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A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion. Remember the good old days of GPT-3 when we could ask the chatbot anything and it would almost instantly and very confidently spit back a plausible but false answer? That’s when the word “hallucination” as applied to AI answers entered the lexicon. Six years and several versions of a few different chatbots later, I still can’t trust the answers I get, even though Claude or Gemini often do useful work for me. That’s the thing about current AI: Trust but verify. And verify you must. Apply that maxim at scale to maintain the integrity of the scientific record and you get an idea of the challenge facing publishers of peer-reviewed scholarship. In “I Used AI to Fake a Scientific Paper,” freelance writer and doctor of applied mathematics Benjamin Skuse tells the story of the human gatekeepers trying to beat back the rising tide of AI slop by attempting to get his own AI-generated papers past IEEE’s defenses. “He basically hit ‘run experiment’ and ‘write paper.’ And then he didn’t do a damn thing to it.” —Eliza Strickland As features and AI beat editor Eliza Strickland told me, Skuse was surprised at just how effortless it was to generate a paper that was more or less plausible and more or less science. For one of his experiments, “he basically hit ‘run experiment’ and ‘write paper,’” she said. “And then he didn’t do a damn thing to it. He just launched it into the maw of the publishing engine.” Skuse discovered that the combination of IEEE’s human-review processes and research-integrity checker programs are currently keeping pace in what amounts to a cat-and-mouse game against people using free tools. But the more advanced LLMs are no longer making the same dumb mistakes the free versions do. That leaves publishers locked in an arms race between increasingly plausible slop and the systems aimed at detecting it. Scholarly publishers aren’t the only ones trying to hold the line against slop. So is that global depository of human-generated knowledge, Wikipedia. For this month’s Five Questions, Strickland interviewed Ellia Lebleu, a founding member of WikiProject AI Cleanup, a volunteer effort to detect and delete AI-generated material from the platform. Lebleu’s team is confronting the same problem as commercial publishers, but with fewer people and fewer tools. “Wikipedia is known as a repository of human-generated information, and they want to hold on to that,” says Strickland. “Who knows how long they’ll be able to do that.” If we can’t collectively assume that the scientific record is trustworthy or that basic facts about the world are accurate, then our understanding of what is real unspools into uncertainty. Perhaps that delights quantum scientists and the grifters who thrive in an information environment where there is no shared reality. But that uncertainty ultimately undermines trust in the institutions that exist to curate the corpus of human knowledge and to promote the advancement of science and technology for the benefit of humanity. Or maybe in a world where AI is doing the science and writing the papers, it won’t matter whether or not humans trust the outputs. As Strickland put it, “Doesn’t it just feel crazy that we’ll have AI writing for other AIs at some point? We’ll just get left out of the loop.”
It’s disheartening how much power gets generated and then promptly lost as it travels through grid networks. This leaking of electricity happens when it vanishes as heat as well as when it is pilfered by thieves and nonpaying customers. More than half of the countries that track these metrics lost at least 10 percent of their electricity in 2023, according to the World Bank. Losses topped 20 percent for 24 of those nations. Two countries lost more than half of what they generated. With numbers this high, cutting down on losses makes sense. Electricity demand is rising beyond what many grid operators can supply; reducing waste would help meet some of that demand without having to build new power plants. Plus, when the power comes from fossil fuels, any loss means emitting even more greenhouse gases into the atmosphere. And electric losses hit the bottom lines of power providers, which ultimately pass those costs on to everyone else. The trouble is, reducing electricity losses is a hard and expensive process that takes a long time. Typically, the less maintained the grid infrastructure, the more electricity that’s lost. And the more fragile the region’s law enforcement and government, the more prevalent the power theft. Natural disasters and war make things worse. Delhi’s Power Grid Comeback Fixing a power grid requires a systemic approach across many sectors. There’s no one technology that will solve the problem. At the outset, the obstacles to success may feel insurmountable. Equipment across entire grid networks must be updated. Multiple arms of government must agree to reforms and coordinate to ensure power providers are set up to succeed. Regulations must be written or revised, investments made, cultures changed. The city of Delhi did all those things. Over the past 25 years, it cut its electricity losses from about 50 to 5 percent. How the city pulled off that impressive feat is the focus of “The Epic Comeback of Delhi’s Power Grid” by Mini Shaji Thomas, an electrical engineer at the university Jamia Millia Islamia who has lived in Delhi since the 1990s. She gives us a view from the inside—as a resident and a power systems expert. Delhi is a shining example, but some other regions have significantly lowered electricity losses over the last quarter century too. The country of Georgia went from losses of over 16 percent in 2002 to about 8 percent in 2023. In Singapore, losses dropped from 6.6 percent to a nearly nonexistent 0.2 percent over the same time period. Global Electricity Theft Crisis But there are many parts of the world where electricity losses remain a problem or have gotten worse. In Jamaica, where power theft is rampant, losses have hovered between 21 and 28 percent for years. Argentina’s losses nearly doubled between 2015 and 2023, going from an all-time low of about 12 percent to an all-time high of nearly 24 percent. The main problem: Transmission and distribution companies lacked the capital to maintain and upgrade their networks, which left equipment operating under stress. A delay in the installation of smart meters has allowed thieves to more easily siphon power and tamper with meters. Thomas says she hopes her account of Delhi’s grid comeback will serve as a blueprint for others. It’s possible to replicate the sweeping changes Delhi made, she says. But it “requires a concerted effort from all stakeholders, customers, the utility, the government, and their employees.”
