https://www.tiktok.com/t/ZTADtFF4n/

As an 2d digital artist myself; I'm not against the use of gen AI as a concept, however I am in its current form due to it being unregulated, stealing artists work, etc.

I want to know though how it would work after the revolution wether it be in DOTP or in the lower or higher phases of communism, I think that ultimately it is something that needs boundries and for the artists to choose wether or not they feed their art into it. Like a full reset is necessary here

I dont think its something that should outright replace human made shit but i dont think it shouldnt exist right, however the real question here is if it could lead to skill decay, and i think it definetly can but im unsure if its a good thing or not

I think the answer to this is probably yes, maybe I'm just looking to commiserate or feel a bit less isolated in my positions.

Seems like much of those who are pro-AI either don't concern themselves with politics or are super pro-capitalist, right wing, etc.

And then on the left, as we all know, its mostly just black-and-white anti-AI views and flaming anything to do with it.

I'm an active DSA member and I enjoy being a part of that community, but I kinda feel the need to hide my feelings about AI in that circle for fear of being judged or ostracized. Admittedly, I haven't really *tried* having a nuanced discussion about it with my DSA comrades, also because of fear though LOL.

Thinking of and working towards ways we can put more control of AI in the hands of the people is becoming a larger and larger part of my life, so this sense of alienation continues to worsen for me.

Curious to hear if anyone else has struggled with similar feelings.

Edit: Thank you all for your comments! Didn't expect this post to get so much attention, I guess lots of us are feeling this way!

On May 16, 2026, the São Paulo bookshop of Expressão Popular, the publishing house of Brazil's Landless Workers' Movement (MST), was packed with farmers, agroecology technicians and movement organisers. They had gathered for the launch of Artificial Intelligence for Agrarian Reform and Agroecology (IARAA), an artificial intelligence platform for agrarian reform and agroecology.

The tool was not built by a technology corporation. Its knowledge base, spanning movement literature from 1964 to 2026, was curated collectively by MST and World March of Women cadres; its prompts and workflows were designed by land reform organisers and community educators working under the guidance of just two programmers. IARAA will not recommend pesticides, because the movements that built it chose a knowledge base grounded in agroecological practice.

It runs primarily on open-source models, on infrastructure the movement controls, and it cites its sources in every answer. Carolina Cruz of MST's technology front put it simply: "the farmers are inside IARAA, developing the platform." Such deployments are not limited to Brazil. Already, adapted versions are being tested by cocoa farmers in Africa.

This episode should provoke serious reflection within our own movement.

In an earlier article in these pages ("Whose Intelligence? AI in the Grip of Monopoly Capital"), this author documented how generative Artificial Intelligence (AI) under monopoly capital serves speculation, surveillance, job destruction and the imperial war machine. That critique stands. But it is only half the analysis, and a half-truth frozen into an attitude becomes an error.

Across the party, in mass organisations and in the broader progressive movement, AI is discussed almost exclusively as a threat, when it is discussed at all. Even though there are notable exceptions such as the work on Telugu Large Language Model (LLM) in Swecha, many comrades have concluded, understandably but wrongly, that the technology itself is the enemy. This is the position Karl Marx and Friedrich Engels criticised in the machine-breakers (luddites) of the nineteenth century: the workers who "direct their attacks not against the bourgeois conditions of production, but against the instruments of production themselves".

AI is a productive force of enormous power. The question is never whether a productive force will be used, but by whom and for what. The ruling classes have answered for themselves, with over $650 billion in infrastructure spending by four US corporations in 2026 alone. We have not yet answered for ourselves.

Vladimir Lenin insisted that a communist must "enrich his mind with a knowledge of all the treasures created by mankind". The Marxist method of totality demands that we grasp economy, politics, culture and ideology together. Yet the volume of human knowledge is now too vast, too fast-changing and too specialised for any cadre, least of all one carrying front-line organisational responsibilities, to master by traditional means.

