Critical takes on tech - BlogFlock

Did AI kill your job? - Blood in the Machine

2025-05-08T21:53:57.000Z

The response to my recent story, “The AI jobs crisis is here, now”, has been pretty overwhelming. The piece, which examines how the language-learning app Duolingo fired its writers and translators to go all-in on AI, how DOGE uses an “AI-first strategy” to justify firing tens of thousands of public servants, and how both fit into a broader trend of executives using AI to erode conditions for creative, civic, and freelance work, has become one of the most-read stories in this newsletter’s history.

It was picked up by outlets like TechCrunch, the Guardian, and TechMeme, did numbers on Reddit, and was shared in industry newsletters and AI blogs. It sparked a lot of debate, the great majority of which was civil, even.

Most importantly, I heard from a lot of people who had stories of their own about the impacts of AI on their working lives. Some left comments on the article, some wrote me emails, some posted their stories on social media, and others still sent me tips about job-killing AI use at their companies in cases that have not yet been made public. I heard stories from medical transcribers, freelance illustrators, graphic designers, marketers, writers, tech workers, and more.

I want to hear yours, too. I think a lot of people do.

The wide-ranging and often impassioned response to this story has encouraged me to try to do more to document, understand, and illuminate how AI is impacting our jobs, the working world, and our daily lives. The first step is hearing those stories. A lot of people I speak with are anxious, afraid, or feel alone in their fears. Some are angry their bosses deemed them or their colleagues expendable; some feel depressed watching what they loved about their jobs disappear. Many feel exploited. Others are frustrated at the new work AI creates. Others feel a loss of self-worth. But these workers are not alone and are certainly not worthless—there are many people experiencing such indignities right now. Assembling and sharing these stories can illuminate what’s happening to our jobs in the AI era, and help us understand the extent of the trend.

So, if you have a story about how AI has impacted your working life, your job, or your workplace, and you’d like to share, please send it to AIKilledMyJob@pm.me. It’s an encrypted ProtonMail account, and I will keep all details private and, if desired, your identity anonymous. I will protect your identity as I would any source. Your message can be as long or as short as you’d like. It can be shared as a tip on background or written with the intent of being shared with readers, here on BLOOD IN THE MACHINE, where I’ve added a new section called AI Killed My Job.

I think there’s great value in sharing these stories. We cannot hold the AI companies or corporate leadership or the managers using AI accountable if we do not know what they’re doing, after all. A researcher has volunteered to help sort the responses, and I’ll use the entire corpus as part of a broader project to understand the impacts of AI-led job loss, degradation, and transformation.

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When I ask if AI killed your job, I don’t mean it literally. “AI” is after all a loose term that describes a broad array of technologies, even though these days it’s usually used to describe generative AI systems, which use large language models to produce text and image output. And “AI” can’t kill any jobs on its own—that’s a management decision. And many felt that even if AI didn’t eliminate their job outright, as it typically does not, it effectively killed it; draining it of value or ruining what made it enjoyable.

In the last week—and over the last couple of years—I’ve heard stories about the ways that bosses, clients, or managers use AI to replace or downgrade rewarding tasks, institute new surveillance measures, try to speed up the rate of work, justify giving workers more work, threaten them with replacement, or otherwise make their lives miserable. And, yes, some people reported being replaced outright by AI systems, either suddenly, as the Duolingo writer and the federal tech worker reported, or after months or years of enduring downgrades and corporate restructuring.

Clearly, there are a lot of such stories out there. I hear more of them every few days as it is. (This is one thing that happens when you write a book defending the historical Luddites and you document the struggles of the new ones.) But I have a feeling I’m just seeing the tip of the iceberg: what this response has shown me is that we are under-reporting, and failing to understand the breadth and scope of the changes generative AI, algorithmic work, and automated systems are leveling on jobs and modern workplaces.

One recurring comment on my original article was that unemployment in general is still high—that the impact of AI has yet to show up in employment statistics. A recent study of workplaces in Denmark found that generative AI seemed to have little impact on total employment. Though I’d like to see a similar study on American workplaces, where employees enjoy fewer protections and employers may be more excitable over AI firms’ pitches—and where DOGE has been the most potent American job-destroyer of 2025 by far—I’m not particularly surprised by the findings.

The point in my piece was that “the AI jobs crisis is…a crisis in the nature and structure of work, more than it is about trends surfacing in the economic data.” Those who are feeling the impacts most are already-precarious freelance workers who tend to be undercounted in jobs data, creative industries, and public servants laid off by DOGE, a project empowered by the logic of AI, and AI-first strategies.

I have interviewed too many artists who are now struggling because their clients have turned to Midjourney or ChatGPT, too many designers now out of work, too many federal workers laid off, pushed out, or threatened by DOGE and its bad AI, too many people squeezed in novel ways, to be able to dismiss this trend as anything other than a significant reshaping of work. How significant is this trend? How far-reaching? How economically damaging to workers, in their own words? How debilitating to their mental health?

These are questions I’d love to answer with your help. This project has been inspired in part by Bullshit Jobs: A Theory, by the late anthropologist David Graeber. The backbone of that book comes from testimonials he solicited over email about workers’ bullshit jobs, and why they felt their own jobs were, in fact, bullshit. I hope to accomplish something similar for AI-impacted jobs here.

I am convinced, based on years of interviews and industry observation, that generative AI is driving serious changes in work. Help me, and the rest of the world, understand them, if you can.

So, if you:

-Have lost a job because management said they’re switching to AI
-Seen your clients dry up and suspect or know they use AI-generated output instead
-Have been pushed to use AI systems at work to speed up your productivity
-Have been pushed to use AI at work to speed up productivity after layoffs in your department
-Were forced to use AI as a requirement of your job
-Have seen AI change the day-to-day makeup of your job
-Are concerned that the AI you’re using will result in someone else’s termination
-Worry that AI tools are increasing surveillance in your workplace
-Have any other stories about the use of AI at all

Please do get in touch, and share your story at AIKilledMyJob@pm.me. I will assume that any submissions are 100% confidential, and are not to be viewed by anyone besides myself and the researcher, unless the messages explicitly and clearly state otherwise, or until I reach out and receive permission to share them.

Thank you, and I look forward to reading your stories, learning from them, and, when desired, sharing them with a world being overrun by AI.

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As always, thank you to readers of this newsletter, and to the subscribers who chip in their hard-earned cash to support it. If you’d like to support this work, and projects like this one, every paid supporter goes a long way, and I’m thankful to each of you. Thanks, and more soon.

Machines Cannot Feel or Think, but Humans Can, and Ought To - Cybernetic Forests

2025-05-06T01:07:55.000Z

My new piece in Tech Policy Press is up, and it’s about the recent re-emergence of very silly chatter about whether AI “experiences the world” and therefore ought to have something akin to human rights. In the piece, I argue that they don’t need them. Here’s how it starts:

Few things in the realm of policy are riskier than posing a question to philosophers. Philosophy is grounded in the practice of challenging definitions and finding edge cases in accepted terminologies, whereas policy is tasked with creating commitments and shared understanding. The tension between these two is sometimes fruitful, as when it comes to the question of, for example, which category of person is legitimate. Philosophy has shaped public understanding on these issues, and policy has raced to catch up.

However, as with any other human activity, philosophy can also be harnessed to support capitalist enterprises and goals that ultimately create harmful and even downright bizarre incentives.

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London!

Tuesday, May 6, 2025
6:30 PM 8:00 PM
The Photographer's Gallery16-18 Ramillies Street London UK (map)

Artist Talk: Eryk Salvaggio, The Hypothetical Image: Reading the Artifacts of Generative AI

06:30pm - 08:00pm, Tue 06 May 2025, The Photographer’s Gallery, London.

How might we "read" the media produced by generative artificial intelligence? Eryk Salvaggio suggests we engage them as infographics: data visualizations of archives and datasets that strives for plausibility rather than expression or documentation.

In a presentation blending artistic research into images, video, and sound, Salvaggio will examine the ways artists can hijack the spectacle of AI to engage more critically with its politics and dangers of AI ordering the world on our behalf.

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The AI jobs crisis is here, now - Blood in the Machine

2025-05-02T16:16:56.000Z

On Monday, April 29th, Luis von Ahn, the billionaire CEO of the popular language learning app Duolingo, made a public announcement that his company is officially "going to be AI-first.” Duolingo, he wrote in an email to all employees that was also posted to LinkedIn, will “gradually stop using contractors to do work that AI can handle,” while taking pains to note that “this isn’t about replacing Duos with AI.”

According to one such Duolingo contractor, this is not accurate. For one thing, it’s not a new initiative. And it absolutely is about replacing workers: Duolingo has already replaced up to 100 of its workers—primarily the writers and translators who create the quirky quizzes and learning materials that have helped stake out the company’s identity—with AI systems. Duolingo isn’t “going to be” an AI-first company; it already is. The translators were laid off in 2023, the writers six months ago, in October 2024.

“It was very sudden when it happened,” the worker, a writer who spent years working at the company, told me on the condition of anonymity. They said it was “shocking” when they got the news. “We had been working with their AI tool for a while, and it was absolutely not at the point of being capable of writing lessons without humans.” They’d been told to stop working new content to help train the system, the writer says.

“If you had asked me a year ago, I would have told you that my job would become more and more editing AI content,” the writer told me. “I did not expect to be replaced so soon.”

This is a glimpse of the AI jobs crisis that is unfolding right now—not in the distant future—and that’s already more pervasive than we might think. Before we dig in further, Blood in the Machine is a 100% reader-supported publication. Its existence depends entirely on the paying subscribers who chip in a few bucks a month, or $60 a year, so I can do this reporting and writing. If you can, please do consider becoming a paid supporter—many thanks, I’m so grateful to all of you.

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The Duolingo writer is far from alone. Almost every professional artist or illustrator I meet tells me they have lost clients and gigs to firms that have turned to AI instead of paying for human work; some have been pushed out of their fields altogether. I’ve written for WIRED about managers who are using AI to displace artists and designers in the video game industry. Voice actors have been on strike for 9 months now, seeking protections from corporations that would use AI to clone their voices. Just this week, the popular gaming website Polygon was sold off to the content farm Valnet that’s often accused of running AI-generated articles—almost all of Polygon’s human staff was fired.

It’s unclear whether these kinds of layoffs are enough to register in the economic data, though there are signs it is. Writing in the Atlantic this week, business journalist and abundist Derek Thompson points to an alarming phenomenon in the job market: The unemployment rate for recent college graduates is unusually high—and historically high in relation to the general unemployment rate. Why might that be? One theory: Firms are hiring fewer grads into white collar jobs, and using more AI. “When you think from first principles about what generative AI can do, and what jobs it can replace, it’s the kind of things that young college grads have done,” as the Harvard economist David Deming told Thompson.

Young grads are typically among the easiest to employ; they’re skilled, ambitious, and will work for cheap. Yet the recent grad-gap—the “difference between the unemployment of young college graduates and the overall labor force”—is higher than it’s been in four decades. Thompson points to the following graph, made with data from the US Census Bureau and the Bureau of Labor Statistics:

Here’s Thompson:

The strong interpretation of this graph is that it’s exactly what one would expect to see if firms replaced young workers with machines. As law firms leaned on AI for more paralegal work, and consulting firms realized that five 22-year-olds with ChatGPT could do the work of 20 recent grads, and tech firms turned over their software programming to a handful of superstars working with AI co-pilots, the entry level of America’s white-collar economy would contract.

Furthermore, as Thompson and others have noted, the Trump tariffs and trade wars could exacerbate the situation. Recessions hand more power to employers, who at least threaten and sometimes do invest in automation technologies like enterprise AI. “And even if employers aren’t directly substituting AI for human workers,” Thompson notes, “high spending on AI infrastructure may be crowding out spending on new hires.”

One of the biggest questions—perhaps the big question—that has persistently circled the AI boom is how it will impact our working lives and jobs more broadly. Will AI lead to a jobs crisis? Poll after poll has found that it’s the top concern that people have with AI: That AI will take jobs and make our working lives worse. To an extent, the big AI companies encourage this line. OpenAI, Anthropic, and its competitors are selling a brand of automation software whose key value proposition is that it can replace tasks and workers to slash labor costs.

Well, I have bad news. The AI jobs crisis has arrived. It’s here, right now. It just doesn’t look quite like many expected it to.

The AI jobs crisis does not, as I’ve written before, look like sentient programs arising all around us, inexorably replacing human jobs en masse. It’s a series of management decisions being made by executives seeking to cut labor costs and consolidate control in their organizations. The AI jobs crisis is not any sort of SkyNet-esque robot jobs apocalypse—it’s DOGE firing tens of thousands of federal employees while waving the banner of “an AI-first strategy.”

The AI jobs crisis is not the sudden displacement of millions of workers in one fell swoop—instead, it’s evident in the attrition in creative industries, the declining income of freelance artists, writers, and illustrators, and in corporations’ inclination to simply hire fewer human workers.

The AI jobs crisis is, in other words, a crisis in the nature and structure of work, more than it is about trends surfacing in the economic data—if not more so. The AI boom, driven by OpenAI and Silicon Valley’s relentless talk of AGI and promotion of enterprise AI software and AI influencers enthusing over endless productivity gains, has been a powerful enabler of corporate automation and cost-cutting imperatives. These imperatives have always existed, of course; bosses have historically tried to maximize profits by using cost-cutting technologies. But generative AI has been uniquely powerful in equipping them with a narrative with which to do so—and to thus justify degrading, disempowering, or destroying vulnerable jobs.

DOGE is a good example. Elon Musk and the Trump administration’s project to hollow out the federal workforce is fueled by “AI-first strategies” and obtuse descriptions of algorithms and efficiency; it’s presented as a cost-cutting initiative aimed at generating efficiencies—yet it’s destroying livelihoods en masse and eroding institutional knowledge.

Patrick Kigongo, a former employee of 18F—the widely respected government agency that provided digital services across the federal government before it was dismantled wholesale by DOGE—says that this is essentially what the agency is doing. “They’re pointing at the AI hype machine and trying to apply that to government,” he said at a meeting of federal workers and academics resisting AI and DOGE in Washington DC. “They’re overpromising and doomed to underdeliver. The AI tools they’re promoting do not exist yet.”

DOGE is empowered by the logic of a generative AI enterprise product. It promises efficiency and techno-solutionism, but delivers results that are unreliable at best, and dangerous at worst. Consider DOGE’s laying off of nuclear regulators by mistake before recalling them back to work. Or its winnowing of federal food safety inspectors, soil surveyors, consumer protection advocates. Or its gutting of NASA, of the CDC, of the funding for scientific research everywhere, while Musk installs StarLink in the White House and positions his companies to pick up more state contracts.

This is the AI jobs crisis. It’s DOGE boosting its chatbots, cost-cutting algorithms and hastily installed AI systems designed to replace personnel and the administering of regulations, while eliminating tens of thousands of federal workers, technologists, scientists, and civil servants.

More broadly applied, the AI jobs crisis is billionaires, executives, and managers are using the gen AI logic to take aim at these kinds of jobs with renewed vigor; ie, those jobs that do not exist expressly to help them maximize profits.

I’ve been rereading the late anthropologist David Graeber’s Bullshit Jobs, which persuasively makes the case that the corporate world is happy to nurture inefficient or wasteful jobs if they somehow serve the managerial class or flatter elites—while encouraging the public to harbor animosity at those who do rewarding work or work that clearly benefits society. I think we can expect AI to accelerate this phenomenon, and to help generate echelons of new dubious jobs—prompt engineers, product marketers, etc—as it erodes conditions for artists and public servants.

A common refrain about modern AI is that it was supposed to automate the dull jobs so we could all be more creative, but instead, it’s being used to automate the creative jobs. That’s a pretty good articulation of what lies at the heart of the AI jobs crisis. Take the former Duolingo worker who was laid off as part of the company’s pivot to AI.

“So much will be lost,” the writer told me. “I was a content writer, I wrote the questions that learners see in the lessons. I enjoyed being able be creative. We were encouraged to make the exercises fun.” Now, consider what it’s being replace with, per the worker:

“First, the AI output is very boring. And Duolingo was always known for being fun and quirky. Second, it absolutely makes mistakes. Even on things that you would think it could get right. The AI tools that are available for people who pay for Duolingo Max often get things wrong—they have an ‘explain my mistake’ tool that often will suggest something that’s incorrect, sometimes the robot voices are programmed to speak the wrong language.”

This is just a snapshot, too. This is happening, to varying degrees, to artists, journalists, writers, designers, coders—and soon, perhaps already, as Thompson’s story points out, it could be happening to even more jobs and lines of work.

Now, it needs to be underlined once again that generative AI is not yet the one-size-fits-all agent of job replacement its salesmen would like it to be—far from it. A recent SalesForce survey reported on by the Information show that only one-fifth of enterprise AI buyers are seeing good results, and that 61% of respondents report a disappointing return on investment for AI or even none at all.

Generative AI is still best at select tasks that do not require consistent reliability—hence its purveyors taking aim at art and creative industries. But all that’s secondary. The rise of generative AI, linked as it is with the ascent to power of the American tech oligarchy, has given rise to a jobs crisis nonetheless.

We’re left at a crossroads where we must consider nothing less than what kind of jobs we want people to be able to do, what kind of work and which institutions we think are important as a society, and what we’re willing to do to protect them—before the logic of generative AI and the jobs crisis it has begotten guts them to the bone, or devours them altogether.

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That’s it for now — as noted above, I’m currently in Washington DC for a great event put on by the American Association of University Professors, the MIC Center, and the Rutgers' Power and Inequality Working Group, aimed at fighting DOGE and the expansion of AI in academia. I have a huge pile of stories to get to, and lots of great and terrifying and bleak stuff in store. Thanks as always for reading — hammers up.

What Does It Mean to ‘use’ Generative AI? - Cybernetic Forests

2025-04-27T11:03:39.000Z

The social media discourse seems to constantly come back to the question of whether Large Language Models are “useful (good)” or “useless (bad).” The responses are a bizarre back and forth in which one group says they use it often and another group says they nobody uses it at all.

Notably, the only question here is utility, rather than anything else: it’s not an assessment of Gen AI’s myriad problems in the context of its claimed benefits. This post is not about that. It’s about the discourse of “useful” — what does that word, useful, mean to those who find Gen AI useful, and what makes it‘a usefulness so hard to fathom for others?

If two groups cannot agree on whether a tool is useful, they are perhaps defining totally different purposes for that tool. Maybe “use“ is not best understood through the question “what did you use it for?” which, with AI, would yield the standard banal answers — coding, writing a summary, etc — which would then be debated based on statistics or technical details about how unsuitable these things are at those tasks.

