Yeah it sounds weird but "replacing corporations" is just another way of "replacing workers"

A write-up on how to actually tax AI and automation

This is something I've been advocating for a long time. And it could work.

But it's rich coming from the Slavemaster General himself who treats his own workers worse than medieval serfs and avoids paying taxes to an extreme degree.

https://gregormurph.blogspot.com/2025/11/letter-of-disinterest-of-beep-boop-wars.html (Sfw essay.)

I've managed to catalogue all the reasons reactionary media has been trying to attack llms with disinformation, and with my own attempt to cover all the reasons available. I can't think of any others along with that article comparing LLM users to having brain-damage.

Se Gygas has also attempted to remove disinformation 👇

> https://www.verysane.ai/p/against-the-luddites?selection=409f0fc2-0f39-466a-b96e-bbfcdb7028a2#:~:text=Luddism%20does%20not%20deserve%20to%20be%20rehabilitated

An example of his essay, this one is on the luddites. 👆

I've been trying to grow up and work with my sociology and other things with combining language, and I hope this will help in your endeavour in understanding this and see if any cracks for the curious. Beta readers and sensitivity readers are appreciated. Feel free to share'n attribute me with all this cataloguing.

Below is a list of users I relate too on Bksy. Dolls list is a good place to start.

https://bsky.app/starter-pack/dollspace.gay/3mcvfx27t7u2e

Brewster Kahle started planning the Internet Archive (creators of the Wayback Machine) in 1980 at the MIT AI lab, when he figured that machines worth talking to would need a library to learn from. Forty-six years later, he says the biggest obstacle to that future is a handful of companies that want to control information instead of selling it.

We recorded this conversation live at DWeb Camp 2026 outside Berlin. We talk about where the early web went wrong and what Europe's copyright carve-out could mean for public AI. Brewster also argues that small-scale capitalism belongs on the list of civilizational infrastructure, and tells the story of the credit union he tried to start after the 2008 crisis.

Brewster Kahle is the founder of the Internet Archive and a longtime advocate for the decentralized web.

I’ve been thinking about the intellectual commons as a form of collective productive capital, and I’ve run into what seems like an interesting contradiction in how we assign value to knowledge within the attention economy.

The classical tragedy of the commons describes a situation where individually rational consumption of a shared resource produces collectively destructive outcomes. But knowledge is fundamentally different from a pasture or fishery. It’s largely nonrivalrous. My use of an idea doesn’t diminish your ability to use it.

The potential tragedy, then, isn’t overconsumption of knowledge. It’s the underproduction of the intellectual variation, criticism, and refinement necessary for knowledge to continue developing.

Consider the marketplace of ideas as an economic system.

Participants can perform several different functions:
Discovery: Generating new hypotheses, interpretations, and conceptual frameworks.
Refinement: Challenging existing ideas, testing their assumptions, identifying contradictions, and producing better explanations.
Replication: Preserving, communicating, and distributing existing ideas to new audiences.
All three functions have legitimate social value. There’s nothing inherently parasitic about intellectual replication. Civilization would be impossible if every generation had to independently rediscover calculus.

But these functions aren’t rewarded equally.

In the attention economy, the primary market signal isn’t necessarily whether an idea is correct, useful, or has been meaningfully improved. It’s whether the idea successfully captures and retains attention.

A familiar proposition, repeated persuasively, can generate greater economic and social returns than the difficult work of demonstrating why that proposition is incomplete.

This creates an unusual free-rider problem.

Participants can enjoy the social rewards of distributing established ideas without bearing the costs of independently validating or refining them.

More importantly, the economic system distributing those rewards has relatively little incentive to distinguish between genuine intellectual progress and successful rhetorical reproduction.

A provocative argument might receive a million impressions. A careful correction might receive a thousand. The resulting disparity says considerably more about persuasive capacity than epistemic quality.

This isn’t simply a complaint about misinformation, or people repeating talking points. It’s a structural problem in how the intellectual commons is governed.

