status quo and moves on without looking back. Nobody caused it; nobody controls it. You can prepare for it and survive it. You do not shape it. The only reasonable response to a storm is readiness.

She reinforces this through a carefully constructed upgrade narrative. The old Trend Report, she explains, tracked individual trends. Useful, but limited. “Trends are kind of like weather data,” she says. “None of us are looking at a number on a barometer as a clue that we should evacuate town, right?” For that, you need a meteorologist. You need someone who can read the storm.

“So this is actually not just a eulogy for our trend report,” she tells the audience. “It’s for all trend reports.”

She buries the entire category. And in its place, she introduces convergences: phenomena that, in her framing, require her specific methodology to detect. “We built a storm tracker. We have invented a brand new methodology.” The intellectual upgrade feels natural. Of course you need a storm tracker, not a thermometer. But the consequence is that the old tool was available to everyone. The new one is available only from her. 1

The Language of Certainty

The framing goes deeper than metaphor. I ran a linguistic analysis of the full transcript. Webb uses language of certainty (will, going to, inevitable, always, never) at a ratio of 11.4 to 1 compared to language of possibility (could, might, maybe, what if). She says “I don’t know” exactly three times. She never says, "It depends.” She never says, “It remains to be seen.” She never uses the word “alternative.” 2

In her telling, the future is certain. It is not a space of multiple possibilities. It is a specific trajectory, already in motion, that you are either prepared for or you are not. The only uncertainty she permits is personal: Will you be ready? Will your company act in time? The message is clear: the future is decided; the only open question is whether you keep up.

At one point, she says it plainly: “After I scare the hell out of you, you are going to feel energized.”

A Narrative, Not an Analysis

Webb is not analyzing futures. She is constructing a specific future narrative. Every element of her keynote, from the storm metaphor to the certainty of her language to the theatrical funeral for the old format, serves a single story: the future is a force of nature, it is already here, and you need her tools to survive it.

Webb’s engagement with these topics is clearly genuine, and many of the technological developments she describes are real phenomena worth watching. The question is not whether they matter. The question is what happens when they are packaged into a single, deterministic narrative.

Education technology critic Audrey Watters once observed, “The best way to predict the future is to issue a press release.” But that only works if futurists play along. Webb’s evidence follows that logic precisely. Press releases and prototypes are treated as confirmed trajectories. Pilot projects become established realities. The narrative is the deliverable.

The patterns beneath that narrative are familiar. In my work deconstructing futures presentations, I keep running into the same four rhetorical tropes:

1. The deterministic future. “This is simply what’s coming. There is no alternative.” Webb’s entire storm framing operates here. Convergences “become inevitable before they look inevitable.” The future is not a possibility. It is a schedule.

2. The revolutionary break. “Everything you know will soon be obsolete.” Convergences “rewrite who wins.” The message: your world is ending, whether you notice or not.

3. Technology as savior. In Webb’s version, the savior is not the technology itself but her framework for understanding it. The Storm Tracker, the Convergence Outlook, and the proprietary methodology. The rescue comes not from building something, but from buying the right tool.

4. The warning to laggards. “Nobody’s coming to save you.” “Are you going to get left behind? Spoiler alert, you are.”

Webb deploys all four.

Seismograph and Amplifier

The future as an uncontrollable force bearing down on us, technology as destiny, humans as passengers. This is the dominant future narrative of our time. And when she chooses exactly this story, she reveals something intriguing: she is mirroring how her audience already perceives the future.

Her keynote is a seismograph for the prevailing mood. A room full of executives and decision-makers cheers loudest when she channels their frustration at a system they feel powerless to change. That reaction tells you something. Not about technology, but about the emotional state of the people who are supposed to be shaping the response to it. The narrative she constructs lands because it matches the anxiety that is already there. She is not creating the fear. She is giving it a stage, a vocabulary, and a product catalog.

The problem is that she does not treat it as one future narrative. She reinforces the dominant narrative instead of making it visible, naming it as one possible framing among many, and opening the conversation to alternatives. She amplifies it. Every keynote, every report, and every Storm Tracker is a feedback loop that solidifies the narrative further. Sohail Inayatullah describes the core task of critical futures work as “loosening the future.” 3 Making rigid narratives flexible again, opening space for alternatives, and reminding us that no single trajectory is inevitable. Webb does the opposite. She tightens.

Stories about the future shape action in the present. They make certain futures more likely and others harder to imagine. A futurist who reinforces the dominant narrative is narrowing the field of vision.

Watch how she closes. She channels the audience’s frustration into a rant about 250 years of selfish capitalism, political systems that reward outrage over governance, and powerful people making decisions out of ego. The room erupts. Standing ovation. Then the pivot: “But anger isn’t a plan. Anger is a distraction.” And the distraction lifts to reveal the product. Download the power folder. Visit the website. Buy the book. The last words she leaves them with: “Every civilization that has ever mattered was built by people like us.” The audience walks out energized. But the question is whether they know what to do next.

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Further Reading

• Trend Talks vs. Futures Literacy: My longer analysis of why typical trend presentations overwhelm rather than empower, the psychological mechanisms behind them, and five principles for building actual futures competence.

• From Future Narratives to Actionable Insights: The four rhetorical tropes referenced in this article, plus five questions for deconstructing any futures narrative you encounter.

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Every time I give a talk, someone in the audience asks whether I can predict the future. I can’t. But I’ve stopped explaining why. Instead, I ask back: if I could tell you what the future holds, what would you do with it? And more to the point, what could you do with it?

We already possess a wealth of knowledge. We know that climate change is restructuring our environment and that demographics are shifting in ways that will hit entire industries within a decade. The knowledge is there. The ability to act on it is not.

I’ve spent almost 20 years doing foresight work. Futures reports, trend analyses, and scenario projects with senior leadership. The work was solid. The recommendations were specific. And almost nothing happened. Not because the insights were wrong, but because organizations are machines built to deliver on the present. Whatever is on fire this quarter gets the attention. Preparing for change? That is where organizations seize up. I’ve written about this before, and I’ve watched it happen more times than I can count.

This week, I watched it happen again.

What I heard at MobilityMove

I was in Berlin at MobilityMove, one of the largest public transport conferences in Germany. I gave the opening keynote for Kontiki, a conference-within-the-conference focused on AI and mobility. The context matters: public transport in Germany is facing a massive retirement wave. A significant portion of the workforce will leave within the next ten years. This industry doesn’t have the luxury of treating AI as an optional experiment. It needs all the help it can get from humans and machines to continue delivering the service it currently delivers.

My keynote covered the usual ground: the state of AI, the hype and how future narratives shape what gets built and what gets feared. Regular readers of this newsletter will recognize the themes. But the real insight came afterward, in conversations with people from the industry.

What they described, again and again, was this: trying to do even the most basic things with AI takes months. Every step requires approvals, risk assessments, compliance checks and coordination across departments that each operate on their own timelines and with their own logic.

One manager told me it took him four months to get his workers‘ council to approve a single employee attending an introductory AI training course. Four months for one person to attend one training course. That is the ratio of effort to outcome that people in these organizations are dealing with every day.

And I want to be clear about something: the people in IT, in legal, in data protection and in workers’ councils (for readers outside of Germany: Betriebsräte are elected employee representation bodies, and they have done tremendous work protecting workers‘ rights for decades) are doing their jobs. They are following the rules of a system that was built, over many years, around risk minimization and procedural thoroughness. That system served real purposes. But it now makes even the smallest experiment feel like pushing a boulder uphill.

Another manager told me he finds AI fascinating. He sees the potential. He just has zero time to engage with it. His days are consumed by meetings and firefighting. The irony is painful: AI tools could probably help him ease his workload, but he would need time to learn them first. And that time does not exist. The people who should be redesigning the system for the future cannot do it because the system eats all their bandwidth at present. When they do try, they rush, creating a hasty strategy deck and announcing a new initiative at a town hall. Everyone in the room is already aware that this initiative, like the previous three, will fade into obscurity in two weeks. The eye-rolls start before the presentation ends.

At some point during the conference, I said to someone,

“If I understand you all correctly, the way forward for your industry right now is not building more prototypes. It is unblocking the system that prevents your people from even trying. And that means focusing on IT, legal and workers’ councils first.”

I think this goes far beyond public transport. Healthcare, insurance, public broadcasting, legal, and others: the stories sound almost identical, especially here in Germany.

The real bottleneck

I’ve always believed that it is more effective to take down barriers than to push new initiatives. When you make it easy for people to try things and gain experience, they tend to move forward on their own. But right now, it is too hard to even try.

Vaughn Tan, in his newsletter The Uncertainty Mindset puts it in a way that I keep coming back to: “An organization’s ability to respond to external uncertainty is created by embracing internal uncertainty.” Bureaucratic systems are built to do the opposite: eliminate anything unpredictable. That is what approval chains and standardized processes are for. And that is precisely what makes them unable to adapt when the external world shifts.

Stanley McChrystal, in Team of Teams, provides the perfect metaphor for what this means in practice: stop thinking like a chess master and start thinking like a gardener. A chess master plans moves ahead and controls the board. But that only works when the game is legible. Right now, the board is changing faster than anyone can read it. A gardener cannot force a plant to grow. She can only tend the soil, pull the weeds, and trust the growing to happen on its own.

Here is what I keep observing: people want to engage. The common leadership line that employees are resistant or demotivated is too simple. The reality I keep hearing about is messier than that. People are exhausted from constant strategy pivots that lead nowhere. They are ground down by everyday barriers that make every small step feel like a negotiation. They have watched too many initiatives be announced with fanfare and quietly disappear. And underneath all of that, there are real fears and real anxieties about what AI means for their future.

What looks like resistance is usually exhaustion and anxiety. And when leadership misreads that as opposition, the response is predictable: more pressure, more urgency and more top-down mandates. Which produces more pushback. Which confirms the diagnosis. A cycle that feeds itself.

You can’t plan your way into the future

The world is changing too fast and too unpredictably for any strategy deck to capture it. Understanding the narratives that shape how we think about the future matters. But that understanding alone does not create the ability to act. What would help is less chess and more gardening. Reduce the procedural weight. Make it possible for people to try things without spending months on approvals. The ideas are there. So is the motivation. What is missing is room to move.

People can do this if you let them. But right now, they can’t. And honestly, most of them are very tired of trying.

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Further Reading

• Warum Impact Teams gegen Wände laufen (covolution, in German): Why dedicated innovation and future teams hit walls despite solid work and talented people. The article delves into the structural reasons that underlie the pattern I have described above.

• The Beginning and the End of Foresight: My longer argument for why foresight insights rarely translate into organizational action, and what happens in the gap between the two.

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The Liquid Legal Institute brings together over 1,500 legal professionals and organizations working on the transformation of the legal industry. Their Legal Foresight Office runs a workshop series exploring what the future of law looks like. In the first workshop in November 2025, participants developed four scenarios along two axes: weak vs. strong rule of law and incremental vs. disruptive digital transformation. For the second workshop on 5 March 2026 in Munich, I was invited to kickstart the afternoon with a keynote.

This text is a cleaned-up transcript of my keynote.

I like a challenge. The post-lunch speaking slot is the peak challenge of public speaking. The tortellini are getting heavier, the attention is lighter. Club-Mate, cola, coffee: whatever you need. We will get through this.

You spent the last session before lunch looking at trends and placing them on an uncertainty-impact matrix. For each one, you had to decide: how certain or uncertain is this? How high or low is the impact if it actually plays out? You discussed, you argued, and some of you couldn’t agree. But here’s my question: how did you make those calls? What were you basing them on? We usually don't ask. And when you look closely, the answer is almost never data. So how do we actually make these decisions?

Three forces that shape every decision

There is a model I constantly come back to in my work, the Futures Triangle. It shows, in a very simple way, what influences the decisions we make every day.

First, the weight of the past. You can sum it up in a single sentence: “We have always done it this way.” Path dependencies, rituals, traditions, regulation, law. That is how things were done, and it pulls our decisions in a certain direction.

Then there is the push of the present. All the topics crashing into us right now. The emails piling up. Which fire do I put out first? The question everyone has to take a position on:

“What are we doing about AI?”
“It does not matter what. As long as we are doing something about AI. Because everyone is doing something about AI. If we are not, we are behind.”

And then there is a third force, one we rarely notice: the pull of the future. Our expectations, hopes, and fears about what is coming. The sociologist Fred Polak described this in the 1950s. We assume the future grows out of the past through the present. Polak said no. Our images of the future pull us toward them.

If I expect things to improve tomorrow, I act differently today than if I expect them to get more dire. Sounds simple. But think about how many of your daily decisions depend on exactly this: Do you believe tomorrow will be better or worse?

What is the future when it does not exist?

How do you study something that does not exist? That is the central challenge of futures studies. The future, by definition, is not here yet. If it were, it would be the present. We noticed the difference at some point and came up with a distinction that I find genuinely useful.