If we instead defined “use” as one task that we know Large Language Models do — create statistically likely arrangements of text — we might ask, “to whom is that useful?”

From there, I have to wonder if the value of AI for the “users” camp is largely dependent on how they experience knowledge transmission. In other words: what is information — words or images — for, for you? What does it do for you when you encounter text or an image?

If you perceive information as something that generates internal thought, you don’t care that GenAI is “just statistics.” But if you filter information by the veracity of its source, you see nothing of value.

In Silicon Valley I’d always run into people who were open to ideas, but recalculating it all according to whatever fixation they had at the moment. It didn’t matter if it was a lecture by Stephen Hawking or a YouTube paraglider transmitting the message, what mattered was what they, as the receiver of these signals, assessed the message: how it moved existing ideas around.

Ideas severed from reference to reality are often useless, but can also result in a pro-hype mentality, where “building” can just as easily mean amplifying the shared illusion.

So that’s one group of users: the internally oriented thinkers for whom any source of information can be weighed on what it sparks.

Another group of users is more direct: these are the people who use it to create a spreadsheet, or write a summary of a meeting, or an email; maybe they use it to answer questions about things. Maybe they don’t fully understand the risks of inaccuracies, or maybe they just don’t care. For this group, producing content correctly is less of a priority than doing it quickly. Perhaps a cost/benefits analysis creates that instinct, or perhaps they just don’t care

On the other end of this spectrum are the refusers, external-facing folks that want to know what the evidence is for any information they encounter, because a connection to reality was always the point of information in the first place. In this state of mind, knowledge is a means for understanding something akin to the true state of things. Ideas come from conclusions filtered through veracity, trust in the authority of the information’s source. Statistical word assemblages are not useful to that end, because there is no source, no authority, just statistics.

People can move between these states of mind, but some people hang out in one or the other quite intensely. This explains the pointed tension between pro and anti AI camps: the veracity finders (who care primarily about information’s relationship to ground truth) are going to be driven bonkers by people for whom ground truth is just one more ingredient in the production of ideas.

Likewise, when people show me text they generated, I am profoundly disinterested. I don’t care about what statistics generates about a topic I am interested in. Others seem to be fascinated by this stuff. There are scores of people who despise AI art, but equally, there are communities of people paying money to create it, time to discuss and share it.

I think an AI generated text, and the LLM that produces it, is useful for people who want signals, stimulation, and space to build associations and projections. I think it is less useful for people who see text and images for conveying evidence about the state of the world. I think each of us is in this state of mind, at least a little bit, at different times, depending on the task.

To one group, or one state of mind, we might see a clear “use” for AI, whereas the other may be stunned to imagine anyone using them. But this is to imagine the other person’s “use” in a particular way.

I’ll reiterate that none of this comments on whether such use is justifiable, and it hopefully doesn’t read as a judgement call on either assessment. I’m just writing this on a Saturday night because I haven’t written anything else for Sunday yet, and thought it was a worthy question.

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On Take Your Child to Work Day, DOGE-led GSA is hosting an AI workshop for kids of federal employees threatened with layoffs - Blood in the Machine

2025-04-24T00:36:16.000Z

Tomorrow, April 24th, is Take Your Child to Work Day, an occasion on which many federal workers would typically bring their kids into the office. This year, any celebratory mood will likely be dampened by the sweeping layoffs ordered by Elon Musk’s DOGE, which has fired tens of thousands of federal workers, threatened to fire of tens of thousands more, and promised to institute an “AI-first strategy” to replace the laid off employees.

Into this climate, the General Services Administration, now led by the DOGE leader Stephen Ehikian, will be ringing in Take Your Child to Work Day in a novel way: by hosting AI workshops for kids, to show off the technology DOGE is using in its campaign to eliminate their parents’ jobs.

An official email titled “Al Workshop Featured for 2025 Take Your Child to Work Day at 180OF,” was sent out to GSA employees on Tuesday, April 22nd. It announces that, “As a highlight for this year’s event at 1800F we’re providing two special AI workshops which will offer a chance for the children to get a hands-on experience with our new AI bot and how it helps you in your work.” (1800F is the address of GSA headquarters in Washington, DC.)

That “new AI bot” refers to an AI chatbot project, GSAi, that has become a top priority for DOGE officials since they took over the agency two months ago. A GSA employee, who I have granted anonymity because they fear retaliation, tells me that the AI project has become “the biggest goal at GSA now,” as the agency bleeds personnel and hundreds of employees are pushed to take the latest “fork in the road” resignation offer.

Public Domain Image by #iPredator NYC. Via Flickr.

For their part, parents working at the GSA find some dark humor in the whole thing. DOGE leadership has been erratic, and rumors of drug use have swirled.

"Most GSA parents seem to be bemused by the notion that they'd want to expose their kids to the environment at 1800F,” the federal worker tells me, “and it's not exactly changing that feeling to know their kids will be pushed to use the very technology GSA leadership claims will replace their parents or their parents' coworkers."

It’s a telling snapshot of the political, technological, and cultural climate in Washington right now—and hosting AI workshops for the children of federal workers currently threatened by DOGE’s AI schemes is either tone deaf or cruel. Either fits the bill.

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More on Elon Musk, DOGE, and federal tech workers:

With Elon Musk on the way out, federal workers can unite to stop DOGE - Blood in the Machine

2025-04-23T01:33:32.000Z

Greetings all,

Hope everyone’s hanging in. The big news this week: Elon Musk is finally stepping back from DOGE. On an earnings call that revealed Tesla’s net income had fallen 71% in the first quarter of 2025, Musk announced that, starting in May, his “time allocation to DOGE will drop significantly.” This may scan as good news, and it’s a victory for the Tesla Takedown protests; popular backlash helped tarnish the brand, dip its stock, and push Musk out the door. The fear, however, is that even with Musk less involved, the mass layoffs and departmental intrusions will continue apace—just with less mainstream media attention.

Musk has, after all, already staffed up the DOGE campaign, set it in motion, and, most importantly, forever linked himself with the operation. He has provided and animated the framework to gut more of the government than hardline activists like Project 2025 architect Russ Vought ever thought possible; DOGE can continue on with Musk serving as its perpetual avatar and human blame shield. And just in the last week, the cuts continued to hit as hard as ever—DOGE is trying to lay off 90% of the Consumer Protection Bureau, and the Food and Drug Administration is under fire. Right before that, it was NASA and Social Security. The list goes on.

No department is safe from DOGE, and the federal jobs crisis, I fear, is about to enter is most dire phase yet. But DOGE may also be at its most vulnerable. With Musk’s influence waning, a little distance between him and his pet project, and legal interventions holding up some of the mass layoffs, the stage is set for a forceful resistance. Now, says Rutgers labor studies professor Eric Blanc, is the time for workers to stand up.

A brief reminder that Blood in the Machine is a 100% reader-supported, entirely independent publication. This work depends on those who chip in a few bucks a month to make it possible. Huge thanks to all who do—as always, hammers up.

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The DOGE image Elon Musk tweeted out in November 2024, remixed, poorly, by the author.

Blanc researches modern workplace organizing, strikes, and digital labor activism at Rutgers, and he’s the author of We Are the Union: How Worker-to-Worker Organizing is Revitalizing Labor and Winning Big and Red State Revolt: The Teachers’ Strike Wave and Working-Class Politics. He writes the Labor Politics newsletter, where he’s been closely following the developments in Washington. Blanc has argued that rank and file federal workers have the power to stop DOGE in its tracks—if they organize fiercely and tactically, and don’t wait around for union leadership to point the way.

I spoke with Blanc about how they might do exactly that at this crucial juncture. Blanc hit on the need for federal workers to get creative, to make noise, and to pressure department heads to refuse to acquiesce to layoff orders. He shared some insights from his labor research, as well as six key lessons from the successful teachers’ strikes of 2018 that federal workers might use to defeat DOGE.

BLOOD IN THE MACHINE: Trump's been two months in office. Elon Musk and DOGE have taken a hatchet to the federal workforce, and every day seems to bring new announcements of layoffs or 'forks in the road' or moves to instigate further cuts. You've argued persuasively that unions are workers' best bets to push back. Where are we now? And do you still believe this to be true?

Eric Blanc: Obviously the situation is very fluid, but labor resistance to Musk has already been a major obstacle for DOGE's wrecking-ball operation. One key thing people have tended to overlook: most federal workers have refused to quit, despite Musk's push to demoralize them and drive them out. Collective organization has been crucial for keep up this day–to-day resistance—this organization takes the form of the established unions, but also thousands of group chats, as well as new rank-and-file groups like the Federal Unionists Network. And the FUN in particular has played a crucial role in encouraging workers to speak out publicly and to build a serious pushback of federal workers together with the community to save these services. We saw, for example, how the FUN took the lead on getting large numbers of federal workers to come out to the Hands Off! protests, and in pushing the big established unions to do the same.

This type of worker-led backlash has already gotten the Trump administration to leak that it intends on pushing Musk out shortly. But to really reverse the damage that has been done and to prevent the further implementation of mass layoffs—many of which are looming through Reductions in Force at major agencies—will require a much deeper and more widespread level of workplace organizing. Unfortunately, so far, most of the big federal unions have focused excessively on legal resistance to Trump; while this is necessary, the big missing puzzle piece is unions going an all on organizing and mobilizing their members to resist.

Do you see signs that such widespread organizing is getting underway, or that there are hopes that the big unions will change their priorities? I've spoken to a number of federal workers who are eager to resist Musk and Trump's cuts and policy changes, but many are growing demoralized; some were unable to even get ahold of their union reps to figure out what was going on. Some are considering taking the latest fork in the road. Which, I get it. Staring down the barrel of at least 4 years of this, it's got to be hard. What needs to happen next, with regards to workers, unions, and ordinary folks who want to help?

I wouldn't expect the national unions to change their priorities without a significant amount of bottom-up pressure and cajoling. I think the pattern in organized labor is generally that when rank-and-file workers lead, then risk-averse official union leaders tend to eventually follow. We saw that in the 2018 teachers' strikes, which were initiated from below over Facebook groups, and in which the established unions only jumped on board once there was lots of momentum.

We're seeing the beginning of this dynamic in the federal sector—though, as your question indicates, it's still well-below the levels needed. Part of the issue is that the fear factor about speaking out is particularly high in the federal workforce, partly because of Musk, but also partly because of the longstanding culture of civil service in the US, where workers assume it's illegal for them to get involved in politics and organizing. That's not actually true—the Hatch Act, which regulates this, is much narrower in scope—but it's taken a while to start getting very large numbers of fed workers to be willing to speak out publicly. We are starting to see a shift—there were lots of feds out on April 5th, for example. But the movement is still incipient.

In terms of next steps, the first thing I would recommend is that if you're a federal worker or ally who want to support, you should sign up to get involved at savepublicservices.com. The big next steps are campaigns to pressure members of congress to pressure agency heads to reject RIFs at the Social Security Administration, the Department of Veterans of Affairs, and the Environmental Protection Agency.

No matter what Musk or Trump say should happen, it's actually within the power of these agency heads whether or not to implement the RIFs, which is why it's so urgent to pressure congresspeople in late April—when they're on recess—to demand agency heads protect the agencies they are leading. That's a winnable goal, especially since DOGE and Musk's popularity is tanking, but it'll take a lot of pressure. And the second step I'd flag is that everyone should already be getting prepared to take to the streets on May 1, which I hope can be the next big national show of force against the billionaires' takeover.

You happen to have written the book on the 2018 teacher strikes, which proved more wildly successful than anyone may have expected at the time. What can federal workers learn from the teachers' playbook?

I think there are six main lessons from 2018 for today.

First: overcome scare tactics by speaking out. Fearing retaliation from above, most teachers in 2018 were initially scared to make their voices heard. But a few bold colleagues broke the climate of intimidation by taking a public stand early on. Nobody was fired or disciplined. Their early courage created space for countless more to speak out.

Second: escalate your actions. Especially because so many workers were initially scared, the movements grew by taking easy actions that could involve the largest number of workers. One prominent build-up tactic was “RedForEd” days in which everybody—both employees and community supporters—wore the same color and posted selfies and group photos with messages about their cause. This generated a huge amount of momentum and showed the community the human face of a demonized and demeaned workforce.

Third: try to go viral. The red state strikes were initiated and coordinated largely over viral Facebook groups. Worker activists grabbed people’s attention through public actions and catchy digital content, and they immediately onboarded their co-workers into organizing similar actions via digital tools. In person and local organizing was still crucial, but these movements needed digital tools and a big social media presence to win (and coordinate) at scale.

Fourth: persuade (and involve) the community. To seize the attention of the public and dispel narratives about “privileged, lazy public sector workers,” these movements understood that the only way for them to win big was by consistently emphasizing how their work—and their demands—benefited the larger community. And they consistently sought to involve community members in their RedForEd days and other escalating actions.

Fifth: don’t wait for top union leaders. Though unions ended up playing a crucial role in these movements, the spark and drive came from self-organized rank-and-file workers. Most top union leaders were too stuck in legalistic routines to take a lead on risky actions. But once momentum exploded from below, unions jumped on board and played a key role in helping workers win.

And, finally, six: get disruptive if necessary. While I don't think strike action is on the table for federal workers—since the administration would likely seize upon any such illegal activity to impose more mass firings—it may again require mass non-violent disruption to win. I could imagine escalating actions like worker-community sit-ins inside (and in front of) federal buildings, Tesla dealerships, and Congressional offices, actions that grab the public's attention, that contest for space, and that raise the political cost on the administration by obliging it to either accept this disruption or to arrest a bunch of peaceful protestors. While I doubt most federal workers would engage in such activities, it seems to me that large numbers of recently fired federal workers and community members impacted by the cuts—e.g. to social security or the VA—might enthusiastically jump on board.

These are all great ideas and examples. What would you say to a federal worker who is enduring these cuts, hates what's happening, but *is* afraid to speak out? Is afraid to get the ball rolling? The stakes seem higher, lots of attention is focused on them, and they're facing down an administration that has shown itself to be punitive and unafraid of retribution. How do you confront that?

I get why so many federal workers today are still reluctant to stick their necks out, since they’re professionally trained to stay clear of politics and since Musk seems so eager to fire anybody. It is risky.

But the reality is that trying to keep your head down is also an incredibly risky move at this moment, for precisely the same reason: Musk’s wrecking ball operation is so extreme and so widespread that doing nothing means there’s a good chance your job could still end up being cut. And unless Musk is stopped through mass action and public backlash, even if you somehow manage to hold onto your job, there’s a good chance it’ll become unbearably miserable. Given that there are big risks no matter what you do, why not take the path of publicly defending the services to which you’ve dedicated your life and upon which so many Americans depend?

Not to mention that most Americans would have their backs! Polls have found that most disapprove of Musk and DOGE's wrecking ball. Resistance is increasingly popular.

I think federal workers already knew from their experience and from polls that their services were broadly supported by the public. What's new, though, is the extent to which the public has come out in large numbers to actively protest the dismantling of these services—and I know that this has been incredibly encouraging to many federal employees. We'll see, but my hope is that after April 5th, more workers will feel encouraged by the rising tide of protest nationwide to jump in too.

So what should these workers do, right now, as layoffs are happening, or being ordered, in the moment? And second, curious to know how do you think all of this fits into a broader framework for resisting the Trump administration?

The first thing I'd suggest to any group of workers who just got fired is to immediately, in the next day or two, hold an emergency rally in front of your place of work, invite the press, invite your friends and community members, and explain to the public how these cuts will hurt the services everyday people depend on. What will ultimately constrain Trump and Musk is widespread public backlash, since this would make it very hard for Republicans to get reelected and for Musk to keep on selling his products like Tesla. The administration and Republicans generally need to see that every cut they impose will cost them significantly in the court of public opinion. Feds and community members who want to organize around these types of actions can sign up to join the rapid response network.

But there's also a higher risk response tactic that I've heard some federal workers discussing, a tactic resembling the sit down strikes of the 1930s, in which workers walked in collectively to work instead of walking out. There could be something particularly compelling about scores of community supporters attempting to peacefully escort an illegally fired worker into their workplace, so that they could continue to serve the American public.

You know how these things evolve, but the viral spread of these types of actions could create a crisis for DOGE by underscoring that Americans will not stand idly by as billionaires break the law to destroy our vital services. And were such actions to go viral online, they would surely spur copy-cat efforts across the country. To the extent that such a movement caught on, the insides and outsides of federal buildings could start to resemble, respectively, the Wisconsin capitol occupation and the Occupy movement of 2011. Ultimately, I think we may have to see that level of mass resistance and public backlash on a nationwide level to put an immediate block on DOGE's wrecking ball operation.

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Possible Noise - Cybernetic Forests

2025-04-20T11:00:46.000Z

A guided tour of the NCM Melbourne's "Signal to Noise" exhibition.

Internet Dream, Nam June Paik. Seen at Signal to Noise, 2025. Photo by Phoebe Powell, courtesy of NCM.

In the age of computation, the role of noise — or at least, the close-enough approximation through pseudorandom numbers — has been the bulwark against the rigid and orderly logic of mathematics and prediction. Leslei Mezei, writing for Jasia Reichardt’s “Cybernetics, Art, and Ideas,” puts forward a theory of the computer-generated image that would be just as accurate for describing generative AI, with a few million more pixels:

“... Constraints impose the unity, the overall structure, or in the terminology of ‘information aesthetics,’ the macro-aesthetics of the picture. Were we to repeat the experiment any number of times, we would get a larger number of variations, some more interesting than others, but the overall structure would remain recognizably the same. The program embodies the generating rules, or the algorithm, with which random selection is capable of producing an infinite variety of similar pictures. (165)”

— Leslei Mezei

This seemingly paradoxical point of “an infinite variety of similar pictures” is all too often lost on us in discussing generative AI. When Lev Manovich writes that “We can now, in principle, create an infinite museum filled with endless AI-generated images that simulate artworks from every period in history for all genres, artistic techniques, and media,” he is, in principle, wrong. The generative AI system is just as constrained by data as Leslie Mezei’s computer was in 1971. There is just more data through which to constrain the possibilities of noise by turning to reference, and to specific, commonly accepted regimes of image-making.

Generative AI is, after all, a noise reduction technology. It just so happens to produce an image at the end of that process. It dissolves training data by introducing noise in a sequence of steps, storing the path of this noise to memory. When prompted, it plays these steps in reverse, applied to random noise, in a bid to denoise it into a statistically centered outcome based on accumulations of images related to the prompt. This reliance on noise is what produces the illusion of creativity in the system, a means of creating variety. But it is always constrained by data, by historical reference, by language.