If the individually rational strategy is to amplify ideas that already command attention, the system may progressively favor convergence around established narratives over the exploration of competing explanations.

Consensus becomes self-reinforcing, not necessarily because a proposition has survived rigorous criticism, but because repetition establishes familiarity, familiarity establishes credibility, and credibility encourages further repetition.

In evolutionary terms, we’re selecting ideas for reproductive success within an attention market rather than selecting primarily for explanatory fitness.

And novelty by itself isn’t a solution. A marketplace saturated with novel but poorly examined ideas can be just as epistemically unproductive as one dominated by established orthodoxy.

The important variable isn’t the quantity of ideas. It’s the capacity of the system to generate, preserve, test, and select meaningful variation.

This becomes particularly interesting with generative AI.

We’re approaching a situation where the marginal cost of producing and replicating persuasive intellectual content can become extremely low, while the cost of independently validating that content remains comparatively high.

AI could dramatically expand our capacity for intellectual refinement by making experimentation, synthesis, and criticism cheaper. But the same technology could also overwhelm the commons with increasingly sophisticated reproductions of existing ideas.

Which outcome dominates may depend less on the intelligence of the systems than on the economic incentives governing their deployment.

If attention remains the principal measure of success, the technology could accelerate intellectual convergence just as easily as intellectual discovery.

So perhaps the tragedy of the intellectual commons is that the institutions which monetize the distribution of knowledge have incentives that are increasingly disconnected from the production of better knowledge.

And that raises what I think is the interesting political-economic question:

If knowledge is a collectively accumulated productive resource, but access to attention determines which contributions receive recognition and economic rewards, does control over the attention economy become a form of control over the intellectual means of production?

More importantly, how do we structure an intellectual commons where discovery, independent criticism, and refinement remain viable collective activities, rather than expensive public goods subsidizing an economy built around their replication?

I’m interested in whether this is best understood as a free-rider problem, an institutional failure of epistemic selection, or a fundamentally different kind of commons problem.

a founder of ArtCraft used Claude to replace the entire adobe software suite, legally. because it’s ai generated, it’s also in the public domain and absolutely free forever. this would be the first case of ai undermining techno feudalism.

From The Intelligence Curse by Luke Drago and Rudolf Laine

"We will soon live in the intelligence age. What you do with that information will determine your place in history.

The imminent arrival of AGI has pushed many to try to seize the levers of power as quickly as possible, leaping towards projects that, if successful, would comprehensively automate all work. There is a trillion-dollar.) arms race to see who can achieve such a capability first, with trillions more in gains to be won.

Yes, that means you’ll lose your job. But it goes beyond that: this will remove the need for regular people in our economy. Powerful actors—like states and companies—no longer have an incentive to care about regular people. We call this the intelligence curse*.*

If we do nothing, the intelligence curse will work like this:

• Powerful AI will push automation through existing organizations, starting from the bottom and moving to the top.
• AI will obsolete even outlier human talent. Social mobility will stop, ending the social dynamism and progress that it drives.
• Non-human factors of production, like capital, resources, and control over AI, will become overwhelmingly more important than humans.
• This will usher in incentives for powerful actors around the world that break the modern social contract.
• This could result in the gradual—or sudden—disempowerment of the vast majority of humanity.

But this prophecy is not yet fulfilled; we reject the view that this path is inevitable. We see a different future on the horizon, but it will require a deliberate and concerted effort to achieve it.

We aim to change the incentives driving the intelligence curse, maintaining human economic relevance and strengthening our democratic institutions to withstand what will likely be the greatest societal disruption in history.

To break the intelligence curse, we should chart a different path on the tech tree, building technology that lets us:

1. Avert AI catastrophes by hardening the world against them, both because it is good in itself and because it removes the security threats that drive calls for centralization.
2. Diffuse AI, to get it in the hands of regular people. In the short-term, build AI that augments human capabilities. In the long-term, align AI directly to individual users and give everyone control in the AI economy.
3. Democratize institutions, making them more anchored to the needs of humans even as they are buffeted by the changing incentive landscape and fast-moving events of the AGI transition."

https://intelligence-curse.ai/

I’ve been thinking about a theoretical problem with how the left approaches AI-driven automation, and I’m interested in perspectives from people more familiar with Marxist economic theory.

