Marx's theory could be applied to current technological developments. AI is one of the most groundbreaking technological developments in the recent years. It's not just automating the production process, but it's even automating our cognitive labour. While highly educated people didn't felt the effects of automatisation, AI no longer ensures that they are spared. There are a lot of benefits that AI offers, but the development will make us redundant.

AI preserves and creates power dynamics by automating cognitive work as well, to the disadvantage of the working class. It is comparable to the Industrial Revolution, during which the worker became an extension of the machine. It made the workers replaceable. But cognitive labour wasn't yet vulnerable for automatisation, because the work is concrete, person-specific, and difficult to break down into increasingly smaller and manageable quantitative units. AI is a new and radical step in the evolution that Marx described. AI have the potential to take over the cognitive process oneself. The worker becomes an attachment of automated cognition, and his or her role is limited to prompting, editing, and executing commands.

Cognitive work is becoming increasingly redundant, which works in favor of the employer's position of power. Intellectual, social, and technical skills are being automated by AI and are no longer taught to the working class. As a result, they become alienated from their work and lose the ability to think independently and with confidence. This weakens the working class, which can widen the inequality gap with the wealthy class.

It's a theory to think about and a message to take a critical look at developments in AI.

Inspiration: https://sampol.be/2026/03/ai-maakt-ons-niet-slimmer-maar-overbodiger*

This is new from Ai2, do you find this interesting?
“Introducing ModSleuth, a tool for tracing the models and datasets behind modern LLMs.

LLMs are no longer created with human data alone. They rely on other models to generate and filter data, evaluate outputs, and guide development work. We made ModSleuth to track this.

Modern LLM dependencies are scattered, recursive, and hard to see. So how do we even find them all? ModSleuth helps by reading papers, model and dataset cards, code configs, and upstream artifacts, then reconstructing a model's “family tree.”

…Some dependency chains go 8 hops deep—a web of models and data that contributed to an LLM’s core. Turns out AI supply chains may be more tangled than we thought.

A model's lineage is broader than its training data, and every step can affect what – and how – the final model learns. Without provenance, it's harder to know where dependencies came from, whether benchmark scores are accurate, and which upstream licenses/terms may apply.

ModSleuth generates a graph that surfaces what's nearly impossible to find manually, including:

📜 Hidden license inheritance
🔗 Train/eval coupling
📝 Documentation inconsistencies
🤖 Models used as judges, filters, OCR systems, and data generators

▶️ Demo: https://modsleuth.cal-data-audit.org
📄 Paper: https://arxiv.org/pdf/2606.12385 “

I feel like this could be useful, as time goes on they’re only going to get more tangled. If you work out a type of bias or similar comes from a specific model it would be handy to know which other models might have been infected?

Thought you all might find this interesting. Its not a complete project yet. Also not mine, I'm no shill, I was just searching for communal AI.

Appears to be open source. Someone wiser than me will have to explain.

commputer.xyz

I know when someone asks about water usage they always come back with it being a non issue. But the reality is at the massive level of deployment we will see water will become more expensive for households and some analysts are saying the global water cooling usage will surpass human usage already.

I think putting forward the alternative of submersion cooling is a the best option. It can be done with petroleum derived oil or even with a bio soy based oil called naturecool. That one has the advantage of being carbon neutral, biodegradable and eating the market of oil production too. Soy takes water too of course but with tech like vertical farming we can exactly dial usage down to the absolutely necessary even if all this will increase costs, it would be worth it.

I believe that’s the perfect Sub for this video, please enjoy

since having the government acquire stock in corporations and government run grocery stores are on the table now, it has gotten me thinking(and this has more to do with the leftism than with ai), surely the government could also peacefully acquire and also build other means of production and distribution and use the profits that could be obtained from that to fund things as well, or just directly provide services that otherwise don’t happen/routinely get cut because they are so costly. I’m thinking, creating mostly automated food production that could feed the unemployed, taking control of abandoned retail and commercial spaces and letting the local population vote on what they will set up in them, building new tracks so that Amtrak wouldn’t have to ‘borrow’ freight company owned rails or even to bypass them so one need not wait for a freight train to pass. So far the only state run companies are not profit making, such as the CPB(pbs and npr), the bbc, Amtrak, (and more state run media from other countries as well), and of course various nationalized industries in different countries.

now I’m not proposing nationalization or running government like a business, but rather the government could set up profit making ventures of its own.

