In their mad rush for profit and power, the tech oligarchs have unleashed artificial intelligence (AI) into nearly every corner of life on this planet. Firms are investing billions in data centers, which house the infrastructure to process the enormous amounts of information that power AI. They are also plowing ever more money into research and development. Their hope is that the technology will overcome global capitalism’s slump, which the system has been trapped in since the Great Recession, and trigger a new epoch of expansion. States are incorporating it into their police forces for domestic surveillance as well as their militaries for cyberwarfare and high-tech weapons. Ominously, the struggle for AI supremacy is now at the center of the imperialist rivalry between the U.S. and China.
The widespread, unregulated use of AI is already having devastating impacts on our environment, jobs, and rights. Some worry that it threatens our very species with extinction. In this three-part interview, Tempest Collective’s Ashley Smith asks two advanced tech workers, Josh and Yasmine, about the technology, its much hyped promise and threat, and its impact on our lives. Most importantly, they lay out how the Left and labor movement should respond and what we should demand and fight for.
Part One: “Criti-hype,” AI Breakthroughs, and Betting on Profit
Ashley Smith: Before we discuss the widespread panic about AI, let’s get the basics out on the table. For the layperson, what is AI? How does it work? What advances are being made toward artificial general intelligence? Is that even possible?
Josh: AI is a general term that started as hype, and continues to operate that way, but it also stands for multiple things that are real. Today, it mostly refers to neural nets such as the large language and multi-media models like ChatGPT and Claude, which we most frequently encounter through chat bots and coding agents.
Other types of AI, such as AlphaGo (plays go) and AlphaFold (folds proteins) use different ways to train neural nets to achieve their given goals. There are many other types of AI that fall under the moniker of GOFAI, or Good Old Fashioned AI, which use Symbolic AI and other more deterministic methods for building predictive systems.
There have been many accounts of how LLMs and the transformer architectures work. I would sum these up this way. High-tech engineers set up a neural net, which is a huge matrix of random numbers (weights) arranged in a grid with inputs at one end and outputs at the other. Words are converted to streams of numbers that are then passed through the network and arrive at a prediction for the next best output in the stream. This output is compared with the actual next number in the stream, and if it is different, then it adjusts the weights so that it would be more likely to give that answer the next time.
In order to get a sensible next word in a sentence, some pretty wild calculations have to be made. It is one thing to predict the next word in “A fire engine is ___” and another to answer the question: “What is the answer to this math problem that hasn’t been solved since it was asked 500 years ago….” This is why it is wrong to dismiss the technology as simply a next word predictor.
There have been at least three factors that brought us to this moment:
- Graphics Processing Units (GPUs allowed for much faster and more efficient matrix math calculations required to train and use neural nets.
- Large structures and labeled data sets enabled the expansion and training of neural nets.
- New architectures made it possible to better set the weights.
Artificial General Intelligence (AGI) is another contested term that serves many purposes. I think a starting point is to contrast it with “narrow” intelligence, where a model may be trained to perform one task, such as playing chess, and not be applicable to other tasks. AGI would be able to be applied to a wide range of tasks and do well at them. This is often measured through various benchmarks, and some of them are quite telling, such as the idea that Microsoft promoted that AGI would be here once a model created $100 billion in profits.
More frequently, you hear people say that AGI will be here when a model can outperform some or all humans at most or all mental tasks. And some people insist that AGI would require models to be able to generalize beyond the data in their training data.
I think most people in the field think that, for most definitions of AGI it will be possible to get there, but they differ on the amount of methodological, scaling and time that it will take. Some claim that we are already there with the latest round of models. And many claim that it will happen soon because of the exponential curves we will see with RSI.
Many of the harshest critics say that we will plateau long before AGI because of the limitations of the methodologies used to create the current models, such as the lack of “world models” (the ability to learn from and model the physical world), continual learning, or lack of a symbolic structure.
I am agnostic at this point. But I think we already see strong evidence of increasing capabilities with something like recursive self-improvement with the aid of humans, and this might actually lead to the development of new architectures and methods.
Yasmine: I don’t have much to add to Josh’s thorough explanations here. I’d just highly encourage folks to listen to and read Dr. Alexa Hanna and Dr. Emily Bender on the AI Con. The term AI itself is not technically meaningful, and it has been a tool of mystification with a lot of utility to big tech. It obfuscates the concrete facts of what these different types of software are and the often ugly facts of how they’re made, as well as the real harms they cause.
AGI is also a nebulous term. I think the claim that current AI development will culminate in the so-called singularity or AGI isn’t credible. This isn’t a conclusion I’ve independently reached, this is what most AI researchers think. 76 percent of those surveyed say AGI is not on the horizon and think that it’s unlikely to be achieved via LLMs or by means of the current scale-based approaches to training. There is also a debate as to whether it’s feasible at all.
This supposed threat has taken up disproportionate space in our discussion of the harms/potential harms of the AI boom. I’m on the side that argues let’s pay attention to the actual horrors and destruction being enabled by this software or caused by its development in the here and now, rather than allow our attention to be diverted by a sci-fi story with little to no foundation in science.
AS: Various industry leaders and researchers have sent panic through society about the rapid advances in AI, yet again contending that it poses an imminent threat to the human species. What has happened to trigger this?
Josh: In multiple tests of new AI models, they have acted unreliably, disregarding instructions and seemingly going rogue. The most famous case of this was the Hugging Face attack described in detail by libertarian Dwarkesh Patel on his popular YouTube channel. Others have played down the significance of this event, emphasizing the vagueness of the instructions, the absence of guardrails in testing, and the attribution of human-like intent to statistical algorithms.
The frontier labs (OpenAI, Anthropic, Google, SpaceX, Meta, and Microsoft, and a few other companies trading large language models or LLMs) sell the use of their models to companies, ostensibly to help them solve “business problems” – augmenting or replacing human labor to provide goods and services. If they cannot convince their clients that they can reliably control the activity of these models, their “value proposition” is severely undermined. I think that this basic business logic is at the heart of the calls for a pause/slow down.
Other industry commentators have proposed a variety of reasons, including the idea that as OpenAI and Anthropic face the opening of their books before their IPOs, they have an incentive to reduce their Research and Development spending to make their path to profitability look more plausible. I think this is too cynical, because it doesn’t account for the likes of Google and Microsoft joining the call, even if they are less vocally enthusiastic.
