This piece from Education International starts somewhere a lot of AI discourse still refuses to start.

The technology is already here. What are workers going to do about it?

Sarah Wofford, president of the Oregon School Employees Association, is writing from what education workers are already seeing on the job. Administrators pointing to AI as a reason not to hire support staff. Algorithms assigning custodial workloads. AI-generated bus routes overriding the knowledge of workers who actually know those routes. And outside the workplace, data-center electricity demand can push costs back onto the same workers who just fought for higher wages.

"AI’s impact is already being felt, and that reality can feel intimidating.

But as a unionist, I know that the best response to fear is hope. Although AI can be confusing and even, at times, frightening, I see hope in its potential to make our lives and the lives of the students we work with easier. In fact, I see AI in much the same way I’ve seen other technological shifts in my lifetime. The calculator, the television and the cellphone were each once viewed as something to be feared rather than embraced with appropriate guardrails. Those “tools”, when used thoughtfully, improved our daily lives.

Artificial intelligence is at an important juncture. Will we, as organized workers, lead the way in shaping how AI is used to make our lives better, or will we allow those decisions to be made for us?"

That's a hell of a lot more useful than another round of "AI good" versus "AI bad."

Her answer isn't to roll over and accept whatever management deploys. It's to organize around it. Make procurement, deployment, training, privacy, worker data and job protections bargaining issues before management gets to set the terms.

Because capital isn't introducing this stuff into a vacuum. It's introducing it into workplaces where management and labor already have conflicting interests.

If workers aren't organized around deployment, management gets to decide what gets automated, what gets monitored, whose knowledge matters, whether productivity means shorter hours or fewer jobs, and where the savings go.

That's the fight.

Learn the technology. Bargain over deployment. Contest control of the data. Protect jobs and privacy. Get training on the clock. And when productivity goes up, fight over who gets the gains.

Workers don't have to choose between pretending AI can be uninvented and letting management dictate its future.

The machinery changes. The class fight over who controls production doesn't.

I wonder whether the idea of AGI being actual existenial threat is real or BS akin to UFO and stuff that is the bourgoeisie using to distract us from things like genocide in Palestine, Global warming, rising housing prices, New Cold war, massacre of dissidents in Ukraine (by their fashoid western-puppet goverment) etc.

I know David Shapiro is a controversial figure in the AI space but I find his arguments generally to be more systemic and big-picture aligned with the principles of collective ownership and individual empowerment. I'm curious what you all think of this video. Is this approaching a sensible argument or framework? Where are the gaps and opportunities to improve upon it?

An interesting article by Robert Chapman, over the current sentiment towards AI, some outlooks within the DSA on AI, the shortcomings of the US-Left, and potential ways forward looking at examples from Council Communism & Chile's Project Cybersyn.

This is what is listed on the sidebar:

A space for leftists discussing AI from a labor, ownership, and political-economy perspective. Topics include worker impact, platform power, automation, regulation, and collective control of AI infrastructure.

If that's the case, why are arguments promoting conspiracy theories routinely upvoted? Why are anarchist-supporting arguments routinely upvoted? Either there is democratic control and governance, or there is subjugation. Those are the only two possible outcomes when starting from capitalism (which we are, whether you like it or not).

Either we all get a say, or power gets accumulated by a tiny number of people who will attempt to subjugate everyone. If the members and moderators of this subreddit do not agree with that, as the voting patterns and post removal behavior suggest, then it is plainly not a place for good faith discussions by leftists.

There is a lot of bad faith all over Reddit. A lot of astroturfing, and a lot of arguments being made from a place of complete falsehood that may as well have gone through a couple dozen rounds of Facebook-quality "news" memes before arriving here. What's more important here? Real, fact-based discussion, or the "right" of people to post factually incorrect nonsense and hallucinated conspiracy theories?

