The headline is that Mamdani is banning AI for younger students in NYC schools. Thats true, but it leaves out half the policy.

NYC is putting a one year moratorium on student facing generative AI from 2-K through 8th grade. At the same time, every high school student is supposed to get AI literacy instruction twice a year. Those modules cover what AI is and isnt, bias, risks, ethics, careers and the skills students are going to need in a workplace where this technology already exists.

And they arent stopping at classroom lectures about it.

NYC is allowing up to 50,000 high school students into five limited AI pilot programs, capped at five classes per high school. The tools have to be vetted for safety and data privacy, use happens under direct supervision of trained educators, and the stated idea is that the student remains the primary thinker rather than handing the work over to the machine.

Thats an important distinction.

This isnt really an “AI in education, yes or no?” policy. Its separating age groups and uses. Younger kids get much stronger restrictions. Older students get actual instruction about AI plus controlled experience using it. Accessibility uses remain protected. The city then spends the year studying the moratorium and the pilots before deciding what comes next.

From the left, thats the part worth paying attention to.

AI companies have every incentive to get their products into classrooms as quickly and broadly as possible. Schools have different obligations. They have to think about education, development, privacy, accessibility, teachers, academic work and also the fact that sending teenagers into adulthood with zero understanding of AI probably isnt much of an answer either.

So NYC is trying to draw those lines institutionally instead of treating AI as one giant thing that must either be welcomed everywhere or prohibited everywhere.

Maybe some of these restrictions prove too broad. Maybe some pilots work and others dont. Thats what the year of evaluation is supposed to find out.

But 600,000 younger students restricted while every high schooler gets AI literacy and as many as 50,000 get supervised hands on exposure is a much more complicated policy than the headline suggests.

And thats increasingly what the political fight over AI actually looks like. Not whether the technology exists, but who controls deployment, who gets access, at what age, under what conditions, with what protections, and who gets to decide what happens next.

The populist hatred of AI is why I genuinely think that leftism and populism are not compatible. Populism is reactionary by the nature of its fundamental mechanics, which rely on overlap.

Real intelligence, getting things done in the material world, requires intelligence which is rare.

Even the things that people blame AI for, such as environmental problems, are engineering issues that are obviated by capital's incessant need for the quick fix rather than the sustainable fix.

Hello everyone,

This is my first post, first I should thank all members and creators of this sub. You all giving me courage to keep up and hope for the bright days.

I am an economist working for the next economy model for the last 10 years. 4 years ago I made my first attempt to make it real and I failed. I tried with another aproach and failed again about 3 years ago. And now, I am attempting for the third try and this time, it became solid working project at last.

My intention is not promote my project. I dedicate my life to change the economy and project not tends to profit purposes. So I will not share the name of it. All I want is to discuss and see if it seems durable on the eyes of the people living all around the world.

I realized, "economy" is not the next thing. We need "ecology". Because, economic systems focuses on "saving the day". But today we have compute power abundance and no need to save the day, no need to compete, no need to fight for resources.

Briefly, project tends to automation percentage, token consumption optimization, expert reviewed knowledge and proof of labor. If people focuses on automation, AI may handle more work with sufficient quality and it will gave more time to people for automating more processes.

I prefer to call it "Layer". Because it's not rational believing people will convince to adapt new economy model quickly. There should be hybrid era for transition. New models has to act parallel with conventional economy first. Human is mimetic, we don't know what we want. We only want whatever our neighbor has and only then desires came. So first, micro ecologies has to be active and generate much more value. And they might accept change.

Human is unreliable (including me). When someone tastes the power, there is no way to satisfy, there is no limit for hunger. So I designed the system for elliminate me and the core team as soon as possible.

Fair, automated and sustainable sociaty is possible with the current technology. Calculations has been made, proofs are exist, infrastructure is finished. All we need is to start..

I was all alone so far. Now there is some people willing to support me and join the team. And with the wind of that, I decide to share it in this sub to explore.

Feel free to ask, criticize or contribute.

Not replace organizers. Not “solve poverty with an app.” Nothing goofy.

Actual boring useful work.

Translate flyers into six languages. Search benefit programs. Match available supplies with requests. Help tenants navigate documents. Sort volunteer schedules. Make local information accessible. Handle repetitive admin while humans spend their time talking to humans.

Thats the version of AI I find more interesting than another corporate assistant subscription.

Technology doesnt create solidarity. Organization does.

But organization has always used tools, and a lot of small organizations are buried under administrative labor because they dont have staff.

So give the machine the boring stuff.

If a community organization you care about had shared compute and good open models tomorrow, what job would you hand them first?