If you own an at-home server, a gaming computer, or just a laptop that doesn’t get much love, listen up. You can now put that spare computing power to use and earn some passive income in the process. AI companies are hungry for more compute to run AI inference—the process of using a pre-trained model to respond to queries—and they’re willing to pay you for it. “Imagine Uber or Airbnb, but for AI inference computing tasks,” says Ilman Shazhaev, founder and CEO of Far Labs, based in Abu Dhabi. The AI boom has spurred on construction of massive data centers, often damaging local communities by raising electricity prices, straining local water resources, causing environmental damage and noise, and being just plain ugly. Huge data centers are likely not going anywhere—training new frontier models and running AI models from leading companies will likely still be the purview of these behemoths. But now, several companies are providing AI inference on smaller, mostly open-source models. They are running inference on pre-existing computing power spread throughout homes and small businesses, and compensating owners. “Everyone thinks the only way to do it is data centers. And data centers are extractive for the communities in which they’re built, and they don’t return services or taxes or much of anything to the people there. So why not just turn this whole thing on its head?” says John Federico, founder and CEO of Evolving Edge, in Austin, Texas. “The compute power is out there. If you can orchestrate it, then you’re actually adding value to those communities directly.” The idea isn’t entirely new: From 1999 to 2020, a volunteer-based project called SETI@Home used spare computers to search for signs of extraterrestrial life in radio telescope data, for instance. But now, commercial companies are eager to use the same strategy. Shazhaev’s Far Labs is launching its platform Far AI in the coming weeks, while Federico’s Evolving Edge is currently in open beta. Other companies, like Bless Network, Salad, and Gradient have started to provide similar platforms over the last year. Connecting to the network Federico has been a computer hobbyist since youth, and he has amassed a whole server in his basement to run his projects. “It just hit me one day, there’s all this talk about not having enough compute, and I just thought, well, 92 percent of the country has broadband, and you have people like me who have mini data centers in a closet,” he says. Federico sees the potential hosts as people much like himself who have already invested in home servers, and he aims to make the process of selling spare compute as seamless for them as possible. “Sign up for the program, install an application,” Federico says. “All we want to do is run jobs on your machine when you tell us we’re allowed to. The only thing we do is monitor the resource usage. And of course, you can give us a schedule.” With a large enough network of devices, the platform would have compute available whenever it’s needed. Privacy and security are primary concerns for such hosts. To reassure the users that their local data is secure, and that no malware will be downloaded to their devices, the team open-sourced their scheduling software. “The node software is open source, so anyone can look at it, see what it does. All we want to do is run jobs on your machine when you tell us we’re allowed to,” Federico says. Far Labs’ Shazhaev explains that the company’s software is designed around a principle known as “least privilege”: granting both the host and the user the least access possible to accomplish the task. Inference runs as an isolated workload with authenticated, encrypted communication and explicit limits on the GPU, CPU, memory, storage, and network resources it may use. Customers do not receive arbitrary access to the host machine, and providers can inspect resource use, pause the node, revoke access, and remove the software at any time. The protection also works in the other direction. Workloads are segmented and only the minimum required information is exposed to an individual node. Sensitive enterprise workloads can be restricted to controlled hardware rather than routed through consumer devices. Divide and conquer Massive data centers still have advantages from the user perspective: top of the line GPUs and CPUs, high speed networking, thick cables, and sophisticated cooling. User devices are usually less powerful, more varied, and less reliably connected to one another. “This is quite a difficult issue from the science angle,” Shazhaev says. “You want to do a similar level of tasks that are happening in those high infrastructure data centers, and run them on the user device with limited capacity.” Evolving Edge’s Federico says this is an issue for the largest, state-of-the art AI models. But those are not always needed and are often not even preferred. “There are numerous companies, once they reach a certain scale, suddenly paying for tokens on a state-of-the-art frontier model [that] no longer makes sense for their needs,” he says. “Instead, they are fine-tuning open-source models for specific tasks that they have in their business. These models don’t require anywhere near the resources that some of the state-of-the-art models do. It’s just using the right tool for the job.” Smaller, open-source models can often fit on a single user device. But if that fails, there are tools to split a single inference task over multiple GPUs or CPUs. Evolving Edge is using an open-source tool called Ray to perform this splitting, while Far Labs has developed its own proprietary software that not only splits the workload, but wraps the splitting in a layer of security and reliability-providing software. “One thing we have done is we shared the model,” Shazhaev says. “We take the model, we cut it into many pieces, then these pieces will be distributed through different devices. And we have an orchestrator and a load balancer which manage the task flow, so each device processes a part of the task. Then we combine the answers in the main brain, the orchestrator.” Through a combination of using smaller, more task-specific models, and splitting larger models between disparate devices, the teams claim they can perform inference much cheaper than a traditional data center “because we don’t have capital expenditure,” Shazhaev says. Gaming PCs are a common source of spare computational power in the home. Dizzaract The distributed advantage Not only is it cheaper to run inference this way, it is also more reliable, Shazhaev claims. The companies have access to a distributed network of computing resources, rather than one giant device that can experience outages. Shazhaev compares this to cryptocurrencies, and their resilience through decentralization. “Today, to shut down Bitcoin, you need to nuke the whole planet. Here, we have the same concept,” Shazhaev says. Federico explains that this resiliency would be beneficial not just for AI inference, but for all kinds of applications, including smart cities, environmental sensors, autonomous vehicles, and more. During an Amazon Web Services outage in 2026, for example, smart beds were stuck in their upright positions and their users couldn’t adjust them. Federico says that a distributed network where everything doesn’t need to be routed through a single data center, say, in Ashburn, Va., would make those kinds of outages much less impactful. “We could lose 100 nodes in a network of 250,000 and it wouldn’t matter,” he says. If the network of user devices is substantial enough, every job can be routed to a nearby device, decreasing the latency. Far Labs claims a latency of 100 milliseconds or less on its platform. The lower cost and lower latency of this approach may even enable new use cases, such as in-game AI video generation, which is currently prohibitively slow and expensive. “OpenAI last year had $30 billion in revenue, but they closed the financial year at an $8 billion loss. Why? The official reason is due to the high cost of inference,” Shazhaev says. “And those are mostly text models. For gameplay, you have audio, video, animations: It’s heavy data, and you need real-time responses. So, we’ve been trying to solve this issue.” All of these companies are trying to tap into an untapped resource of local compute, and hoping it’ll benefit the device hosts and users alike. “All these big guys are running around building data centers,” Shazhaev says, “but I believe there is enough compute power that already exists in the world.”
