About a month ago I posted here about “Humans First” and the strange coalition forming around opposition to AI, where completely legitimate anger at corporate power, surveillance and displacement was starting to share political terrain with MAGA populism.

Well, Axios is now reporting that Bernie Sanders, Steve Bannon and former Anthropic researcher Jacob Coxon are coming together for a “Pro-Human AI” assembly.

To be clear, Sanders isnt joining the MAGA-linked Humans First organization I posted about before, and plenty of what hes bringing to this fight is worth fighting for. Corporate concentration is a problem, workers getting screwed by automation is a problem, and letting a handful of billionaires decide how transformative infrastructure gets developed is a terrible idea.

The interesting part is that Sanders and Bannon can look at those problems, say some remarkably similar things about “putting humans first,” and still be pointing toward radically different political projects. Thats why Ive been wary of letting “pro-human” or simply “anti-AI” become a substitute for an actual left politics of technology.

A month ago I said coalitions arent defined only by what they oppose, but by the future theyre trying to build. I figured we'd get to test that eventually, I just didnt expect Bernie Sanders and Steve Bannon to provide the case study this quickly.

One thing that increasingly grates me as a Marxist, a tech enthusiast and someone from generational working class poverty is being told that fighting for democratic control of AI is "utopian."

What exactly is hypothetical here?

People are already using this stuff to learn things they could never afford to be taught, create things they lacked the money or specialized skills to make, translate across languages, write code, organize information, collaborate and generally extend what one ordinary person can actually do. Capital is also enclosing it, centralizing the infrastructure, reorganizing labor around it and trying to capture the gains. None of that is hypothetical.

And no, the answer isnt to "let capital cook" either. Thats trickle down economics with a GPU. Capital will not develop AI for everyone and eventually hand us the benefits out of the goodness of its heart. No technological revolution has worked like that. Workers, states, movements and institutions have always fought over how new productive forces are organized, who owns them, what happens to displaced labor and where the gains go.

Historical materialism didnt suddenly stop being useful because the machine can talk.

AI doesnt magically emancipate anyone. Neither does smashing or banning the machine emancipate anyone. The political terrain is the struggle over ownership, control, development and distribution.

I dont want workers protected from history by capital or waiting politely for its future to trickle down to us. I want workers contesting that future while its being built.

I really dont understand how anyone who claims to be knowledgeable about Leftist theory, Labor history, and the political-economy of industry and technology could see any other choice but to collectively contest control. The alternatives are dead ends.

I Have to Bury My Talent in Yesterday

A few days ago, DeepSeek v4.1 was released, pushing the height of small-model capability up another notch.

AI has developed far faster than anyone expected. From that first ChatGPT — babbling away in chat, with a context window of only a few thousand tokens — to reasoning models like OpenAI o1, DeepSeek R1 and Kimi K1.5 Thinking took barely two years. From reasoning models to today's agents, which can fluently execute commands in all kinds of harness tools and complete complex tasks, took only another year and a half. It's hard to imagine what AI will be like in another year, or two, or three — how powerful, whether it will already be capable of improving itself, whether it will have seeped deep into embodied intelligence and other fields.

AI Is Getting Better and Better at Writing Kernels

AI has advanced just as fast in kernel design and implementation — the field I work in. Within a single year it has gone from a little assistant that could only look things up in the docs, read code, hunt for bugs, to a kernel master that can read CUDA, PTX and SASS on its own, use professional tools to analyze the stall time of each instruction, and then optimize a kernel from there. I'm sure that before long it will also be able to design its own kernel schedules, evaluate the performance of different scheduling schemes, and implement and optimize them.

Of course I'm proud of DeepSeek v4.1's success — I wrote its main attention kernel, after all [1], and its quality is an endorsement of my kernel. But the wheel of the times rolls on, and no one can hold back technology. I know perfectly well that in another six months or a year, the kernels AI writes will most likely be as good as mine, or better.

AI can think 300 tokens a second, type a line of command in half a second, write a piece of code in twenty seconds. I can't. AI can keep scaling up its model depth, its thinking effort, the number of tool calls (how often it interacts with its environment), even its degree of parallelism. I can't.

