Beyond AI

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.

Keep this in mind on native #openweb paths.

Why WhatsApp plebiscites, and #dotcons in general are a crude and negative democratic instrument

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.

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.

Some first #KISS step tools

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.

#openweb #mutualaid #care #solidarity #deathcult #climatechaos #Oxfordboaters

People destroy things they love, not from hate, more from possession

AI didn’t break the web. The dotcons did – AI just turned up the volume

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.

https://kolektiva.social/deck/@jpl99@vivaldi.net/116691642387749842

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.

Compost “digital sovereignty”, build working commons

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.

Why do we keep bringing this up?

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:

run the servers
maintain the software
fund development
handle abuse, moderation, and #UX

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.

At AtmosphereConf, the signal was stark:

“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.

Let’s try and simplify the #OMN

It’s not about features. It’s about culture.

#ActivityPub comes out of the #openweb tradition.

#Bluesky comes out of a split lineage – #openweb roots, shaped by #dotcons incentives, with an #encryptionist upbringing.

The Tech “Empiricism” Problem

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.

If this work matters to you, help support it.

Public Money, Private Hype: From Blockchain to AI – and the #FOSS Path Less Taken

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.

Who or What Has Consciousness?

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.

#Oxford

The Blavatnik worldview, book talk

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.

#Oxford

Open Media Network: A Manifesto for the Digital Commons

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.

There is no intelligence in AI – and no path to any

Despite the constant #mainstreaming hype, the branding, and the trillions of dollars being poured into it, there is a simple reality that needs to be stated plainly: There is no intelligence in current “AI”, and there is no working path from today’s Large Language Models (#LLM) and Machine Learning (#ML) systems to anything resembling real, general intelligence.

What we are living through is not an intelligence revolution, it is a bubble – one we’ve seen many times before. The problem with this recurring mess is social, as a functioning democracy depends on the free flow of information. At its core, democracy is an information system, shared agreement that knowledge flows outward, to inform debate, shape collective decisions, and enable dissent. The wisdom of the many is meant to constrain the power of the few.

Over recent decades, we have done the opposite. We built ever more legal and digital locks to consolidate power in the hands of gatekeepers. Academic research, public data, scientific knowledge, and cultural memory have been locked behind paywalls and proprietary #dotcons platforms. The raw materials of our shared understanding, often created with public funding, have been enclosed, monetised, and sold back to the public for profit.

Now comes the next inversion. Under the banner of so-called #AI “training”, that same locked up knowledge has been handed wholesale to machines owned by a small number of corporations. These firms harvest, recombine, and extract value from it, while returning nothing to the commons. This is not a path to liberal “innovation”. It is the construction of anti-democratic, authoritarian power – and we do need to say this plainly.

A democracy that defers its knowledge to privately controlled algorithms becomes a spectator to its own already shaky governance. Knowledge is a public good, or democracy fails even harder than it already is.

Instead of knowledge flowing to the people, it flows upward into opaque black boxes. These closed custodians decide what is visible, what is profitable, and increasingly, what is treated as “truth”. This enclosure stacks neatly on top of twenty years of #dotcons social-control technologies, adding yet more layers of #techshit that we now need to compost.

Like the #dotcons before it, this was never really about copyright or efficiency. It is about whether knowledge is governed by openness or corporate capture, and therefore who knowledge is for. Knowledge is a #KISS prerequisite for any democratic path. A society cannot meaningfully debate science, policy, or justice if information is hidden behind paywalls and filtered through proprietary systems.

If we allow AI corporations to profit from mass appropriation of public knowledge while claiming immunity from accountability, we are choosing a future where access to understanding is governed by corporate power rather than democratic values.

How we treat knowledge – who can access it, who can build on it, and who is punished for sharing it – has become a direct test of our democratic commitments. We should be honest about what our current choices say about us in this ongoing mess.

The uncomfortable technical truth is this: general #AI is not going to emerge from current #LLM and ML systems – regardless of scale, compute, or investment. This has serious consequences. There is no coming step-change toward the “innovation” promised to investors, politicians, and corporate strategists, now or in any foreseeable future. The economic bubble beneath the hype matters because AI is currently propping up a fragile, fantasy economic reality. The return-on-investment investors are desperate for simply is not there.