“The lowest-cost place to put AI will be in space, and that will be true within two years, maybe three at the latest,” SpaceX founder Elon Musk told the World Economic Forum in Davos this past January, as his company was preparing to go public. Later that month, SpaceX filed an application with the Federal Communications Commission for an orbital data center constellation of up to 1 million satellites in low Earth orbit, 500 to 2,000 kilometers above Earth. And just three days before the IPO, he discussed some initial design specifications for a new AI-1 satellite data center in a video interview. Musk is prone to hyperbole when it comes to timelines. Full self-driving cars by 2017. First human mission to Mars in 2024. Ten thousand Optimus humanoid robots by the end of 2025. Et cetera. For orbital data centers, which he says will be a cost-effective alternative to terrestrial data centers within three years, the math won’t make sense for several years, if ever. Consider this: There are roughly 14,500 active satellites in orbit. Musk’s Starlink constellation accounts for about two thirds of those. Both the launch cadences and satellite-manufacturing capacity would have to scale up astronomically to deploy a million orbital data center satellites. For context, there have been roughly 7,000 orbital launches in all of human history. To loft 1 million satellites into low Earth orbit on SpaceX’s Starship, which is designed to carry up to 60 satellites per vehicle, would require 16,666 launches exclusively devoted to satellite deployments. Considering that SpaceX launched a record 165 orbital missions in 2025, even at 10 times that cadence, it would take a decade. And how long would it take to build 1 million satellites, given Starlink’s current pace of around 4,000 per year and a generous tenfold increase in capacity? Short of a manufacturing revolution, try 25 years. The reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized. As this month’s cover story, “Why Orbital Data Centers Are So Hard” by Andrew Cavalier of ABI Research, makes clear, the reality is that the vision of massive constellations of orbital data centers is nowhere close to being realized. Dina Genkina, IEEE Spectrum’s computing and hardware editor, put the idea into perspective: “Starcloud (a startup that has applied to the FCC for an 88,000 orbital data center satellite constellation) sent one Nvidia H100 GPU in space so far. Their radiator was too weak to let the chip run at full power.” As Cavalier shows, cooling even a single Nvidia H100 GPU in space is difficult: It draws 700 watts, which will require 1.4 square meters of radiator at 60 °C. A 40-kilowatt rack of servers will need an 80-m² radiator; a 100-megawatt data center will require 2,500 of those radiators. Some astronomers are understandably concerned that a million satellites with giant radiative wings would blot out the stars. So if the economics doesn’t make sense, if the chips are at the mercy of the radiative ravages of space, and if humanity will lose its view of the stars, not to mention increasing the risk of triggering the Kessler syndrome, why are the hyperscalers hyping orbital data centers? Genkina offered the obvious answer: sweet, sweet moolah. “The Elon Musk part of it is honestly genius because he’s got xAI building the data centers, SpaceX sending them to space, and Tesla building solar panels,” Genkina says. “It’s almost like he’s paying himself.” Two Analyst’s Views of SpaceX’s Proposed AI1 Data Center Satellite Michael Pierce, Principal at Technology Strategy Partners Musk’s timelines are notoriously overly ambitious, but I think SpaceX’s orbital data centers might reach cost parity with terrestrial data centers in 5 to 10 years. The Starlink laser-link network already exists as the communication backbone for any SpaceX compute constellation, and that infrastructure is what no new entrant can replicate quickly. The chip-agnostic payload design probably reflects their disclosed difficulty securing AI silicon as much as any modularity philosophy. My view is that the only realistic near-term application is a SpaceX mega-constellation for inference. Training workloads likely cannot tolerate the synchronization and latency constraints of a distributed orbital system. Our report analyzed the market from the integrator’s vantage point, but AI1 is what it looks like when one player has assembled all the necessary advantages simultaneously. The question is whether the terrestrial data center industrial base will degrade or improve on economics. I don’t have insight into SpaceX’s internal costs, as opposed to public pricing, on all their components, so it’s hard to say if they’ll completely dominate or not. Even if they are not cost competitive with terrestrial data centers for another 5 to 10 years, it may simply be faster to get new compute that just happens to be in space. Matt Hasan, AI strategist and independent consultant My initial view is that AI1 does not fundamentally change the rationale for space-based data centers as much as it changes the timeline and scale. The underlying drivers remain the same: escalating AI compute demand, growing power constraints on terrestrial grids, and the desire to colocate energy generation with computation. What AI1 does signal is that the concept is beginning to move from theoretical discussion toward engineering and capital allocation decisions. The announcement adds credibility to the idea that hyperscale computing infrastructure may eventually expand beyond terrestrial constraints rather than simply competing for increasingly scarce grid capacity on Earth. That said, significant economic and technical questions remain. Launch costs, maintenance, hardware replacement cycles, thermal management, latency-sensitive workloads, and overall system economics will ultimately determine whether space-based data centers become a mainstream extension of AI infrastructure or remain a niche capability for specialized applications. The key development is not that these questions have been resolved, but that major industry players now appear willing to invest resources toward answering them.
