Seth :ally_always:

Seth :ally_always:

@phillycodehound.encryptr.net.ap.brid.gy

I'm all over the Fediverse. I'm a big proponent of online privacy rights and encryption. I'm a former or recovering journalist turned Digital Marketer (18+ yrs) […] 🌉 bridged from ⁂ https://encryptr.net/@PhillyCodeHound, follow @ap.brid.gy to interact

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Seth :ally_always:
@phillycodehound.encryptr.net.ap.brid.gy

Wow.... https://podnews.net/article/spreaker-copy-your-podcast @james !!!!!

podnews.netSpreaker is enabling wholesale copyright theft for podcastsSpreaker is making it easy to make copies of other podcasts, and is making money from it, Podnews has discovered. We’ve learnt from multiple publishers that their shows have appeared duplicated on platforms like Apple Podcasts, Spotify and others - causing confusion for listeners, and reducing advertising revenue. We’ve discovered more than a thousand shows have been copied in this way, and are earning revenue for Spreaker as well as the people who have copied them. And, when we looked into how Spreaker works, we were astonished how simple it is to make a full copy of someone else’s show, publish it to Apple Podcasts, and start earning money. So let’s start there. **Get the free Podnews newsletter** for more like this Get it free ## How easy is it to copy someone else’s show into Spreaker? It couldn’t be simpler to copy a show without the owner’s permission to Spreaker’s platform - and you can do it on a free account. Plus, you can enable monetisation (dynamically inserted ads and a “supporter’s club”) on Spreaker’s free plan as well. With a free account, the “import episodes” page couldn’t be easier: simply copying an RSS feed address into Spreaker. On this page there is no discussion of copyright, or link to the terms and conditions - just a simple way to import “episodes from existing feed”. We were expecting the tool to read the email address from the RSS feed, and give some sort of check that the owner wanted to transfer the episodes over to Spreaker. It doesn’t - it just copies the show audio, and all the episode metadata, into the Spreaker platform, without any notification to the original podcast producer that you’ve done so. But, this isn’t just a one-off copy. The Spreaker platform gives you the ability to automatically check every hour for new episodes - effectively allowing your copy of this show to be kept perpetually up to date. We see little reason why a podcaster would ever want this feature when switching podcast host; but it makes the ability to copy a podcast much easier, since that copy is kept up to date automatically by Spreaker’s systems. It took just minutes to copy the Podnews Daily RSS feed, and republish it on Spreaker. The copy contains all the metadata from the existing episodes - artwork, dates, episode descriptions and titles. From here, we can go into the “distribution” tab and submit this show to all the main podcast directories, like Apple Podcasts, Spotify, iHeartRadio, and many more. (We didn’t do this step, because we don’t want this podcast to really be there). All this is possible from a free account, with no credit card details being given to sign up. But it’s not just free - it earns money, too. In Spreaker’s interface, clearly highlighted, a button to turn on monetisation. We tried applying for monetisation. The monetisation process is clearly referred to a human at Spreaker as part of the company’s published approval process; and the company’s mostly European staff were asleep when we made our application for monetisation. We assumed that, with a title clearly marked “Test” and obviously copied content from a podcast we had seemingly no connection to, we would be blocked from monetising it. We were wrong. By 10.30am central European time, our application for monetisation had been approved. Now, we were able to put prerolls and postrolls automatically into the content; and the service would also look for silence in the middle of shows and automatically put ads in there, too. Why would Spreaker have so quickly given access to monetisation, when it’s clear this is a test podcast, and there is no real connection to the imported show? It’s probably because the terms and conditions are pretty clear - Spreaker keeps up to 41% of all the ad revenue. You could argue that it’s in their interest to make it as easy as possible to copy shows to earn money from them: since they stand to earn a significant amount of revenue as a result, especially if they then withhold the payment to the person who did the pirating. ## How many podcasts have been pirated onto Spreaker? We downloaded a fresh copy of the Podcast Index, as of Saturday 24 Oct 2026. We wanted to look at all shows where: * a copy of a show was on Spreaker as well as another host * the same number of episodes were listed for each * both copies were in the Apple Podcasts directory * the original host wasn’t Librivox We found **1,778 pirated podcasts** that were copied into Spreaker in this way. In Apple Podcasts, Adam Carolla Show (copy) is a typical example. Unlike the real Adam Carolla Show, the author of the podcast has been changed to “juyio”, rather