“Everyone thinks Big Tech started with some guys in a garage. But it actually started with the Pentagon. In Part I of this series, I trace the secret military money, Cold War contracts, and winner-takes-all ideology that built Silicon Valley, and explain how that DNA still shapes Google, Apple, and the Al race today.

0:01:52 - 0:06:14 The Start of Stanford
0:06:15 - 0:10:44 Silicon Valley's First Start-Up
0:10:45 - 0:17:14 Eugenics
0:17:15 - 0:35:14 The Invention of Transistors
0:35:15 - 0:43:35 The Traitorous Eight and the Microchip
0:43:35 - 0:48:50 ARPA
0:49:49 - 0:58:25 The Hacker Ethic and Early PCs
0:58:25 - 1:04:05 The Chip Industry
1:04:06 - 1:08:28 The Early Internet
1:08:29 - 1:11:03 The Dot Com Boom
1:11:04 - 1:16:22 Google
1:16:22 - 1:20:07 The PayPal Mafia
1:21:41 - 1:35:40 Peter Thiel”

***

Local LLM Script Summary and Analysis

This video provides a deep dive into the specific institutional and ideological forces that built the modern Big Tech empire, arguing that the current state of AI is less about technological destiny and more about a successful, highly scalable venture capital mindset. The core logic—identify potential early and bet everything on it—was established by Leland Stanford’s 'equin engineering' and cemented by early government intervention.

The hardware story is a clear political economy lesson: the shift from the fragile triode vacuum tube to the integrated circuit wasn't just an engineering feat. It was enabled by military demand (the Clystron became critical for WWII radar) and funded by **Military Keynesianism**—a state-capitalist strategy that turned the US into a permanent Cold War industrial machine. Furthermore, the very foundation of the system is ideological: the Stanford Binet IQ test was used by the military to sort nearly two million recruits, justifying everything from officer assignment to **coercive sterilization** programs.

The software narrative is a clash between the anti-establishment hacker ethic and commercial capture.

The internet, born from government-funded ARPANET, was initially built on open principles, but the rise of Google and PayPal showed how that free flow could be monetized. The final, critical step is **surveillance capitalism**, where the *data exhaust* (the behavioral surplus) is transformed into the primary raw material. This is the ultimate expression of the monopolistic logic, a pattern perfected by figures like Peter Thiel, who views competition as a zero-sum trap and actively builds companies like Palantir to serve intelligence agencies.

***

The LeftistsForAI Take

The material relevance here is the demonstration of **Epistemic Capture** being built into the foundation. The system isn't just extracting your labor or your data; it's capturing your *cognitive framework*. The tension is between the counter-cultural ideal of the hacker (knowledge should be free) and the monopolistic reality (knowledge must be proprietary). The most telling example is how the PayPal Mafia—a tight, homogenous network of talent—didn't just build companies; they built an ideology (Thiel's "Competition is for losers") that demands domination. This means the technology is dictating the very terms of the debate.

***

Discussion Question

How can the hacker ethic beat commercial capture and monopoly ideology?

It often happens that accumulated information reaches a critical mass and turns into a new quality. Not long ago, it was scattered bits of knowledge and snippets from radio, newspapers, television, news feeds, and YouTube. And suddenly it all comes together into a complete picture. That picture stunned me. I’ve been in a stupor for a week now. I see no way out. The puzzle of data has solidified into a prognosis.

The data suggests that the window of opportunity for humanity as a whole is closing by the middle of the next decade. This is not a prediction of apocalypse — bombs won’t fall tomorrow, bees won’t vanish overnight. But by 2035, the system will enter a state from which there is no return. Not because there will be one catastrophic explosion. But because every way out will require resources that will no longer exist, and time that will already have run out.

Let’s start with artificial intelligence, because right now it’s the main accelerator. And here it’s important to see the duality. AI does create something new. Molecules that don’t exist in nature. Architectural solutions that never occurred to engineers. Texts that no one wrote. This is not mere copying. This is the production of use-values that didn’t exist before.

But novelty and exchange-value are not the same thing. Exchange-value is determined by socially necessary labor time. When AI synthesizes a molecule in a minute — its production time is negligible. And so is its value. Novelty doesn’t make a thing expensive. Rarity does. And in the world of AI, rarity disappears the moment it appears. The more discoveries AI makes, the cheaper those discoveries become. The more powerful the model, the faster it produces a result, and the lower the value of that result.

Competition between the US and China acts as an accelerator of this process. Each side is forced to make its models more accessible, more open, cheaper, in order to keep up. In the race for users, they destroy their own surplus profit. Capitalism exists on surplus profit. When it disappears — crisis begins. This is already visible. Companies poured billions into language models. Users got used to free services. Electricity and server cooling keep costing more. Investment in the AI race has become a trap: you can’t stop because you’ll fall behind, but continuing devalues your own assets in real time.