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Today's links Autodoxing: The road to hell is paved with badly thought-through tech policy. Hey look at this: Delights to delectate. Object permanence: Shakespeare programming language; DeCSS is "pure speech"; Blue Coat sold surveillance tools to Syrian secret police; "Extraordinary Catalog of Peculiar Inventions"; Cable prices rose at double the rate of inflation for 20 years; Supercuts; Antivax x Trump. Upcoming appearances: Kilkenny, Courtenay, Vancouver, Folkestone, Oxford, Sheffield, Virtual, Oxford, Montreal. Recent appearances: Where I've been. Latest books: You keep readin' em, I'll keep writin' 'em. Upcoming books: Like I said, I'll keep writin' 'em. Colophon: All the rest. Autodoxing (permalink) After 25 years of fighting for human rights amid the wreckage of bad tech policy, I'm here to tell you that the road to hell is paved with badly thought-through tech policy. The most galling thing about this wreckage is that it was both foreseeable and foreseen. Back when Sun Microsystems CEO Scott McNealy was telling the world "Privacy is dead, get over it," digital rights advocates were saying, "Not only are digital tools pretty dang good at helping you protect your privacy, but a society-wide abolition of privacy sets us up for infinite harms long into the future": https://pluralistic.net/2025/10/31/losing-the-crypto-wars/#surveillance-monopolism It wasn't just business leaders who hated privacy, it was also our political classes. Bill Clinton tried to put a backdoor into every microchip (no, really): https://en.wikipedia.org/wiki/Clipper_chip The NSA managed to enact a total prohibition on civilian access to working encryption, on the grounds that we're all safer if our spooks can spy on "bad guys" even if that means that every creep, petty crook, corporate or state-sponsored spy, and mafia can spy on us: https://pluralistic.net/2026/04/09/bernstein-2/#win-some-lose-some Even after that ban was overturned, governments encouraged the private sector to spy on us without limit, precaution or permission. Those private-sector troves of intimate, compromising data about every person alive constitute a warrantless, off-the-books surveillance system that cops, spies and government agencies can tap at will: https://pluralistic.net/2026/10/13/petards-r-us/#nobus To protect these surveillance capabilities, private firms and governments colluded to pass "anticircumvention" laws that make it illegal to reverse engineer our devices and apps to add privacy blockers to them, with the effect that every device we own and every app we use spies on us constantly: https://pluralistic.net/2024/03/12/market-failure/#car-wars At the same time, states either failed to pass new privacy laws (as in the USA, where federal consumer privacy law was last updated in 1988), or passed privacy laws that went unenforced (as in the EU, where "pretending to be Irish" allows US tech giants to flout EU privacy law): https://pluralistic.net/2023/05/15/finnegans-snooze/#dirty-old-town Our surveillance free-for-all world is objectively very bad. Over and over, companies and agencies who have been permitted to nonconsensually harvest and retain our data demonstrate that they are not capable of keeping that data safe, and no matter how badly they fuck up, nothing changes. It's been nearly a decade since Equifax doxed 148 million Americans and not only is the company still in business, they've been permitted to buy up and absorb more commercial surveillance firms. US regulators let Equifax buy Teletrack in 2021. Canadian regulators let them buy the Food Industry Credit Bureau in 2023. The same year, Brazilian regulators let Equifax buy Boa Vista Serviços. This all despite the fact that Equifax blamed its breach on shitty security practices after a string of mergers that led to it haphazardly combining databases from newly acquired surveillance companies. Data isn't the new oil. Data is the new oily rag: profitable only if you can warehouse it in vast quantities under unsafe conditions and have society pick up the bill for every conflagration: https://locusmag.com/feature/cory-doctorow-zucks-empire-of-oily-rags/ Equifax is a serial reoffender, but rather than getting the corporate death-penalty, they've been made to pay coins-down-the-back-of-the-sofa fines that amount to a fraction of a fraction of a fraction of the costs they imposed on society by amassing these vast, flaming storehouses of oily rags. Why wouldn't Equifax reoffend? Our policymakers keep demanding less privacy, not more, and their indifference to the consequences is (almost) comical. Take Malcolm Turnbull, the former Australian Prime Minister who uttered what remains, without a doubt, the single stupidest thing anyone anywhere has ever said about tech policy. Like many of his peers before, during, and since his reign, Turnbull had been trying to outlaw working encryption, insisting that every device have a Bill Clinton-esque back-door for the purpose of spying on "bad guys." When told that the laws of mathematics dictated that any measure that let Australia's cops spy on "bad guys" would also let bad guys spy on Australians, Turnbull replied, "The laws of mathematics are very commendable, but the only law that applies in Australia is the law of Australia": https://www.newscientist.com/article/2140747-laws-of-mathematics-dont-apply-here-says-australian-pm/ We could