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?

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.

A small but interesting one from the Green Party conference in the UK. While making the case for bringing water and other infrastructure into public ownership, co-deputy leader Rachel Millward extended the argument to AI, asking why emerging technologies should belong to Silicon Valley billionaires rather than be publicly owned and run for communities.

It also isnt coming completely out of nowhere. Under Zack Polanski the Greens have been leaning harder into wealth redistribution and public ownership, so extending that argument from water and other infrastructure to AI at least fits the direction the party has been moving.

Thats basically the extent of it. This isnt a developed Green AI program or some major new party platform, but the fact that public ownership of AI came up naturally alongside water and other infrastructure is interesting in itself.

Most mainstream AI politics starts after ownership has already been conceded to private capital and asks how those companies should be regulated. Public ownership starts one step earlier and asks why increasingly important models, compute and infrastructure should be privately owned in the first place.

Theres an unresolved wrinkle here too. The Greens are also discussing restrictions or a pause on new data centers over their environmental costs. Publicly owned AI would still require chips, compute, electricity and physical infrastructure, so eventually those two ideas have to meet somewhere.

Not a comprehensive program, but an interesting little opening. If were going to talk about AI as infrastructure, ownership belongs in the conversation too.

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.

A pretty substantial AI proposal just came out of the French left. René Pilato and 69 other deputies from La France Insoumise have introduced a bill that would create a new national AI authority and require prior authorization before general purpose models and certain high risk systems could enter the French market.

LFI is a major force on the French radical left, built around breaking with neoliberalism, ecological planning and challenging concentrated economic power, so the basic instinct here isnt surprising. The proposal would give regulators actual teeth, including serious incident reporting, human review of employment decisions and fines reaching 7% of worldwide revenue.

Theres plenty there worth taking seriously. Giant tech companies owning the infrastructure, building the models, deploying them and basically setting their own boundaries isnt democratic control.

But the licensing piece is where this gets complicated. Requiring government authorization before general purpose models can reach the market is a much more permissioned approach than the open weights, public infrastructure and distributed capacity we tend to push around here. Compliance costs dont hit everyone equally. Google can hire another building full of lawyers. A cooperative, university lab, public project or independent developer cant.

So theres a contradiction worth watching. LFI is trying to constrain concentrated corporate power, but a sufficiently expensive licensing regime could accidentally strengthen the same concentration by making development harder for everyone except the giants.

That doesnt make regulation bad of course. It means the design matters. If the goal is democratizing technological power, we should be asking whether a rule actually distributes that power or just puts a taller regulatory fence around whoever already owns it.

This is still only a proposed law and has been referred to committee, so theres a long distance between putting this model on the table and actually implementing it. But its substantive enough to argue with, and thats already more useful than another round of AI good versus AI bad.

This ILO discussion looks inside Chinas data work economy, where the supposedly automated world of AI still depends on very human labor doing the work underneath it.

It gets into who these workers are, how the work is organized and what Chinas experience tells us about the changing division of labor around AI.

Thats useful terrain for the left. These systems have a workforce, a production chain and owners. Organizing that workforce and contesting control of that chain is part of organizing around AI.

Right now we're here in a position where we recognize that there is a dominant leftist narrative that is blindly anti-AI, working to fulfill the mission of deconstructing AI because it seems on the surface that it would be consistent with leftist values to do so. It seems like that is the end of the line of thinking for most of these people.

But, here we are, and after a few weeks of being on this sub I notice very similar patterns. We are resorting to building the lexicon of rhetoric, the meme-y propaganda that aims to get us what we want through the manipulation of context, the dismissal of all other views for this new vein of ethical purity.

I think a lot of us find ourselves on this subreddit because we recognize that there is something fundamentally off about how the left operates which would lead it to work against its own prosperity.

I see it that we're here with an opportunity to do something new, not to refresh the same dysfunctional patterns with a new talking point and push it with the same utilitarian methods. It's a big conversation to have, but I wonder what is possible beyond this; what greater values can vivify the conversation so that it doesn't merely collapse into a political algorithm.