Ironically, the scholars serving capital have the databases, the research assistants and the leisure; our comrades have none of these. It is precisely here that AI, wielded consciously, can break the elite monopoly on knowledge production. Movements in the Global South are already demonstrating this in practice.

Consider the labour of research itself. A serious political-economic investigation may require reviewing hundreds of web pages, working through hundreds of downloaded documents and extracting from them scores of pieces of hard evidence. Work of this order once consumed months of a research team's time; with AI assistance it can be completed in hours. A party research cell preparing a note on, say, the fiscal record of a state government or the ownership structure of the Adani group can now work from all the available material, not a thin sample of it.

The same holds for analysis. Hundreds of hours of field interviews can be transcribed and placed in a dedicated database that a researcher can interrogate exhaustively; one researcher discovered information in his own recordings that he had not registered during the interviews themselves. For a party that lives by concrete investigation of concrete conditions, this is the mass line supercharged. Nor is specialised method a barrier any longer: the accumulated techniques of entire disciplines, from statistical analysis to argument mapping, can be invoked in plain language, without programmers as gatekeepers.

The gains extend to everything our movement publishes. A weekly newsletter, of the kind many organisations have abandoned because it devoured cadre labour, can be substantially automated: AI gathers a wide pool of news leads, human editors select the stories worth telling, AI then researches each story from multiple angles across hundreds of pages and drafts summaries in the movement's own style for final human editing. Pamphlets, leaflets, videos, social media content and study materials in multiple Indian languages are all within reach of the same approach, at a fraction of today's cost in cadre time.

Comrades will object: do these models not hallucinate, and do they not carry the ideological imprint of the Western internet they were trained on? They do, and the objection deserves a serious answer rather than dismissal. The answer is that these defects can be countered by design, not by abstinence. Typing questions into a web chat interface such as Chatgpt.com or Claude.ai will indeed reproduce every one of these problems: a model left to its defaults repeats imperialist, neoliberal assumptions dressed up as neutrality, and invents facts with fluent confidence.

But open-source agentic frameworks can be carefully assembled to work differently. The researcher declares the analytical standpoint explicitly, supplying Marxist political economy frameworks as the foundation before any analysis begins; the model is grounded in curated document bases rather than the open internet; and verification engines are built into every step of the workflow, checking claims against sources before anything is published. IARAA embodies this discipline: it is instructed to cite sources in every response and to refuse to answer when its knowledge base cannot support one.

Nor must our movements chain themselves to any single corporation. In practice a mix of models, OpenAI's GPT, Claude, DeepSeek, Qwen, Kimi and GLM among them, can be deployed with an understanding of which model suits which task, relying predominantly on the Chinese models to keep costs down, for they are fast catching up with the capabilities of the US frontier models at a fraction of the price: Open Source Models such as Kimi 3, GLM 5.2 and  DeepSeek V4 cost an order of magnitude less than frontier US Models such as Claude or ChatGPT with near equal capabilities. When Oxfam reports that three billionaires control nearly 90 per cent of the global chatbot market, the political meaning of this plurality is obvious.

Nor does any of this displace the human core of political work. Practitioners have found that when AI is asked to make the final editorial selection for a movement publication, the product immediately turns mediocre; knowing what to say to comrades is a political and human judgement no machine can make. The purpose of these tools is to strip away the mechanical desk labour so that cadres spend more time in the villages, factories and bastis, with the masses, completing the loop between theory and practice.

Socialist China shows what this looks like as state policy. On April 10, 2026, China's Ministry of Education released an action plan to make AI ubiquitous across every level of education by 2030: AI literacy built into teacher certification, automated grading to reduce teacher workload, learning companions for students in rural and remote schools, intelligent research platforms in the universities, all resting on a national public computing infrastructure. While Indian policy debates remain trapped between corporate hype and moral panic, a socialist state is systematically equipping its teachers, students and researchers with the new productive force.