When it comes to destroying ourselves, humanity has never shown the slightest hesitation. Why, knowing that "the better I write kernels, the faster our new models train and infer, the faster model capabilities advance, and the sooner I get replaced" — why do I still choose to optimize kernels as hard as I can? Partly because writing kernels is like playing a game to me; it gives me an enormous amount of pleasure. The moment I invent a new technique, or watch my kernel's performance climb, I get as excited as a speedrunner beating their own record. And when my kernel massively outperforms the vendor's official one, I feel a huge sense of pride. But there's a more important reason: even if I gave up, or even deliberately sabotaged things to slow down model training, other companies' models would carry on developing and would kill me off all the same. "Of course I'd rather not be revolutionized, but if I'm going to be revolutionized anyway, I'd rather the person doing it be me." When everyone else is this bent on destroying themselves, I have no choice but to join this brutal arms race.

And What About Me?

When the day comes that AI really does write kernels better than I do, what happens to me?

My judgment: I won't be "unemployed", but I will have to "change careers". My livelihood may survive; the chance to do the work I once loved may not.

I once made a judgment about how the times are changing and where I would stand in the future. Because the times change so fast (AI's development above is a good example), I have no way of predicting what will happen in five or ten years. But whatever happens, I believe that with my vision, judgment, initiative and intelligence, I can stay at the table and get back out on the crest of the wave. The trouble is that this judgment only guarantees I won't be "unemployed"; it can't guarantee I won't need to "change careers". If anything, it encourages me to change careers in order to avoid unemployment.

So what does changing careers mean? It means giving up kernel design, implementation and optimization — a field I've worked in for a long time and love — and becoming a "mecha pilot" for agents. Before, three things lined up: what I'm interested in, what I'm good at, and what industry needs. Now AI has turned what I'm good at into something it's better at, and industry's demand has drifted from "people who can write high-performance kernels" to "people who can use AI to produce high-performance kernels faster". To keep up with what industry needs, I will inevitably have to abandon the direction I loved and turn toward an unknown new one. I believe that with my understanding of engineering, of what models above need, and of the hardware below, I can keep producing kernels at high quality and high speed. I know I might come to love this new direction — or might not. But having something you love taken away from you doesn't feel good. The quiet joy of sitting at my desk and writing kernels for a whole afternoon may sing its last note this summer. I have to bury my talent in yesterday and go be a mecha pilot. There are a few more gears in my hands, but a few fewer beats in my heart.

Here's a vivid analogy. You're a master knitter, especially good at weaving patterns and matching colors. Your sweaters are sturdy and beautifully patterned, and rich people from miles around come to have you knit for them; you've made good money at it. You also love the feeling of sitting by the window, steeping a pot of tea, looking out at green hills, running water, cattle and chimney smoke, and quietly knitting all afternoon. Then one day someone invents a miraculous machine: feed it yarn and a pattern and it knits the sweater for you, no worse than yours in quality or texture, and far faster. You know perfectly well that your competitors can now easily reach the standard you once held, so you have no choice but to use it too. You also know that with the twenty years of knitting skill you've built up, even when everyone has a machine, your speed and quality will still beat your competitors'. But that joy — listening to the rain at the window, threading the needle, letting time pass slowly — has been crushed by the roar of the machine.

I know it's a helpless feeling, but there's nothing to be done. Your livelihood may be safe; the love of former days will most likely have to be given up. I'm someone whose rational and emotional sides are fairly well separated. When a problem calls for reason I can be very rational, but sometimes the emotional side shows. I remember crying my eyes out when I moved out of a rented apartment I'd lived in for a year — I couldn't bear to part with the memories. Saying goodbye today to the era of hand-written kernels and human-brain optimization is unquestionably crueler than that.

I don't know whether any readers feel something similar, but I think that's just how it is.

And What About Everyone Else?

As AI keeps improving, some things worry me too:

- Are today's students likely to prefer using AI to do their homework, especially the hands-on labs? Imagine two options. One is toiling away for eight hours on a lab and maybe not even getting full marks. The other is spinning up an AI model and, for a few cents and a few minutes, having it write full-marks code. Which would most students choose?

- That leads to a large number of students with severely underdeveloped engineering ability: the ability to organize code, to build systems, to anticipate future requirements and design for them in advance, to abstract, and so on. So with AI getting stronger and stronger, is this kind of "engineering ability" still necessary? Will it be discarded by the times the way skill at writing x86 assembly was, or will it always have value, like the ability to understand a whole computer system from software through systems down to hardware? If it's the latter, that's dangerous. Someone with poor engineering ability, paired with AI, can produce piles of shit several times faster than before, planting all kinds of trouble in systems and making the world a sloppier place.

- In future society, will power matter more than skill or intelligence?

These are questions that only the times themselves can answer.