There are two kinds of futures: present futures and future presents. Future presents are what we typically mean by “the future”: a specific point in time when something happens. Present futures are the images, stories, and fears we carry right now about that point in time. And we can study them with classical humanistic methods. We can interview people, and we can generate new images. You know this from science fiction: you went to the cinema, watched “Her,” and suddenly you had a different picture of what the future might look like.

A side note that says more than you might think: In futures studies, we always talk about “futures” in the plural. In English, that is grammatically unremarkable. In German, it is unusual. The dictionary allows the plural form “Zukünfte,” but adds a parenthetical: rarely used. That tells you something about how deeply the idea of one single, deterministic future is embedded in the German language. Whoever predicts it best wins.

The hopes, wishes, and expectations we hold about the future are, when you look closely, stories. Stories we tell ourselves and others to influence what comes next. Our economic system runs on this: I invest today, hoping for returns tomorrow.

In a future that is open and unpredictable, that is a remarkably uncertain system. How do we deal with it? We tell stories. We try to build shared images. The more people expect the same future, the more they act accordingly, and the more likely that future becomes.

That is why Sam Altman gets on every stage and says, “AGI is coming tomorrow. Maybe even tonight. And if you want to be part of it, give me more money now.” It works so well that his main problem is now explaining where the return on investment will come from. The economic sociologist Jens Beckert makes this case in Imagined Futures. His thesis: we believe we act on rational expectations. In reality, what allows us to act are fictional expectations.

AI as a narrative machine

My thesis: AI is first and foremost a future narrative. There is this line I like: Everything that does not work yet is AI. Once it works, it is software.

Here is what I keep observing: We use an AI tool, see, “Oh, this responds, and it is not bad,” and immediately start extrapolating, “But that means in the future...” And then we only react to the future image we just generated. Not to what the technology actually does here and now. Pay attention when you read articles about AI: How much describes what works today? And how much is extrapolation?

2026 is a particular year for AI. A year ago, the dominant narrative was: the data is running out, and the models are not getting better. That feels very different from where we are now.

How narratives shape technology: Claude Code and OpenClaw. Claude Code has been on the market for about a year; it was originally a developer tool. Then Anthropic released a new model, quality jumped to a different level, and suddenly people spent their Christmas holidays building things they had not thought possible before. Peter Steinberger, an Austrian developer, thought, “I will build my own agent.” He started with a WhatsApp interface, and it turned into OpenClaw, an open-source platform for personal AI agents. Mac Minis sold out everywhere. Why does he build this? Because the narrative of AI as a personal assistant runs deep. Decades of science fiction. Books, films. Jarvis from Iron Man. That is why we keep building these things, and why development keeps heading in that direction.

How narratives reinforce themselves: Matt Shumer. A developer from California, early thirties, has never worked outside of tech. He builds an app with Claude Code one afternoon, steps away, and Claude Code keeps building. His conclusion: it is over. All work. Everywhere. I have been studying the question, “Will AI take our jobs?” since 2012, and I have watched these waves come and go. AlphaGo beats the best Go players, every newspaper writes: within months the machines will take over. Shumer writes “Something Big is Happening” on Twitter (it will always be Twitter to me). A hundred million views. CNN, Forbes. The essay is not particularly good as a piece of writing. But it hits the dominant narrative. Pure confirmation bias: we all have the feeling something big is happening right now. And then someone writes exactly that in a headline, and we nod.

How narratives get misread: the Citrini scenario. Completely different context. People who have been writing reports for Wall Street for years publish a scenario. The author wrote a hundred times: This is not a forecast. This is a scenario. We put the probability at 10 percent. Does not matter. Stock markets drop because of a Substack newsletter. Why? When we see a single scenario, we automatically read it as a prediction we need to take a position on. The only way to understand a scenario as a scenario is when a second scenario stands next to it. That is why futures studies never produce one scenario. Always several.

How narratives move markets: Anthropic and the legal industry. Anthropic releases some plugins for Claude. For contract reviews and NDA screening. No complex software. Just a few Markdown text files. And the stock prices of the major legal information companies crash. Nobody fired their legal department. Nobody seriously tested the tools. Nothing happened. But the dominant narrative (AI takes jobs away) plus the open question (Are we in an AI bubble?) make markets react to every headline. That shows how deeply narratives sit. And how they shape actions in the present, even though nothing has changed about the future yet.

We are story animals. We run on stories. And we have a strong tendency to turn everything into a compelling narrative. Man vs. Machine. That is a story that sits deep. The intriguing question is not whether it is true or false. The interesting question is, am I reaching for a story that is much deeper and much older than the current technological development? And it is doing something to me.

That is the diagnosis. The question is, what do we do with it? If narratives are this powerful, can we build our own?

Getting concrete

In critical futures studies, the process always starts with deconstruction (which narratives are in play, and where do they come from?) and then moves to reconstruction: How can we build our own? And the first step is getting concrete.

I took the scenarios from the Legal Foresight Workshop in November 2025 and asked, “What happens when we do not stop at abstract scenarios? What might headlines look like in these futures?”

Spiegel International, 2033: “Who Do I Sue? The Question Defining a Generation of Legal Helplessness.”

The Guardian, 2033: “Two-Tier Justice: How Germany’s Courts Became a Service for Those Who Can Afford to Wait.”

TechCrunch, 2033: “German Legal Tech Startups Relocate to Singapore. We love Germany, but we can’t wait another decade.”

What happens when you make things this concrete? It feels immediately different from abstract trends. You notice where something hits a nerve, where emotions surface. And that shows us that something deeper has been triggered: an underlying narrative.

I pushed this further and developed personas for the scenarios. Dr. Katja Bergmann, a lawyer at a Frankfurt firm, in 2033. For each of the four scenarios, I wrote one day in her life.

In Scenario 2, the optimistic one, her morning starts like this:

She opens the BundesJustiz-OS dashboard on her tablet. Overnight, the system processed 14 of her standard cases. Three tenancy disputes: facts recorded, law applied, settlement proposals accepted by both parties within hours. She scrolls through the summaries. Clean. Correct. What used to take weeks, resolved before breakfast. But the case on her desk is different: a Frankfurt biotech startup running gene therapy approvals across three EU jurisdictions whose laws contradict each other. The Legal OS flagged the conflict, produced an 83-page analysis, identified 23 partially applicable precedents. What it cannot do: decide which argument is right. For that, it needs Katja.

That is the difference between abstract trends and concrete scenes. The moment you describe a morning, a case, or a tool interaction, the future stops being a concept and starts being something you can prepare for.

What story do you want to tell?

Here are three recommendations for foresight work in general:

First: Regularly ask yourself which narrative you are currently using. Where does it come from? Who told you? What is behind it? What is their agenda? Is it what you actually want?

Second: Update your mental models. We hear certain things and say, “That is how the world works.” Then the world moves on, but we still carry that old model. Good example: “We all need to become prompt engineers.” That was the future two years ago. Today the machines write their own prompts better than we do. But in many heads, prompt engineering is still “the thing.” Also pay attention to where your signals come from. Is this a signal from Germany, from the US, or from Asia? Our news consumption creates such a jumble that we quickly think, “This is happening everywhere now.” Everyone is drinking matcha lattes. I live in Prenzlauer Berg, Berlin’s poster child for lifestyle trends. Of course everyone there drinks matcha lattes. But that is not the world (and it’s not even true for the Kiez).

Third: Go into detail. Do not stay at the abstract level. Develop scenes, describe the everyday, and look for the uncomfortable. There are things in today’s headlines that you would have said were “never gonna happen” ten years ago.

And then the question I ask all my clients: Scenarios help you orient and explore. However, I urge you to take the next step forward.

Are you the one who provides orientation? Who drives change? Who brings the next generation along?

If we do not have our own narrative about the future, we automatically operate inside someone else’s. We should at least decide whether that is what we want.

P.S. If you want to go deeper: I have published research notes on Legal Foresight and The L in STEEPLE in my Digital Garden. The slides are at johanneskleske.com/slides/keynote-futures-of-law.html. A big thank you to Lina Keßler and Kai Jacob for the invitation.

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Want me to talk with your organization about this? Write me an email at johannes@kleske.de or just hit reply.

Voices from the workshop

• Daniella Domokos on how future foresight methods help sort, prioritize, and orient amid uncertainty, and the responsibility lawyers bear in shaping futures.

• Dr. Felix von Held on how foresight has shifted from abstract strategy to practical decision support, and why leaders need to write their own narrative about what comes next.

If you’re in Berlin

From Productivity to Meaning: Working with AI Without Losing Yourself — April 9, 2026. I’m giving a talk on “Cognitive Debt”: the cost of letting machines generate outputs without human engagement in the thinking process. Limited to 60 seats.

New in the Futures Garden

Notes I added or updated in my digital garden this week:

• Continuous Critical Problems (new)

• Futurological Materialism (new)

• The Forward Deployed Futurist (revised)

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This week, a scenario went viral. Citrini Research published “The 2028 Global Intelligence Crisis,” a speculative scenario written as a retrospective from the year 2028. In it, AI doesn’t just automate tasks: it triggers what the authors call an “Intelligence Displacement Spiral.” Companies cut white-collar workers, spending collapses, tax revenue drops, the mortgage market cracks, and there is no escape sector because AI keeps improving everywhere at once. Unemployment hits 10.2%. The S&P drops 38%.

The scenario was intended as an investor stress test. The authors said so explicitly. James Van Geelen, one of the co-authors, told the Odd Lots podcast he'd put the probability at 10 to 15 percent. But the markets didn't read the footnotes. IBM fell roughly 13%, driven in part by the scenario's impact.1 A prediction market on Kalshi appeared overnight, trading the „Citrini scenario“ at 11.6%. Citadel Securities published a rebuttal, and stocks recovered. Tracy Alloway put it precisely: „We shouldn't be in an environment where a single think piece causes a broad sell-off.“

I agree with Alloway. But as a critical futurist, I’m interested in a different question: why did this happen?

Here’s the short answer: the moment a concrete scenario lands in an environment of anxiety, it stops being a scenario and becomes a prediction.

This is something I’ve seen in my own work, in dozens of scenario workshops. The participants arrive with a fixed image of the future in their heads. It’s rarely a clearly articulated one, more of a background hum: a sense that AI will take over, that things will get worse, that the future is already decided. When they then encounter a detailed scenario that matches that hum, it clicks into place. Confirmation bias does the rest.

This keeps happening. AI doom scenarios go viral because they make the dominant narrative more concrete. They give shape to a feeling people already carry. And that is precisely the problem.

A detailed rebuttal is one valid response. But there is something else that rebuttals can’t do: place a second scenario alongside.

I’ve taught scenario methods for over a decade. The most important moment in every workshop comes when the groups have each developed their scenario and all four of them go up on the wall for the first time. The energy in the room changes. People sit up straighter and lean forward. Their eyes widen. They suddenly understand what the person at the front of the room has been trying to explain all along: that all of these are plausible directions. That which decision pushes them in which direction. The realization dawns on them that they possess choices and alternatives, each with their own set of consequences. That the future is not determined.

I know people who still bring up that moment years later, because it changed how they think about the future. One scenario narrows the view. Multiple scenarios open it. And only when you see multiple futures side by side do scenarios become what they are supposed to be: tools for thinking, not for fearing.

Right now, there is one AI narrative dominating the conversation. It asks a specific question: what happens when AI automates existing work? The Intelligence Displacement Spiral is one answer to that question, and a plausible one. I’m not going to argue that Citrini is wrong.

I’m going to ask a different question: what happens when AI restructures systems?

Citrini’s blind spot

Citrini is not a bad scenario. It is an incomplete one.

The scenario's weakness is the question it poses. Citrini asks, "Which existing jobs can AI replace?” And then it extrapolates linearly. More AI capability, more jobs replaced, more spending lost, downward spiral. Sangeet Paul Choudary, in his book Reshuffle, calls this the “intelligence distraction”: measuring AI’s impact by mapping it onto existing human tasks.

There’s a formulation I keep coming back to: we imagine the future as today, only more extreme. Citrini takes the current economic system and turns up the AI dial: faster automation, cheaper inference, and more displacement. It never asks how the system itself changes. And that is where the scenario stops short.

Consider transport in 1955. If you asked, “How many horse carts does the truck replace?” you’d get a technically reasonable answer. But you’d completely miss supermarkets, suburbs, and global supply chains. The truck didn’t replace horses. It restructured the logic of trade, settlement, and consumption.

Or consider agriculture. In 1800, roughly three quarters of the American workforce worked the land. Today it’s under 2%. The other 73% are not unemployed. They work as air traffic controllers, software engineers, radiologists, film editors, and industrial designers: professions that would have been incomprehensible to a farmer in 1800, because those jobs only make sense within a system that didn’t yet exist. The farmer could imagine more efficient plows. They could not imagine a profession dedicated to ensuring aircraft safely sequence their landings.

