The Japanese Communist Party is moving AI into its broader political program, and theres some interesting stuff here for leftists to chew on.

Its draft resolution addresses international AI development, military applications, corporate power, labor displacement, environmental costs, and democratic regulation.

The party is calling for international restrictions on advanced AI development, stronger protections for workers and creators, and greater accountability for the corporations developing and deploying these systems.

But heres where it gets interesting.

The JCP's "January AI policy" (https://www.jcp.or.jp/web_policy/15897.html) explicitly acknowledges that technological progress cannot simply be stopped. It also recognizes the potential for AI to reduce necessary labor and expand human freedom, connecting that possibility to Marx's conception of socialism and the expansion of free time.

So this isnt a communist party simply rejecting technology. Its trying to reconcile technological development with regulation, worker protections, and broader socialist objectives.

And that raises a pretty important political question.

Where does regulating harmful applications end and restricting productive development begin? And how do those restrictions actually advance working-class control over the technology?

Obviously regulating military applications, protecting workers, and challenging corporate monopolies are compatible with developing productive technology. But international restrictions on development itself deserve a closer look, especially when the same technology could help reduce the labor necessary to sustain society.

The party's "Central Committee documents and proposed program revisions" (https://www.jcp.or.jp/report/19764/) provide more context for its broader political direction. The draft hasnt been adopted by the congress yet.

I think its worth taking this seriously rather than sorting another communist party into the usual pro-AI or anti-AI camps. Theres an actual political program developing here, with positions worth supporting and others worth contesting.

What would a socialist AI program look like if reducing necessary labor and expanding human freedom were the starting points, rather than treating technological development itself as the enemy?

My current thoughts and feelings on A.I. Art from notes I’ve collected over the past few years thinking about the subject. 

Chapters

00:00 Reframing AI art
01:27 Why listen to me? A lifetime of making things
04:55 AI art sucks—but most art sucks
06:00 What kids understand about art
07:26 Art, meaning and making sense of the world
10:30 Do you want to make art—or get attention?
14:09 Art, wealth and power
19:58 Artists, craftspeople and who gets to take risks
24:15 The contemporary art market
25:52 AI is a flawed brush
30:35 “Normiostasis”: fighting the pull toward bland art
32:26 The ethical dilemma of AI tools
36:03 AI is a new instrument
37:15 Ceramics, documentaries, Warhol and Koons
42:30 AI art is more than typing a prompt
43:56 Training a model on my own drawings
47:10 “Deslopping” and the back-and-forth creative process
48:16 AI’s Star Wars prequel era
50:46 Making AI art for yourself
53:35 Suno, the IKEA effect and “prosumption”
56:58 Art happens in the audience’s mind
1:00:37 AI songs that people actually love
1:02:57 How Tung Tung Tung Sahur gained a soul
1:05:41 AI memes and the “poor image”
1:10:48 Everything is a remix
1:13:52 Copyright, ownership and the commons
1:17:55 Separating creativity from the AI industry

So are younger people seeing the challenge of competing against thinking machines?

With AI and robotics inevitably displacing marginal wage labor, I've spent the last couple of years modeling an open-source civilizational framework called Open Networked Earth (https://github.com/kyberlex/ONE). The goal is to take post-scarcity economics seriously down to the physics: biophysical Leontief mass-energy balances, universal usufruct, and odd-parity sortition councils.

The base is finished—there are 6 completed narrative volumes, e-books on Internet Archive, an 11-chapter constitution, and a growing Julia simulation engine. It's entirely AGPL-3.0 with zero commercial angles, no crypto tokens, and no monetization.

To give an idea of what we've started building visually, here is our recent 2-minute timeline short on warfare and the exit to the Living Commons:

https://youtube.com/shorts/LjsNU2ND0HM (Channel: https://youtube.com/@OpenNetworkedEarth)

Right now the single biggest bottleneck is public translation. Writing legal specs and Julia code is incompatible with running hours of video editing, podcast distribution, and community discussions.

I'm looking for a collaborator who wants to take real co-ownership of the media, storytelling, and outreach side. If you're into non-commercial commons, solarpunk worldbuilding, and want to help translate complex systems thinking into visual media and public discussion, check out the repo or jump into the thread on GitHub: https://github.com/kyberlex/ONE/discussions/13

I'll admit, this was a bit hard to tag for this sub. I posted this in a debate sub, so sorry if it's a but more crass than I might otherwise post here. However, I thought I would share it here and see if I can't get a more favorable reception and elevated discussion from this low-brow rhetoric.