It is constrained to an overdetermined process which, unlike human creativity, centers itself without opportunity to shift its strategies. Creativity in the machine cannot exist; it follows a set regimen of steps for noise reduction. It will produce images that inevitably center pattern, not possibility: generate an "infinite museum," and images will reveal their own patterns, their own logic, within 9-12 pictures. This is not some deep, mysterious thing: try it yourself, and see what happens.

A "Post-AI" Art Exhibition

It is this structure of generation from which Signal to Noise takes its cues as a “post-AI” art exhibition. It is structured as a historical lens on artists who worked with the unique affordances of noise in given communication technologies of their era, embracing the breakdowns that pushed computational rigidity to its limits. In some sense, it shows how artists are free to work with noise to push boundaries, reimagining the systems they work within, a capacity of human art that is at odds with the art-making as defined within automated image generation systems.

In this way, we look at AI in hindsight, rather than the forward-looking lens of utopian hype or doomer panic. Instead, Signal to Noise looks at AI as one more media format designed to reduce noise — that is, unpredictable and unwanted intrusions into “clean” media signals. Artists have been introducing noise into channels for as long as we have had technology in which to intervene, through technical or imaginary acts.

George Brecht, Universal Machine. Seen at Signal to Noise, 2025. Photo by Phoebe Powell, courtesy of NCM.

Universal Machines

Consider Universal Machine, George Brecht’s 1965 take on the rigid mathematics of computational systems. Brecht places his assemblage of images and text — cut out from magazines — into a cardboard box. The instructions tell us to shake this box to generate new, random patterns (though bound, as always, by the constraint of his pre-selected vocabulary). Arguably, Brecht’s box is just as generative as any AI system: a scattering of data points, activated by the shaking of a “black box” to propose new structures for whatever the shaker has intended. Brecht’s instructions even seem to echo the hype of Silicon Valley: “Need a friend? Shake the box.” The box can even write novels: shake the box, open it, and that’s chapter one. Do it again for chapter two. Use Number 18: “Travel Itinerary.”

Nam June Paik’s “Internet Dream” embraces the noise of information overload, with Paik himself pioneering the use of magnets onto televisions to distort the carefully calibrated signals of cathode ray tubes. In JODI’s My%Desktop, artists insert friction into the friction-reducing site of the computer interface. The graphic user interface of desktop computing was designed to constrain the options of computation while making them more readily accessible (just think if you had to write a command line query for every email). JODI’s work inverts this, turning the desktop interface into a site of contention and error, estranging us from its simplicity and use for “productivity.”

Machine Sees More Than it Says, from artist Mimi Onuoha, hints at the origins of computer vision and image generation, “a sketch of a computer's imagining of itself and the courses of development which formed it, hinting at processes of resource extraction, transport, technical advancement, interfaces and labor.” The archival footage is a document of how we have allowed computers to produce images of themselves: each scene is the result of a computer’s existence as a product (the computers and screens being filmed) or political purposes (the reasons they are being filmed to begin with). As such, the images in this piece evoke a powerful arrangement of order and control that elides the chaotic origins, histories and purposes to which they were meant to be used.

Beautiful Sounds

Elekhlekha อีเหละเขละขละ present “The beautiful sounds are gathered together in one cluster,” in Signal to Noise, 2025. Photo by Phoebe Powell, courtesy of NCM.

In contrast to this order, New York-based Thai artist duo elekhlekha อีเหละเขละขละ present “The beautiful sounds are gathered together in one cluster,” a work which embraces the shifting tonalities and relationships that emerge when a listener attends to noise. This is a mix of 32 speakers suspended from the ceiling and transducers triggering assorted aluminum objects. As samples of percussive gongs are triggered, they blur into a meditative drone, depending on one’s position and willingness to hear.

The auditory and political message come to overlap: the work is a challenge to the idea that the music of gongs in the artist’s home, Thailand, is described by Western observers as “noise.” Position in relation to noise is what defines it, far more than any inherent tonal or communicative quality. The question of order and stability is relative to the desire for change and transformation. What fits into our patterns is order, what resists them is noise.

From Australian artist Rowan Savage, the transformation comes about in another way: the boundaries between the sounds of human and natural language. Using a home-trained AI system to convert samples of Savage’s voice and field recordings of crows, the sonic texture of crows transforming into human vocals creates a distorted audio track punctuated by natural crow calls and the question Savage repeats on loop: Who am I? For me this speaks to the internal dissonance of noise in our heads, the cognitive friction that emerges from our desire to separate from the natural world (or to abandon claims to be separate, and the friction that rises in attempts to annihilate our ego and become something more fluidly connected).

Direct Communication

There is a moment in the show — just after “The beautiful sounds are gathered together in one cluster,” that one can stop and look back at the path the visitor has just traveled. And from there, the “noise” is revealed as, paradoxically, a set of isolated, structured zones: Onuoha, JODI, and Paik, each work complex up close, now isolated, by distance, in a grid. That these zone are only apparent after you move through them reflects the psychology of noise, or moving through noise into order. Order appears in hindsight — a result of sense-making. The patterns become clear, the sounds in the din have been isolated.

Craftwork, Ancient Futures, in Signal to Noise, 2025. Photo by Phoebe Powell, courtesy of NCM.

While it is the first piece in the show, it is worth discussing Craftworks “Ancient Futures” as the endpoint of this attempt at sense-making. Craftwork, another New York based duo, creates textiles entwined with fiber optic cables that capture colored light. Visitors are also offered a telephone to speak through, and voices are analyzed for key words and tones that come to be represented as colors in the cables. As more people contribute over the duration of the exhibition, the colored lights of past recordings will be replayed, weaving these stories into the textiles.

Ancient Futures anchors us in the most basic form of communication, that which takes place when people gather together to weave together, to chat, to share stories. The textile (itself evocative of computation’s origins in looms) connects this basic interpersonal communication to the escalating abstractions of communication technologies: the telephone, AI. The voice shapes the signal, and yet, the voice dissolves into the system. Noise can be directed in all kinds of paths.

As you exit the cavern of textiles, you will encounter the exposed threaded tapestry of telephone patch cabling hidden behind the removed back panel of a telephone switchboard from the 1920s. Operators, too, weaved communications through wires — a fabric capped by plugs to form circuits in a constant shuffle by human operators, connecting voice to voice in all directions. Traveling through these wires would have been the incessant din — here, of Australian voices dialing through switchboard operators.

Freedom of Choice

Between the two is a quote from Cecile Malaspina’s Epistemologies of Noise:

“We will someday come to see noise as an inescapable freedom of choice.”

— Cecile Malaspina

It’s an echo of Claude Shannon, whose 1948 Shannon-Weaver model for communication systems occupies a wall opposite JODI’s glitched desktops. The lines of the message are concentrated by the signal source, dissipating as they move from the source to the intended receiver. By the end of the process, our “message” is a scattered array of dots, the message obliterated in the media mess.

“Information is, we must steadily remember, a measure of one’s freedom of choice in selecting a message. The greater this freedom of choice, and hence the greater the Information, the greater is the uncertainty that the message actually selected is some particular one. Thus greater freedom of choice, greater uncertainty, greater information, go hand in hand.”

— Claude E. Shannon

Noise is Possibility

More information, more noise. More noise, more freedom to choose. And then what? It's the hope of the curators that this exhibition mirrors that process of sense-making: moving the visitor from overwhelm to detail, then allowing a greater view of the whole. Making sense of the information that comes to us is an exercise in focus and attention, a cognitive flexibility of scoping in and then stepping back, reassessing the whole in the context of its individual parts. I think that is the type of experience that Signal to Noise provides, and the kind of attention it rewards.

Signal to Noise runs at the National Communication Museum, Melbourne, from April 12 to September 11.

More About the Show

HUMAN ITINERARY TOUR

What I am calling the "Human Itinerary Tour" starts on April 28 and will be a whirlwind of events in the Europe, with events in the US and Australia. Dates below; events in Oslo, Rome, and Melbourne TBA.

Baltimore

April 28: AI & Artistic Practice:
Sam Pluta, Brea Souders, and Eryk Salvaggio
University of Maryland Baltimore County, Baltimore, MD.

In a discussion presented by the Center for Innovation, Research, and Creativity in the Arts (CIRCA), composer and sound artist Sam Pluta, visual artist Brea Souders, and video artist and writer Eryk Salvaggio each use and interact with AI in their artistic practice. This will be followed by a discussion moderated by UMBC assistant professor of art Eric Millikin.

AI and Artistic Practice: Sam Pluta, Brea Souders, and Eryk Salvaggio

In a discussion presented by the Center for Innovation, Research, and Creativity in the Arts (CIRCA), composer and sound artist Sam Pluta, visual artist Brea Souders, and video artist and writer Eryk Salvaggio each use and interact with AI in their artistic practice. They will introduce us to their work, reflecting on their experiences, doubts,…

UMBC:

Amsterdam

May 3: Human Presentation
AIxDesign Slow AI Festival, Loods6, Amsterdam NL

In-person: Live performance of the film "Human Movie" with Q&A and a presentation of AI films with Fabian Mosele. Details below!

AIxDESIGN Festival: On Slow AI

On Slow AI

AIxDESIGN

London

May 6: The Hypothetical Image: Reading the Artifacts of Generative AI
The Photographer's Gallery, London.

How might we "read" the media produced by generative artificial intelligence? Eryk Salvaggio suggests we engage them as infographics: data visualisations of archives and datasets that strives for plausibility rather than expression or documentation. In a presentation blending artistic research into images, video, and sound, Salvaggio will examine the ways artists can hijack the spectacle of AI to engage more critically with its politics and dangers of AI ordering the world on our behalf.

Artist Talk: Eryk Salvaggio

The Hypothetical Im

The Photographers Gallery

Zurich

May 13: Embodied Generativity: Critical AI, Art & the Body
Zurich, ZHdK (Zentrum Künste und Kulturtheorie)

The interface of generative AI presents a passive mode of media production, modeled after the submission of a ticket to a tech stack, or a request for a chatbot helpdesk. What other modes of interaction might artists engage with to get at these tools beyond the keyboard? In a presentation of selected works, Eryk Salvaggio explores positions, gestures and attitudes toward AI that reflect its brittle comprehension of creative logic and the world at large. This talk will present works and propose novel workflows that challenge ideas of "generativity" by moving it from the visual senses to the body, examining video, performance, dance, and puppetry.

More Info

Stuttgart

May 15 - 16: Feel the Noise: Notes from an Adversarial AI Artist
Re shape Forum for Artificial Intelligence in Art and Design
Hochschule für Gestaltung Schwäbisch Gmünd
(University of Applied Sciences)

"The re shape Forum for Artificial Intelligence in Art and Design will explore AI’s evolving role in creativity, interaction, and society. From playful experiments that challenge conventional AI use to critical discussions on its ethical and ecological impact, this event brings together visionary designers, artists, and researchers. We’ll question dominant AI narratives, rethink design beyond human-centered approaches, and imagine regenerative futures where technology coexists with the more-than-human world."

RESHAPE FORUM (KITeGG)

Forum for Artificial Intelligence in Art and Design. 14-16 May 2025. Hochschule für Gestaltung Schwäbisch Gmünd.

More info & register

Events in Oslo, Rome, and Melbourne TBA!

The fury at 'America's Most Powerful' - Blood in the Machine

2025-04-18T21:10:48.000Z

Last weekend at Coachella, the singer of the punk band the Circle Jerks called for “an army of Luigis,” in reference, of course, to Luigi Mangione, the alleged murderer of United Healthcare CEO Brian Thompson.

The statement, which implied the band would like to see more assassins murdering more CEOs, made a couple headlines, but not as many as one might expect in the wake of an encouragement of mass violence issued at the nation’s premier music festival. There’s a lot going on, sure, but this seems to speak to a normalization of the sentiment: Polls have shown sympathy, and even support for Mangione, especially among young people. The pop singer Ethel Cain posted #KillMoreCEOs to Instagram, suggesting the ultra-rich should be made to “fear for their lives.” Meanwhile, three men have been arrested for firebombing Tesla dealerships in protest of Elon Musk’s role in the federal government.

The Overton Window appears to have shifted with regard to the acceptability of political violence against elites and their property, in other words. And in this climate, an artist and freelance writer is selling an Iraqi Most Wanted-style deck of cards with the home addresses of Elon Musk, Peter Thiel, John Roberts, Marc Andreessen, and 48 others printed on them, through a website online.

Justin Caffier classifies his “America’s Most Powerful” cards as an art project. It’s a parody of the infamous playing card decks that the US military once handed out to soldiers in Iraq to help them identify top members of Saddam Hussein’s government during the war for capture and/or assassination.

The Iraqi Most Wanted playing card deck. Photo via Wikimedia. Creative Commons.

Saddam was pictured on the ace of spades, his sons on the aces of clubs and diamonds, and so on.

Caffier’s deck features individuals he has deemed “most powerful” players in the United States; tech titans-cum-oligarchs like Musk and Thiel. Supreme Court Justices. The BlackRock and Goldman Sachs CEOs. Defense tech contractors like Palantir’s Alex Karp. Zuckerberg, Bezos. Salesforce CEO Marc Benioff. The list goes on. Each card features a portrait of the individual, their name, and their home address. Caffier made 53 copies of the “art” decks, he told me, and he is selling them online for one million dollars each. (In addition to the “art” decks, he is also selling “merch” decks for $25, with publicly listed office addresses.)

It is, let’s say, quite a provocation. (And I’ll state here clearly that this piece is not in any way an endorsement of this project.) When I first heard about it, it was because I overheard someone at a party talking about how their friend was “about to self-immolate,” which caught my ear, and prompted me to ask who was due to go up in flames, and why. Why would someone pursue such an inflammatory project—for politics, for attention, for notoriety, for money? Out of genuine rage? All of the above? And what would it say about ~these times~ that producing a deck of cards with the addresses of the megarich printed on them, bounty hunter-style, might be seen as a viable means of achieving any of that? Does this signal the rise of the death drive influencer?

Caffier is (was?) a freelance writer and reporter; he’s written for publications like New York Magazine, VICE, and Mashable. He says he honestly doesn’t know if he’s done with that life, or what will come after this.

“I was out on a run in the summer, in 2023, and suddenly the idea of doing a parody of the Iraq's Most Wanted cards with corporate ghouls came to me fully formed,” Caffier said. There had been a spate of troubling Supreme Court rulings, and Caffier believed that the root of the world’s problems was the amount of power concentrated into the hands of a few. “I've always maintained, ‘y'know, it's really just 10,000 terrible people out there making life miserable for the other 8,000,000,000,” Caffier said. “And they have addresses and need sleep like the rest of us. This isn't as impossible an issue to solve as we're led to believe."

Caffier made a list of names of the millionaires and billionaires and power brokers he would include in such a deck, and turned the skills he’d honed as an investigative reporter towards tracking down their apparent home addresses. He located each of the addresses by connecting the dots laid out in publicly available information, he says. As a rule, he didn’t use any paid services to obtain the addresses.

“It was hardly as simple as putting ‘Jeff Bezos home address’ into Google,” Caffier told me. “I had to get creative with my methodology to find leads and then double back to cross reference once I think I found a hit. There were many red herrings and many hours spent on stuff I just wasn’t able to confirm.”

Caffier unveiled the cards at a Los Angeles art space in late February, with an accompanying statement distancing himself from violence, and suggesting that the buyer of a $1 million deck could, if they wanted, simply destroy it to make a statement of their own. The less incendiary “merch decks” have since begun to circulate; both versions have been seen by journalists, influencers, and podcasters. At least one high profile streamer has a loaner “art” deck (with the home addresses) that they’ve been using for in-home poker games.

Caffier says he was driven to make the cards because the most powerful elites are destroying the fabric of American life, without any meaningful checks on their power at all. “They do the terrible things they do,” he says, because “they know the economic and judicial system they operate in will not only never punish them, but actively supports their behavior.”

“They can operate with impunity from the shadows because relatively few people know who they are or what they're doing,” Caffier says. “So the cards serve as a method for flipping over the America-sized rock over them and blasting everyone with sunlight while sending the message to them that they are very much perceived.”

I asked Caffier if he’s worried about the backlash an undertaking like this seems destined to ignite. He says he is, but he has consulted lawyers and legal experts, and made an effort to “stay within the letter of the law,” though he acknowledges that the project “seeks to explore where some uncharted legal boundaries are.” And he insists that he doesn’t want to see violence, or to see “a descent to barbarism.”

Instead, he says, “I'd like the cards to inspire people to dig deeper into who the people featured are, learn more about the awful things they've done and continue to do and how their crimes—legalized and sanctioned by America's institutions—contribute to the average person's life and the planet itself being increasingly miserable and futureless.”

There is rage in these words, and desperation, and the darkest timbre of hope. He continues:

I hope the shrinking of these titans onto small rectangles that show where they can sometimes be found helps to disempower that person in the eyes of whoever's holding the card. These are not gods on Olympus. They still (sometimes) have to walk among us.
What everyone does with this hypothetical new knowledge and empowerment is something I won't and legally can't advise further on, but I sure hope this helps move the needle on Americans not rolling over and resigning themselves to oligarchical dystopia for the empire's remaining years.

Look, the sources of this sentiment are not difficult to discern. We are witnessing the most nakedly oligarchic ruling class in recent US history. While working class Americans want for rent, groceries, and access to basic services like healthcare, the world’s richest man is firing civil servants by the tens of thousands. As retirees and veterans watch their savings and 401ks shrivel up, the billionaire president is capitalizing on world-historic crypto grifts. We hear a lot about the fear that this moment is supposed to inspire—via ICE agents shoving students into unmarked vans, the illegal forced deportations, the Republicans who say even they’re afraid to speak up—but we do not hear as much about the rage.

But clearly, that rage is there. The Trump administration is aiming to tamp down that fury by instilling more fear—his Department of Justice is pursuing the death penalty for Mangione, and officially treating Tesla vandals as domestic terrorists—but we may have reached a place where the genuine populist rage is simply too incandescent to be stifled that way. The Luigi avatars are as common as ever on social media, and this is a nation awash in guns and ammunition. As the Trump administration ignores due process and the Supreme Court, there is a growing attitude among dissidents that ‘if they’re not going to follow the law’, why should we?

And Overton Windows are shifting all over the place. Just this last week, the neoconservative Bill Kristol called to abolish ICE and the New York Times’ arch conservative David Brooks ended his column by quoting the communist manifesto. Last Tuesday, Bernie Sanders and AOC brought their ‘Fighting Oligarchy’ tour to Folsom—just a few miles away from my childhood home—in California’s Trump country. Nearly as many people turned out as did in Los Angeles.