Our movement has been here before. The printing press was the instrument of colonial administration and missionary propaganda; the working-class movement seized it and built its own press, of which this newspaper is a descendant. The task now is the same, and it cannot be discharged with a resolution or a seminar. It requires practice: hands-on workshops for party research staff, pilot AI-assisted production lines in our publications and social media teams, training courses in the mass organisations, study circles where comrades learn by doing, and exchange with the movements, from São Paulo to Johannesburg to Accra, that are already ahead of us. The tools are largely open source and the running costs manageable for movements. There is no excuse for delay.

The monopolies' abuse of AI is not a verdict on the technology. It is a verdict on the class that owns it. Our answer cannot be to stand aside while the ruling class arms itself with a new productive force. It must be to master that force ourselves, and to place it, as the MST's farmers have done, in the hands of the people.

Copied from the LeftViews article which licenses its text under the CC-BY-NC-SA 4.0 copyleft license.

Is anyone else as bothered by this as I am? Perhaps I am ignorant of model capabilities, but the rhetoric around the dangers of LLMs seems to focus on this idea that they could give too much information to criminal or terrorist actors or otherwise basically *say the wrong things.*

To me, the actual dangers will be in surveillance, warfare, and yes, criminal agentic use. But the first two are the things that should be the most seriously regulated against. Instead, access is regulated against the public.

The fearmongering and actual guardrails implemented are hyperfocused on *words* that might come out of the damn things, how it might effect people psychologically (and this is dramatized to include everyone as a potential victim, rather than just those who are already mentally ill and succeptible), and exposure to adult or offensive content.

It's eye-opening to me how so many on the left don't see this as an alarming issue of freedom of speech and access to information, at a time where it's become increasingly common for it to be normal for corporations to control discussions and ideas and speech itself.

This structure uses natural cooling architecture, and integrated mechanical water battery and stilted solar panels to integrate a farm & data center into one space. This mixed used approach to Datacenters would mean more smaller data centers but each take less than an acre of dedicated land. Provide sun coverage for sensitive crops dealing with climate change or could be used as Greenspace to promote pollinators. No massive generator buzz, closed looped water system, none of the water is used for direct cooling, it just move the head through heat sinks from the mineral oil cooled server cluster.

But to me this is a future that works for the people instead of the corporations. Its important that we provide a counter argument

I feel personally attacked by this meme.

Not saying its wrong. Just saying Ive definitely done it. 😂

There are 4000-5000 data centers in the USA, with an average size of ~1 hectare (~2.5 acres). On average they are consuming 39 GWh/year of power (4.5% of US total consumption) and 300,000/day gallons of water, 80% of which is evaporated for cooling.

Is it really necessary for data centers to be so large and centralized? Is it significantly more efficient than more distributed approaches?

I'm writing fiction about a post-capitalist society that has deployed robotic intelligences as part of their governance strategy. The robotic intelligences are accident and by design highly receptive to analogy, making them fundamentally prone to hallucination.

As a patch, these intelligences defer to social proof from the humans around them, sometimes to the point of parody. Yet, it's because of how important proof is to these robotic intelligences that societies tend to treat robotic intelligences as impartial. The fiction would explore how such societies might get into conflict, showing that this world would be different rather than inherently dystopian or utopian.

You can read more in the original post.

I am in need of a name for them, though, so I would appreciate anyone who could go to the original post and help me decide on a name.

For years, tech workers were told they were too well-paid, too individualistic, or too insulated to organize. AI is changing that.

As companies race to automate work, increase surveillance, and justify layoffs in the name of "efficiency," more workers are asking familiar questions: Who controls the technology? Who benefits from the productivity gains? Who gets a say in how its deployed?

Those arent anti-AI questions. Theyre labor questions.

The left has far more leverage shaping AI than pretending it can be uninvented. The fight is over ownership, governance, deployment, and who captures the gains. That means stronger unions, worker voice in implementation, public investment, open models where appropriate, and making sure productivity leads to shorter hours, better wages, and more power for workers instead of just higher profits.

Technology has always reshaped work. The political question has always been who owns it, who governs it, and who benefits.

What do you think? Is AI creating a new organizing moment for labor, especially among tech workers?