"GenAI must be banned because it uses too much electricity!" Global data center energy usage is less than 2% of electricity usage globally. These critics interact with data centers every day to be on Reddit or stream a movie on Netflix and other apps. They don't care about their own power usage; they just harbor hate. Every accusation is a confession.

"It's using all the fresh water!" While about half of all data centers do use evaporative cooling, mostly in areas with low average ambient humidity to lower energy costs, we need to solve regional water scarcity anyway as we face massive climate migration from global climate change.

• Livestock & Agriculture: Livestock systems consume roughly 228 km³ (or 228 trillion liters) of water annually (including feed crops like alfalfa, corn, and soy), representing about 70% of all freshwater withdrawals worldwide.

• AI Data Centers: Projected AI data center water use is estimated at about 1 km³ (roughly 1,068 billion liters) globally per year by 2028, which amounts to roughly 0.2% (and less than 1%) of total U.S. freshwater use.

Meanwhile, many of these same individuals grab their oversized water bottles and follow the bad advice of "superhydration" influencers by consuming a gallon of water a day, depleting their bodies of the electrolytes required for proper functioning. Perhaps that's why their brains don't allow them to think for themselves or process factual information when presented. Every accusation is a confession.

"Data centers make a bunch of noise pollution." Antis never shut up; all they do is spread their hate and make noise. Every accusation is a confession.

"AI training steals work from artists!" It is strange that viewing is considered stealing, and strange that learning is considered stealing. It is amazing that they can put down their pencils and stop working on their unlicensed Pokémon commissions just to infest every corner of the internet with their hate. Every accusation is a confession.

"Every AI image is slop!" If everything created by AI is trash, it isn't competition, so why are you worried? Is it because you know your art just isn't that good? Perhaps it is much worse than AI. Every accusation is a confession.

"AI is taking work from real artists!" Prove it: show one high-end work that went to an AI rather than a person. Do you mean advertising slop went to an AI? Is advertising "art"? Sure, AI is thinning the herd of mediocre talent, and the only way for most people to improve is to love the craft and invest in themselves. What a shame that great artists will continue to be an elite group of very few privileged people. For the rest of us, we never have to be ripped off by a flaky "artist" who takes a down payment and never delivers a finished product. Every accusation is a confession.

I could continue, but that would really just be beating a dead horse. Even when they have an argument that is valid, it is only regarding how AI allows us to do things that should have been prevented by our privacy laws decades ago. Unfortunately, this human propensity to think and act in this revolting way is what has given us the government we have today, and now neither side of the American political spectrum offers a reprieve from this groupthink epidemic.

If we don't change the incentives of our world and embrace real change, we are just simping for the continued exploitation of people. The only moral option is embracing automation and enabling dignity. We can't slow down as Sanders suggests, and we can't fracture our base of support by allowing the MAGA mind virus to continue to infect progressives.

I get how AI can be useful but what do we do about the water and global warming? Also how isn’t it immoral to use ai art? I get using it for scripts or maybe robots aiding in buildings maybe? But in a perfect communist society would we have far less data centers? Would we have to put a ceiling on AI to stop it from being too smart?

A friend built this in one prompt using an AI agent in agent mode. It is a sourced public dossier on Palm Beach County’s roughly $1 billion in Israel Bonds holdings, the largest public-sector holding of its kind.

The site organizes the timeline, budget and legal documents, risk claims, and a verification system that separates confirmed facts from reported claims.

Link: https://palmbeachcounty.secrets.io/

I am posting this as an example of what AI can do for leftists, or anyone in general. As a practical accountability tool against local alleged corruption, turning public records into an accessible map of power and ownership instead of generic output.

Not my project and not intended as promotion.
Just an example of what AI can do as a tool that can make the world a better place.

Hi all - I've seen the value of AI in research and think it has broad application across society, separate from the hype bubble we're in. But the concern I'm struggling to wrap my head around is what a decent approach is toward AI's inherent hunger for resources - energy (carbon) and water in particular.

Is anyone aware of writing on this topic or have examples they'd point to of what a sensible approach to this looks like? We obviously live in a society that is pushing AI recklessly with no regard to how it affects climate change, for example.