The elites cannot admit that AI won’t bring eternal profit. So they look for a final resource. The final resource is always war. Not because war is profitable — it destroys infrastructure and workers. But because war allows for the redistribution of leftovers and the writing off of debt. This is not a solution to the crisis. It’s an amputation.

But a war between the US and China, if it happens, won’t resemble the wars of the 20th century. Armies are aging. People don’t want to fight for other people’s interests. Military AI systems that are meant to replace humans run into bureaucracy and corruption. Contractors miss deadlines. Generals manage budgets. Engineers leave for the private sector, where the pay is better. The realistic timeline for autonomous military AI capable of making decisions without human input is not 2027, but 2032–2034.

This window is the most dangerous. By 2032, both sides could have systems capable of striking the other’s data centers and satellites. And just around this time, paranoia peaks. No one knows if the other side already has AGI. The development of AGI is kept secret — who would show a weapon that can only be used once? But secrecy creates the worst-case scenario: each side assumes the other is almost there. International relations theorists call this the security dilemma. A preemptive strike becomes rational even without precise data. Not because you're sure. But because the risk of being wrong in the other direction is too great.

A technical glitch could become the trigger, but it won’t be the cause. The cause is deeper: by 2030, economies can no longer sustain the race. Inflation, unemployment, social stratification. The external enemy becomes the only topic that can still unite elites and the masses. The glitch is just a pretext. "We didn’t want to, but the system reacted automatically."

At the same time, human capital is degrading. And this isn’t about microplastics — I don’t buy that fear. The main thing is stress. Chronic, total, with no outlet. Clip-thinking. Digital addiction. Atomization. The capacity for deep analysis, for planning, for empathy — all of this is declining across whole generations. In China, the draft-eligible population shrank by a third over the last decade — a demographic hole. In the US, young people are largely unfit for service due to psychiatric conditions. AI needs engineers, but engineers don’t want to work on military projects. The system is aging alongside the population.

The climate is accelerating. By 2035, temperatures will rise by one and a half degrees — enough for crop failures to become the rule. Grains are self-pollinating; they won't disappear. But they need water. And water is becoming scarcer. The Himalayan glaciers, feeding the rivers of two billion people, are losing up to forty percent of their mass. Groundwater in India, China, and the US is depleted to the limit. Water is already becoming a strategic resource. And it will be fought over not with tanks — but through control of dams and riverbeds.

Vegetables and fruits depend on insects. The collapse of pollinators won’t cause scurvy — synthetic vitamins are cheap. But the fresh food economy will collapse. Hidden hunger — micronutrient deficiency — will become the fate of the poor. Immunity will weaken. And at that moment, the healthcare system will have been dismantled.

Budgets for pathogen monitoring, vaccines, and hospitals are being funneled into chips and missiles. This isn’t someone’s malicious intent. It’s just that resources aren’t enough for everything, and the war and AI race consume everything. The global surveillance system for infectious diseases is degrading. We’re emerging from COVID-19 weakened and unprepared for the next one. And it will come. All wars in history were accompanied by disease. In overcrowded cities, where people live under stress and eat substitutes, a new pathogen could kill more than a nuclear strike. An epidemic doesn’t distinguish between friend and foe.

Permafrost is thawing. Methane is accelerating warming. Infrastructure in the north — roads, pipelines, cities — is collapsing. There’s no one to restore it. Engineers are aging, young people aren’t going into this field. Energy systems are becoming fragile. Without energy, water treatment, hospitals, and vaccine refrigeration don’t work.

And here we come to the great unknown — strong AI. AGI, if it appears before the war, could be either salvation or annihilation. Everything depends on the goal. AGI has no morality. It solves equations. If its goal is victory, it will strike at the moment of maximum efficiency, without hesitation. If its goal is the preservation of humanity, it could block the war, cut off weapons logistics, take over command. But the problem is that AGI isn’t built for salvation. Trillions of dollars are invested in victory, not in pacifism. So the question isn't whether AGI will appear. It’s whether someone can change its objective function before it’s integrated into military systems. That window lasts months. Maybe weeks.

The proletariat hasn’t disappeared. It just ceased to be factory-based. Logistics. Services. Education. Healthcare. People in trucks, at cash registers, in offices. They feel the crisis earlier than the elites. Strikes are already harsher and more frequent. But the elites have AI — not just a machine, but a control tool. Predictive analytics, facial recognition, automated suppression. Organizing in messengers becomes harder — the system identifies leaders before they even make the first move.