So-called “AI agents”, beyond trivial and tightly constrained tasks, will default to being just more #dotcons tools of algorithmic control. Beyond that, thanks to the #geekproblem, they represent an escalating security nightmare, one in which attackers will always have the advantage over defenders, this #mainstreaming arms race will be endless and structurally unwinnable.

Yes, current #LLM systems do have useful applications, but they are narrow, specific, and limited. They do not justify the scale of capital being burned. There are no general-purpose deliverables coming to support the hype. At some point, the bubble will end – by explosion, implosion, or slow deflation.

What we can already predict, especially in the era of #climatechaos, is the lost opportunity cost. Vast financial, human, and institutional resources are being misallocated. When this collapses, the tech sector will be even more misshapen, and history suggests it will not be kind to workers, let alone the environment. This is the same old #deathcult pattern: speculation, enclosure, damage, and denial.

This moment is not about being “pro” or “anti” technology. It is about recognising that intelligence is social, contextual, embodied, and collective – and that no amount of #geekproblem statistical pattern-matching can replace that. It is about understanding that democracy cannot survive when knowledge is enclosed and mediated by #dotcons corporate capture beyond meaningful public control.

To recap: There is no intelligence in current #AI. There is no path to real AI from here. Pretending otherwise is not innovation – it is denial, producing yet more #techshit that we will eventually have to compost. Any sophist that argue otherwise need to be sacked if they arnt doing anything practical.

The only question is whether we use this moment to rebuild knowledge as a public good – or allow one more enclosure to harden around us. History – if it continues – will not be neutral about the answer.

There is such a thing as society -and the #openweb depends on it

There is such a thing as society. The entire #openweb is built on that assumption 🙂
Deny it, and everything collapses into noise, power grabs, and enclosure. That denial, dressed up today as “post-truth” – is killing us.

Our current media ecology is broken. So called #AI and Google are no longer a useful way to find information about most things that actually matter. This isn’t accidental; it’s a structural #dotcons problem. Extraction, advertising, and algorithmic manipulation have replaced human discovery, context, and trust.

The same sickness runs through much of today’s open-source and free software world. Its governance models are still rooted in medieval political ideas: aristocrats, benevolent dictators, kings and courts. That might have muddled through in the 20th century, but it is obviously useless for the world we now live in.

The last twenty years trying to mediate this with neoliberal #stupidindividualism has only made things worse. The result is towering piles of steaming #techshit, endlessly churned, rarely useful, and increasingly disconnected from any healthy social reality. This is the #geekproblem made in: code, silicon and concrete.

The #mainstreaming disaster driven by #dotcons is obvious. We don’t need to relitigate it every five minutes. For motivation and clarity, let’s put them to one side and focus on what we can actually change. Our own tech culture is still hopelessly mired in the #geekproblem. So yes, we need to compost a lot of our own mess.

The path out of both the #closedweb and the geek cul-de-sac is not new. It’s old, boring, and powerful: trust, shared responsibility, and human-scale democracy. If we are serious, the #openweb has to be rebooted with grassroots democracy at its core. Social tech needs social governance. Without that, we are just recreating vertical power with nicer licences.

This is where #OGB (Open Governance Bodies) matter. With real democratic process, it becomes relatively simple to push the #dotcons back out of spaces they currently dominate by default. Without democracy, they will always win, not because they are smarter, but because they are organised.

Right now, we are drowning in the #mainstreaming mess. And worse, we are still adding to it. Every pointless project, every ego-driven fork, every governance-free platform accelerates #techchurn and deepens the rot. We need to stop pretending this is neutral.

Yes, “open standards” are a mess, always have been, but they are the mess we must build on until enough of the #openweb is rebooted – including democratic decision-making – to rejuvenate and civilise the standards bodies themselves. Strong democracy changes the game. With it, enclosure becomes contestable. Without it, we just get louder arguments and faster failure.

If you care about this direction, add a statement of support here https://unite.openworlds.info/…/wiki/Statements-of-support You don’t need permission. You don’t need to convince everyone. You need to show up and help build.

And when people doing obviously stupid things can’t understand what the #OMN hashtags mean? Click the hashtags and think, or stand and shout, then hit the block button. You get to choose 🙂 This is not rudeness, it’s focus. And focus is how we stop adding to the mess and start composting it into something that might actually grow.