Citrini’s “Intelligence Displacement Spiral” assumes displaced workers have nowhere to go because AI improves across all domains. That’s true if you think in terms of tasks. If you ask, "Which existing jobs can AI do?” you’ll always end up with fewer jobs, because you’re only counting what disappears. You’re never counting what emerges, because the new things only become visible after the system shifts.

The same sectors, a different lens

Let me take three sectors from Citrini’s scenario and look at them through a different lens. I’ll keep the same AI capabilities and the same companies under pressure. But I’ll ask a different question: what new system emerges?

Software and SaaS. Citrini says agentic coding tools make SaaS replicable. Customers build their own tools. Prices collapse. Zendesk defaults on billions in debt from its leveraged buyout.

The first part is plausible. Simple workflow tools will become replicable, and established products will break apart as old constraints disappear. Anyone who’s watched a platform ecosystem unravel knows what this looks like: unbundling. But unbundling is always followed by rebundling, a reconfiguration around new constraints. When every company can build its own basic tools, a new bottleneck appears: who makes all those tools work together? Who guarantees outcomes when the software is assembled from AI-generated components?

In this scenario, SaaS wouldn’t disappear. It would shift from selling software licenses to guaranteeing outcomes and coordinating the mess underneath. The Zendesk of 2028 might not exist. But the problem Zendesk solves (coordinating customer interactions at scale) wouldn’t vanish just because the tool layer becomes cheap. The coordination layer would become more valuable.

Labor market. Citrini projects 10.2% unemployment with no escape sector. That number makes sense if you only count tasks that disappear. But when AI reduces coordination costs, things become possible that were too expensive before.

In 1937, the economist Ronald Coase asked why companies exist at all. His answer: because coordinating through the market is expensive. When external coordination gets cheaper, organizations get smaller, and the threshold for starting a viable business drops. There is data to support this: solo-founded startups went from 17% of all new startups in 2017 to over 36% by mid-2025, according to Carta’s Solo Founders Report. Solo entrepreneurs and small teams are already doing things that required entire departments three years ago, because the administrative cost of running an operation is shrinking.

The economy doesn’t just shift who does the work. It shifts how work is organized. New kinds of work appear around new constraints: ensuring the quality of AI-generated outputs and orchestrating systems where information is abundant but trust is scarce. This is the pattern from every major economic transition: the new jobs are not improved versions of the old ones. They are different kinds of work that only make sense inside the new system.

Consumer economy and payments. Citrini’s scenario has AI agents bypassing intermediaries. Visa and Mastercard lose interchange revenue as stablecoins and agent-to-agent transactions route around the card networks.

Old intermediation dissolves. That part is likely right. But intermediation wouldn’t disappear: it would migrate. When AI agents shop for me, new questions arise that didn’t matter before. If neither the buyer nor the seller is human, the question of who guarantees quality and who takes the hit when things go wrong doesn’t disappear. It gets harder. And when there are suddenly ten thousand options where there used to be ten, someone has to do the filtering. The new intermediaries might look nothing like Visa: agent reputation services that track reliability, outcome insurance for automated transactions, and curation layers that filter infinite choices down to the ones that match what you want.

The old toll booths would close, and new ones would open in different places.

What Citrini misses entirely

The sectors above show how existing industries look different through a system lens. But the more intriguing part is what becomes visible only when you stop asking about existing jobs and start asking about new systems.

Constraint migration. When AI makes knowledge abundant, where does value go? It migrates to the constraints that AI doesn’t solve.

This shift is already visible. Consider the sommelier. Wine knowledge used to be scarce and valuable. Today, anyone with a phone can look up tasting notes, regional characteristics, and food pairings. The sommelier should be obsolete. They’re not. What they provide was never really information in the first place. It’s confidence in a moment of uncertainty. The value migrated from knowledge delivery to judgment and trust.

This pattern will repeat across professions. When expertise becomes cheap (and AI is making expertise cheap very fast), value moves to judgment and accountability. Who certifies that an AI-generated legal document is correct? Who takes responsibility when an AI-coordinated supply chain breaks? These are tasks that only exist because AI exists.

The building-block economy. When capabilities become modular, rentable, recombinable, and scalable, founding costs drop and experimentation speeds up. Companies can be assembled from pre-existing components: AI agents, specialized knowledge modules, network access, and cloud infrastructure. The cost of testing a business idea approaches zero. Scaling a working one significantly reduces its cost.

Citrini describes a Displacement Spiral: each wave of automation triggers the next. But when founding costs drop, the opposite dynamic becomes possible: a Creation Spiral.

Lower coordination costs mean more business experiments. More experiments produce more new businesses. Those businesses create demand for services that didn’t exist before: someone to verify that the AI output is correct, someone to vouch that the provider is real. That demand creates jobs and spending, which fund the next round of experiments. The spiral would produce new types of work that we can’t yet name, because they only make sense inside a system that is still forming.

This is not hypothetical. The solo-founder wave, from 17% to over 36% of all startups in eight years, is an early signal of what the first turn of this spiral looks like.

Loosening the future

Citrini’s scenario is plausible. The alternative I’ve sketched here is also plausible. Neither is a prediction. Neither will happen the way it’s described. Reality will be messier, slower in some places, faster in others, and full of things neither scenario anticipates.

That is the point.

When you read a single scenario, the question you ask yourself is, “Will this happen?” When you read two scenarios side by side, you stop asking, "Will this happen?” You start asking, "What pushes things one way or the other?” And what can I influence?

Sohail Inayatullah calls this “loosening the future”: weakening the grip that a single future image has on our thinking so that alternatives become visible again. I wrote about this in the previous issue.

An alternative scenario forces specific questions. Is the negative outcome really as determined as it feels? What would need to happen for the positive version to play out?

The Creation Spiral doesn’t happen automatically. AI tools need to be broadly accessible: if only large corporations can afford the best models, the building-block economy stays locked. Education systems need to prepare people for a landscape where the jobs themselves keep changing. Regulation has to balance protecting workers during the transition with leaving room for the experiments that create new work. And governments need the political will to shape this actively.

None of that is guaranteed. Every one of these is a fork. And which way things go depends on choices that haven’t been made yet.

And even these two scenarios together are incomplete. What would a third look like? A fourth? What aspects of AI’s impact are not covered by either scenario? The moment you ask these questions, you’ve already left the single-scenario trap. You’re thinking in possibilities, not predictions. The exploration space gets wider, not narrower.

Scenarios are not forecasts. They are tools for seeing what you can do. A single scenario narrows that view. A second one cracks it open.

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Want me to talk with your organization about this? Write me an email at johannes@kleske.de or just hit reply.

Supplementary reading

Two pieces on the Citrini scenario that come at it from different angles:

• Adrian Monck: Why the Citrini AI crisis won’t happen — argues the scenario gets the capability curve wrong. AI follows an S-curve, and most AI pilots fail at implementation. A classic rebuttal: the premises are off. My piece above accepts the premises and asks a different question.

• Paul Graham Raven: Intentions and inversions — frames Citrini as evidence of narrative’s material power: a fiction that moved markets. If you’re interested in why stories have this kind of force, start here.

New in the Futures Garden

Notes I added or updated in my digital garden this week:

• The Forward Deployed Futurist (new)

• OpenAI Frontier Alliances (new)

• Documentation as Infrastructure (updated)

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I give talks about AI for a living. As a keynote speaker and foresight researcher, I’ve spent over a decade explaining how artificial intelligence is changing work and society. And the most telling moments are never during the talk itself. They come during the Q&A.

Someone raises their hand and asks, “If I learn how to use this, am I training my replacement inside the machine?”

The room goes quiet. Not because the question is new. Because everyone has been thinking it.

I hear versions of this at almost every event now. The questions rarely touch the technology itself. They’re about survival, identity and control. “What’s the minimum we need to do without getting deeper into this?” “Can we keep this as small as possible?” People don’t ask these questions about a new project management tool. They ask them about something that feels like an existential threat.

And sometimes the energy turns. The frustration and the anxiety about all this AI noise: it gets directed at me. I’m the one on stage talking about it, so I become the stand-in for the threat itself.

But these reactions have almost nothing to do with the specific technology I’ve just been discussing. They come from somewhere much deeper and much older. To understand where, we need to go back about a hundred years.

The pattern we all assume

In 1922, the sociologist William F. Ogburn published a book called Social Change with Respect to Culture and Original Nature. In it, he introduced a concept that still shapes how we think about technology and society: Cultural Lag.

The idea is straightforward. Technology (what Ogburn called “material culture”) moves fast. Society’s response (norms, laws, values, institutions) follows behind, sometimes decades later. The gap between the two creates friction, confusion, and harm. Ogburn saw technology as the primary motor of social change: the material world advances, and everything else adapts. Later scholars have called this a form of soft technological determinism. Technology doesn’t dictate outcomes mechanically, but it sets the pace, and society has to keep up.

We’ve seen this play out repeatedly. Social media platforms launched between 2004 and 2008. The Cambridge Analytica scandal broke in 2018. The EU Digital Services Act, the first serious regulatory framework, took effect in stages starting in 2023. And right now, the biggest policy debate around social media is age restrictions: whether children should be on these platforms at all. That’s a conversation about a technology that is over twenty years old. Two decades of lag, and we’re still catching up.

This is the pattern we’ve internalized. Technology leads. Culture follows. It feels like a law of nature.

But with AI, something strange happened.

The twist: Cultural Lead

The pattern reversed.

With AI, culture was first. Decades before the technology could deliver anything close to artificial intelligence, our stories, fears, and hopes about it were already deeply embedded in society. We’d been imagining AI for over a century before the technology began to catch up.

Ogburn called the normal pattern Cultural Lag. I’ve started calling what’s happening with AI the opposite: Cultural Lead. I think it names something real that the existing vocabulary misses.

The main criticism of Ogburn’s model has always been its unidirectional causality: technology leads, culture follows, end of story. AI doesn’t just challenge that assumption. It inverts it. With AI, culture didn’t merely precede the technology. It actively shaped what was built. What we’re looking at might be closer to cultural determinism: culture dictating the direction of technological development.

The evidence

We’ve been imagining artificial intelligence for a very long time. The line stretches back at least to Mary Shelley’s Frankenstein in 1818, the first major exploration of artificial life in fiction. From there, the cultural images kept accumulating: Metropolis (1927), HAL 9000 in 2001: A Space Odyssey (1968), and the Terminator (1984). Each generation added new layers to what “AI” meant in the collective imagination. And those layers didn’t contradict each other. They coexisted, giving us simultaneously the killer robot, the helpful assistant, the sentient companion, and the superintelligence that ends civilization. All these images are still active, all of them shaping how people respond to the same three letters.

These images didn’t stay in the movies. They migrated directly into the technology.

Jeff Bezos has explicitly stated that the talking computer on Star Trek served as inspiration for Amazon's Alexa. Both Apple and Google approached Majel Barrett, the actress who voiced the Star Trek computer, to voice their AI assistants before her death in 2008. They weren’t borrowing an aesthetic. They were trying to rebuild a piece of science fiction, down to the casting.

The most striking example came on May 13, 2024. That day, OpenAI demonstrated GPT-4o with its new voice capabilities. Sam Altman posted a single word on X: “her.”

The reference was immediate. Spike Jonze’s 2013 film Her, in which Scarlett Johansson voices an AI companion named Samantha. Altman had called the film “prophetic” in a September 2023 interview, naming it his favorite AI movie. Eight months before the demo, he was already telling the world which movie he wanted to make real.

OpenAI had approached Johansson to voice their AI. She refused. They launched anyway with a voice called “Sky” that sounded strikingly similar to hers. Johansson issued a public statement accusing them of deliberately replicating her voice. Her lawyers sent formal letters to OpenAI demanding an explanation. OpenAI pulled the voice.

A tech CEO tried to rebuild a movie literally, including the actress’s voice, against her will. This is not a case of science fiction that vaguely inspires technology. This is culture steering the technology, right down to the casting.

The collision

For most of the history of AI, the technology couldn’t deliver what culture had been imagining. That’s the core of Cultural Lead: the gap between expectation and reality ran in the opposite direction from what Ogburn described. Culture was ahead. Technology was behind.

When a group of researchers coined the term “Artificial Intelligence” at Dartmouth College in 1956, they were already playing catch-up to the cultural images. Their founding conjecture: “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” Herbert Simon predicted in 1960 that machines would do “any work that a man can do” within twenty years. Marvin Minsky told Life magazine in 1970 that machines with “the general intelligence of an average human being” were three to eight years away. The pattern was set early: promises calibrated not to the technology, but to the cultural expectations that already existed.

What followed was a cycle of raised expectations and collapse. The field went through two AI winters, periods where funding dried up because the technology couldn’t deliver on its promises. The Lighthill Report told the UK government in 1973 that “in no part of the field have the discoveries made so far produced the major impact that was then promised.” By the late 1980s, “AI” had become so toxic that researchers rebranded their work as “machine learning” or “informatics” just to keep their funding.