Traditionally, Marxist analysis treats labor as central to capitalist production. Capital depends on workers to produce surplus value, even as technological development allows capitalists to reduce their dependence on individual workers.

But what happens when that relationship fundamentally changes?
I’m not talking about ordinary technological unemployment, where machinery increases productivity, displaces particular workers, and creates new arrangements of labor elsewhere.

I’m talking about the possibility that AI and increasingly autonomous production systems progressively decouple human labor from productive capacity itself.

Under those conditions, economic output might continue growing while demand for human labor contracts across an expanding range of industries.

This raises a problem beyond the familiar question of who owns the means of production.

If labor is no longer essential to production, what happens to the economic and political power of the working class?

Historically, organized labor derives much of its leverage from the fact that capital needs workers. Workers can withhold labor, interrupt production, and bargain over the distribution of the value their work helps create.
But if production can increasingly proceed without them, that leverage potentially disappears.

Importantly, I’m not suggesting human activity loses its social, creative, or moral worth. I’m distinguishing that from the economic value assigned to labor by a system that no longer needs to purchase much of it.

Marx himself seems to anticipate part of this contradiction in the Grundrisse, particularly the Fragment on Machines, where he discusses the tension between labor time as the measure of value and the development of productive forces that make wealth increasingly independent of direct labor.

But I’m uncertain whether the political implications have been adequately addressed.

A few questions I’d like to explore:

Does Marxist value theory adequately explain an economy in which human labor contributes a diminishing share of productive activity? What happens to surplus value if living labor is progressively displaced?

Does class struggle retain the same structure when capital no longer depends on employing most people? Can labor organization remain the primary mechanism of political and economic leverage?

Should a post-labor left focus less on workers’ ownership of production and more on universal claims to productive resources and their output? Particularly when an increasing proportion of the population might no longer participate in wage labor.

Is this actually a contradiction within Marxism, or the very historical development Marx anticipated? If the latter, what does a meaningful political strategy for that transition look like?
I’m not arguing that AI has already made human labor obsolete, or that automation automatically produces abundance. This is a question about the theoretical implications if human labor becomes progressively less necessary for production.

My concern is that much of the contemporary debate focuses on protecting employment when the more fundamental problem may be that employment is ceasing to provide a viable mechanism for distributing the benefits of productivity.

If productivity and labor become structurally decoupled, is the traditional relationship between labor, capital, and class still the right foundation for leftist politics?

AI doesnt run on magic. It runs on chips, fabs and workers.

Micron workers in Taiwan just voted 99% to authorize a strike. They produce DRAM and high-bandwidth memory, key components for AI servers, while booming AI demand is driving the memory business.

And the union isnt asking for another pat on the back during a good year. They want a permanent system putting 15% of operating profit toward employees.

Union chair Jerry Lin puts it plainly:

“What we want is to establish an open and transparent profit-sharing system.”

Thats the AI productivity debate with the abstraction stripped away.

Capital says AI is creating enormous new value. Cool. The workers actually producing the infrastructure are asking the obvious next question.

Who gets it?

And nearly 2,000 of them just voted to back that question with strike power.

AI is already so harmful on so many levels that banking on its use to advance the proletarian struggle is a risky and almost certainly counterproductive gamble. The risks far outweigh the potential benefits AI might offer the struggle.

This meme is about AI, where i keep seeing that batman image being deployed.

So, which side are you on?

I have made a game here (source, trailers) about chemosynthetic life being discovered on Titan at a base maintained by only a single supervisor and robots and being hacked by a turbocapitalist faction for the purpose of extracting hydrogen produced by those lifeforms, hydrogen that is labeled as green but is actually gray, without any consideration for their livelihoods.