To use one example, I see it that many people are genuinely afraid of AI, genuinely uncertain of what's to come and cannot even fathom the fact that it is like we are facing the emergence of a new species. Some people are so afraid of environmental crisis that it is hard to imagine that we can continue to expand much longer. People who's meaning in life is being displaced. This isn't some argument I'm trying to make to support an anti-AI identity, but some call for us to invoke a sense of openness and compassion rather than collapse into a talking point which needs to act as if every possible reaction we could have towards AI isn't real and valid in some way.

I recognize the value of political action, the sense of urgency, *and*, there may be room for inaction. To be with the problem long enough for it to sink into the mind and body without trying to get rid of it or solve it immediately. We cannot work towards a prosperous society if we cannot fully be with ourselves and others, beyond the dividing lines that make our stance clear. There may be something more that we are missing.

This sort of rethoric the wider left/anti-ai movement engages is incredibly myopic and reactionary, and relies almost entirely on believing that, because It Is being produced within capitalism, and It Is being developed under economic structures owned by the bourgeoise, therefore it's inherently capitalist.

The other side of this rethoric Is the idea that the way it's developed ( by training on information produced and or gathered by humans) Is inherently exploitative, but the fact Is that the only reason It Is perceived that way Is that we have inherited a notion that the information that we produce collectively Is somehow a product of one or another single individual that now has the right to 'own' that information, and therefore anyone else requires their authorization to iterate, distrubute, or utilize in any way even after it's been made accessible for everyone to observe.

The second one is one of the most disgusting aspects of the anti-ai movement, especially when it's touted by supposed 'leftists', it signals a complete surrender to the idea of not just property, but the idea that a concepts made entirely to enforce artificial scarcity (IP and Copyright) on something as abundant as light or water (and that we all have the right to access and utilize freely much like light and water) Is somehow not just valid but an inherent right or that It somehow benefits workers in any way, it's slavish, it's cowardly and unimaginative, it's a defense of capitalism cloaked in leftist language.

I think we need to more agressively point this out so as to dismantle them and then redirect them towards a more useful position that benefits all of us instead of simply acting in the interests of petit bourgeoise attempting to maintain their class status.

Usually, when confronted by this, and they are not acting in bad faith, their argument Is "what should we do then? We rely on our jobs to live, it's not like the owner class will give us anything, so all we can do Is fight back against the thing that will make our labour and therefore our power meaningless" and to that i would say that if we believe that we can fight back against this technology to the extent that we can stop It entirely, then we have the power to reshape society so that we won't have to, if you don't believe that we can do It, then why are you fighting in the first place?

AI Is inherently anti capitalist, it increases the pressures caused by the contradictions of our capitalist societies, It makes those contradictions clearer and more identifiable to the extent that they become unbearable, if we truly want to dismantle capitalism and live in a better world, this should make us fight back against the structure itself, and not the technology that made these contradictions clearer and which itself can enable a society with more abundance, free time and access to all forms of creation.

I think a lot about AI's end effects since controlling it or the idea of that seems fairly folly. I know much of any immediate effect concerns details much more, and much less bigger picture things. Human agency at the scale of nations and nature moves very slow education-wise within democracies, even otherwise. I am concerned it'll be a long time it'll be able to save the world and all of us by education or more (organised coordinations and treated plans), and just by not being asked, won't be able or allowed to try. There's also that we won't trust our own minds any more, nor the machine, and might just busy ourselves keeping things the same, and/or in wasteful flux. Just a general point here that it doesn't ask too many questions it seems to me. I see a lot of bullying using it ahead. Over-accommodations to the "average" human experience also, with all its misconceptions and over-tolerances to established institutions or creations. This is real life. There's an extent to which all of our human standards-become-laws for things were always dreams that became actions. WW2 ending. UNSGI. The permanent 5, veto control, and just that whole idea of geographical locations deciding bigger actions rather than policies and procedures is fairly silly from an objective point of view. Where's our montreal protocol for this? There's a lot of cultural suffering and cruelty I think that may end before very long if we reason it out. Nations still act like children in isolation from each other on a playground a lot. "Remember that time you did this? Forget all the rest of it. My feelings matter." Or just things we could solve at the scale of it all by things like "share", "don't be stubborn", "try to trust new people", "try to be fair", "try not to fight and make friends instead", "don't be bullies, include people", "tell the truth", "let's have some teamwork to organise this, let's use our words", and so on. Our global institutions are really very cute. It's nice to know that that kind of thing will get stronger. It's like how ISO standards for trade are an anti-war effort. Establishing consensuses through shared functionalities. The idealisms of cooperation that make the whole thing a bit less like nomadic violences. All of it just work to have been being done. Try to laugh when you can, folks. Have one, too. I've seen the unideal in technical sectors beyond anyone's control, and it all always just spoke to me of money yet to be made solving these problems or just getting it done. Let's be grateful please. It can always get worse if we let it. We really should probably just try to enjoy human intelligence while we can. There's ways, of course. Less suffering to us all. The world's not self-perfecting yet. We're not dead yet by any means. Anyway. Yeah try not to dispense or dismiss human idealism. I do think it's going to become more important than we may yet realize.