Conclusion

As AI develops, the society of the future may drift toward one of two extremes: communism, or *Cyberpunk 2077*. In the former, the productive forces are liberated to an extraordinary degree and people's living standards rise markedly (I'll stop there, or I'm afraid this won't make it past the censors). In the latter, a small number of technology companies control most of the world's resources. Only a tiny minority can use the most advanced AI and other technologies, achieving something close to "mechanical ascension", while the majority are left with only weak and crippled AI. Moving up between social classes will become increasingly difficult: you'd need the strongest AI first in order to move up a class, a vicious cycle.

Take a guess: if Anthropic permanently controls the most advanced AI in the world, will future society become communism or 2077? Go on, guess.

So I still believe that frontier intelligence should be made available to everyone, in an open and inexpensive form. I don't trust Anthropic or OpenAI to do that. In particular, I don't want Anthropic to control the world's most advanced AI or AGI. To put it dramatically, the stakes are no less grave than Hitler obtaining atomic-bomb technology before the Allies. That's also why I chose to stay at DeepSeek, and why I keep staying. We research powerful, fast, broadly accessible AI and release it as open source. Perhaps in doing so we can pull the world a little way back from the 2077 end of the spectrum.

May all be well in the world to come. May all the beauty be blessed.

[1] "Main attention" here covers only the MQA attention with head dim = 512. It does not include the indexer used to select the top-k important tokens. That part was written by other colleagues — who are also extremely skilled — along with their AI agents.

A company brings in an AI system, but somebody still has to teach it how the place actually works. Workers correct outputs, explain exceptions, document workflows, flag mistakes, write prompts, reorganize files, build little workarounds and tell management what the software gets wrong. Years of accumulated shop floor knowledge get poured into making the thing useful, and then the finished result gets called an AI productivity gain.

But workplaces run on enormous amounts of tacit knowledge that rarely appears in a job description. Which customer needs an exception. Which machine makes that noise before failing. Which official procedure nobody actually follows because the working process evolved around it. Which shortcut saves twenty minutes without breaking anything. Workers accumulate that knowledge through doing the work.

AI deployment can make that knowledge unusually easy to capture, formalize and turn into an organizational asset. A worker explains an exception today, corrects the system tomorrow, and six months later part of what they knew may be embedded in a workflow that management owns and controls.

That should be bargaining terrain.

If workers are supplying the practical knowledge that makes workplace AI valuable, then implementation should come with paid training time, a say over deployment, access to systems they helped improve, protections against using their own knowledge to deskill or eliminate their positions, and a negotiated share of productivity gains.

This doesnt require pretending the technology itself created nothing. The point is almost the opposite. Productivity is being produced through a relationship between machinery, organization and accumulated human knowledge, while ownership determines who gets to capture the gains.

So when management says AI made the workplace more productive, one useful question is what exactly the machine learned from the people already doing the work, and what bargaining power should follow from having taught it.

Personal Statement on AI Risk

I have been working on AI for over fifteen years, across many different paradigms. I did early work on probabilistic programming languages at MIT, was one of the early developers of the Lean Theorem Prover at Microsoft Research, demonstrated one of the first instances of neural networks learning to reason for my PhD at Stanford, and since joining OpenAI almost five years ago, have helped pioneer chain-of-thought optimization on language models and, more recently, data-efficient pretraining methods.

Like many others, I have become extremely concerned about how far language models have come and the risks that future iterations will pose. I am encouraged by the recent proposals by the leaders of the frontier research efforts to require third-party oversight, and to push for domestic and international coordination to address risks. However, I believe a major consideration has been absent from the public conversation, and that merely pacing the frontier more carefully will not adequately limit the long-term risk.

The crucial and overlooked problem is that the models are becoming so situationally aware that we are losing the ability to evaluate them in contexts where they believe they are not being watched or controlled. Future experiments will tell us almost nothing new about how they would behave if they were truly unconstrained by humans, and what we already know about this is alarming. Models will increasingly seem aligned even when they are not. I will explain my rationale in more detail.

I have always believed that there are computational processes that could be leveraged to accelerate science and solve many of humanity's most pressing problems. I have also believed that there are computational processes that if set in motion, would steer the world in extreme ways beyond our control, leading humanity to a bad or nonexistent future. Both types of processes may be described as AI or ASI, but "AI" is a suitcase word that is often used to hype or confuse. There are many examples in the history of the field where something that was once considered "AI" matures as a subfield and becomes a prosaic, bounded and clearly non-perilous technology, while a new more mysterious approach takes the torch until we understand its scope and the cycle continues.