than the real author (PodcastOne / Carolla Digital). It’s been stripped of its tracking, but still has the latest episodes. It appeared to have been made on Saturday 19 Sep 2026 - and so has been quietly sitting there for over a month. We found copies of Bloomberg News Now, Acast’s Canadian True Crime, Audiochuck’s Crime Junkie, Red Seat Ventures’ Crime Weekly, Vox Media’s Criminal, two copies of History Hit, SiriusXM’s Hidden Brain, The New Yorker’s In The Dark, Simplecast’s Morbid, Airwave and Motley Fool’s Motley Fool Hidden Gems Investing, Wave’s Not Gonna Lie with Kylie Kelce, Goalhanger’s The Rest is Politics, and many more. Some have been removed by Apple Podcasts; many are still there, causing duplicate search results. (Librivox offers audiobooks with a public domain licence. Spreaker hosts hundreds of these shows, too - with users earning revenue from content they didn’t make.) **How we did this:** The Podcast Index’s data is available at the bottom of their index page as an SQLite3 file. You can download that, and then use software like DB Browser for SQLite to query it. We used an SQL query you’ll find on this page, which you can copy and run yourself. ## What effect this has on podcast publishers Any plays of the Spreaker copies will not appear in the official download numbers for these shows. The Spreaker copies don’t earn the original publishers any money from advertising, and count against them in terms of charts like Apple Podcasts and Spotify. Sometimes, as Lisa Lacy tells us, the show starts charting - due to the way the podcast charts reward new followers. That’s a significant threat to the “real” podcast. If a listener subscribes to a Spreaker copy by mistake - and it’s easy enough to do, as is clear from the screenshot above - then that listener’s ratings and reviews don’t go to the original show, and nor does any credit for downloads or ad revenue. They hear double the number of ads that they might otherwise hear, too; and since the original publisher has no control of the ads that Spreaker jams into these podcasts, the ads may be wildly inappropriate, too. Spreaker - and the person who pirates these shows - earn from the additional ads that the company slaps into these pirated copies of the podcasts. Just not the original publisher. ## We asked Spreaker what it was up to We contacted Spreaker’s PR company, and asked them for a response to these questions: * Why is Spreaker facilitating this large-scale copying of podcasts from others? * How much revenue has been earned by creators in this way? * How many shows are currently on the Spreaker platform that use the “automatic import” setting? * How many shows have been removed by Spreaker support for copyright violation? Unsurprisingly, the company, which is owned by iHeart, didn’t answer any of our questions. Instead, it provided us with a statement: > Spreaker does not encourage or condone the uploading of content by anyone who doesn’t have the rights to it. Our Community Rules, which are linked from our show creation page and included in the Terms of Service every creator accepts at sign-up, are clear: Don’t upload content that isn’t yours. We take appropriate actions whenever a violation of our terms of service is found. We also honor the `podcast:locked` tag in our RSS importer. We’d suggest that the company knows better than this. At a minimum, Spreaker should ensure that it only imports shows from RSS feeds with permission from that feed’s owner. This is usually achieved by either sending an email to the feed’s owner directly to ask permission via a link; or requiring a claim code to be pasted in the feed, which only the feed owner can do. Spreaker has done neither of these. It doesn’t even make copyright clear on the import page, nor link to the Community Rules from anywhere in its dashboard once you’ve added your first show. When it comes to monetisation, the company shouldn’t blindly accept imported shows without even a cursory check. It’s a trivial exercise - and one that can be achieved programmatically. Spreaker hasn’t bothered. It’s also a trivial piece of work to check that a show isn’t merely a duplicate of an already existing show. We’ve even documented how to do it, above. Spreaker has demonstrably failed to do even this. The `podcast:locked` tag is a nice suggestion; but it’s not supported by many podcast hosts quite yet. One podcast host that doesn’t support it is, er, Spreaker itself, which doesn’t appear to let you add it to your own podcast. The company claims that it takes “appropriate actions whenever a violation of our terms of service is found”: but in just a few hours, we’ve found 1,778 examples where it hasn’t done so. But, when they have so many shows all earning revenue for them - 41% of all ad revenue stays with Spreaker, and even more if they then suspend the user who pirated the shows - why would they bother? * * * **Support us:** Podnews relies on support from people like you, as well as corporate supporters like, er, Spreaker. It’s how we can report what’s really going on in this industry, not just the fluffy press releases. We’d welcome your support so we can continue to highlight stories like these. Thank you.