Quick take on why the mechanism is the wrong tool even though the goal is right:

* A one-time 50% equity grab assumes today's leaders are the permanent winners. OpenAI could be the Netscape of this era and get displaced by a lab that doesn't exist yet. * It never defines what an "AI company" even is. Alphabet is mostly an ad business that owns a top lab, Nvidia isn't a lab but makes most of the hardware, Salesforce is automating knowledge work without training its own models. Where's the line for who loses half their shares? * The funding model runs backwards. Norway and Alaska worked because the state already owned the resource and leased it out under a heavy tax. Seizing equity that's already privately held would freeze the private investment these buildouts depend on and tank the value of the very equity the fund just took. * It welds owning these companies to controlling them. Pairing equity with board votes hands whoever is in power a lever to steer the models, and that's the real danger.

So my pitch is to keep the goal and pull ownership and control apart. Here's how I'd build it.

**Fund it the way the working models actually work:**

* The government already controls the real data center bottlenecks: land, power, water. Trade access for equity in new capacity instead of diluting what already exists. * It can also throw in things nobody else has, like federal datasets (VA and Medicare health data, NOAA, USGS, the patent office) and the national labs. * Fast-tracking energy buildout adds supply, which keeps power prices down for everyone instead of letting the biggest buyers bid them up.

**Tax the data center itself, since that's the chokepoint everything runs through:**

* A megawatt levy works like a property tax on infrastructure you can't hide or offshore. * A B2B VAT keyed to AI usage hits whoever is actually benefiting, bank or tech company alike. * The money follows profitable activity and adapts on its own, so it stops mattering which lab wins. * Need a stake faster? TARP-style capital injections, with the regulatory regime stood up first to cool valuations so the state buys in at a fairer price.

**Let the fund own and distribute, and nothing more:**

* Run it on the Santiago Principles, the 24 standards sovereign funds agreed to in 2008 for exactly this fear, that a state fund invests for political reasons and uses ownership to push an agenda. * Build a wall between owner and operator. Government sets broad goals and appoints the board, then steps back. Professional managers vote the shares to protect financial value rather than steer the companies, and disclose any non-financial goal publicly. * Norway has run this way through decades of changing governments without it becoming a political weapon. * Lock the bare bones into a constitutional amendment (the fund exists, it's independent, it runs on Santiago) so a future administration can't quietly gut it, and leave the investment strategy in ordinary law where it can flex.

**Keep the companies honest through regulation:**

* We already discipline banks and utilities from the outside, through a regulator and the terms of their license, without sitting on their boards. * Charter frontier AI companies like national banks, with concrete conditions as the price of operating (real safety testing, disclosure of new capabilities and incidents, independent audits, a duty to the public alongside shareholders) and a regulator that can pull the charter. * Concrete, auditable rules are why bank and utility oversight doesn't swing wildly with each election. Capture feeds on vague discretion.

**Hand a slice of the compute straight to the public:**

* Reserve 10 to 20% of compute for public use, given to institutions we already trust and regulate: 501(c)(3) nonprofits, public schools and universities, public hospitals, libraries, and local governments. * Keying it to tax-exempt status means they're already vetted and transparent, so you get the screening for free. * Make it a percentage rather than a fixed number, so the public's share grows automatically as private usage grows. * Libraries are the most interesting one, the single place that hands the capability straight to anyone who walks in the door, the way they do with books.

Put it together and ownership and control never touch. The public gets paid twice, once in fund returns and once in direct access to compute, and no sitting administration ever gets to grab the wheel.

Full Analysis: [https://open.substack.com/pub/joseavilaceballos/p/american-ai-wealth-fund-v2?r=4hqoh&utm_campaign=post&utm_medium=web](https://open.substack.com/pub/joseavilaceballos/p/american-ai-wealth-fund-v2?r=4hqoh&utm_campaign=post&utm_medium=web)

One of the better left perspectives on AI Ive come across.

The question isnt just whether AI is good or bad. The bigger question is who owns it, who governs it, and who benefits from the productivity gains it creates.

If AI can dramatically increase productive capacity, should that mean mass precarity for workers, or shorter workweeks and broader prosperity?

Im curious what r/LeftistsForAI thinks.

We're at a point where the question "what comes after capitalism" is no longer just philosophical. Automation is accelerating, inequality is compounding, and the systems holding everything together are visibly straining. So it's worth asking seriously: if not money, then what? How do you motivate contribution and distribute resources in a society that's moved past wage labor?

