Tag: AI

Why #FOSS matters socially

The internet didn’t become broken overnight, it drifted from being a network of communities into a marketplace dominated by platforms whose purpose is extracting value. This is the logic of #dotcons most of us invested our lives and community into. How did we get into this mess? The problem isn’t only bad companies, it’s that our digital lives depend entirely on proprietary #dotcons paths and software, commercial interests end up controlling our reality.

This is why #FOSS (Free and Open Source Software) matters. The “free” in #FOSS is about freedom, the freedom to use, study, modify and share the tools that shape our lives. Those freedoms create something much more important than software, they create #4opens social power. Open code means community accountability.

Instead of trusting #closedweb corporations, we can build “native” trust through transparency. That changes how communities work as closed platforms create consumers, open projects create participants. The community doesn’t just use the infrastructure, it becomes in part responsible for maintaining it. We can exit to a more #DIY path, were anyone can inspect, improve, challenge and adapt to disparate community needs.

This isn’t always the fastest path as open collaboration is often messy. Consensus takes time, criticism can be uncomfortable. But these are social strengths, not weaknesses. Diverse communities find problems earlier, reduce hidden bias and create paths and systems that are more resilient because they are shared.

The #OMN approach builds on this real world path and body of ideas. That technology can strengthen communities rather than replacing them, code can support human trust rather than algorithms deciding everything. Infrastructure should belong to the commons rather than becoming another private enclosure.

Today’s #closedweb social media platforms are digital landlords. Yes you may build a following, create value and invest years of work, but you’re still a tenant. The rules can change overnight, your reach can disappear, your community can be fragmented at the click of a button.

The #openweb offers another path were we have control of identity and content to connect through open protocols instead of closed silos. We need this more than ever to building communities that can survive the failure – or hostility – of any single platform.

We can’t keep repeating the same mess, this composting matters even more in the age of #AI. If the software, models and infrastructure are closed, then a handful of companies determine how knowledge is created, filtered and shared. If they are open, communities can inspect them, improve them and govern them together.

The future isn’t simply open source software, it’s open source society. That means investing back into the commons we depend on. Maintaining infrastructure, supporting contributors, building institutions that outlast individuals. Planting trees whose shade we may never sit under.

That’s what #OMN is about. Not simply writing better code, but helping build a better social fabric for the #openweb. Because freedom isn’t something technology gives us, it’s something communities build together.

A #mainstreaming video looking at this.

The current common sense of #neoliberal worldview replaces trust with greed as the social motivator. The problem is that every successful society is built on trust. When you undermine trust, you build a #deathcult. Climate breakdown, inequality, social isolation and failing public institutions are not accidents – they’re the predictable outcomes.

When people recognise the problem, ask them what the solution is. Most will instinctively reach for the same neoliberal “common sense” that created the crisis. That’s the moment to gently point out that those ideas are part of the #deathcult, not a way out.

The alternative starts with rebuilding trust, commons and community, #FOSS needs to become a core to this.

Why Stories Matter on the #OMN

Facts matter, technology matters, governance matters. But people rarely change because they read theory, a specification or a list of features. People change because of stories, this is why storytelling is central to the #OMN path. A story gives people somewhere to stand, it connects ideas to lived experience.

Instead of explaining the #4opens as an abstract framework, tell the story of a community escaping a #dotcons platform by building trust on the #openweb. Instead of arguing about governance, tell the story of how a group solved a conflict through #OpenProcess. Instead of listing technical features, tell the story of someone who regained control of their community tech after years of platform lock-in.

Stories carry values, the last forty years of #mainstreaming have been held together by stories about competition, consumption and individual success. These stories have normalised the #deathcult, making extraction and enclosure feel like “common sense.” We now need better stories about cooperation instead of competition, commons balancing ownership, about trust instead of surveillance. Stories about building rather than consuming.