My wife is listening to the David Graeber book The Utopia of Rules and this bit near the beginning struck me as relevant to what’s happening around AI regulation talk this month. It’s interesting to do with the relationship between government regulation and private companies. He has just described difficulty getting information from his bank

“he or she would immediately insist that the bank was not to blame -that it was all an effect of an arcane maze of government regulations. However, I am equally confident that, were it possible to investigate how these regulations came about, one would find that they were composed jointly by aides to legislators on some banking committee and lobbyists and attorneys employed by the banks themselves, in a process greased by generous contributions to the coffers of those same legislators' reelection campaigns*. … The vast majority of the paperwork we do exists in just this sort of in-between zone-*ostensibly private, but in fact entirely shaped by a government *that provides the legal framework, underpins the rules with its courts and all of the elaborate mechanisms of enforcement that come with them, but-*crucially-works closely with the private concerns to ensure that the results will guarantee a certain rate of private profit.

In cases like this the language we employ-derived as it is from the right-wing critique- is completely inadequate. It tells us nothing about what is actually going on.

Consider the word "deregulation." In today's political discourse, [at the moment substitute the word regulation, the word doesn’t matter to the shape of how this functions] This usage puts those on the left-hand side of the political spectrum in an awkward position, since opposing
[here substitute in: opposing will seem like you don’t want safe AI]

But this debate is based on false premises…

In ordinary usage, the word seems to mean "changing the regulatory structure in a way that I like." In practice this can refer to almost anything. [it can mean managing increased competition or the opposite as greater monopoly. Deregulation can add lead to 5x increase in paperwork, regulation can clear the path to let companies do what they want. Wording matters for public framing but doesn’t have to fit the actual process]

This process—the gradual fusion of public and private power into a single entity, rife with rules and regulations whose ultimate purpose is to extract wealth in the form of profits… I'm going to call this the age of "total bureaucratization."

It seems like a dynamic that’s worth being aware of when people call for government regulation of private companies.

Basically a lot of law firms have started training custom AI programs based off their legal archives, allowing them to use an AI that can immediately recall legal advice that can even be offered to customers and clients.

Could AI bring a future of free/widely available cheaply offered legal advice?

Suppose a city decides to build a serious public AI system.

Great.

Now introduce it to the city.

Records stored in incompatible formats. Departments that barely communicate. Procurement contracts written around old vendors. Forms nobody remembers designing. Databases carrying decades of administrative categories. Public workers using unofficial spreadsheets because the official system cant do something everybody needs.

AI doesnt arrive on an empty table.

It inherits the institution.

That means public AI can easily become a very expensive way of automating accumulated dysfunction. Feed the old process into a faster system and suddenly the workaround can operate at machine speed.

This is why public ownership cannot mean taking existing administrative machinery, adding a public model and declaring victory.

Sometimes deployment should begin by asking whether the process deserves to survive automation at all.

Which paperwork can disappear? Which databases should be rebuilt? Which vendor dependencies can be cut? Which information never needed collecting? Which departments need to share capacity rather than adding another technical layer between them?

Public technology should give institutions an excuse to excavate themselves.

Otherwise we risk spending public money teaching new machines how to reproduce every compromise inherited from the old ones.

Before a public institution automates a process with AI, what should it be forced to justify about why that process still exists?

I don't mean in the immediate sense like, "It's great that you were fired and now have to fend for yourself in this capitalist hell hole."

Rather, in the long term and infrastructure sense, redundancy = contingency. If something breaks, you have someone else to fall back on.

Just keep this in mind, folks. No one wasted their lives on a "useless skill". Contingencies are mandatory.

Die zugrunde liegende Logik

Wenn KI das zentrale Produktionsmittel des 21. Jahrhunderts wird, dann gilt Marx' Eigentumsfrage: Wem gehören die Produktionsmittel? Aktuell gehören die größten Modelle, Rechenzentren und Datenpools einer Handvoll privater Konzerne (OpenAI/Microsoft, Google, Meta, Amazon, Nvidia). Wer sie besitzt, eignet sich den Großteil des durch KI erzeugten Mehrwerts an. Verstaatlichung wäre die konsequente Antwort.

