There’s a common understanding that we’re living inside a new digital world order – one that feels like enclosure, where people are “controlled” by platforms they can’t easily leave, where behaviour is shaped at scale, and where #AI intensifies this mess. That’s why #fashernista terms like #technofeudalism are being used to argue that we’ve moved beyond capitalism into something structurally different: a system based not primarily on profit, but on rent extraction through “cloud capital.” In this narrow framing:
Capitalism = wage labour, markets, profit from production
Technofeudalism = platform power, behavioural capture, rent from digital dependency
The “cloud lords” (Google, Amazon, Meta, Apple etc.) operate their own digital fiefdoms. We are the new “cloud serfs” producing data and metadata for free, generating value through participation while feeding systems that learn from us and reshape our behaviour. Even businesses and workers are pulled in, sellers depend on Amazon marketplaces, workers are managed through platform systems and, as the #EU is finding, entire infrastructures depend on AWS-style cloud provision.
From this narrowing view, capitalism starts to look secondary to rent extraction from controlled digital spaces. But is this actually a new economic system? The argument starts to break down, critics point out that many of these dynamics are not historically new as supermarkets already control supply chains and choice, advertising has shaped desire for decades, monopolies are standard capitalist patterns and enclosure of markets predates the internet by centuries.
Even “lock-in” effects (like Amazon Prime convenience or #dotcons platform dependency) can be explained through familiar capitalist mechanisms: convenience, pricing, network effects, and infrastructure dominance. From this wider view, what we’re seeing is not a new epoch, but late-stage capitalism with intensified digital tools, not a structural break.
The more grounded #OMN view suggests something different, the change is not mystical “cloud feudalism”, but that data and metadata has become a primary economic resource. We now live in systems where behaviour is continuously captured as data, that data to be aggregated, sold, and operationalised, then feedback loops reshape what people do next and participation itself becomes productive labour. This shifts relationships, not only worker vs capitalist, not just consumer vs market, but user-as-infrastructure. Yes, people are no longer only buying and selling inside systems, they are constituting the system itself through participation. That is a shift in how production works, even if it doesn’t replace capitalism as the dominant structure, it is reshaping it.
The infrastructural question? Where the #technofeudal framing becomes useful is infrastructure. The real power isn’t just in apps or interfaces, it is in cloud hosting (AWS, Google Cloud, Azure), database and compute control and dependency of entire industries on a few providers. This is less “digital shopkeeping” and more systemic dependency at the infrastructure layer. At that level, switching costs are enormous, entire economies are built on top of systems owned by a handful of #nastyfew actors. Yes, that does mean the enclosure of the commons, not of in the old sense land, but of digital operational reality.
Where this leaves us? The danger is less about whether we label this “capitalism” or “technofeudalism”, more, assuming the system is stable, assuming it is neutral or assuming it is too new to understand through older tools. In reality, we have old capitalist dynamics operating the data-driven extraction layered on top of this older mess, with infrastructure consolidation amplifying both. Not a clean break – but a messy transition stage.
From an #OMN perspective, the useful question is not the label, it is where is agency located? Who controls infrastructure? How is knowledge produced and shared? What forms of network are open vs enclosed? Because whether we call it capitalism or #technofeudalism, the political problem is the same that control is concentrating, while participation is being harvested. And that is the terrain we actually have to work in, not the theoretical naming of it.
Let’s be clear on the background mess, before the personal attacks start, this is not about individuals. It is about patterns, systems and ideas. The danger is that criticism becomes an #adHominem argument – “you just dislike this because…” – instead of looking at the actual structures being discussed.
The point I am making is that parts of dead #postmodern thinking have ended up embedded inside #neoliberal culture: fragmentation, individual identity, endless discourse and difficulty building any shared collective action. That does not mean every idea, person or piece of work in those spaces is the same, it means we need to look at how ideas interact with power.
The question is – What helps us build collective capacity in a time of #climatechaos, inequality and the #dotcons mess? What creates commons? What creates shared action? This is the conversation.
So with that in mind lets look at the major problem with the #dotcons attention economy the advertising model. The platform logic and the attention economy are now becoming harder to simply ignore. For most of mass media history, the commercial transformation of media was hidden behind a layer of journalism, culture and public value. The advertising model was presented as simply a way to pay for content. Platforms were presented as neutral spaces for communication. Algorithms were presented as tools to help people discover what mattered.
But the #dotcons direction has now stripped this bare – the direction has become clearer, the media landscape looks less like a place for shared knowledge and more like a shopping catalogue with occasional content attached. The focus is no longer even the fig leaf of informing people, connecting communities or building public understanding. The naked goal is simple – more clicks, more engagement, more time captured, more data collected and more consumption encouraged. This is the logic of the #dotcons.
The problem with this #deathcult worshipping mess is not only that companies make money. The deeper problem is that the structures built around making money reshape our culture itself. When attention becomes the product, everything starts being measured through extraction. A story is only valuable because it generates traffic – A person is only valuable because they generate data – A community is valuable because it creates engagement – A conversation is valuable because it keeps people inside the platforms. Any, social value gets pushed aside.
The original #openweb grew from a different idea. People built websites, forums, mailing lists, software projects and communities because they wanted to share, collaborate and create. The value was not only in the information produced, the value was in the surrounding relationships. People corrected each other, developed trust, knowledge was maintained collectively.
The internet worked because there was social infrastructure around the technical infrastructure. The mess we made, was thinking that communication could simply be handed over to commercial platforms without catastrophic changing the nature of communication itself. A platform is not just a tool, it comes with incentives, has owners, rules, a business model. When every space becomes a marketplace, the culture changes.
The mess we have made is that extraction replaces participation, the #dotcons path works by turning human activity into resources. People create, platforms capture. Communities produce culture, companies monetise attention. That extraction eventually damages the thing being extracted from, creators become exhausted, communities fragmented, trust declines as people become audiences instead of participants.
The internet becomes full of “content”, but much poorer in meaning, more information does not automatically create more knowledge, more communication does not automatically create better communities, without care, context and collective responsibility, abundance becomes noise. To compost this mess we have made in the media tech path – the question is not “How do we get more people producing?” The question is “How do we build systems where what people produce strengthens the commons instead of feeding extraction?”
The fashionable people of #AI are pushing at changing the scale of content creation, lowering barriers to producing books, apps, music, legal documents and academic papers. Thus, “output” is exploding. But the #OMN second question is what happens when production grows faster than the ability to filter, discuss, trust and maintain? More books, but more noise, More apps, but more clutter. More papers, more pressure on review systems, more music, but harder to value human creativity.
The #dotcons logic says: more content = more value. The #openweb lesson is different – value comes from communities, trust, context and care. We don’t just need more production, we need better commons, better mediation and better ways to separate signal from noise.
The current wave of generative AI (#GenAI) is presented as inevitable, the message is everywhere: adapt, adopt, integrate, or be left behind. But technology is not neutral, as every tool carries assumptions – who benefits, who controls, what values are embedded, and what damage is accepted as “the price of progress”.