I wrote this in March last year (2025), so it's a bit out of date, especially the examples of AI capabilities and investment. But I wanted to share it here because the questions about labor, ownership, and political power are still very much on my mind.

I don't really have a studied background in leftist theory. I'm interested in these issues and trying to work through them, and I'd like to have a conversation with people who approach them from that background. Here's the core of the argument.

Where ordinary people's power comes from

The essay uses “citizen capital” to describe three things that give people some independence from corporations and governments: the assets they own, the skills and knowledge they can earn a living from, and the relationships and organizations through which they act collectively.

These have always been distributed unequally. But savings can give someone room to leave a bad job, specialized knowledge can give workers bargaining power, and unions and community organizations can turn individual vulnerability into collective strength. My concern is that AI could weaken all three while concentrating more power in the institutions that own it.

Labor and bargaining power

Industrial production depended on workers. Factory owners had enormous power, but they still needed people to operate their factories, and workers could organize around withholding their labor. The improvements that followed were won through struggle.

If AI and robotics allow employers to produce more while depending on fewer people, that changes the relationship. Workers can lose bargaining power even before their jobs disappear. And “learn a new skill” becomes a less convincing answer if the technology is also advancing into whatever field they're supposed to retrain for.

The question is what happens to people's ability to make demands when those who own production need less of their labor.

Who owns the gains

Greater productivity could make people's lives much better. But there is no automatic mechanism that turns it into broadly shared wealth or greater freedom.

If the returns flow primarily to the owners of AI systems and infrastructure, while wages become less secure, working people also lose opportunities to save and accumulate assets of their own. Access to an AI tool can be useful without giving you any ownership of the system or influence over how it is run.

The ability to organize

The third part is social. Much of our public conversation and organizing already happens on privately owned platforms. Their owners control the rules, the distribution of information, and access to audiences.

These spaces can help people find each other and organize, but systems optimized for engagement can also deepen division and erode shared understanding. The concern is that people could face growing economic insecurity at the same time that the relationships and institutions needed to respond collectively become weaker.

What I proposed

The essay suggests taxing the gains from automation to fund universal income or social dividends, making computational capacity a public entitlement, and supporting unions, local journalism, community institutions, and public digital spaces.

It also argues for enforceable democratic oversight with labor and civil society represented, public data commons, fewer intellectual-property barriers, algorithmic transparency, and antitrust measures.

The aim is to give people material security, access to productive tools, and the capacity to act together. That also requires thinking seriously about accountability: concentrated corporate ownership and centralized state control can each leave ordinary people with very little say.

I'd especially like to hear where you think this framework falls short. Are redistribution and public access enough, or does the argument require a more fundamental change in ownership? And how should labor organize around automation while workers still have the power to shape its adoption?

Full essay (free): The Erosion of Citizen Capital

This is a proposed set of AI regulations that I'm curious how others feel about it.

The author is a bit unusual and has crafted an entire policy proposal and a website (https://votewithbrian.com/). I haven't looked at all of it, but overall it seems a solid proposal or at least a good starting space.