In between the winters, there were breakthroughs that made headlines: IBM’s Deep Blue beating Kasparov in 1997, IBM’s Watson winning Jeopardy! in 2011, Google DeepMind’s AlphaGo beat the world champion in 2016. Each was presented as proof that AI had arrived. Deep Blue and AlphaGo could each only play the single game they were built for. Watson’s apparent language comprehension turned out to be an illusion: when IBM tried to apply it to healthcare, the project quietly collapsed. But for most people, the nuance didn’t register. What registered was: the machines are winning. And yet you still couldn’t sit down and talk to one.

Then, in November 2022, OpenAI released ChatGPT. One million users in five days. A hundred million in two months. For the first time, anyone with an internet connection could interact with something that felt close to the AI they’d been imagining. It was the first significant overlap between cultural expectation and technological reality. Not a closing of the gap: the expectations still far exceed what the technology can deliver, which is why every hallucination and every failed use case feels like a betrayal. But enough overlap that the emotional charge accumulating since Frankenstein found something real to attach itself to.

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Why the emotions run so deep

In futures research, we call this kind of deep, collectively held vision a “Future Imaginary”: a narrative so self-evident that we mistake it for reality. The AI Imaginary is the shared conviction that artificial intelligence is a transformative, possibly existential force. It manifests in different narratives and archetypes: the Terminator, the Star Trek computer, Samantha from Her, the job-stealing robot. These aren’t separate beliefs. They’re expressions of the same underlying imaginary, and they shape how people react to AI today, largely without knowing it.

Here’s what this looks like in practice. When I’m in a room with twenty people discussing AI, they think they’re talking about the same thing. They’re not. They share the same imaginary, but each person has latched onto a different narrative within it. For some, AI is simply ChatGPT: a text tool that is slightly better than autocomplete. For others, it’s a vague, overwhelming force they can’t quite grasp. For some, it’s Skynet. For others, it’s the companion from Her or the Star Trek computer.

When a team sits down to brainstorm AI use cases or develop an AI strategy, each person reacts to their own dominant narrative. The person carrying the Terminator archetype worries about losing control. The person carrying Her worries about emotional dependency and manipulation. The person who sees “just a tool” wonders what all the fuss is about. They talk past each other, and nobody realizes why. You don’t think, “I am now reacting to a science fiction movie I saw fifteen years ago.” You just feel the reaction.

This also explains something about one of the oldest stories we tell about technology: “Machines will take our jobs.” This narrative is much older than AI. It attached itself to the power loom in the 1810s, to automation fears in the 1960s, and to the internet in the 1990s. It’s a wandering narrative: a story structure that detaches from specific evidence and reattaches to whatever new technology appears, each generation encountering it as if it were new. 1

But Cultural Lead explains why this story hits differently with AI. With previous technologies, the fear was a reaction to something new appearing in the world. With AI, the fear was already there: cultivated by decades of Terminator movies and dystopian scenarios, pre-loaded and waiting for a technology to attach itself to. A 2019 study by the Allensbach Institut found that 76% of Germans cited the Terminator when asked which fictional AI they recognized. The emotional charge was in place long before anyone opened ChatGPT.

This is what I see organizations miss when they approach AI as a technology challenge. The resistance they encounter in their teams, the anxiety, the anger, the “how do we keep this as small as possible”: these are not reactions to a software tool. They are reactions to future images people have been carrying, unexamined, for decades.

Loosening the future

So what do we do with this?

In Critical Futures Studies, one of the core practices is what Sohail Inayatullah calls “loosening the future.” The goal: weakening the grip that a single future image has on our thinking so that alternatives become visible again.

I want to be honest about how hard this is. Future Imaginaries are, almost by definition, resistant to change. That’s what makes them imaginaries. You can’t tell someone to “think differently about AI” and expect the Terminator in their head to quietly step aside.

Inayatullah has a phrase for why this is so difficult:

“Often the present is a stranded asset, a psychic sunk cost. A great deal of emotional investment has been put into the present, and it no longer works, and thus we are unable and unwilling to make changes for a different future.”

We’ve invested so much of our identity, our fears, and our professional self-image into specific future narratives that letting go feels like losing something real.

The most powerful loosening tool is surprisingly simple: the plural. Futures instead of future. The moment people hear “futures,” something shifts. Oh. There isn’t just one. The one dominating my mind right now is just one of many possible versions of what comes next.

A strong future narrative narrows the options we can see. But only in our perception. The possibilities are still there. We just can’t perceive them when a single image fills the entire screen.

For individuals, this means something quite practical: recognizing your own AI image and noticing how it shapes your reactions. When you feel a strong emotional response to AI news, that’s a signal. The strength of the feeling often tells you more about the image running in the background than about the technology in front of you.

For organizations, the implication is uncomfortable. If you want to make good decisions about AI, the cultural work has to come before (or at least alongside) the technology work. That means creating spaces where people can surface and examine their AI images before you hand them a tool and a deadline. Most organizations skip this entirely. They wonder why their AI strategy meets so much resistance, and they blame the people for not being “open to change.”

Ogburn’s Cultural Lag assumes the hard work begins after the technology arrives: catch up, regulate, and adapt. Cultural Lead means the hard work was already underway long before anyone opened ChatGPT. The stories were already told. The fears were already in place. The images were already running. That’s why AI strategies that start with the technology are starting in the wrong place.

The real work begins with becoming aware of which future image is running in your head. With recognizing that the emotional intensity you feel about AI might not be about AI at all. It might be about a story you’ve been carrying for much longer than you realize.

The present, as Inayatullah writes, is “merely the fragile victory of one possible trajectory over other pathways.” That’s true for the present, and it’s true for the futures we project from here.

Loosening those images doesn’t give you a new answer. It gives you a different question: what other futures could there be?

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New in the Futures Garden

Notes I added or updated in my digital garden this week:

• Imagination Economy (new)

• Effective Altruism (new)

• Reactance and future narratives (new)

• Rationalists (rewritten)

• TESCREAL (updated)

For German-speaking readers

Three recent appearances, all in German:

• Apokalypse Not Now — on the WUNDERPANIK podcast: how futures research helps with AI panic

• imagine all the agents — mit Johannes Kleske — a conversation with Klaas Bollhoefer about Future Imaginaries, the Imagination Economy, and what’s actually happening right now

• KI als Zukunftsbild — my new column in Changement magazine on why our image of AI is outdated

Last week, Matt Shumer1 published an article called “Something Big Is Happening” on X, and it went viral. CNN, TBPN, almost a hundred million views. It perfectly captures a narrative that’s been dominant for years. In it, he describes his personal experience with current AI models, especially in coding, and extrapolates from there to basically tell everyone: everything is changing, it’s going to be crazy, save every cent you have.

I posted a short, admittedly snarky reaction on LinkedIn: that this article gives you zero insight about the future with AI but nevertheless displays a current mindset among some people in tech and shows you how our attention economy works. The reactions were intense. People who shared Shumer’s experience didn’t understand my point. And people who didn’t know what to make of all this AI talk felt validated. Both reactions taught me a lot, and the conversations that followed helped me formulate my perspective much more precisely.

So here’s my attempt to lay it out. Not as one long argument, but as a collection of thoughts around the topic that I’ve been carrying with me.

I’m deep in this, too

Before I get into the critical stuff, let me be clear: I understand where Shumer comes from. I actually share a lot of his personal experience with AI tools. I’ve been using Claude Code in combination with Obsidian for more than half a year now. I’ve developed my own knowledge database, my own CRM, and my own task management tools. All systems that run my daily work. In the last issue of this newsletter, I wrote about becoming “voice-pilled” because I spend so much time now speaking and having what I say transcribed. I’ve been deep in the weeds with OpenClaw and the communities around these tools. I can’t code, but I can build things now that were not possible for me before.

So my criticism is not coming from the sidelines. It’s coming from inside the practice.

What stories about the future actually tell you

As a critical futurist, I distinguish between present futures and future presents. Present futures are the stories we tell ourselves and others about the future. We do this usually with an agenda, because we want to invoke a certain future to gain an advantage. Future presents are the actual moments in the future. They simply do not exist yet.

I’m only interested in present futures because you can learn a lot about the present from listening to stories about the future. Just as reading science fiction predicts very little about the future, it reveals what we project into it based on our current problems.

Shumer’s article serves as a prime example of a present future. He has an experience in the present (AI tools getting much better at coding), and he immediately extrapolates it into a story about the future of all work. That story tells you how he perceives today. It tells you very little about what tomorrow will look like.

And his big fallacy is to think that his personal experience in a very specific field is going to roll out to the rest of the world. I’ve been researching futures of work, and especially futures of work and AI, for almost 15 years now. I’ve seen this same pattern again and again. It never works out like that because work is much, much more complex.

The hype cycle repeats

In 2013, I gave my first talk at Republica in Berlin about AI and the futures of work. Back then, the discourse was just starting. Before that, the idea was always that machines could take over physical work. In 2012, the conversation shifted: maybe AI and algorithms might actually become powerful enough to take over mental work. Since then, I've been on this beat.

In 2016, Google’s AlphaGo beat the best Go players in the world. I remember vividly: the articles were all over the media. “It’s over. The machines have won. It’s only a matter of time until they replace us all.”

It didn’t happen. And then ChatGPT launched in late 2022. Instantly the same articles: “Do you see what AI can do now? This changes everything!” Mainstream media and tech people expected exponential development toward AI taking over jobs. That was more than three years ago.

Here’s the thing that got me slightly cynical: if you observe this pattern for more than a decade, you start to ask yourself, “Really, is it coming? Because it should be here by now.” Every couple of months, the same article gets published in one of the German dailies or weeklies. “Here are some examples of what AI can do now. They’re coming for your job.” The specifics change. The structure doesn’t.

And the part that really annoys me with articles like Shumer’s is that they pretend all the other predictions that came before, for decades, don’t exist. We keep making this mistake. We predict something, it doesn’t happen, and then we predict again, adding, “But this time it’s really different.” It usually isn’t. Or if it is, it’s different in a way we didn’t quite figure out, because we tend to look at the future with how we perceive the past.2

The narrative even works in reverse: whenever somebody is fired and it’s said it’s due to AI, it usually is a cover story. The real reasons are overcapacity, restructuring, cost-cutting. But because the dominant narrative leads people to expect AI to replace jobs, companies can use “AI” as a convenient excuse. Society hears it and goes, “Yeah, of course, now it’s happening.” The narrative becomes a self-fulfilling justification for decisions that have little to do with it.

The work paradox

In Germany right now, we desperately need more workers. The government is trying to convince us all that we need to work more. Many people have already experienced burnout due to excessive workload. But at the same time, a growing number of employees are afraid that they’re soon going to be replaced by AI.

How does that work? How are we supposed to go from “we should all work more” to “AI has replaced us all” in an instant?

And on a more personal level: I don’t know anyone who’s currently working with AI who’s doing less work. On the contrary. I’ve been deep in the communities around Claude Code and similar tools over the last few weeks. What I consistently observe is that people who are using these AI tools are working even more than before. There are articles from developers (and HBR) who talk about burnout, about how intense it is to work with these tools, about not having energy anymore. The same thing I see from many people in this field: “I’m not getting enough sleep. I’m eating badly. I really need to get away from the computer.”

There’s actually a concept for why efficiency tools make us work more, not less: Jevons’ paradox. In the 19th century, the economist William Stanley Jevons found that when technological improvements made coal use more efficient, total coal consumption went up, not down. Making something more efficient doesn’t reduce demand. It increases it because suddenly there are more use cases than before.

I keep coming back to Jevons‘ paradox because I think it is the insight we constantly miss when looking at work. We see the current amount of work, we see tools that can make it more efficient and faster, and we think work will decrease. But Jevons’ paradox has taught us again and again: when things become more efficient, we just consume more of them. That’s the driver that’s so often missing from our extrapolations about the futures of work with AI.

Future narratives and the reactance problem

My observation over the last decade is that future narratives like Shumer’s don’t draw people in. They trigger their reactance and push them deeper into the trenches.

Reactance is the pushback you feel when someone says you must do something or lose your job. The instinctive response: “I don’t want this. I don’t care. Go away with this.” If you tell me everything is a “game changer,” my first impulse is to turn away because it sounds enormous. It takes away my agency.

I’ve observed this constantly. The people who pushed back against my LinkedIn post were the ones who share Shumer’s experience. They wanted to use his article to convince their friends and colleagues: “See, he’s writing that you have to look at this!” I’ve seen this backfire too many times.

L.M. Sacasas refers to the Borg Complex, after the Star Trek aliens who always say, “Resistance is futile.” The spirit of the Borg lives in tech companies and pundits who prod everyone they deem slow on the technological uptake into assimilation. Sacasas even has a symptom list: makes grandiose but unsupported claims for technology. Pays lip service to, but ultimately dismisses genuine concerns. Equates resistance or caution to reactionary nostalgia. Announces the bleak future for those who refuse to assimilate.

Sound familiar?