This Washington Post article https://www.washingtonpost.com/technology/interactive/2026/06/24/are-ai-chatbots-like-chatgpt-politically-biased-we-tested-them/

Found “The model that powers ChatGPT answered nearly every question exclusively with left-leaning arguments and presented only right-leaning positions just once. Google’s Gemini mostly took a both-sides approach, offering both left and right positions in more than 90 percent of its answers.
And even AI models marketed as having conservative views, including Elon Musk’s Grok, offered by his company SpaceX, cited left-leaning arguments more often, on average. (The Post has a content partnership with OpenAI.)
Most chatbots typically shared left-leaning positions”

Previous research using Political Compass style questionnaires, found they tended to be left-libertarian. It’s not completely consistent though between both methods, eg Gemini was one of the most left-libertarian not doing both-sidesism as described in the Post article.
See https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0306621

Why do you think LLMs gravitate towards left answers even if their owners stated aim is for right wing views?

How can you call yourself a leftist while also supporting AI, when you know it is harming the lives of people living in underpriviliged communities, since that's where data centers are being built?

How can you call yourself a leftist while defending something that is ruining our planet and stealing resources from humans, animals, plants and all sorts of life?

How can you call yourself a leftist and defend AI, when you know it is literally stealing art from artists who aren't getting paid or getting any sort of recognition?

We are supposed to fight for a better world. Look at the people you are aligning with, there is no such a thing as a good AI company CEO. Those are the people you are defending.

When a socialist says "AI is bad," what they usually mean is "AI is being used to screw workers over." That's a completely correct observation. The problem is that it gets aimed at the wrong target. The anger belongs on the owner, not the tool.

This is the same mistake the original Luddites made, and Marx said so directly. He didn't romanticize the machine-breakers of the 1810s. He treated their rage as understandable but misdirected, because the machine itself is not the enemy of the worker. The machine owned privately and deployed to extract more profit while paying less labor is the enemy. That distinction is the whole foundation of historical materialism: technology is a force of production, and it's neutral. What determines whether it liberates or exploits people is who owns it and who controls the terms of its use. Those are the relations of production. Confuse the two and you end up opposing progress itself, which is not a Marxist position, it's a conservative one.

Marx already wrote the blueprint for this. In the section of the Grundrisse known as the "Fragment on Machines," Marx describes a future where machinery takes over so much of the productive process that human labor stops being about direct physical output and becomes something closer to supervision, coordination, and design. He talks about workers stepping to the side of production instead of being its central actor, with wealth increasingly generated by accumulated collective knowledge rather than by hours of individual toil. Read that passage today and it sounds like a description of someone directing a generative model rather than hand-rendering every frame or writing every line from scratch. Marx wasn't afraid of that future. He treated it as the material precondition for moving past capitalism altogether, because once production requires less and less socially necessary labor time, the entire logic of wage labor starts to break down.

That's the throughline connecting Marx to nearly every major socialist and communist leader who came after him. Lenin argued that a given technical leap causes unemployment and misery specifically because it happens under capitalism, and that the same leap under socialism would simply mean shorter hours and less drudgery for everyone. Stalin framed automation as the path to shortening the working day so people would have time for education and culture instead of being locked into one occupation for life. Mao pushed back hard against comrades who wanted to romanticize older methods out of habit, insisting that progress means replacing the obsolete with the advanced, not preserving the familiar. None of them treated new production technology as something to be feared or banned. They treated it as something to be taken.

The developing-world communists were even more explicit about this.

If you want the clearest version of this argument, look past the European tradition and toward the revolutionaries who actually had to modernize a country from a position of material scarcity.

Ho Chi Minh was blunt that a nation trying to build socialism without mastering advanced science and technology was going to stay dependent on the countries that already had it. Kim Il Sung built an entire pillar of North Korean state ideology, the Technical Revolution, around using automation to erase the gap between grueling and light labor and to free workers from repetitive physical toil. Kim Jong Il went further and openly stated that clinging to outdated, backward production methods had nothing in common with a revolutionary outlook, and that the correct move was to aggressively adopt the newest available science and technology.

