Eric Hoffer once said, “In times of change, learners inherit the earth; while the learned find themselves beautifully equipped to deal with a world that no longer exists.” The line sounds abstract until you begin to see how often it describes real moments of transition.

For much of the industrial era, knowing how the system worked was the path to influence. Organisations rewarded accumulated expertise and long familiarity with established processes. Experience translated into authority because production relied on specialised judgment applied within stable structures. Busyness signalled relevance because the ability to execute was limited. To be deeply embedded in the machinery was to occupy a position of security.

That order began to shift with the internet, which greatly reduced the cost of coordination. The shift is now accelerating as machines take on more execution and generative systems lower the cost of producing structured knowledge. As building becomes easier and execution becomes widely available, the constraint moves. Influence shifts toward those who decide what deserves to be built before building begins.

This is why busyness now tells a different story, not necessarily one of influence. A full calendar can mean you’re busy executing, not deciding. Work can pile up while pivotal choices are made elsewhere. Those who shape outcomes spend less energy doing and more deciding what should be done in the first place. They examine assumptions and use AI to extend their judgment instead of surrendering it.

This future is already unfolding, and in such a world clarity becomes scarce. What endures is the capacity to set direction before scale amplifies the wrong choice. Past expertise may explain yesterday, yet it does not guarantee relevance tomorrow.

In this sense, everyone is becoming an entrepreneur.

Death of the Knowledge Worker

You’re busy inside a system that no longer pays for busyness. That’s why you feel powerless. That’s why you feel helpless. The good news is that value didn’t disappear. It relocated to people who can frame problems worth solving, see what others normalize or ignore, use AI to scale judgment (not replace it), create approaches that didn’t exist before, and decide what matters before execution begins. These people are not busier. They are decisive. They are not reacting to the future. They are shaping it.

Category Pirates | 32 Minutes

Future Skills Without a Skill for the Future

Maybe the most important future skill is not adaptability. Maybe it’s the ability to pause, step back, and ask: Whose future are we preparing for — and why? Until Futures Skills become a core part of how we educate, strategize, and lead, most “future skills” will remain strangely present-focused. Busy preparing for a future, while remaining unskilled at dealing with the future itself.

Jan Oliver Schwarz | 5 Minutes

What Comes Next Is Bigger Than SaaS Ever Was

With AI, everyone has the team. Everyone has the technical ability. The constraint is no longer resources or engineering – it’s vision. What everyone lacks is a strong enough point of view on what should exist. The drive and willingness to break reality. That’s the edge today. Not access to capital, not access to talent, not access to technology. All of that is becoming commoditized. The rarest thing in this new era is a founder who can see what the world should look like and has the conviction to build toward it before anyone else can see it.

Gigi Levy-Weiss | 11 Minutes

Inside Claude Code With Its Creator Boris Cherny

I think for yourself, it’s kind of beginner mindset and I don’t know, maybe just like humility. I feel like engineers as a discipline, we’ve learned to have very strong opinions and senior engineers are kind of rewarded for this…In my old job at a big company, when I hired like architects — and it’s kind of a type of engineer — you look for people that have a lot of experience and really strong opinions. But it actually turns out a lot of this stuff just isn’t relevant anymore. And a lot of these opinions should change because the model is getting better. So I think actually the biggest skill is people that can think scientifically and can just think from first principles.

Boris Cherny | 50 Minutes

Hannah Arendt understood freedom as the capacity to begin.

When you look back at history through that lens, you begin to notice how often the right to begin has shifted hands. For nearly two centuries, it mostly lived inside industrial institutions. Factories, corporations, and bureaucracies determined what mattered, translated ambition into plans, and organised people into stable roles. Software reinforced that world, embedding procedures into systems that could run with consistency and scale. We even learned to ship software as if it were produced on an assembly line.

Beginning was something you did inside an organization, within rules that were already written.