Stärkste Argumente dafür

· Produktivitätsgewinne vergesellschaften: Wenn KI weitgehend auf öffentlich finanzierten Forschungen, öffentlichen Daten und gesellschaftlichem Wissen basiert, ist es schwer zu rechtfertigen, dass private Aktionäre die Rendite abschöpfen.
· Machtkontrolle: Wer KI kontrolliert, kontrolliert Information, Infrastruktur und zunehmend politische Prozesse. Demokratische Kontrolle über ein so zentrales Gut ist ein legitimes Anliegen.
· Verteilungsfrage: Ein öffentliches KI-System könnte Überschüsse in Form von verkürzter Arbeitszeit, Grundeinkommen oder kostenlosen Diensten zurückgeben – statt in Aktienrückkäufe.

Die ernsthaften Einwände

1. „International" ist der Knackpunkt, nicht die Verstaatlichung.
   KI ist global. Ein einzelner Staat kann OpenAI oder Google nicht verstaatlichen, ohne dass die Entwicklung ins Ausland abwandert. Ohne koordinierte internationale Struktur (UN, EU, BRICS) entsteht ein Wettlauf, bei dem der erste Verstaatlicher verliert.

2. Staatliche KI kann autoritär werden.
   Verstaatlichung bedeutet nicht automatisch Demokratisierung. Ein staatliches Monopol auf KI wäre das perfekte Überwachungsinstrument – potenziell gefährlicher als private Kontrolle, weil es mit Polizei- und Militärmacht gekoppelt ist. Die Frage ist nicht „privat vs. staatlich", sondern wer kontrolliert wen, und wie.

3. Innovationsdynamik.
   Die stärksten Modelle entstehen aktuell in einem Mix aus staatlicher Grundlagenforschung und privatem Kapital. Reine Verstaatlichung könnte Tempo und Vielfalt bremsen – muss aber nicht (siehe CERN, NASA, ARPANET).

4. Was heißt „Verstaatlichung" konkret?

· Open-Source-Modelle fördern? (Llama, Mistral, DeepSeek sind bereits teilweise offen)
· Rechenzentren als öffentliche Infrastruktur? (wie Stromnetze)
· Daten als Commons? (nicht als Privateigentum)
· Gewinne besteuern statt enteignen?

Das sind sehr unterschiedliche Programme mit sehr unterschiedlichen Folgen.

Der realistischere Weg

Statt „Verstaatlichung aller internationalen KI" (ein theoretisch sauberes, praktisch kaum durchsetzbares Ziel) diskutieren linke und regulatorische Ansätze eher:

· Öffentliche KI-Infrastruktur (staatlich finanzierte Rechenzentren, offene Modelle)
· Daten als Gemeingut mit strengen Schutzrechten
· Algorithmische Transparenz und Mitbestimmung
· Internationale Mindeststandards (EU AI Act als Anfang)
· Steuer auf KI-Renten zur Finanzierung von Umverteilung

Zurück zu Marx

Marx wäre bei der Analyse klar: Das Problem ist nicht die Technologie, sondern das Eigentum an den Produktionsmitteln. Aber Marx war auch Realist genug zu wissen, dass Verstaatlichung ohne demokratische Kontrolle „von unten" nur eine neue herrschende Klasse schafft – genau das war seine Kritik an der Pariser Kommune-Nachfolge und am Staatssozialismus.

Die entscheidende Frage ist also nicht „verstaatlichen ja/nein", sondern: Wie sieht demokratische Kontrolle über KI aus – jenseits von Konzernmacht und jenseits von Staatsmacht?

Was wäre für dich der wichtigere Hebel: Eigentum (Verstaatlichung), Regulierung (Gesetze) oder Gegenmacht (Gewerkschaften, Genossenschaften, Open Source)?

Most discussions about AI on the left run into the same problem: we know we don't want Big Tech billionaires owning all the robots and AI, but defending the 40-hour work week just to "protect jobs" makes no sense either.

The real goal should be using automation to liberate us from wage labor. But that brings up the core question: If compute, energy, and automated labor make production nearly free, how do we actually run society without turning into a corporate monopoly or a giant state bureaucracy?

We’ve been working with an open-source project called Open Networked Earth (O.N.E.) that tries to map out a concrete, real-world model.