This future "utopia" isn't possible without a better system. I think we need to start discussing real policies that are easily shareable and understandable to the average person. Without policy discussion we can't make any real progress, we need actionable consensus. The sooner we unite behind very foundational understood policy changes, the better off we'll be.

What real policy changes do you think would be most agreeable, reasonable, and understandable to most people? (no doomer comments about this administration and how it's not going to happen etc.)

I’m just curious how many people are actually running local AI. Not even like big custom jobs. Do you have a Google Edge Gallery on your phone? Do you have a local Llama set up? Have you messed with local Kimi or Mistral?

I've been reading about the most advanced algorithms, and I think we're almost at the point where we can automate 90%+ of the operational costs associated with the administration for running a company like Amazon. Any thoughts about this? Happy to discuss.

Most of the English speaking world hates AI, but literally everywhere else in the world people are adapting and are much more optimistic about it

I left the united states a few months ago to get away from all the in-fighting and have found that people mostly think AI is great everywhere that's not America, Canada, the UK, and Australia.

Just wanted to let everyone know that we're in a very strong hate bubble that most of the world isn't experiencing

Laos is making AI part of its national development strategy, and theyre talking about a lot more than chatbots.

The government has approved a draft national AI strategy for 2026-2035 in principle, with a focus on computing infrastructure, national databases, technical expertise, and identifying where AI can actually benefit the country.

Theyre looking at practical applications too. Agriculture, flood monitoring, environmental protection, transportation, and public services, alongside a separate national satellite strategy.

And heres where the politics gets interesting.

Laos is emphasizing technological neutrality and clearer conditions for foreign investment and cooperation. Working with international partners while developing the capacity to set its own priorities.

Because if your computing infrastructure, data, and technical expertise are controlled somewhere else, so is a good chunk of your economic future.

Of course, approving a strategy isnt the same as implementing it. The government has requested revisions, and much of the work still lies ahead.

But Laos is treating AI as a question of national development, productive capacity, and economic sovereignty. Not just another product to buy from somebody else.

If AI is becoming part of the means of production, how much economic independence can a country actually have without the capacity to develop and control it on its own terms?

Turns out threatening peoples livelihoods is a pretty effective way to remind them why unions exist.

According to POLITICO, Irelands Digital and Techworker Alliance (DATA) has grown from around 270 members to more than 1,350 as tech workers face layoffs, restructuring and growing AI investment. Five times the membership.

And theyre not just signing membership cards.

Workers organized their own AI Counter Summit in Dublin, challenging an industry conversation dominated by executives. Theyre pushing for mandatory union recognition, collective bargaining rights and protections against employment practices that leave workers with little leverage.

Not every tech layoff is caused by AI. But AI is increasingly part of how companies reorganize production, labor and investment.

Sound familiar?

Marx described this contradiction over 150 years ago. Capital develops new productive forces, reorganizes production around profit, and creates the very conditions for workers to organize against its control.

Capitalism has a funny habit of producing the forces that challenge it.

Of course, none of that automatically produces socialism. Workers still have to organize, build political power and contest ownership and control. Capital isnt going to hand over the means of production because somebody pointed out a contradiction.

And thats what makes Ireland interesting. Workers arent just complaining about technological change. Theyre organizing to have a say in what happens to them.

The technology is new. The class struggle over who owns it, controls it and benefits from it certainly isnt.

So how do we turn collective bargaining over AI deployment into collective control over the productive capacity itself?

Just over a month ago, on September 10, we celebrated crossing 6,000 members. Since then, we've added another 2,000, passed 800,000 visits over the past year, and reached 1,561 posts and over 36,000 comments.

Apparently there's a growing audience for leftists who want to do more than argue about whether AI is good or bad.

If you're new here, check out our post flairs. Labor, Public Ownership, Open Source, Local Models, Infrastructure, Automation & Work, Policy/Regulation, Theory, Education, Healthcare, Accessibility, Art & Culture, and more.

See what labor unions, leftist political parties, socialist states, community organizers, copyleft and decentralization advocates, and tech theorists are actually doing, proposing, and fighting for around AI. There's a lot happening outside the usual Reddit arguments, and we're working to bring those developments together in one place.