From a #OMN perspective, the question is not simply “can this technology do impressive things?” Of course, it can. The question is what kind of society does this technology build? Does it strengthen human creativity, collective intelligence and open participation? Or does it deepen the existing #dotcons path of centralisation, extraction, dependency and enclosure? The promise and the reality of large language models (#LLM) represent a technical development, they can summarise information, translate languages, generate text, assist coding, and help people interact with large amounts of information. These are real, if floored capabilities.
But the current #techshit hype jumps from useful assistance to much bigger claims: that these systems will replace expertise, solve social problems, revolutionise education, transform science, and create a better future. This is currently not true, and, on the LLM path will never be true as the current GenAI systems do not understand the world. They generate likely patterns based on huge amounts of training data. They do not know truth from falsehood, meaning from appearance, or ethics from probability, a convincing answer is not the same as a system that understands. This matters because the native #openweb was built on a different idea, that knowledge comes from people, communities, discussion, correction and shared responsibility.
The #geekproblem is confusing capability with wisdom is a recurring problem in technology culture – it is the assumption that if something can be built, it should be built. The technical question becomes “Can we?” while the social question “Should we?” gets pushed aside. This is part of what #OMN calls the #geekproblem – the tendency to reduce complex social questions into technical problems. A better search algorithm does not automatically create a healthier information system, a faster way to generate content does not automatically create better knowledge. More automation does not automatically create more freedom. The missing piece is the social context around the technology.
Then we come to the ecological cost of scaling, the current GenAI boom depends on enormous infrastructure. In the era of out of control #climatechaos data centres require huge amounts of electricity, water for cooling, specialised hardware, constant replacement cycles leading to massive extraction of resources. At a time of #climatechaos, we should be asking whether increasing consumption is the only path available.
The lesson is not that technology is bad, the lesson is that technology without social responsibility becomes a tool for whoever already has power. The question is not “how do we make AI bigger?” more it is how do we make technology serve human communities rather than making communities serve technology control systems, it is about who controls. The current dominant systems are owned by a few powerful companies controlled by the #nastyfew actively working to destroy our ecology and societies.
The future is not decided by whether we use AI, it is decided by whether we allow the same old #dotcons logic to shape every new technology. The work remains the same to build alternatives, keep processes open, grow the commons. The answer is not simply rejecting technology, the #openweb has never been anti-technology. The question is what kind of technology grows from what kind of culture. We need tools that strengthen human networks, not replace them. Tools that support commons, not enclosure, that increase agency, not dependency.
If we change this can there be an ethical AI? A socially useful technology? Possibly, but it would require a very different path, it would need many of the things the #openweb has argued for from the beginning.
The biggest question is not whether #AI becomes useful. It is who shapes the surrounding paths? A future controlled by a few #dotcons will reproduce the same mess we have now of centralisation, extraction, enclosure. Were a future built through #4opens paths would look different.
The #geekproblem is believing the next tool solves the old problem. But many problems are not tool problems, they are relationship problems. The next stage is not replacing humans with smarter machines, it is building better human paths that can use machines without becoming dependent on them. Beyond AI is about making communities capable, the real upgrade is not artificial intelligence, it is collective intelligence.
AI is changing the scale of content creation, but not raising the quality. Generative AI tools have lowered the barrier to producing average books, apps, music, legal documents, academic papers and endless streams of text. The result is a massive increase in output, but what happens when production grows faster than our ability to filter, discuss, trust, maintain and give meaning to what is produced?
More books, but more noise, more apps, but more clutter, more papers, but more pressure on systems of review, more music, but a harder struggle to recognise human creativity and care. The #dotcons logic says – more content = more value – were the #openweb lesson is different, value comes from communities, trust, context and care. The challenge is not creating more things, the challenge is building better commons around the things we create.
The AI question is bigger than the technology, as the current wave of generative AI (#GenAI) is presented by our #fashionistas and there servants as inevitable. The message is everywhere to adapt, adopt, integrate, or be left behind. But technology is never neutral, every tool carries assumptions about who benefits, who controls it, what values it embeds and what damage is accepted as the “price of progress”.
From an #OMN perspective, the question is not simply “Can this technology do impressive things?” Of course, it can. The real question is “What kind of society does this technology build?” Does it strengthen human creativity, collective intelligence and open participation? Or does it deepen the existing #dotcons path of centralisation, extraction, dependency and enclosure? This is the wider #openweb question we should be focusing on.
Large language models (#LLM) and generative AI systems represent a real technical development. They can summarise information, translate languages, generate text, assist coding and help people interact with large amounts of information. These are useful capabilities, but the hype jumps from assistance to much larger claims – That AI will replace expertise – That it will solve social problems – That it will transform education and science – That it will create a better future automatically.
The problem is that current AI systems do not understand the world, they generate patterns based on huge amounts of training data. They do not know truth from falsehood, meaning from appearance, or ethics from probability. A convincing answer is not the same thing as understanding.
The missing social layer in our narrow conversations is that the #openweb was built around a different idea, that knowledge comes from people, from communities, discussion, correction, disagreement and shared responsibility. This is where the #geekproblem appears – the tendency to confuse technical capability with social wisdom – the technical question becomes “Can we build it?” the social question “Should we?” often disappears.
A better search algorithm does not automatically create a healthier information system, a faster way to generate content does not automatically create better knowledge. More automation does not automatically create more freedom. The missing piece is the culture around the technology, as technology without social responsibility becomes a tool for whoever already has power.
This is not even touching on that the ecological cost of scale is a catastrophe in the era of #climatechaos and social backdown. The current AI boom depends on enormous infrastructure, huge amounts of electricity, water for cooling, specialised hardware with constant replacement cycles leading to the large-scale resource extraction. At a time of #climatechaos, we should question whether endless expansion is the only possible future. The #dotcons model has always worked through scale, more users, more data, more infrastructure and more dependency. Generative AI is arriving inside the same economic system that created the catastrophic problems it claims to solve.
Then we have the open internet problem, the #openweb was built around participation, people created #4opens websites, communities, documentation, software and culture. GenAI introduces a different path, that the internet becomes raw material, this human creativity becomes training data. Communities produce knowledge, while large companies extract and monetise it. This creates a dangerous cycle were there is less support for creators → less motivation to create → less genuine knowledge → more dependence on generated content. Its #KISS to understand that healthy commons cannot survive if everything is extracted and nothing is returned.
The #Fediverse and the question of growth, a few years ago there was a feeling that the #Fediverse development culture was running on leftovers. Social movements arrived in waves, and many feared that more waves was moving into #mainstreaming. Since then, the Fediverse has grown, with more people knowing about decentralised social media, more organisations paying attention. Ideas that once lived mostly in activist and technical circles have moved closer to wider adoption.
But growth always creates a question – What happens when a movement becomes successful enough that the surrounding culture starts changing it? The early #openweb was built around different assumptions – People have agency – Communities shape their own spaces – Experimentation matters more than optimisation – Trust matters more than control and Commons matter more than platforms. #Mainstreaming brings pressures, these are not automatically bad. But there is a danger that the technology scales while the culture that created it gets diluted. Federation is a technical idea. Living commons is a social one, the challenge remains – now do we grow without losing the roots?