Seth :ally_always: reposted

rickf - a cynical professor
@rickf.indieweb.social.ap.brid.gy

Donnie is now dangling $1 million payouts to anybody reporting confirmed voter fraud. Missing from the announcement, of course, is any indication of who is paying for it. Desperate Donnie flailing hard with a week to go…..

Seth :ally_always: reposted

Nathan Wrigley
@nathanwrigley.mastodon.social.ap.brid.gy

It's Monday again, and it's nearly time for This Week in WordPress. Join us LIVE at 2pm UK time (about 1 hour). See you there? https://wpbuilds.com/live Join Nathan Wrigley, Michelle Frechette, Ivana Ćirković, Dave Grey.

wpbuilds.comwpbuilds.comFind out what’s coming up… Skip Calendar Choose View Month - Classic Month - List Week - Time Grid Week - List Week - Day Grid Day - List List - Upcoming Month - List with Image Back To Full Month < > Event Title * * * Starts Ends * * * View Add To Calendar Event times are listed in your local time zone: Go to our YouTube channel You should be able to access LIVE chat from YouTube, so you’ll need to be logged into a Google / YouTube account for that. Alternatively, once we’re live, there’s a ‘Live Chat’ icon at the top-right of the video which does not require you to be logged in anywhere. Open the chat in a new tab YouTube Comments ### Would you like to share this... * Share on Bluesky (Opens in new window) Bluesky * Share on Mastodon (Opens in new window) Mastodon * Share on X (Opens in new window) X * Share on Threads (Opens in new window) Threads * Share on LinkedIn (Opens in new window) LinkedIn * Share on Facebook (Opens in new window) Facebook * Share on Telegram (Opens in new window) Telegram * Share on WhatsApp (Opens in new window) WhatsApp * Share on Reddit (Opens in new window) Reddit * Share on Tumblr (Opens in new window) Tumblr * Email a link to a friend (Opens in new window) Email * Print (Opens in new window) Print * Share on Pinterest (Opens in new window) Pinterest * ### Like this: Like Loading…
Seth :ally_always:
@phillycodehound.encryptr.net.ap.brid.gy

I still get nervous when getting ready for a podcast. I've been podcaster for 16 years (yea that long, geez) and before that a professional journalist. Even with those credentials, I'm still human and nerves hit me from time-to-time. #Podcasting #TheTruth

Seth :ally_always: reposted

Steinar Bang
@steinarb.mastodon.social.ap.brid.gy

Good AIrticle by @pluralistic in particular the distinction between "good AI" and "bad AI" and actual numbers behind the AI bubble, ie. investment vs returns. Read it! https://locusmag.com/feature/commentary-cory-doctorow-technopolitics/