laugh Turnbull off…if he was an outlier. But while Turnbull had the world's stupidest rejoinder to the scientifically irrefutable fact that banning encryption will let bad guys hack everything, he was hardly alone in wanting to ban encryption. Banning encryption is current policy in Canada: https://blog.mozilla.org/netpolicy/2026/10/08/canadas-bill-c-22-threatens-encryption-users-privacy-and-the-security-of-digital-economies/ It's also current policy in the UK: https://pluralistic.net/2023/03/05/theyre-still-trying-to-ban-cryptography/ And in the EU: https://fightchatcontrol.eu/ The difference between Malcolm Turnbull and, say, Canadian PM Mark Carney is that Turnbull was stupid enough to try to defend his ban on encryption, while Carney simply shakes his head at his critics' naivete and continues to press forward with his catastrophic proposal to welcome every cybercriminal in the world to hack every morsel of Canadian data. When it comes to bad privacy policy, our political classes aren't content to rest on their laurels: hardly a week goes by without news of a new way to encourage (or even require) the private sector to recklessly collect and retain our data, despite the dead certainty that this data will eventually end up breaching and getting plastered all over the internet. Take "age verification" laws. There's no such thing as "age verification" – there's just spying on everyone, all the time, creating (even more) records of all of our internet activities: https://pluralistic.net/2026/05/19/shes-dead-of-course/#consensus-hallucination Every promise of "privacy preserving" age verification is bullshit: https://pluralistic.net/2025/08/14/bellovin/#wont-someone-think-of-the-cryptographers If you think kids are experiencing internet harms today, just wait until we expose them to the harms of having even more of their data harvested, stored, breached, and weaponized against them: https://pluralistic.net/2026/06/23/destroy-the-village/#to-save-it If you want to protect kids from algorithmic manipulation and targeting by bad actors, what about banning spying on kids so no one can target them? https://made-not-found-by-danah-boyd.ghost.io/no-one-paid-me-to-disagree-with-jonathan-haidt-it-was-teenagers-who-taught-me-that-he-was-wrong/ Now, at this point, you might be thinking, "How can we know who the kids are so we know who not to spy on?" You can't. In order to not spy on kids, you'd just have to not spy on anyone: https://pluralistic.net/2023/04/09/how-to-make-a-child-safe-tiktok/ "Don't spy on anyone" is the kind of thing that has a reasonable chance of enhancing online privacy. But in the time-honored tradition of "we've tried nothing and we're all out of ideas," governments keep finding ways to make digital privacy worse, for example, by procuring Flock cameras for every lamp-post: https://www.eff.org/deeplinks/2026/09/high-crime-lmao-how-cops-are-treating-mass-surveillance-joke And then there's UK Labour: after the success of a pilot program for an incredibly effective and safe privacy-preserving way of analyzing the country's health records for research processes, they commissioned the Goldacre Report to evaluate the possibility of making this permanent: https://pluralistic.net/2024/03/08/the-fire-of-orodruin/#are-we-the-baddies After that report was completed and had a chance to be metabolized by policymakers, Labour acted upon it…by throwing it out and handing all of the country's health data to Palantir, a serial mishandler of data and violator of privacy: https://www.theguardian.com/technology/2026/sep/30/legal-objections-palantir-nhs-platform-handling-data Here's the scariest thing about bad privacy policy: it has a long fuse. The harms of commercial and private-sector digital surveillance can occur years or even decades after the decision to permit it. There's a straight line from the post-9/11 decision to encourage public agencies to gather, merge and warehouse data on every person in America, and the ability of ICE ethnic cleansing squads to break down people's doors and drag them off to concentration camps; just as there's a straight line from the decision to do nothing about the breach of every Social Security Number and the later theft of all Social Security data by adolescent DOGE criminals: https://www.pcmag.com/news/is-your-ssn-in-the-national-public-data-breach-heres-how-to-find-out But despite how obvious these connections are, the fact that they're separated by a lot of time and space provides our policymakers with cover for making terrible decisions with foreseeably terrible outcomes. As a society, we lack object permanence, the ability to connect acts in the past with present-day phenomena. When the press reports on a "Black October" of catastrophic, AI-fueled data breaches and hack attacks, they don't mention a generation of policymakers' indifference and hostility to information security: https://pluralistic.net/2026/09/12/god-in-the-box/#llms-are-fake Policymakers benefit from politics' eternal now, in which disasters simply befall us, without any history or precedent or cause. Remember the One Big Beautiful Bill? Its architects designed it to wreak its worst harms after the midterms, with Medicaid work rules and food aid restrictions starting in December, and cuts to affordable housing and rural hospitals phasing in through the year: https://www.tumblr.com/mostlysignssomeportents/832146932089700352 Black October is going