The narrow lesson from #FOSS – it is one of the greatest successes of the #openweb era. Without it there would be no Linux, no Apache, no Firefox, no Wikipedia-scale infrastructure and no Fediverse ecosystem as we know it. It has created extraordinary shared value, but success should not stop us asking difficult questions. The question is not whether FOSS works, the question is – Who does it work for? Where does it struggle? What social lessons can we learn? One recurring problem is the idea that open source is simply a marketplace of independent individuals.
When building the future we actually want – The question is not whether we use AI, more It’s whether we allow the same old #dotcons logic to shape every new technology. The future depends on whether tools strengthen human networks or replace them. Whether they support commons or enclosure, whether they increase agency or dependency.
But what we are seeing is that the tools we need most are often the first things stressed, messy and elitist systems try to defund, discredit and dismantle. Why? Because they require uncertainty, require questioning assumptions, require admitting complexity. Those are not weaknesses, they are survival tools.
A plebiscite (or simple poll) reduces complex questions to binary or multiple-choice outcomes decided by raw headcount. This works reasonably well for large nation-states were aggregating millions of preferences is practically necessary. But in small community groups – like a WhatsApp boating community – it undermines democratic values rather than express them, for several reasons.
The participation fallacy – Whoever happens to be on their phone when the poll appears votes; everyone else is excluded by timing. In a WhatsApp group, this might mean a dozen people determine policy for two hundred. The result carries the appearance of collective legitimacy while actually reflecting a self-selected subset. True democratic representation requires deliberate, structured participation – not whoever checks notifications first.
Suppression of minority interests – This is perhaps the deepest problem. A poll asking “should we allow X at the mooring?” can produce a 60/40 result that completely ignores why the 40% disagree. In a functioning community democracy, minority positions deserve to be heard, reasoned with, and sometimes protected. A simple poll flattens all of that. The liveaboard who depends on a particular mooring has the same one vote as the weekend visitor who barely uses it.
The tyranny of the majority in microcosm – John Stuart Mill’s classic concern about majoritarian democracy – that it can become a form of collective tyranny over individuals – is almost more acute in small groups than in states. In a national election, your minority view is still represented through opposition parties, courts, constitutions. In a WhatsApp poll, you simply lose, with no appeal mechanism, no minority rights protection, and often no transparency about who voted or why.
Social pressure distorts the vote – In a small group, people know each other. Polls are rarely secret. Vocal members who post before the poll closes visibly shift the outcome. Quieter members – often those with the most legitimate concerns – may not vote at all to avoid conflict. The result reflects social dominance as much as genuine preference. A WhatsApp poll in a group like that might ask something like “should we organise a group clean-up on Saturday?” which seems harmless – but even this excludes people who work weekends, who have caring responsibilities, who are moored further out and can’t get there. A poll that produces “yes, 23 votes to 4” then generates social pressure to participate that bears down hardest on the most vulnerable members.
For contentious issues a WhatsApp poll is the worst possible instrument, as itt short-circuits exactly the conversation and negotiation that would surface the real interests at stake. What works better in community groups is face to face or federated trust based deliberative democracy rather than plebiscitary voting. Distinguishing between decisions that affect everyone equally and decisions that affect specific individuals far more than others – the latter should require consent, not just majority approval.
The irony is that small community groups like boating communities are ideal for genuine deliberative democracy – people know each other, stakes are concrete, conversations are possible. WhatsApp polls squander that by importing the bluntest majoritarian tool into a context that could support something richer.
Fluffy mess makeing
A second problem with #dotcons digital community decision-making is the hidden layer underneath the visible conversation: metadata is when organising becomes evidence in court cases.
People think privacy as the content of messages – what someone wrote, what someone posted, what opinion they expressed. But modern platforms collect something much broader: who joined a group, who attended an event, who reacted to a post, who communicated with whom, when people were active, who organised conversations, who supported a campaign, the patterns of relationships and activity.
This information will reveal the structure of a community even without reading the actual conversations. A WhatsApp group, Facebook group, or online community is a map of social relationships. That matters because grassroots organising often happens through relationships. The same online networks that allow communities to defend their rights, challenge poor decisions, or hold powerful actors accountable can also become visible records of who is involved.
The danger appears when, activism turns spiky and there is a conflict between less powerful groups and privileged actors. A campaign group, activist network, neighbourhood organisation, or community project might simply be trying to protect a shared space or challenge unfair treatment. But the digital traces created while organising can later be used against those people.
This does not require some dramatic conspiracy, it happens through ordinary legal processes. A court order can require a platform to provide information relevant to a legal case. Large platforms hold enormous amounts of stored data, and when authorities or private actors successfully obtain legal access, information that people assumed was just part of a conversation can become evidence.
The issue with this is imbalance, a large corporation, wealthy individual, or powerful institution have far more ability to navigate legal systems than a small grassroots group. They have lawyers, resources, and institutional support. Community activists have only their networks and their ability to organise.
This creates a contradiction, the “common sense” digital tools that allow ordinary people to coordinate can also create permanent records of that coordination. The answer is partial – there are #FOSS and #NGO tools that make this less of a problem – Healthy commons needs people to be able to organise, disagree, challenge power, and build alternatives without automatically creating a legal danger to everyone involved.
The question is not whether communities should be accountable, the question is: accountable to whom, and who has the power to use the information? Because in struggles between grassroots groups and privileged actors, metadata can become another form of power.
The lesson is simple – Build open movements, but do not naively confuse openness with exposure. Commons needs trust, but they don’t need to leave a #dotcons surveillance trail. Yes people will use bad tools anyway, but it’s good if some people use better tools to start stepping away from this digital and social mess.
Use the #openweb as core organising, do not use the #dotcons – an example here is open collective website not WhatsApp chat or Google Docs. Tools shape behaver and metadata gets people prosecuted.
Use #signal for chat, it’s not a perfect tool, but it’s better than the rest, use a common platform.
Use #torbrowser for web searches and browsing of any sensitive subject, if you want to use AI, Then don’t logged-in inside tor for any sensitive questions. All AI questions are stored as a part of your account and can be used agonist you – this is true even when you are not logged in.
Do not rely on #AI for activist research or grassroots legal thinking – its hallucinations and training data will endanger you. The AI default is always wrong on this path without inside knowledge to prompt past the #mainstreaming output.
I’ve come to think that caring for people requires a degree of resistance to the culture around us. Not because people are bad, but because so much of the dominant culture is built around values that put profit, status, and competition ahead of human need. In that sense, care becomes a quiet act of rebellion.
Every few months another AI company executive suggests that their latest Large Language Model possess values, ethics, judgement, emotions, or even a form of consciousness. The latest example is claims around Claude, where discussion has drifted toward the idea that the system possess “a functional version of emotions or feelings.” This is a good moment to step back and look at what is actually happening.
They are software, very sophisticated software, certainly. Useful software, maybe. Sometimes surprisingly capable software, but software nonetheless. The current generation of LLMs works by processing enormous amounts of human-produced content and generating statistically probable responses based on patterns found in that content. What people mistake for intelligence is the reflection of our own intelligence. What people mistake for morality is often the reflection of our own moral language. What people mistake for emotion is the reflection of our own emotional expression. The machine is mirroring us.
The #geekproblem strikes again – a recurring problem in technological culture is the blinded tendency to mistake technical processes for social processes. If you spend enough time around code, it becomes tempting to imagine that social problems can be reduced to technical ones. That human complexity can be transformed into engineering complexity. That ethics can be encoded, governance can be automated, community can be replaced with platforms. This is not a new mistake.