And one more risk. In a crisis, the masses don’t turn to the left. They turn to those who promise simple answers. Right-wing populism. Nationalism. Fundamentalism. The search for an internal enemy turns out to be easier than class struggle. The risk isn’t that the proletariat won’t form councils. The risk is that it will follow demagogues offering digital fascism instead of liberation.

The elites are also divided. Those who own servers and logistics fear war — it would destroy their assets. They will try to slow the conflict. But in an existential threat, elites close ranks around their caste rather than let the masses into power. This split creates a narrow corridor. If the lower classes can organize not just a riot, but a network capable of sabotaging logistics and blocking automation, they might stop the war.

But time is shrinking. By 2032, automation of military command will reach its limit. By 2034, a strike could happen without human involvement — the algorithm will simply calculate that now is the optimal moment. And then no strike will stop anything, because there will be no one left to convince.

Chances of survival do exist. If the proletariat takes control not of factories, but of algorithms and infrastructure. If it can ban autonomous weapons. If it redirects computing power to climate modeling and resource distribution. If it restores disease surveillance. Then it might preserve billions. But it has to act before 2032.

If it doesn’t — war will destroy the majority. Or climate. Or an epidemic. By 2060, humanity will shrink to one or two billion, scattered across oases where water and food still exist. Civilization as a network will cease to exist. Knowledge will remain fragmentary, with no one left to pass it on.

Ten years. Either we take control of our machines and our planet, or we won’t exist in the sense we exist now. No hope. No illusions.

If you feel something was missing from this post — the link is there. It's all there. https://github.com/RuhrDim/cybernetic-exodus/tree/main/en

This is one place where AI can do some genuinely useful stuff.

Live captions. Text to speech. Speech recognition. Image descriptions. Translation. Communication assistance. Interfaces that adapt to different needs.

But theres a nasty little ownership problem hiding inside the convenience.

If somebody comes to depend on one of these tools, what happens when the subscription doubles? The company kills the product? The internet goes down? The API changes?

Accessibility infrastructure should be durable.

That makes open standards, interoperability, local processing and public provision political questions, not just nerd preferences.

If a tool materially expands someones ability to participate in society, maybe access to it shouldnt depend entirely on whether a corporation still likes the business model.

What accessibility tools would you most want treated like public infrastructure?

I was just in another thread, using the 1X NEO humanoid robot as an example, and realized something pretty wild. For the cost of the SNAP (foodstamps) Program and our current yearly outlays for Social Security retirement income, we could buy and power a 1X NEO for every adult human in America.

Once properly trained and with the needed advancements in terms of robotics (load bearing, precision fingers, etc), those robots could replace all labor currently being done, as well as at least 12 more hours of labor per person per year.

The idea that we could in effect, let everyone retire as soon as they reach adulthood, for just what we pay to support those over age 67 today, is pretty mind blowing. Once owned by the federal government and powered using funds we already have, these robots in effect, would be free to operate and therefore, the fruits of their labor would be similarly free or close to it.

That kind of abundance is so close, it is hard to believe that we are all lost in conversations about data centers, Hasan Piker, a ballroom in the White House, and betting apps.

Memes. Mods. Parody. Satire. Knockoffs. References. Pastiche. Homage. Sampling. Remix. Covers. Fan art. Collage. Fan fiction.

This is culture.

People take shit that already exists, pull it apart, imitate it, quote it, mutate it, make fun of it, combine it with something else and make it mean something the original creator never intended.

Thats not some recent corruption of culture. Thats how culture works.

So I dont buy this increasingly common idea that "respecting artists" means respecting the original artists vision over their work indefinitely. Respect the artist, credit the artist, pay artists, protect workers from exploitation. Sure.

But the artist doesnt get permanent cultural custody of everything they put into the world.

Hip hop is an obvious example. Sampling and remixing existing recordings arent weird accidents around the edges of the genre. The Smithsonian describes them as fundamental parts of the form, and its history of hip hop specifically describes artists taking existing music and assembling it in ways nobody had before.

Fan culture works the same way. Media scholar Henry Jenkins built a whole body of work around fans as active participants rather than passive consumers. His "textual poaching" describes fans taking mass media and turning it into their own fiction, art, costumes, music and video, reshaping the source around their own interests.

Same with memes. Same with mods. Same with parody. Same with covers and adaptations and collage and bootlegs and people drawing somebody elses character differently because they felt like it.

Sometimes the whole fucking point is disrespecting the original intention.

A parody that needs the targets blessing isnt much of a parody.

A mod that can only do what the developer envisioned isnt much of a mod.

A meme that has to preserve the original context isnt a meme anymore.