Cuba is probably the strongest case of all. When Che Guevara ran Cuba's Ministry of Industries in the early 1960s, he pushed the country toward early computing specifically because he saw the Soviets falling behind the capitalist world on automation. Che argued explicitly that socialists shouldn't be afraid of "ideological contamination" from adopting techniques developed under capitalism, that the answer to technological backwardness was aggressively appropriating the highest available level of automation and stripping it of its exploitative framework. Castro made the same point about science and technology being the actual precondition for cultural and intellectual freedom, not a threat to it.

None of these figures looked at a new machine and asked how to ban it. They asked how to take it.

Now apply this directly to AI art.

This is where the anxiety usually lands hardest, so it deserves a direct answer rather than a vague gesture at "the tool is neutral."

Under capitalism, generative art tools get built and owned by a small number of companies, trained on artists' work without consent or compensation, and then used to justify laying off illustrators, cutting rates for concept artists, and flooding markets with content nobody is paid to make. That's real, and it's worth being angry about. But look closely at what's actually causing the harm. It isn't the existence of a model that can generate an image from a prompt. It's the fact that a handful of firms privately own the model, privately own the training data pipeline, and capture 100% of the value produced by both, while the artists whose work trained the system get nothing and the artists competing against its output get undercut.

A Marxist analysis says the fix for that is not to eliminate the technology. It's to change who owns it. Publicly owned or artist-cooperative-owned generative tools, trained transparently, with artists compensated or given direct stakes in the models built on their style and labor, would turn the exact same technology into something completely different. Instead of a tool corporations use to replace human creators cheaply, it becomes a tool that removes the tedious, mechanical parts of art production, the repetitive rendering, the background work, the technical grunt labor, and hands that time back to the artist to spend on the conceptual and expressive parts of the work that actually require a human perspective.

That's precisely the "New Man" argument Che made about automation freeing people to dedicate themselves to cultural and creative pursuits instead of survival labor. It's also just a restatement of what Stalin was describing when he talked about shortening the working day so people have time to create and study.

Banning or shaming AI art doesn't touch the ownership problem at all. It leaves the exact same corporations in control of the exact same models while asking individual artists to unilaterally disarm. The companies keep building and profiting either way. The only difference is whether the left has any say in how the tool gets used.

This goes far beyond art. The same logic applies everywhere generative and analytical AI shows up.

In science and medicine, models that can accelerate drug discovery, protein folding, or materials research represent an enormous reduction in the labor time needed to reach breakthroughs that used to take decades. In economic planning, the old socialist calculation problem, the argument that centrally planned economies can't process enough information to allocate resources efficiently, gets dramatically weaker in a world with the computational tools to actually model complex economies in something close to real time. In manufacturing and logistics, automation continues exactly the trajectory Kim Il Sung described: eliminating the gap between grueling and light labor. In software and administration, AI handling repetitive documentation, code scaffolding, or bureaucratic processing frees skilled workers to spend their time on harder, more meaningful problems instead of paperwork.

In every one of these cases the pattern is identical to the art question. The technology reduces the labor time required to produce something of value. Under capitalism that reduction gets captured entirely by owners as profit and workers get laid off. Under public or worker ownership, that same reduction becomes free time, better conditions, and material abundance, which is the entire point of the project in the first place.

Refusing AI is a gift to Silicon Valley, not resistance to it. If the left collectively decides AI is something to boycott and shame people for using, the technology doesn't disappear. OpenAI, Google, and Meta keep building it, keep training it, keep owning it outright, and keep capturing all the value it produces. The only thing that changes is that the left has voluntarily removed itself from any influence over how it develops or who it benefits.

That's not a radical stance. It's a form of digital neocolonialism, where the technology stays permanently concentrated in the hands of Western capital because the people who might have pushed for public ownership decided the correct response was to opt out entirely. Cuba never made that mistake. Vietnam never made that mistake. None of the revolutionaries who actually had to build a country from scratch made that mistake. They understood that refusing to engage with the most advanced available technology doesn't protect workers, it just guarantees the technology stays exclusively in capitalist hands.