For decades, we have watched technologists claim that algorithms can replace editors, platforms replace communities, markets replace politics, and code can replace governance. The result has been a mess. Now the same pattern is repeating with AI. Human judgement emerges from lived experience, social relationships, culture, responsibility, memory, and consequences.
Ironically, the real danger is not that these systems become conscious, the danger is that people increasingly behave as if they already are. The public relations narrative coming from many #AI companies encourages this confusion. The more human-like these systems appear, the easier it becomes to sell products, attract investment, and generate media attention. The result is a kind of digital anthropomorphism.
People begin treating software as trusted friends, therapists, advisers, teachers, and companions. Meanwhile, the actual human institutions that should provide these functions continue to weaken. This is a familiar pattern from the #dotcons, rather than building stronger communities, we build stronger platforms. Rather than strengthening relationships, we optimise engagement. Rather than supporting public institutions, we create private substitutes. The technology becomes a replacement for the social fabric it quietly helps unravel.
The deeper issue is that morality does not exist in isolation, ethics is not simply a set of rules, it emerges through social processes. People learn morality through families, communities, traditions, cultures, institutions, and struggles. We argue about values by negotiating differences. We face consequences for our actions. We inherit stories and experiences from previous generations. This process is messy, often contradictory. But it is fundamentally social.
An AI system can reproduce ethical language because ethical language exists in its training data. It can discuss justice because humans discuss justice. It can talk about compassion because humans write about compassion. But discussing a value is not the same thing as possessing it. Repeating ethical language is not ethical behaviour. Generating moral arguments is not moral agency.
From an #OMN perspective, the important question is not whether machines are becoming human. The important question is whether humans are becoming less social. The #openweb was built around the idea that people communicate with people. The current AI boom increasingly promotes a future where people communicate with machines that imitate people. That should concern us.
Not because the machines are evil, not because AI is an existential threat. But because every step in this direction risks reinforcing the existing trend toward isolation, atomisation, and #stupidindividualism. The challenge is not to fear AI, it is to keep social processes social. To remember that governance requires communities. That ethics requires accountability and culture requires participation. That intelligence without social context is simply computation, machine can generate words, but people can create meaning.
People add a lot of mess, this toot is a diagnosis of a small shift, but it’s thinking is trapped inside a narrow, liberal property lens on what the internet is and was supposed to be. What’s being described as a “split” between a Free-For-All quarry and gated communities is what happens when you assume the web was primarily about enforceable intellectual property contracts in the first place. That framing already accepts the #dotcons worldview – that value is created by ownership, extraction, and legal enclosure.
From an #openweb and #OMN perspective, that was never the path. The early web (and the cultures that fed into it – FOSS, mailing lists, blogs, wikis) wasn’t held together by copyright enforcement. It was held together by norms: reciprocity, attribution, sharing, trust, and rough social accountability. That’s much closer to the #4opens than to IP law. Open code, open standards, open data, open process – not because the law enforced fairness, but because social relations did.
What #AI scraping has broken is not a legal equilibrium, but a fragile social one that the #dotcons had already been hollowing out for decades. They didn’t rely on “fair use” or reciprocity – they relied on enclosure, centralisation, and extraction, #AI simply accelerates that logic. So yes, “anything reachable by HTTP becomes fuel” is accurate – but the mistake is thinking the alternative is stronger copyright walls or more contractual gating, that deepens enclosure. The split you describe is real, but it’s not new, and it’s not caused by #AI, it’s the endpoint of a long enclosure of commons → platform capture (#dotcons), trust → contracts, sharing → surveillance + monetisation and public space → login walls.
The current AI mess is not the origin of this, it’s just a new layer of extraction sitting on top of the #mainstreaming mess. From an #OMN view, the interesting question isn’t how to reassert IP over scraping. It’s how to rebuild social and technical spaces where contribution, context, and reciprocity matter again – where value isn’t just extracted but circulated in ways communities can govern.
AI is not an existential threat to the #openweb, it’s an asshole amplifier inside an already broken system. The real loss we need to compost isn’t only copyright protection, it’s the erosion of the social commons that made openness meaningful in the first place.
The #KISS secret about the noise in “digital sovereignty” is very simple – Ignore most of this branding and build commons tech instead. That’s the path, not another layer of management, another funding bureaucracy for a glossy strategy document. Not another NGO conference circuit explaining why nothing can happen without another round of funding. Just build working commons.
This matters because much of the #EU “digital sovereignty” conversation is simply more churn inside the same #neoliberal#mainstreaming logic that created the problem in the first place. Europe spent decades outsourcing infrastructure, privatising public space, undermining local autonomy, and feeding the #dotcons.
Now the consequences are becoming impossible to ignore, dependence on US platform monopolies, fragile infrastructure, imperialist surveillance capitalism, cloud centralisation, shrinking democratic accountability, and growing geopolitical vulnerability.
So suddenly everybody is talking about “sovereignty”, but what do our chattering class of institutional actors mean by sovereignty? Too often they mean procurement contracts, compliance frameworks, consultancy ecosystems, defence posturing, startup hype and fashionable funding narratives. The same old structures wearing a new outfit.
This is where the #fashionistas rush in to cash out of the latest cycle of #techshit, every crisis produces a new branding wave #Web3, #AI, #blockchain, smart cities, trusted identity and now digital sovereignty. The words change, the consultants were the same clothes, to push funding applications with different buzz words. But underneath, the social relations to often stay exactly the same. This is why so much “innovation” produces so little durable social value, the energy and focus gets consumed by branding, positioning, institutional competition, and funding capture.
The #OMN approach is to compost this mess rather than feed it. Composting means recognising that some parts of the existing system still contain nutrients technical knowledge, infrastructure, institutions, legal frameworks, public funding, developer skills. But these need breaking down and re-rooting into commons processes instead of simply reproducing the same dead structures.
The #KISS approach is important because complexity is often used as a control system, the more complicated the governance path becomes the harder it is for normal people to participate, the easier it is for insiders to dominate, and the more power flows to the parasite class managing the process. People then confuse institutional complexity with competence, but most healthy social systems are not built this way, healthy systems tend to be transparent, iterative, federated, participatory, and grounded in practical trust.
That’s why the native #openweb worked when it worked, people built things together directly like mailing lists, forums, blogs (bit more messy), federated publishing, open protocols, community hosting, shared standards. Messy? Yes. Human? Yes, but functional. The current “digital sovereignty” debate ignores this history because acknowledging it would undermine the need for the giant managerial layer now feeding on the crisis.
A lot of the current policy noise is about preserving institutional power during systemic decline, that’s why signal-to-noise matters, most of the noise performs concern, manages perception, protects careers, and absorbs dissent into harmless process. Signal is rarer, it’s about building actual commons’ infrastructure, creating durable trust networks, supporting federation, sharing governance openly, and keeping paths simple enough that communities can understand and maintain them.
This is one reason the #4opens remain central, without these, “digital sovereignty” simply becomes another enclosure strategy under a different flag. European-owned silos are still silos, state-managed platform capitalism is still platform capitalism. Replacing Silicon Valley landlords with Brussels landlords is not liberation.