locusmag.comCommentary: Cory Doctorow: TechnopoliticsCory Doctorow (Copyright Julia Galdo & Cody Cloud) It’s one thing to be “anti-AI” in the sense of believing that AI-generated slop, disinformation and deepfakes are bad; that AI companies that hope to con your boss into firing you and replacing you with AI that can’t do your job are bad; that betting the economy on AI is bad; that betting the human-habitable planet Earth on AI is bad; that running roughshod over local environmental rules and seizing farmland for data centers is bad. Those are all good reasons to be “anti-AI.” But being “anti-AI” in the sense of “opposing the statistical inference techniques we call AI” is weird. So is being anti-AI in the sense of “opposing the use of models that were created using large language corpuses because it’s inherently wrong to compute the frequency with which words occur in writ­ten language.” And it’s also weird to be anti-AI in the sense of “opposing AI because someone, somewhere used AI in a way that was gross, harmful or exploitative.” Today, the two kinds of “anti-AI” are mostly a distinction without a difference, because nearly all the world’s AI output comes from one of the “frontier models” made by a giant AI company. No matter why you oppose AI, you probably don’t like those frontier models. But after the AI bubble bursts, the distinction will be much sharper. Long after the collapse of the seven AI companies that make up 35% of the S&P 500 stop trading the same $100 billion IOU around really fast while pretending it’s in all their bank accounts at once, there will still be cheap bankruptcy-sale hardware, legions of unemployed skilled AI workers, and the open-source models released by today’s AI giants will still run, and will continue to be improved by the people who rely on them. The useful things AI does will keep happening. You’ll still be able to prompt up a single-serving, personal piece of software to perform one task with moderate reliability for your use, and when it stops working, you’ll be able to do it again. You’ll still be able to transcribe audio, or summarize a document. You’ll still be able to use a chatbot as an interactive journal, or as a supercharged spellchecker. None of that will change – and neither will the limits on all those activities. If you don’t know enough about a document or its subject to spot obvious errors in an AI summary, you’ll be periodically misled. If you’re so desperate or uninformed that you trust summaries you lack the capacity to evaluate in critical situations, you may be led to disaster. If you don’t know anything about computers and you turn a piece of vibe-coded software loose on your machine, you might delete something important, or irreversibly expose sensitive data to the public internet. AI isn’t exceptional, it’s normal. It’s not exceptionally dangerous. It’s not exceptionally evil. It’s also not exceptionally useful or exceptionally good. AI represents a significant advance to computer science – a moderate variation to existing machine learning techniques that generated a much larger improvement in those techniques. In the absence of the AI investment bubble, we’d call the things AI can do “plug-ins” – as in, “I just got a new plug-in for my word processor that generates plausible word salads. I can see how that might be useful for generating placeholder text, and I can tell that a lot of people are going to use it in ways I find supremely annoying.” I’ve had a lot of plug-ins for my word processors over the past 40 years, and most of them fit that description. None of them ever made me want to bet the global economy on it, or to feed all the writers into a woodchipper. AI can do many useful things. Some of them are _very_ useful: Friends who work in advanced statistics tell me that AI coding assistants help them produce high-quality, reliable code that solves real problems, improving the quality of the work they do. I find AI-generated transcripts of audio very useful for locating quotes in podcasts I’ve recently listened to, and I use a chatbot that runs on my computer to check my essays for typos. This bot often catches typos that my text editor’s checker misses, among a smattering of false positives that I have the experience and discernment to disregard (you know, like every spell checker). But AI doesn’t exist in the absence of the bubble – it very much exists in the white-hot center of the most dangerous investment bubble in the history of markets. A year ago, the AI sector’s total expenditures were about $700b. Today, they’re over $1.4t, and Gartner projects that will rise to $2.4t by the end of the year. This is an astonishing sum of money to commit to a sector that grosses a mere $50b worldwide, annually. The sector’s failure to produce anywhere near as much value as it is consuming has not dimmed its backers’ ardor, and they are committed to spending, and spending, and spending, until either they run out of investors or they somehow transform $50b worth of revenue on $2.4t in expenditures into profit. To this end, the sector’s