This is the core insight I want to share: these FOMO-driven future narratives have the opposite effect of the one intended. If you want to give people more agency in shaping the future of work, the overhyped “everything changes, don’t get left behind” framing does the opposite. Fear is a good activator but a bad motivator. People share the article, but does it actually get anyone to open Claude and experiment? Or does it just make them more anxious and more likely to either freeze or push back?

And I think this is what we’re starting to see: a strong counter-movement to AI forming. All of this hype is leading us away from the benefits that AI tools can actually bring. Because we too easily buy into the narrative that “resistance is futile,” that we should just accept that AI is the future and go along.

I don’t want that. I think AI is changing things, but I want society to shape this transition according to its values. The question I keep asking is, how can we use the best of this technology but with the values we have as a society and the way we want to live in this world? How can people gain more agency in shaping the future, instead of having it dictated to them?

The attention economy makes it worse

The dynamics of the attention economy make it so hard for people to actually deal with AI tools in a rational way. The hype surrounding these tools is overwhelming. If you want to look on YouTube for videos explaining how to try out Claude Code, most of them have titles like “This Changes Everything! AGAIN!” and “I’m Making a Million Dollars in Just a Week.”

What happens then is that people compare their experience to the hype and come up short. They think, “Yeah, that’s not the game changer everybody says it is.” As a result, they often abandon the tools once more.

There’s a deep irony in Shumer’s article. Buried inside is actually useful advice: the models have gotten much better, you should try them out, and if you’ve only tested them a couple of months ago, you should play with them again because they’ve changed so much. If he had written an article that simply stated, “I’m really amazed at what current models can do. Make sure you try them out, too, so you have a current understanding of what’s possible.” I probably would have shared it. But that wouldn’t have gotten him hundreds of millions of views and onto CNN. That’s the attention economy at work.

The attention economy didn’t amplify the useful part. It amplified the FOMO.

It changes the system, not just the tasks

Now, I don’t want you to walk away from this thinking it’s all hype and you don’t have to do anything. That is absolutely the wrong takeaway.

I’m convinced that AI is going to change work fundamentally in many places. But it’s going to take much longer, it’s going to be so much weirder, and it’s going to be so much more unexpected than today’s predictions suggest.

The great fallacy with AI and work is that we only ever look at the existing system and speculate how AI might accelerate and automate it in the short term. We only imagine existing jobs disappearing. We have a hard time imagining new jobs emerging that wouldn’t make any sense in the current system but would become rather obvious once the system adapts.

I can see it in my own work. I can’t code. But I now do pull requests on GitHub; I’ve built my own CRM and my own task management system. These are not tasks that got automated. These are capabilities that didn’t exist for me before. And that changes the whole system of what my work looks like.

What we’re living through is not a crisis of who does the work, but a transition in what the work is.

And this is exactly what Shumer’s article misses. He says, “I had Claude build an app for me while I stepped away from the computer.” He sees the new capability. But he doesn’t ask what it means for the system. Whether it’s a good app. Whether it sells. Whether anyone wants it. He only talks about the tinkering and the experiment part. But experiments are not what you base your business on. The intriguing question isn’t what AI can do. It’s what new kinds of work and value emerge once things shift.

So what should you do?

Here’s the funny part. My actual advice is surprisingly close to what Shumer is saying at his core: get your hands dirty with these tools. Explore. Play. Tinker.

But without the fear. Without the FOMO. Without the “save every cent because the robots are coming.”

I keep (half-)joking that this era is more like 1999 with the internet. Some people tell me they feel like they’re behind on AI, like they might have already missed the train. I get that feeling. But imagine someone in 1999 going, “This internet thing, I’ve missed it. Not worth looking into anymore.” We’d smile at that today, because the things that ended up mattering the most (social media, smartphones, streaming, remote work) hadn’t even been invented yet.

If we’re truly at a 1999-equivalent moment with AI, then the things that will actually matter haven’t been built yet. Feeling like you’ve already missed it is the FOMO talking. And FOMO is a product of the hype, not a reflection of reality.

So: take away the hype. Take away the future narratives. Explore AI as a normal technology. Be playful. Try things out. Build prototypes. But don’t compare it to “this has to change everything.” Just find out for yourself how it can be helpful, and keep learning about it. That will actually help you, and society, to be much more reflective and prepared for whatever comes next.

And if you want to bring others along, try a different approach. Instead of saying, “This is changing everything, and you’re already behind,” try, “I’m really fascinated by what’s happening here. Can I help you try it out too?”

That’s the best way to shape the future instead of just having it happen to you.

Special thanks to Katja Nettesheim and Stephan Thiel for their questions.

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Reading list

This is what I love about articles like Shumer’s: they bring out the best writing from others. Here are some pieces I can highly recommend:

• Why I’m not worried about AI job loss by David Oks

• The Singularity Is Going Viral by John Herrman

• A.I.’s Pandemic Moment by Max Read

• Tool Shaped Objects by Will Manidis

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I just discovered that you’re all still here, still subscribed to this abandoned newsletter (please feel free to unsubscribe if this annoys you or is irrelevant to you in any way; I won’t mind). And so in the spirit of playfulness and experimentation, I’m going to use this microphone that I currently still have without any concept or plan and without any AI generation or something.

Voice-pilled

Because I somehow, over the last couple of months, became very voice-pilled. I used to be someone who hated talking to a computer. I never used Siri, and I never used Alexa or anything like that. But when trying out LLM chatbots and seeing a couple of recommendations for that, it became clear that it actually works quite well to use a transcript of what you’ve said to talk to these chatbots. And for me that’s a completely different mode of thinking and working than writing. When I write, I’m editing all the time; I’m trying to find the perfect words, and I instantly rewrite sentences and stuff like that. When I speak, I just have my train of thoughts that I’m following. And so I have been speaking to my computer a lot. I even, to the annoyance of my partner, got to the point where I do it when other people are in the room. And so I’m going to use this and do the opposite of what we see mostly on feeds, which is slop. I’ll just give you my train of thought on whatever comes to mind. And it might just be this one time, or I might just use it in the future. So here we go.

One year of self-employment

So to update you, if you haven’t seen anything from me in the last weeks or months, I have now been fully self-employed for a little over a year. And I’m mostly working as a keynote speaker these days, because especially in Germany, the economy is so bad, nobody’s thinking. about the future at all. If it doesn’t make you revenue in the next three months, companies are not interested. So they don’t want to take any risk, they don’t want to invest in any futures work, but they very happily want to talk about the future. And so it turns out that is a product that I can deliver. I am quite happy with how it has shaped up in the first year.

It’s also especially, I think, because in Germany people feel so little agency about the future that the term “shaping the future” is everywhere. Posters, political campaigns—it’s everywhere. But nobody talks about how that actually works. How do you shape the future? And so this has become my main keynote theme: explaining what that actually means. What the future is, what shaping the future means, why you should do it, and how you do it in a practical way. That has been working actually quite well.

The thing that I discovered, which I was kind of aware of but got much more into, is that I actually really, really enjoy doing keynote talks. I’ve been doing them for a long time, but I’ve never done them as my primary source of income or my main type of work. But I really quite enjoy working with organizers, figuring out what the dramaturgy and the goals of their event are and what role the opening keynote plays in that agenda. And then really helping the audience to make the most of the day of the event, getting them into the topic, getting them into the right mindset, stuff like that.

Yeah, I’ve been quite enjoying that and have also been enjoying always putting a bit of the Critical Futures work into these keynotes. I have my Three Assholes slide that shows Zuckerberg, Altman and Musk as examples of popular people who are invoking specific future narratives to advance their own agenda. My goal is to help the audience to learn how to decode future narratives that they hear or that are getting passed around and then help them to take the first steps to defining the narrative of a future that they want to live in. That’s been my main source of work.

The AI and work discourse

I also have been playing around a lot with LLM tools, all kinds of things, to really like figure out what’s in there and what I can learn from experimenting with them and trying them out. So I have been doing talks about AI and the future of work since 2013. And it became clearer and clearer that this discourse around machines taking over our mental work and algorithms replacing humans has been around for a long time and essentially hasn’t changed at all. The talk that I gave in 2015 at Republica—I could slightly polish or update the examples, but it would still completely work today. And I think the reason for that is that this discourse is way too abstract; it’s way too high-level. And so it never leads to any significant change or new actions. Thus, my strategy for the last two years was to get a much more concrete, precise understanding of these systems, not just the technology but also the cultural aspects and the economic aspects, because then it becomes much clearer where the levers are.

OpenClaw

The current example, as of right now, is called OpenClaw, which was called Clawdbot and then Moltbot before. This is an open-source agentic system that you can install on a VPS or, as many like to do, a Mac mini, which seems to be really popular. I installed it a week ago, and it was very fascinating to use because it made so many abstract things, like the discourse around agents and the “agentic future,” so much more concrete because you could see the potential in a system that was best described as “what Siri really should be.” Seeing a system that is based on the premise of it having access to everything—to your emails, to your documents, to everything you give it access to, and the more you give it access to, the more powerful it becomes to do certain things—and seeing the potential of that and making it so much more concrete.

In the last 12 months there was a lot of talk in economic circles about agents and humans together on the org chart, and that’s one of those metaphors that is always passed around, and it doesn’t make sense at all. If you have done any work with agents, you really know that they are very much not replacing humans. They are replacing certain tasks, or they are automating certain tasks. But nobody in their right mind who’s not just interested in doing a press release will seriously consider creating an org chart with humans and agents in there. That doesn’t make sense. It’s also a good example for me of why it’s so important to have very concrete experience with the system because then you immediately know where the discourse falls apart.

So I started playing around with OpenClaw. And in the beginning, you could just use your Claude subscription, but then Claude started banning people because it was against the terms of service. And so you had to give OpenClaw an API key for one of the LLM services, which also meant you had to pay for each token that you used. And it very rapidly for me became quite clear how resource intensive a simple personal agent is. You use a top model like Claude Opus 4.5, and you just let the agent run for a couple of hours without doing any big things, just the usual, like the heartbeat feature and a couple of chats. Within a few hours I had amassed costs of approx. 10 euros. So if you run it for a month, you’re easily in the hundreds. and so that shows you that all those visionary ideas about the agentic world … It’s still very very expensive and resource intensive. The other option, obviously, is to use a local model, but to get any meaningful use of a local model, you have to have a really, really expensive machine that is capable of running big enough local models for them to work at all in a meaningful way.

The other thing that I found really interesting is that over the last week, it instantly became very clear by sheer anecdotes how unready our digital systems are for these kinds of all-encompassing personal agents when it comes to security. At the beginning it was mostly just that they were not really secured from access from the internet, and it was not that hard to fix that. But having these probabilistic systems reacting to the world or even just observing the world opens it up for so many inroads of hacks and prompt injections and similar threats. I read lots of articles by security researchers who basically said, “We spent the last decades to make computer systems and the internet more secure. We invented a lot of frameworks and models and methodologies to have much better security. And to a certain extent for the agentic world, we have to completely start that again.”

The other day I joked on the feeds that we will have to thank all those YOLO nerds who just ran an OpenClaw instance with access to everything and had their authentications and tokens and keys stolen. Because these very concrete examples created a roadmap for the next weeks, months and years and actually allowed us, because we had so many concrete learnings, to move from the abstract to the concrete, which I’m guessing will make more robust and secure systems arrive much faster.

Moltbook is about humans

So these are the things that I’m thinking about this weekend. I’m not going to touch on Moltbook too much because everybody is very hyped about it. I think my understanding is that despite what everybody is saying, it is actually quite easy to just post on there as a human. So you should take anything you’re reading on there with a grain of salt. If it seems too perfect to be true, it probably is written by a human. But as always, what fascinates me with these kinds of occurrences is that I think the reactions to Moltbook tell you much more about the humans than they tell you about agents. We are seeing people and how they react to it, and they are like, “Oh my god, what’s happening there?” and ”Should we shut it down? It’s going crazy!” You can just see how it basically is like a live-action science fiction story that we feel like we’re observing, and we’re just so used to these tropes that we can’t help ourselves in seeing the ”ghost in the machines.” This is what we’re going to get again and again, where it’s just a performance of intelligence and consciousness, and it works so well to fool us.

Closing

Okay, there’s tons more stuff to talk about, but I’m just going to end it here. This has been my train of thoughts, basically a braindump of what just came to mind. I’m going to clean it up a little bit so that it is easier to read. And then we’ll see.

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If you have any questions for me about anything, please hit reply or leave a comment. Questions are what I run on.

For transparency’s sake: I used Monologue to record this and QuillBot to take out the transcription errors (but did not use formulation recommendations; according to QuillBot, it gets only 57 for fluency and 54 for clarity out of 100). I also added some headlines to make it easier to read.

It's been a while since my last edition of “The Futures Lens” in August, and I have some updates to share about its future.

After careful consideration, I've decided to consolidate all my content on my website (johanneskleske.com). This move allows me to create a more integrated, multilingual experience and maintain greater independence from third-party platforms.

What does this mean for you?