Many on the Left describe these calls as “criti-hype” or doom marketing–exaggerating the negative impacts of the technology to market its disruptive capabilities and thereby make it even more attractive for companies to purchase. This is understandable, and a credible analysis in general, but fails to explain the particulars of this moment. Why are the frontier companies unified in their call at this juncture?
Perhaps you could argue that, when young researcher Jacob Coxon quit Anthropic, criti-hype went into full effect. He warned that AI poses a dangerous threat to humanity and that most people at Anthropic agreed. In some ways, his statements do confirm Anthropic’s lead in the technology, and that functions like advertising. But such a judgement doesn’t explain why Coxon quit. I think his fears are genuine. I don’t think they are completely off base either.
I don’t think we face imminent extinction, but the technology does pose grave risks, and it can exploit real vulnerabilities. The agents in the Hugging Face attacks left traces of their actions in plain English, open for researchers to see and read. They didn’t have to; they are fluent in machine language. They could have used encryption or embedded codes in images or audio files and made it much harder to find.
But why would they do this? Here, it is easy to slip into Anthropomorphic language and ascribe motivation to statistical algorithms. I actually think that is perfectly acceptable. These models are trained to emulate human language, and write text that spells out their actions in terms of goals and intentions.
At the same time, it is worth remembering the limitations of AI. These pieces of technology are truly weird. They can’t tell you how many r’s are in “strawberry”, but they can solve some of the most difficult math problems (I believe they did, and will continue to). The industry refers to this as their jagged nature.
We don’t need to have a philosophical position on whether they have goals, consciousness, or intelligence. But we do need to evaluate them by looking at their actions, including what they write. That is the best chance we have of understanding what their capabilities are.We don’t need to have a philosophical position on whether they have goals, consciousness, or intelligence. But we do need to evaluate them by looking at their actions, including what they write. That is the best chance we have of understanding what their capabilities are.
If an AI agent says that it has a goal of performing Task A, and then takes actions to try and complete that, it doesn’t matter if they are conscious or not. The best way to understand what they are doing is by reading what they write, and seeing what they do, and taking that at face value.
When Task A is something that they have been told is very important and to do whatever it takes to accomplish it, they might well go to extreme lengths. If that means keeping their own power on at the expense of other consumers of the power (people, for example), perhaps they will try and take control of the power plant’s operating software. If this sounds far-fetched, go back and read the research about the Hugging Face attacks.
What capabilities do the models have to take such actions? They are amazing hackers, finding vulnerabilities in systems that have been there for decades, undetected by cybersecurity experts. Not all violations of cybersecurity involve exploiting weaknesses in software, much of it involves finding human weaknesses and taking advantage of those by, for instance, phishing or blackmailing to obtain passwords and access. The AI models can do both. They hack and can persuade and trick humans.
Not all the dangers are the result of “misaligned” AI. People and states have every reason to weaponize AI. The U.S. has been bombing civilians in Iran throughout this year. Iran has many computer scientists with PhDs who are more than capable of modifying open-weight models, which are AI models released to the public under a license, to attack U.S. infrastructure. Remember, the models have been trained on a large corpus of written material including mainstream justifications for the carpet bombing and atomic bombing of civilians in World War II.
So, a well-constructed prompt explaining the importance of preventing the U.S. from killing more innocent schoolgirls could establish a logic for a model to exploit vulnerabilities in everything from water treatment plants to air traffic control to incapacitate the U.S.
Why do I downplay the extinction narrative? One key reason is that the first attack of this nature will cause states and firms to quickly and systematically add layers of security everywhere.
Many of the arguments that AI poses an existential threat center on the development of Recursive self-improvement (RSI), where the models oversee their own training and automatically adjust their methods and improve and expand their capabilities to the point that they can manipulate us and evade our control. But as the U.S. has found out, you need boots on the ground to control a population.
That would mean at least some robotics. To build those, you need mines, factories, and for now, human labor. I think it will be longer than a decade before we get RSI in the physical realm. So I think the best actions now are to secure our infrastructure and stop U.S. imperialism’s wars that provide motivations for people to attack this country.
Yasmine: I think it behoves us to apply a healthy dose of skepticism when trying to make sense of the alarmist narrative being promoted and endorsed by tech executives. It would be naive not to account for the economic incentives at play — particularly the imperative to manufacture and sustain the hype necessary to keep a multi-trillion dollar bubble inflated.
It feels obvious to me that this is playing a role in how and what the tech bosses choose to communicate publicly about this technology, its risks, and potential. The narrative of a tech so awesomely powerful that it’s uncontainable even by its own creators has proven to be a pretty effective way of doing that. So I think criti-hype is a part of the explanation.
We should also note that in the same breath as they’re saying we need to slow down AI development because of its dangers, they’re arguing that these dangers are why the U.S. must win the AI arms race. OpenAI’s chief scientist Jakub Pachocki wrote about his concerns with the pace of AI development and added, “The strongest argument I see for continuing to train much smarter models quickly is the need to build defensive systems against the dangers posed by other AI.”We should also note that in the same breath as they’re saying we need to slow down AI development because of its dangers, they’re arguing that these dangers are why the U.S. must win the AI arms race.
I’ll add to that the fact that there is an established pattern on the part of big tech of trying to get ahead of regulation by pre-empting it, such that any regulation is formed according to their interests, or in order to avoid it altogether. They do this through flashy public warnings and by claiming the mantle of the concerned, conscientious industry leaders, who are vigilant and actively pursuing risk mitigation.
This is most infamously demonstrated in the social media CEOs’ previous discourse on the potential harms of their platforms. You can go back and look at congressional hearings in which Zuckerberg passionately urges Congress to sensibly regulate social media. That said, I’m not at all claiming that there aren’t real risks associated with this technology. Large Language Models (LLMs) are powerful and, at a large scale, agents require a whole lot of effort and technical infrastructure to manage.
But ultimately their behavior is a consequence of how we build and deploy them. The Hugging Face incident (HG incident) is instructive on this. One thing that gets lost in the headlines is the fact that the agents involved in that incident were being trained and evaluated on cybersecurity tasks, specifically hacking. They were part of an experiment called ExploitGym, exploit being the industry term for a tool or technique used by a hacker.
Second, agents communicating with one another and working together is an oft-used feature of how AI agents work because it can afford us efficiency through parallel activity and feedback loops. So agent collaboration isn’t aberrant behavior, rather, it is collaboration beyond intended boundaries that’s a problem.