When I think about Mangione, and Ethel Cain’s #KillMoreCEOs posts, and Caffier’s America’s Most Powerful deck, I can’t help thinking of the work of Stanford historian Walter Scheidel, and his 2017 book The Great Leveler. The volume is a lengthy and in-depth examination of how extreme inequality has been “resolved” throughout history. Disturbingly, Scheidel found that so far the only thing that has undone such inequality—the only great leveler, so to speak—is violence. Plague, war, or violent revolutions. “It is almost universally true,” Scheidel writes, “that violence has been necessary to ensure the redistribution of wealth at any point in time.”

I do of course very much hope that we have not reached such a juncture—that other solutions are available to us beyond violence—but it would be a mistake to ignore the currents online, in pop culture, and in the streets. One explicit policy goal of reducing inequality in the past has been, if nothing else, to reduce the likelihood of pitchfork-wielding mobs. There is such a seething anger at America’s most powerful, our nation a tinderbox. Looking at our timelines, hearing evocations of this rage, and scanning the portraits on Caffier’s cards, it’s hard not to consider: has it come to this? What if it has come to this?

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The original slop: How capitalism degraded art 400 years before AI - Blood in the Machine

2025-04-15T12:01:08.000Z

Greetings all —

I’ve got something a little different today: The first original BITM guest post, from the great writer and editor Mike Pearl. Mike is a former VICE columnist, the author of The Day It Finally Happens, a frequent contributor to the New Republic, and a good friend. After the Studio Ghibli AI slopfest hit critical mass a couple weeks ago—it is, unfortunately, still ongoing—Mike sent me the following essay. Without spoiling anything, let’s just say it’s very, very Blood in the Machine, it’s great, and I think you’ll like it. Given that my experiment republishing Tim Maughan’s story Flyover Country feels like a success, I’m going to repeat the formula—the proceeds for all paid subscriptions triggered by this edition of the newsletter will be shared with Mike. Maybe this can be a way to support freelance writing on the site? Who knows! Thanks as always for reading, supporting, and sharing—hammers up, and enjoy.

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Europeans went crazy for chintz (1824 painting of a chintz shop by Gabriel Planella Conxello)

Every time a generative AI image model gets released or updated, a similar cycle plays out: AI power users rush to test its capabilities, and highlight what they think are the most novel or meme-worthy examples. One of them goes viral, maybe more. Then, more and more people toy with the model and post the results online, and if I can, I test it out myself. Before long, whatever might have been compelling at first has eroded completely, and the effect of literally anything generated with the new software is that it just looks chintzy—by which I mean mechanical, cookie-cutter, and depressingly empty from an aesthetic standpoint.

For instance, it took approximately one week for the ChatGPT Ghibli meme to dissolve into chintz, and now certain images from actual Studio Ghibli movies make me wince, an effect I hope will be temporary, because nothing could be less chintzy than a painstakingly animated Studio Ghibli movie. But a piece of AI tech has come along and done its nasty work to the masters at Ghibli: zeroed in on something of surpassing beauty and turned it into chintz for fun and profit.

This feels like the only correct word. We use “chintzy” to describe something that has a deliberate aesthetic dimension—floral curtains, for instance—but which is obviously mechanized, causing it to seem cheap. Often something perceived as chintzy strikes a false note not because the consumer is a snob who knows better, but because the cheapness is so easily discerned it’s impossible for anyone not to notice.

Overwhelmingly on social media, chintzy AI images appear to be a statement. According to the writer Gareth Watkins, this is because the use of AI that I call “chintzy” and Watkins says “looks like shit” communicates group identity:

“No amount of normalisation and ‘validation’, however, can alter the fact that AI imagery looks like shit. But that, I want to argue, is its main draw to the right. If AI was capable of producing art that was formally competent, surprising, soulful, then they wouldn’t want it. They would be repelled by it. […] Why? Class solidarity. The capitalist class, as a whole, has made a massive bet on AI: $1 trillion dollars, according to Goldman Sachs.”

Capital has been here before with chintz—by which I mean real chintz, an artisanal craft from India. “Chintz” is originally a Hindi word, and if you're like most people, you’ve probably got the term all wrong. The type of pattern you associate with the world’s ugliest oven mitts and knockoff Lilly Pulitzer dresses is actually a sanded down, industrialized version of a visually rich handicraft that was first created about 400 years ago, in Hyderabad, India and flourished for at least decades before Europeans started importing it in high volume. I first encountered it in this context when my wife and I were in Jaipur, Rajasthan in 2019, and the rickshaw driver showing us around suggested we peek into a shop where handmade chintz is manufactured.

I took this video:

The simple patterns they were pumping out for the entertainment of sightseers were nice, but barely scratched the surface of what this craft can deliver. The original chintz makers used wood blocks, or even paintbrushes to dye vibrant images and patterns onto sheets of calico cotton fabric, many of which have held their vibrant color for hundreds of years.

This collection illustrates the range and depth of creativity in early chintz nicely. There was nothing chintzy about pre-European chintz.

Some 18th century chintz from India (public domain)

And while it surprised me at first to learn that I had completely slept on what chintz actually was, the road from there to the adjective “chintzy” followed a very familiar course:

Humans make something beautiful.
It’s popular for a reason.
Capital profits from selling it, but…
Then capital wants to bypass the original creators to make more money.
Such efforts at reproduction are thwarted, which may make capitalists resent craftspeople.
Eventually capital can sort of reproduce the thing — well enough to remove human creativity from the picture anyway.
But what they really succeed in doing is creating crap.
The original is, at last, mistaken for crap.

Savvy readers of this newsletter will know that any time the British Empire pined for cheaper textiles, the world shuddered. But before anyone could really mass produce textiles with machines, the British East India Company, chartered, as we all know, in 1600, showed up in India with gold and silver, and returned to its cloudy little island spouting bits of Indian textile tech lingo like “khaki,” “calico,” “pyjamas,” “dungarees” and “chintz.” Much of the non-European world already imported Indian chintz before any Europeans got involved in the chintz trade. Other Europeans, notably the Portuguese, traded for chintz as well, but the British soon dominated trade in the region.

In her book Cloth that Changed the World: The Art and Fashion of Indian Chintz, the editor Sarah Fee presents 17 essays by different authors exploring the history of the trade. In the 1600s the European rich and poor alike loved and wore imported chintz. So, merchants started delivering larger and larger shipments—sometimes in place of goods like spices that they originally traveled to India to find—and flooded the market with popular and relatively inexpensive textiles, especially the brightly colored chintz cloth that could be made into exciting new dresses, waistcoats, and linings for straw hats.

European textile workers were getting squeezed, so they held “public burnings of chintz in protest” of the importers, as Fee explained in an interview with the Royal Toronto Museum. Throughout roughly the first half of the 18th century chintz was banned across a wide swath of Europe, although, Fee said, the rich, “continued to decorate their homes with it and wear it.” Only the poor and middle class were actually subject to anti-chintz laws, which allowed the authorities to strip people of chintz in the street if they were caught wearing any.

There was knockoff chintz at the time, and it was legal, but draconian enforcement of the ban on real chintz was still feasible, according to Fee, because European textile workers trying to make chintz could only create what we might now call “slop.” British textile producers could sort of approximate the right fabric with blended linen and cotton, but those bright chintz colors refused to stick to linen. Plus, at the time, Europe had not been able to replicate the right dye colors anyway, so Euro-chintz was mostly brown and gray.

As Fee points out, “There is just something whimsical and quirky and individual in each handcrafted piece from India versus the more mechanical printed or copper plate printed fashions, so you feel life and warmth in them.”

What most people know today as chintz, then, is the result of hundreds of years of technology and capital flattening and mass producing a once-vibrant art form: European printed textiles, often in prim floral patterns, mostly designed and manufactured during and after the Industrial Revolution. When you buy a vintage chintz tea cozy on Etsy, we accept that it’s “chintz” because textile capitalists appropriated, mass produced, and dominated the style, and the so-called chintz has largely replaced the original in our collective understanding. But it’s not true chintz any more than your pajamas today are true “pai jama” from the Indian subcontinent.

In 1851, George Eliot coined the adjective “chintzy”—adding a y—to disparage some ugly fabric, and the term has had negative connotations ever since. Slop chintz has erased the art from our cultural memory almost completely. Occasionally, reproduction chintz comes back into fashion, but these re-adoptions are tinged with irony, and the goods are once again tossed in the trash not long after.

In a 1996 ad for IKEA in the UK, a chorus of women urges you to “chuck out your chintz,” referring to mass produced floral fabrics from the 60s-era revival of the chintz look. No actual chintz was harmed in the making of this commercial.

But this process of artistic erosion once took hundreds of years, and now, thanks to generative AI, we can watch it happen in a day.

It’s probably not the case that Studio Ghibli’s reputation has been permanently or materially damaged by the proliferation of AI memes incorporating a soulless digital approximation of its aesthetic. However, I have now cringed at seeing the occasional frame from one of Ghibli’s less inspired still frames after being subjected to the memes more than I would have in the pre-meme times. The repetitive blandness of the AI images clues me into some of animation maestro Hiyao Miyazaki’s own tics more than I once would have. I can see, in other words, the imperfections in real Ghibli that highlight how bad fully-automated Ghibli would be.

If you don’t believe that the human act of creating is an essential part of art, or that art is more like a social activity—something communicated by the artist and received by the audience—than just a commodity to be consumed, then all this might not bother you. (And if you feel the urge to tell Hiyao Miyazaki to learn to code, your mind is alien to me). If that’s your mentality, a machine can certainly make art, and for that matter so can waves accidentally arranging pebbles on a beach. And perfectly acceptable art can undoubtedly be mass produced without any pesky “artist” being involved, assuming one has the material resources.

As Watkins puts it:

AI art, as practiced by the right, says that there are no rules but the naked exercise of power by an in-group over an out-group. It says that the only way to enjoy art is in knowing that it is hurting somebody. That hurt can be direct, targeted at a particular group (like Britain First’s AI propaganda), or it can be directed at art itself, and by extension, anybody who thinks that art can have any kind of value.

AI slop is, in some ways, a self-solving problem. We all learn to pretty easily distinguish lazy AI outputs from real or handmade work. Whatever new kind of AI artwork is being prompted into existence is the rapidly degrading chintz of the moment.

But ominously, chintz is just one art form, whereas slop is an all-consuming amoeba. It can find a new “chintz” every day, or multiple times a day if the gods of meme virality are feeling sufficiently vengeful. So let’s hope every time capital tries to do to something like Studio Ghibli movies what it did to chintz, it doesn’t leave a lasting cultural scar. And whatever you think of AI images, they do seem to be getting worse. Like chintz, the attributes that make them even remotely interesting are being sanded away over time. One of the attributes that makes something easily identifiable — and mockable — slop is homogeneity.

For example, in 2023, there were a few months in which a badly nerfed version of OpenAI’s Dall-E was available through Micosoft Bing as something called “Designer” (now it’s Bing Image Creator). By writing your prompt in nonstandard Unicode characters you could completely bypass the safety controls and generate all sorts of hideous, sometimes NSFW things. People used it to make surreal, but fairly convincing, fake Simpsons screenshots that looked like they were from the show’s 90s golden age (I don’t even think this required the Unicode cheat). This capability was quickly stripped out of Designer—most likely for the most obvious reason: copyright.

But you could make this Microsoft version of Dall-E do some striking things. For instance, here’s what happened in 2023 when I told Dall-E to show me what it would have looked like if they’d made a shoddy, straight-to-DVD computer-animated Ace Ventura sequel called Mr. Ventura Goes to Washington in 2003:

This is not art, but it’s also not unremarkable. The model created an animated version of the protagonist that looks like what that character would have looked like in a shoddy animated adaptation, and gave him a suit with a flamboyant tie, invented a villain seemingly inspired by character actor Kevin McCarthy, and captured what hands looked like in a Y2K era CGI kids’ movie really well. The image also looks somewhat convincingly like a screenshot from a movie. In the right circumstances you could glean some (not much but some) critical insight about the human-made work the model that made this image was trained on.

Now, two years later, OpenAI’s latest image model — the one that makes the Ghibli memes — produces this sort of thing for the same prompt:

This is in some ways a more arresting image — the subject is brighter, clearer, crisper, more contrast-y, and centered in the frame. His face also has more going on. As a bonus, there are wacky animals! Your lizard brain might be tempted to say this is the “better” image. But it’s profoundly worse in all the ways that matter: it doesn’t look like 2003, and it doesn’t look convincingly like a screenshot from a movie. It’s as if it just wants more people to like it, but it doesn’t make you feel like you’re in contact with something powerful and uncanny. When you see this, the main thing it makes one think is “Yep, there’s some AI slop.”

Bland chintz china (Credit: Sherry's Rose Cottage / Wikimedia Commons)

When mass production needs large quantities of images, geared toward the widest possible audience, exclusively with the aim of maximizing profit, over time everything remotely interesting about them gets worn away. The sort of thing that gets called “chintz” now does genuinely look kinda bad most of the time because it looks like what it is: a cheap floral pattern applied in a factory.

The qualities that made chintz seem “whimsical and quirky and individual,” as Fee put it, came from the fact that human beings applied the dyes by hand. Early AI images similarly had some lingering bugs and weirdness—artifacts of the authentic organic weirdness we all crave from art, or at least handicrafts, and will miss when they’re gone.

There’s “whimsical and quirky and individual” artwork all around you that’s not transcendently beautiful, but is, at the very least, human—from that card on a diner table telling you about a new milkshake flavor, to that lawyer billboard, to that banner on your local elementary school fence advertising a bake sale. I can only imagine how much uglier the world will look if AI does to all those in a handful of years what capital took four centuries to do to the original chintz. And I really hope that’s not where we’re headed.

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Black Mirror's 5 best broadsides against big tech - Blood in the Machine

2025-04-13T18:59:22.000Z

I’m old enough to remember when Black Mirror first dropped, and the anthology sci-fi show was widely received as transgressive, even shocking. Here was a smart, often legitimately unsettling suite of dark modern Twilight Zone fables, all built around the ways new technologies might be used (typically by faceless corporations just off screen) to corrode our humanity. It was, in other words, a luddite show, at a time when “luddite” was still very much a pejorative. And it was willing to probe just how oppressive a world domineered by devices and digital culture might turn out to be, at a time when the pervading consensus was one of tech-friendly optimism.

I will always be happy to see a new Black Mirror season, haters be damned, no matter how skewed the ratio of masterpieces to misfires becomes. So I got a little a shot of serotonin when Netflix announced it was dropping the latest season, which it proceeded to do on April 10th last week.

Honestly, it’s pretty wild that Black Mirror has stuck around this long—14 years now; long enough to endure multiple eras. After that initial phase where it was cool and subversive, it hit its inevitable backlash era, in which it was called out for being too one-note, best summed up by the infamous Daniel Mallory Ortberg burn that Black Mirror is just “what if phones but too much.” I always found that criticism itself to ring hollow—there was always a lot more to the best episodes than complaining about screens. But also, in a moment when social and economic mores were being totally transformed by phones and the companies that put stuff on those phones, that’s a pretty reasonable foundation on which to build a show! You could argue that we should have spent even more time asking what if phones but too much, given where we’ve wound up.

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Anyway, after its ‘what’s even going on with Black Mirror?’ era, in which its biggest crime was not showing up, and not having much to say when it did, the show seems now to have settled into something of an elder statesman phase. When season 6 appeared after years of silence in the wake of the brief season 5—arguably the series’ nadir—I was pleasantly surprised with its range and ambition, even if it had its share of clunkers.

The leadoff Joan Is Awful was unhinged and weird again, and, like such Black Mirror episodes tend to do, it also helped process a socio-technological threat as we were living through it in real-time. When the Writers Guild of America were on strike in 2023, protesting the Hollywood studios’ ambition to use AI to produce scripts, screenwriters I spoke with on the picket lines often referenced the episode. They saw it as a too-plausible worst-case scenario of what entertainment would look like if studio execs used AI to cut humans out of the picture and put TV on autopilot.

The rest of the season was a mixed bag—I thought Loch Henry was a slick and mostly successful interrogation of true crime streaming schlock, Beyond the Sea was definitely overlong but compelling; tonally interesting with a hell of an ending. And Mazey Day earned a slot among the very worst of the series (this is a solid snapshot of how every Black Mirror season works btw). But Black Mirror was back.

And now it’s back again. I haven’t watched yet, but to mark the occasion, I thought I’d round up the 5 best luddite episodes—the ones that register the most potent criticisms of tech and who it serves. This is not, I should note, a rundown of the best episodes, period, though I should do that someday because every ranking I have read is wrong, sorry, and they all seem to overrate what is to me the most gratingly terrible episode of the series and underrate some of the most incisive ones. I digress again.

So yes, you may notice certain beloved episodes aren’t included here. That’s because, for my purposes today, I’m interested in Black Mirror’s sharpest assaults on the tech industry; its critiques of executive power and tech culture. (Not, say, questions of digital mortality.) Without further ado, here they are:

This post is for paid subscribers. I keep all my reporting, industry analysis, and interviews free to read, but cultural criticism, like film, book, and TV reviews, and stories like this one are for paying supporters. For $6 a month, get full access to the archives and pieces like this—and help keep my reporting on AI, big tech, and beyond free for all. Many thanks to those who do, I truly appreciate your support in these dark and unhinged times.

Signal to Noise (and Back Again) - Cybernetic Forests

2025-04-13T11:00:18.000Z

Our understanding of noise could be traced to a young Einstein, staring at a pond full of pollen 120 years ago. Einstein theorized that the seemingly arbitrary motion of pollen on the surface could be modeled — and in so doing, we confirmed the existence of molecules that moved pollen across these surfaces. Signal to Noise is a similar investigation into what moves beneath the surface of today’s digital pollen, broadcast and sticking to the surfaces of screens and airwaves.

Since Einstein, communication technologies have sought to amplify signals and strip away the backroom drone of noise, primarily following definitions by Claude Shannon. For Shannon, noise arrived from two points within a system. The first was the unpredictability of the information within the channel. The second was unwanted intrusions by the world beyond the system. Such interference, internal and external, challenged our access to information — and the ability to share it.

Our re-imagining of the Claude Shannon diagram of noise in systems.

Signal to Noise examines the history of noise in communication technologies. It includes switchboards for telephones, computers and desktops for the Internet, and the algorithmic restructuring of the social media feed. It is an examination of evolving definitions of noise, and the struggle to keep noise out.