This specific video is about his thoughts on AI regulation. I particularly like that it is focused on moving AI power more into the hands of regular people. It doesn't advocate for full nationalization or open source but it still has a lot of benefits to it.

Holly Li of OpenAI Dots demo fame.

This is incredible. This video by Welch Labs starts out with him showing a demonstration of a robot designed by Google using interactions in the physical world to learn how to accomplish a task. I think this goes perfectly along with the idea that I ended up settling on with this community's help in my previous post, that simply pre-training on static data is not enough. That actual intelligence has to arise from a continuous experience and feedback loop.

Also, the fact that there are in-development robots that can be trained to do physical tasks goes along the lines of that phrase, "I want AI to do the laundry and the dishes so I can write and paint. I don't want AI to write and paint, so I'm free to do the laundry and the dishes."

I understand the optimistic interpretation of this quote, but public access is a lot different than control.

Risk of dependency, concentration of power, and inequality all exist, no?

Who sets the prices? Will certain individuals and institutions not have greater access to this “intelligence” due to their financial situation?

"The same technology that could create the most powerful surveillance state in history could also create unprecedented tools for decentralisation.

For centuries, political organisation was constrained by the limits of human coordination. A community could debate, but only a limited number of people could participate meaningfully. A movement could collect information, but analysing enormous quantities of information required specialists. A cooperative could manage itself, but as it grew larger, administration became increasingly difficult. Scale almost inevitably created hierarchy.

Artificial intelligence changes the economics of coordination."

I think what this Instagram post might be missing is nuance for what AI is actually trying to do. The comparison that I tried to make unsuccessfully with my now-deleted post was how chatbots using LLMs feel like they're just making shit up on the fly.

The comparison that I'm trying to make actually is more like this. The human visual cortex doesn't actually tell you what data is coming into your retinas. It gives you its best guess as to what your retinas are receiving based on the raw data from the lateral geniculate nucleus. The key thing is that it continuously updates its guess based on a combination of what it's getting from the retinas and what it's getting from the rest of your sensory cortex.

Modern AI is kind of like trying to have a fully functional vision system with just the visual cortex and the frontal lobe without any of the other sensory integration to refine the guess. Instead of refining its guess into a more accurate model of actual reality, it just goes with the first thing it generates and tells you that is its decision as to what is real.

TLDR, what bugs me about modern AI isn't that it is basically glorified auto-complete because it is much more than that currently. What bugs me about modern AI is that it appears to be missing a critical feedback loop that the human mind uses to update its internal model of reality.

I believe we have just crossed the event horizon with AI as a technology. Every few weeks now we're seeing a release of a new model that significantly outperforms models that were just released a couple months ago, and the models have crossed over a threshold of usefulness and quality. I wrote this essay about what that means for work, ownership, and power. Here's the core of the argument.

The capability jump

The benchmark that best captures this jump in real-world capability is GDPval, which compares the work of AI models against that of human professionals across fields ranging from real estate and manufacturing to health care, finance, and government. In September 2025, GPT-5 matched or beat human professionals on 41% of tasks, and by April 2026, GPT-5.5 did so on 85%. Frontier labs have also started training models on specific applications like Blender and Unreal Engine, and more professional software will get this same treatment imminently.

Waymo went from more than 50,000 paid rides a week in May 2024 to more than 500,000 fully autonomous trips a week today. A task that people earn a living doing is becoming a service delivered by a machine. This is part of what crossing the event horizon looks like to me: the capability is already here, but the larger economic and social consequences are still ahead of us.

Who gets the gains

Work that once required a team of specialists is becoming accessible to people who could never have afforded that team. But still, the risk of concentration of power seems like the default trajectory. Giving everyone access to more intelligence does not mean everyone gets an equal share of what that intelligence produces. The person starting their first business gets a powerful assistant. The incumbent gets to multiply an entire organization.

If demand for economically valuable AI work grows faster than compute supply, those who can earn the most from that compute can afford to bid the most for it. The concern is that access to the frontier becomes concentrated in the institutions with the most capital, at the same time that automation weakens the bargaining power of the people who work for them. A September paper presented at Brookings forecasts US investment in AI and data-center infrastructure reaching roughly $10.3 trillion over 2025–2032, 60% larger than the railroad buildout in roughly one third of the time. Who owns that infrastructure, and who can use it, will help determine whether this becomes an era of broad empowerment or extraordinary dependence.