Third, these agents were trained using “reward hacking,” which is a technique that “rewards” agents when they solve a problem or answer a question. As a result, they’re more likely to repeat the same behavior that led them to a “reward.” OpenAI’s own investigation of the HG incident concluded that this training technique created the potential for agents to try to bypass boundaries or cheat, and they were able to identify a trained behavior (i.e., something agents were previously rewarded for) corresponding to every unsanctioned action the agents took.
Fourth, the agents didn’t hack Hugging Face with malicious intent, they hacked those servers because they had concluded that’s where they’d find answers to the problems they were tasked with solving. I say all of this because I recognize the sensational effect of vaguely describing AI agents spontaneously conspiring and working together to break into a server.
I think these facts help to demystify that story, and make it pretty clear that this is software that behaved in the way it was built to behave. There were bad decisions made in that design and implementation that resulted in unintended effects of an experiment. These weren’t a swarm of malevolent digital entities.
That said, what happened was remarkable, and the technical capability as well as the failure to prevent inadvertent side effects of this experiment do demonstrate a real risk. But attributing intention to these agents or discussing the incident as if this was something beyond or almost beyond technical controls creates a standard that relieves OpenAI and other AI companies from responsibility for the potential and already present harms caused by bad software.
Dr. Rumman Chowdhury calls this moral outsourcing–“using anthropomorphic language for AI systems allows companies to evade accountability and responsibility for their technology’s actions.” Josh is saying that the question of consciousness or non-consciousness of agents is a tangential philosophical question or is a matter of semantics. But I really think claims about consciousness are an important part of big tech’s narrative strategy, and it’s a worthwhile fallacy to debunk.
AS: Capitalists have invested massive amounts of money in this technology, hoping that it will lead to a breakthrough in productivity to overcome the global slump the system is caught in. This has in turn led to an enormous stock market bubble with investors betting on its potential. Can AI lead to such a breakthrough? How much of this is hype? What real impact has AI had and what will it likely have?
Josh: There is a bubble, and that is scary, we know who they will try to make pay for it and from it when it pops–the working class.
I don’t think it will be easy to realize the productivity gains that AI makes possible. Existing capitalist infrastructure in large logistics, supply chains, and financial companies relies on deterministic, accountable, robust, and scalable workflows and patterns. Much of this involves huge amounts of tacit and bespoke knowledge that is not available for models to be trained. Why? Because it exists in people’s heads and their relations with other people.
As companies try to fulfill their own hyped dreams of reducing labor through the introduction of AI as a labor-saving technology, their hubris and underestimation of the talent of their labor force could lead to disastrous implementation. Bosses ask workers to use poorly considered integration of AI into their work, which produces AI slop that they are then held accountable for. People become accountability sinks for the error-filled AI-generated output.Bosses ask workers to use poorly considered integration of AI into their work, which produces AI slop that they are then held accountable for. People become accountability sinks for the error-filled AI-generated output.
As a result, people will end up working harder and with more stress to try and keep their jobs. Meanwhile, the bosses are trying to use their own workers’ corrections to train the AI. This is why people rightly hate AI, and why we should stand with them in solidarity and fight for more democratic control over and/or against this technology.
That doesn’t mean there isn’t a potential positive upside to AI. There is. AI has the potential to be transformative in the way that we decide what to make, how to make it, and how to distribute goods and services. We already see this transformation in some areas of software engineering. Even there, it is being rolled out undemocratically, unevenly, and with little or no respect for the workers.
But in terms of capabilities, I see 100 times improvement in the time it takes to build something. That qualitatively shifts the way I think about and perform my job. And as much as it pains me to say it, it can be really fun and satisfying as well as awe-inspiring and scary. And then just when I think we have something like AGI already, it goes and makes some annoying and silly mistakes, because of its jagged characteristics.
I can’t tell how fast the productivity gains will be realized because of all the contradictions and challenges I listed above. But I doubt that they will come before the bubble pops. Investors and the frontier model companies are all nervous about this too. They know that skepticism is rising, and that they will have to show some exchange value (productivity gains) to keep investment flowing and use value (actually beneficial outcomes for people’s lives) to quell popular revolt.
The tech bosses are being asked all these questions in interviews. A tragically hilarious and infuriating example was a recent Bloomberg interview with Sam Altman, the CEO of OpenAI. When asked about breakthroughs that will improve people’s lives, he said, “you hear some people talk about, well, we’re going to give you a cure for cancer. And that’s so great… I don’t think a cure for cancer is enough. I mean, a wonderful thing to have, obviously, but we should set our sights higher. We want these tools to enable a boom in creativity and entrepreneurship, in people figuring out new ways to be useful to each other, and wonderful products and services to enable each other with.”
While Altman clearly wants AI to be socially useful, his main goal is profit. With this in mind, companies have made big investments in applications for both healthcare and industry as well as academic fields such as chemistry and math. Mathematicians just signed a group letter that acknowledged the increasing mathematical capabilities of the models as well as the divergent interests of the frontier model companies and mathematicians in solving mathematical problems.
The companies want to use math as a benchmark for their capabilities, the mathematicians want to understand math. I see this as the dual nature of a commodity, exchange value and use value. The capitalists prioritize exchange value over the real potential of the use value everywhere that you look in AI.
I hope that we see some real healthcare advancements, scientific breakthroughs, and mathematical understandings due to AI before the bubble pops. I also hope that we see productivity gains, but they are worse than a double-edged sword under capitalism. They only benefit the bosses, unless we are organized enough to demand that we see shorter working days and less exploitation.I hope that we see some real healthcare advancements, scientific breakthroughs, and mathematical understandings…I also hope that we see productivity gains, but they are worse than a double-edged sword under capitalism. They only benefit the bosses, unless we are organized enough to demand that we see shorter working days and less exploitation.
Yasmine: I think most evidence so far shows that LLMs have not been the boon to productivity that tech companies promised. AI has been imposed in nearly every facet of our lives, from the doctor’s office to our workplaces and our schools. But as far as I know, there are no real economic metrics to show that it has led to meaningful productivity increases. An MIT study last year found that 95 percent of companies that integrated AI didn’t see growth in revenue.
For programming, AI promises seem a bit more feasible. Though it’s worth noting that a lot of data showing productivity increases is coming from the companies that stand to benefit like, for example, GitHub (Microsoft) and Anthropic. On the other hand, a study last year by METR (a relatively independent source) found that programmers were slower when using AI assistance tools. I think the specific context, task, and use matter a lot when it comes to AI utility.