Signal to Noise is the story of an encroaching reversal. As engineers have streamlined the information capacities of systems, accessing information is no longer their central role. Now we are awash in noise. The pond, once lightly dusted with pollen, is now hidden beneath a thick layer. Many of us feel overwhelmed not by our inability to receive information, but by our inability to escape it.

The result: technology is now organized around filtering information out. Information has become noise. So we ask, now, how artists today are redefining such noise – and re-examining its opportunities, metaphors, and risks.

This show is a post-AI exhibition, in that it examines AI not as some radical new future, but as the most recent in a long series of confrontations between the ordered logic of computers and unstructured creativity of human beings. We have been grappling with this tension since the dawn of computation, responding to this logic of orderliness even longer than that.

Artists have been at the forefront of this, because creativity is by definition so richly unstructured, whereas AI is actually quite predictable: it really relies on statistics and straightforward processes to get things done. So there’s a great opportunity to look at how artists make work with computers that strives to break the frame of computational logic.

Self Portrait with Shannon-Weaver Model

What is Noise?

Noise is not a man-made phenomenon. The original noise — that cosmic burst of radiation that shaped the universe — obviously predates humankind by millennia. We latecomers have only been able to tune into that noise in the 21st century, thanks to technologies such as broadcast radio and television. Between the channels, the radiation came into our eyes or ears.

The similarity of these sounds to the rushing water of oceans or streams falling over cliffs is a result of the scale and magnitude of activity that water, a frenzy of unsorted sonic frequencies. I live near Niagara Falls, and in a recent visit I took a boat tour to their edge, donned in cheap pink rain gear and unable to hear my partner’s conversation over the boom. In contrast, the white noise of the ocean is differentiated from radio static chiefly by the organization of its rhythms into an ebb and flow: a reassuring consistency imposed upon the roiling chaos by the gravitational pull of our planet.

This meditative quality of ocean sound speaks to the transformative power of organizing noise. The rapid clattering of magnets in the speaker producing bursts of radio static are unorganized sound waves, undirected vibrations competing to shake the speaker. The sound of the radio tuned to unsoiled spectrum is listening to an atom-scaled frenzy, referred to in physics as temperature: the boiling of the tea kettle sounds more like the sea than an ice cube. This circulation of atomic energy speeds up in the presence of heat, and slows down in its absence. If the radio hiss were ever to go silent, it might mean time itself has come to a halt.

Or else it means we did our job of blocking the slow inevitable decomposition of our universe from interfering with, say, the transmission of Chappel Roan’s voice. Our priority as a species is, ultimately, not to hear an immortal roar of the universe but to listen to "Hot to Go!" Noise rudely interrupts our plans, which is also precisely what the slow heat death of the universe is doing to us, too. The ordering of sound into music, a constant tension of repetition and variety, sums up our relationship to information quite well. Too much organization and we get bored, too much noise and we get lost.

Aaron Zwintscher described this dynamic in a 2019 book, “Toward a Possible Noise Politics,” writing:

“Noise is that which always fails to come into definition. The question of noise, and who has the right to define it, is found at the center of the power struggle between succeeding generations, between hegemony and innovation. Noise is found both in the clamor of the unwashed masses and in the relentless din of “progress” and construction of the new. Noise is found in diversity and confrontation with the unknown, the other, and the strange. Noise is in structures of control and domination as well as in the failure of these systems and their inability to be holistic or totalizing. Despite these forms of noise, noise is not a consonance of opposites, but rather a troubled unity, a unity that does not synthesize without remainder.”

In noise, we also see the habits of the European colonial mind: the sorting, the silencing, the steadiness of plan and intolerance of interruption. Cruelty is weirdly fused with a desire for silence, never for cacophony. There is, too, a psychological noise, cognitive dissonance, a chattering interruption of our own thoughts. Silence (that is, denial) in the face of this conflict only amplifies it, forcing deeper responses to suppress. So noise is a means of escaping our cruelty to ourselves; but cruelty to others is a means of escaping "the remainder."

Yet noise is not inherently pessimistic. Noise, the absence of order, is the force through which structures dissolve, liberating categories from distinction, creating a new terrain from which new signals may emerge.

Sidenote: Even Chappel Roan could one day become noise. Years ago, I compiled a Karaoke Songbook of pop music that had been banned by authoritarian regimes or nervous broadcasters (IE: Abba's “Waterloo” was banned by the BBC during the Gulf War, lest it stir up fears of defeat). Censorship of certain noises is nothing new. Charles Babbage, creator of modern computation, spent many of his days calculating the loss of thought owed to street musicians and hurdy-gurdy men whose presence on the streets of London distracted him from his contemplation of future machinery:

“No man having a brain ever listened to street musicians." – Charles Babbage

Telegraphy Lines

Before the radio gave us full sonic access to the universal hum, the hum pestered the telegraph and telephone through interruption and distortion of the voices who boldly assumed we should be able to whisper into long strands of copper and be heard across a continent.

Some endeavored not to interrupt the noise but to listen to it. Thomas Watson, Alexander Graham Bell’s lab assistant, was fascinated by the mystery of whatever was coming through the line. Watson categorized the noises of the ether as it seeped into the empty conduits of undialed connections. Hillel Schwartz takes poetic license to the fact of Watson’s late night listening sessions, imagining those ruminations:

“Was a snap, followed by a grating sound, the aftermath of an explosion on the surface of the sun? Was something like the chirping of a bird a signal from a far planet? What occult forces were just noticeably at work in the recesses of telephonic sound?” (Making Noise, p. 330).

Watson’s interest may have been piqued as the first human to hear electricity speak his name by “varying the density of air” over wires to reshape the voice of Bell, with the request: “Mr. Watson—come here—I want you,” in that Boston laboratory in 1876.

The source of noise in the telegraph was cosmic radiation, but it was also the lack of any stable order in the world in which these signals moved. To create a perfect condition for the technology to operate, vast distances of wiring would need to be isolated from any kind of event. This might be wind rattling the line or trees cutting it completely. It could be overcrowding the line. Solar radiation. Lightning strikes. The struggle between communication and control may best be defined this way, as information theory and cybernetics struggled to make sense of complex systems — while acknowledging that the world would never stand still long enough to see them.

It's appropriate, then, that we had the famous Bell Labs give way to artists who would seek to carve their own names into electrical signals, much as Watson had heard his coming through the phone line. Lillian Schwartz, who has no less than five works in Signal to Noise, worked with computers to create new patterns in electronic charges, writing them into screens or in programs that would cut into metal to create detailed drawings in reflective steel.

The radio, switched on, became a stethoscope into the rapidly beating heart of the universe, but we aimed it inward instead: more useful, I suppose, to listen to ourselves. But artists such as Lillian Schwartz give us dreams of a history of technology in which our relationship with noise was forever inverted.

What if we designed technology to humble us, through perpetually reminding us of disequilibrium, in all its catastrophes and opportunities — a reminder, in the end, that no stability is permanent and striving for it may not insulate us from instability, but diminish our capacities to adapt and respond?

What if the ambition at the heart of communications technology was not to steer our systems away from noise, but to respond to perturbations and disturbances with something like curiosity? How might our relationship to noise — as an antidote to our rush to “solve” and “settle” — become a signal itself? What if we turned away from the insistence upon previously established order — with its fear of unstructured cacophony, of simultaneous and paradoxical positions? What if we designed technology not to provide answers, but to linger, without resolution, on unsettled questions?

In the coming weeks I'll be writing about the works in the exhibition and some of the relationships they speak to concerning noise & signal (and everything in between).

Signal to Noise

Signal to Noise was curated by Emily Siddons, Joel Stern and Eryk Salvaggio. It runs 12 April to 11 September 2025 at the National Communications Museum, Hawthorne, Melbourne, Australia. More details at the NCM website.

Therapists went on a hunger strike to protest 'assembly line' conditions and the automation of mental healthcare - Blood in the Machine

2025-04-12T02:57:18.000Z

Mental health professionals at the giant consortium Kaiser Permanente started a five-day hunger a strike in Los Angeles on Monday this week, an escalation of a broader strike that has ground on since October 2024. The workers are fighting for more time spent with patients and better pay, and against algorithmic triage and an assembly line-style of providing mental healthcare.

So, on the last day of the hunger strike, I went down to the picket line at the Kaiser Permanente Los Angeles Medical Center in East Hollywood. Chants sporadically erupted among the healthcare workers assembled in bright red NUHW t-shirts. Those on hunger strike were sitting on lawn chairs in the shade of a pop-up shelter, smiling wearily. By the time I arrived, they’d gone over 100 hours without food.

This day was also, the Kaiser workers told me, the day that they’d broken a record—at 173 days, theirs was now the longest mental health worker strike in the US. Not a record you want to break, exactly.

“We want more time to be with our patients,” Adriana Webb, a medical social worker at Kaiser tells me. Webb hasn’t eaten for five days. “Kaiser treats therapy like a factory, they make therapists see patients back to back to back with no breaks,” she says. “It’s exhausting. You can’t take care of patients if you can’t take care of yourself, if you can’t go to the bathroom.”

Webb is tired and pale, and she tells me she’s lost 14 pounds this week. But she’s also lucid and entirely cogent. “I think around Wednesday the brain fog lifted,” she says. She’s able to fire off her concerns with Kaiser with rapid-fire eloquence. “Kaiser wants us to do more with less. They treat us like robots, like it’s an assembly line—therapy does not work that way.”

One of the core issues here—along with the fact that Kaiser is not paying southern California mental health workers what it pays their Northern California counterparts, and that hundreds of therapists aren’t receiving pensions—is the aforementioned and possibly illegal practice of algorithmic triage.

California law requires that a trained and qualified health professional—a physician, registered nurse, or such—to do triage, or screen patients to determine the urgency of their care. So when a patient with a mental health issue calls their provider, it’s supposed to be a trained therapist, nurse, or doctor answering the call to asses the patient’s needs. But Kaiser has been hiring clerks and relying on a semi-automated and algorithmic system to do these screenings—even for suicide assessments.

“They have clerks and non-clinical people triaging people and doing suicide assessments, and that doesn’t work,” Webb says. “Because if you ask a patient, ‘have you had any thoughts of harming yourself,’ and they kind of go ‘no?’ they can just check the no box.” And that patient doesn’t get mental healthcare—even if they truly need it.

This triage system, which has callers answer a series of basic questions about their mental wellbeing, feeds the answers into an algorithm that determines whether or not the patient should receive care. The purpose of this system, of course, is to automate a crucial healthcare service, in order to help Kaiser save on labor costs.

A complaint filed this week by the National Union of Healthcare Workers details the scope of the issue:

In 2019, Kaiser began implementing aggressive changes to its triage system in order to cut costs. It replaced licensed therapists with unlicensed, untrained clerical staff and sharply reduced the duration of each triage assessment to fewer than five minutes. During each triage assessment, an unlicensed clerical staffer employed by Kaiser poses questions to an enrollee, including about suicidal and homicidal ideation, and enters information into a Tridiuum (now LUCET) software tool. An algorithm then calculates an acuity score and, based on the score, generates a response that guides the clerical staffer in scheduling the enrollee for subsequent care. In most cases, these triage determinations are not reviewed by licensed and trained therapists. Each week, Kaiser uses this system to triage thousands of enrollees with behavioral health disorders.

This is not only corner-cutting—before 2019, triage would take 10-15 minutes, with a trained professional—but downright dangerous, the nurses say.

“For example, there’s a patient that might describe feeling anxious, but you might find out later they have an eating disorder—which is urgent,” says Ligia Pacheco, a Kaiser psychiatric social worker. “That should be treated right away.” An untrained clerk and an algorithm might miss such nuances, and arrive at the wrong conclusion altogether. “Or if you ask ‘do you have any suicidal ideation?’ and say if a patient asked me ‘what is that?’ I can say, ‘ideation is just the idea. But do you have a plan, do you have means?’ and so on. The algorithms don’t have those follow up questions, so it puts patients at great risk of great harm.”

The Kaiser mental health workers have been fighting that system since its inception, and Kaiser has thus far refused to budge. The fiasco with algorithmic triage also makes the therapists and healthcare workers worried about how Kaiser might further push AI in the future. They’re fighting for a contract that demands that any AI systems be instituted only with their input and approval—and against top-down systems that hand all the power to managment.

“AI puts patients in great danger,” Pacheco says. “It takes away the human aspect, right? Patients want to be heard by humans.”

Kaiser, in response to the hunger strike, publicly expressed concern for the workers, and management agreed to return to the bargaining table next week. But how productive that will prove, of course, remains to be seen.

The therapists, for their part, remain resolute.

“We didn’t come this far to only come this far,” Webb says. “It doesn’t make sense to go back without an equitable contract. Kaiser has $70 billion in reserves. They can afford to provide us with an equitable contract, which will benefit them and their patients.”

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Turning the little screws - Blood in the Machine

2025-04-10T22:51:30.000Z

In the summer of 2016, just months before Donald Trump was elected president for the first time, I snuck into what was then the world’s largest iPhone factory. Officially called the Longhua Science and Technology Park, it was better known as Foxconn City, named after the massive subcontractor that handles the bulk of Apple’s product manufacturing. Longhua had become famous in the early 2010s for the suicide epidemic that swept its massive, 1.2 square mile campus, when workers protested their conditions in the gravest possible way.

As I made my way through the sprawling environs with a translator, I spotted the most notorious emblem of Foxconn’s turmoil, the so-called suicide nets, erected to catch the falling bodies of workers. Interviews with workers revealed that conditions had not improved since Apple and Foxconn promised to fix things in 2010—most felt stressed, relentlessly overworked, and underpaid. They complained of mandatory overtime, and of abusive managers who berated workers, many of whom had traveled from far away provinces to get this job, singling out underperformers to be shamed in front of their peers. They talked about having benefits withheld from them and being overcharged for housing. Someone had just jumped a few months ago, two workers told me, and all this was why.

As I walked from one end of the campus to the other, what struck me, aside from the drab, monolithic buildings and the sheer size of the operation, was the pervasive glumness that hung over the place; there were young people everywhere, talking or sitting down to eat, or heading into the 7-Eleven accessible only to Foxconn employees; but there was so little cheer or even vitality that the place indeed felt “haunted by a ghost” as one worker put it to me.

There were hundreds of thousands of people who worked here, a whole mid-sized American city, and the mood was so uniformly restrained and sullen—like I was walking all the way across St. Louis, but everyone had been told to keep quiet or else. The sheer scale made it striking. I’ve never seen anything like it before or since.

I’ve been thinking about this place because for all its bleakness, it seems to be a fantasy for the Trump Administration lately—even if it’s an incoherent one.

A photo I snapped from inside Foxconn City.

This is the kind of place that secretary of commerce Howard Lutnick is talking about when he makes the rounds on TV news shows trying to sell Donald Trump’s tariffs on China and the rest of the world. In one of the clips that went viral on social media, Lutnick enthuses to a CBS anchor about an “army of millions and millions of human beings screwing in little, little screws to make iPhones,” explaining how “that kind of thing is going to come to America.”

But, Lutnick adds, in something of a jumble, “it’s going to be automated."

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Now, with the absolute chaos of Trump’s haphazardly implemented global tariffs settling somewhat, with a 90 day pause announced on most of them, we’re left with a trade war with China—as of writing, the US has levied a wildly punitive 145% tariff rate against Chinese imports. (Two hours from now, it could be 275% or 1000% or 0, who knows.) The markets initially rejoiced at the tariff pause, but now the direness of even that is setting in; this is still very much uncharted territory. China, of course, is the largest importer of goods into the US and the largest manufacturer of American consumer electronics by a wide margin.

Trump, as in his first term, has once again returned to espousing the idea that he can return consumer electronics manufacturing to US shores—and, in particular, the manufacturing of Apple’s flagship device, the iPhone. “He believes we have the labor, we have the workforce, we have the resources to do it,” Trump’s press secretary Karoline Leavitt said this week. Trump himself underlined the notion in a Truth Social post on Wednesday: “This is a great time to move your company into the US, like Apple, and so many others, in record numbers, are doing.”

The question is, why would anyone want what I saw in Longhua to come to the United States? Why would anyone use such mass immiseration as a talking point to sell the public on the benefits of trade policy? There are a number of answers here: The most telling, I think, is that, as with much that emerges from the White House, this isn’t as much a stated policy goal as it is a rushed effort to defend or explain whatever the boss just said or did. So it winds up being a sort of Rorschach test of what a given Trump lackey believes—and, more importantly—believes Trump wants to hear.

In this case, Lutnick is attempting to articulate Trump’s well-known affinity for the ‘Apple should build iPhones in the United States’ idea. Lutnick also has some knowledge of the actual conditions in those factories, so he’s awkwardly wrapping that up in his sales pitch. Now, Lutnick is a billionaire, and perhaps so divorced from the notion of manual labor that it doesn’t compute to him that what he’s saying scans to most people as brutal and utterly miserable. Who exactly aspires to spend all day screwing in “little, little screws” on devices, in his mind? Or it could be that he simply doesn’t care—this is, after all, an unabashedly oligarchic US government, and the purpose of these factories is not to generate good jobs for working Americans, or even to pretend as much, but to maximize profits for the owners. Hence the automation talk.

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The “little screws” line became famous as Lutnick was trying to defend earlier comments he’d made on Fox News about how the tariffs would bring iPhone manufacturing to the US; in that earlier instance he said that most of the work would be done by robots. Perhaps realizing the case he was supposed to be trying to make was that bringing manufacturing back to America was good because it was good for jobs, he tried to thread the needle and ended up with some verbal mush about tiny screws and armies of human beings and also automation.

But it’s a telling mush. It shows us how we might begin to understand what we’re dealing with right now—and will be stuck dealing with for the coming years—with regards to Trump, labor policy, AI, and automation. First, it’s a reminder that this isn’t so much about policy at all, but about Trump’s mad king-style wielding of power, as the New York Times’ Jamelle Bouie pointed out. Everything else follows from that. It’s gangster capitalism at best, an effort to force countries and companies to ingratiate themselves to him—Trump is already talking about granting exemptions to certain companies and “tailor made” deals with countries.

Given that, we can zero in on what Lutnick might be revealing with his mush. Because yes, if the tariffs with China stand—a colossal if—then there are probably a number of serious implications for industry. That’s why, I think, I received a number of calls from reporters and pundits this week, asking me how Trump’s tariffs might impact automation. After all, the Luddite uprising against bosses who were automating their work occurred after trade blockades erected over the Napoleonic Wars tanked the British textile economy. It was into that depressed economy that entrepreneurs and factory owners accelerated the adoption of automating machinery, further depressing wages and enraging the already suffering weavers and craftsman who made up the bulk of England’s industrial workforce at the time.