Then the internet inverted the architecture and gradually rendered that script obsolete. By connecting everyone to everything, it shortened the distance between idea and distribution. Audiences became reachable, and demand became visible. Coordinating action no longer required the same institutional weight. Scarcity shifted from pure production capacity toward attention and network position. Stability gave way to fluidity, and organisations built for control had to learn how to move within environments shaped by shifting preferences and constant change.

This rewiring has not been seamless. As inherited structures lose their grip, society experiences a growing disorientation. The old playbook no longer offers reliable guidance, yet a new one has not fully taken shape. In moments like this, uncertainty creates space. When the script weakens, authorship becomes possible.

Over time, participation itself began to look different. Economic life now unfolds through millions of small operators such as creators, developers, consultants, and micro-teams, many of whom would never call themselves businesses yet carry the responsibilities of one. Their work forms around projects and communities, supported by tools that once required significant institutional backing. Increasingly, they define the environments in which they operate, shaping their own niches rather than competing inside someone else’s structure and, in doing so, building their own ponds.

At the same time, this fragmentation rests on a parallel consolidation beneath it. A powerful layer of infrastructure has formed through cloud services, payment systems, and platforms designed to make these capabilities widely accessible. Scale continues to matter in this environment. It shows up as shared foundations that enable many smaller actors to build and operate on their own terms.

What this amounts to is a profound shift in authority. Individuals today can build, distribute, and monetise with forms of leverage that once belonged almost exclusively to institutions. Organisations that once required entire teams can now find themselves competing with a single focused operator who, together with many others like them, forms part of a loosely coordinated and increasingly capable network. Systems can generate and scale at extraordinary speed, yet they do not determine what is worth pursuing. Direction remains a human act.

As economic life disperses into countless small beginnings supported by common infrastructures, authority moves closer to the individual, and with it, freedom.

Beginning no longer depends on institutional permission. It rests with those willing to take responsibility.

Becoming an AI-first software-intensive company

This doesn’t reduce human responsibility; it changes its nature. Humans remain accountable for outcomes, but they express their intent at a higher level: what should be optimized, what trade-offs are acceptable and what values must never be violated. AI systems then operate continuously within these boundaries, making thousands or millions of micro-decisions that would be impossible for humans to manage.

Jan Bosch | 8 Minutes

Petri Dishes, Not Factories

However, our mental models are lagging behind our enthusiasm. The pieces of the puzzle are there but we’re held back by a monolithic definition of what ‘work’ is. Looking around, most software engineers are fervidly vibe-coding every idea they have and looking for tools to build more, faster. So where’s the problem..? Well, most ideas are bad. In practice building the thing is not nearly as much work as designing it.

Ben Follington | 6 Minutes

The Five Pillars of Work: The Hidden Identity Criss of Portfolio Careers

What I didn’t expect was how much I’d have to unlearn. Leaving my job turned into what I can only describe as unlearning life. Peeling back the scripts I’d absorbed through out life without even realizing. Taught in school, reinforced in university, hardened in the early years of my career. Scripts that we adopt eagerly because they offer direction, safety and approval. But to choose a different path and to actually do things differently, you have to unlearn nearly everything you’ve ever been taught.

Iwana Johannsen | 15 Minutes

AI ads, the end of SaaS, and the future of media

Today, if you want to be a writer, what you want to do is make your own pond. The internet enables the creation of a million different ponds. You get to define your own pond and be the only and biggest fish in it. That’s how you succeed. AI makes that possible for all sorts of content and all sorts of businesses—lots of smaller-scale individual entrepreneurs or small teams who don’t really fit in the Salesforce-driven, seat-based model of many of these companies. There might be a big return to self-serve, or maybe people will just roll their own because their needs aren’t that large.

Ben Thompson in Stripe | 90 Minutes

Rainer Maria Rilke, known for extraordinary sensitivity and a lifelong unease with ordinary life, once wrote that “the future enters into us, in order to transform itself in us, long before it happens.” It feels especially apt for this moment. Something has already shifted in how we see, decide, and organise, even as established institutions remain in place. The future is shaping our orientation, working through us before we can fully name what is changing.