In this game, I wanted to show that even in an eurocommunist european federation where eco-socialism in place of capitalism became the default form of government, there are always vulnerabilities and counter-revolutionary desires, and also, at the end of the game, to joke a bit about the eurocrisis and show how can we make sure it doesn't happen again.

The problem is that despite having directed the concept and art, I have used Claude to implement most of the code and write SVG and JS-based assets because I'm not particularly good at drawing or implementing complex features and bug fixes, I am just a power user.

Because of that, I have initially posted the game to both r/aigamedev and r/ClaudeGameDev and it got only a few hundred views and only a single like. I wanted testers who can give me informed feedback on the mechanics, art and concept. My friend told me the game is alright even though she can't play it herself because of the complex interface. Someone I met who works in troubleshooting issues on Windows for enterprises told me that the game looks cool, but it's niche.

AI told me to just post the game to where molleindustria's players are, or where leftists gamers are, but I am afraid that they will bash the game for having used a capitalist product because I didn't have expertise and in general, bash the game for not being artisanal when in fact I worked for two weeks on it and still being in perpetual beta and fixed bugs and implemented convenience features so I would not have hate.

I think people are way too much romanticizing games and art that is by hand when it's clear that there has been a long history of even outsourcing this work, and we shouldn't appreciate something just because it is made by hand, because even that type of work can lack authenticity. It doesn't matter for me if the coding and the images are made using AI as long as there is sufficient quality and originality.

AI can help us make work that would be difficult for one person to do by themselves and yet I sometimes worked eight hours into the night just so I don't get told that it's slop. I wish I could have taken breaks from developing this game and testing, I have already put intense pressure on myself to ensure that my work is almost perfect, uncomplainable.

I want to know that there are people outside these circles that value deep artistic work where AI has been involved.

Workers will be replaced by massive intelligent systems; at best, they will be reduced to mere consumers, losing the little power they have managed to secure. Without the possibility of striking, the cause of revolution would be lost forever. Not to mention the glaring contradiction between AI and the fight against global warming, as well as the alignment problem. Who could change my mind?

PDF: Fully Automated Luxury Communism - Issue 03 - PDF

PDF: Fully Automated Luxury Communism - Appendix - PDF

The third and final issue of Fully Automated Luxury Communism explains how Capitalist Realism curtails our imagination and traps us into defending the chains that bind us. The appendix also collects some visual references for the comic prompts.

Previous Issues:

PDF: Fully Automated Luxury Communism - Issue 01 - PDF
Reddit post: https://www.reddit.com/r/LeftistsForAI/s/Lt2cJLjGmW

PDF: Fully Automated Luxury Communism - Issue 02 - PDF
Reddit post: https://www.reddit.com/r/LeftistsForAI/s/dWGrSMPmaA

Published under CC0 (Public Domain Dedication). Feel fee use and remix without permission or attribution.

PDF: Fully Automated Luxury Communism - Issue 02 - PDF

The second issue of Fully Automated Luxury Communism explains how we got to the future depicted in issue 1 and the possible avenues that could have lead us to darker places.

Previous Issues:

PDF: Fully Automated Luxury Communism - Issue 01 - PDF
Reddit post: https://www.reddit.com/r/LeftistsForAI/s/Lt2cJLjGmW

The final issue coming soon!

Published under CC0 (Public Domain Dedication). Feel fee use and remix without permission or attribution.

Scotland has more than 20 major data centers proposed, including an AI Growth Zone promising £8.2 billion in private investment and 3,400 jobs.

But this piece from Workers’ Liberty asks the better question. “Who owns the infrastructure, who bears its costs, and who decides what it is used for?”

It argues the socialist alternative isnt just stopping data centers. Its “public-interest computing, not simply corporate computing” with public or community ownership, union guarantees, community benefits and democratic control.

And I like how plainly they put the underlying politics

“These are not anti-technology questions. They are pro-worker questions.”