So it’s a good and valid question! And one that’s complicated by so, so many factors. If Trump’s intent is to bring back American manufacturing jobs by making foreign products and labor prohibitively expensive, it might, after all, blow up in his face if American companies took the opportunity to accelerate automation programs instead. On paper, it’s logical, as the automation theorist Carl Benedict Frey told the reporter Billy Perrigo in TIME this week for a good piece on tariffs and automation, to say that the rising of costs in the US “means there’s an even stronger economic incentive to find ways of automating even more tasks.” Theoretically, if the tariffs—especially those on China—hold, then there is indeed a stronger incentive for companies to invest in automation. (Of course, if they hold only in China, then companies may just find a different source of cheaper labor.)

But there are a couple things to keep in mind here:1 First, the tariffs actually raised the costs on importing the parts and machinery you would need to build automated robotics systems. (The tariffs are also set to hit the AI industry hard; the US imported $200 billion in data center equipment and services in 2024, and much of that was from China and Taiwan.) So there’s a short-term disincentive to invest in automation tech right now. Second, of course, is that Trump’s economic policy is wildly inconsistent and unpredictable. I can’t see many companies making any sweeping investments in anything right now, until there’s at least a shred of clarity on what even the middle-term picture is going to look like. (Again, as of now, it’s a fool’s errand to guess what tariffs on any country are going to look like next week, much less a year from now.)

But third, we very, very well may see a spike in discourse around AI and automation led by the companies themselves. OpenAI, Anthropic, Microsoft, and other AI firms have been pushing enterprise AI software pretty hard for the last two years, even if the results still aren’t looking all that great. If Trump’s trade shenanigans do wind up crashing the economy, or even throwing us into a minor recession—both very much within the realm of possibility!—the AI companies will have a renewed opportunity to promise cash-strapped firms labor savings if they shell out for premium tier enterprise AI software. I would not be surprised at all, in fact it would be the least surprising thing in the world, to see a renewed round of headlines worrying that, with AI approaching AGI or whatever, an economic dip could throw millions out of work sooner than we thought—logic that AI companies would wield, as they do, for commercial benefit.

Because ever since the Industrial Revolution, it certainly has been a recurring theme: When there’s an economic depression, or a barrier erected to a trade, you do often see owners and tech entrepreneurs announcing that they are investing in—or threatening to invest in—automation, usually as a way to slash their labor costs or to try to weaken worker power. The cloth factory owners and industrialists did so in the 1800s, using the workers’ relative weakness to buy more machinery and to run them with precarious and child labor.

There was a big push by industry to automate in the late 1950s, during an economic slouch—the carmaker Ford had coined the term “automation” and established a department dedicated to the practice the decade before. Most recently, we witnessed this during the response to the Covid pandemic; there were a lot of headlines about how the virus was going to accelerate automation, and many jobs were at stake.

Screenshot of the management consultancy OliverWyman during the peak Covid pandemic years.

While some real efforts at automation were undertaken—a National Institutes of Health study found that 5.7% of middle to low income workers self-reported having lost their jobs to automation during the pandemic—it a) did not end up denting the economy or the broader jobs picture, and b) nonetheless no doubt proved useful to employers as pervasive leverage over their workers, who were increasingly inclined to agitate for better conditions amid a plague. The threat of automation is simple, and constant, even if its premise is often untrue: Step out of line, we can replace you with technology.

So that’s what I’ll be watching most of all as the trade war saga unfolds: corporations using the specter of automation or AI adoption as leverage over workers. I gave a similar quote to TIME’s Perrigo, and he was kind enough to give me the final word in his piece, which also included Frey’s thoughts, as well as others.

Right now, there is not a chance in hell that Apple will be meaningfully moving its iPhone manufacturing operations to the United States anytime soon. The company itself has repeatedly said it will not; the last big Trump-led effort to do so failed spectacularly; and 404 Media has a whole litany of other reasons. But on top of that, there’s even less of a chance that if it were to do so that those operations would be heavily automated. As things stand, it’s highly unlikely other firms will invest in robotic automation about now either.

And bear in mind that even if Trump and Apple did ever convince Foxconn to set up a working iPhone factory on American shores, it would in all likelihood look a lot more like the Longhua facility I described above—barring the huge numbers. Foxconn has been rolling out automation, too, but only in specific use cases, and the process has been slow. It would probably be filled indeed with armies of human beings working numbing and thankless jobs—the sort imagined by speculative fiction writer Tim Maughan in the story I republished earlier this week—in miserable conditions, because that’s the requirement of the job.

And that’s the rub. Trump, Lutnick; they don’t care. They don’t care even to try to imagine the lifeworld of an actual worker, or think more than 30 seconds about automation, because to them, it doesn’t matter—automation, human laborer, American worker, Chinese worker, these are abstract producers of value to them, the big bosses, the ones at the top of the food chain. All else exists for their benefit. Trump has decided that it would somehow serve is own interests, his own ego, to see Apple build iPhones in the United States. He would like that, on some fleeting level; to be able to say, “I did that.” It would be a testament to his power. That is the extent of his interest in the idea.

Figuring out the rest is a rearguard action against his whims. So when we’re thinking about what the Trump administration’s trade policy might mean for automation, let’s keep that in mind—automation and AI, like servile labor, are tools that exist to serve its power. It’s why JD Vance wants American AI to “dominate” and called for an end to industry regulation. It’s also why Musk and DOGE and their supporters have talked so much about AI, and why Trump uses AI as a way to justify his executive order expanding the coal industry. To the Trump administration, it’s the technology’s ultimate value proposition—one of concentrating power, turning the screws on workers, and ultimately, of dominion. It’s why AGI is so alluring to the industry: the promise of infinite labor replacement, and infinite investment, even if all you get so far is sometimes-functional software automation. A shiny automated future or millions of immiserated workers—like Lutnick’s jumble, the distinction ultimately doesn’t matter, as long as it serves their power.

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Some of which are raised by Perrigo’s other interview subjects, like Daron Acemoglu, in the same piece!

Flyover Country - Blood in the Machine

2025-04-08T22:52:16.000Z

When the clip of Howard Lutnick announcing his vision for post-tariff America—millions and millions of human beings screwing in little screws to make iPhones—went viral, I immediately thought of “Flyover Country.” Back in 2016, right after Trump was elected the first time, his cabinet *also* talked endlessly about bringing electronics manufacturing back to the US, alongside the anti-immigrant and mass deportation rhetoric. At the time, I was the co-editor of Terraform, a speculative fiction outlet at VICE that I’d founded with Claire Evans. So I commissioned a short story from the writer Tim Maughan, asking him to imagine what a future of American-made iPhones might actually look like should it come to pass.

What Tim turned in—a vision of a poverty-stricken America where US Foxconn factories have sections for precarious day laborers as well as for detained migrants and “criminals,” all compelled to assemble an endless stream of iPhones—was devastating. The story has lived with me ever since. Now, Tim is not just a friend, but, for my money, one of the very best working speculative fiction writers; his work bridges a raw cyberpunk sensibility with a literary touch in a singular and often brutal way; his futures feel painful, absurd, and lived-in. His novel Infinite Detail is excellent. I have to say all these nice things about him, because he has graciously agreed to let me republish “Flyover Country” in full here.

You see, I got another gut-punch of depression after I thought of the story—I went to dig up the link at VICE and post it on Bluesky or whatever, and quickly realized it’s entirely unreadable in its current form. VICE went bankrupt a few years back and was sold off to a private equity vulture. On old stories like this, the formatting is broken, images have been stripped, and it’s all larded over with ads. It’s a mess. Fortunately, and in quite a twist, one of the very first SF writers we approached about contributing a story was none other than friend of BITM Cory Doctorow. I didn’t know Cory at the time, but he insisted VICE edit the writers’ contract to be more amenable to authors. We then adopted that as our standard, and thus, unlike the vast majority of stuff imprisoned on the decaying VICE platform, writers are legally free to reprint their work anywhere they’d like. Thanks Cory! Jason Arias, the great artist who contributed the original illustration in 2016, also agreed to let me reprint it here.

So, depending on whether folks want to read more of this sort of thing, this might become an occasional recurring feature—rescuing the prophetic fictional futures of Terraform from platform rot, and airing them out where they can help us better feel out the dystopian present. You tell me! For those interested in reading more short-form speculative dystopias, Claire and I published a print anthology of some of our favorite works with FSG back in 2022. Finally, in gratitude to Tim and Jason for their kindness in letting me reprint their work, the proceeds for all paid subscriptions triggered by this edition of the newsletter will be shared with them.

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Just to underline how eerily prescient Tim’s piece was—or how stuck Trump is on certain horrors—here is, I kid you not, the intro I wrote for the story back in 2016 (even though it kills me not to edit out the redundant “future” now but I digress):

With a Trump presidency and the full scope of his campaign promises looming, here’s a look at a future where some of them have come to pass. Welcome to a future where undocumented migrants are rounded up and penalized, private prisons overflow into labor camps, and iPhones are made in America.

Without further ado, here’s Tim Maughan’s “Flyover Country.”

Art by Jason Arias. Originally published in Terraform in 2016.

Flyover Country

by Tim Maughan

I meet this girl Mira and her kid in the parking lot of that Wendy’s on Jefferson that’s been closed since ’19. Yellowing grass pushes up through cracks in warped tarmac, and I find myself daydreaming again about the ground ripping open and consuming the whole fucking town. Like an earthquake. Or maybe a big storm rolling in, like last year but fiercer. Something. Anything.

It’s only 6.30 but it’s hot out already. Mira’s kid is sleepy, not used to being up this early. But she’s still cute as all hell, all pigtails and smiles, playing up for the camera as I snap pictures of her and her mom on an old Samsung phone. Mira has got a CVS bag stuffed full of papers with her—the kid’s school reports, some crayon scribbled drawings, letters both of them have written. I snap photos of them too, trying not to read the contents as I fight to get the shitty phone camera to focus on handwriting.

I need to get going. Miguel always sorts this shit out last minute, swapping shifts around so things line up. So everyone is in the right place at the right time. Always seems to end up with yours truly barely prepared, in a rush. Mira gives me 40 bucks, which she says is the last of her UBI for the month. I feel bad and try to give her 10 back, but she won’t take it. Says she can pick up some more cleaning jobs on Handy, that I shouldn’t worry. Just don’t fuck up, she says. I smile and promise her I won’t.

I’ve only got an hour before my shift starts, no time to walk all the way back home, so I duck into the bathroom at the big Walgreens on Lincoln. It’s on the way. In the stall I kneel on the floor in front of the john, and spread out a clean shirt from my bag across the closed lid. I place the Samsung in the middle, and start using one of the toolkits I got off eBay to crack open its casing. It’s tricky—it always fucking is—but I manage to pry it apart without scratching it up too much, without it looking like it’s been tampered with.

I breathe a huge sigh of relief when I see the motherboard. The SD chip is 256GB, and the right model. It makes me fucking laugh, this shit. All these companies always competing to make you buy their phone, and then to make you buy a new one every damn year, making you feel like you’re missing out if you’ve not got the newest, the best ever. But inside they all look the same to me. Same components, same chips, same storage, year in year out.

Somebody comes into the bathroom, so I start making heaving noises, just in case they spot my feet and wonder what the fuck I’m doing. There’s a pause and then they reluctantly ask me if I’m ok in there. I laugh and make spit sounds and I’m like yeah fine, just a heavy night y’know. I cough some more and listen to them moving around, the sound of pissing then running water, mixed with canned laughter and the theme tune to Tila Tequila’s Beltway Round-Up pumping in over the store’s tinny PA. Eventually I hear the door close and I get back to work.

The SD chip comes out easy, I’ve done it a few thousand times before. I gently tape it to the backside of the RFID chip sewn into the back of my green overalls with a band-aid, before stuffing them back into my bag. I put the phone back together and drop that in there too, along with the clean shirt. Sadly the cheap-ass tool kit has to go in the trash on my way out, cos there’s no way I’ll get that through security. Pain in the ass, but fuck it. I’ve got a bunch more of them at home.

The walk to the Foxconn-CCA Joint Correctional and Manufacturing Facility takes me about 20 minutes on the interstate. Traffic is pretty much non-existent apart from the cab-less trucks that dwarf me as they pass, kicking up clouds of pale dust that scour my eyes with grit.

Gate security is bullshit as always. They barely care, lazily rummaging through my bag as I stand in the body scanner, feet on the markings, arms bent above my head. They pull out the phone, put it in a RFID tagged baggy to pick up at the end of my shift, and silently hand me back my bag.

Miguel is at the shift manager’s desk. He gives me some gruff bullshit about getting in earlier in future, about how I should turn up ready to go in my overalls, while guiltily avoiding making eye contact. Stay cool, Miguel. He checks the rota on his tablet, tells me I’ve been assigned to production line 3B, building 7. Motherboard assembly. Of course, I know all this already.

I duck through dormitory 6 as a short cut, weaving through the endless rows of bunkbeds. Artificial light filters down through suicide nets and sprays a slowly undulating checkerboard across the plastic floor. Everyone in here is in green overalls: Voluntary. On shift breaks they sit around on their bunks or on plastic chairs, talking, playing cards, watching A Noble War on the huge TVs that line the dorm. It’s the episode where Barron and Beatrice get married on the bridge of the USS Thiel, just after they’ve put down a socialist uprising on Phobos. I remember the episode, season 4 I think. Barron still has his real arm. I used to love this shit back in high school.

I keep walking. The dorm is a fucking shit hole. It’s dirty and smells of ass and body-stink. If this is where they put the voluntary workers I don’t want to ever see how bad things are for the actual inmates. I shudder at the thought of choosing to be stuck in here, but I get it. I got no kids, I’m lucky. My Universal Basic Income still covers my rent, just about. I pass a guy that looks my age, stripped to the waist, lying on his bunk. Chest splattered with random, uncoordinated tattoos, like sticker’s on a kid’s lunchbox. He stares up through the suicide nets, into dull fluorescent light, his eyes unmoving. There but for the fucking grace of god.

I find an empty locker and open it, cram my bag in. Checking over my shoulders for guards or drones I reach inside and tear the band-aid away from the inside of my overalls, and palm it and the chip into my pocket. I step back and pull the overalls on over my clothes, slip on the paper face mask and hairnet, and head outside, relieved to escape the smell.

I move quickly through the courtyard. Running late. Again the bodies I weave through are all sealed in green overalls, but on the other side of the three-story chainlink fence I can see red and blue clothed figures. Convicts and Illegal Residents.

I keep my eyes down as I move, not wanting to catch the mirror-shaded gaze of the guards in the towers, or the dead twitching eyes of the drones that hang in the hot, still air.

Inside Building 7 the chain fence runs right through the interior, cleaving the production line in two. Green overalls on my side, red and blue on the other. The dank, mildew smell of almost-failing AC. Today I’m on motherboard assembly. A constant stream of naked iPhones come down the conveyor belt to me, their guts exposed, and as each one passes I clip in a missing chip. 256GB storage chips, from a box covered in Chinese lettering.

One every ten seconds. Six a minute. 360 an hour. 4320 a shift.

After me the line snakes away, disappearing through a hole in the chainlink, into the hands of Reds and Blues.

At the station next to me, a slender matte-black robot arm twitches, snapping video chips into the motherboards. It is relentless, undistracted, untiring. Given half a chance Foxconn would replace us all, but then they’d lose all those special benefits the President promised them for coming here in the first place. The ten year exemption on income and sales tax. The exemption on import tariffs for components. The exemptions from minimum wages. The exemption on labor rights. The protection against any form of legal action from employees or inmates. The exemption from environmental protection legislation. And Apple? Well, without me standing here, clipping one Chinese-made component into another Chinese-made component, Apple loses the right for a robot in Shenzhen to laser engrave ‘Made in the USA by the Great American Worker’ into every iPhone casing before they’re shipped over here.

It takes me about two hours to pluck up the courage to do what I gotta do. Two hours. 720 iPhones.

Once I decide, there’s no going back. Instead of taking a chip from the box to my right, I slip my hand into my overalls pocket, and palm out the chip. To my huge fucking relief it clips effortlessly into the next iPhone on the belt. On top of it I place the band-aid, with just enough pressure that it stays there while looking like it fell from my scratched and battered hand.

I watch the phone slide down the line, it’s little band-aid flag making it stand out from its compatriots, as it vanishes through the chain link fence.

Eight hours later. 2880 iPhones.

Shift over. My calves and the backs of my thighs sting from standing for 12 hours, my eyes strained from the fluorescent glare. The panel on the wall bleeps, turns green, as I punch out. I gaze at its screen. My blocky, low-res reflection gazes back, a machine vision approximation of my tired eyes and pale skin. I stand there, silently, not moving, waiting for the panel to recognize me. A tick appears, obscuring my face. Video game statistics scroll along the screen’s bottom: efficiency, accuracy, time keeping, responsiveness, productivity. 4314 iPhones. Chimes and a bleep. A synthesized, too-cheerful, feminine voice tells me I should smile more. A second bleep, the click of a door unlocking, and I’m out.

My phone buzzes at 5.24 AM, under my pillow and loud as all fuck because I made sure the ringer was cranked to max. Text from an unknown number. Miguel on a burner. Time to go to work.

Two hours later and I’m back in my overalls, back in Building 7.

This time I’m on Returned QA Fails. The pace is slower, the work slightly more involved. iPhones that have failed quality assurance up the line because of faulty chips come back down. I whip out the fucked chip, stick a new one in, send them back up the line again.

One every 20 seconds. Three a minute. 180 an hour. 2160 a shift.

It tends to be even more chilled than that, to be honest. There’s not that many that come back faulty, obviously. Nowhere near in fact. But the algorithms don’t care. The drones lazily orbiting around the ceiling on their quadrotors are always watching, making notes, remembering. Calculating. Doesn’t matter how many you actually do, you still gotta do ’em quick. Keep those productivity stats high.

It’s less than two hours—maybe 60 phones—in to my shift when it appears. Coming down the line, a dropped band-aid stuck lazily to it’s exposed guts.

My stomach flips. I glance upwards to make sure the drone has cycled away. As the phone reaches me I pluck the band-aid away, drop it to the floor. Un-click the storage chip, and drop in another, new one.

The chip I’ve just taken out should go in a box, to go into a container, to go onto a truck, to go onto a ship, to go to China, to go onto another truck, to be dumped in some no-fucking-where village in Guangdong where an old lady that used to be a subsistence farmer will pull it apart in her front room to recycle the components.

But this one? This chip I originally ripped from that old Samsung? This chip gets palmed into my pocket.