to give way to Black November, Black December, Black 2027, and the Black 2030s – a string of cybersecurity catastrophes that will befall us until policymakers decide that keeping us safe is more important than making it easy for them to attack their chosen "bad guys." Here's a preview of Black November: there's a new strain of the Volta infostealer malware that uses AI tools to dox you even as it hacks you: https://www.wired.com/story/kernel-panic-volta-autodoxer-ai-infostealer-malware/ How that works: your computer gets hacked and your data gets stolen, then it gets merged with breach data from Ticketmaster, Disney, AT&T and other companies that have recklessly compiled vast troves of data on all of us, stored it insecurely, and leaked it onto the internet where it will live forever. Volta's makers prompt their LLM "to act like an intelligence analyst and produce structured citation-backed assessment of the victim." This lets the criminals prioritize their victims based on the expected return from exploiting them. It augments this dossier by combing through your Gmail as well as your Doordash and Uber Eats profiles. This merged data makes it much easier for criminals to call you up or email you (or have a chatbot call you up or email you) and trick you into giving up even more data: https://pluralistic.net/2024/02/05/cyber-dunning-kruger/#swiss-cheese-security Volta seems especially interested in younger internet users, targeting "Discord, Telegram, Steam, and Roblox sessions." Of course, once our "age verification" services breach vast, eternally persistent, detailed troves of data on young internet users, this kind of AI-supercharged malware will be able to target kids with much greater fidelity and efficacy. AI capabilities have made infostealers much worse, but the reason for that new potency lies in how AI capabilities are supercharged by breach data that is the foreseeable and foreseen consequence of generations of hostility to privacy on the part of policymakers and corporations who responded to the incentives policymakers created. We are living in the age of oily rags. For decades we fucked around with letting the private sector warehouse our data and weaken our security so that public sector agencies could plunder our data and our computers at will. Now, we keep finding out that everything is on fire. It's going to take a long time to fix this, but we can do it. We can build more secure systems. We can make it much more of a legal ball-ache for companies to gather our data in the first place, and we can legalize and promote the creation of privacy blockers for browsers, phones, computers, cars, thermostats and smart speakers. We can enact corporate death-penalties for breaches to reflect the true societal cost of warehousing our data. We can ban Flock cameras and tear down the post-9/11 "fusion centers" that neatly package up our data for use by DOGE fascists and ICE thugs. We can ban our spy agencies from hoarding defects in widely used software, and instead require them to divulge these bugs so vendors can fix them: https://en.wikipedia.org/wiki/NOBUS And before we do any of that, we can just stop making it worse by ending the campaigns to require companies to spy on us and deliberately introduce security defects into their products. Hey look at this (permalink) ICE wants its mask back, and hired a company to ask https://blog.flokinet.is/2026/10/29/ice-wants-its-mask-back-and-hired-a-company-to-ask/ Is There Life After AI? Cory Doctorow Thinks So. https://jacobin.com/2026/10/ai-bubble-labor-copyright-competition Online censorship is crushing indie webcomics https://xtramagazine.com/culture/online-censorship-indie-webcomics-284463 Fascists Thrive On Bad Economics https://economicpopulist.substack.com/p/fascists-thrive-on-bad-economics PrivacySafe Foundation Releases Free Security Tools to Combat Global Cyber Threats https://www.einpresswire.com/article/952907125/privacysafe-foundation-releases-free-security-tools-to-combat-global-cyber-threats Object permanence (permalink) #25yrsago NY Post blames pagans from Indianapolis for spam malware https://web.archive.org/web/20011103073038/http://www.nypost.com/news/regionalnews/33077.htm #25yrsago The Shakespeare Programming Language https://web.archive.org/web/20011207071324/http://shakespearelang.sourceforge.net/report/shakespeare/ #25yrsago Appeals court: DeCSS is "pure speech" https://web.archive.org/web/20011102003502/https://www.wired.com/news/business/0,1367,48075,00.html #20yrsago Apple drops Trusted Computing https://web.archive.org/web/20070106150844/https://www.osxbook.com/book/bonus/chapter10/tpm/ #20yrsago Microsoft threatens China pull-out over jailed bloggers http://news.bbc.co.uk/1/hi/technology/6102180.stm #15yrsago Righthaven copyright troll’s victims send in US marshals to seize its assets https://arstechnica.com/tech-policy/2011/11/us-marshals-turned-loose-to-collect-6372080-from-righthaven/ #15yrsago Supercuts: rapid-cut videos that collect repetition https://web.archive.org/web/20111103190737/http://www.wired.com/epicenter/2011/11/supercut/ #15yrsago Study: why parents help their underage kids pretend to be 13 in order to use Internet sites https://firstmonday.org/ojs/index.php/fm/article/view/3850/3075 #15yrsago Blue Coat “deep packet inspection” tools used by Syrian secret police and other repressive regimes https://www.eff.org/deeplinks/2011/10/blue-coat-acknowledges-syrian-government-use-its-products #15yrsago Extraordinary Catalog of Peculiar Inventions: awesomely dangerous pranks from the age of fraternal lodges https://memex.craphound.com/2011/11/01/extraordinary-catalog-of-peculiar-inventions-awesomely-dangerous-pranks-from-the-age-of-fraternal-lodges/ #10yrsago Cable prices have risen at more than double the rate of inflation for 20 years https://cordcutting.com/news/cable-prices-have-risen-faster-than-inflation-for-each-of-the-past-20-years/ #10yrsago Typical English village mystified by its cult status among Chinese tourists https://www.bbc.com/news/uk-england-oxfordshire-37820663 #10yrsago Nebraska’s billionaire governor couldn’t preserve the death penalty through the law, so now he’s buying a ballot initiative https://www.motherjones.com/politics/2016/11/ricketts-nebraska-death-penalty/ #10yrsago Anti-vaxx conspiracy leaders back Donald Trump, claim it’s mutual https://scienceblogs.com/insolence/2016/11/02/donald-trump-and-disgraced-antivaccine-scientist-andrew-wakefield-best-buds-forever #10yrsago New, fast-spreading IoT botnet hybridizes two less-effective strains to achieve quick dominance https://arstechnica.com/information-technology/2016/11/new-iot-botnet-that-borrows-from-notorious-mirai-infects-3500-devices/ #1yrago There's one thing EVERY government can do to shrink Big Tech https://pluralistic.net/2025/11/01/redistribution-vs-predistribution/#elbows-up-eurostack Upcoming appearances (permalink) Kilkenny (Kilkenomics), Nov 6-8 https://kilkenomics.com/ Courtenay: Enshittification (Sid Williams Theatre Society), Nov 10 https://www.sidwilliamstheatre.com/events/cory-doctorow-talks-enshittification/ Vancouver: BC Policy Solutions Gala, Nov 12 https://bcpolicy.ca/gala/ Folkestone Writers Festival, Nov 16 https://www.creativefolkestone.org.uk/whats-on/cory-doctorow-the-internet-is-broken-now-what/ Oxford: Ministry of the Future, Nov 23 https://ministryforthefuture.co.uk/events.html Sheffield: Reverse Centaur at Juno Books, Nov 25 https://www.outsavvy.com/event/40274/cory-doctorow-in-conversation Virtual: Enshittification, AI, and the Future of Technology (UBC Master of Educational Technology), Nov 28 https://met.ubc.ca/2026/09/met-speaker-series-cory-doctorow-enshittification-ai-and-the-future-of-technology/ Oxford: Bennett Oxford Symposium on Open Code in Science, Dec 9 https://www.bennett.ox.ac.uk/events/2026-bennett-institute-symposium/ Montreal: World Science Fiction Convention, Sep 2-6 https://montreal2027.ca/en Recent appearances (permalink) Clint Eastwood (No Gods No Mayors) https://www.patreon.com/NoGodsNoMayors/posts/clint-eastwood-173008088 Life After AI (Brain Bar) https://www.youtube.com/watch?v=IPPa43-n0vI Terms of Service with Clare Duffy (CNN) https://www.cnn.com/audio/podcasts/terms-of-service-with-clare-duffy/episodes/458ce968-af5d-11f0-b539-13ed2afe25f8 AI, Work, and Power (Software Engineering Daily) AI, Work, and Power https://softwareengineeringdaily.com/podcasts/cory-doctorow-on-ai-work-and-power/ AI, Corporate Power, and the Fight for Worker Control (Plutopia) https://plutopia.io/cory-doctorow-ai-corporate-power-and-the-fight-for-worker-control/ Latest books (permalink) "The Reverse-Centaur's Guide to AI," a short book about being a better AI critic, Farrar, Straus and Giroux, June 2026 https://us.macmillan.com/books/9780374621568/thereversecentaursguidetolifeafterai/ "Canny Valley": A limited edition collection of the collages I create for Pluralistic, self-published, September 2025 https://pluralistic.net/2025/09/04/illustrious/#chairman-bruce "Enshittification: Why Everything Suddenly Got Worse and What to Do About It," Farrar, Straus, Giroux, October 7 2025 https://us.macmillan.com/books/9780374619329/enshittification/ "Picks and Shovels": a sequel to "Red Team Blues," about the heroic era of the PC, Tor Books (US), Head of Zeus (UK), February 2025 (https://us.macmillan.com/books/9781250865908/picksandshovels). "The Bezzle": a sequel to "Red Team Blues," about prison-tech and other grifts, Tor Books (US), Head of Zeus (UK), February 2024 (thebezzle.org). "The Lost Cause:" a solarpunk novel of hope in the climate emergency, Tor Books (US), Head of Zeus (UK), November 2023 (http://lost-cause.org). "The Internet Con": A nonfiction book about interoperability and Big Tech (Verso) September 2023 (http://seizethemeansofcomputation.org). Signed copies at Book Soup (https://www.booksoup.com/book/9781804291245). "Red Team Blues": "A grabby, compulsive thriller that will leave you knowing more about how the world works than you did before." Tor Books http://redteamblues.com. "Chokepoint Capitalism: How to Beat Big Tech, Tame Big Content, and Get Artists Paid, with Rebecca Giblin", on how to unrig the markets for creative labor, Beacon Press/Scribe 2022 https://chokepointcapitalism.com Upcoming books (permalink) "The Post-American Internet," a geopolitical sequel of sorts to Enshittification, Farrar, Straus and Giroux, 2027 "Unauthorized Bread": a middle-grades graphic novel adapted from my novella about refugees, toasters and DRM, FirstSecond, April 20, 2027 "Enshittification, Why Everything Suddenly Got Worse and What to Do About It" (the graphic novel), Firstsecond, 2027 "The Memex Method," Farrar, Straus, Giroux, 2027 Colophon (permalink) Today's top sources: Currently writing: “Once Is Enemy Action,” a science fiction novel about the origins of modern technofascism. Today's