The real challenge is rebuilding public digital commons, that means the hard part is cultural, not only technical. People are deeply trained by #mainstreaming to look upward for solutions to governments, corporations, experts, influencers, NGO etc. But commons culture grows sideways instead, through participation, trust and through practical collaboration, yes this is slower at first, but far more resilient over time.
That’s the real #KISS secret, ignore much of the spectacle and quietly build the alternative underneath it. Less noise, more compost – Less branding, more commons – Less #techshit. More grounded infrastructure. That’s how you compost the #mainstreaming mess instead of endlessly feeding it.
If we want a better web, we have to stop pretending this is just about “bad tech companies doing bad things.” Of course, they are-that’s what capitalist incentives produce. The real question is: what are we doing differently?
That means accepting some uncomfortable truths. The better path will be less convenient, at least at first. We will have to socially support things that used to look free on the #dotcons. Because the cost we didn’t want to face is simple: the #openweb was always going to be harder, someone has to:
The fantasy wasn’t that this work didn’t exist. The fantasy was that the market – advertising – would cover it without consequences.
In the current mess in tech paths, this becomes visible again. Bluesky and #ATproto keep getting lumped in with #ActivityPub under the easy label of “open protocols, yay”… but that’s just not true. Yes, they both sit in the #openweb space, but there’s a real structural problem here, and we’re seeing it play out in real time.
“Why would anyone fund an Atmosphere project if Bluesky, with $100 million in the bank, might ship a competing feature at any moment?”
That’s not an ecosystem. That’s a platform with enough gravity to crush its own edges. And people are noticing. The old pattern is back:
invite the community in
let them build the value
then absorb and replace them
Same playbook, again and again. It feels open – but the centre still holds the power. The same dynamic we saw with Twitter. The DNA is obvious.
The difference really matters. #ActivityPub was built as a commons path from the start – messy, flawed, but natively open. #ATproto is something else: a platform-first model with openness layered on top. That’s why it keeps drifting this way. It’s not a bug, it’s the design.
Too much #techshit, and everything starts to stink. Why would anyone step into the #openweb if that’s the smell? This creates a bigger problem, that it’s a mess that keeps coming back, and as usual we’ll be the ones left to compost it, underfunded, unrecorded, and unthanked.
We’ve been here before – with the #encryptionists and the #blockchain mess. Big promises, lots of noise, overlapping hype cycles. Now there’s a clear overlap with #Bluesky and #AI. The risk isn’t just that this fails. It’s that when it fails, it leaves a miasma behind, making it harder for people to trust the actually working open paths. That’s the real damage.
Neglect is not innocence, this isn’t about blaming users instead of power. Power matters. Monopolies matter. Venture capital mess matters. But still, if the #openweb mattered, why didn’t we support it?
Why do people pay for streaming, cloud, and delivery, but not support publishing tools, independent media, hosting, or open infrastructure?
Why did so many #NGO organisations that talked about openness still push people onto closed platforms the moment growth and analytics are on the table? We keep choosing short-term convenience over long-term stewardship, not just a market failure, a cultural one.
So lets look at this mess again. I’ve been trying to find a way to express my view of the people who took over outreach in the #Fediverse, and in doing so helped shape the current #openweb reboot.
DRAFT: naïve, controlling, and self-interested.
They’ve left a mess that the people they pushed aside now have to compost. It’s really useful to look at how we got here.
In the early years, outreach was organised by a genuinely diverse, native crew. It was a good time – three open conferences, and even getting the EU to adopt the standard. But that group burned out, focus splintered, self-interest crept in, driven by the need to control resources. The balance shifted, and grifters gradually outnumbered them, eventually tearing it apart. In the space left behind, a new crew stepped in – filling the vacuum with centralised power and influence. And that’s where we are today.
We don’t fix this by arguing harder. We fix it by building – and holding – open spaces that don’t follow this pattern.
A recent essay on deadSimpleTech makes a point the #openweb community should hear: the biggest problem in technology is not only the tools, it’s also the culture behind them. For years the tech world has operated under a form of narrow “tech empiricism”: the belief that if something produces results quickly, then it must be working well. In this mindset, success is measured by novelty, speed of production, and the ability to create something new. The heroes of this culture are disruptors and iconoclasts who ship fast and build shiny things that capture #fashionista attention.
But this basic #geekproblem ignores a simple #KISS truth: technology only has meaning inside the culture that builds and maintains it. And this is where the real problem begins. In the dominant tech worldview, the culture rewards novelty, disruption, rapid production, and personal prestige. Inside this environment of #deathcult worship, producing new code becomes a way to gain status among peers. Shipping quickly matters more than maintaining systems or improving what already exists.
But there is another culture that exists alongside this, the culture of engineering and maintenance. In fields like civil engineering or infrastructure design, the heroes are not disruptors. They are the people who quietly maintain systems, improve reliability, and prevent failures. The emphasis is on responsibility, long-term stability, and care for systems people depend on. This difference in culture matters enormously. Because what counts as something working “well” depends entirely on what the culture values.
From the perspective of blinded tech culture, a tool that generates lots of new code and features appears incredibly successful. But from the perspective of infrastructure and engineering culture, that same tool may look deeply flawed – even dangerous. Real systems require debugging, maintenance, testing, and institutional memory. Most importantly, they require people who accept responsibility when things fail.
In mature systems, the first prototype is only the beginning. The real work comes later: years of maintenance, improvement, and adaptation. Yet this long-term work is largely invisible in tech culture and funding systems, which celebrate the person who creates something new but rarely honour the people who keep it running. This cultural blindness leads to fragile systems and recurring cycles of hype and #techshit to compost.
The same problem is in the #OpenWeb. Unfortunately, this problem is not limited to Silicon Valley, it also appears inside the #openweb, #NGO, and #FOSS ecosystems. Many conversations focus almost entirely on: code, protocols, scaling, features and UX. All of these are important, but without balance they are not enough to sustain a functioning ecosystem.
Without the native social culture that originally shaped the open web, open technology slowly drifts toward the dominant norms of the wider #dotcons tech industry of status competition, short-term innovation cycles, neglect of maintenance and eventual capture by institutions or corporations. This is one reason so many promising #openweb projects stagnate or collapse.
The technology works, but the social infrastructure fails. It’s in part why the #OMN exists as a project. This is the gap we need to address, not primarily as technical project. Most of the protocols and software already exist. What is missing is the social infrastructure that allows them to function as a public commons. Instead of focusing only on building new non-native platforms, the #OMN focuses on growing the wider ecosystem around what all ready works.
This means recognising that the real value of a network comes from the people who maintain it, moderate it and build communities around it – not just from the code itself.
From tech “empiricism” to social infrastructure, if we want the #openweb reboot to succeed, we need to move beyond the narrow mindset that treats technology as purely technical. The lesson from history is simple, code builds systems, culture makes them work. Without a healthy culture, even the best open technologies will eventually fail or be captured by more powerful institutions.
A deeper mess is “The End of Theory”, tech empiricism problem is really the #geekproblem amplified by ideas like this, the claim that massive data sets make traditional scientific thinking unnecessary. This idea, popularised by Chris Anderson, suggests that with enough data we no longer need theories, models, or human understanding. But this is a dangerously narrow view as large data models are epistemologically weaker than scientific theories. They can recognise patterns, but they do not understand them.