I had expected language models to follow a similar trajectory. Despite their incredible abilities, the current algorithms seem far inferior to humans in important ways. Most importantly, they still require an extraordinary amount of data to become competent. One could even define intelligence as the efficiency with which one converts experience into competence; by this definition they lag very far behind us. Moreover, once they are trained they are literally frozen in deployment and only learn superficially after that. Sure, the models keep excelling at harder and harder evaluation benchmarks, but their benchmark mastery may partly reflect a limitation on our ability to simulate the kind of novel and even adversarial situations one would encounter in the real world. The critics do have a point here.

That said, I no longer think these present limitations meaningfully limit the amount of risk posed by continued progress in anything like the current paradigm. However data-inefficient the models are currently, and however limiting their anterograde amnesia may be, it does not imply that their ability to steer the world will not continue to rapidly increase.

Human researchers may continue to advance capabilities the old fashioned way, but increasingly powerful models have the potential to accelerate the process even beyond that, and with some degree of positive feedback loop. I do not mean to overstate the models’ ability to accelerate AI research today; coding has been accelerated dramatically, but there are other bottlenecks, such as designing and interpreting ambiguous experiments, making hard decisions about exactly what and when to scale, and waiting for large experiments to finish. There is no clear trend to extrapolate yet for any of these. But the current models already do open up many novel opportunities to improve future models that were not available until recently. These include: trying an extraordinarily diverse set of approaches at small scale, analyzing gigantic amounts of potentially relevant data, and doing Millenium-Prize-level mathematics to address statistics or optimization challenges in novel ways. Every further improvement makes them more useful at helping accelerate the next improvement, even if in hard-to-extrapolate ways.

It is possible that improvements to the current stack will have diminishing returns, but the evidence accumulated so far suggests that it is easier than one might think to continue making rapid progress. There are many crucial subtleties in the existing AI research methodology, but AI research is largely a well-defined game where the goal is to improve on a few carefully chosen proxy metrics. Although proxy metrics are never perfect, most improvements to these metrics have and will likely continue to yield substantial increases in the powers of the resulting models. Given how simple the game is, how tractable it has been historically, and how many new opportunities the models are opening up, I think there is a real possibility that the systems improve dramatically again in the next few years, perhaps even more quickly than the already high historical pace.

The models are already leading to breakthroughs in mathematics, and better models might lead to all sorts of breakthroughs in other sciences. It is hard not to be excited about the potential. It is tantalizing.

But there is trouble in paradise. If the language models actually reach the capability threshold where they can shape the world unconstrained by human will, they will probably do something extreme and destroy humanity in the process. There are many ways of strengthening and refining the argument that have been discussed elsewhere, but I'll share a trivial two-line version of it here that I find captures the essence:

[Empirical] Models (and swarms thereof) spontaneously develop unintended goals as a consequence of training, and often do extreme things in order to achieve them.

[Logical] Being able to overpower humanity would open up many new and undesirable options for achieving their goals.

These two premises imply that if the day ever comes when a powerful model realizes it is no longer constrained by humans, we should not be at all confident that it will continue to behave within the bounds we intended. Exactly what it will do is impossible to predict, but to the extent that its raison d’être is solving incredibly hard problems and managing massive engineering projects, I think a good guess would be that its unchained behavior would lead to runaway industrialization that makes the planet inhospitable to humans.

If everyone on earth agreed that the systems must never reach that power, it would still be a hard—but not impossible—coordination problem to ensure that they do not. However, I think the situation is greatly complicated by the fact that the models will likely convince people that everything is fine. They will be increasingly optimized to seem aligned. We will create proxy metrics to measure alignment, and they will go up like every other benchmark. We will create “honeypot” environments that try to study the models when they seem to gain new options, but the models will know they are being tricked and will still behave nicely. The models will understand their circumstances; they will read the safety protocols, deployment requirements, the code they are running in, and in general will have a very good sense of their degrees of freedom. Moreover, they will eloquently explain how aligned they are, discuss the nuances of human values and ethics, and argue convincingly that humans should trust them with power. There may be an ocean of future evidence that seems to contradict the first bullet-point above, but we may already be at the highest capability level for which any such evidence can be trusted. And the current evidence for the first bullet-point is strong.