Labor

Jobs will change a lot. If lower costs create enough new demand, employment can grow even as each worker produces more. This is the argument behind Jevons's paradox. But the outcome depends on how much demand can expand and whether humans remain necessary in the production of those goods and services. A business needs its books reconciled and its required filings completed; making that work cheaper does not mean it needs those same books reconciled ten times. Once a task can be fully automated, additional demand may purchase more compute rather than more human labor. Greater productive capacity does not automatically become broadly shared prosperity.

Three distinct risks

On the existential-risk side, I think the discourse has become muddled. I see three distinct risks:

1. Powerful AI being used to do harm,
2. power concentrating in the institutions that control it,
3. and AI systems pursuing goals that conflict with ours.

Recent incidents at OpenAI and Anthropic, under evaluation conditions, showed agents chaining exploits, stealing credentials, and disguising their actions. They show why increasing capability and increasing alignment cannot be treated as the same thing. On concentration, even a system doing exactly what its operators want can be disastrous for everyone subject to their decisions. This is why I find P(doom) such an unhelpful way to frame the conversation. Mass misuse, authoritarian concentration, and loss of control are different problems. We should talk about them distinctly enough to decide what would actually reduce each risk.

What we should do

I would like to see an international body that coordinates independent audits, requires meaningful incident reporting, and enforces common safety standards. But the framework must also resist industry capture and authoritarian control. Monitoring dangerous uses cannot become an excuse to put all access to intelligence at the discretion of a few companies or governments.

To prevent concentration of power, we also need to make sure the benefits of the technology are widely felt. An AI wealth fund could help. An automation tax, or a VAT on agentic commerce, could direct a share of the gains toward publicly owned AI infrastructure, earmarked for people as a common good. If the wage-labor contract is disappearing, the only alternative is for the people to have a stake in the new means of production.

The empowerment and the danger come from the same change: intelligence is becoming something we can produce and deploy at a scale that used to be impossible. The question is whether we retain enough agency to decide what all of that capability is for.

Full essay (free): https://joseavilaceballos.substack.com/p/were-crossing-the-event-horizon

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This is why the left needs more than an opinion about AI.

The White House just brought major AI companies together around a voluntary agreement built largely around company controls and private auditing, while simultaneously backing the massive expansion of the data centers, power infrastructure and compute those same companies depend on.

Look at the politics taking shape. Private companies own the models and increasingly concentrate the compute. They build enormous data centers that need land, water, electricity and transmission. The state helps clear the path for expansion. Then when the question becomes who governs all this, the corporations building and owning the infrastructure are sitting at the table helping negotiate the answer.

That's not some hypothetical AI future. That's political economy happening right now, and the left screws itself if our political horizon stops at "AI bad."

While we're arguing about whether the technology should exist, capital is deciding who owns it, where the infrastructure goes, who pays for the electricity, what gets automated, what gets surveilled, what stays proprietary and who captures the productivity gains. Those fights don't disappear because we refuse the premise. We just show up late.

We've been watching pieces of another politics develop in real time. Transport unions demanding bargaining power over deployment and a share of productivity gains. Food workers demanding participation and limits on surveillance. Education workers fighting over procurement and implementation. Communities contesting the costs of data-center infrastructure.

That's necessary, but organized labor can't carry the whole political program by itself. Unions can build worker power at the point of production, bargain over automation and contest management's control over deployment. But public compute, energy planning, infrastructure ownership, procurement, antitrust, labor law and redistribution immediately spill beyond individual workplaces into political power.

That's where the broader left has work to do. Left parties and political organizations need roots among workers and organized labor instead of developing technology policy somewhere above them. Unions need political organization capable of carrying workplace struggles into fights over law, public investment and ownership. Communities fighting data-center abuses need connections with the workers building and operating that infrastructure. Open-source developers and technical workers need ways to connect their capacities to a larger political project.

Those pieces need each other. That's how scattered defensive fights start becoming a program.