That tension between clarity and uncertainty, between movement and unfinishedness, is not only personal. It is structural.

Every paradigm shift shaped by technology brings with it a new way of seeing the world, and with that, a new way of organising ourselves. Industrial firms took shape alongside mechanisation and electrification, when the central challenge was turning raw materials into goods at scale. Corporations emerged with telecommunications, computing, and managerial systems, as the challenge shifted from production to coordination, aligning people and decisions across growing complexity. Platforms followed, enabled by the internet and software infrastructure, reshaping organisation around access and distribution.

In each case, the organisational form took shape around the pressure point of its time, where something essential struggled to move.

Over time, these technological shifts take on architectural form. What begins as a change in capability finds its way into questions of organisation.

Once these forms take hold, the economy reorganises around them. What counts as value shifts, along with the paths to legitimacy and influence. Platforms pulled attention toward participation and visibility, and from that shift the creator economy emerged, placing ordinary people closer to the surface of value creation. Work began to feel less like execution inside an organisation and more like navigation within an open landscape shaped by reach, relevance, and presence.

For those living inside these transitions, the shift rarely announces itself clearly. Established structures continue to operate, often convincingly, even as their internal logic slips out of step with reality. Plans still circulate, and decisions still get made under a familiar sense of authority. At the same time, outcomes begin to drift as effort accumulates without producing movement, and language that once explained progress starts to point in the wrong direction. This moment is often described as being between systems, though experience suggests something else is already underway. The environment shifts first. Explanations follow later.

It is here that the question of permission surfaces. We were taught to follow the instructions, to wait for alignment and coherence before acting. Now it feels like you are being asked to decide what direction matters, even while the map remains unfinished. Repermissioning asks for agency. It means choosing direction when certainty remains incomplete, and accepting responsibility for movement rather than waiting for it to arrive from elsewhere.

Permission becomes unavoidable as inherited scripts stop working and clarity begins to emerge through movement. Direction takes shape through action. The stories we tell change, and traditional assets start to matter in different ways as value moves elsewhere. Scarcity shows up in new places, changing where pressure builds and where progress becomes possible. In moments like this, orientation matters more than certainty, creating space to act for those who can sense how the system is tightening and taking on a new shape.

Which brings this edition to its open question. Generative AI introduces another shift in capability and speed. Intelligence floods the system and output multiplies with ease. The decisive issue concerns gravity. Where does constraint now move. What slows progress. What shapes leverage. If earlier eras organised themselves around different pressure points, what is the bottleneck emerging in the GenAI era, and who is already learning to build in response to it?

The future is already working through our institutions and our choices. The question is whether we recognise its shape in time to organise around it.

Things Fall Apart

This is where I want to challenge the idea that we’re somehow “between systems,” waiting for new rules to arrive as some organic part of change. Previous Unravellings have taught me that when inherited scripts stop working, no one hands you the new ones. This is where permission matters — this is why I bang on about the idea - not as motivation or self-help, but as an operating stance. As the starting point for navigating an Unravelling, because I’ve watched them before, I’ve lived them before. What I learned is that the only way to make new things out of everything being broken right now is by giving yourself permission.

Jill Reilly | 6 Minutes

Collecting The Sparse Bits Between

For sixty years, code has been an asset. Something maintained, documented, understood, protected. Version control exists because code is precious. Code review exists because code is consequential. Technical debt accrues because code persists. But if code can be regenerated on demand from specification, it is no longer an asset in the same sense. It becomes a derived artifact, like a compiled binary. You do not read the binary, you read the source. In the emerging paradigm, you do not read the code. You read “the spec”.

Greg Ceccarelli | 6 Minutes

Building a 100 year business.