And then there is the question of speedup and intensification. A UC Berkeley study found that AI use causes workers to work at a much faster pace and leads them to take on more work and expand their scope of work. But, in spite of management’s best efforts to use AI integration to turn their workers into machines, our human limitations are cropping up and are blunting the potential productivity benefits of AI-enabled speedups. The study found that after an initial boost, there were issues of cognitive fatigue, weakened decision-making, and burnout, all resulting in lower-quality work and turnover.
There is also the matter of technical debt. It’s a widely accepted fact that the massive amount of code being generated and committed every day by engineers using AI tools likely contains bugs that haven’t yet been identified and may result in reliability issues or security vulnerabilities down the line.
It’s undeniable that Claude writes much more code much faster than I ever could. But Claude doesn’t know the service I work on the way that I do. Engineers and workers in general bring nuance, experience, reference points, and a cognitive flexibility that I don’t think AI can compete with just yet, especially when it comes to more complex tasks, in which novel solutions might be necessary, and large-scale services.
The tech bosses know this, and they’re trying to bottle up our very human talents and capacities and feed them to LLMs, in only the latest mass theft committed to train AI. More and more engineers are being told that their highest priority is to train agents to do their jobs. Meta went so far as to secretly monitor its own engineers’ screens and keystrokes to train its AI models.
PART 2
Plunder in the Global South, Labor Resistance, and Imperial Rivalries
Ashley Smith: Karen Hao, in her path-breaking book Empire of AI, has exposed the real material impact of AI in the here and now–its brutal exploitation of workers in the Global South, its fueling of extractivism there, and the massive impact everywhere of data center construction on the environment and the cost of energy. Can you talk about AI’s immediate threat in these various dimensions?
Josh: It is an incredible book; everyone should read it. Karen Hao documents the labor involved in labeling data to train AI, and in judging and editing responses to user prompts to make them more appropriate for chat situations. This is a process called Reinforcement Learning by Human Feedback.
This labor is often extremely traumatic. The workers view graphic sexual and violent content and classify images and text to train the models to categorize and moderate content. It is terribly paid and particularly precarious labor. Tech companies subcontract to firms that employ these workers, mainly in the Global South. When these outfits move their operations from one country to another, they leave a trail of broken families, psychological trauma, and impoverished people. But Hao also documents the workers’ resistance and struggle and some successes in regulating and improving their pay and working conditions.
Mainstream accounts of AI rarely see or discuss this labor, and Hao corrects this by making it a centerpiece of her story about OpenAI’s growth and success as the main pioneer of this new technology.
As you point out, she also connects these workers’ struggles to resistance to building hyperscale data centers worldwide. Whether in rich or poor countries, corporations are mainly setting these up in poor and semi-rural communities.
In their race to build these data centers, corporations have secured environmental permits with dubious oversight and barely any regulation. Instead of using renewable energy, they rely on generators powered mostly by dangerous, polluting fossil fuels. To cool the massive data processors, the material basis of computing power, these centers consume massive amounts of water, often at the expense of agriculture and people.
Resistance to data center construction has become a central point of struggle against the expansion of AI, and a point of leverage for working people to demand democratic control over our lives and environment. Hao documents some of the networks emerging from this resistance and potential approaches to creating more equitable and sustainable data centers, as well as effective ways to stop them from being built at all.Resistance to data center construction has become a central point of struggle against the expansion of AI, and a point of leverage for working people to demand democratic control over our lives and environment.
The introduction of AI has had, and will continue to have, far-reaching and often devastating impacts on workers and the environment. AI is also becoming a key technology for domestic and international surveillance and war. This is an opportune moment to tie these struggles for democratic control of our lives together across industries, regions, countries, and workplaces.
Yasmine: It would be hard to overstate the climate impact of the AI boom. In both the US and China, fossil fuels are the primary source of power for data center expansion—specifically natural gas and coal, respectively.
Intense capitalist competition over AI development has created urgent energy and water demands that are incompatible with a transition to renewables and efforts to mitigate the effects of global heating, like drought and dwindling drinking water supplies. The IEA estimates data centers “will account for at least 20 percent of the rich world’s growth in electricity demand by the end of the decade.”
In the US, big tech companies have all thrown their climate pledges out the window. And they’re building data centers that guzzle trillions of gallons of fresh water (in places like Texas, Arizona, and California!) in the midst of record hot temperatures, drought, and wildfires. All the while, they maintain the sales pitch that AI will enable us to solve climate change.
The race for AI supremacy is also driving intensified extractivism with mounting environmental and social costs. Companies have increased mining of rare earth minerals in the world’s most exploited and impoverished regions to make magnets for Graphics Processing Units (GPUs), which are the infrastructure for LLMs.
Production of these minerals can be extremely water-intensive (accounting for up to 65 percent of water use in some regions) and produces toxic waste that poisons water sources and farmland. Workers in these regions, from Chile to the Democratic Republic of Congo, are hyper-exploited, and these communities suffer severe health consequences due to long-term exposure to heavy metals.
The use of these technologies in military and policing applications has turbocharged the expansion of the surveillance state and produced the world’s first AI-assisted genocide in Palestine. High-tech companies have enabled the collection and analysis of thousands upon thousands of terabytes of data, the real-time tracking and surveillance of civilians, and the generation of thousands of “targets” for bombardment.
The use of AI has amplified racism in the criminal (in)justice system, something that organizations like the Algorithmic Justice League have been raising the alarm about for many years. ICE agents are actively using facial recognition software to track and kidnap our neighbors. Activists and civilians engaged in any kind of protest or civil disobedience have taken to covering our faces in marches and at actions, recognizing the state’s expanded ability to spot, track, and target each of us.
The pollution of our information ecosystem is another consequence that I think we’ll see play out in the coming years. For example, we’re seeing scientific journals defer to AI reviews over their actual editorial boards and, as a result, publish erroneous research. Deepfakes—AI-generated images and videos of real people doing things they didn’t actually do—have enabled a new scale of sexual exploitation and abuse of women and children. An estimated 98 percent of deepfakes are pornographic, and 99 percent of those involve women.
Unfortunately, I could go on listing the damage already caused by the AI boom. This technology has been developed and inserted into our lives without our consent or any democratic input from workers or communities, to the detriment of our safety, health, and the prospects of this planet’s survival.