This becomes even more problematic in the age of opaque and unexplainable #AI systems. Deep learning models can be efficient at pattern recognition, but they lack human comprehension and produce opaque but believable outputs. At the same time, the increasing “datafication” of society means that communication and public life on the #dotcons platforms are moderated by these same algorithms. These systems prioritise engagement and behavioural prediction over needed values like: accuracy, truth, democratic deliberation. The result is a social environment driven by metrics rather than meaning.
It is past time to compost the mess as it is becoming easier and easier to see. But seeing the problem is only the first step. The next step is to compost it – to take the failures of the current system and use them as nutrients for something better. The future of the #openweb will not be decided by better code alone. It will be decided by whether we build the social infrastructure to support it. That is the work the #OMN is trying to grow.
In tech funding, over the last decade, the #EU poured hundreds of millions of euros into the #blockchain mess. The promise has proven to be illusion, we built no working transformation: trustless systems, frictionless governance or new economic layers for Europe. The reality? By any honest social metric, 99.9% of that public funding was poured straight down the drain.
Now we are lining up to do the same with AI. Another wave of hundreds of millions, based on another cycle of hype, feeding frenzy for consultants, startups, and policy conferences. And if we are realistic, 99% of this funding will follow the same path: absorbed into closed, corporate-driven ecosystems with minimal public return, poured down the drain.
In between these two hype cycles, we invested comparatively little in the #openweb and #FOSS. And yet that is where we actually saw meaningful results. Even if we are conservative and say 70% of public funding for #openweb and Free and Open Source Software was wasted, that still leaves 30% that worked. Thirty percent that built tools people use. Thirty percent that created infrastructure that continues to function. Thirty percent that delivered measurable social good.
Compared to less than 0.001% meaningful return from blockchain projects (and that’s being generous), and perhaps 1% from AI funding (also generous), this is an extraordinary success rate. So why aren’t we talking more about this?
The Pattern: Funding the Closed, Ignoring the Commons
The problem is not technology, it’s political economy. Public money is repeatedly funnelled into closed ecosystems. #Blockchain projects were built around proprietary platforms, based on financialisation. They all failed to deliver public infrastructure, most were simply vehicles for extraction.
#AI is following the same pattern. Instead of building public infrastructure rooted in openness, transparency, and shared governance, we are too often simply subsidising closed models and corporate consolidation. The result will be the same: dependency, vendor lock-in, and very little democratic control.
Meanwhile, the #4opens and #FOSS quietly power the world.
Servers run on open-source operating systems.
The web runs on open protocols.
Community platforms run on federated code.
Critical infrastructure depends on open libraries.
And yet funding for these projects remains very marginal, precarious, and treated, if at all, as an afterthought.
Why This Matters
This is not only about waste, it is about direction. We are living in an era of climate breakdown, democratic fragility, and accelerating inequality. Public investment needs to strengthen commons-based infrastructure, not deepen dependency on mess of speculative and corporate-controlled #dotcons. When we fund the #fashionista hype cycles we increase centralisation, reduce public oversight and lock ourselves into closed ecosystems, which hollow out our needed local capacity.
When we fund #openweb and #FOSS we build shared infrastructure, increase resilience, enable local innovation to create tools that can be forked, adapted, and reused. Even a poor 30% success rate in commons-based funding creates compounding social value. Code written once can be reused globally. Infrastructure built openly becomes a foundation others can extend. Knowledge stays in the public sphere.
Closed projects don’t compound in the same way. They expire, pivot, get acquired, and then disappear behind paywalls.
The Incentive Problem
So why does this mess keep happening? Because hype is easier to support than maintenance. The current #mainstreaming is to blind, Blockchain and AI come with glossy narratives of disruption and geopolitical competition. They promise growth, dominance, strategic autonomy. They flatter policymakers with the illusion of being at the frontier.
The #openweb and #FOSS, by contrast, are mundane. They are about maintenance, collaboration, and long-term stewardship. They don’t produce any unicorn valuations, the smoke and mirrors that feed splashy policy headlines. But they work, and in public policy, “working” should be the gold standard.
What We Need to Talk About
We need to keep asking direct #spiky questions about what percentage of publicly funded tech projects remain usable five years later? How many are open, forkable, and independently maintainable? Who owns the infrastructure we are building with public money? And does this investment strengthen the commons or subsidise enclosure? If we measured blockchain funding by long-term public utility, it would be exposed as a massive misallocation at best and fraud at worst. If we measure AI funding the same way in five years, we may reach the same conclusion. We #KISS need structural change:
Default to #4opens – Public funding #KISS should require open licenses, open standards, and transparent governance.
Fund Maintenance – Not just #fashionista projects, but long-term stewardship of critical open infrastructure.
Measure Social Value – Not hype, not valuation, not patents, but actual public use and resilience.
Grassroots tech as seedlings – to be open to real change and challenge in tech.
Support Commons Governance – Fund communities, not more startups.
Why We Need to Act
If we do not challenge the current messy #techshit cycle, we keep pushing ourselves into a future defined by the #dotcons, closed platforms with extractive models. To say this is not anti-technology, it is pro-public infrastructure. The choice is simple, do we keep pouring public money into, closed ecosystems with near-zero public return or invest systematically in the messy, imperfect, but functioning #openweb commons.
The data – even by generous estimates – is clear. Thirty percent real return beats 0.001% every time. We need to stop funding hype, we need to fund what works, and we need to say this loudly, before the next billion euros disappears down the same drain.
A simple question: who – or what – has consciousness? Humans, animals, #AI, or perhaps matter itself? What is consciousness, and why is it different?
Philip Goff (Philosophy, Durham University) Consciousness is everywhere
Heather Browning (Philosophy, University of Southampton) Evidence for consciousness in non-human animals
Patrick Butlin (Global Priorities Institute, University of Oxford) The case for AI consciousness
One recurring theme was that consciousness is not just another scientific object to measure. We already know consciousness from the inside, we are born into subjective experience. Science can describe physical processes mathematically and externally, but subjective qualities – feeling, sensation, the experience of “I am” – resist straightforward physical explanation.
This creates a gap where physical science explains structure, behaviour, and observable mechanisms, but not questions about experience, just function. #Philosophy enters here, asking not only what consciousness does, but what it is.
Some perspectives suggested a spectrum, simple systems may have simple forms of consciousness, while complex organisms have richer ones. This takes physical reality and asks what happens if consciousness is treated as a fundamental feature rather than an emergent accident.
The discussion of non-human animals focused on suffering, feeling, and ethical implications. We cannot directly access animal minds, so researchers rely on behavioural and neurological markers to infer consciousness. Despite this, there is growing consensus that animals experience subjective states, especially those capable of learning, emotional responses, and adaptive behaviour.
The ethical consequences are obvious that if animals feel, they can suffer, if they suffer, human systems must reckon with this. The discussion touched on animal cruelty and the moral responsibilities emerging from this understanding of non-human consciousness.
The most contentious section involved #AI consciousness, intelligence vs experience. One argument suggested that modern AI has reached human-level inference in certain domains, even systems trained purely on historical text. From this view, sufficiently complex information processing might be enough for consciousness.