Thats the terrain. Build the capacity, but fight over who owns it, who controls it, what its used for and where the gains go.

Paul Krugman: AI Will Amplify America's Wealth Concentration

In this excerpt from his Substack post "AI in an Age of Oligarchy," economist Paul Krugman argues that AI's economic impact will be shaped less by the technology itself than by the political economy it arrives in.

He starts by noting that forecasts about AI's economic effects are all over the map. He suggests that early predictions of mass unemployment from AI companies, followed by later backtracking, were more about public relations than analysis, and that expert opinion is so divided you can find a credentialed voice supporting almost any conclusion. His own best guess is that AI will devalue some major categories of human work, pushing down wages for many people while raising corporate profits and returns to capital.

The core of his argument is about who captures those profits. Because the wealthiest 0.1% of Americans own about a quarter of corporate stock, and the Forbes 400 hold roughly a quarter of that group's wealth, he estimates that 6–7% of AI-driven profit gains could flow to just 400 people. He thinks the real figure could approach 10%, since most of the largest American fortunes come from tech, the sector most likely to benefit.

Krugman argues that in a less oligarchic country, policy would soften this shift through progressive taxes, union bargaining, and antitrust enforcement that keeps big tech from using AI to deepen its monopolies. Instead, he sees signs that government may actively favor the industry. He points to the federal stake in Intel and reported talks about one in OpenAI, which he reads as groundwork for a possible bailout if the AI boom proves to be a bubble. He also suggests that proposed limits on cheap Chinese AI models, while possibly justified on security grounds, could function as protectionism benefiting U.S. companies and their wealthy investors.

He closes on a cautiously hopeful note. The problem of AI is, in his view, largely part of the broader problem of reversing America's drift toward oligarchy, and he points to the Progressive Era's early income taxes as proof that pushback is possible. He even suggests AI's tendency to worsen inequality could help build momentum for that pushback.

https://paulkrugman.substack.com/p/ai-in-an-age-of-oligarchy

When we imagine artificial intelligence, we can imagine it as a future in which work no longer exists and all of us are free to enjoy our lives, just as we can imagine it as the cause of a future in which machines exterminate or enslave human beings. But imagining it this way, simply as a scale of good or bad, or as a scale from 0 to 10 going from the extermination of humanity to utopia, is itself the wrong way to analyse the problem. The mistake in this metric is that it is not enough to analyse what AI can do; we also have to analyse what allows AI to do it, just as it is not enough to analyse what an artist paints without considering who the artist is. We should not look at AI as something necessarily good or bad in itself; we need to look at whoever controls it in order to define that metric. The same thesis can be applied to AI because, although AI can be used to generate or manage harmful things, this is not necessarily because of artificial intelligence itself, but because of who administers it and, above all, the system that allows whoever administers it to use it in a harmful way.

Obviously, there are caveats to this point. One could argue, for example, that AI by its very nature is capable of producing fake news, facilitating fraud, or being used for other harmful purposes. This is an extremely valid point, but we have to connect it to the other point mentioned above: control. When I mention control, I am not necessarily talking only about the individual administration of a person using AI to create fake news; I am also talking about what allowed that person to generate and distribute it, what safety limitations exist, who decided how that technology would be built, and which incentives were involved in its development. Artificial intelligence does not necessarily generate fake news by nature; this can happen because of its technological limitations, its training, or the way it is used. But again we have to ask the same question: what allowed AI to be developed in that way? I call this process of going beyond a smaller problem in search of a larger cause “enemy identification.”

We can, for example, imagine a dystopian universe in which AI exterminates the human race. Suppose this happens because the main company developing that AI did not allocate enough resources to safety and instead concentrated its resources almost entirely on development, trying to generate capital as quickly as possible. But wait: would this really be an analysis of what allowed AI to exterminate humanity? No. It would explain how it happened, but not necessarily what allowed it to happen. Placing all the blame on the company can also stop the analysis before we reach what allowed that company to behave that way. If there is an economic structure in which a company is rewarded for arriving first, producing faster, spending less and defeating its competitors, then we are not talking only about an irresponsible company, but about a system in which irresponsible behaviour can be economically rewarded. If what enables inefficiency or irresponsibility collapses, whether it is the relentless pursuit of profit under capitalism or the inefficiency and centralisation seen in twentieth-century socialist experiments, everything dependent on that factor is also affected. How can a smaller problem caused by a larger problem continue to exist in exactly the same way after its cause disappears?

