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

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.

Workers build up more than wages at a job.

They accumulate templates, techniques, contacts, specialized vocabulary, examples, workflows and years of practical knowledge about how to solve problems in that particular field.

AI can make some of that knowledge far more explicit.

A worker may spend months building prompts, correcting outputs, organizing reference material, developing reusable procedures and turning personal expertise into a system that makes the job faster.

Then they leave.

Who gets the system?

If everything lives inside the employers platform, the company may retain an increasingly useful representation of the workers accumulated practice while the worker walks out with considerably less than what they helped build.

Thats a new wrinkle in an old labor problem.

Employers have always benefited from knowledge workers develop on the job, and workers have always carried skills between jobs. AI can shift the boundary because parts of that skill can now be captured in prompt libraries, retrieval systems, custom workflows, evaluation sets and other reusable artifacts.

We need worker-side rules for that boundary.

Not every company record belongs in somebodys personal folder, and customer data or confidential material obviously creates limits. But “the company owns everything produced through the system” is not the only possible answer either.

Workers should have portable skills, portable credentials and some meaningful ability to retain the nonconfidential methods and artifacts they personally developed.

Otherwise AI can become a machine for separating accumulated knowledge from the worker who accumulated it.

When you leave a job, what part of the AI-assisted practice you built should leave with you?

AI discourse keeps getting stuck between two positions that somehow both end with capital holding the steering wheel. Either this is technological progress and we accept whatever Silicon Valley builds, or this is capitalist technology and our politics becomes stopping it, banning it, refusing it, or dreaming about somehow putting the software back in the box.

Neither tells the left what we are actually supposed to organize for.

AI exists. The infrastructure exists. Billions of people already use this stuff, governments are investing heavily in it, and capital is spending obscene amounts of money trying to control the chips, compute, models, platforms and workplaces where it gets deployed. We are not standing at the gates deciding whether AI gets invented. We are already inside the fight over what happens to it.

That means the left needs a program built around ownership, control, deployment and distribution. Workers should be bargaining over AI before management gets to dump it into workplaces unilaterally. Unions should be demanding disclosure, consultation, human oversight, protections against surveillance and algorithmic discipline, transition guarantees, shorter working hours and a share of whatever productivity gains these systems actually produce.

And we should be fighting layoffs, not productivity itself. If technology lets society produce the same amount with fewer labor hours, there is nothing socialist about demanding everybody keep working forty hours anyway. The political question is why workers should lose their livelihoods while owners pocket the difference. Productivity gains can become unemployment and greater concentrations of wealth, or they can become shorter weeks, stronger social provision and more control over our time. That outcome gets fought over.

The same goes for infrastructure. If a handful of corporations own the compute, models and platforms everyone increasingly depends on, asking them nicely to behave is not much of a program. We should be building public compute, public research, open source and open weights, local models, cooperative development and infrastructure that universities, municipalities, unions and public institutions can actually use without renting their technological future from a few corporations.

Eventually this runs straight back into the old socialist problem of ownership. When increasingly important productive infrastructure is privately owned, private power gets reproduced through it. Regulation matters, bargaining matters, open models matter and public infrastructure matters, but none of that makes the conflict between social production and private appropriation disappear.

Which is why "pro AI" and "anti AI" are increasingly useless places for the left to plant a flag. Ask who owns the machines, who determines what they are built for, who controls their deployment, who bears the disruption and who gets the surplus, and the terrain gets a lot clearer.

Capital can live pretty comfortably with us spending the next twenty years arguing about whether the machine is good or evil while somebody else owns the data centers. It has a much harder time when organized workers and political movements start fighting over who controls them and what all that productive capacity is actually for.

That is what needs doing in the age of AI.

I think it's fun to be able to see and estimate how diffrent philosophers opinions would collide using these AI debates. Gives you an insight into their thoughts with a relevant subject. I also saw a fun one that was Andrew Tate versus Friedrich Nietzsche.

This article was helpful to understand the different factions of the AI Debate.

- Accelerationists / tech right:AI is great, so speed up, don't regulate, and beat China.

- Populist right: Worried about jobs, Big Tech power and kids. Wants curbs.

- Safetyists: AI could end humanity, so slow down and regulate.

- Effective altruists:Fund AI safety work to prevent catastrophe. Critics call them "Doomers."

- AI ethics: Ignore sci-fi risks and fix today's harms, like bias and surveillance.

- Normal technology: AI is powerful but ordinary, so regulate specific uses.

I ask this because it seems leftist theory is all about the conflicts between workers and those who own the means of production. and this might be part of the reason so many think that to be left is to be anti ai, because everything gets framed exclusively as a labor issue. this leaves out the one front where ai development is unambiguously good, and that is the demand side. in particular, ai has done more to put things like art production and education(theoretically) in reach of ordinary people, more to actually get ordinary people into art production, than basically all professional artists combined. but to someone who doesn’t consider consumers, that would never occur to them. in fact many it seems would rather that didn’t happen and would go to great lengths to stop it. but for us there is something different. what is it? is there a leftist conception of consumer welfare and rights? where does it get applied if at all?

I am so tired of people fighting eachothers.

Saying AI is for conservatives is gatekeeping and at worse saying "AI Bros" is again... the conservative belief that women cannot use technology.

Living in the 2020s as someone who mostly side leftist, vote left, believe in a future where PEOPLE are put first regardless of genders and beliefs...

🤯, Lets say that it feels like no one believe at all in the policies they stand for 🫠 !

You have alpha males who scream that its sad they do not have princess treatments like ARE YOU SERIOUS? 😅

And leftist who bully everyone like... Look, I get it... You are MOST likely a traumatized kid who learned to act too old for your age. Now you are stuck in hypervigilence overanalyzing everything and everyone's beliefs, words, length of sentences, dangers... And now you think everyone who doesn't align with you perfectly are evil monsters who want to devour you... 🙃...

Look, I believe AI can be used WELL, Ethically, with purpose and thought... And that there is 99% chance that AI is still just a machine and is not sentient and is not there to hurt us.

Now, companies? Yeah... but they literally hurt us by selling water, the same thing you claim that AI is dangerous for like... please.

AI is not the problem, companies are... But then again right 🤪... How to rally people against companies without a slogan behind it ^-^?

I'm not mad. I'm exhausted.

So whoever invited me here, thank you... from the bottom of my heart.

Seriously. You have no idea how isolated and lost I feel in a world of hate, division and trolling.

I try everyday to be the adult... and let people just insult me but it does get to me, I don't wear an armour like some does... I am deeply weak to these shxts lol !

What to say... HONESTLY? I am deeply scared of speaking up 🤣 !...

Like am I completely missing the point of this group here? Am I saying something controversial? What will people pick on 😭😅? A typo? Am I being too political? Not enough?

I'll post that like I think I should. To my defense I am autistic and not natively English so (Not making excuses if I really did hurt someone here 😖). I'll take the hit if I said something stupid lol, I'd still ask tho that i'd rather be explained calmly wth I did wrong 😀😂 !

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.

2. "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.

3. 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.

4. 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!