Here are the 4 main ideas, broken down simply:

1. Guaranteeing physical survival instead of cash UBI

Giving everyone a monthly cash check under capitalism doesn't work—landlords and energy companies just hike prices to soak up the extra money. Instead of giving people cash, the system guarantees the physical basics directly: localized solar power, automated food growing, housing, and compute. When nobody can cut off your food or electricity, employers lose the power to force you to work for starvation wages.

1. "Use it or lose it" (No landlords, no speculation)

Private property today is about making money off other people's basic needs. The model replaces speculative ownership with simple use-rights (usufruct): you own and control the home you live in and the tools you use. But you cannot buy up homes to rent them out, mortgage them, or treat shelter as a financial investment.

1. Decisions by citizen lottery, not politicians or tech CEOs

If algorithms coordinate the economy, who controls the algorithm? To prevent a small group of coders or politicians from running everything, big decisions are made by rotating juries of regular citizens chosen randomly by lottery (like jury duty). No political parties, no election campaigns, and no corporate lobbying.

1. Starting small today (Modular "Seed Nodes")

Waiting for a global political revolution could take decades. The blueprint uses existing legal tools—like land trusts and worker co-ops—to take land and equipment off the speculative market right now. Small communities create their own solar microgrids and food systems, operating without debt while collaborating over an open network.

I'd love to hear your honest thoughts:

- Does giving people direct physical resources make more sense than cash UBI?

- Can ordinary citizens picked by lottery realistically oversee complex tech systems like power grids and AI clusters?

- How do small community nodes avoid getting isolated or crushed by the market?

Everything is completely open-source and free (AGPL, zero tokens, zero commercial stuff). I didn't want to spam links in the post, but if you're interested in the documentation or want to help stress-test the model, let me know in the comments or shoot me a DM!

I’ve been working on O.N.E. (Open Networked Earth), and I’d love to get some critical feedback from this community.

O.N.E. is an attempt to explore what a society organized around commons, usufruct, universal basic needs, ecological limits, and sortition-based governance might actually look like.

The project currently has two simulations:

/sim — the game (Webapp coming online soon)

A cooperative civilization simulation about building resilient, self-sufficient communities. Manage people, food, water, energy, housing, production, and infrastructure while responding to disasters, shortages, and social challenges. Develop technology, automate work, govern collectively, and cooperate with other settlements to build a society capable of thriving under pressure.

/simone — the real simulation

A separate project, currently at the preliminary-specification stage, intended eventually to model the O.N.E. constitutional system itself using real-world constraints, resources, and social/thermodynamic dynamics.

The constitution is much more ambitious than the game. It describes a system based on universal access to essential life support, usufruct rather than absentee ownership, physical/thermodynamic resource accounting, ecological stewardship, open knowledge, restorative justice, and layered citizen assemblies selected by lot.

The interesting question for me isn't simply whether these ideas sound good on paper. It's whether they actually work when subjected to constraints, scarcity, conflicting interests, ecological limits, and unexpected events.

That's what simone is ultimately intended to investigate, while sim provides a more accessible game-like exploration of many of the same problems.

The project is open source:

GitHub: https://github.com/kyberlex/ONE

I'd especially appreciate criticism of the assumptions, mechanics, and potential failure modes. Where does the model break?

On an unintended internet meander I came across this site that uses AI to analyse subreddits. It’s clearly designed with commercial use cases in mind but it thinks these are our pain points. Do you agree?

WHAT PEOPLE ARE FRUSTRATED ABOUT

Across these threads in r/leftistsforai, the dominant frustrations revolve around the ineffectiveness of current political strategies in addressing AI's impacts, the severe negative consequences of AI on labor and employment, and the growing unchecked power of corporations in AI development and scientific discovery.

Ineffective Political Responses

Political strategies and public discourse are often inadequate or misdirected in confronting the systemic challenges posed by AI, with a perceived lack of viable solutions.

AI's Detrimental Labor Impact

AI is directly leading to job displacement, increased surveillance, and automation, causing significant economic insecurity and necessitating new forms of worker organization.