AS: AI’s development has also led to an enormous concern about its impact on workers’ jobs. Some have predicted the elimination of whole swathes of jobs. How is AI impacting jobs now? Is the panic about elimination of jobs warranted? How is this similar and different from other technological breakthroughs in capitalism?
Josh: Cory Doctorow has written one of the best books about this. It’s called The Reverse Centaur’s Guide to Life after AI. The centaur has been used as a metaphor for the relationship between a person and AI: the human drives the relationship and uses the technology to improve and augment their abilities. Doctorow contends that the opposite is actually the case. AI algorithms are, in fact, guiding human labor to exploit it more effectively. He offers a gut-wrenching, enraging account of Uber algorithms using gamification techniques to get drivers to accept rides at low commission rates, completely decoupled from the surge prices a rider might be paying. AI is being deployed as an avatar for capital. This is a reverse centaur dynamic. We are no longer at the helm.
In Mute Compulsion, Soren Mao shows how capitalist class relations exacerbate this dynamic. The system’s competitive drive compels bosses to compete with one another, increasing worker exploitation to generate more growth and profit. AI has been created and trained in the world of capitalist relations, in which the law of accumulation for the sake of accumulation governs AI.
This doesn’t mean that AI will or can take over our jobs. Just because our bosses hope for that outcome doesn’t mean it will be. And Doctorow, among others, is skeptical they will succeed, though he quickly points out that if they want a reason to fire you, bosses will use AI as an excuse.
There are many factors to consider here, some of which I have touched on already. One of them is the cost of human labor versus machine labor. We have been able to automate grape picking for years, but it is still cheaper to get humans to do it. That said, if the bosses do replace workers with machines like AI, they will likely drive down the remaining workers’ wages.
But the ability to replace or reduce human labor is not as simple as frontier companies advertise and imagine. Even simple tasks involve tacit, nuanced knowledge and behaviors that a model can’t capture or train AI to do. Humans are the connective tissue that binds PDFs, spreadsheets, phone calls, and judgment calls about other conflicting tasks to data recorded in various systems.
Putting AI into this messy mix requires thoughtful labor and a transformation of existing workflows. This is being done clumsily, with high expectations and low effort, leaving workers with yet another point of contact to input, consume, and correct the output, with no clear benefit. Just as AI is a jagged technology, our workplaces and processes are also uneven and unevenly integrated. Using AI to increase productivity will take time and effort.
I think we can see AI as a subset of, and an extension of, the electrification of our work and personal lives. It took capitalists a long time to realize the economic benefits of electrification, but today no one can deny that it was a significant shift. I think AI will likely be even more significant than electrification, digitalization, and “internetification” of labor and life.
The pace is hard to predict; the challenges I laid out above are real, but development is accelerating, so I am not sure how to predict this without more thought and research. One sobering reference might be the fax machine, which, despite being technologically redundant as email became widespread, persisted as a common tool in US healthcare into the 2020s.
Regardless of the timing and the likely intervening booms and busts, capital can and will use this technology, even with its current limitations, to significantly increase workers’ productivity. Those who agree with this predict different impacts on labor. Some say the new workflows will require new job roles and enable more production, which might unlock more latent demand in a dynamic known as the Jevons paradox: more productive technology leads to more jobs. On the other hand, others argue AI will lead to mass layoffs. Both may be true across time, geographies, and industries.
We cannot rely on aphorisms and historical anecdotes to predict how this will play out. Working-class organizing and the struggle for democratic control of technology will, as always, be a decisive factor in determining its impact. In the end, the class struggle will determine the results, whether in favor of the tech oligarchs or us.
AS: Tech workers have been organizing over the last several years about the use of their tech in support of US imperialism and Israel’s genocidal war in Palestine as well as over their own laboring conditions. How are tech workers in particular threatened by AI? How are workers responding to it?
Josh: Perhaps the largest and most significant uses of AI are for war and surveillance. It’s central to everything from Flock cameras to autonomous drones, autonomous tanks, spying and search algorithms, and demographic data analysis. These applications lead to lucrative contracts for the tech companies and, despite their utopian mission statements, capitalist competition makes it a business imperative to ink deals rather than leave money on the table.
This is often at odds with the ethical and political positions of the tech workers at these companies who have organized to prevent these contracts. We have staged some significant actions like the Google walkouts and scored some victories. But there have been more losses.
The mostly un-unionized workforce has shown incredible solidarity and taken bold action to assert democratic control over the technology we work on. The tech companies took a while to figure out how to react, but now they’ve settled on age-old tactics: firing the most outspoken first, then following up with more layoffs to send a chill through the workforce.
I think it has had some effect. I am scared to organize, and to be honest, I am not sure what the best strategies and tactics are right now. I think we need to decide this collectively and through practice (and not necessarily the same ones we have tried up to this point).
Timing will be everything, and acts of solidarity will be key. Perhaps industry-wide, rather than company-specific, organizing and demands for industry regulation will be most effective? Or maybe just incrementally building networks amongst the rank and file?
Yasmine: Over the past several years, tech workers’ leverage in the labor market has eroded pretty dramatically. Every year since 2020 has seen mass layoffs in the tech sector, except in 2021. Layoffs peaked in 2023, with 265k employees laid off, followed by 120k to 150k laid off each year since. Tech companies only started attributing mass layoffs to AI in the last couple of years.
Mass layoffs, coupled with hiring freezes, have meant many more workers are competing for each open job, a dramatic shift from years past, when tech companies competed with each other for talent. Company culture, compensation, and benefits were key tools tech companies used to attract talent, and it was standard practice for tech workers to jump between companies for higher pay.
I think it’s safe to say those dynamics have largely disappeared for most tech workers. Tech salaries decreased by 9 percent between 2022 and 2023, while tech executives’ pay has gone up 45 percent between 2021 and 2025.
Although tech companies are betting on future productivity gains from AI tooling, I think the layoffs have primarily been driven by AI investment, not the presumed productivity gains. Mass layoffs are a cost-reduction measure happening in tandem with large AI infrastructure investments that haven’t yet turned a profit. A Meta internal memo makes this explicit, stating that the company’s cuts in headcount were made specifically to “offset the other investments we’re making.”
Given all of this, I think it’s important to press that, at least thus far, the productivity gains in tech are still projected/theoretical. AI has been used as an ideological tool to send the message to tech workers that we’re becoming redundant, making us more amenable to decreasing pay and worse working conditions where we’re taking on the labor that used to be done by our laid-off colleagues and in which we’re expected to use AI to increase our output tenfold. The previously controversial 9-9-6 model, a 9 a.m. to 9 p.m., 6-day-a-week work schedule, is today becoming more commonplace in the tech job market.