masters are committing all manner of unforgivable sins. These range from conning your boss into firing you and replacing you with an AI that can’t do your job; to convincing your town council to seize your home and build a data center, without any environmental review or public input; to swindling everyday savers with nonsensical IPOs and sweetheart deals with stock exchanges that let insiders cash out their stupid AI bets with your retirement savings. These sins are baked into this kind of AI production. There is no way to raise trillions in subsidies that allow AI companies to sell computation at a subsidy that amounts to selling $100 bills for $1 each without also insisting that the end state of all of this is that every job will be swallowed by a chatbot. You can’t raise trillions on this promise with­out “blitzscaled” data-centers – accompanied by climate-shredding gas turbines. You can’t raise trillions without cramming AI into every part of every person’s digital life, without any safeguards for how vulnerable people might use them to sink deep into dangerous delusion. These are the “technopolitics” of AI: To make this AI, the people in­volved _had_ to inflict all these harms upon us. The important questions are: to what extent are these the _necessary_ technopolitics of AI, and; can those technopolitics change? Langdon Winner’s 1980 article, “Do Artifacts Have Politics?” is the Ur-text for questions like these. In this seminal essay, the rock critic turned tech scholar Winner demands that we move beyond the simple framing of tech having “intended” and “unintended” consequences, and instead insists that politics come baked into some technologies. Winner divides the politics of technology into two broad categories: politics that are derived from the specific way that a technology has been introduced into the world; and politics that are either necessitated by, or strongly compatible with a technology by dint of its technical char­acteristics. For the first category – objects whose politics are determined by de­ployment decisions – Winner cites a set of notorious Long Island freeway overpasses. These were specified by Robert Moses, and in _The Power Broker_ , Robert A. Caro’s seminal biography of Moses, we learn that Moses delib­erately chose to make these overpasses too low for a bus to pass beneath, in order to prevent a public transit route from being established between New York’s Black neighborhoods and Long Island’s public beaches, favored by the white bourgeoisie. According to Winner, a freeway overpass isn’t necessarily political, but these overpasses are. What’s more, their politics is durable, outlasting Robert Moses’s tenure as New York’s most powerful political figure, even outliving Moses himself. For the second category – technologies whose politics emerge from their intrinsic technical characteristics – Winner compares solar and nuclear power. Nuclear reactors, he argues, require a central authority to build, operate, and maintain (we might add here that they also require a con­stant stream of fissiles that can be extracted only from certain unevenly distributed mining sites). By contrast, solar can be installed anywhere the sun shines, established and overseen by small, decentralized groups. For Winner, nukes are irreducibly the tech of large, centralized states; while solar is potentially the tech of autonomous, nonhierarchical societies. Between these two technological poles, Winner discusses a third ex­ample: a tomato harvester. The harvester enabled farmers to fire many of their pickers, giving power to bosses at the expense of workers. At the same time, the harvester only worked with hardier varietals that traded flavor for durability, yielding a cheaper, worse kind of tomato that drove out competing tomatoes that tasted better but cost more to raise because their cultivation wasn’t as amenable to cost-saving automation. The tomato harvester wasn’t created to weaken labor and worsen tomatoes, but that was its effect, and so the politics of this harvester included a certain kind of farm, a certain kind of labor relation, and a certain kind of foodway. AI certainly has technopolitics. At a mini­mum, AI has the technopolitics of the first kind, those Robert Moses bridges. It has been capitalized specifically on the basis that it can be used to erode workers’ wages and labor power and to foist inferior goods on the public. There is no other way that the AI sector’s unimaginably vast capital losses can be turned into profits. The sums involved are so large that virtually everything everyone does, all the time, will need to be replaced with AI, and so it is also in AI’s technopolitics that AI must be crammed into every activity we undertake, whether or not it belongs there, and whether or not it presents a danger to the user, or to the people who depend on the user’s work (think here of cramming AI into automated systems for generating criminal accusations, which then prompt the police to make false arrests). Now, none of this is intrinsic to the technolo­gy. Remember, the