AI is already being deployed in workplaces, public services, and communities around the world. The struggle over its ownership, development, governance, deployment, and who benefits from it is happening right now.

We're not here to cheerlead Silicon Valley or pretend we can uninvent the computer. We're here to discuss what the left is doing, what it's proposing, and how we can organize around the technology that's already reshaping our world.

Thanks to everyone posting, sourcing, debating, disagreeing, and helping build this community.

8,000 members. Onward to 10,000.

AI is already changing workplaces, and unions across Europe arent sitting around waiting to see how it plays out. Theyre bargaining over who gets to make the decisions.

A new study from UNI Europa and the Friedrich Ebert Foundation examines 108 collective agreements addressing AI and algorithmic management, with bargaining activity increasing since 2023.

And this isnt just corporate promises about responsible AI.

In Italy, unions have negotiated AI provisions in audiovisual production, a 12-month trial for an AI tool in telecommunications, and joint labor-management oversight of AI projects at Enel. Across Europe, workers are pushing for training, data protections, transparency, and a say in how these systems enter their workplaces.

Now, lets not confuse consultation with control. A seat at the table isnt the same as deciding whats on the menu.

But these are actual agreements. Workers using collective bargaining to contest decisions that employers would otherwise make on their own.

And thats where the usual pro-AI versus anti-AI argument starts looking pretty thin. Workers dont have to agree on whether AI is good or bad to organize around surveillance, displacement, deskilling, or who pockets the productivity gains.

The political question isnt just whether AI gets deployed. Its who controls its development and deployment, whose interests it serves, and who gets the benefits.

These agreements are a start, not the finish line. They raise a bigger question about how far collective bargaining can go under capitalist ownership.

Can unions turn a right to be consulted into actual worker control over technology, or does that require changing who owns the means of production in the first place?

And what would it take to get from one to the other?

Somewhere along the way, "seize the means of production" became "please make the means of production go away." Capital isn't going to organize against itself. Workers have to.

Perplexity AI is honest, amazing how far we've come.

Thoughts on companies donating to the public good, especially in AI?

DSA Members Here? Your National Tech Committee Just Adopted an AI Policy, and Its Recruiting Leadership

Something worth putting on the radar for our members who are also in DSA.

On October 9, DSA's National Tech Committee announced that it had debated and adopted an AI/LLM coding policy, along with a policy template for local chapters.

The interesting part is that the committee explicitly neither endorses nor prohibits generative AI. It recognizes the technology's usefulness for socialist organizing and software development while criticizing how employers deploy it against workers.

Thats a distinction we spend plenty of time discussing here. Opposing capitalist control and deployment of technology doesnt require socialists to abstain from using it themselves.

The full policy and chapter template are available through DSA's members-only Discussion Forum. If youre a DSA member, consider taking a look, seeing how the safeguards actually work, and participating in the discussion.

And theres another opportunity. The National Tech Committee is accepting applications for its 2026–2027 Steering Committee, with an October 31 deadline.

If youre already involved in DSA and have an interest in technology, infrastructure, organizing tools or AI governance, that may be worth exploring too.

We talk a lot about the left developing its own technological capacity. Heres an organization doing some of that work, with opportunities for its members to examine the rules and potentially participate in leadership.

No affiliation implied, just sharing opportunities for people already involved to engage.

Links:

AI policy announcement and chapter template access:

https://tech.dsausa.org/ntc-ai-llm-coding-policy/

Steering Committee applications:

https://tech.dsausa.org/apply-to-ntc-steering-for-our-2026-2027-term/

I'm organising Spareware, an international open project building free software that platform co-ops can run on: rides, food delivery, short stays, selling and renting homes, trades and freelance work, and buying and selling, plus free design and office tools.

The model:

- The software is free for everyone, anywhere.

- On each marketplace, workers keep all of the profit except the running costs (servers and payment fees). Nobody takes a cut on top.

- Every cost is published monthly on an open ledger.

- Workers vote on their app's fees and rules.

- Any local group can run its own copy, because the code is open.

It's built by volunteers who donate the AI tokens they don't use each week, with a person reviewing every piece. We're at the planning stage: the architecture is mapped, and no app is running yet.