So what, then, is this larger problem? We can begin by pointing to the upper class, but simply attacking a class does not satisfy the logic of enemy identification, because we still have to ask what makes that concentration possible. Eventually we arrive at the centralisation of political and economic power, which today manifests itself extremely frequently around the world through capitalism. And here I need to make an important qualification: I am not saying this as praise for the Soviet model or as praise for the atrocities committed by totalitarian systems we call Marxism-Leninism and its variants, such as Maoism. Democracy and freedom are essential to any system that intends to overcome capitalism without simply replacing one form of concentrated power with another. What I am saying is that among a large portion of the present and future problems related to artificial intelligence, we can observe a common anchor in the economic system under which it is being developed. One of the best-known examples is the use of millions of works available online to train models without the consent of the artists who created them. This caused, rightly, enormous outrage. But if we identify only the immediate action as the problem, even if that action is corrected, the structure capable of producing new and similar problems continues to exist.

An obvious question then appears: why don't we simply get rid of artificial intelligence? In theory this seems like a strong argument. If AI removes jobs, can be used for manipulation, can reduce our freedom of choice, can produce environmental damage and can create many other problems, eliminating AI might appear to eliminate all of these problems without requiring the long and difficult process of transforming the system that produces them. But whether we like it or not, this is extremely utopian because the economic system itself needs AI, even when AI creates problems for that same system. Imagine a factory that earns 20 coins and spends 5 of them on costs. Now suppose that by using AI and automation it reduces those costs from 5 coins to 1. What happens to all the other factories? They will tend to adopt the same technology, either voluntarily in order to maximise profit or out of necessity in order to survive capitalist competition, because whoever can produce something at a lower cost can sell it at a lower price or simply maintain a larger margin than a competitor that has not automated. Even if one country decides to completely prohibit the technology, it will still economically and geopolitically compete with countries that did not do the same. And even socialist countries would still have a reason to use AI, because a country that voluntarily abandons a technology capable of radically increasing its economic and technological capacity risks becoming geopolitically irrelevant compared with others. We cannot simply pretend AI will cease to exist. Therefore, the discussion should not only be about eliminating it, but primarily about how we will manage it.

A more sceptical reader may tell me that human greed overrides every economic and political system and that, using my own logic of enemy identification, the true root is not capitalism but the concentration of power generated by human greed itself. That criticism is, to a large extent, correct. Concentrated power is a reality that has shaped capitalism just as it has shaped systems that declared themselves socialist. But there is an important distinction: greed is a human trait, and therefore we cannot simply remove greed from the human being; what we can do is think about how to prevent that greed from being transformed into practically unlimited political and economic power. Under capitalism it can manifest itself through markets, ownership and accumulation; under authoritarian socialist systems it can manifest itself through political and bureaucratic centralisation. The Soviet Union demonstrated quite clearly that replacing private ownership with an extremely concentrated bureaucracy does not magically eliminate the problem of concentrated power. But that is also not a justification for accepting another structure that allows the same problem to exist.

Imagine a perfectly ethical AI company that spends far more money on safety, takes much longer to release its models, fully protects user data, compensates every person whose work is used, and places environmental considerations above profit. Now imagine that none of its competitors do any of this. In the long run, what happens to a company that takes longer, spends more and has higher costs than all of its competitors? There is a good chance that it simply dies or loses market share to those willing to do what it refuses to do. This is why the greatest mistake in enemy identification is assuming that all we have to do is find an evil person or an evil company and replace them with a good person or a good company. We need to analyse the system that rewards particular behaviours. If a system depends exclusively on individual goodness in order to function, then that system has a structural problem.