We often talk about where we’ve come from in organizations. We hold a kind of myth about this - a story about our beginnings, our founding ideas, and rationale behind decisions made that got us here. That is well and truly a myth. Because the past is revised constantly. And it’s deeply subjective. It’s something, therefore, just like the future, that we can influence. We can shift how we reflect on where we’ve come from, how we think about the potential of our future. Without a doubt, things are better when we embrace change and when we take control over the narrative of our story.

Joanna Lepore | 7 Minutes

Why the Smartest AI Bet Right Now Has Nothing to Do With AI

The history of the corporation, ironically, illustrates this perfectly. Every dominant organizational form emerged to dissolve a specific bottleneck. Whoever solves the binding constraint captures disproportionate value. Everybody else participates in the abundance that’s created. The AI era absolutely has its own bottlenecks and they’re not the ones most people are watching.

The interesting question is really not whether AI creates abundance. It does. The interesting question is where are the bottlenecks? Because that’s where value concentrates. Of course, AI is creating an unprecedented abundance of intelligence. But that just means that the bottleneck flows downstream. And that’s where the leverage lives. And that’s where fortunes will be made or lost in the next decade. Abundance is super hand-wavy. I’m not interested in hand-wavy. Bottlenecks are specific. And specificity is where strategy happens. It’s where careers happen. And it’s where companies happen.

Nate B Jones | 24 Minutes

There are moments when it feels right to pause.

Enough has happened that standing still for a second reveals a shape. This is one of those moments.

If you’ve been here from the beginning, a heartfelt thank you. And if you joined somewhere along the way, welcome to the scene. This past year has been full in ways that are hard to summarise without flattening them. The writing has grown more deliberate, and the conversations and questions have stretched across more disciplines and lived experiences. Over the past year, this scene nearly doubled in size through shared curiosity and ongoing exchange. That growth matters as a signal of resonance, of people finding something here that helps them orient themselves in a world that has become harder to read.

Three years in, Scenius has also changed in a deeper way. With time, ideas have had the chance to return and develop, allowing earlier signals to settle into something more legible. What once appeared as isolated moments now begins to read as a pattern. That is what makes this feel like a natural pause point: enough has accumulated for its shape to come into view.

This work has reached further through people whose judgment is trusted, and who chose to stand behind it. Noémie Aubron from 👁️😮🔮 Futur(s) and Robin Good from TRUST-able have both done so. Recommendations like these carry care and responsibility. They reflect a willingness to invite others into something one considers worthwhile. In an environment shaped by abundance, those gestures shape trust.

Scenius has always been an experiment in paying attention together. It values noticing shifts early and staying with ambiguity, trusting patient sense-making to do its work over time. At its core, Scenius is about ordinary people doing extraordinary things together, by learning how to think alongside one another with care and presence. Over the past year, that practice has grown in importance.

Where We Stand

Looking back across Vol. 3, a few things become clear.

We are living through a period where technology is advancing faster than our collective ability to integrate it. AI, automation, platforms, and infrastructures have become environments we inhabit. Automation shapes the flow of work and decision-making, often invisibly, while AI mediates language, creativity, and judgment. Together, they influence how we work, how we relate, and how we make sense of ourselves. Much of the tension people feel right now arises from a lack of shared orientation and from the absence of common ways to situate these shifts in lived experience.

At the same time, the organising structures we relied on for decades, even centuries, are loosening. Work is leaving the firm. Careers are fragmenting. Identity, time, and meaning are being renegotiated at the level of the individual. The old scripts still exist, but they no longer hold with the same authority. Many people are discovering that what once felt stable was simply rigid, and what now feels uncertain also carries new forms of agency.

Through all of this, one thing has become unmistakable: progress is no longer about doing more, moving faster, or scaling harder. It is about learning how to hold complexity without defaulting to control. It is about judgment, taste, trust, and the ability to sense what actually matters amid abundance.