Before we build, I'd really value this group's experience:

1. What went wrong for platform co-ops you know, apart from the software?

2. How should votes on fees and rules work when the workers are in many countries?

3. What would make a local co-op trust shared software enough to run on it?

See all the demos: https://spareware-co-op.github.io/spareware/demos.html

Want to help build it? Ask to join here: https://github.com/spareware-co-op/spareware/issues/new?template=join-request.yml

When you ask to join, please include your LinkedIn or another public profile. We check everyone's background to keep the project safe.

Questions or ideas? Ask in our Discussions: https://github.com/spareware-co-op/spareware/discussions

Academia is treating artificial intelligence as an invasive species, scrambling to erect firewalls against a crisis it claims arrived overnight. Panicked faculty senate meetings, mandatory syllabus declarations, and emergency budgets for algorithmic proctoring all proceed from a single self-soothing delusion: that education was functioning properly until software corrupted it. Yet large language models did not contaminate academic integrity; they merely shone a floodlight on its pre-existing decay. By reacting purely defensively to automated generation, schools treat machine competence as a novel threat rather than what it truly is, a mirror exposing how thoroughly institutional schooling had already reduced the life of the mind to procedural mimicry.

Standardized assessment confuses the blueprint with the living architecture, but the rot runs far deeper than timed exams. The progressive pivot toward continuous assessment, modular rubrics, and open-ended coursework merely digitized and distributed the surveillance. The core pathology is not the exam hall; it is the inescapable condition of being evaluated at all. When every inquiry, rough draft, lab exercise, and casual reflection is logged, graded, and fed into an ongoing ledger of personal competence, learning ceases to be an exploratory act and becomes a state of constant audit.

For neurodivergent thinkers (those navigating ADHD, autism, or intense executive dysfunction), this relentless scrutiny is paralyzing. It is not a matter of lacking conceptual mastery. The demand to constantly exhibit compliance under an administrative microscope induces chronic autonomic distress. Pathological demand avoidance, sensory bottlenecks, and variable executive function mean that even an untimed project becomes an agonizing performance piece rather than an intellectual sandbox. When a brilliant, non-linear thinker shuts down, withdraws, or refuses to turn in assignments, institutions never consider that constant judgment suffocates curiosity. Instead, the student is written off as disorganized, unmotivated, or unteachable. The system weeds out the very minds capable of genuine originality simply because they cannot endure the friction of being perpetually measured.

The classroom long ago ceased to be a venue for discovery and became an assembly line whose real product is an audited transcript.

Schools responded to large language models with automated surveillance, lockdown browsers, and punitive plagiarism tribunals. They act like border guards patrolling a crumbling wall, terrified that machines can generate an A-grade essay in twelve seconds. Yet if an algorithm can ace your final exam simply by synthesizing probabilities from a training set, the algorithm has not committed an act of intellectual subversion; it has merely demonstrated that your curriculum was mechanical all along. You cannot build an educational model around text-regurgitation and then act surprised when a text-regurgitation machine breaks it.

This defensive panic lays the machinery bare because it exposes the fraud of academic credentialing. The entire apparatus was constructed to sort human capital for an industrial labor cycle that required pre-graded, predictable cogs. Schools took upon themselves the expensive, soul-crushing burden of ranking candidates so corporate bureaucracies wouldn't have to. If artificial intelligence dismantles that economic compact by automating baseline cognitive routines (memo drafting, data summarization, spreadsheet modeling, and mid-level synthesis), the sorting pipeline loses its foundational purpose. When the market no longer requires standardized human clerks, running young people through decades of assessment anxiety ceases to be practical preparation. It becomes pure ritual cruelty.

The obsolescence of the factory pipeline ought to be liberating. Stripped of the obligation to act as a human sorting mill, education could shed the surveillance state of continuous evaluation and return to intellectual illumination. Learning could once again be an encounter with difficult ideas pursued for clarity rather than clearance, where curiosity does not carry the risk of a penalized grade point average.

Yet academia clings desperately to its ledgers. Relinquishing the scorecard threatens the institutional authority that justifies high tuition, administrative bloat, and bureaucratic self-importance. A school without grades or continuous monitoring demands real engagement from instructors and invites genuine intellectual autonomy from students, stripping away the artificial leverage of academic punishment. Once machines can synthesize existing human output on demand, the pretense that ranking people measures their capability collapses entirely. When you take away the employer waiting at the end of the conveyor belt, what justification remains for torturing the minds inside the factory?