But tensions emerged that AI systems are not embodied. Solving “geek problems” does not imply subjective experience, highlights the divide: Computationalists – see consciousness as potentially arising from information processing. Where biological or embodied perspectives, argue that lived, physical existence may be essential. The discussion felt unresolved.
Cultural observations of the event: the engineers and “geek” audience clustered at the back of the room, reflecting the broader cultural divide between technical and philosophical approaches. Much of the debate mirrored this tension, information-processing models versus lived experience and embodiment. There was also a sense that many people rarely reflect on their own use of consciousness, how we attend, choose, or engage with the world.
No clear resolution emerged – and perhaps none is coming soon. What became clear is that consciousness sits at the boundary between disciplines. Science struggles to capture subjective experience, while philosophy cannot avoid engaging with empirical discoveries.
The question of who or what has consciousness remains open, but the debate itself reveals something deeper: our theories of consciousness often reflect our cultural assumptions about intelligence, technology, and what it means to be alive.
A talk on a new book by Pepper Culpepper on how corporate scandals could be used to save liberal “democracy”. This talk is the familiar fantasy of elitist institutions like the Blavatnik School, Oxford. Culpepper and co author Lee reframe disasters from Enron to Cambridge Analytica not as structural failures of a system built to concentrate power, but as healthy “corrections” that supposedly can be used by people like them to renew democracy.
In this telling, public anger is something to be safely channelled into regulation, corporations remain indispensable, and democracy survives as a managerial process overseen by the normal “progressive” liberalish policy priests. It is #deathcult logic, polished up, to worship the system while denying its violence, recurring catastrophe not as proof of collapse, but as evidence that the machine still works – if only the right people are allowed to control it.
The Blavatnik worldview in one sentence “Capitalism is broken, but only experts can fix it, without threatening those who benefit from it.” The tone is elitists pessimism dressed as realism, the talk opens with managed the pessimism “Yes, things are bad…” “…but lives are improving” “…and the liberal order still basically works” “…we just need better policy”. Everything else is ornamentation, democracy is talked about constantly, but control is never offered.
This is the #deathcult chant, not in any way apocalyptic enough to demand rupture, and also not hopeful enough to empower people. It’s pessimism, justifying elitist management, so no real change. They talk about democracy, but notice how it’s framed: Democracy = policy capacity, regulatory competence, party systems and institutional continuity. Democracy is not found in any real popular control, public ownership, exit, refusal, redistribution, or material power. The people appear as voters, outrage generators, legitimacy providers, but never as agents who might take any part in control, the old mainstreaming tradition of social democracy as crowd management.
The book is worship of policy nerds vs fear of the #techbrows, a strange inversion at work, that billionaires are dangerous, reckless and markets are running amok. The solution for them, is therefore, “we need policy experts to save us.” who can circulate through the same elitist institutions, depend on the same funding systems and never threaten ownership or accumulation. Yes, capitalism is “broken” – but only as a governance problem to solve. This is instead of any stress of public vs concentrated power, in their book, it’s an intra-elite turf war, sold as democracy.
They get very close to truth here “capitalism is a minority of people with a lot of power, unafraid to use it.” But then they refuse any logical conclusion, if what they say is true, then regulation is insufficient, as any real accountability requires ownership change and democracy requires material leverage to function. Instead, they do a quick pivot to stakeholder capitalism and value generation as a path to “put capitalism back on its feet”. This is a system that’s killing people, while insisting itself must stay alive.
Public capitalism is a bloodless fantasy that might sound radical to a privileged chattering class. But it’s the same failed mess, where the public gets, exposure, risk, volatility while the elitists keep control and set the agenda. It is inequality, endlessly acknowledged, but never touched, the normal elitists preference disguised as inevitability.
There, assumptions are wrong, yes, the is a very real fear of autocracy, but not of oligarchy, they are worried about autocracy, but they are not worried enough about billionaires controlling media, capital, thus veto over policy, regulatory capture and economic coercion. Why? Because oligarchy is their ecosystem. Autocracy is framed as something external, crude, foreign, where oligarchy is polite, networked, respectable… and pays for book launches at the Blavatnik School we sip wine at, after the event.
They are scared by “bad populism” but love “good populism” as outrage without power, believing, outrage can be used to drive a very narrow idea of reform, scandals and anger can be “harnessed” as a fuel for what they see as elitist balance. The public is a matchstick, a controlled burn to open up a space for their class (literally their children) the future“policy entrepreneurs” who, with generational wealth, still rich enough to volunteer, bored enough to care and insulated enough to fail, its politics as a hobby of the ideal rich.
In the Q&A they talk about media fragmentation = democracy in trouble (but not elitist paths). They worry we “can’t agree on facts”. But they don’t worry about who owns platforms, who shapes narratives, who funds think tanks, who sets the Overton window. Fragmentation is blamed on the public, concentration is never blamed on capital. Then we have #AI outrage already being pre-neutralised, the AI bubble “will pop”, they say. The question is, “how do we use that outrage?” Not, how do we let people decide, how do we transfer control, how do we prevent enclosure in the first place.
Outrage is something to be channelled into managerial politics with the Churchillian cop-out “democracy is the worst system except all the others.” Which translates into, lower expectations, accept elitist rule to manage decline politely.
In this path, corporations are treated as unavoidable, people are treated as incapable, you get a strong feeling from this talk and book that this is it is not democratic theory, rather paternalism with footnotes. The core lie, unspoken underneath everything, is “we can fix capitalism without shifting power.” Every answer assumes that capitalism must remain, corporations must remain, and that the elitists must mediate and guided the public not to challenge this.
It’s elite self-soothing, but yes, they aren’t wrong that the system is broken, they’re wrong about who is allowed to fix it.
A cohesive manifesto is needed as the world we inherited is fractured. Wealth, power, and knowledge are concentrated in the hands of the #nastyfew: platform owners, data hoarders, and corporate monopolies who extract value from our work, our attention, and our trust. Democracy has been hollowed out, captured and controlled by algorithms that decide what is knowable, profitable, and even true. Ecology, community, and care are sacrificed on the #deathcult altar of growth and consumption.
In this mess, the Open Media Network (#OMN) is a #KISS project that exists to reclaim the digital commons, reshape society, and redefine what is possible when power, knowledge, and technology are returned to the people.
In the current #dotcons economy, access to infrastructure, information, and governance is rent-based and extractive. Communities pay to participate, and the surplus flows to distant shareholders.
The #4opens – open code, open governance, open data, open processes – upend this system. Putting tools of creation and coordination into grassroots democratic, collective stewardship. Value no longer flows automatically upward; it stays with the communities that generate it.
On this path, inequality stops being “natural.” Rich and poor are revealed as structural outcomes of enclosure and extraction. By reclaiming infrastructure as a commons, we recompose power, and inequality becomes a historical memory, not a permanent fact.
The logic of capitalism equates growth with progress, but infinite growth on a finite planet is impossible. Digital goods – knowledge, code, culture, and coordination – are non-rivalrous, replicable, and shareable. By moving value into open, digital abundance, the material basis of economic expansion shrinks.