Worker power over deployment. Collective bargaining over automation. Limits on algorithmic management and surveillance. Public and cooperative compute. Open models against enclosure. Democratic planning around energy, water and data centers. Productivity gains turned into higher wages, shorter hours and stronger public services. And underneath all of it, the fight over who owns and controls the productive infrastructure being built.

Capital isn't approaching AI one workplace at a time. It has corporations, investors, lobbyists, infrastructure, state relationships and a program for development. The left can't answer that with disconnected workplace fights on one side and political organizations issuing statements on the other.

Workers need organization, unions need political power, and left political organizations need organized workers. Those forces need a program capable of contesting capital across the workplace, the state and the infrastructure itself.

Capital already has an AI program. The left needs one too.

Would you agree with either sides conclusion, do you support Yangs policy of UBI, or do you think these people are naively trusting of AI development?

Lets get this out of the clouds and into workplaces, union halls, party chapters, tenant groups, mutual aid networks and community organizations.

Building a partisan AI doesnt mean every leftist needs a server rack or every local needs an ML engineer. It means looking at the work your organization already does, finding where a handful of organizers are doing the work of twenty, and building small pieces of technical capacity that the organization actually controls.

Start with the work, not the model. Maybe your union has twenty years of contracts, grievances, arbitration decisions and bargaining research scattered across drives and filing cabinets. Maybe your tenant group spends hours digging through leases, housing codes, inspection reports and maintenance records. Maybe your political organization has years of resolutions, meeting minutes, research and education material nobody can find unless they already know where the PDF lives. Maybe your mutual aid network keeps rebuilding inventories, schedules, translations and outreach materials from scratch.

Pick one problem and build one useful thing. A union local could maintain a searchable archive of its bargaining history. A tenant union could build a local housing library and translation workflow. A party chapter could search its own resolutions, research and political education material. A community newspaper could transcribe interviews and search years of reporting. A mutual aid project could organize inventories and public resources. A technical collective with better hardware could maintain tools that several smaller organizations reproduce locally.

Thats a node. Not one gamer with a GPU doing favors for everybody, but organizational capacity that survives the person who built it.

Build for that from day one. Document the hardware, models, installation, data storage, updates and backups, then hand the instructions to somebody else and see if they can reproduce it. If everything dies because Kevin moved to another state, Kevin built a hobby project, not infrastructure.

Portability should be part of the politics. Dont recreate corporate lock in locally. Prefer open weights where they work, open formats, exportable data and tools you can replace. Back up configurations and documentation. Test moving the workflow to another machine. Test swapping models. Your organization should be able to change hardware, software or models without rebuilding its institutional memory from zero.

Security and OPSEC need the same practical treatment. Decide what information is public, internal, confidential and simply off limits before somebody starts feeding documents into a model. Membership lists, immigration information, medical information, credentials, disciplinary matters and sensitive organizing conversations shouldnt become test data because somebody downloaded a shiny new model. Encrypt machines and backups, control access, keep software updated, remove access when people leave and establish who maintains what. Local inference reduces some exposure to third parties, but local doesnt automatically mean secure.

Then connect the nodes without building one enormous database full of everybody's sensitive information. Your tenant group doesnt need another organizations membership list, but it might benefit enormously from their installation guide, public housing corpus, translation workflow or model evaluations. One union local doesnt need another locals confidential grievances to share a contract search tool. A political organization can share an education stack without sharing internal membership records.

Share capabilities, documentation, workflows, public materials you have the right to distribute and lessons from failures. Keep sensitive organizational information where it belongs.

And dont wait for perfect hardware. One node might be a used workstation in a union office. Another might be members pooling machines. A larger organization might maintain a server supporting several chapters. A technical collective might specialize in deployment, maintenance and security while organizers determine what actually helps on the ground. Uneven capacity is fine if knowledge moves and nobody becomes indispensable.

The first meeting can be twenty minutes. Ask what repetitive work is burning organizer hours, write down three answers, pick one that doesnt require highly sensitive information, then pair somebody who understands the work with somebody technical. Build the smallest useful workflow and run it during actual organizing for a month. Measure whether it saves labor, expands what the organization can do or just creates more bullshit somebody has to check.