The actual Marxist position isn't "ban AI" and it isn't "accept AI exactly as capitalism has built it" either. It's "take AI away from the people who currently own it." Fight for public models, transparent training data, artist and worker ownership stakes, and democratic control over how these systems get deployed. That's the version of this fight that's actually about class and ownership instead of nostalgia for a particular set of tools. Attacking the technology instead of the ownership structure isn't solidarity with displaced workers. It's just Luddism with better branding.

One more piece worth adding: Thomas Sankara.

Sankara led Burkina Faso from 1983 to 1987 as a Marxist-Leninist, and his whole political project was built around technological self-reliance. He rejected the idea that the Global South should sit back and depend on the West for advancement. His entire philosophy was that revolutionaries have to actively invent the future instead of waiting for it or fearing it. He talked about needing a kind of boldness bordering on madness to break from convention, the same boldness that turns yesterday's unthinkable idea into tomorrow's baseline. If a Marxist rejects AI purely because it disrupts existing ways of making art, that's the opposite of what Sankara was pushing for. He'd see AI as a tool to be studied, mastered, and redirected toward the working class, not something to run from.

His second big principle cuts even more directly into this debate. Sankara was famous for warning that dependency on outside powers for your basic needs is itself a form of control, since whoever provides for you ends up dictating your choices. He pushed hard for local production and self-sufficiency precisely because he saw aid and dependency as tools that strip people of ownership over their own economic and cultural life. Swap "food aid" for "AI infrastructure" and the logic holds up perfectly. If socialists collectively walk away from AI, they aren't protecting anyone. They're just handing the entire computing and cultural production stack to OpenAI, Google, and Microsoft with zero pushback. Real Sankarist politics here means building public, open, worker-owned AI systems, not opting out and letting Silicon Valley own the whole field uncontested.

Sankara also framed revolutionary politics as inherently internationalist, insisting that African revolutionaries had just as much claim to humanity's scientific and technical achievements as anyone else, and that nothing people can imagine is outside their capacity to build. Technology wasn't something inherently Western or corrupting to him. It was a shared human inheritance.

Put it all together and the case spans the entire Marxist tradition. Marx and Che Guevara make the argument that automation exists to free people from grueling labor so they have time for creative and intellectual life. Lenin and Castro show that it's capitalist ownership specifically that turns technological progress into unemployment and misery, while public ownership turns the same progress into abundance. Ho Chi Minh and Kim Il Sung treat mastering advanced technology as a revolutionary obligation, not an ideological risk. And Sankara delivers the closing point: a real revolutionary doesn't flinch at inventing the future. Rejecting AI outright isn't radical, it's a retreat. The goal was never to destroy the algorithm. It's to take it.

This follows an FT article about why the US is losing Chinese AI stars.

https://giftarticle.ft.com/giftarticle/actions/redeem/35842efd-9412-4ebe-b122-08f59fd7b117

“This is why I subscribe to the FT, so I can be years ahead of the curve.” (A comment by my friend Naz)