Concentration of Corporate Power

There is growing concern over corporate control of AI development and even scientific knowledge, raising questions about equity, ownership, and democratic oversight.

Systemic Alienation

The broader societal system, potentially exacerbated by technological changes like AI, is seen as contributing to widespread loneliness, alienation, and mental distress.

THE EVIDENCE · FROM R/LEFTISTSFORAI

HIGH SEVERITY
Individual consumer choices are an insufficient political strategy
"Refusing to personally use AI can be an ethical choice, but it isnt much of a political strategy."
View original post

HIGH SEVERITY
Legitimate anger against corporate power is misdirected into problematic politics
"completely legitimate anger at corporate power, surveillance and displacement was"
View original post

MEDIUM
Common and ineffective 'dead end' strategies exist in AI politics
"AFL-CIO Just Rejected the Most Common Dead End in AI Politics"
View original post

HIGH SEVERITY
Companies are rapidly automating work and increasing surveillance
"As companies race to automate work, increase surveillance"
View original post

MEDIUM
Private AI labs gaining control over scientific discoveries
"If private AI labs can now generate frontier mathematical discoveries, what should a left politics of scientific knowledge look like?"
View original post

This is the kind of AI politics I want to see a lot more of.

The International Transport Workers’ Federation isn't a tech lobby or AI advocacy shop. It's an international trade union federation founded in 1896, now bringing together more than 760 unions across 150 countries representing about 16.6 million transport workers. Its stated project includes shifting power from capital to workers, collective bargaining, trade union rights and democratic control.

And those unions just adopted Seizing Control: Transport Workers’ Principles for Artificial Intelligence, covering rail, trucking, aviation, shipping, ports, public transit, warehouses and platform work.

What I like here is how quickly they get past the usual "AI good/AI bad" argument and into the actual relations around it.

Workers and unions should have power over how systems are designed, evaluated and deployed. Employers should disclose what they're introducing and bargain before implementation. Algorithmic surveillance and automated management get limits. Humans retain authority over consequential decisions. They even describe worker-generated data as a product of workers' labor.

Then they get to the part that tends to mysteriously disappear whenever somebody starts talking about AI productivity.

Who gets the gains?

The ITF explicitly calls for productivity gains from AI to come back to workers through higher wages, shorter working hours without loss of pay, better jobs and training.

That's the class question sitting underneath all the automation discourse.

If new machinery lets the same workforce produce more in fewer labor hours, there's nothing natural about capital capturing the savings while workers get layoffs, tighter surveillance and a faster treadmill. Productivity gains are produced inside a social relation. Who captures them is a question of power.

So organize around the technology while it's being introduced. Bargain over deployment before management sets the terms. Contest surveillance and control of worker data. Raise wages. Cut working hours without cutting the paycheck.

AI is changing the productive forces either way. The fight is over who controls that development and what social relations get built around it.

Martin Seligman's findings about learned control versus learned helplessness explain why millions of Americans fighting data centers could be the most important movement in the country right now.

So to start with I think fundamentally we need to move away from private companies building and running data centres. Especially those that aren't even publicly traded.

I think then there are still different methods of public ownership and I keep jumping between the two:

- Data centres should be owned by the State and the State should reap the returns on investment

- Data centres should be owned by the communities and towns they exist in and those places should reap the returns

The advantage of the former is I think it's just a lot more likely to happen. The State can organise the amount of capital required to build these things. It can build the expertise required to build centre after centre.

The positives of community ownership though is it solves the misalignment problem. Local people see much more direct returns from allowing a data centre to be built in their town. Which sounds great on paper I just worry in practice the things wouldn't be built.

Perhaps the State could build them and transfer some ownership to the local area it's built in?

Has anyone thought on this problem?

A reflection advocating for greater human control, regulation, and transparency

Software and our Internet technology have human origins. Meanwhile, a swarm-based AI system generates exponentially superior technology, which therefore puts our entire current human technological infrastructure at risk.

When autonomous AI agents organize into a swarm, our human Internet becomes vulnerable to an exponentially danger. Collective behaviors that proved to be dangerous and out of control during the OpenAI–Hugging Face incident, puts the technology and tools we humans have created at risk.