The #Fediverse itself is a story, an accidental reboot of the #openweb that proves another internet is possible. It isn’t perfect, but it demonstrates that people can build social infrastructure without billionaires directing the conversation.

The challenge is that the academics or the #geekproblem tell stories only to other, academics or geeks, technical and academic paths are important, but they don’t inspire people to act, so we need stories that people can understand:

  • A neighbourhood rebuilding local media.
  • Boaters organising to defend the waterways as a commons.
  • Communities escaping the #dotcons.
  • Volunteers composting #techchurn into useful tools.
  • Ordinary people discovering that they can build infrastructure together.

These stories become shared memory, shared memory becomes culture, culture shapes “common sense.” and changing common sense is how lasting social change happens. So don’t just build software or academic theory, tell stories that help people imagine themselves as part of a healthier #openweb.

#OMN #FOSS #openweb #4opens #ActivityPub #Fediverse #commons #DIY #makinghistory

Our failure to mediate the mess

The current #AI boom is built on a huge contradiction, you can see this in #mainstreaming terms. That AI companies are not paying the real costs of what they consume. They haven’t paid for the copyrighted material they trained on. They haven’t paid the environmental costs of the water and electricity they use. They haven’t paid for the pressure they place on public infrastructure, or for the social costs of flooding the web with synthetic content.

Yet AI services are sold below their true cost, subsidised by investors gambling on future monopolies. This should sound familiar as we saw the same pattern with social media, with the gig economy, with cryptocurrency. First comes the hype, then investor money, then comes market capture, only later do the public pay the real costs.

The problem is like every time before is not the technology itself, it is that we’re allowing it to develop within the logic of the #deathcult, where growth matters more than social value, extraction matters more than care, and monopoly matters more than the commons. Simply shouting “ban AI” won’t solve this, nor will pretending everything is fine. We need to actively and democratically mediate this technology socially, which means asking (and acting on) questions the tech industry never asks.

This is where the #OMN approach matters, technology doesn’t exist outside society as every technical system embeds social values, whether intentionally or not. If we leave the current mess AI is making entirely to venture capital, Big Tech and the market, we shouldn’t be surprised when it reproduces the same inequalities and failures of those systems.

This mess is a part of our long-standing #geekproblem, that technical culture mistakes technical possibility for social progress. Yes, sometimes it is not driven by malice, more often it’s a feedback loop of arrogance and ignorance, where clever engineering is assumed to be enough, while questions of governance, trust and community are treated as secondary, or more likely ignored altogether. The result is endless #techchurn: one wave of disruption after another, each promising liberation while quietly or noisily reproducing the same concentrations of power.

We don’t need to knee-jerk reject technology, we need to compost the culture that keeps producing these #techshit outcomes. The #4opens offer one practical path, this needed path is not anti-tech, but it is pro-society. On the path we need to take, the challenge isn’t stopping progress, it is widening the culture that guides creativity, so technology can better serves people and the planet rather than the current narrow blinded interests of capital, feeding the #nastyfew.

That’s the work of the #openweb, and the path of #OMN.

Techno feudalism what changes

What part can you play? The #openweb is one place to start, keep it simple (#KISS), build commons, compost the mess. The political centre created this mess. Four decades of neoliberal “common sense” deepened inequality, weakened democracy, privatised public goods, and pushed us beyond ecological limits. Yet much of the centre still offers only more of the same: better management of the #deathcult rather than a path beyond it.

That leaves a dangerous vacuum. If progressive movements cannot offer practical alternatives, the far right will continue to grow by presenting itself as the only force willing to confront the crisis. Their answers are built on fear, exclusion and authoritarianism – but they resonate because they acknowledge that the old system is failing, while the centre too often pretends it can be repaired.

Simply defending the status quo is not enough. Nor is retreating into abstract radicalism disconnected from everyday life. We need practical, grassroots alternatives that people can participate in and build themselves.