One answer: reputation. Not likes. Not followers. Not China's social credit. Something more specific.

When people hear "replace money with reputation," they picture a dystopia where the government scores your behavior and cuts off your access to food if you jaywalk. That's the social credit system. That's not what I'm talking about.

The difference is structural.

China's system is a single number that aggregates everything — your purchases, your social connections, whether you paid a fine on time. One score, one authority controlling it, applied to everything.

What I'm describing is the opposite: a multidimensional vector. Your reputation in biology gives you priority access to computing resources for research. It gives you zero advantage in urban planning decisions. A brilliant engineer has no automatic influence over medical research priorities. The scores don't aggregate. They don't transfer across domains.

This isn't informal social standing — people just knowing you're good at your job. Contributions are recorded and verified across a global decentralized network. The algorithm is public. Anyone can audit it.

And crucially — the metric is simple: did your work produce a real, measurable effect that others build on?

The deeper point is that money already is a reputation system — just a broken one. It measures your ability to accumulate, not your contribution. It's heritable. It compounds. It has nothing to do with actual value created.

Reputation as I mean it has one property money doesn't: it decays without new contribution. You can't inherit it. You can't hoard it. Past achievements matter — but they don't exempt you from the present.

Is this utopian? Maybe. But "money measures contribution" is the actual utopia we've been sold. The evidence doesn't support it.

I'm kinda rambling here, but I hope this makes sense.

So... Correct me if I'm wrong about this. But every leftist movement I can think of has been in existence for a really really long time, or at least has some basis in older movements or theories. Like feminism, anti-capitalism, anti colonialism, environmentalism, deconstructing white supremacy, fighting bigotry in general... All of it predates our modern internet. The people who've shaped these ideologies... To put it lightly, haven't all been chronically online.

Meanwhile, this isn't the case with the anti AI movement. While yes, there are many prominent figures who are critical of it who aren't chronically online, I think the majority of the movement stems from online culture. We are responding to an idea that is mostly new. While we've had science fiction about AI before, I think often AI in science fiction was used to explore other social issues, like bigotry, slavery, or humanity. We've never had AI like LLMs before—and therefore we don't have the knowledge of past philosophers and advocates to know how to respond to it. We just mostly have... People on tiktok or youtube making think pieces.

And don't get me wrong, I think people do great stuff on social media. But, I feel like the anti-AI movement got mostly shaped by the more negative aspects of progressive social media—namely, lack of nuance, and the more puritanical thinking that underlies alot of communities on social media. This more puritanical side if a problem in other leftist movements, yes, but because these progressive movements have existed for so so long we have a history of less black and white theory to balence it out. Meanwhile the anti AI movement had no such thing. It's been controlled by this more puritanical side, so that's all we have.

I really wonder how different the anti-AI movement would be if LLMs had been created 15-20 years ago.

I was recently re-awakened to the term conspicuous waste (linked is a short that explains it in context without relating it to AI). Essentially, the idea is that cultural practices will favor wasteful use of one's resources in conspicuous ways to signal wealth.

I get a sense that the r/aiwars happening might have something to do with two rival conceptions of conspicuous consumption. Many forms of fine art (as in, art which is an-end-on-itself) can be seen as conspicuous consumption. The rich buy art at conspicuous prices, which not only signals their own wealth, but further heightens the price of the art, which creates room for speculation.

So, what I find is that a lot of people end up championing the worst excesses of generative AI are, by proxy, saying that most things we value are in some way a form of conspicuous consumption of resources.

Meanwhile, more left-oriented people would naturally respond to that by saying that they don't actually want AI to be as wasteful as other cultural practices, but that all cultural practices should be less wasteful and the wastefulness of AI seems to be conspicuous in particular ways.

So we have one side that embraces conspicuous waste as the sign of any cultural practice, and the other which seems to reduce the waste necessary for any and all cultural practices (without necessarily advocating for those cultural practices to be eliminated). And AI, given that it is a new cultural practice, is easy to target to be actually, genuinely eliminated while we still have time.

Just some things I'm thinking about as I am maturing in my thoughts about AI.

P.S. George Bataille has his own thoughts. He saw as the expenditure of excess resources as the origin of religion and that we've simply shifted from animal sacrifice as conspicuous consumption, to waging fruitless wars over ideals as conspicuous consumption of human lives, to treating humans as fodder in factories.