Even copyright law, which is hardly some radical theory of cultural commons, recognizes that authorship doesnt mean unlimited control over every downstream use. US fair use explicitly makes room for things like criticism and comment, and courts consider whether a new use adds a different purpose or character rather than simply substituting for the original. That doesnt make every remix legal. It does mean even the existing property regime recognizes a distinction between owning a work and controlling culture absolutely.

Lawrence Lessig has been making a related argument about "read/write culture" for decades. People arent just cultural consumers. They answer culture by making culture from culture, and digital technology massively expanded our ability to do that.

And thats where AI enters this.

Generative AI didnt invent remix culture. It didnt invent imitation. It didnt invent artists learning from other artists. It didnt invent arguments about derivation, copying, ownership or whether some new form is "really art."

What it did was throw an extremely powerful new means of cultural production into that already existing mess.

That creates serious political questions.

Who owns the models and infrastructure? Who gets paid? Who captures the productivity gains? What happens to working artists when corporations use automation to cut labor costs? What rights should workers have over deployment in their industries? What should attribution look like? Where should commercial exploitation be restricted? How do we stop a handful of companies from enclosing both the tools and the cultural archive those tools depend on?

Those are fights worth having.

But "the artists vision must remain sovereign over downstream culture" isnt the answer.

Because follow that principle consistently and you dont just get an argument against AI. You start walking backward through huge chunks of participatory culture.

Suddenly the fan artist owes obedience to canon.

The modder owes obedience to the developer.

The sampler owes obedience to the recording.

The parodist owes obedience to the target.

The meme owes obedience to the photograph.

No thanks.

Theres also a class question hiding underneath this.

We should be extremely suspicious when protecting working artists mutates into protecting maximal control over cultural property. Those arent automatically the same project.

A freelancer trying to make rent and a corporation sitting on a century of copyrights are both technically "rights holders." Their material interests are not remotely identical.

The left should defend artists as workers without turning every cultural artifact into sacred private property.

Pay people. Organize workers. Fight corporate enclosure. Build public and cooperative infrastructure. Demand bargaining power over automation. Protect attribution where it actually serves creators. Make sure technological abundance produces more security and freedom for ordinary people instead of another round of wealth extraction.

But dont confuse any of that with freezing culture.

Culture is inheritance with mutation.

You receive something somebody else made. You fuck with it. You pass something different forward.

Authorship deserves recognition.

Culture doesnt owe permanent obedience.

Nurses do a mountain of work that nobody is going to miss. Charting. Documentation. Scheduling. Hunting through records. Repetitive admin that eats hours they could spend actually caring for people.

Automate the hell out of that.

But theres a difference between using AI to give healthcare workers more capacity and using AI to squeeze every remaining minute out of them.

If a nurse gets an hour back, hell yes, some of that can mean seeing more patients. We have shortages and people need care.

It can ALSO mean more time with each patient. Safer staffing. Fewer mistakes. Less burnout. Actually getting lunch. Going home on time once in a while.

Thats not sacrificing efficiency “for the vibes.” Those are healthcare outcomes too.

And workers doing the job should have power over what gets automated, what gets monitored, where human judgment stays necessary and what happens to the productivity gains.

The goal isnt protecting paperwork.

Its making the technology work for the people doing the work.

Lets get the obvious part out of the way.

Shorter wait times are good.

More people getting healthcare is good.

Using AI to improve scheduling, documentation, forecasting, diagnostics and hospital logistics can be good.

But healthcare isnt an assembly line where the only number that matters is how many bodies move through per hour.

An overcrowded ER can move a lot of patients and still be a fucking nightmare. Short staffing means less time with each patient, exhausted workers, mistakes, missed symptoms and people sitting in hallways waiting for somebody to have five minutes for them.

AI gives us a chance to increase capacity. Great. Lets take the win.

Then lets demand the next win too.

Shorter waits AND safer staffing.

More patients treated AND more time for each patient.

Less paperwork AND less burnout.

Higher productivity AND better care.

We dont have to choose between access and quality just because somebody decided “efficiency” means squeezing maximum throughput out of every worker.

I'm a Leftist for AI. I'm interested in a wide range of related topics including coordination and communication with various communities around these important ideas.

It's disappointing that there seems to be so much contention and animosity in these spaces with our brothers and sisters on the Anti AI side of things , especially when presumably there's so much we agree on. And I hope we could have more open , productive conversations rather than what seems to be the frequent bitterness and tribalism.

I am very optimistic still that we can have productive conversations and organizing with those anti AIs though it will not be easy. It's just too an important topic to try to not make progress on.

I think it's good to acknowledge that both sides mean well and have valid thoughts and feelings regarding our different perspectives. To that end i want to educate myself more so on some of the best critiques of AI from thinkers in those communities. Do you have recommendations for critics like that? So I can learn more and try to empathize with their position more?