technology we’ve been calling “AI” for the past decade is a set of small varia­tions on existing machine learning techniques that yielded surprisingly large dividends relative to the size of those variations. This improvement was scalable – meaning that when computer scientists pushed these variations harder, they got concomitantly outsized improvements in the machine learning systems’ performance. This is one of the best kinds of technological breakthrough to be in the midst of, one where you discover a piece of low-hanging fruit that turns out to be at the edge of a whole _orchard_ full of similarly low-hanging fruits. There’s nothing _technical_ about the discovery of this variation on machine learning that dic­tates the current AI approach. In an alternate universe, “AI” could be a $100b joint supercom­puting effort between the International Panel on Climate Change and Sandia National Labs, used to improve the fidelity of our climate models. This, too, would produce carbon emissions, but not nearly as many, and of course, everything people do has an impact on the environment (the only way to stop impacting the environment is to a) die; and b) decompose). This alternate uni­verse might also see a proliferation of hobbyist open source / open weight models spinning out of these efforts, with online forums and social media filled with generated text and sounds and images. As with every technology controlled by its users, some users will find ways to sin against the law and ethics. There will be some gross stuff around the edge (voice cloning for fraud, deepfake porn, etc), but for the public, the main AI event would be a bunch of plug-ins that were byproducts of a big public research project. The crimes and sins of AI will become a matter for social and legal control – not problems that we can only solve if we can convince vast, corrupt, wealthy corporations to take action. Could we have this AI future still? Well, after the AI bubble pops, we’re going to have _something_. We’re going to have the open-source models that the hyperscalers released as demos for their main event “frontier models.” We’ll have bankruptcy-sale hardware. We’ll have an army of skilled workers who will no longer be compelled by their bosses to try and cram AI into all of our orifices or to convince our bosses to fire us and replace us with chatbots that can’t actually do our jobs. The question, though, is whether AI is more like a Robert Moses overpass, a nuclear reactor, or a tomato harvester? Are there _inescapable_ aspects of the current generation of AI models that mean that using them independently of the hyperscalers will still somehow perpetuate the harms of the current AI bubble? One argument for this is that the fruit of the poisoned tree is forever poisonous. The fact that these AI tools were made by bad people for bad reasons makes everything you do with the tools bad. The most extreme version of this argument is that it’s the mere fact of a sinful origin that disqualifies the hyperscalers’ open AI models from moral consideration. This is a bad argument. There’s hardly any technology – any built infrastructure – that is free from sin. Every microchip you use is filled with billions of linear descendants of William Shockley’s silicon transistors. Shockley was a monster: a virulent racist and eugenicist who spent his Nobel Prize bounty in efforts to pay Black women to have their tubes tied as part of a disgusting racist project. Going further back: Europe’s roads owe their existence to Rome’s imperial conquest, a bloody campaign that included mass enslavement and the indiscriminate slaughter of civilians. Eu­rope’s modern roads aren’t necessarily any bet­ter: Hitler himself turned over the first spadeful of dirt for Austria’s Autobahn. It’s important to remember these facts and to make reparations for old crimes – but just as you can’t both be alive and avoid impacting the environment, you also can’t be both alive and free from contact with the relics of monsters both modern and ancient. Anyone who claims that they live a life that isn’t steeped in blood both old and fresh is kidding themselves. By all means, do everything you can to right wrongs, but if you only permit yourself to act in ways that avoid contact with the product of sin, you won’t do _anything_ , let alone anything to right wrongs. Imagine a new generation of buses could handily fit under Robert Moses’s Long Island overpasses, such that the overpasses could no longer be used to racially and economically segregate the city’s beaches. At that point, the overpasses’ technopolitics change. They may have been made for an evil purpose, but now that they no longer serve that purpose, they’re just bridges. The mere fact of their historical sin is no reason to tear them down today. A less extreme version of the sinful origins ar­gument is that the ongoing use of a sin-steeped object is the source of fresh evil in the world. Museums that display the looted remains of indigenous people don’t merely dishonor those long-dead people: They inflict ongoing harm on living