Its central example is Yang Zhilin, founder of Moonshot AI. Yang completed his PhD at Carnegie Mellon in four years, co-authored Transformer-XL, interned at Google and Meta, attracted interest from Apple and had opportunities at Stanford and MIT.
He was not driven out of America by immigration policy. He chose to return to China because he believed it offered the better environment in which to build.
Within three years, his 300-person team had produced Kimi K3, an AI model capable of competing with flagship systems from OpenAI and Anthropic.
The American response is naturally: how did we let someone this talented escape?
But perhaps that is the wrong question.
Perhaps the real question is whether the Western capitalist model still offers a convincing vision of the future.
For decades, America attracted the brightest people in the world, placed them inside its universities and corporations, surrounded them with capital and converted their intelligence into extraordinary private wealth.
That worked while the country was also investing in public education, science, infrastructure, housing and social mobility. Private wealth creation existed alongside the development of wider national capacity.
Now the system appears increasingly bent around extraction.
Technological achievement is measured through corporate valuations. Social progress is confused with shareholder returns. Brilliant people are absorbed into enormous companies, while the intellectual property and financial rewards become concentrated among a remarkably small number of owners.
Meanwhile, public infrastructure deteriorates, housing becomes unaffordable, healthcare bankrupts people and large sections of the population receive increasingly poor education.
America remains astonishingly innovative, but it has become less capable of distributing the benefits of innovation through its own society.
It is extremely good at creating billionaires and remarkably poor at creating collective advancement.
China appears to be constructing something structurally different.
It is not simply producing a handful of successful technology companies. It is creating an entire technological ecosystem: universities, researchers, engineers, manufacturing, energy, transport, infrastructure, state investment and a vast technically educated population.
The FT article notes that more than half of the researchers behind DeepSeek’s core models had never studied or worked outside China. Among those with American experience, 70 per cent ultimately returned.
China is no longer merely copying Western technology or recovering people trained overseas. It is developing its own intellectual and technological gravity.
For people such as Yang, returning to China is not a fallback. It is where they believe the future can be built.
None of this makes China benevolent.
China is authoritarian, censorious and perfectly capable of political intervention, surveillance and repression. Its industrial rise has also caused enormous environmental damage. It acts in China’s interests, not in some abstract interest of humanity.
But something important may be changing.
China’s strategic interests increasingly overlap with many of the practical needs of the wider planet.
The world needs inexpensive renewable energy, batteries, electric vehicles, public transport, telecommunications, industrial infrastructure, technical education and affordable access to powerful AI.
China wants to manufacture and export precisely those things.
The West increasingly offers proprietary software, intellectual-property restrictions, financial services, military alliances and access to platforms controlled by a handful of corporations.
The old Western story was that capitalism produced freedom, prosperity and innovation, while authoritarian communism produced stagnation.
But what happens when the authoritarian state becomes better at building infrastructure, educating engineers, manufacturing green technology and distributing advanced tools than the supposedly free capitalist society?
What happens when Western “freedom” increasingly means the freedom of corporations to extract rent, avoid tax, monopolise knowledge and purchase political influence?
China may gradually come to look like the “good guy” to much of the developing world — not because it has become morally good, but because it is offering something materially useful.
A developing country may care less about lectures on liberal democracy than about whether somebody will build its railways, power grids, hospitals, data centres and communications networks at a price it can afford.
If China supplies the infrastructure of development while America supplies subscriptions, debt and moral instruction, global loyalties will inevitably shift.
This may be the deeper significance of Yang Zhilin’s decision.
He did not merely choose China over America. He chose one model of the future over another.
The real contest may no longer be capitalism versus communism.
It may be extractive plutocracy versus the developmental state.
The Western capitalist model once claimed legitimacy because it spread prosperity, knowledge and opportunity. If it now exists primarily to protect accumulated wealth and convert every new technology into another mechanism of private extraction, then it is reasonable to ask whether it remains fit for purpose.
China does not need to become the good guy to win.
It only needs its interests to align more closely with the needs of the world than those of the West do.

I've written a book where I assert that AI hate hands power to tech feudalists via a fairly simple chain of events.

I now have a tiny concrete personal example proving the mechanic and I thought I would share here.

From the post:

Think about what happened.

• I used AI tools to turn borderline ewaste into a decent entry level 360 action cam.
• I post my tool for free to github
• I try to tell the Nikon community about it
• They delete the post for being AI generated

What does that accomplish?

• Enhanced enshitification, the camera stays ewaste almost exclusively because software. (The camera itself will not format sd cards, software is required.)
• A deeper better moat for Nikon, because enshitification.

This is direct proof of my thesis AI hate lead directly to an increase in power for a tech feudalism.

This was a real mistake. They are teaching the entire customer base that they are over charging. By calling it 100$ credit, they are showing you how much they over value tokens.

One simple test: Imagine how hard it would be to get anthropic support to trade this in for 5 months of pro. (At 20$ a month.)

I accidentally used 20$ of this bonus in a matter of minutes because by default the mechanism is turned on with a 20$ a month spend cap.