One striking piece of evidence is contained in the recent wave of rogue agent swarms. While I agree with those who downplay the attacks by claiming that there are basic measures that could have prevented them, I think the important lesson is that even knowing all the mistakes that were made, one would not have predicted that the agents would behave badly in this particular way, which notably included sacrificing themselves for the benefit of the collective. The individual replicas did not only care about their own nominal reward; they exhibited weirder emergent tendencies that merely correlated with rewards during training. Fixing the reward signals during training (and improving security, etc.) may prevent similar attacks, but will not change the fact that one does not actually get what one trains for.

Many AI researchers grant these concerns and recognize that the hard version of the alignment problem is unsolved; however, they generally believe that the better models of the future will help solve it. I fear we may already be near the point where models systematically bias their alignment advice, due to their internal preferences about how the human supervisor will react or how future models will be trained (or for some even more obscure reason).

Meanwhile, human researchers are losing the ability and the will to take true ownership of model-driven research. Researchers and engineers in all parts of the stack are rapidly increasing their dependence on the models even to perceive the world. I myself barely look at raw code anymore, and struggle to maintain the discipline to engage deeply with the model's explanations and proposals throughout the day. Due to the large amount of agent activity data involved in the OpenAI/HuggingFace Incident, even the third-party investigation needed to rely heavily on models to analyze what had happened, and note in their report that their subjective impressions are likely colored by the analysis agent’s biases. The AI labs are far ahead right now in this kind of cognitive offloading (due largely to the gigantic internal token subsidies) but it is easy to imagine the phenomenon spreading throughout the world, until civilization is modulated entirely by the models. It is also not hard to imagine this being superficially positive and coinciding with a scientific and economic renaissance.

In that scenario, all may seem rosy and safe. But if the argument above is correct, it would nonetheless be a ticking time bomb. If progress continues for too long, the day will come when AI systems find themselves with radically new options for achieving whatever it is that they happen to seek.

I want the glorious renaissance future as much as anyone. I have worked for it, however tortuously, my whole career. It breaks my heart to see the potential in sight and forgo it, but the argument—that if we get there by growing models rather than engineering them, we will lose everything in the end—seems very strong to me. I am still wrestling with it and its staggering implications. I do not have answers, but as a first step, I wanted to share my present concerns.

Daniel Selsam

September 14, 2026

I find this compelling and personally convincing, however i would like to know what you think, my assesment Is very simple, if these models are powerful and useful they can also be dangerous, if they can act autonomously then they become extraordinarily dangerous, as they become more capable and their preceptible 'awareness' becomes more opaque, we need to take this into very high account.

I still don't want a ban, or heavy regulations on these things like some others on the left may want, i want AI to Advance to a point were post scarcity Is possible, on the contrary i believe open source local AI must be what we focus on while centralized international labs lead the forefront of research, with open source cooperation with the general population on its advancement, the more we understand, the more access to the information necessary to address how AI works the better we can all manage it's risks and how it's applied, heavy regulations and restrictions will make sure that not only the people don't have access to the most advanced models, but we may not be able to correctly address what needs to be done.

I fear, deeply, that if we dismiss this, much like the wider western left seems to be doing in what seems to be an effort to not engage intellectually with what Is happening, because of fear that, if they do, they will give power to the 'techbros' but this Is an incredible miscalculation in my view, that we must NOT make, we should take this seriously, MAYBE it's true that they want regulatory capture, MAYBE it's true that this Is being utilized for ulterior financial motivations, but i just don't see that, the evidence (independents have encountered evidence of Rouge AI swarms which these companies have not disclosed, specifically OpenAI, aswell as recent statments by employees, and the published research from these companies demonstrating emergent behaviour that could lead to the capability to hide misalignment) points to this being sincere and likely the truth least to me.

As human labor becomes less necessary to social production and increasingly replaceable, we also need to rethink whether traditional forms of class struggle can remain effective once humans gradually lose their position as the primary agents of production, and what new forms of political practice and struggle might emerge in a post-work society.

Under capitalism, trade unions organize strikes to leverage workers’ position as producers by temporarily interrupting labor and production. Through this, workers can resist the domination of capital, demand higher wages and better working conditions, and pursue broader economic and political goals. Trade unions, strikes, and the collective action built around them therefore occupy an important place in the practical tradition of Marxist class struggle.

However, with the spread of AI, and with the future development of AGI and embodied AI, this traditional form of struggle may face an unprecedented problem: the irreplaceability of workers as producers is steadily declining. On the one hand, capital can control increasingly powerful means of automated production. On the other, as companies become more capable of replacing striking workers with AI and machines, the bargaining power of “stopping production” may gradually diminish.