Three Patterns That Kept Returning

Across the editions of Vol. 3, three patterns surfaced again and again, sometimes from different angles, sometimes through different stories, but always pointing in the same direction.

➤ First, value is moving beneath the surface.

Interfaces are becoming fluid. Execution is getting cheaper. Outputs are easier to generate. What holds its shape is the substrate: data, logic, infrastructure, context, and meaning. Whether in organisations, platforms, or creative work, the durable advantage no longer lives in polish or speed, but in what sits underneath and how reliably it can be trusted.

➤ Second, work is reorganising around judgment rather than execution.

Automation does not remove work. It relocates it. As systems become more powerful, labour moves into interpretation, verification, oversight, exception handling, and accountability. The more capable the system, the more it depends on human sense-making. This shifts what it means to be valuable. Craft still matters, but orientation matters more.

➤ Third, scenes are replacing institutions as sites of coherence.

As firms struggle to hold identity, continuity, and meaning, people are forming smaller, more intentional constellations around shared questions and projects. Trust networks, portfolios, creative collaborations, and informal communities are taking on roles institutions no longer perform well. Scenius shows up here as a living pattern: genius emerging not from individuals or organisations alone, but from scenes that know how to think together.

These patterns are not predictions. They are already visible. What remains open is how we choose to engage with them.

What Vol. 4 Will Explore

Vol. 4 begins from this moment of recognition.

Some of this work is also beginning to ask for a different container. The questions behind Scenius did not start three years ago. They have been forming over the course of nearly a decade, across projects, organisations, conversations, and lived experience. The newsletter became a place to surface and test them in public. That longer arc, and the question of how to act on it, is what Futuring Architectures will publish this year, giving the worldview a more durable form and offering people something to work with. More on that as it unfolds.

If Vol. 3 was about tracing the shifts and naming what is changing, Vol. 4 will be about learning how to live inside what is emerging. Less speculation, more orientation. Less reaction, more discernment.

We will explore:

• How people build continuity when institutions no longer provide it

  What replaces the firm as a source of identity, learning, and long-term relevance.

• What human leverage looks like in intelligent systems

  Where judgment, taste, and responsibility concentrate as execution becomes easier.

• How trust forms in a world of infinite generation

  Why care, presence, and selection matter more than ever, and how trust networks slowly take shape.

• What it means to design for intention rather than attention

  In work, in technology, and in life.

• How scenes are cultivated, not scaled

  And what it takes to hold space for creativity without flattening it.

This is not a roadmap toward certainty. It is an invitation to stay curious, to keep noticing, and to resist the urge to outsource meaning to systems that were never designed to carry it.

The future is not something that arrives fully formed. It is assembled through the choices people make about what they build and who they choose to think alongside.

Thank you for being part of this scene.

Welcome to Scenius Vol. 4.

Automation is usually sold as a replacement story: a way to cut costs by removing the most expensive resource, people. Much of the conversation around generative AI starts from this assumption, revealing how eager organisations are to convert technological ambiguity into a cost-cutting mandate. But if you squint and look at what actually happens, a different pattern emerges. Automation rarely plays out so cleanly. More often, it shifts costs rather than eliminating them, leaving organisations supporting more work, not less—just in less visible places.

What tends to change first is the surface. As coordination collapses at the interface, interactions become faster, cleaner, and easier to execute. Tasks that once required human mediation disappear from view. From the outside, it looks like work has vanished.

It hasn’t. It has moved.

As systems automate, labour is driven deeper into the stack—into design choices, oversight, verification, exception handling, and responsibility. New forms of work emerge that only become necessary once an automated system exists. A bank transfer no longer requires a physical interaction, yet an extensive web of human and organisational effort still sits behind the scenes to ensure the money arrives safely. Fraud detection, reconciliation, compliance, error resolution, customer support. The surface appears simple because the complexity has been absorbed elsewhere.

This pattern is not new.