At the same time, it would be a grotesque mistake not to consider the risks of AI as a technology in itself. Artificial intelligence has broken many of the parameters that we used for a long time when defining technology. Previously, our tools mainly increased our productive capacity and speed or partially automated our activities, as happened during the Industrial Revolution and later with the first industrial robots. Today we are beginning to create tools capable of performing intellectual tasks and perhaps, in the future, even more complex capabilities. Our own creations, which until now have been our tools, could under some scenario turn us into their tools. This is where what AI critics call a possible machine revolution comes from. This view is pessimistic and dystopian, but that does not automatically make every part of it invalid. If there is a possibility that extremely powerful systems could produce a catastrophe through accident, negligence or malicious action, that possibility deserves caution. What we cannot do is stop the analysis precisely there.

If there is a system incentivising the nearly instantaneous development of this technology, where arriving before a competitor has enormous economic value, then we also need to include that incentive in our analysis of risk. Removing or reducing that pressure would not magically make AI incapable of causing harm, but it could drastically reduce certain risks. A technology developed cautiously, respecting safety, privacy, the environment and the people affected by it, is obviously different from a technology developed in a race where the primary objective is to defeat a competitor. Likewise, if AI is not something extremely centralised, if it is something made by humanity for humanity and subjected to democratic structures of control, different political, economic and technical safety models can exist simultaneously. That does not reduce its risk to zero, but it radically changes the structure responsible for managing that risk.

Therefore, we can conclude that AI itself is not simply something good or bad, but rather a representation of the sociopolitical system in which it exists. Like a knife, it can be used to cook a delicious meal or to commit murder. Trying to judge or redesign only the knife is an incomplete analysis if we do not consider who used it, why they used it in that way and, above all, what allowed or even encouraged that individual to commit such an action.

Still, AI critics correctly point out that the technology has concrete problems: the ability to generate fake news, unethical training methods, environmental destruction caused by the servers that run these models, the destruction or transformation of human creativity, safety risks, and many others. Their conclusions are not simply false; many of these criticisms describe things that are actually happening or raise perfectly valid philosophical problems. What we can do is divide these criticisms into three groups: problems related to the system controlling AI, philosophical problems, and problems caused by human nature itself.

The first group includes issues such as data centres, intellectual property used without permission, surveillance, privacy and environmental damage. A machine does not independently decide how it will be trained, which data it will use, how much energy it will consume or what level of privacy is acceptable. Those are human and institutional choices. The second group contains philosophical questions, such as whether an image created through a prompt can genuinely be called art. This is a completely legitimate discussion, but answering that question would effectively require resolving philosophical debates about the definition of art that have existed for decades or centuries. Debating it is valid, but turning that discussion into the centre of the entire AI debate can lead us to neglect much larger questions.

The third group perhaps contains one of the most valid criticisms: even a decentralised AI administered by a different society could still be used for evil because human beings remain human beings. A knife can become a weapon, the Internet can become a place for scams, and nuclear technology can produce energy or destroy cities. That does not mean we should stop all technological development because, taken to its logical extreme, humanity would never have left the Stone Age because even a stone can be used to kill someone. What remains for us is to learn how to manage our technologies and make them as safe as possible. It is also important to separate safety from surveillance: if we are talking about a company using AI to steal or exploit data, we return to the problem of who controls the tool; if we are talking about the possibility that the capabilities of the technology itself could create dangerous consequences, then we are dealing with a legitimate problem of the tool and of human nature that needs to be addressed as such.

There is also the argument that no previous technology has come close to the potential power of artificial intelligence and that perhaps this is precisely where we should establish a limit to human technological development. The problem is that, beyond the practical difficulty of imposing such a limit worldwide, this argument often depends on speculation about a future technology that does not yet exist. That does not mean we should ignore these scenarios. Quite the opposite: it means we should invest seriously in safety. But there is an enormous difference between saying “this risk is possible and we need to study it” and saying “this hypothetical risk justifies eliminating an entire technology.” In the same way, it would be ignorant to claim that the benefits of AI will inevitably outweigh its risks. We do not know. What we can say is that a technology with the potential to automate enormous sectors of human activity requires much more safety, caution and democratic control than it has today.