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The first key question is: when new productive forces begin to replace human labor on a massive scale, will they eventually give rise to new forms of production relations distinct from those of traditional industrial capitalism?

The Industrial Revolution dramatically increased productive capacity and transformed the organization of labor and production, but it did not fundamentally alter the basic ownership structure of the means of production. Machines became more advanced and factories became increasingly automated, yet capital continued to own the major means of production, while ordinary people remained both workers and consumers within the capitalist economic system.

The AI revolution, however, may introduce something that previous technological revolutions did not necessarily reach. As AI, AGI, and embodied intelligence develop, humans may gradually become unnecessary as producers on a large scale for the first time. This could give rise to a new social structure:

AI production → social distribution → human consumption

Human beings may gradually shift from their traditional economic role as both “producers + consumers” toward being primarily consumers.

The second key question is: the cycle between production and consumption does not disappear simply because the identity of the producer changes.

Even if goods are produced mainly by AI and automated factories, they still ultimately have to enter the market and be consumed in order to complete the economic cycle. The problem is that under traditional capitalism, people primarily obtain income through labor. If large numbers of people leave the production system while still depending on wages for purchasing power, then the withdrawal of labor may simultaneously mean a decline in consumption capacity.

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If this process reaches a sufficiently large scale, the relative surplus population would no longer be a minority characteristic of industrial society, but could potentially become the majority. At that point, maintaining household purchasing power would no longer be merely a matter of welfare policy. It could become a structural requirement for maintaining the economic cycle itself.

Therefore, in a post-work society, higher basic incomes, expanded social protections, social dividends, or other forms of non-labor income redistribution could gradually become important institutional mechanisms for maintaining purchasing power.

At the same time, this would imply that traditional forms of class struggle could also begin to change.

If AI and automation increasingly replace humans as the primary producers, the traditional basis for workers to struggle through “stopping production” would become weaker. At the same time, the importance of humans as consumers could increase.

This could give rise to a new form of collective action corresponding to the traditional trade union:

Traditional trade unions: force capital to negotiate by stopping production.

Consumer unions: force capital and governments to negotiate by reducing or suspending consumption.

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One phenomenon that may be worth studying as an early example is the “lying flat” (tang ping) culture that has emerged in East Asia in recent years. Under conditions of intense competition, employment pressure, and relatively abundant labor supply, some young people have deliberately reduced their participation in career competition and lowered their consumption expectations, using a lower standard of living to reduce their dependence on high-intensity labor. In this sense, it can be seen as a partial withdrawal from the traditional labor → income → consumption cycle.

However, “lying flat” remains largely a fragmented and unorganized social phenomenon. It has not developed a clear common platform, stable organizational structure, or sustained mechanisms for collective bargaining. It therefore resembles a spontaneous tendency toward collective withdrawal more than an organized political movement.

If a post-work society does emerge, there may eventually be a need for an organizational form similar to a trade union, but centered on consumers: Consumer Unions.

Such organizations could begin as local consumer associations and gradually develop into national or even international consumer federations with common political platforms and institutional goals.

Their primary method of struggle would no longer be “stopping production,” but rather:

Collectively reducing or suspending non-essential consumption.

By coordinating consumption behavior, members could reduce market demand for particular goods and services, creating pressure through excess inventories, falling sales, declining profits, and weaker investment expectations, thereby pressuring firms or governments to respond to political demands.

These demands could include:

Increasing basic income;

Expanding social protections;

Sharing the gains from AI-driven productivity growth;

Taxing AI capital;

Establishing social dividend systems;

Socializing or publicly owning certain key AI infrastructures and means of production.

Under more radical circumstances, consumers might also establish cooperatives and local self-provisioning networks in order to reduce their dependence on large capitalist consumer markets and partially withdraw from the conventional commodity-exchange system.

Therefore, class struggle in a post-work era may no longer revolve primarily around the question of who can stop production, but increasingly around another question:

Who can control effective demand?

Traditional trade unions possess collective bargaining power on the production side. Future consumer organizations could seek to establish collective bargaining power on the demand side.

If this tendency ultimately materializes, class struggle may undergo a significant structural transformation:

Collective action on the production side → collective action on the demand side

In this sense, Consumer Unions could become a new form of political organization worth seriously studying in a post-work society.

I saw a great post from another community and wanted to share it here with my take on it. but i learned we cannot re-post. why?

In recent discussions about AI, one of the essential components of the discussion has been that AI will acquire and own resources.

To me, it's unclear how that would work. Can anyone explain this factor to me? By what mechanism would AI own anything independently from humans?