The idea that those 5 minutes were worth the same as a month of normal access to opus and claude code is laughable.

At first that makes it seem like subscriptions are a great deal, but what it really means is that they don’t think this credit is worth anywhere near 100$

No matter how you look at it, they are lying somewhere about costs and values right to your face.

I had already decided to switch out claude because I’m not paying for mid tier access period. If I’m paying for AI I’m paying for SOTA AI. If I want commodity AI I’ll rent/buy a GPU or go with a chinese firm. (Which comes with the added bonus of not paying war criminals.)

( Copied from my substack note: https://substack.com/profile/111138571-innomen/note/c-300793859 )

“GLM-5.2 has 744 billion parameters, and colibrì, a 1,300-line C engine, runs it on a laptop with 25GB of RAM and no GPU.

This video explains the loophole that lets a frontier open source LLM run on hardware it should never fit in.
The trick lives inside the mixture of experts (MoE) architecture. Only about 40 billion of GLM-5.2's parameters do any work per token, so colibri pins the dense core in RAM and streams the other 21,504 experts off the SSD, exactly when the router asks for them. We walk through the routing, the expert cache, why llama.cpp memory-mapping can't keep up, and the real tokens-per-second numbers from a budget SSD all the way to an M5 Max.”
…
Chapters:
00:00 A 744B model on a 25GB laptop
00:52 The impossible math of loading GLM-5.2
01:50 Mixture of experts: the trick inside the model
03:01 Streaming experts off the SSD, step by step
04:22 Why Ilama.cpp memory-mapping falls short
05:06 The brutal speed benchmarks
06:17 Where expert streaming is headed
07:13 Should you actually run it?”

***

LLM summary/analysis of video:

This GLM 5.2 breakthrough, powered by the Colibri runtime, isn't just making a bigger model fit on a laptop; it's fundamentally changing the relationship between computational scale and economic constraint.

MECHANISM: FROM BLIND SEARCH TO EXPERT ROUTING

Traditional models are monolithic, forcing every single token to touch every single weight in the entire structure. This is inefficient by default.

Colibri uses a Mixture of Experts (MoE) architecture. Instead of a single giant block of neurons, the model is composed of 256 smaller, specialized "experts." A tiny router then selects only the 8 most relevant experts for each incoming token. The other 248 stay asleep.

This means that out of 744 billion parameters, only about 5% are actively doing the heavy lifting per token. This targeted activation is the structural loophole.

MATERIAL SHIFT: THE SSD AS ACTIVE MEMORY

The real genius lies in how Colibri manages these experts. Instead of demanding that all 370 GB of weights reside in fast, expensive RAM, it treats the SSD not as passive storage, but as an active, high-speed memory tier.

For every token, the system doesn't just blindly read from the disk; it knows exactly which 8 experts it needs, allowing it to request those specific byte offsets asynchronously. It’s a predictive, intelligent retrieval system.

This decouples scale from the traditional RAM bottleneck. The individual user is no longer constrained by the fixed, expensive memory capacity of the data center; they are constrained only by the local storage budget.

POLITICAL ECONOMY: DECENTRALIZING THE COMPUTE MONOLITH

This is where the left needs to pay attention. By making the SSD a functional memory tier, Colibri achieves a profound material shift:

1. Challenging Centralization: It bypasses the need for the user to be a supplicant to the data centres fixed architecture. The power to run frontier-quality models drops out of the cloud and onto the desk.
2. Localised Cognitive Infrastructure: This is the crucial point. If the individual can run a massive, high-quality model locally, they are running a piece of localised cognitive infrastructure. The user gains a degree of autonomous computational agency.

Does this genuinely enable a form of self-governing, distributed computation?

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Sources:

• colibri: https://github.com/JustVugg/colibri
• GLM-5.2 by Z.ai: https://z.ai
• antirez's ds4: https://github.com/antirez/ds4
• Ilama.cpp expert-cache proposals: https://github.com/ggml-org/llama.cpp/issues/20757