This frees human effort to focus on ecological outcomes. Energy systems can localise, circular economies can flourish, and extraction-driven industries can shrink. Consumerism no longer masquerades as culture. Life becomes about care, collaboration, and sustainability. In a post-consumption economy, human needs are met without destroying the biosphere
What we need to compost is the closed, corporate networks, that, reduce people to metrics: clicks, views, and engagement scores, where connection is commodified, communities dissolve into attention economies. Moving to #4opens networks reverse this. Open, modifiable, and transparent paths and systems allow communities to rebuild trust, care, and reciprocity. Collaboration happens without permission, and relationships can persist across distance and time. Communities stop belonging to brands and start belonging to people. Social infrastructure becomes a tool for power and resilience rather than extraction.
The capitalist world naturalised exploitation, scarcity, and secrecy. Our “common sense” became a prison: work more, compete, hoard, distrust. The #4opens world undoes this conditioning. Open infrastructure and governance teach us that scarcity is artificial, cooperation is powerful, and secrecy serves control, not communities. Common sense is no longer what capitalism told us, it is what we collectively choose, this open thinking makes new realities possible.
The transitory shaping of privacy as we imagined it is gone, the #dotcons and surveillance states already see everything. Closed systems cannot protect us; secrecy is a lost battle. The solution is radical transparency. Open metadata, and commons-based governance shift power away from hidden extractors and toward the public. Privacy becomes collective control over visibility: who sees what, and with what accountability. In this world, transparency is justice, and knowledge is a tool of liberation.
In a #4opens world, exchange is no longer driven solely by money. Scarcity loses its grip when knowledge, code, and infrastructure are freely shared. Value can be recognized, tracked, and distributed openly. We give not to accumulate, but to re-balance. Contribution is measured in social and ecological impact, not profit. Capitalism made money sacred; #4opens break that spell, opening paths to redistribute both material and social power.
The next bubble, current #AI – #LLMs and ML #systems – is not intelligent. There is no path from these tools to general intelligence. What exists is pattern-matching, statistical correlation, and corporate extraction of public knowledge. But handing locked-up data to corporate systems strengthens anti-democracy structures. Instead of enabling “innovation”, it reinforces surveillance, centralisation, and algorithmic control. Real intelligence is collective, embodied, and social. True change and challenge emerges not from hype bubbles or closed corporate labs, but from communities building shared knowledge and infrastructure in the open.
Fascism vs. Cooperation – Fascism treats collaboration as weakness, hierarchy as inevitable, and domination as the only path to power. It cannot be trusted and cannot survive in open, cooperative networks. The #OMN path is the opposite: power through participation, resilience through trust, and flourishing through shared infrastructure. Communities that cooperate can sustain themselves, adapt, and grow, while isolationist, extractive paths, systems and tools wither. Cooperation is not optional, it is the foundation of any path to security, survival, and progress.
The choice before us, the world we inherited, is extractive, enclosed, and unsustainable. But the tools to reclaim power, knowledge, and community already exist. In #FOSS, the #4opens – applied to infrastructure, governance, culture, and knowledge – allow us to reduce inequality structurally, not through charity, but with rebuilding social trust and care, aligning human activity with ecological limits to make knowledge a public good, not a corporate asset.
Open Media Network is not a platform. It is a social path, to a world where power is distributed, knowledge is shared, and society is governed by the people who live in it. We are not asking for permission. We are building the commons, the question is not whether we can succeed, the question is whether we will choose to. History will remember what we did in this moment.
Why do we need to be this change and challange – when the vertical stack is captured, this is not simply a “shift to the right” in technology, ideas, or voting patterns. It is something deeper and far more dangerous: the capture of institutions themselves, the state as infrastructure. What we are witnessing is the hard right learning how to weaponise liberal, vertical systems against the values those systems claime to uphold.
This capture runs all the way down the stack. From the #dotcons to national governments and regulatory bodies; from university chancellors to local councils; from courts to media regulators. Structures that were designed – at least rhetorically – to mediate power are being repurposed as tools of repression, exclusion, and control.
Crucially, this is done using the language and procedures of liberalism itself: law and order, efficiency, neutral administration, security, common sense. The shell remains liberal. The content is no longer so.
Vertical systems are inherently brittle. They concentrate authority, normalise hierarchy, and rely on trust in institutions rather than participation in decision-making. When functioning well, they can stabilise society. When captured, they become perfect instruments for authoritarianism.
Once the hard right gains control of vertical institutions, it does not need to abolish democracy outright. Instead, it quietly redefines who counts, who is heard, and who is excluded. Algorithms are shaped. Funding rules tightened. Governance boards reshuffled. Enforcement priorities rewritten. Dissent is hollowed out while everything is insisted to be “within the rules.”
Universities become compliance factories. Local councils become enforcement arms. NGOs are defunded or disciplined. Media becomes “responsible.” Protest becomes “extremism.” This is not a breakdown of the liberal system, it is the system functioning as designed, but for different ends.
A dangerous illusion persists: that when the political pendulum swings back, these systems can simply be “returned to normal.” History tells us otherwise. Once vertical systems are captured, they are extremely difficult to bring back to any liberal-centrist path. Rules have been rewritten. Personnel replaced. Norms broken. Trust eroded. Appeals to fairness or precedent no longer land, because the system’s function has shifted from mediation to domination.
This is why “defending institutions” on its own is not enough. Institutions built on vertical authority cannot defend themselves once their legitimacy has been repurposed. At that point, asking them to save democracy is like asking a locked door to open itself from the outside.
Why horizontal power matters, and grassroots, federated power stops being a nice idea and becomes a necessary tool of change. Horizontal systems – commons-based networks, federated media, open governance, mutual aid, cooperative infrastructure – do not depend on permission from captured institutions. They distribute power, knowledge, and coordination across communities instead of concentrating it at the top.
In #OMN terms, this is about balancing power, not fantasising about purity, collapse, or revolution-as-spectacle. When vertical power becomes hostile, horizontal power provides resilience. It creates parallel capacities for communication, care, legitimacy, and collective action.
Federated systems are harder to capture because they have no single choke point. They can route around repression. They can survive attacks. They can continue to function even when formal institutions turn against the people they claim to represent.
We should be clear-eyed about where this leads. When vertical systems are captured and horizontal power is absent, pressure builds. History shows the likely outcomes: civil unrest, civil war, or international intervention. These are not abstract risks. They are structural consequences of power being monopolised without legitimacy.
Building horizontal power is not about accelerating conflict. It is about reducing the likelihood of catastrophic collapse by giving societies non-violent ways to rebalance power. When people have no voice, no access, and no agency, conflict becomes inevitable. When people can organise, communicate, and build alternatives, escalation can be resisted.
Its the strategic choice, the question is no longer whether horizontal power is desirable. The question is whether we build it before the remaining liberal structures are fully repurposed against us. The Open Media Network, the #4opens, federated governance, and open knowledge are not ideological luxuries. They are infrastructure for democratic survival in a world where vertical systems are increasingly hostile.
We are entering a period where balance – not dominance – will determine whether societies fracture or adapt. Horizontal power is what remains when the state forgets who it is meant to serve. Then the future will not be decided by who controls the top of the stack, but by whether people at the edges still have the means to organise, to speak, and to act together.
And that is a fight worth taking seriously, while there is still time.