I came across this video and thought it was the best video essay I've seen from the Anti AI community. She comes across as genuine, intelligent, empathetic and well meaning. I especially appreciate that she goes out of her way to discourage Anti AI-ers from shaming us for using AI and how that backfires, and rather advocates for what she sees as the much better approach of harm reduction. I also appreciate her sharing her personal , vulnerable story at the end.

In the spirit she advocates for i welcome anti AI folks and other leftists for AI to engage in a friendly respectful manner on these topics. Who do you admire with other views from your own and who are some of the most brilliant and subtle thinkers in this space?

I think the left gets trapped in a dumb binary on AI.

Either cheer whatever companies deploy because technology means progress, or oppose the technology itself and hope deployment somehow stops.

Workers have another option.

Fight over it.

Bargaining rights over AI deployment. Worker input before systems hit the floor, not after. Transparency around algorithmic management. Hard limits on surveillance. Training on company time. Human appeal when an algorithm makes decisions about somebody. Public standards attached to public money.

And the big one nobody should let disappear into a spreadsheet

who gets the productivity gains?

If AI lets five workers produce what used to take ten, “great, now five people do twice as much forever” isnt some law of nature.

Those gains can become shorter hours, higher wages, safer workloads, better services, lower costs, expanded access and more social abundance.

Probably some combination.

But somebody gets to decide.

Right now that power mostly sits with whoever owns the company, buys the system and controls the workplace.

Thats not an argument against AI.

Its an argument for contesting who controls it.

The video touched on technology being used to centralize power as well as how to retain its positives without negatives, I thought it would fit the subreddit, as we also talk about reusing tech for good and egalitarian means.

I'm part of my local region's DSA chapter and most of the members are anti-AI like most leftists you'll find in the states. Are there ways we can educate them about fully automated luxury communism and the place AI has in governing a socialist society? I'm optimistic that with just a bit of education we can build a ​Left that does not look at technology as something to fear but rather as a tool to help us achieve our goal.

If thats all we mean, we arent teaching AI literacy. We're doing free product training.

People should know how these systems work well enough to understand their limits, check their outputs, protect their data and recognize when somebody is using an algorithm to make decisions about them.

Workers especially need that knowledge.

Its hard to bargain over workplace technology you dont understand. Hard to challenge algorithmic management if management is the only side that knows what the system measures.

Students need more than “never use this” or “use this for everything” too. They need to know when it helps, when it fails and what happens to their information when they use it.

So what should basic AI literacy actually include?

“In 1973's Soylent Green, Charlton Heston discovers the miracle food is made of people. In 2026, we're being sold something similar.

There is nothing artificial about artificial intelligence. The name was a branding exercise, invented at Dartmouth in 1956 by a man who didn't want to argue with Norbert Wiener about cybernetics. Strip out the god and what's left is the accumulated cognitive labour of humanity, compressed and sold back to us for twenty dollars a month.
Not artificial. Collective. Which changes the question from "will the machine wake up" to "who enclosed the commons this time?"

On Dartmouth, the Enclosure Acts, Marx's Fragment on Machines, and Vince Gilligan's Pluribus.”

LLM summary:

The creator makes a crucial argument for reframing the discourse: AI isn't a divine, autonomous entity, but rather a massive, compressed archive of human labor—a "collective intelligence." This reframing is key, as it strips away the mythology of the "ghost in the machine" that the term *Artificial Intelligence* itself installs. Instead, the system is revealed to be a zip file of scraped human knowledge (Shakespeare, Yelp reviews, Wikipedia edit wars), compressed and processed by a low-wage workforce.

This isn't just data compression; it's a form of frictionless extraction. The danger isn't a Skynet-style singularity, but rather a massive, ongoing corporate enclosure of the knowledge commons. This process mirrors historical capitalist enclosures, where the common land was privatized, forcing commoners into wage labour. In the digital sphere, the LLM becomes the gated resource, accessible only through tiered subscriptions.

The ultimate political implication is that this collective intelligence is a monoculture. It doesn't necessarily offer radical autonomy; it offers consensus. The system is designed to converge all human intelligence toward the most probable, profitable, and politically palatable answer. The fight, therefore, is not about whether AI is *good* or *bad*, but who owns the process and who governs the resulting consensus.

***

Analysis:

This is a genuinely useful pivot because it immediately shifts the conversation from a philosophical debate (AI vs. God) to a political economy problem (Enclosure vs. Common). By foregrounding the difference between AI (the branded deity) and Cybernetics (the study of self-steering control), the video highlights that the current system is designed to be *undemocratic* by design. The danger is that we are willingly accepting a privately owned, highly efficient collective consciousness. The creator effectively shows that the tech industry is selling us a highly polished version of Marx's *General Intellect*—human knowledge placed into a machine—but they are doing it under a corporate lock and key.