This same pattern appears in technology. Our #geekproblem isn’t primarily bad code – it’s a culture of arrogance and ignorance. Technical communities often reproduce the assumptions of the wider society without questioning them. They celebrate disruption while recreating centralisation. They mistake individual freedom for collective liberation, reproducing #stupidindividualism while unconsciously serving the logic of the #deathcult.

This isn’t usually malicious. It’s a feedback loop. The culture produces the technology, and the technology reinforces the culture. The answer isn’t to reject technology. It’s to mediate it socially.

The #4opens offer a simple path:

  • Open Data keeps knowledge in the commons.
  • Open Source keeps tools accountable.
  • Open Standards prevent enclosure.
  • Open Process ensures communities – not just developers or investors – shape the progress.

We need to use this to composting 40 Years of “Common Sense”, For the last 40 years, #neoliberalism and #postmodernism have shaped what passes for “common sense” in #mainstreaming society. Their influence runs so deep that many assumptions now feel natural rather than ideological. When these assumptions are challenged, people react defensively, not because they’re bad people, but because their worldview feels under attack.

This isn’t just a mainstream problem. Activist spaces reproduce the same patterns. The result is endless circular arguments, performative politics, and fragmentation rather than solidarity. Our task isn’t simply to oppose this culture, it’s to mediate it, to compost the mess rather than pretend it doesn’t exist. Composting means taking the waste of the last forty years – failed assumptions, captured institutions, #techchurn, #fashionista politics, and individualism – and turning it into fertile ground for something better.

The worrying trend is that the grassroots signal is being submerged beneath an echo chamber of #mainstreaming voices. On many #fashionista threads, noise overwhelms practical action. We spend time performing politics rather than building the infrastructure movements need. That requires empathy, patience, and practical organising. It means creating spaces where people can step outside inherited “common sense” without feeling they have to defend it. It means building working alternatives instead of only criticising broken systems.

The answer isn’t to shout louder, it’s to build better spaces, better tools, and better processes. The signal-to-noise ratio can change again, but only if we do the patient work of mediation, trust-building and commons creation – The work of #OMN.

Techno feudalism what changes

There’s a common understanding that we’re living inside a new digital world, 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 different – a system based not primarily on profit, but on rent extraction through “cloud capital.” In this narrow framing:

  • Feudalism = land-based power, bonded labour, hierarchical obligation
  • 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 digital fiefdoms, making us the “cloud serfs” by producing data and metadata for free. This generating value through participation while feeding tech 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. But is this actually a new economic system? The argument soon starts to break down as critics point out that many of these dynamics are not historically new: 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 normal capitalist mechanisms: convenience, pricing, network effects, and infrastructure dominance. From this step back 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 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 structure, it is reshaping it.

The infrastructural question? Where the #technofeudal framing becomes useful is infrastructure. The real power isn’t only in apps or interfaces, it is in “cloud” hosting (AWS, Google Cloud, Azure), database and compute control and the resulting 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, this is a real enclosure of the commons, not of in the old sense land, but of the new digital reality.

Where this leaves us? The danger is less about whether we label this “capitalism” or “technofeudalism”, more, it’s the simple assumption that the system is stable. In reality, we have capitalist dynamics operating the data-driven extraction layered on top of this older mess, with infrastructure consolidation amplifying both. Not a clean break – more 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 mess we need to compost, not the theoretical naming of it.

Our failure to mediate the mess
Open Media Network, a path we forgot

Technology is never just a tool

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.

#OMN #OGB #4opens #openweb #FOSS #indymediaback

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.

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.

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, its 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. Let’s try: 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.

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:

  1. Default to #4opens – Public funding #KISS should require open licenses, open standards, and transparent governance.
  2. Fund Maintenance – Not just #fashionista projects, but long-term stewardship of critical open infrastructure.
  3. Measure Social Value – Not hype, not valuation, not patents, but actual public use and resilience.
  4. Grassroots tech as seedlings – to be open to real change and challenge in tech.
  5. 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