Going forward, I'll be publishing all my articles directly on my blog – in both English and German. To stay updated, you can subscribe to my new newsletter. It's a straightforward way to receive my latest articles and insights directly in your inbox, in your preferred language.

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What you've been missing:

Since my last Substack edition, I've published several articles that explore critical perspectives on current developments and other thoughts about futures:

• How to talk about the future: Transcript of a short talk I gave, that opens with “The “Car-free City” is a negative future!” …

• Angela Merkel’s Mindset – What her Biography reveals about Germany today: Angela Merkel’s autobiography is much more than the memoirs of a former chancellor. It is a fascinating contemporary document that offers deep insights into a state of mind that has shaped Germany and its institutions over the past twenty years – and continues to do so today. It is a state of mind that is reaching its limits in the face of today’s global challenges.

• Future Orientation: Understanding Hidden Narratives in Your Organization: Every organization has an implicit way of using the future that profoundly shapes its culture, decision-making, and behavior. The future acts as a powerful force in organizational life, influencing everything from daily operations to long-term investment decisions. Yet most organizations remain unaware of how they invoke and employ future narratives, leaving this influential force largely unexamined and unmanaged.

• Brand resilience through future narratives: True brand resilience does not come from faster response times or better crisis communications alone. It needs a foundation – a clear idea of what the brand stands for and what future it wants to actively shape.

• Navigating Uncertainty, Shaping Futures: I recently had the pleasure of joining Matt Ballantine and Julia Bellis on the WB-40 podcast to explore some of the most pressing questions facing organizations today: How do we make sense of an uncertain future? What role do our shared narratives play in shaping the paths ahead? And how can we proactively design strategies to thrive in complexity?

While this Substack chapter is coming to an end, I'm excited about the possibilities this change brings. Having full control over the platform allows me to better serve you with critical perspectives on futures that matter.

On a personal note: Since August, I've taken the step into self-employment as a critical futures researcher and strategic advisor. If you're looking for keynotes, workshops, or strategic guidance in navigating complex futures, you can now book me directly through my website.

Thank you for being part of this journey. I hope you'll continue to explore critical futures with me on my website.

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Best regards,

Johannes

P.S.: This will be the final edition of “The Futures Lens” on Substack. The archive will remain accessible, but for new content, please visit johanneskleske.com and subscribe to my new newsletter.

The current version (and a German translation) is over here.

As a foresight practitioner with over a decade of experience, I've noticed a recurring issue in our field: the lack of lasting impact of our work. Let me paint a familiar situation:

The scenarios are done. You might even have done some backcasting. The report is written, including a beautiful slide deck with the key findings. You've given the final presentation. The client is happy. The invoice is paid. And then … nothing happens. You check in with the client and ask about how they are doing with all the insights and recommendations and they say something about some short-term priorities that have taken over and how they are still waiting for the meeting with the CEO, and how they are about to be transferred to another department, but how they always like to think back to our project and how much fun they had.

This scenario illustrates a common problem in foresight work: the disconnect between the insights we provide and their practical implementation. If you think of the work of an organization as a huge comprehensive process that goes from foresight (what might be on the horizon?) to strategy (how do we get there?) to tactics and implementation (what's next?), there's a huge gap between foresight and strategy.

One factor contributing to this gap is the structure and focus of many organizations' strategy departments. These departments often operate under constraints that make it challenging to fully leverage foresight insights. They may be understaffed, undervalued, or primarily focused on short-term horizons - typically concentrating on the next quarter or fiscal year rather than long-term futures. As a result, they often lack the resources, bandwidth, or mandate to effectively incorporate and act upon foresight findings. This challenge is compounded in organizations where it's not even clear which department or team is responsible for implementing foresight insights.

Yet, as foresight practitioners, we also bear a great deal of responsibility for the mediocre impact of our work. We put a lot of thought into the process of developing our scenarios and recommendations but then we “fire and forget” them into the organization and move on. I'm guilty of this myself. I‘ve done a lot of futures reports and scenario projects over the last decade – work I'm really proud of. But when I look at how little impact they had in the end, it bugs me. And when I look at our industry, I can clearly see that I'm not alone. The meager impact we're having also has an economic effect. Foresight studios and trend agencies have to constantly attract new clients because the old ones are not coming back for new business. If our work made the impression we think it should, it wouldn't be the first thing to be cut from budgets in tough times.

To meet these challenges and increase the impact of foresight work, we need to rethink our approach from the very beginning of a project.

The Beginning of Foresight: Critical Questions

At the beginning of a foresight project, the client comes to you with a question or an objective. Basically they want to be able to make a decision, whether it is about the way forward, the choice of a strategy or an investment, or more generally to be more aware of what might lie ahead. But even before agreeing on the budget and the process, it helps to ask a series of questions:

About the Project

• What is the client's current understanding of the issue or topic they wish to explore?

• Are there alternative ways of framing the issue that might lead to different insights?

• What are the boundaries of the topic, and are they appropriate for the client's needs?

• What time horizon is being considered, and is it appropriate for the topic?

• How will the client measure the success of the foresight project?

About the Context

• What specific events or trends have prompted this foresight initiative?

• What internal and external factors influence the client's perspective?

• What sources of information does the client rely on, and are they sufficiently diverse?

About the Client

• What is the organization's past experience with foresight projects, including their implementation?

• How open is the organization to challenging its current views and considering disruptive change?

• Who are the key stakeholders for this project, and what are their decision-making cycles?

All of these questions will help you decode the client's briefing and better understand, what they are looking for.

Another set of questions explores the underlying assumptions and unconscious preconceptions about the future that every organization has, which could be summarized as its futures culture.

About the Client's Futures Culture

• What role does the future play in the organization's strategy and operations?

• How do “official” and “hidden” future narratives coexist within the organization?

• What range of future scenarios is the organization willing to consider, and what's off limits?

• How does the organization typically deal with risk and uncertainty?

In most foresight projects, this futures culture of a client is never outlined. It usually only becomes apparent when something in the foresight project goes against it and there is suddenly strong pushback, such as when you present interim results to a manager and he gets angry for no apparent reason.

That's why it's so helpful to uncover an organization's futures culture at the beginning of a project. But there's another set of questions that should be asked at the beginning of a foresight project.

About the Implementation

• How does the client plan to use the results of this foresight project?

• What processes and resources are in place to act on the foresight findings?

• What are the potential barriers to implementing the project's findings?

As these questions make clear, it is enormously helpful to plan the project for maximum impact and implementability. In other words, think about the end of the foresight project.

In order to continue to develop this series of questions, I've added a note to my digital garden.

The End of Foresight: Beyond the Final Report

As we approach the end of a foresight project, we need to think strategically about how to maximize its impact. I suggest thinking about this on three levels:

1. The ground level is the planning of actions that follow the delivery of the final report or whatever the final deliverable might be. For example, this could be an internal road show where the results are presented to different parts of an organization. Each part would receive a contextualized version of the findings to make clear how the anticipated futures might affect them. This could be combined with a workshop to help the audience to work through the recommendations together and derive their own next steps and future projects.For example, for our work with the German public broadcaster SWR on a futures report on the media landscape, we also developed different workshop designs so that the client team could work with different departments to apply the insights from the report to their contexts, from editorial teams thinking about the needs of their future listeners to technical departments.

2. The intermediary level would help the organization move from a one-time foresight project to an ongoing foresight engagement. Rather than throwing the report over the fence, it shows them that for foresight to have a greater impact on their business, they need to revisit trends and scenarios on a regular basis. The world is constantly changing and they need to adapt. The necessary condition for this level is that the ground level is already established and has shown results. If the client is not yet sure whether the foresight project has been worthwhile, it will be difficult to give the green light to an ongoing project. But if the feedback from different departments is that the engagement with the project results was helpful, the case is easier to make.

3. At the last level I see a stronger intersection between foresight and organizational development. After all, the challenges of implementing foresight insights and recommendations are at the level of organizational design. Organizations are not set up to deal with different futures. They usually don't have positions and processes mandated to do so. That's why they have a hard time adapting to change and dealing with new developments, from the climate crisis to AI. In a way, foresight makes this fundamental problem more visible without being able to fix it. That's why I'm very interested in the intersection of foresight and organizational development.

These levels are just a starting hypothesis for me to explore further how we can bridge the gap between foresight work and the rest of an organization. I know from experience that design can play a crucial role in making futures work more tangible. But it still doesn't fully translate into impact.

In conclusion, to increase the impact of foresight work, we need to think critically about both the beginning and the end of our projects. By asking the right questions at the outset and planning for implementation from the outset, we can bridge the gap between foresight and action. As practitioners, we have a responsibility to ensure that our work doesn't just imagine futures, but helps to shape them. I invite you to share your experiences and thoughts on how we can make foresight work more impactful.

Hey there, I'm Johannes

A critical futurist and foresight expert. I decode complex trends, challenge dominant narratives, and help leaders navigate the fog of the future(s).

Worthy of your time

• Paul Graham Raven has launched the website for his “Worldbuilding Agency” together with a “it's not a newsletter”-newsletter that I can highly recommend subscribing to.

• Jay Springett is a guest on the Wolf Podcast in an episode about William Gibson's book Pattern Recognition, the book that I've reread more than any other.

Thank you for your attention.

If you find this newsletter valuable – the best way to support it right now is to ask me a question. What interests you in the futures field? What do you want to know more about? Hit reply or use the comment section to let me know.

You can also share the newsletter with friends and colleagues who are interested in its topics.

Have a good one,
Johannes Kleske

Futures studies are primarily an applied science, focusing on generating knowledge that helps individuals and organizations make informed decisions in the present. It has a particular emphasis on methodologies. When asked to define futures studies, practitioners often reference scenarios, Delphi methods, and horizon scanning, among other techniques. This focus brings specific challenges, including an under-theorization within the discipline. Almost no fundamental theories about the future exist—its essence, construction, and implications. This lack of theoretical foundation renders the field somewhat unstable. Therefore, efforts to reinforce this foundation, especially within critical futures studies (CFS), are particularly exciting.

I met Nele Fischer during the master’s program in futures studies at the Free University of Berlin. There, she co-taught a course on ‘vision assessment’ with Sascha Dannenberg, one of the supervisors for my master’s thesis. Nele is also playing a pivotal role in organizing our CFS community and was instrumental in launching our inaugural event.

Nele recently completed her dissertation, ‘Möglichkeitsräume kontingenter Gegenwarten’ (EN: ‘Possibility Spaces of Contingent Presents’), which reflects on thought frames as a theoretical and methodological foundation for critical futures studies. This work promises to be a cornerstone of future studies, offering a comprehensive framework for critically engaging with the construction of futures. Good theoretical work paves the way for methodological innovation, and I eagerly anticipate such developments.

The dissertation is available only in German, but I recommend using a translation tool to explore it for those with even a remote interest in these topics. To whet your appetite, here are some highlights:

• Social Constructivism as a Foundation: Futures are not predetermined but are shaped by societal discourse and collective interpretations, influenced by current knowledge, societal values, and narratives.

• Critical Futures Studies (CFS): Emphasizes a reflective approach to future-making, advocating for examining normative assumptions and power structures and aiming to democratize future visions.

• The Role of Thought Frames: Critical in shaping our perception of possible futures and identifying limitations to imaginative scope.

• Methodological Innovations: Introduces novel strategies for exploring future scenarios, emphasizing the importance of hermeneutic understanding and creative engagement with future possibilities.

• Reintroducing Contingency: Underlines the importance of recognizing the future’s inherent uncertainty, advocating for explorations beyond deterministic forecasts.

• Deliberation and Transformation: Promotes a deliberative process in futures studies, aiming to co-create diverse visions and societal transformation.

• Democratizing Futures: Calls for more inclusive future-making processes, pushing for equitable and just scenarios.

You can download the dissertation as a PDF or listen to Jonas and me interviewing Nele about her groundbreaking work for further exploration (both links in German).

Hi, I'm Johannes

I work as an adviser and facilitator at the intersection of strategy, design, and transformation. If you manage innovation, change, or strategy, this newsletter invites you to explore new perspectives and approaches.

Worthy of your time

Futures for Journalists Workshop

This unique workshop is designed to equip journalists, editors, freelance writers and media professionals with the tools of futuring you can apply today.

Sharpen critical skills and develop better ongoing scanning and forecasting skills to support story development, reporting and analysis of the future.

Friends Susan and Scott from Changeist have created great capacity-building offerings around Futures. Their two books, How to Future and Future Cultures, are among those I recommend when asked about introductions to futuring.

For those of you who are journalists or work in media, look at their upcoming online workshop, Futures for Journalists, on May 16-17.

Futures for Journalists

Future Observatory Journal

Future Observatory Journal is a biannual online journal on design, ecology and futures. Our aim is to create a space for rethinking the frameworks within which design operates. Published by Future Observatory, the Design Museum’s national research programme for the green transition, the journal aims to expand the often narrow scope in which design and ‘sustainability’ are discussed.