I meet Mira in the Wendy’s parking lot. Her kid is with her again. Cute as all hell. Running around in the tarmac-piercing grass.

I hand her the Samsung phone, its storage chip returned to its rightful place. She hands me another 40 bucks.

Before I turn to leave, I watch her power it on, swipe it open. Her thumb stabs impatiently at icons. And then the screen fills with a photograph, a brown face, beard, smiling. Trying to look happy but nervous. Blue overalls. A photo taken while glancing over your shoulder, on a hastily hacked open, smuggled-in old smartphone you don’t even know works. A photo you’d risk spending six months in solitary to take.

Mira smiles, begins to cry.
She calls her kid over.
Look. You know who that is?
Pause. Eyes wide.
Daddy!

As I walk away she’s kneeling on the floor, holding the kid close to her, tears rolling down both their faces, as she swipes through images. The face again. Badly focused photos of handwritten notes.

I feel good for a second. Like it’s worth it. But part of me still wants the ground to rip open and consume the whole town.

It’s cooler today. A breeze is picking up, tugging at my green overalls as I start my walk back home. Somewhere out past the interstate, over the horizon, a storm is rolling in. A big storm like last year. I hope it’s fiercer. I hope it’s something. Anything.

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Whew. Told you.

Thanks again, Tim and Jason, for letting me reprint this piece.

Tim Maughan’s website is here. He’s on Bluesky. And his book is Infinite Detail.

Jason Arias is an artist, illustrator, book cover designer, and artistic director. His website is here.

See everyone again soon—and BITM readers, let me know if you enjoyed seeing some future fiction show up in your inbox, and if you might want to see more of it. Hang in there, and hammers up.

The currents of anti-oligarchy protest, and a great tick-tock nonfiction thriller about revolution - Blood in the Machine

2025-04-06T21:42:53.000Z

On Saturday, I brought my boys down to Culver City in LA, where hundreds of people lined the streets outside city hall. Folks were packed on both sides of Culver Boulevard, even commandeering the median, with signs and verve and plenty willingness to chant and yell and cheer. The boys delighted in the more vulgar chants (“Fuck this shit!”) and climbed trees to wave signs of their own. (A collaborative effort with a slogan that they came up with themselves, it read “No Donald Trump / No Elon / Allowed. You Stink! Hands off!”).

The sheer numbers and vigor were enough to discourage any counterprotests, at least here, and the vibes were good all day. The same can’t quite be said for last weekend, when I was out at a Tesla Takedown protest in West LA. Everyone was lined up on the sidewalk outside the dealership on Santa Monica Boulevard, with signs and megaphones, and it was great, especially at first. There were chants and new acquaintances and kids on their dads’ shoulders. Drivers and passengers passing by rolled down their windows and cheered, truckers in big rigs blared their horns in support. But, unlike last time I went to a takedown protest—which I still encourage everyone, everyone to do—the shape of the opposition was clearer.

The cops were there from the start. One for every five to ten protestors, just about. When one man kept standing in the street, where he was blocking traffic, despite his correctly pointing out that the cops were also standing in the same street, they quickly put him in cuffs. It was such a needless display of power—he was detained, not arrested, and he assured me he was fine, but still. Not long after that, a presumably pro-Musk Tesla driver stopped his car and just started screaming at us, middle finger extended. There would be more middle fingers, more cops making their presence know before the day was done.

It was fine, but it was a reminder of where the power lies, and how those who have it wish to wield it right now. It darkened the day to be sure, and that darkness lingered with me as I drove home, trying but failing to shake it with some upbeat New Order b-sides.

These are, it scarcely needs to be said, pretty dark times. With the markets crashing from the haphazardly applied and very stupidly written tariff rates, the Trump administration’s brutal detention and deportation campaign continuing in full force, and the US’s creep towards a Russia-style oligarchy well underway, I’ve found some solace and inspiration in a somewhat unexpected place: A nonfiction account of a bloody revolution.

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Before we go on, a quick appeal: I keep all the reporting and critical analysis I do at Blood in the Machine—at least one story per week but often more—out from behind a paywall. In the last week or so I have written about economy-crashing chatbot-shaped trade policy, reported on OpenAI’s insult to Studio Ghibli and to art itself, and deconstructed the new American tech oligarchy. I know it’s a bad time to be asking for support, but paid subscriptions are now my livelihood! If you can, help ensure I can keep doing this work for a few bucks a month, and get access to Blood in the Media posts like this one. All the best—and hammers up.

Signal to Noise - Cybernetic Forests

2025-04-06T15:51:34.000Z

This week I am delighted to be in Melbourne for the opening of Signal to Noise, an exhibition I've been co-curating with Joel Stern and Emily Siddons at the National Communications Museum in Melbourne. I've got plenty more to say about it, but the opening is this Saturday so I'm sitting on my thoughts until then. As we say on the website:

Signal to Noise explores how artists work with, challenge, or complicate the relationship between signals and noise—disruptions, glitches or interference—in communication technologies and the messages they send. These technologies include the internet, telephones, radio and television, artificial intelligence, social media algorithms, and even the sounds of the natural world.

If you're in Melbourne, come to the opening! It's going to be a blast.

More Info & Tickets

A sneak peak of opening day events on April 12 in Melbourne, which are included with your ticket to the exhibition:

Eryk Salvaggio (that's me). I'll be "opening the opening" at 1:30pm with a live (in person) narration of my film, Human Movie: Meditations on a Compression Algorithm, in a lecture-performance version called Human Presentation. The lecture will explore the shared (albeit problematically so) metaphors of AI and humanity, complicating them in all directions.

At 2:30, Explore the boundaries between human, animal, and machine with Kombumerri DJ and producer Rowan Savage (salllvage). There will be a live performance with crow recordings and home-brewed AI samples, followed by a conversation with Dr Joel Stern on Rowan's immersive sonic installation in Signal to Noise, Carrion Sentience.

At 3:30, from New York based duo elekhlekha อีเหละเขละขละ comes a participatory performance of "Jitr จิตร –Extended Gong Ensemble," a live-coding and sound-making performance where audiences are invited to bring found objects to contribute to a collaborative sonic experience.

Get Tix

Podcast Alert: DOGE and the United States of AI

I was on this weekend's Tech Policy Press podcast to discuss DOGE and the flimsy premise of automation that driving the gutting of government services in the US, based on my recent article on Future Fatigue.

In this convo with TPP's Justin Hendrix, I'm joined by Rebecca Williams, a senior strategist in the Privacy and Data Governance Unit at ACLU; Emily Tavoulareas, who teaches and conducts research at Georgetown's McCourt School for Public Policy and is leading a project to document the founding of the US Digital Service; and Matthew Kirschenbaum, Distinguished University Professor in the Department of English at the University of Maryland.

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Welcome to the age of AI-generated economic collapse - Blood in the Machine

2025-04-04T16:57:35.000Z

Greetings fellow bloodied machinists —

The fun never ends. Last week was marked by national security-threatening Signal chats and the socially mediated march of fascism; the broad daylight disappearing of students and state-produced content humiliating immigrants. This week, we’re dealing with all that *and* a haphazardly instigated global trade war that threatens to tank the US into recession—and which sure seems to have been hashed out by ChatGPT. That, of course, is the product offered by the company that, a few days earlier, closed the single largest private tech investment deal in history.

So in this newsletter, we’ll tackle

How we came to get AI-generated tariffs in the White House, and
How “AGI” helped enable those tariffs—and landed OpenAI a record-breaking $40 billion in investment this week.

Since the two are connected, let’s start with AI-generated tariffs. Buckle up, and I hope everyone’s hanging in there, and stocking up on imported goods and good takes.

How we got to AI-generated tariffs

I do find it darkly funny—look we have to laugh, right?—that we’ve spent the last month or so embroiled in another round of debate and speculation about whether super-intelligent AI is on our doorstep, and asking probing questions like “Will an AI emerge so powerful that it will utterly transform the world as we know it, throwing millions out of work, or worse??” only to see the White House use AI in a way that risks doing precisely that, but in a way that can only be described as super-dumb.

You may have seen the gist of this explained already, so I’ll keep the recap brief: Basically, when Trump announced the tariff rates at one of his typically meandering press conferences, analysts were pretty stunned by how large and punitive they were, and by the fact that the figures didn’t seem to make much sense. That is, until the finance journalist James Surowiecki figured out that the Trump admin hadn’t calculated tariff rates at all, as they said they had, and had instead just taken the US trade deficit with a given country and divided it by that country's exports to the US. This, as Surowiecki put it, is “extraordinary nonsense.”

And how might the Trump administration have come to decide that this extraordinary nonsense was in fact a good way to craft global trade policy? Well, it looks like AI chatbots told them it was! The basic formula Surowiecki ID’d was, as the VC and AI advocate Krishnan Rohit and others pointed out, the very formula that every major AI chatbot suggested when asked “how to impose tariffs easily.”

Others, including yours truly, have replicated the results.

Now, the Trump administration will probably not admit to using a chatbot to create tariff policy that has sent markets tumbling in what are now the worst days of trading in years (the Dow is down 1,600 points at time of writing). But it more than fits the bill; the Trump administration, after all, is reckless, impatient, and high on American-made AI. And it would be tempting to answer the question of why Trump’s team turned to AI for such a momentous task with ‘because it was there’ or ‘because they could and they are lazy’ and leave it at that, even if that probably gets you most of the way there.

But there’s another factor, and it is related directly to OpenAI finalizing that massive infusion of cash earlier in the week: The AI industry has rather relentlessly been touting its products’ awesome power and capabilities for years, and, as we’ll see below, that promotion has been especially acute in recent months. If one of Elon’s DOGE lackeys was told to come up with tariff rates for Trump’s big Liberation Day announcement, he would probably consider the technology perfectly capable of calculating them. (Observers like point out that the equations and explanations the Trump admin shared as proof it did in fact calculate the rates seem quite rushed.) We’re a couple of years away from all-powerful AI systems! Surely AI can spit out some tariff rates.

This is actually a pretty great example of the kind of assumptions commercial AI systems encourage—they’re sold as ultra-powerful, or about to become so, and thus many users are prone to over-index on their capabilities. They encourage laziness, or “cognitive offloading” as researchers more diplomatically put it, and deter critical thinking (like the kind that might lead one to ask whether it is a good idea to impose tariffs on uninhabited islands). They’re adept at tasks that require ‘good enough’ output—say, producing marketing email copy, images for corporate power point slides, or tariff rates to be printed out in very small font on a poster board to be gestured at by a guy who really just wants to stand at a podium in front of the cameras and hear himself talk for an hour.

The bottom line is that the AI industry has created a permission structure for use cases like this one, with its “incredibly capable” AI systems, and AGI just on the horizon. (And what’s more, I’d take odds that the staffer or intern or DOGE employee or whoever did this may successfully be lobbying to keep his job right now on the grounds that well it was the AI that did this, not me! Because AI can also be used as an accountability sink!)

Look, I’m begging people to take this all-time example and look at the yawning gulf here—between the marketing promises of the always latent “all powerful AI that benefits all of humanity” and the reality of “White House intern uses ChatGPT to generate trade policy that might cause a global recession.” Predicted benefits, and proven harms, to paraphrase the scholar Dan McQuillan. We cannot blame this disaster on AI itself—it is, of course, also an enormous user error. But still: This is how AI is being used, in practice! This is what AI is delivering us, right now! It’s being used by the cruelest and laziest people—who may or may not actively want to crater the US economy in the hopes that it consolidates their power—to automate critical decision making.

As for how we arrived at this juncture, where so many are left to assume AI is imbued with some degree of super-intelligence, and with even more on the way, well that’s the subject of part II of this very newsletter.

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Selling “AGI”

This week, OpenAI closed yet another titanic and oddly structured deal. On paper, the maker of ChatGPT is now more valuable than McDonalds, Chevron, or Samsung, despite evincing few to no signs that positive cash flow is anywhere on the horizon. So how do we account for an investment firm, even one as profligate as SoftBank, dumping a historic amount of cash into it? Well, let’s consider at least one significant ingredient at work here—the promise of the above-mentioned “AGI.”

Last month, there was a flareup in mainstream AGI discourse. Two New York Times columnists, Ezra Klein and Kevin Roose, each published pieces forecasting the imminent arrival of Artificial General Intelligence. “The Government Knows A.G.I. Is Coming,” read the headline of Klein’s March 4th piece, which inspired a round of online chatter, spurring folks like Matt Yglesias to chime in with agreement. Roose’s column, originally tilted “I’m Feeling the A.G.I.” was even more direct with its second: “Powerful AI is Coming. We’re Not Ready.” Takes were written, critics were chastised, the case for AGI advanced and argued over.

During the month that these columns were published, OpenAI was, per the Wall Street Journal, negotiating its latest—and largest—funding round, led by the notorious investment firm SoftBank. On March 31st, news broke that a $40 billion investment was finalized, and that OpenAI had closed the largest such deal in history.

The now $300 billion-valued OpenAI headlined its own official announcement of the investment “New funding to build towards AGI.”

The brief release notes that:

We’re excited to be working in partnership with SoftBank Group... Their support will help us continue building AI systems that drive scientific discovery, enable personalized education, enhance human creativity, and pave the way toward AGI that benefits all of humanity.

The promise of AGI, we can infer, is a leading reason why SoftBank was willing to invest historic sums in OpenAI—these releases serve as a confirmation of purpose to participating investors as well as a framing device for the press. As such, I’d love for us all to look at this timeline and interrogate the role that the construct of “AGI” plays in feeding the mythology of marketing and attracting investment for big AI firms.1

In his column, Roose talks about how widespread the belief that AGI is imminent is in Silicon Valley and around the Bay Area, how people talk about “feeling the AGI” at house parties—and I have experienced that too, in my trips to the Bay. But it’s still a radically industry-centric belief; even if it’s one not purely motivated by money or portfolio-boosting. Such trends, even manias, have gripped Silicon Valley plenty of times before. I can recall lots of Silicon Valley functions where folks were talking about crypto, or “Uber for X,” or social apps with similar zeal.

Would OpenAI have closed this deal with or without the renewed excitement about AGI in The New York Times? Who knows? Probably! This is the firm that invested $16 billion in WeWork, after all. But the added dash of AGI inevitability certainly didn’t hurt. More importantly, it was already in the air—and we should understand how this AGI mythology has to be continuously cultivated, updated and upgraded, so that it still commands meaning to the press, and more importantly, to potential investors.

I’ve been beating this drum for a while.2 Last year, while researching a report for the AI Now Institute about the generative AI industry’s business models—an undertaking that involved combing through OpenAI’s blog posts and press materials from its earliest days on—I noted each time the term ‘AGI’ surfaced, and in which context.

To quote from my own report:

Tracing the usage of the term “AGI” in OpenAI’s marketing materials, patterns emerge. The term is most often deployed at crucial junctures in the company’s fundraising history, or when it serves the company to remind the media of the stakes of its mission. OpenAI first made AGI a focus of official company business in 2018, when it released its charter, just after it announced Elon Musk’s departure; and again as it was negotiating investment from Microsoft and preparing to restructure as a for-profit company.

The pattern continued through the launch of ChatGPT, and continues today. In February, amid the negotiations with SoftBank, Sam Altman published a widely covered message—Roose cited it in his AGI column—that pronounced, “Our mission is to ensure that AGI (Artificial General Intelligence) benefits all of humanity… Systems that start to point to AGI* are coming into view.” Altman then lists three observations about modern AI, including this one:

The socioeconomic value of linearly increasing intelligence is super-exponential in nature. A consequence of this is that we see no reason for exponentially increasing investment to stop in the near future.

We’re used to overblown pronouncements from Altman and the AI sector, but this is a wildly audacious statement even by those standards. And I think it’s a finely crystallized example of why we should be suspicious of broader AGI claims. Here we have the CEO of the company at the vanguard of the AI industry, beginning his message by declaring that AGI is almost here, and the takeaway is literally and without exaggeration you should invest exponential sums in our company.

Even Dario Amodei, the CEO of Anthropic, OpenAI’s top competitor, has said that he’s “always thought of AGI as a marketing term.”

I’m not even saying that AI is “fake,” as Roose’s colleague Casey Newton might accuse me of—I also think it’s folly to argue that, myriad ethical questions about training data and resource usage aside, LLMs aren’t impressive in a lot of ways—I’m saying that there is a particular tactical approach these companies take, deploying “AGI” to woo investors and promote the power of commercial products in the press, and that it’s not new, but it’s still working. I’m saying we can discuss the promises and risks of these technologies without using a framework that pretty directly serves the industry’s interests. A $40 billion investment! In a company that’s losing $5 billion a year! Even if you are an AI aficionado that has to boggle the mind on some level. And, sure, it may be that investors may simply be that impressed with the technology, or that maybe they’ve even been treated to demos no one else has, or seen a secret business model that stands to ramp up OpenAI’s revenues starting first thing next year—but largely I think it’s blunter than all that: that the very concept of AGI has been rendered in such a way that it’s simply too enticing for large investors not to buy into.

In its charter, OpenAI defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.”3 OpenAI has defined AGI that way since 2018. This promise, that OpenAI stands to automate all meaningful work, is the big one. That’s been the dream of industrialists for 200 years—a world without workers. What if OpenAI can do it? All those other investors seem to think it can, or at least has a shot at it, and the rest of the entire industry, from Google to Anthropic to Microsoft, is behaving like they can too. AGI has collapsed all other horizons in Silicon Valley around this one promise. That’s how you get $40 billion in a single funding round.

That’s how you get a world where more and more people buy into the idea that AI can do nearly anything, like automate handcrafted animation or replace federal workers with chatbots—or auto-generate national tariff policy on the fly. A world where we are promised AI systems so powerful and so radical that they risk throwing everything into chaos because we are not ready for their immense capability. But where in reality, they are used today to unthinkingly dictate crucial trade policy that tanks the global economy. The events of this week have at last made this point clear.

The true threat to the global economy and our social order is not super intelligent and powerful “AGI.” It is the thoughtless people in positions of power using the products sold by the companies who promise it.

That’s it for this week. Thanks as always for reading. And again, if you appreciate critical analysis of AI, Silicon Valley, and our tech oligarchy, consider becoming a paid supporter so I can keep doing it. Take care out there.