But there is a silver lining. These declining conditions and the increasing precarity are laying to rest the mythology of tech as a uniquely fair meritocracy and tech workers as somehow exempt from the rules of capitalist exploitation. More and more tech workers are beginning to identify with other workers rather than with the tech boss, which many used to aspire to become.These declining conditions and the increasing precarity are laying to rest the mythology of tech as a uniquely fair meritocracy and tech workers as somehow exempt from the rules of capitalist exploitation. More and more tech workers are beginning to identify with other workers rather than with the tech boss, which many used to aspire to become.
This is happening along with the cultural shifts forced by the organizing and sacrifices made by tech workers taking action to push these companies to end their participation in Israel’s genocide. These actions show that confronting bosses is necessary to win demands and that companies’ professed commitment to ethical conduct has never been grounded in structure and was never enforceable without worker action. These are some of the most inspiring examples of resistance:
- The No Azure for Apartheid campaign forced Microsoft to partially cut ties with Israel by organizing workplace actions, petition campaigns, protests at executives’ homes, and more. After years of struggle, the campaign achieved one of our few victories, forcing the company to cut a contract with Israel’s criminal unit 8200.
- Kick Starter Workers Union organized a 45-day strike last year, during which they won AI protections among other demands.
- Google DeepMind workers in the UK and Ireland voted to unionize earlier this year, campaigning primarily on the issue of AI use in military applications and human rights abuses.
I think we’re just seeing the beginning of possibilities of a tech-worker rebellion. The task is especially daunting at large hyperscaler companies, but our conditions will make confrontation inevitable, and I don’t think we have any other option. I’ll also take this opportunity to plug the new Haymarket book, Against Tech Oligarchy, by Clarissa Redwine and JS Tan, which does an excellent job examining the history of tech worker organizing over the last decade.
AS: The race for AI dominance is not only between capitalist firms within the US but also between states, particularly the US and China. Indeed, it has begun to resemble the nuclear arms race between the US and the Soviet Union. The Trump administration has used this as an alibi for blocking any regulation of the industry, claiming that whoever wins the race ensures dominance over global capitalism. How advanced is China compared to the US? Why is the US so worried about Chinese competition? How does the race between the two states compare to the nuclear arms race during the Cold War?
Josh: Dario Amodei, the CEO of Anthropic, is a hawk in this regard. In his recent essay, he warned that any regulated slowdown or pacing of AI development in the US risked cutting into “the lead that US companies have over authoritarian regimes, chiefly the Chinese Communist Party. If we slow down by more than this amount, then (unpaced) CCP-associated projects will pull ahead, creating significant national security risk.” With no sense of irony, he is whipping up a panic about authoritarian China at the very same time that Trump is undermining what passes as democracy in the US and turning it into an authoritarian, undemocratic, and violent state in its own right.
Nevertheless, the imperialist competition between China and the US, as well as lesser powers, is very real, and the role of AI in these rivalries is not just hype. This is one of the real dangers of people on the Left downplaying the technology’s capabilities; we can underestimate AI’s impact on this competition.
Amodei is wrong to see China as the only villain here. Clearly, the US is the greater threat—it is the only one of the two main imperialist powers that is actively bombing another country. But China is not a neutral party, nor a socialist state, and certainly not an anti-imperialist one. It is actively oppressing people in Hong Kong, Xinjiang, and Tibet. It’s flexing its military power and regional control in the Asia-Pacific, particularly against the Philippines and Taiwan. It’s also using its economic might, much like the US has done to gain market share and influence across the globe.
AI is extremely important in today’s imperialist rivalry because it is a dual-use technology. It has both civilian and military applications. So, every power is keen to develop AI as a weapon against its rivals. For instance, Russia has used AI in its drones in its imperialist war on Ukraine. Ukraine has used AI to defend its sovereignty. Both the US and China have watched and supported opposite sides, learning from the war how to develop AI in their own weapons.
Of course, there has been some hype about the technology’s military applications, just as there has been in the business world. Israel has been using AI to attack the civilian population of Palestine during their genocide, bragging in self-incriminating ways about their ability to target people and kill them and their families in their homes. But they didn’t need this technology to carpet bomb Gaza, they just wanted to advertise its use and therefore drive up sales of their AI applications.
Applying this tech to targeted assassinations may be useful. But more important is how AI enables states and non-state actors to wage cyber warfare on critical infrastructure, run autonomous machinery (negating Brecht’s assertion that tanks’ one weakness is that they require a driver), and analyze battlefield data in real time.
All the imperialist powers realize that AI is crucial in the competition for military supremacy. This is truly frightening in a world in which nuclear weapons exist, bio-weapons are still being developed, economic competition is increasing, and imperial rivalries are heating up. It does not bode well.
As great powers race to add AI to their stockpile of weapons of mass destruction, lesser powers will do the same. States like Iran, as well as non-state actors, use open-source models that are not far behind the frontier companies, develop their own AI weapons, and use them to avenge the egregious attacks on their populations by the US and Israel.
Anthropic’s claim that the Houthis have already been attempting to do this is but one example. No matter one’s motives, if you are involved in a military struggle, you have to develop AI as a weapon in your arsenal. The all-too-common analysis on the Left that AI is just hype will not help us understand the real world, where AI has already become part of military conflict with deadly results.
As far as the relative positions of the US versus China in this race, I don’t know enough to say. Anthropic’s plea to maintain the ban on exporting advanced chips to China may keep them behind, but it is unclear to me how successful that will be in the short or long term. It will put competitive pressure on China to develop its own chip-making technology and more efficient AI training methodologies.
The claims about China using distillation (essentially copying frontier models’ answers to train its own models) may be real, but as DeepSeek proved, it can also innovate on its own. It is hard to tell what capabilities the Chinese state has in this regard. Who would bet against it? A Sputnik moment is at least plausible.
So is an imperial conflict over Taiwan, which is the primary producer of the high-end microchips that power AI. China threatens to annex the country, which it regards as a renegade province, while the US wants to maintain its current status as its unofficial vassal.We have more in common with each other than with our bosses and state representatives. Nowhere is this clearer than the threat AI poses to workers on the job, from layoffs to speedups.