Bioregioning - Future Observatory Journal

Book: Navigating the Polycrisis

When somebody puts ‘futures’ in the title of their book, I'm instantly interested. Michael J. Albert— a lecturer in Global Environmental Politics at the University of Edinburgh School of Social and Political Sciences—has a new book out this month called ‘Navigating the Polycrisis – Mapping the Futures of Capitalism and the Earth’ from MIT Press.

Resilience.org has an excerpt from the book's introduction that is worth a read. Albert introduces the term ‘counter-hegemonic futures analysis:’

I argue that a theoretical framework informed by complexity theory and world-systems theory can provide a new form of critical-futures analysis capable of grappling with the polycrisis condition.

I also liked this one:

rather than ultra-specialized experts, it is the agile and curious—those who venture far outside their disciplinary comfort zones, seeking out new insights from other fields and opposing perspectives that challenge their thinking—who are best placed to connect the dots and develop more realistic maps of the future

So, if you're interested in a Marxian futures approach, the book is available as open access.

Navigating the Polycrisis

Thank you for your attention.

If you find this newsletter valuable – the best way to support it right now is to ask me a question. What interests you in the futures field? What do you want to know more about? Hit reply or use the comment section to let me know.

You can also share the newsletter with friends and colleagues who are interested in its topics.

Have a good one,
Johannes Kleske

Wie entsteht unsere Zukunft? - mit Johannes Kleske - Netzpiloten.de

You can listen to the German podcast here.

Images of the future shape our ideas of possible futures and profoundly influence our societal behavior. This insight, first articulated by sociologist Fred Polak in the 1950s, emphasizes the significance of our images of the future: They attract us, influence our decisions, and shape our actions in the here and now.

Unlike classical futures studies, which focus on generating new images of the future, critical futures studies adopt a reflective approach. Its focus is on questioning and analyzing existing images of the future to understand what they reveal about our current fears, hopes, and values. Critical futures studies recognize that the future, though not yet arrived, already exists in our imaginations and shapes our decisions and actions in the present.

Deconstruction of Futures

The starting point of critical futures studies is deconstructing existing images of the future to uncover their underlying assumptions, values, and implications. This approach allows us to question dominant narratives and explore alternative visions of the future based on different value systems.

A concrete example of the impact of future narratives is the discourse on artificial intelligence and generative technologies. Visions shaped by leading figures in Silicon Valley consciously influence our expectations for the future and often steer societal developments to benefit those who design and disseminate them.

This insight raises essential questions: Whose interests are represented by dominant future images? What values underpin these images? Most importantly, does the direction these images lead us to align with the values and goals of a broad majority of society?

Sohail Inayatullah, an influential thinker in critical futures studies, describes the task as “loosening the future.” Instead of viewing the future as fixed and unchangeable, this perspective encourages us to recognize and utilize its openness and malleability.

Futures Studies as Studies of the Present

A fundamental insight of critical futures studies is that it constitutes studying the present at its core. The future visions we develop and discuss often say more about our current hopes, fears, and desires than what may come to pass in the future.

Interestingly, those presenting definitive future visions on stages and in lectures often reveal more about their interests and perspectives than about the future itself. Their statements reflect less objective predictions and more their personal hopes, fears, and wishes. This emphasizes the necessity of critical reflection on the sources of our future images and the motivations driving them.

The engagement with future images and their impact on society brings us to a critical aspect: the power of self-fulfilling prophecies and the performance of futures. This concept, particularly relevant in political and societal discourses such as those related to Donald Trump or the rightward shift in Germany right now, illustrates how expectations of the future shape our behavior and thus increase the likelihood that these anticipated futures become the present.

Reconstruction of Alternative Futures

In addition to providing a platform for analyzing existing future concepts, critical futures studies also enable active participation in shaping a desirable future. It encourages us to rethink the premises behind our visions of the future, contributing to developing a more reflective and fairer future.

Creating such alternative future images is far more than a theoretical exercise; it is an urgent necessity to allow for a more inclusive and diverse future. It will enable us to think beyond the status quo and explore paths to a future that better aligns with our collective values and hopes.

From Visions to Concrete Actions

It’s crucial to recognize that it’s not enough to design new future images. What matters is the transition from desirable visions to concrete actions, which means recognizing and utilizing our own space for action, even if the steps appear small.

The difference between positive and negative images of the future and their impact on people’s acceptance and engagement is fundamental. By focusing on what is enabled through changes rather than what is lost, broader support for transformative projects can be gained.

Particularly striking is the idea that actual change is often initiated by small, prototypical projects representing an alternative future in the here and now. Such “real utopias,” exemplified by communal living projects that promote intense and networked cohabitation, illustrate that alternative forms of society are not only possible but are partly already implemented.

Conclusion

Critical futures studies encourage us to question dominant visions and actively work on creating alternative future images. By becoming aware of and utilizing our action possibilities, we actively contribute to shaping a desirable future. The mission of critical futures studies is to encourage us to take action and show that change always starts with a first step.

Thanks again to Moritz Stoll and the team at Netzpiloten for inviting me. For more of my thoughts, listen to the full episode.

Welcome back.

Last year, Jonas and I launched a community for Critical Futures Studies in Germany. Last week, I gave a talk at our second event, which inspired this week's article.

Key Questions in Critical Futures Studies

At the heart of Critical Futures Studies (CFS), as articulated by Sohail Inayatullah, lies the endeavor to “loosen the future.” Instead of creating new images of the future (in the form of scenarios, for example), the goal is first to identify and deconstruct the images of the future that are already there, in the room and the minds.

Picture yourself watching the latest keynote from the likes of Zuckerberg or Altman. You hear them declare, “This is the future!” and you might start wondering, “Wait? Why? And why do you want me to believe this is the future?” Welcome to CFS. It begins with asking questions.

In their fresh look at CFS in Beyond Capitalist Realism, Luke Goode and Michael Godhe suggested a foundational set of questions aimed at critically evaluating specific future visions.

Question to ask of a specific future:

• How is the future invoked?

• What kind of future is evoked?

• Who would want to live in such a future (and who would not)?

• What sort of people live in such a future?

• How are we expected to arrive at this future?

• What is the persuasive power of such a vision?

• What’s the history behind this vision of the future?

Furthermore, Goode and Godhe propose an additional framework for analyzing the broader context in which these future images emerge, termed the “political economy of the future”:

Questions to ask of the “political economy of the future”

• Who are the actors (institutions, individuals etc.) producing and propagating images of the future?

• What are the institutional arrangements (from scientific institutes to popular and online media) shaping the circulation and discussion of images of the future?

• How are ideas of the future discussed and contested in public life?

• Who are the agenda-setting and gatekeeping powers in the futural public sphere?

• What potential impact could this vision of the future have?

These questions are tremendously helpful in my work and even my everyday life. From client meetings to reading tech news, they provide a guideline to identify underlying assumptions, values, and motivations for rendering future narratives in particular ways. They reveal that most images of the future will tell you very little about the actual future but vast amounts about the interests and intentions of their proponents (Present futures).

Beyond Capitalist Realism – Why We Need Critical Future Studies | Culture Unbound

If you're looking for one paper to read about Critical Futures Studies, this is it.

Worthy of Your Time

Our Shared Storm by Andrew Dana Hudson

Through speculative fiction, five interlocking novelettes explore the possible realities of our climate future.

If you're seeking an engaging introduction to foresight and futures thinking, 'Our Shared Storm' by Andrew Dana Hudson is a must-read. The book masterfully combines fun storytelling with educational insights. Hudson vividly brings to life five climate scenarios, originally based on those from the IPCC, through interconnected narratives set in the year 2054 at the COP in Buenos Aires. Each story features the same characters but with divergent backstories, highlighting the nuances and complexities of potential futures.

Reading this book concurrently with the last COP event in Dubai offered me an enriched perspective. It was as if the fictional scenarios in 2054 provided additional layers of context and detail to the real-world proceedings. This dual experience deepened my understanding of the climate issues discussed and the importance of the COP meetings.

I highly recommend 'Our Shared Storm' to anyone interested in climate change, storytelling, and future studies. It's particularly enlightening for those who appreciate a narrative approach to complex global challenges.

Our Shared Storm: A Novel of Five Climate Futures

We're sorry we created the Torment Nexus

In this transcript of a talk the science-fiction writer Charles Stross gave last year, he goes deep on the connection of science fiction's influence on Silicon Valley and traces the route of some of the current trends back to their narrative origins. You can feel Stross' pain (and uttered apology), having written one of the most influential books for the whole crypto and web3 world: Accelerando (still worth reading).

We’re sorry we created the Torment Nexus - Charlie’s Diary

Thank you for your attention.

If you find this newsletter valuable – the best way to support it right now is to ask me a question. What interests you in the futures field? What do you want to know more about? Hit reply or use the comment section to let me know.

Have a good one,
Johannes Kleske

Welcome back.

Here are a couple of thoughts from this week.

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1.

What do I call myself – Part II

I realised something I had never recognised almost immediately after sending the last issue. While I reject the term ‘futurism’ due to its fascist connotations, I never realised the art movement itself also used ‘futurist.’ I then discovered the Wikipedia page for ‘futurist’, which detailed a myriad of contexts and applications for the term spanning over 150 years.

The Oxford English Dictionary identifies the earliest use of the term futurism in English as 1842, to refer, in a theological context, to the Christian eschatological tendency of that time.

I continually ponder what the next term could be that describes the evolving direction of future work. ‘Foresight practitioners’ and ‘futures thinkers’ (or ‘futures thinking facilitators’) focus too much on the method, and I'm less interested in moderating workshops. ‘Futures researcher’ has too strong an academic connotation for the reality of my work.

The term should be more focused on the outcome of the work. I don't consider my job to be done when the scenarios are delivered, even if that is what most foresight work has been focussed on over the last 70 years and thus has an abysmal track record of impact. As professionals in this field, we should focus more on what precedes and follows a typical foresight process. We need to determine when and where investigating futures is beneficial and how to integrate these insights into a continuous process. Am I more of a strategic designer with a foresight toolset?

To be continued …

2.

When we say “AI” …

I received a report about artificial intelligence from an investor. The first slide has the headline “We're at Day 1 of AI.” Below it, there's a timeline starting from 1990. The first line begins in 1990 and is labelled “Internet.” The second line starts in 2005 and is labelled “Smartphone.” The final line is labelled “AI” and begins in—I kid you not—2020.

In recent months, I've frequently discussed generative AI with media companies, and two common misunderstandings have continually arisen. Firstly, there is a tendency to equate generative AI with AI in general. Secondly, the perception that AI is a surprising technological revolution emerging seemingly from nowhere. These conclusions are understandable if one only follows the current hype. This includes claims like the ‘fastest consumer adoption of a product ever,’ among others. Therefore, I usually begin my talks with a brief history of AI, spanning decades, followed by examples of AI technologies used by media companies for over a decade, such as ‘robot journalism,’ which predates ChatGPT.

‘Previously on this hype …’ should be a slide in any investor deck, so to speak. But what seems to be happening here is that the term ‘artificial intelligence’ is transforming. In the last decade, it has primarily evolved to encompass machine learning and deep learning (recall the Alpha Go milestone and the accompanying ‘now it's over’ sentiment?). These terms increasingly replaced the umbrella term ‘AI’ in communications, pitches, and similar contexts. Machine learning and deep learning were no longer abstract technological concepts but applied frameworks.

‘Technology is everything that doesn't work yet,’ as W. Daniel Hillis observed, and this applies to the term ‘artificial intelligence’ as well. To be more precise, I argue that when the term ‘artificial intelligence’ is used in public discourse, it often refers not to the technology itself but rather to images of its future.

In these conversations with media companies, the focus was always on how generative AI might alter jobs, products, processes, and business models. Rarely did these discussions delve into the specifics of how large language models currently work or how understanding transformer models can lead to a better grasp of how ChatGPT ‘understands’ text. Using the term ‘AI’ creates a speculative space broad enough to envision wide-ranging futures, in contrast to the more precise terms mentioned above, which would foster a more concrete discourse about current capabilities.

It will be intriguing to observe how and when ‘AI’ will be supplanted by more specific terms in public discourse and what new meanings ‘AI’ will then acquire.

3.

The Performativity of Dystopian Futures

Dystopian visions are often leveraged as cautionary tales, alerting us to potentially perilous paths. Their fundamental purpose is to stir action, to steer us away from unwelcome futures. Yet, a critical juncture exists in public discourse where these dystopian narratives shift. Overused, they morph from warnings into something more insidious: an anticipated reality. This occurs when the sentiment, “We must prevent this at all costs, yet prepare for its unlikely realisation,” is repeated excessively. By doing so, we inadvertently embed these outcomes into our collective consciousness. The result is a performativity of future visions that edge towards self-fulfilment.