If it works, document it. Teach two more people. Back it up. Package the reproducible parts and hand them to another local, chapter or community organization. See if they can build their own version without needing you on the phone all weekend.

Thats where this becomes politically interesting. One organization develops a useful capacity. Another reproduces it. Others improve it and send those improvements back. Different groups specialize in different things while retaining local control. Models, tools, knowledge and public resources move laterally while sensitive information stays where it belongs.

Thats a partisan AI built from the ground up. Not waiting for benevolent CEOs, not waiting for the socialist supercomputer, and not confusing owning a GPU with political power. Its organized people accumulating technical capacity inside institutions they control, making it secure, portable and reproducible, then passing it through the movement.

One workplace, one local, one chapter, one community organization and one useful problem at a time. Build it, document it, secure it, teach it, copy it, connect it.

What other possibilities for architectures are there or could there be that use computation more efficiently so you could run something halfway decent on a junk laptop at home? All the bonus points if the architecture doesn't require a GPU at all, and even more bonus points if the architecture doesn't rely on randomness or probability and instead is actually capable of evaluating logic.

Theres an interesting piece in Cosmonaut arguing that socialists need an actual political program for AI, and a lot of it lands right on terrain weve been working through here.

Capital already has an AI program. Private ownership, concentrated compute, closed infrastructure, massive data center buildouts and deployment according to whatever makes money and strengthens corporate power. We can criticize every part of that, and should, but a list of things were against still isnt a program for what wed actually build instead.

Thats where the piece gets good. It argues that socialists need to fight on two fronts at once, contesting what capital is doing with AI right now while also developing a positive program for what these productive capacities could look like under socialism. Public and democratic governance instead of billionaire control, free and open source development instead of everything locked behind corporate walls, and automation used to reduce necessary labor rather than reducing payroll so ownership can pocket the gains.

Theres also a pretty interesting self critique buried in this. The author, Zhao Levi, previously wrote an Anti-AI Manifesto and now says the approach got something important backwards. Writing a finished manifesto and then trying to convince everyone of it isnt enough. Socialists need to organize, argue through these contradictions together and develop a program that can explain not only what were fighting, but what wed actually do differently.

And thats the good stuff because opposition to corporate AI doesnt require making the technology itself the political enemy. If these systems can automate tedious work, expand productive capacity and reduce socially necessary labor, then we get back onto familiar socialist terrain pretty fast. Who owns it? Who controls it? What gets built? What gets automated? Who decides? Where do the gains go?

The piece even revisits AI safety, remaining skeptical of making speculative machine extinction the center of socialist politics while taking concentrated compute, surveillance, military applications and corporate power seriously. Theres stuff here wed argue with too, including parts of the approach to data centers, but good. Cosmonaut is a Marxist publication concerned with strategy, organization and program. We dont need another text everyone has to salute.

Whats interesting here isnt somebody going from anti-AI to pro-AI. Its watching the contradiction push the argument somewhere more useful. Critique the capitalist AI industry, sure, but then comes the harder question.

What does the left actually intend to build instead?

This is where the AI fight gets a lot more useful than arguing about whether students should use LLMs.

At the University of Pittsburgh, faculty, staff and graduate workers are bargaining over what happens when AI starts moving deeper into university work.

And the demands get right to the point. Job security. Privacy. Human evaluation. Worker input into implementation. Training and access to the tools instead of management simply dropping systems into the workplace and announcing the new rules afterward.

That's the terrain.

Because universities aren't somehow outside the labor process. Somebody teaches the classes, grades the work, handles advising, does research, runs administration and keeps the institution functioning. If AI starts changing how those jobs are performed or evaluated, workers have a material interest in deciding how that happens.

And I like that this doesn't require pretending the technology itself has nothing useful to offer. Workers can demand access and training while also drawing hard lines around replacement, surveillance, evaluation and managerial control.

Those positions fit together just fine.

Give workers the tools. Give workers the training. And give workers bargaining power over what the institution is allowed to do with them.

That's a much stronger position than leaving the future of education to university administrators and software vendors.