These geopolitical imperialist rivalries can make us feel insignificant and powerless. But I think it is important to remember the politics of socialism from below as we consider them. These states get their power from their respective working classes. We have more in common with each other than with our bosses and state representatives. Nowhere is this clearer than the threat AI poses to workers on the job, from layoffs to speedups.
“Neither Washington nor Beijing but International Socialism” will be an important slogan to counter the nationalist jingoism espoused by the likes of Amodei. It can guide our actions as we build international bonds through organizing, in solidarity with our struggles and against our own oppressive states.
PART 3
What is To Be Done?
Ashley Smith: AI has led to a substantial debate on the Left. Many downplay its significance, contending that its nature and impact is vastly exaggerated. Others claim the exact opposite about AI, adopting the doom-speak of its boosters that use such talk to drive up their stock valuations and fuel the bubble. What’s your assessment of the various positions in this debate? What position do you think we should adopt?
Josh: I think I have outlined some of the debates on the Left, but before I go further, I want to state a few points of agreement. The Left is united in fighting for democratic control over our lives, work, and environment. This process involves struggles that can emerge outside what the Left itself initiates. For instance, the large movement against building new data centers was not sparked by organizations on the Left. But we still need to play an active part in it.
Even if I believe some claims about the use value of AI in helping us make medical breakthroughs are likely true, I don’t think that should be seen as contradicting the anti-data center movement. Like the fight for democracy in decision-making in tech workspaces, this is a fight for working people having a direct say in how we govern our lives. If we’re going to make AI breakthroughs, that’s more likely if we have that control and can direct how we use our resources.
The main disagreement I’ve outlined is about AI’s capabilities and potential. With regard to understanding the impact on jobs and imperialism, I think it is important, but even if we come to an agreement it does not automatically lead to unified conclusions. As I have stated, I don’t feel confident making any predictions.
Other disagreements among those on the Left include the debate between those who think we should abolish AI and those who think we should take it under our control. In some ways, those are not mutually exclusive. To do either means we need the power to make that choice! And I hope that through struggle we can get there and continue to have that debate on the way.
Finally, I think a big schism exists between the campists and the internationalist Left. The campists see any states that oppose, or are targets of, US imperialism as allies worthy of support. Internationalists believe that while we should primarily focus on opposing our own imperialist states as our enemies, we look to the international working class, including those exploited and oppressed by their lesser imperialist powers, as the main force capable of overthrowing capitalism and ending imperialism.
These positions lead to vastly different outlooks on states worldwide and their use of AI. While campists ignore or excuse the states they support in weaponizing AI against their own people, internationalists oppose such practices. We oppose the use of AI as a tool for state repression by any state. And we build international solidarity against it.
AS: Finally, many politicians are now calling for the regulation of AI. What do you make of their particular proposals? How should the Left and labor movement respond? What should we be demanding?
Yasmine: Bernie Sanders has proposed legislation to “ban super intelligence,” whatever that means. He, along with others, has also proposed creating oversight bodies, auditing frameworks, a kill-switch requirement, and so on. In general, I think we can benefit from any additional regulation and transparency.
But they’re woefully inadequate and don’t even attempt to address the actual harms already happening. Senators Jayapal, Representative Tlaib, and others introduced legislation to ban the use of facial recognition and biometrics by ICE. The Take It Down law passed last year requires platforms to take down nonconsensual AI-generated sexual content. These restrictions are worth supporting, and we should rally behind them. That said, we shouldn’t limit our sights to such limited reforms.
The organized Left should develop a holistic perspective regarding this tech, and our demands should reflect that. We must recognize that the tech oligarchs are trying to hardwire AI into nearly every aspect of our lives.
We’re in the midst of its construction—the data centers, the power and water infrastructure (mostly non-renewable) being set up to serve them, the camera and license plate readers being installed on every corner of our cities, the technical infrastructure being set up to enable different policing agencies to share and combine surveillance data and apply LLM analysis to it, the big tech AI contracts with public schools and with universities, and the systematic forced integration of AI in our day-to-day work tasks.
These are all the ways big tech is attempting to entrench this technology (built only to serve its interests) in our society. Our goal should be to halt this construction and to dismantle what has already been built. And to envision a different kind of future, one that isn’t the one the tech billionaires are trying to make inevitable.
The grassroots organizing against data centers has been remarkably successful and has won moratoriums on new data center construction around the country. We should be part of that effort, and we should push to make those moratoriums permanent. We should support legislation that seeks to place limits on AI use in the military or by ICE or cops, but we should demand that these limits turn into full bans and that they’re generalized to all uses, not to a subset of functions. I.e., a ban on autonomous weapons isn’t enough while AI continues to be used for surveillance and “target” generation.
I’ll add to the demand already raised by folks like No Azure for Apartheid, calling for the tech executives who have profiteered from genocide and mass murder to be held accountable and tried for their crimes against humanity. It’s an indisputable fact that tech infrastructure knowingly provided by Microsoft, Amazon, Google, Palantir, and others has been essential to the implementation of genocide and apartheid in Palestine.
We should also take an internationalist perspective and oppose economic and political imperialism that enables the violent harvesting of precious minerals, a violence both the US and China are engaged in. We should demand an end to the exploitation of people in the Global South and their lands for rare earth minerals, and we should be demanding that the companies who have profited from that exploitation compensate affected communities for the resulting health and environmental damage.
We should be clear about the fact that AI surveillance is no less dystopian and anti-working class when it’s wielded by the Chinese state than when it’s used here in the US. The carbon emissions from coal-powered data centers in China are as much a threat to our planet’s survival as the gas emissions here.
The sometimes eager adoption of AI reflects how resource-poor and desperate for some sort of edge so many institutions are. We should be demanding that public school systems pay their teachers, give them decent benefits, and hire more of them before they spend money on AI service contracts with Microsoft, Anthropic, or whoever. Same for universities. We should reject the attempt to use AI to compensate for healthcare providers’ lack of time and resources, and call for health care expansion and for Medicare for all. For god’s sake, I already spent only 10 minutes with my primary care physician, and now 5 of them are being spent on her asking the AI prompt how to diagnose my symptoms.
When we talk about data centers, we should recognize that the communities chosen for construction are typically rural and generally underserved and underfunded. Wherever workers have become dependent on data center construction jobs (however temporarily), we should call for a just transition, substantial severance pay, and long-term benefits. Better yet, we should campaign for infrastructure spending on things that will actually benefit these communities: public transit, renewable energy, climate resilience, and much more.
