Tag: GenAI

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.