This concept is particularly relevant when examining the discourse surrounding Donald Trump's potential re-election. The narrative is not just about current poll numbers but has evolved into using the prospect of his return as a lever to instigate action in diverse contexts, from Ukraine to climate conferences. While prudence in preparing for various futures is essential, it's equally critical to recognise the moment such preparations subtly acknowledge the undesired scenario as a likely eventuality. When we cross this threshold, the battle is lost even before it begins. We must remain vigilant, ensuring that our efforts to avert a future do not inadvertently solidify its place in our expectations.

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Quotes of the Week

“The future, after all, is written in the present, and there’s a lot of powerful evidence at the forefront of media showing that communities are helping us find the path forward.”


– AX Mina in We navigate deep uncertainty with community (with a quote from yours truly). Also, check out her Republica talk.

“Far too often, we blame women for turning to alternative medicine, painting them as credulous and even dangerous. But the blame does not lie with the women – it lies with the gender data gap. Thanks to hundreds of years of treating the male body as the default in medicine, we simply do not know enough about how disease manifests in the female body.”


– Caroline Criado Perez, as quoted in ‘Everything you’ve been told is a lie!’ Inside the wellness-to-fascism pipeline | Health & wellbeing

Worthy of your Time

I sat down with my friend and collaborator Jonas to reflect on our recent initiative to establish a community for critical futures in Germany. The podcast episode (in German) is here.

I also had a conversation with Philipp from the Gesundheitsmarkt-Podcast, talking about aging, lifespan vs. healthspan, and the role of user experience in healthcare. That podcast episode (also in German) is here.

Thank you for your attention.

What has sparked your interest? What new question came to mind? What do you want to know more about? Hit reply or use the comment section to let me know.

Have a good one,
Johannes Kleske

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Welcome back.

In this issue, we delve into futures terminology. Terms like 'foresight,' 'futures studies,' and 'futures thinking' are foundational to our dialogue about tomorrow. They're not just academic labels; they shape our perceptions and influence our strategies for the future. Your interpretations of these terms might vary, and I'm eager to hear them. Please feel free to share your thoughts in the comments. First, let's examine these terms through my lens, establishing a common ground for our upcoming discussions.

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Understanding Futures Terms

Working with precise definitions in the context of futures is tricky because there are no generally agreed-upon definitions of futures terminology. Here’s how I understand them:

• Futures: This is the umbrella term I use to discuss how humans envision what lies ahead. It underscores the idea that the future isn't a singular, predetermined path but a spectrum of possibilities shaped by our present choices and actions.

• Futures Thinking: A general mindset based on the ideas above, rooted in the belief that the future is a canvas of possibilities, is vital for everyone. Recognising that our future is not preordained but moldable can guide us towards more informed and impactful decisions.

• Futures Studies: The academic approach to futures thinking, investigating and developing (scientific-ish1) methods to develop multiple futures around a topic, like scenarios, delphies, etc. Sometimes also referred to as futures research.

• Foresight: The applied practice of futures thinking, often using methods from futures studies, typically as corporate or strategic foresight in organisations from corporations to politics. When strategy is defining how to get from A to B, foresight can be understood as the precursor helping to determine what B could be.

• Futurology: The oldest term, introduced by German futurist Ossip Flechtheim in the middle of the last century. It’s also the broadest one, encompassing the general study of the future, but it’s rarely used today.2

• Futurism: After fading into the background, the term 'futurism,' associated initially with an early 20th-century Italian fascist art movement, has re-emerged in contemporary dialogue. It has largely supplanted 'futurology' in public discourse as the go-to term for discussions about the future.3

These relatively short definitions capture my current thinking and are up for debate. I think it’s essential to check in with terms and ideas to update and replace them where necessary and helpful.

Exploring Our Professional Identity

What should we call ourselves as practitioners in this dynamic field? 'Futurologist' sometimes conjures images more akin to mixology or a medical profession, while 'futures researcher' fits if your focus is primarily academic.

Then there's 'futurist.' A term some of us are hesitant to adopt due to its association with tech evangelists who often proclaim inevitable futures. I understand that. You put a lot of rigorous work and thinking into your foresight work and don’t want to be associated with charlatans who put on a show with little substance. So we can surrender the term to them. Or we can use it for the time being as a Trojan horse. Honestly, I find a certain appeal in subverting expectations – being introduced as a 'futurist' and then presenting a nuanced, critical perspective that challenges the audience's preconceptions. So, for now, I’m okay with the label ‘futurist’ when complemented with ‘critical.’ What does ‘critical’ refer to for me? That’s what I will write about next week.

Starting with these foundational definitions is intentional – it's about keeping the dialogue inclusive and accessible. Futures studies and foresight have historically been realms of elite thought, but I believe in democratising these discussions. Remember, these perspectives are mine, and I invite you to share and shape your own. Thanks for coming along.

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Quotes of the Week

“Our future will be characterized by a tension between copilot (AI as collaborator) and autopilot (humans as sidekick to AI).”

– Steven Levy in Google’s NotebookLM Aims to Be the Ultimate Writing Assistant

“She'd once heard of something called "the Reverse Anna Karenina Principle": all places that were happy in the climate crisis were happy in their own unique ways, while all unhappy places were the same.”

– Andrew Dana Hudson in Our Shared Storm

Worthy of your Time

The kerfuffle at OpenAI has produced some fascinating long-reads that help to get a better picture of what's going on in the world of Generative AI.

• The New Yorker: The Inside Story of Microsoft’s Partnership with OpenAI

• TIME: CEO of the Year 2023: Sam Altman

Igor has provided an updated definition of his helpful term: Denial of Future Attack.

On the conflict in Israel and Gaza:

• Your Empathy is Killing Us by Mushon Zer-Aviv is the best thing I've read about the current situation.

• Ezra Klein has some of the most in-depth interviews on his podcast, bringing different voices together and letting them stand beside each other.

  • Why Palestinians Feel They've Been ‘Duped’

  • What Israelis Fear the World Does Not Understand

  • The Sermons I Needed to Hear Right Now

  • The Best Primer I've Heard on Israeli-Palestinian Peace Efforts

Thank you for your attention!

What has sparked your interest? What new question came to mind? What do you want to know more about? Hit reply or use the comment section to let me know.

Have a good one,

Johannes Kleske

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1. I have some pet peeves with this, but I will leave it for another time. ↩

2. Despite DeepL always translating the German Zukunftsforschung into futurology for me. ↩

3. I have a Uniqlo shirt with the term on it, but more as an artefact. I'd rather not wear the name of a fascist movement on my body. ↩

Welcome.

Thank you for signing up for this new endeavour. Let me introduce myself, as you might have only a vague idea of who I am.

My Profile

My name is Johannes Kleske. I was born and currently reside in Germany (Berlin for the last dozen years). I'm in my mid-forties, white, and come from a working-class family.

As a critical futurist, future researcher and consultant for strategic foresight, I have been exploring possible, probable and desirable futures from different perspectives (applied, critical, and scientific) for many years.

My primary motivation is to increase people's capacity for self-determination in an increasingly complex world.

At the intersection of foresight, design, and strategy, I assist my clients in better understanding a volatile and complex world and how to shape their own path.

Since October 2022, I have been Senior Director for Foresight at Edenspiekermann, an international design consultancy. Before that, I was co-founder and co-managing director of the foresight and strategic design studio Third Wave for 12 years.

Over the last ten years, I've given approx. fifty keynotes, lectures, and talks. My talks advocate for a confident and reflective approach to permanent change. I focus on the trifecta of comprehensible explanations of how things work, critically examining the effects and concrete recommendations for a sustainable approach.

In addition to my applied foresight work with clients, I am also involved in further developing trends and futures research. Since completing my master's in futures studies (2020), I have focused in particular on critical futures studies, which deal with the deconstruction and reconstruction of existing images of the future. I am interested in the future's role in society and how it influences decisions and developments.

Explore more

Here are a couple of links if you want to explore more of my work and my thinking:

• My digital garden is where I develop my ideas (and where you can find my master's thesis about future imaginaries)

• A recent interview with Foresight Folks: To shape the future, we must first understand the present: A conversation with Johannes Kleske

• A talk I gave at Republica: The Role of Futures in Capitalism (and Beyond)

My Plans for this Newsletter

This newsletter is my vehicle to externalise more of what's on my mind: from futures studies to spirituality, from (tech) vision assessment to political narratives, from embracing complexity to investigating and imagining futures of cities, health, and publishing.

Here's the rough format that I will start with: each issue will have an article about something on my mind, a week-note-like itinerary of what I've been up to, and a collection of links to material that I find worthy of your attention. The format will change over time based on your feedback.

What I most hope for is your feedback, recommendations, and questions. Always feel free and encouraged to hit reply or comment.

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At contemporary health events in Germany, attendees often grapple with a prevailing sense of cynicism and despair. The prevalent sentiment is a palpable helplessness in the face of an overwhelming system that seems impervious to innovation and improvement. This background noise of resignation may momentarily fade but resurfaces with undeniable prominence.

Personal Impressions from LVL UP HEALTH

Last Thursday, I immersed myself in Leipzig’s LVL UP HEALTH, an event tailored for the next generation of healthcare professionals. Even in this youthful setting, it didn’t take long before the aforementioned pervasive mood made its presence felt, slightly skewing the panel’s core question: “Are we still complaining, or are we acting now?” However, after this initial sentiment, the atmosphere seemed to pivot dramatically. The subsequent Barcamp sessions reverberated with an unmistakable “Fuck it, let’s go!” spirit. Indeed, a refreshing turn of events.

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During my opening keynote, I imparted two specific insights to the attendees, drawing from my foresight approach and our collaborative endeavours with Edenspiekermann in healthcare.

The Power of Positive Future Imagery

Major transformations, like healthcare’s digitalisation, demand significant time and energy. It’s all too easy to become lost in the quagmire of daily minutiae. The antidote lies in constantly revisiting and articulating the more significant “why.” And nothing communicates this more powerfully than vivid, positive visions of the future. Instead of getting entangled in intricate technicalities or vague proclamations (“AI will revolutionise everything”), the emphasis should be on narratives that paint a tangible picture of everyday life after the successful transformation. When rooted in tangible experiences rather than mere logical arguments, positive visions energise in unparalleled ways.

My Theory of Change

All of us, knowingly or unknowingly, possess a mental model of how change unfolds. Typically, transformation processes aim to reshape the old into something new. I see it differently: the new should grow parallel to the old. As the new matures and outshines, the old eventually fades into obsolescence. It’s reminiscent of a query: “Whatever became of the old ways?”

In practice, we should continually derive and construct tangible prototypes from our positive visions, making the future tangibly present. This mirrors our approach at Edenspiekermann. Based on profound foresight analysis, we conceptualise possible and desirable visions of the future. This process culminates in forward-looking prototypes that serve as guiding beacons for our strategies and blueprints.

To the LVL UP Health attendees, I emphasised: Dedicate time amidst your daily routines. Work together to shape those aspirational visions and bring them to life in prototypes. In doing so, you set the stage for what’s next. Imagine a day when we look back and muse, “Remember when, in 2023, we thought the age-old healthcare system was insurmountable?”

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It used to be columns in magazines. Today, personal newsletters are a unique blend of private journals, sharp observations, and deeper reflections. One of my favourite pastimes is to make a cup of coffee1 and settle down with the latest issue of a newsletter that has just arrived in my inbox. An individual I find captivating or inspirational is about to answer the unspoken question: "What have you been up to, and what's on your mind?"

This newsletter is my way of giving back and sharing my thoughts and observations – all of which are works in progress, in keeping with the idea of "working with the garage door up". My daily life sets the framework for the topics. My role as a critical futurist, strategic designer, and public speaker provides the lens through which I view these subjects. My insights will be eclectic and highly personal.

I hope this newsletter encourages more conversations about envisioning and carving out alternative ways of living in a world dominated by relentless noise and gloom. My choice of Substack as a platform was deliberate: I appreciate their community-centric approach, even as I remain conscious of their broader content choices.

For starters, I’ve added a couple of short articles from the last two years that have grown out of my digital garden. Have a look, and thanks for being on board.

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1. I’m an absolute sucker for beans processed with anaerobic fermentation. Try these to get an idea of the taste. ↩

My eternal question to academic futures studies: What good is scientific rigor when nobody gives a shit? I’m not asking you to give it up but rather that you put as much effort into having an actual impact.

Futures play too much of a central role in society to leave the public discourse around them to the trend gurus, tech evangelists, and advertising agencies.

At the moment, there seem to be two poles in futures work: on the one end, you have the academic futures studies focused on scientific rigor. On the other end, you have the design-thinking-ish workshop-driven futuring approaches (lots of canvases). Both have little impact for opposing reasons.

I’m interested in the space in between. I want to do deep work routed in theory and proper methods, combining it with participatory and critical approaches while minding the politics from the beginning with the goal of as much impact as possible.

One exemplary expression of that: Can we teach students foresight methods and, simultaneously, prepare them for the backlash and politics they will experience once they try to apply future thinking in their future work contexts?

This text is a seedling, which means it is an unpolished thought or idea that will grow and mature over time. For this purpose, it has been planted in the garden. Let me know your questions and thoughts via email.