words: 574 (7730 total). "The Post-American Internet," a sequel to "Enshittification," about the better world the rest of us get to have now that Trump has torched America. Fourth draft completed. Submitted to editor. A Little Brother short story about DIY insulin PLANNING This work – excluding any serialized fiction – is licensed under a Creative Commons Attribution 4.0 license. That means you can use it any way you like, including commercially, provided that you attribute it to me, Cory Doctorow, and include a link to pluralistic.net. https://creativecommons.org/licenses/by/4.0/ Quotations and images are not included in this license; they are included either under a limitation or exception to copyright, or on the basis of a separate license. Please exercise caution. How to get Pluralistic: Blog (no ads, tracking, or data-collection): Pluralistic.net Newsletter (no ads, tracking, or data-collection): https://pluralistic.net/plura-list Mastodon (no ads, tracking, or data-collection): https://mamot.fr/@pluralistic Bluesky (no ads, possible tracking and data-collection): https://bsky.app/profile/doctorow.pluralistic.net Medium (no ads, paywalled): https://doctorow.medium.com/ Tumblr (mass-scale, unrestricted, third-party surveillance and advertising): https://mostlysignssomeportents.tumblr.com/tagged/pluralistic "When life gives you SARS, you make sarsaparilla" -Joey "Accordion Guy" DeVilla READ CAREFULLY: By reading this, you agree, on behalf of your employer, to release me from all obligations and waivers arising from any and all NON-NEGOTIATED agreements, licenses, terms-of-service, shrinkwrap, clickwrap, browsewrap, confidentiality, non-disclosure, non-compete and acceptable use policies ("BOGUS AGREEMENTS") that I have entered into with your employer, its partners, licensors, agents and assigns, in perpetuity, without prejudice to my ongoing rights and privileges. You further represent that you have the authority to release me from any BOGUS AGREEMENTS on behalf of your employer. ISSN: 3066-764X
A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion. Remember the good old days of GPT-3 when we could ask the chatbot anything and it would almost instantly and very confidently spit back a plausible but false answer? That’s when the word “hallucination” as applied to AI answers entered the lexicon. Six years and several versions of a few different chatbots later, I still can’t trust the answers I get, even though Claude or Gemini often do useful work for me. That’s the thing about current AI: Trust but verify. And verify you must. Apply that maxim at scale to maintain the integrity of the scientific record and you get an idea of the challenge facing publishers of peer-reviewed scholarship. In “I Used AI to Fake a Scientific Paper,” freelance writer and doctor of applied mathematics Benjamin Skuse tells the story of the human gatekeepers trying to beat back the rising tide of AI slop by attempting to get his own AI-generated papers past IEEE’s defenses. “He basically hit ‘run experiment’ and ‘write paper.’ And then he didn’t do a damn thing to it.” —Eliza Strickland As features and AI beat editor Eliza Strickland told me, Skuse was surprised at just how effortless it was to generate a paper that was more or less plausible and more or less science. For one of his experiments, “he basically hit ‘run experiment’ and ‘write paper,’” she said. “And then he didn’t do a damn thing to it. He just launched it into the maw of the publishing engine.” Skuse discovered that the combination of IEEE’s human-review processes and research-integrity checker programs are currently keeping pace in what amounts to a cat-and-mouse game against people using free tools. But the more advanced LLMs are no longer making the same dumb mistakes the free versions do. That leaves publishers locked in an arms race between increasingly plausible slop and the systems aimed at detecting it. Scholarly publishers aren’t the only ones trying to hold the line against slop. So is that global depository of human-generated knowledge, Wikipedia. For this month’s Five Questions, Strickland interviewed Ellia Lebleu, a founding member of WikiProject AI Cleanup, a volunteer effort to detect and delete AI-generated material from the platform. Lebleu’s team is confronting the same problem as commercial publishers, but with fewer people and fewer tools. “Wikipedia is known as a repository of human-generated information, and they want to hold on to that,” says Strickland. “Who knows how long they’ll be able to do that.” If we can’t collectively assume that the scientific record is trustworthy or that basic facts about the world are accurate, then our understanding of what is real unspools into uncertainty. Perhaps that delights quantum scientists and the grifters who thrive in an information environment where there is no shared reality. But that uncertainty ultimately undermines trust in the institutions that exist to curate the corpus of human knowledge and to promote the advancement of science and technology for the benefit of humanity. Or maybe in a world where AI is doing the science and writing the papers, it won’t matter whether or not humans trust the outputs. As Strickland put it, “Doesn’t it just feel crazy that we’ll have AI writing for other AIs at some point? We’ll just get left out of the loop.”
Video reading by Tim Foley:
A lesson in learning, summarization, and explaining via LLMs