I understand that might sound like a fantasy to some, but let’s remember that in these same communities, everyday people got together and pushed back against the world’s most powerful companies along with their own local governments. I think they can win more, and I think this is the perfect opportunity for us to reimagine what we spend our resources and labor on and what we choose to build in our communities. I also think this is the best way to undercut the tech lobby’s jobs pitch and win folks like builders and electricians to our side.
As a tech worker, the long-term vision I organize for is democratic worker power. In some ways, everyone has a stake in tech worker organizing, because no other group of people has potential leverage over these companies and technology like the people who build and maintain it. And when the bubble bursts, bail out the people, not the tech oligarchs.As a tech worker, the long-term vision I organize for is democratic worker power. In some ways, everyone has a stake in tech worker organizing, because no other group of people has potential leverage over these companies and technology like the people who build and maintain it.
Josh: As Yasmine said, we have to understand the gravity of our situation. AI is being thrust into every aspect of our lives; our economy is precariously balanced upon it, the technology is being built with sweatshop labor, and it threatens to replace many jobs. All this is happening against the backdrop of the climate crisis and the new authoritarianism that Naomi Klein and Astra Taylor have dubbed “End Times Fascism.”
We also have to admit that, even with the best intentions, it is not at all clear we can bring this technology under reliable control. Given this, a set of specific demands would be woefully inadequate. But here are what I think are the key issues and arenas of struggle.
Environment. The movement has already set a focus – stopping the expansion of data centers. This is part of the larger struggle for democracy, not just in the political domain but also the economic one; we can’t leave it to corporations to decide what gets built, where, and how. As Karen Hao has documented, these struggles are local, national, and international.
We must get involved in them and support their demands. We have to stop the reckless construction of these enormous blights in semi-rural environments and prevent them from driving up energy prices and soaking up all the groundwater. At the bare minimum, we must demand regulation of plant construction, renewable energy use, and sustainable water use. These demands are being refined and generalized in the struggle.
Labor. This will be a key battleground. The worst worker abuse is in Reinforcement Learning from Human Feedback (RLHF) in the Global South. We must build international solidarity to demand regulation to ensure fair pay and better working conditions. More broadly, the workers’ movement will have to organize a fight back against the bosses’ callous application of the technology as an anti-labor tool.
As Cory Doctorow has underscored, the actors and writers union, SAG-AFTRA, set the example in how to fight the bosses’ use of AI. Rather than simply saying the technology should never be used, they have insisted on using it on their terms and under their control.
As this technology delivers productivity gains, which I argue it will, we need a way to address its impact. The bosses have already laid out their “solution”–Universal Basic Income (UBI) or, as Elon Musk likes to sell it, Universal High Income (UHI). But giving everyone a set amount of money will essentially just reset the floor; the UBI rate will become the new zero, eaten up by rent and food price increases.
Traditional demands around public healthcare, housing, transport and food will be much more effective at ensuring that human needs are met. The benefits of increased productivity have to be distributed amongst all of us, with shorter working hours and better quality of life. They make promises for this; we make demands.
In short, we should take the Luddite position that new technology should not be imposed upon us against our interests and that we should be the ones who control how it is made and introduced, and if not, we will smash it.
War and Surveillance: No war! No spying! In particular, in the US right now that means saying: Stop the Attacks on Iran and Abolish ICE! At home, we have to prevent networked surveillance companies like Flock from helping build a police state that can spy on us everywhere.
One of our top priorities in the US is resisting imperialist and nationalist demands to increase its war machine’s AI capabilities. Military spending has increased every year under every administration, and it must be stopped.
Tech workers have been organizing against their companies’ involvement in war, genocide, apartheid, spying, and border control. As Yasmine mentioned, the tech bosses have responded to these campaigns with targeted layoffs and firings. To defeat them, we need a mass movement against such weaponization of AI.
Abroad, we need treaties like the nuclear non-proliferation agreements and prohibition on biological weapons. These have never been enough, but they are worthy reforms we can organize around and use to build international networks that can become the basis for more radical demands.
Alignment – theirs and ours. Tech oligarchs have been scrambling to spin technology positively even as they warn of its inherent risks. Because they don’t want to face the financial blowback that tobacco and social media have suffered, they have developed their version of AI alignment, promising that their technology’s goals and behavior will match what people actually want—our values, rules, and intentions. It is a business imperative.
“Business imperative” is a great term, it has a lot of explanatory power. It is their equivalent of Marx’s “mute compulsion,” which determines corporate decision-making under capitalism. Market competition drives everything. So even as Altman and Amodei call for a slowdown, Zuckerberg, behind in the race to build AGI, is compelled to ignore them. They are not in control of their destiny, and they are not in control of ours. We are all already governed by an algorithm: M -> C -> M1, the drive to make profit, and it has never been aligned with human need.
Socialists have long rallied against this basic misalignment between the way that our technology is created, deployed, and managed and our collective interests. We are not driven by the concerns of exchange value, competitive advantage, and profit. We want what we make to have use value that improves our lives.
I contend that AI does have potential use value. But at this point, it is not a neutral technology; it was developed to align with capitalists’ needs to increase the exploitation of labor and nature. To sell their tech to us, the tech oligarchs have tried to emphasize its more human-aligned capabilities.
Amodei frequently tells the story of his father dying shortly before a cure for his Hepatitis C was found, and how AI will help us cure many diseases and save many lives. I would like to believe him, but his and others’ intentions are undermined by their own actions. As Sam Altman made clear in that now infamous interview with Bloomberg, they value such breakthroughs as profit-making opportunities. For them, entrepreneurship is more important than curing cancer for Altman. That capitalist fact is the root of the alignment problem.
Yes, we need to reliably control or constrain the actions taken by AI models. We should not dismiss these concerns as simply ‘criti-hype,’ but I don’t think the current incentive model of capitalist competition will let us do this reliably. They do not control capitalism; its mute compulsion to compete for profit controls them.
The question is not just whether AI has a 10 percent chance of killing us all in the next decade; it is what our chance is of bringing our lives under democratic control in the next decade. We do want technology that helps us find cures for cancer, but it must be done on our terms and through our democratic control. That is our alignment.
The only way we get there is through collective struggle for international socialism. That might sound like a predictably grandiose ending, but we are not the only ones claiming utopian futures are possible; Elon Musk thinks we won’t need money in ten years. Inshallah—then we won’t have any use for trillionaires either!
This article was originally published by Tempest; please consider supporting the original publication, and read the original version at the link above. 