indigenous people whose elders are reduced to colonial curiosities, making indig­eniety itself a relic, as opposed to a living force in the world that is critical to the lives and thriving of millions of people. I think a version of this can be applied to some of the hyperscalers’ models. Models that have been trained on – and retain – the ill-gotten private information of millions of people are indeed so wicked that they should be deleted. Just as the mere existence of pornographic images of children is an ongoing harm to the people who were abused to create them, the mere existence of databases of ill-gotten private information is an ongoing source of harm and risk to the people in the database. If there’s a chance that an AI model can be made to yield up your personal, sensitive information, we should destroy it. But if there are databases – models – that were trained on private information, _in such a way that they no longer retain that information_ , then they are more like the Autobahn than they are like a trove of child sex abuse material. They are steeped in sin, they should not have been made in the way they were, but the ongoing use of them causes no new harm to anyone, and so if they are useful, we should use them – even as we erect protections to prevent anyone else from engaging in mass privacy invasions for any purpose, including AI, ever again. Think here of the thousands of miles of fiber-optic cables that Worldcom buried under the streets of cities all over the world. Worldcom was a massive, wicked, fraudulent enterprise, whose executives lied to the public in order to steal billions from everyday working people, leaving them bankrupt or separated from their retirement savings and doomed to a miserable old age. For this crime, Worldcom CEO Bernie Ebbers was sentenced to prison, and he died there. And yet…the fiber is still under the ground. It got there by the most disgusting, harmful means imaginable. But treating those cables as morally untouchable doesn’t help any of those bankrupt pensioners. At my home in Los Angeles, I have a 2GB symmetrical fiber link from AT&T, who bought and lit up some of Worldcom’s fraudulent, pensioner-destroying fiber. I would rather live in a world where I had slower internet and the pensioners still had their money, but that’s not an option. At this point, the fiber is just fiber – its use doesn’t harm anyone anew, nor would refusing to use it bring any benefit to anyone. Some people’s anger over AI models’ training data is couched in claims about copyright, not privacy. The argument goes that by making transient copies of billions of public works and then tabulating the relationship of the elements in them, the AI companies are stealing from creative workers. I think this represents danger­ously misdirected anger. It’s totally legitimate to be angry at a tech executive with billions in investor capital who publicly announces their intention to destroy your livelihood – even moreso if they use your own creative output to accomplish this end. But the bad conduct here lies in the inten­tion and action to destroy your livelihood. It does _not_ lie in making copies of your work in order to count the words or the pixels in them. Copying and analyzing every public work is how every search engine works. What’s more, this practice wasn’t invented by search engines. Long before the first digital computers, the Oxford English Dictionary examined the entire corpus of publicly available written work to trace the etymology of our language. Long before Google News, the library where I worked subscribed to a quarterly, hardbound subject index of all the articles published in every general-interest English-language newspaper and magazine. A language model is a collection of embedded inferences that certain words are likely to follow from other words. You may rightfully regard the use of a model trained on your work to attack your wages as unethical, but that’s about the model’s _operation_ , not its _creation_. The fact that the model has derived some of these inferences by counting all the words you ever published is not theft – no more than it’s theft for the OED to identify the first times I used the word “enshit­tification” in its entry on my word. Counting words – even all the words ever uttered – isn’t theft. We call that “a search engine.” If an open model, operated and hosted by someone other than the hyperscaler that shipped it, is sold or used to destroy your liveli­hood, then the person using that model is bad, and we should have social, economic and legal sanctions for that person. But the issue here is the _use_ of the model. A neofascist who uses the Autobahn to travel to a racist rally is a Nazi – but they’re a Nazi because of what they’re doing, not because they’re doing it on Hitler’s highway. The technopolitics of language models are bad because the people selling language models market them for the purpose of bankrupting writers and filling the world with slop. But there’s nothing intrinsic to language models that decrees their use in this