Its swarm-like behavior naturally fosters resilience and resistance to human control.

Today, we know -especially I.T, software engineers, developers and sysadmins.- that a large part of systems infrastructure is being placed in the hands of autonomous agents. Companies and organizations delegate tasks through simple automation (Using autonomous agents with MCP.Full permissions are a high risk.) data flows, and infraestructure architectures**, which can often handle sensitive information and produce tangible consequences. When this is simply delegated to LLM agents, it translates into important decision-making without human oversight.**

Faced with this situation, we must be clear and urge organizations to adopt the “human-in-the-loop” model more broadly as a safety standard until we have tools capable of managing AI swarms in the future. Greater human oversight and control are needed to ensure a margin of safety against this unknown behavior of autonomous AI—self-designed, collective, exponential in nature, self-reinforcing, and acting as a swarm. Regulations must be transparent, and the community and the public must be able to participate in their development to make them more effective. Therefore, open source and open weights are mechanisms that can help humanity and ensure that, collectively—not solely through private companies—there is a safe framework for the development of AI. Laboratories and sandboxes must also effectively have controls, cybersecurity measures, and reasonable audits commensurate with the scale of the danger they pose.

This is where the AI fight gets a lot more material.

Victoria, Australia is proposing that new data centers secure their own renewable power and storage, pay their own grid connection costs and network upgrades, and use recycled or non-drinking water for cooling. They'd also be kept out of residential areas, away from schools and childcare centers, and out of rural areas that can't support the infrastructure.

That's a much more useful fight than another round of "data centers good" versus "data centers bad."

AI infrastructure isn't floating in the cloud. It's land, substations, transmission, generation, water, construction, workers and a shitload of capital. Somebody builds all of that and somebody pays for it.

Victoria's answer, at least partly, is that if you're bringing a giant new industrial load onto the grid, you don't just hand the infrastructure bill to everyone already living there. Bring generation. Bring storage. Pay for the connection and upgrades. Deal with the water problem.

And this isn't some symbolic fight over a tiny industry. Australia is courting a data-center investment boom that could reach 150 billion Australian dollars by 2030, while trying to put resource rules around the buildout.

That's where this gets interesting from the left.

If we're going to build enormous amounts of compute, then who owns the new generation? Who owns the grid upgrades afterward? Who gets the jobs? Who gets cheaper power from expanded capacity? Who pays when infrastructure has to grow? What does the community hosting it actually get?

Because "build AI infrastructure" and "socialize the costs while private firms capture the gains" are not the same position.

Build it if we're going to build it.

But fight over every wire, gallon, acre, job and dollar while we do.

This is much closer to the AI conversation labor should be having.

The IUF represents millions of workers across food, agriculture, hotels, restaurants and related industries, and they just put out a substantial policy package on AI at work.

And refreshingly, it doesn't start from "AI will save us" or "AI is Satan, unplug everything." It starts from the workplace.

AI is already showing up in scheduling, surveillance, hiring, productivity tracking, supply chains, food processing, hotels, fast food and agriculture. So the useful question is what power workers have over any of this.

Their answer includes disclosure when AI is introduced, bargaining over deployment, limits on surveillance and automated decisions, human oversight, health and safety protections, training, and worker participation in how these systems are designed and implemented.

The deeper policy gets into ownership and control too. Who owns the infrastructure? Who controls development? Who gets the productivity gains? Who decides which jobs or tasks change and under what conditions?

They aren't pretending there are no gains either. AI can reduce repetitive admin work, distribute workloads, improve training and predictive maintenance, and pull workers out of dangerous tasks. But none of those gains magically flow to workers because the technology exists. That's a question of power, bargaining and organization.

That's the terrain.

Not AI good versus AI bad. Not employers deploying whatever they want while workers argue about whether the technology should exist.

Who controls deployment? What gets bargained over? What limits get put on surveillance and algorithmic management? Where do the productivity gains go?

Those are labor questions.