Consider a simpler example. Before electronic elevators, operating an elevator was a job. When elevator operators disappeared in the mid-20th century, many assumed the work had vanished with them. In reality, it had shifted. Automated elevators depended on engineers, safety standards, maintenance teams, inspections, liability rules, and emergency procedures to function reliably.

Electricity did not remove work. It reorganised it wherever automation made sense.

What changed was the amount and type of coordination required to make systems safe, scalable, and dependable. Someone had to absorb that coordination. For much of the industrial era, large organisations became effective containers for complexity, risk, and accountability. That organising form is now under pressure to change shape.

This is how the future tends to be built: not through sudden replacement, but through new organising structures silently absorbing complexity until a different way of working becomes normal.

Generative AI fits neatly into this longer pattern. It is an emerging technology with the potential to transform tasks where that makes sense, even if what does make sense is still being figured out—much like in the early 2000s, when we could sense that the World Wide Web mattered long before we understood the kinds of organisations it would give rise to. The uncertainty then was not about whether work would change, but about where coordination would settle.

As coordination shifts into technical systems, execution becomes easier, but responsibility does not disappear. Instead, new demands emerge around human judgement, verification, oversight, and accountability for outcomes.

Because AI systems are probabilistic rather than deterministic, these roles become more consequential as automation becomes more capable. The more powerful the system, the more it depends on people to interpret outputs, catch errors, define boundaries, and take responsibility for consequences. The work moves to where the system still relies on human sense-making.

The most interesting question, then, is not whether AI will replace work, but how it will reorganise it.

As tasks are transformed, the centre of gravity shifts away from execution and toward interpretation, trust, and accountability. That shift can feel unsettling when framed as a binary choice between humans and machines. History suggests something steadier: work changes shape, coordination moves, and new forms of value emerge where human judgement still matters.

Technology alters the surface.

The work goes on.

For individuals, the challenge is to keep moving with the work.

AI and the Human Condition

That’s because humans didn’t just sit on their hands; rather, entirely new kinds of work were created, which were valued dramatically higher. Much of this was in factories, and then, over the last century, there was the rise of office work. All of that could very well be replaced by AI, but the point is that the history of humans is the continual creation of new jobs to be done — jobs that couldn’t have been conceived of before they were obvious, and which pay dramatically more than whatever baseline existed before technological change.

Ben Thompson | 16 Minutes

Ed Zitron on big tech, backlash, boom and bust: ‘AI has taught us that people are excited to replace human beings’

The biggest thing we’ve learned from the large language model generation is how many people are excited to replace human beings, and how many people just don’t understand labour of any kind. Over the past few years we have had escalating prophecies of the technology laying waste to work as we know it. The current generation of AI large language models will not be doing that. My evidence is they’re basically the same as they were a year ago. They have the same efficacy. And every attempt they make to try to turn these into something that can actually do things autonomously has failed.

Steve Rose | 18 Minutes

AI Can Build Anything. Social Dandelions Decide What Spreads.

The code is a commodity. The distribution channels are saturated. As we move deeper into the age of AI, the bottleneck for success has shifted. It is no longer about who can build the best technology or buy the most ads. The bottleneck is now sociology. The founders and creators who will define the next generation are those who understand the invisible physics of how groups of people decide to trust something new. They are the ones who know how to identify a narrow community, spot the social dandelions within it, and lower the cost of adoption until saying “yes” feels like the most natural thing in the world.

Lewis Kallow | 17 Minutes

Inside the AI bubble

At the moment, there are lots of professions and medicine and law would be two of them, journalism is another one, where I think if LLMs can’t be relied upon, that puts a hard ceiling on how much they can transform those professions. Is that a problem that’s gonna get fixed, or is that something that is inherent to the technology? I think it is inherent. It’s like a statistical mathematical reality that they will always have some level of error. And so the best you can do is sort of add ways to verify things, source things, have citations, but I don’t think it’s something we can ever get rid of.

Tech Tonic | 42 Minutes