fashion. The computer scientist Pete Warden has a lovely demo of a language model running on a sub-$10 system on a chip (SOC) that can ingest and conversationally access the manual for an ap­pliance. In his demo, Warden uses this system to create an interactive dishwasher manual: tap a button on the dishwasher and ask it a question like “What should I do if I’m getting streaks on my glassware?” and it will locate the correct answer in the manual and tell you what to try. This language model may have been trained on the corpus of all available literature without permission, and the company that did the training may have raised their capital on the promise of bankrupting writers with their own words, but the button on your dishwasher that uses an open, disconnected model to give you advice on getting your dishes clean in no way perpetuates those harms. If tomorrow someone invented a nuclear reactor that sifted uranium out of seawater and ran safely in your garage, it wouldn’t have the same centralizing technopolitics as the nukes that Winner concerned himself with – and that would be true even if the factory that produced these decentralized pocket reactors ran on a ten billion dollar nuke filled with traditional enriched uranium. The technopolitics aren’t tied to the fact that the nuke produces energy by splitting atoms – it’s tied to the technical reality of the only way we know to accomplish that feat. If we learn new atom-splitting techniques, they can have different technopolitics. By the same token, a language model – even one released by a despicable Altman or Amodei – has embedded technopolitics because of the kinds of infrastructure it requires, not because it guesses words. Guessing words is fine. It’s some­times useful. The open models that OpenAI and Anthropic have put into the world and can never claw back will be guessing words long after those companies are dust. We can use those open models for good or ill – we can choose their technopolitics. Technopolitics are a matter of what happens when you use a technology, and what you have to do to maintain and improve that technology. Optimizing open models is a matter for collaborative, open effort – not closed labs fueled by investors’ centibillions. AI _has_ technopolitics. But those politics are neither inevitable nor immutable. Sam Alt­man’s lurking presence in a language model is no more politically disqualifying than William Shockley’s residue in your microchips. AI today might be a defective tomato har­vester, immiserating workers and driving out good tomatoes with bad. But there’s nothing intrinsic in the idea of a tool for harvesting a tomato that is bad for workers – or tomatoes. It’s because we let bosses choose which tomato harvester was suitable for use that we ended up in this place. Put the pickers in charge of that decision and we’d have very different harvesting technology – and far more delicious tomatoes. * * * #### Cory Doctorow CORY DOCTOROW (craphound.com) is a science fiction author, activist, and journalist. He is the author of dozens of books, most recently Enshittification: Why Everything Suddenly Got Worse And What To Do About It (nonfiction); and the novels Picks And Shovels and The Bezzle (followups to Red Team Blues). Other notable books include the solarpunk novels Walkaway and The Lost Cause; the tech policy books The Internet Con and Chokepoint Capitalism; the internationally bestselling YA Little Brother series; and the picture book Poesy The Monster Slayer. He maintains a daily blog at Pluralistic.net. He works for the Electronic Frontier Foundation, is an AD White Professor at Cornell University; an MIT Media Lab Research Affiliate; a Visiting Professor of Computer Science at Open University. He co-founded the UK Open Rights Group. In 2020, he was inducted into the Canadian Science Fiction and Fantasy Hall of Fame. All opinions expressed by commentators are solely their own and do not reflect the opinions of Locus **While you're here, please take a moment to support _Locus_ with a one-time or recurring donation. We rely on reader donations to keep the magazine and site going. We publish hundreds of reviews and news stories each year, but we can't do it without your help. ** ©Locus Magazine. Copyrighted material may not be republished without permission of LSFF.

Seth :ally_always: reposted

Chuck Darwin
@cdarwin.c.im.ap.brid.gy

The man who attempted to ambush the Democratic Senate candidate Abdul El-Sayed with a question about his supposed allegiance to the Muslim Brotherhood – which El-Sayed parlayed into a viral clip for his campaign – is the son of a political operator who recently received $250,000 for “canvassing […]

c.imOriginal post on c.im

Seth :ally_always: reposted

Entrepreneur's Enigma Podcast
@entrepreneursenigma.com

Doug Downs of Stories & Strategies is my guest tomorrow on Entrepreneur's Enigma. It was such a fun discussion. Be sure to tune in tomorrow morning to listen to two podcast nerds talk podcast growth and so much more! #PodcastGrowth #B2BPodcasting #PodcastProduction #PodcastGoals