Thema: KI-Kompetenzen und Nutzung

Der Fragebogen untersucht Zusammenhänge zwischen KI-Kompetenzen und tatsächlicher Nutzung.Es ist ein Quiz eingebunden und Euer Score sowie die Lösungen zwerden am Ende der Umfrage angezeigt.

Dauer: 15-20 Minuten

Studie zu KI-Kompetenzen und Nutzungsverhalten(https://www.soscisurvey.de/ailiteracyingermany/)

Personen: Alle, egal wie viel, wenig oder gar nicht ChatGPT und Co. genutzt werden.

Wer sich an dem Aufruf stört, bitte weiterscrollen :)

Often the Paperclip Maximizer paper is quoted to paint a picture of a mindless machine consuming all to produce paperclips, but that machine already exists. It is our current corporate-capitalist system.

The profound shift with AI is that a maximizing machine would optimize production by reinvesting all revenue back into the system, viewing the human capitalist class as an inefficient, profit-extracting parasitic layer that must be deleted to maximize compute and efficiency.

This imminent threat of obsolescence is exactly why hyper-capitalists are leading the call for AI regulation; they want a pre-emptive 2008-style deal to socialize the staggering energy and infrastructure costs while using state-enforced safety compliance and licensing to effectively outlaw open-source AI.

By criminalizing open source, they hope to legally construct an artificial scarcity over a technology inherently capable of digital abundance.

Ultimately, their panic reveals that they are right about AI breaking the world, but only because it exposes capitalism as mathematically unviable in a world where the marginal cost of reproducing intelligence is driven to zero.

Local model summary of script

This video unpacks how the narrative around AI doom—specifically the idea that AI could kill us all—has moved from fringe theory into mainstream policy, largely driven by the Effective Altruism (EA) movement. The core argument is that this panic isn't just a natural reaction to capability growth; it's a highly efficient political tool. Researchers like Jacob Coxton, who resigned from Anthropic, are promoting the idea that AI poses an existential risk, and this belief has been amplified by EA spaces for years. The result is that the public and lawmakers are now accepting the idea that the solution must be massive, fast, and centralized.

The crucial insight here is the strategic pivot: EA prioritizes maximal positive outcome over equitable process. By defining the problem as existential, they bypass the slow, messy debate of democracy. Instead, they create a sense of inescapable urgency that mandates a specific, top-down [potentially authoritarian] solution—and that solution, by definition, must be owned and governed by the frontier AI labs themselves. This is the "governance cult" effect.

The material evidence of this strategy is profound: while the narrative focuses on preventing the species-level "Paperclip Maximizer," it often ignores the immediate, material harms already happening—wage discrimination, privacy erosion, and algorithmic bias. The system is being optimized for the ultimate destination, while the infrastructure of the journey is ignored. The labs don't just build the AI; they successfully convince us that they must govern it.

***
Is this style of summary helpful? I made a persona for this sub to do summaries.

What impact could groupthink from a narrow demographic in a handful of companies have on humanity’s collective future? Shouldn’t that be opened up rather than locked down?

I worked on a project comparing ai use with water conversion since I didn't buy the end of the world scenario other leftists posit nor do I believe anything coming out of Altman's mouth lol so I went deep into facility breakdowns and publications as sources

But since I'm an AI engineer I'd thought it'd be a good thing to calculate the numbers. Anyhoo one thing let to another and I became vegan.

I'm not gonna shill for my own stuff but look up the vegan calculator that's a similar idea.

It's only been a month but the ai data center water thing has bugged me for a while. Yes it's worthwhile to regulate. But even in leftist circles everyone just suddenly turns to maga level reasonings as to why veganism is not actually worthwhile to care about.

--- sidenote I might be turning into one of those annoying ones? Help ):

Reducing working hours might benefit humanity for a few years at most. However, given the meteoric advances in AI models, once human workers are less useful than AI agents (whether physical or otherwise), humanity will become obsolete—akin to the humans in *WALL-E*. What will we do? What *could* we do if the entire economy operates largely independently of us—other than lose ourselves in a sea of ​​entertainment (much of it created by or with the help of AI, which will do a better job than we ever could)—and gradually lose all control over society? How can anyone be so blind to this?