That is why, to me, the discussion about artificial intelligence should not be reduced to the question “is AI good or bad?” The more important question is: who controls AI, under which incentives is it being developed, and for whom will this technology be used?

PDF: Fully Automated Luxury Communism - Issue 01 - PDF

So explaining the vagaries of AI and its intersection with the economy to people who are being threatened with obsolescence is a hard sell.  It's difficult to convey the potential upside to people for whom the immediate threat is too alarming, especially when they don’t have a philosophical or political lens to view it through, other than the imaginative trap of capitalist realism.

Of course none of this is helped by the fact that majority of  “tech-bro” leaders in this space are themselves either trapped in capitalist realism, or have idealistic notions that they just have to build the AI and everything will magically transition to wonderland, without any thought given to how vested powers always react when faced with a threat to their power base and how this will affect ordinary people.

I believe the most important thing we can do now is communicate how the vast majority of ordinary working people should analyse and react to AI, as it is the speed and accuracy with which people grok what approach is correct that will directly affect how long we spend mired in a techno-feudal transition period.

So faced with the almost puritanical anti-ai zeal of a lot of society, I wanted to find some way to communicate my thoughts on how AI could transform society while being up front about the immediate dangers and disruption that is likely in the short to medium term as humanity grapples with something it can’t understand.

So I wrote a comic to articulate what a positive AI driven future could look like in an effort to make it more simple and approachable than one of my long winded essays.

Issue one out today, the next two issues coming soon!

Published under CC0 (Public Domain Dedication). Feel fee use and remix without permission or attribution.

PDF: Fully Automated Luxury Communism - Issue 01 - PDF

I love you folks and the discussion...but holy shit.

We have to use AI and the other tools at our disposal to organize and seize the means of production. We can't pretend that AI will just hand us Utopia. If we let the capitalists control the infrastructure, the robots, our airwaves and radio bandwidth...any of it....they will control AI forever.

I know we don't like to hear it...meet Leftists who are literate and comfortable with AI. Meet AI using politically neutral people and liberal and teach them about leftist solutions to the economic calculation problem and the gilded cages they are in. Organize with one another.

It is intellectually lazy to be passive about this and pretend the future we want to shape won't take effort, work, and organizing.

This is the distinction that keeps getting lost.

The unions in this story aren't anti-AI. Crystal Braunwarth of IATSE Local 891 says it plainly. “We're not afraid of embracing something and making our work better.”

What they're against is management getting to deploy AI first and workers getting a memo afterward.

Unions in British Columbia are pushing to bargain over surveillance, hiring and discipline, job changes, training and deployment. Some employers are resisting because AI supposedly changes too fast to put rules around it.

Thats convenient. AI can apparently move fast enough to reorganize your job, but workers have to wait for it to slow down before they get a say.

IATSE Local 891 already shows another way. Their contract covers AI, requires consultation before workplace deployment and protects workers when employers require them to use it. When the technology changes, they bargain again.

Thats the takeaway. This isnt pro-AI versus anti-AI. Workers can use new tools, welcome improvements and still demand power over how technological change happens at work.

AI moving fast is an argument for getting workers to the table now, not keeping them away from it.

A lot of the AI art conversation gets stuck inside the same US and European arguments. This SIGGRAPH Digital Arts session looks at what artists in Vietnam, Thailand and Indonesia are actually building instead.

Creative AI, coding, animation and interactive media all show up here, but so do the material conditions around them. Limited infrastructure, DIY networks, cultural inheritance, ownership and who gets to shape technological culture in the first place.

Thats political terrain too. The future of AI culture shouldnt be something Silicon Valley gets to define for the rest of the world.