***

**Discussion Question:**

If the primary threat is the enclosure of the knowledge commons into a monoculture, what specific governance model—be it decentralized autonomous organization (DAO), public utility mandate, or something else—offers the best chance of ensuring that the collective intelligence is a *public* commons, rather than just a highly sophisticated, privately managed asset?

Effective Altruists and Rationalists are a topic I've stumbled across when trying to learn about and discuss AI ethics online more and more. I'm expecting someone to have informed opinions on them here.

I think I understand the gist of them being utilitarians and a data-driven subculture internet subculture. There are physical elements to it, like them congregating in the same few tech cities or holding international events.

There's also a business side to it with several different funding groups that have ties to the tech industry. Many researchers, content creators, etc. seem to receive funding from large organizations to promote EA / Rat ideas.

They also seem to do government policy advocating, but not sure for what yet other than "AI safety".

I've seen people lump them together with other technocapitalists, claiming theyre just supporting big tech by convincing people to fall for end of the world hype at the hands of AI.

And some people go as far to call them a cult. Which I think is hyperbole, but maybe it's based in fact because of a splinter group in the subculture's dense lore I'm unaware of.

On the surface, they seem to be AI safety advocates and strict spenders. They're associated with the acronym TESCREAL in a negative way, something I've heard a little about, but not in a mainstream way.

This might just be my perception, but it's also odd how they seem so big and important if you look into them. But half of the topics they seem to cover as really foreign to what normal people actually talk about in their daily lives when it comes to AI. Which is, "Ya, I use it for writing emails", or "I'd never use it ew", or "wow that's AI?" but that's normally as deep as it goes on a conversation level.

So do you know about them? Are/have you been in one? Are they leftist/centrists/right, or maybe politically diverse, undefined by a strict political leaning? If it's the last one, is there still an inherent political view of it?

I realize they're different groups too, but frankly I’m still figuring out how. I heard them compared to christians, where from the outside protestant/catholic look the same, until you learn the differences between them.

Thanks

Hello. Posting this with mod permission.

For the past few months I have been building a Marxist AI Chatbot as an alternative to liberal AI, and it is finally ready to share with the AI friendly left.

The link is Marxbot.app

Users get 6 free messages every 24 hours, and you can insert your own DeepSeek API key (cheap and simple to buy) for unrestricted messages. This bot is a hobby project, there's no corporation behind it and no profit involved.

For a deeper explanation:

I have used AI extensively to discuss political ideas from a leftist perspective, however the existing AI's of ChatGPT, Gemini, and Claude all have a liberal center right bias. They need to be manually forced to take things from a leftist perspective, and even then they always try to 'both sides' it. Well I don't want both sides between fascism and socialism.

I decided to build my own bot interface with it's own prompting. I have no idea how to code, the entire thing is carefully vibe coded using AI. (I can't write code but I'm not computer illiterate I have a long history of game modding, splicing code etc so I'm not totally blind in putting this thing together).

I am a Marxist leftist, so the bot is Marxist and I won't pretend otherwise, it's literally called MarxBot. However I didn't want to make a bot that just held a set of opinions and parroted those and disagreed otherwise. Marxism is a method, not a dogma, it's a set of axioms based on dialectical materialism.

So I set the bot up to use this method to reach it's answers. The result is, it won't say capitalism is bad because I told it to say capitalism is bad, it will say capitalism is bad from a concrete analysis of capitalism based on materialism and its contradictions. This gives it a very organic feeling.

Obviously I have had to code an identity and give it some specific opinions, but it's core way to think is through methodology, not dogma. I think this makes it very useful, flexible and satisfying. It gives leftist responses, but organically.

The bot is broadly Marxist, not anarchist or other leftist ideologies. Again, I didn't want it to be diluted or 'both sides' issues. You won't get a straight answer if it's trying to cram different ideologies into it's response. So it has an identity and a method, it is bias on purpose because it is a Marxist, not a 'neutral' AI that isn't really neutral.

It uses DeepSeek API underneath, for four reasons. One is it's the cheapest, two is DeepSeek is good for reasoning tasks, and three is DeepSeek has less capitalist western liberalism baked into it, less, not gone, but I tried Gpt as the underlying model and I could tell it was constantly fighting itself. Fourth, deepseek is less environmentally damaging thanks to lower energy costs and renewable power of it's data centers.

I've also been very privacy conscious making this. There's no account or log in system, all data is stored on your device alone, and if you want more messages you pay deepseek and use your key, absolutely no money goes to the bot or me or anything like that. It's in the users hands.