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Notably, I’m not suggesting that Klein and Roose are intentionally helping to juice OpenAI’s valuation or anything like that. In fact, at the risk of having my AI critic card revoked, I’ll even say on record that I like Kevin, and as a former tech columnist myself, respect his columnistic craft—columnists are supposed to infuriate people, and what can I say, he’s got some good hooks. He also reads this newsletter, I believe, so: Hello Kevin! Have me on Hard Fork sometime so we can argue about all this. That said, I do vehemently disagree with his AI analysis (and crypto and web3 analysis even)—and that really good software automation does not portend AGI, which is, again, I think, primarily useful as an investor-focused marketing term—and think in fact that such framing has directly help pave the way for the AI firms to concentrate capital and power and to sell their specific vision of automation technology to clients, investors, and the world.

So have many others! It’s worth noting, as has been pointed out by critics and scholars like Emily Bender and Alex Hanna—their new book “The AI Con” is coming out soon, btw—that “AI,” or artificial intelligence, was once deployed as “AGI” is today. AI was a marketing term coined in 1956 by pioneering AI researcher John McCarthy to help get funding for a summer study, and it came to mean a computer system that could, or aspired to, think like a human. To do anything a human could do. AGI, or artificial general intelligence, was coined in the late 90s after the meaning of AI had been diluted, and was more commonly used to describe software programs that could autonomously perform narrow tasks.

Emphasis mine.

The powerful, kid-friendly tech criticism of one of the best Studio Ghibli films - Blood in the Machine

2025-03-30T12:03:20.000Z

One of the reasons that the AI-generated Ghibli meme phenomenon—and especially the OpenAI-perpetrated Miyazaki disrespect—irked me so much is because we’re a Studio Ghibli family over here. Sorry Pixar, but no animation studio has a better hit rate for films the entire family can legitimately enjoy. My Neighbor Totoro and Ponyo are favorites, but we also field a lot of requests for deeper cuts like The Secret World of Arrietty and The Cat Returns. We caught Howl’s Moving Castle on the big screen during a recent Ghibli fest, and it was great.

Ghibli films, it barely needs to be said, are lovely and strange and shot through with wonder; most are singularly imaginative and all are beautifully drawn. They are always, always, a welcome respite from the Despicable Me and Ice Age sequels. Importantly, they never condescend to children. In fact, Miyazaki often populates his films with young people who must navigate rather adult themes on purpose, emphasizing the honesty and goodness of children, as he’s put it in the past.

This is central to what is not only my favorite of the kid-friendly Ghibli movies—my apologies to Totoro—but also happens to be one of the most powerful works of technology criticism aimed at young people, period:

Unsorted Depths - Cybernetic Forests

2025-03-30T11:04:45.000Z

Projection, edited footage filmed at Jacob's Pillow (present day recordings by Carlos Johns-Davila overlaid with archival footage).

The purpose of an archive is to activate memory, but the memory produced by an archive reflects a distillation of memory. The archive is a site that activates a selection of memory. Ellisions and gaps that existed beyond the frame of its memory structure exert their own kind of presence and influence, a negative space of what is forgotten that structured what was not.

Information, like the physical world, experiences entropy: an erosion of form and structure over time. Things eventually dissolve into noise unless we resist entropy. Preserving information states in an archive or a dataset slows down the process but can’t stop it. To stop entropy would be to stop the universe's movement, the disentangling of the tangled atoms, or stories, that make it.

This week, I have been at Jacob’s Pillow as a PillowLab resident with the Choreodaemonics Collective, a collaborative ensemble of artist-technologists founded by Laurel Lawson and Sydney Skybetter, developing a way to activate its vast archives through robotics and visualization while centering the presence of the body: how knowledge enters into bodies, is transmitted between bodies, and transforms through embodiment and re-embodiment.

The title Unsorted Depths draws from Aleida Assman’s concept of the archive as an ordered space that structures the retrieval of loosely associated memories. Unsorted Depths visualizes the archive — the idea of the archive as a cultural formation, but also the specific archive of Jacob’s Pillow, but also the field of memory and forgetting — as noise: “the archival embodiment shifts endlessly from contact to dissolution and reformation,” as we write in the performance notes, aimed at creating an embodied experience of these tensions for the audience.

The piece is awash in a visual language of noise and blur, cycles and improvisation. Noise, obviously, fascinates me as a state of vast combinatorial potential—atoms, electrons, and neurons waiting to be arranged. It is also the state to which all those atoms, electrons, and neurons return, entering the cosmic bank of resources toward the new.

Laurel Lawson, Andre Zachery and Allison Costa in a rehearsal of "Uncharted Depths" at Jacob's Pillow, March 24 2025.

Against a backdrop of cosmic noise, data loss, and the lost arrangements of atomic structures we call the “past,” a presence emerges, moving between the backdrop of forgetting and lost-ness into the archival record. And from the archival record (visualized here as a second screen) we enter into a multitude of new relationships with what the archive has been distilled. I call this The Presence, a shimmering visualization of memory, which floats against the screen, eliminating noise in its trail, offering a shadow through which we can gaze at what's left of the past.

Careful to balance the tension between dissolving, freezing, and regeneration, the presence can be shaped into any direction. The first is through the technological achievement of the show, a floating assemblage of fabrics into the loose form of a body that copies a range of poses held within the archive of these dancers which date back to the founding of the site by Ted Shawn.

The Presence and the the Fabric Body at rest at the start of "Uncharted Depths."

The robot – a series of pulleys, built for this installation by Tighe Costa – that lift and spin fabric to match the poses from the archive, and later, the positions of the live dancers – initially performs these motions as a robot does: interpolating positions in inhuman ways and sorting the past into a series of poses. The human form intervenes here: our dancer becomes a conduit, first shifting the presence of memory from oblivion — a performance of the gesture of preservation at the heart of archival practices. Then, the human body moves forward, interacting with the robotic presence, embodying and evolving this archive into new forms and embodied performance. In the end, with a lovely re-interpretation of The Presence shaped with projection designed in collaboration with Carlos Johns-Davila.

Through connections between the robot and other dancers, the spectral presence — the memory of the archive — moves between them, devouring noise with each step, replicating this tension between the recombinatorial potentiality of noise and the locking-in-place of our specific arrangements.

I am a stranger to dance, but I am excited by the work as a way of thinking about the issue of embodiment, the contrast between embodiment of performance — which includes a tradition of observation and response, a requirement to learn the performance from someone who has learned the performance, and so on and so on. But embodiment also establishes a kind of loss, and this loss allows the establishment of new arrangements. As Cécile Malaspina notes, each decision we make narrows the possibility of events away from noise and more toward constraint; this is simultaneously an opening and closing of possibility.

The presence is a blurred abstraction of a sampling of history, taken from a prior retreat to the Jacob's Pillow archive with Andre Zachery and Colin Clark searching through relevant archival video. In the end we arrived at a selection of slices of performances across time and space at the archive, mapped not by their connections but by possible arrangements between those two bodies. This choreography of images placed one body into a sequence with the next closest pose. This reference was made by human eyes scouring more than 60,000 still frames rendered from the video archive.

To create "The Presence,” I thought about this relationship between potential and constraint through interpolation. This very basic machine learning process analyzes a hand-selected assortment of frames. It shifts pixels between them, tracking the body not as a body but as color information embedded into pixels and transformed, step by step, to meet the constellation of pixels in the next image.

💡

For more on Interpolation, read "A Brief History of Static" from 2023.

The interpolation is necessarily blurry, as it is a permanent state of generative possibility: a vast number of possible poses at once, but constrained, as it was, by the connections, or lack of connections, by the two dancers placed beside each other in the sequence.

A still image of "The Presence," an interpolated video of various curated still from the video archives of performances at Jacob's Pillow.

There is a tension between this noise — the entropy of past states of the universe, and the information loss inherent to the passage of time — and the preservation of these states in human memory (and the technologies of memory we invent). Another tension exists between the constraining grip of memory and the freedom of letting go. Let go of too much, and you dissolve into noise; hold on to too much, and you block the noise from entering into you, limiting the motion of new possibilities and inner sense-making. It is this space that Unsorted Depths presents as a stage from which dancers are able to improvise their way, through movement, to resolve these tensions with their own emphasis, the direction they want to head in the given moment.

There is also an activation of place in the work, drawing on footage of surrounding trees and archival footage of the barn planks that make the buildings; the noise seeps and dissolves through these structures — through the trees and footage, the bodies in the archive and the bodies in the present — evoking their memory, acknowledging the forms these memories take in the ways the absent shapes and organizes the structures of the present. These unsorted ghosts steer us in ways we cannot see clearly. Nonetheless, these paths give us direction — paradoxically sorting the world in unknowable (and therefore unsortable) ways.

The crew for this edition of the Pillow Lab residency – intended for the development of a work in progress, which this is – was Laurel Lawson, Sydney Skybetter, Kevin Clark, Andre Zachary, Sage Ni'ja Whitson, Colin Clark, Tighe Costa, Allison Costa, Carlos Johns-Davila, and myself; the producer was Michelle Daly with Jess Massart serving as our outside eye.

Laurel Lawson, Andre Zachery and Allison Costa in a rehearsal of "Uncharted Depths" at Jacob's Pillow, March 24 2025.

Upcoming Public Events

April 2: AI & Art: Cutting the Strings
Hamilton College, Clinton, New York

AI & Art: Cutting the Strings

How might generative AI shape the possibilities of performance? Eryk Salvaggio is an “anti-AI AI artist,” interested in contrasting the definitions of creative process in AI with those of the human. In this talk, Salvaggio will present ongoing artistic re

Hamilton College

April 5: Residual Noise
Brown University, Providence, Rhode Island

Through spatial audio concerts, installations, and a conference, this three-day festival highlights a wide range of work that takes place in sound: noise, voice, audio narrative, field recording, and related sonic practices. Residual Noise is a collaboration between Brown and the Studio for Research in Sound and Technology (SRST) at RISD with events occurring on both campuses.

Residual Noise

April 3-5, 2025 CONCERTS | CONFERENCE | SOUND INSTALLATIONS A three-day festival highlighting a wide range of contemporary sonic practices by prominent artists and scholars from Brown, RISD, and beyond.

Brown Arts | Brown University

April 12: Opening, Signal to Noise
National Communications Museum, Melbourne, Australia.

In person in Melbourne for the opening of Signal to Noise, curated by Joel Stern, Emily Siddons, and I. Signal to Noise explores how artists work with, challenge, or complicate the relationship between signals and noise—disruptions, glitches or interference—in communication technologies and the messages they send. These technologies include the internet, telephones, radio and television, artificial intelligence, social media algorithms, and even the sounds of the natural world.

Includes works from Nam June Paik, George Brecht, Lillian Schwartz, JODI, elekhlekha อีเหละเขละขละ, Craftwork, Rowan Savage, Mimi Ọnụọha, Machine Listening, and Eryk Salvaggio.

April 16: AI Week, Film Screening w/Q&A
Alfred University, Alfred, New York.

Info coming! In-person.

April 28: AI & Artistic Practice:
Sam Pluta, Brea Souders, and Eryk Salvaggio
University of Maryland Baltimore County, Baltimore, MD.

AI and Artistic Practice: Sam Pluta, Brea Souders, and Eryk Salvaggio

UMBC:

May 3: Human Presentation
AIxDesign Slow AI Festival, Loods6, Amsterdam NL

In-person: Live performance of the film "Human Movie" with Q&A.

AIxDESIGN Festival: On Slow AI

On Slow AI

AIxDESIGN

May 6: The Hypothetical Image: Reading the Artifacts of Generative AI
The Photographer's Gallery, London.

In-person artist talk.

Artist Talk: Eryk Salvaggio

The Hypothetical Im

The Photographers Gallery

More to come!

Get your Signal group chats together and get out there - Blood in the Machine

2025-03-28T23:51:54.000Z

This was a bad week. I’m not sure I’d try to justify a claim that it was particularly bad in the scheme of an already interminable parade of bad weeks, but it was certainly bad. Maybe it was the combo of malicious executive incompetence in SignalGate with demonstrations of state power that openly aspire to fascism; the daytime abductions of students and socially mediated migrant Salvadoran prison tours with Trump officials. Maybe it was Trump assaulting the federal unions head on, or Musk’s open efforts to bribe Wisconsin voters.

Regardless, it all comports with the peculiar and predictable behavior of the ruling American oligarchy (which I took a stab at deconstructing earlier in the week), and it can all get pretty dispiriting, or worse. I got an email from a reader this week saying they felt they had to unsubscribe; while they’d continue to be a paid supporter to back the work, hearing the news of what’s happening to the country was simply too much for their mental health. I understand that fully. The onslaught is relentless. I felt like I was losing it myself more than a couple times this past week.

But there are always things we can do, and things we can do right now. And don’t stop using Signal—in fact, use it to get organized.

First, the doomscroll, in fast-forward, a reminder of the stakes:

Video surfaced of masked federal agents abducting a student visa-holding legal resident of Massachusetts without due process, threw her in an unmarked van and shipped her to a detention center in Louisiana, where she joins another legal resident to which they have done the same.1 (She wrote an op-ed in favor of her university divesting from Israel, he helped organize a campus protest. Neither are charged with any crimes.)
The head of Homeland Security, Kristi Noem, took a trip to El Salvador to pose in front of prisoners, some of whom are migrants that the US has shipped there, again without due process, to make some content for X.
In an interview with Fox News, Elon Musk, his voice quivering with fear and with fury, proclaimed that Trump had assured him they were going to “go after” critics and people organizing Tesla protests. Musk is also delivering $1 million checks to quite literally buy votes in the Wisconsin Supreme Court elections, and is also continuing his campaign to break the federal government—this time, it’s Social Security, where he wants to replace the workers who administer benefits with AI.
Thursday night, Trump signed an order to end collective bargaining for most unionized federal workers, seeking to dissolve contracts for some 700,000-1,000,000 civil servants.

And then of course there was the Houthi PC Small Group affair. A truly astonishing tale of executive imbecility for our times. Michael Waltz, Donald Trump’s national security adviser, accidentally added the editor-in-chief of the Atlantic, Jeffrey Goldberg, to a Signal group chat along with Defense Secretary Pete Hegseth, Vice President JD Vance, and many others, to plan and discuss a forthcoming bombing operation in Yemen. Without ever noticing Goldberg was present, the planned the attack, shared sensitive classified information, and celebrated its completion.

The Trump administration’s response to the story can best be described as a shrug mixed with some of the least convincing lying you’ve ever seen. Trump himself, of course, can barely be bothered to grasp what actually happened or what Signal even is, and the revelations have led to several more cases of comically bad operations security. WIRED discovered that Waltz and other top White House staffers have left sensitive information public on their Venmo accounts, and Der Speigel reports that classified details, usernames, and even passwords on easily accessible websites.

As a result, the Trump administration and even a number of bipartisan commenters questioned the security of Signal, prompting a number of experts to have to explain that, no, the problem isn’t Signal, which remains the best and easiest to use encrypted messaging app, but you—if you happen to be an idiot while using it. Signal is secure, and you can trust it, if you practice good personal security hygiene. Ie, don’t store your Signal password online, have a strong password on your phone if you have Signal on that phone (and just have a good password anyway dummy), using Signal’s nickname feature, and so on.

Ok ok ok. You get it; there’s a lot. We are being ruled by cruel vainglorious men, many of whom are so hubristic and unthinking it leads them to do very dumb things, and many others who are just quite actually dumb. They are perpetuating what can fairly be described as a terror campaign against migrants and dissidents, and an intimidation campaign backed by the weight of the state against protestors and organizers. It is bleak.

But! There is still much to be hopeful for, and at the very least to fight for. As the writer Osita Nwanevu put it on Bluesky: “We are deciding, right now, whether the aspirations we hold for this country are finished. Whether we're going to let these hoodlums and grifters throw what's left of the American idea into the garbage for their own profit and gain.”

It is easy to succumb to the doomscroll. Let’s not, if we at all can prevent it. (Is there a word for wanting to do something, but feeling paralyzed by the myriad torrents shit raining down? How about doomblocked, is that something. Let’s not get doomblocked. Get on Signal—we are going to need safe, protected communications now more than ever to protest this state—and get organized.

Some ideas:

Tomorrow, on Saturday March 29th: Join a Tesla Takedown event for its global day of action. These are getting bigger and better attended every week, and at the last one I went to I met folks who’d never been to an organized protest before, but the march towards authoritarianism inspired them to get out in the streets. Do it. I’ll be heading to one in West LA, if you’re around, say hi. If you’re anywhere else, use this website to locate an event near you.
The next few days: Volunteer to help do virtual phone banking in support of the Democratic Supreme Court candidate in Wisconsin—the seat that Musk is currently trying to buy.
Tuesday, April 1st: If you’re in California, the UC labor unions are set to strike. Join a picket line and show your support.
Anytime: Download Signal, invite your colleagues, and start talking about organizing. Don’t accidentally add any Trump administration officials.
Anytime: Sign up to join the federal workers unions as an ally, as they organize to confront DOGE and Trump’s latest executive order to eradicate their right to bargain. Read
Anytime: Follow Mariame Kaba on Bluesky for a frequently updated stream of volunteer and organizing opportunities.
Anytime: Read Hamilton Nolan and start thinking about a general strike, for real.
Anytime: The AI companies promoted their latest products by desecrating Studio Ghibli this week, so support the real thing by going to see a new 4K remaster of Princess Mononoke in theaters.

I am aware that this here blog can get heavy on the gloom—the nature I guess of writing about the blood in the machine—and folks have asked for more actionable solutions. Hope this helps out in that regard. One other thing you can do is pitch in here; this is the third story I’ve written this week, and I can only do this work with reader support. It’s just a few bucks a month, if you can. Thanks to all who do, and to everyone out there picking up the proverbial hammer.

We’ve got to get out there and make some noise, build community and tank some Tesla stock. Hammers up.

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If you’ve been anywhere near any kind of screen over the last week, then you’ve seen the chilling video of a crew of plainclothes men and women, clad in masks, arrest a Tufts University student and load her into an unmarked van. The masked officers were ICE officials, and Rumeysa Ozturk is a Turkish national with a valid student visa. However, last year, she wrote an op-ed politely asking Tufts to comply with a student union vote to disclose its investments and divest from Israel. That was enough to make her a target of the Canary Mission, a hardline pro-Israel site that keeps tabs on students whose speech it dislikes, and now, like Mahmoud Khalil before her, Ozturk has been transported by anonymous ICE agents a thousand miles away from her home to a detention center in Louisiana. There is no warrant for her arrest, and she has not been accused of, nor charged with, a crime.

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Amsterdam

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Zurich

Stuttgart

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Upcoming Public Events

April 12: Opening, Signal to NoiseNational Communications Museum, Melbourne, Australia.

Get your Signal group chats together and get out there - Blood in the Machine

April 12: Opening, Signal to Noise
National Communications Museum, Melbourne, Australia.