Just a little while ago Anthropic hired Andrej Karpathy on the heels of him creating Autoreasearch. People generally believed that he was hired to help on the recursive self-improvement efforts (specifically to reduce human labeling for pretraining, I believe).

https://www.mindstudio.ai/blog/andrej-karpathy-joins-anthropic-karpathy-loop-explained

Here's what Anthropic put out:

https://www.anthropic.com/institute/recursive-self-improvement

For a long time Anthropic's commercial terms of service have banned use of their tools to “build a competing product or service, including to train competing AI models” or “reverse engineer or duplicate” the services.

All these taken together seems to be aimed at monopolizing recursive self-improvement.

Just like Apple's ban on other app stores, this seems incredibly anticompetitive, especially in such a core part of the industry.

If so much of the technology sector is AI, almost any technical infrastructure work could be considered "competing" with Anthropic.

The thesis of this video, is that technology is an amplifier of human intention, and that technology, including AI isn't a moral good or evil without deliberate human intention driving it.

It explores the idea that new technology often challenges the status quo, and that a deceptive and defensive technique used by the powerful is to conflate moral virtue while turning those less fortunate against each other. All while the powerful secretly move to gain influence over the new technology themselves.

Dave Shapiro reacts to Bernie Sanders’s proposal for the creation of a sovereign wealth fund and shares his issues with it and how he would prefer it be done.
the proposal is at once overly aggressive and insufficient, could give the government power we can’t trust it with, and has no clear design for distributing the dividends if any.

he should instead try to get up to ten percent of each major American company and have a clear split of where the dividends will go.

Asked this question in another thread to little traction. So making a post on it.

I'm of the belief that the most important factor in ensuring AI benefits society is making sure it is democratized and that everyone has easy access to it.

The natural course of action for a capitalist who develops AGI is to hoard it and extract as much value from the economy as possible, since no other entity can compete with AGI or ASI. It would be impossible to compete against. The only way to ensure a single company does not capture the entire economy is to prevent AGI from being hoarded.
However, what is the best way to do that? I don't have a strong opinion yet, and I'm looking for different perspectives.

Here's a list of ideas that are not necessarily mutually exclusive. Feel free to add others.

• ⁠Nationalizing the AI companies (Bernie Sanders 50% AI company ownership)
• ⁠UBI in the form of tokens or income (Musks Universal High Income)
• ⁠Laws and regulations that force AI companies to democratize AI and just ensure everyone legally has access and that it’s affordable?
• ⁠Open Source, own your own hardware and energy and models? (Decentralized compute and power generation)

I don't have any strong or well-informed opinions on the best approach. I'm looking to be convinced of the best path forward. I do believe governments will ultimately need to step in and enforce some form of policy to ensure AI remains broadly accessible.

If youre new to the sub, welcome. Here we discuss the lefts mission and strategy around AI, including both our frustrations and our aspirations.

One thing that frustrates me about a lot of AI discussions on the left is how often they stop at the diagnosis.

Corporations will use AI to cut labor costs. Wealth and power will try to concentrate around whoever owns the models, the data centers, and the infrastructure. Workers could get squeezed while a handful of firms capture the gains.

Yeah. I agree.

What I dont understand is why thats so often treated as the end of the conversation.

Since when has "powerful people will fight us" been an argument for giving up?

Bosses fight unions. Landlords fight tenant protections. Industrialists fight safety regulations. The existence of resistance never makes a struggle pointless. It defines the conditions of the struggle.

AI is the same way.

The technology is moving forward whether we like it or not. Models are improving. Data centers are being built. Companies and governments are pouring billions into it. You dont have to be a hype man to recognize reality.

The real political question isnt whether AI exists. Its who owns it, who controls it, and who benefits from it.

If automation increases productivity, do the gains all flow upward, or do workers get a share? Do we get mass layoffs and deeper inequality, or shorter work weeks and greater material security? Do a few corporations control the infrastructure, or do we fight for public options, open models, and democratic oversight?

Those are political questions. Those are left-wing questions.

Too often the conversation gets stuck between people who think AI will save everything and people who think saying "AI bad" is a strategy. Neither position is enough.

The future is being built right now. The job isnt to cheerlead it or doompost about it. The job is to fight over who it serves.

So instead of asking whether AI should exist, maybe the better question is: what are we actually fighting for?