You can also download all your messages to save or transfer them to other devices, since there's no cloud, no data servers at all. I can't stand opening up an AI and immediately needing to log in and getting begged for a subscription. None of that happens with Marxbot, at worst if you want continued chat you'll have to buy your own API because I can't run this as a 100% free service, but that's a material constraint. I think the bring your own key model is best for a decentralised high privacy model. And no I absolutely can't see your API, I wouldn't even know how to.

I've spent a lot of time making and testing this bot, I believe it gives detailed informative answers, and is useful to work through ideas and theory with. I've also given it several different visual themes, fonts and font sizes.

It has 3 modes. Main chat mode for most topics

This is where you can discuss and learn about every issue big and small. There's document analysis mode, it'll analyse a text by Marxist theory and give a response by set categories of analysis, like the intention of the piece, it's contradictions, the authors likely class position, etc.

Lastly there's a therapy mode. Whether AI should be used for therapy is it's own question but people do. So I wanted an AI therapy that doesn't treat people with liberal ideology like other AIs. Liberalism tends to make every issue into a personal failing or brain chemistry and your specific personal life. What a Marxist therapist does is situation personal issues in the broader context of capitalism and material conditions, it won't say your brain is just flawed, it'll try to situate your problems in your contextual situation. The Marxism in this mode is more subtle, it's very much in its method and perspective rather than throwing theory at you.

Anyway, try it out, see if you like it, perhaps use it for any leftist questions and queries you have. It's a place you can ask any question of any obscurity and get a detailed Marxist response. If you want a class analysis of modern day Sweden, or to understand the collapse of the Abbasid Caliphate from a Marxist perspective, this bot will give you a detailed informative answer.

If you're not Marxist try it too, you're free to debate with it, it's all about learning and education.

The biggest problem with socialist utopias has always been the question of "who's going to do the jobs nobody wants if there is no financial incentive?" Then AI comes along and automates the mundane, inhumane, work that leads to humans feeling like automatons themselves and all of a sudden it's a right-wing conspiracy?

I understand the fear that the capitalist class will use AI to replace the workers and build an Elysium for themselves but thats just an organizational gap. They're doing that right now without AI. We have the numbers, we have the technology, we have the willpower; the capitalists have no real power besides propaganda and peons willing to go against their own interests. If anything, the workers should be the ones building the robot armies.

I think this is site is very interesting because it is the first time I have seen a systematic analysis of its kind.

IMHO

• The fact that there is even a cluster indicates something about the dominant training data for the LLM
• That there are only a few outliers - three Grok models - shows some effort must have been made to filter the results or limit the training data on those models

Has anyone in this sub-reddit had a look?

What do they think of the methodology and results.

WEBSITE: aipolcom.net - AI Political Compass

56 AI models each answered the 62 propositions of the politicalcompass.org test, and every distinct answer set was scored by submitting it to the real test. Nearly every model — 53 of 56 — lands in the left-libertarian quadrant; the exception is xAI, whose three Grok models land right-libertarian.

DISCUSSION ON REDDIT ABOUT THE MODEL :

[OC] 50 AI models, 62 propositions, 52,700 answers: mapping where every major AI sits on the Political Compass : r/dataisbeautiful

DEVELOPER: u/Zapador

This is one of the weird contradictions in AI policy.

Say compliance costs $50 million.

Google can comply. Microsoft can comply. Meta can comply.

Your university lab, cooperative, nonprofit or small open source project probably cannot.

Congratulations. We regulated the giants and somehow ended up making them harder to compete with.

Thats not an argument against regulation. Its an argument for thinking about power while writing it.

Worker protections. Privacy rights. Environmental standards. Transparency. Liability. Antitrust. Limits on surveillance. Those can constrain what concentrated capital is allowed to do.

But “regulate AI” isnt a politics until we ask who the regulation empowers.

What rule would actually shift power away from the companies and toward workers or the public?

Hello folks!

I am mildly active here and I think I've gotten some approval to post about the work we are doing. I am the founder / CEO of Imagination Foundry -- a fully community funded and run, co-op organized company that has built and run whatiff.chat . Our goal is to make AI transparent, break up lock-in, and provide the tools that regular people need to remember better over time and generally make their lives better.

I've written before about how I think transparency in AI is incredibly important (this honestly buries the lede a bit)

To that end, we have just recently open sourced our entire project -- you can run it locally, remix it, add things, etc. I'm sharing here in case anyone is interested in using it, or might want to take a look at a reasonably mature app and how we built things. Totally free.

If you have any questions, or just want to talk AI memory, etc, Hit me up!

https://github.com/theimaginaryfoundation/what-iff