Link: Amazon pledges $1B to data center communities, warns that local opposition threatens U.S. AI lead, by Todd Bishop in GeekWire This is a sign that protests are working: > “Amazon says it will spend more than $1 billion over five years in the U.S. communities where it builds and operates data centers, responding to a growing backlash across the country against the tech industry’s massive AI infrastructure buildout.” But I’d consider this a _settlement_ rather than a _win_ : companies like Amazon need the data centers to be built in order to continue their growth. Communities opposing them should be able to keep pressing and get bigger, ongoing concessions at the very least. The objections here follow a predictable playbook: claims about widespread foreign interference (because surely no _real_ American could oppose data centers in their communities) and a warning that America will lose to China if it doesn’t keep building. It’s a scaremongering technique designed to keep people in line without offering further benefits to communities. America won’t lose; _Amazon_ might. The current concessions over five years represent 0.1% of Amazon’s annual capital spending. It’s less than pocket change — _and_ Amazon isn’t forgoing tax breaks, so it may get it all back anyway. And it seems odd that these benefits are set up as something like an altruistic program through Amazon’s own “Built Together” program. Really it should be money that flows directly into towns and cities, on an ongoing basis, with zero strings attached and no involvement from the company. Those towns can do the same things Amazon is claiming to do, but on their terms, and in a way that doesn’t tie them to Amazon’s infrastructure and isn’t a weird PR win for the company. There’s more to do. This is a sign that the protesters can get much more for their communities.

Link: OpenAI doubles support for Lenfest’s AI and local news fellowships, by Andrew Deck in Nieman Lab I participated in this program in my former role of Senior Director of Technology at ProPublica. Because I’m no longer there, it’s no longer my place to say how it went / how it’s going, but I’ll summarize it this way: I have no regrets. The two AI fellows I got to hire are genuinely wonderful people who I hope to work with again, and I learned a ton from other newsrooms in the program. If you’ve followed my writing for any length of time you know that I’m very far from being an AI fanboy, and there was never a sense that I needed to stray from the extremely careful approach that is appropriate for a newsroom like ProPublica. We did not need to use any particular approach or vendor, and most importantly, nobody ever asked us to do anything that would compromise the trust or safety of the newsroom or its community. (The idea isn’t to displace reporters, or any human; it’s to give them new tools.) At its core, it was just an opportunity to explore a new technology in community with other mission-driven newsrooms. It’s also a way for newsrooms to regain their confidence in sharing their technical approaches and code, collaborating with other newsrooms, and (sometimes) open-sourcing their work. That, to me, is a benefit that probably outweighs the use of any technology. Newsrooms _should_ be sharing; they _should_ be collaborating; they _should_ be working together. Hopefully, even when the cohort is over and everyone has moved on to think about something else, they will retain this muscle. It’s important.

Link: First steps of the PLC organization, by Public Ledger of Credentials Organization Since Bluesky’s foundation, its mission has been to work on an open social media protocol that gives users a “credible exit” from any one company’s services while allowing them to continue participating in the network. This has two important effects. The first is that users can feel safe investing in the network knowing they’re insulated from any one company’s individual business decisions (if their social media provider makes a boneheaded decision, they can move to another one without losing their networks or content). The second, which is even more important to me, is that the network is insulated against monopoly power. When one company owns the way everyone communicates and learns, their incentives and priorities dominate how the network works, granting them unprecedented power over democratic discourse. The most obvious example is Elon Musk, who bought Twitter to spread his values and suppress those he disagrees with. But he’s not alone: the US attempted to co-opt TikTok because they were worried the Chinese government might have undue influence over content viewed by American citizens. In an open network, that level of control is much harder to obtain; anyone can start an alternative application provider, and anyone can migrate to it. In a fully decentralized network, no-one — not a would-be oligarch, nor a nation — can have outsized control. One criticism of Bluesky has historically been that it’s _not_ been fully decentralized. Originally, it provided the only application and datastore that sat on the network, and it was the only organization doing identity management — so, while there was an open protocol underneath it, in practice users couldn’t pick anyone else’s services or really migrate. There have been alternative application and datastore providers for a while: Blacksky broke the ice with an application, algorithms, and data stores that allow communities to govern their own data and fund collective needs. Eurosky, which hosts and processes inside the EU, and Gander, a Canadian alternative, are some of the providers that followed. More are showing up every day. Which left identity resolution — the underlying address book that takes a user’s ID on the network (their Decentralized Identifier) and figures out where their data is stored. That had previously been handled by Bluesky itself, but today’s announcement heralds the first steps of transferring it into the hands of an independent Public Ledger of Credentials Organization, headquartered in Switzerland. This is exciting to see, and is another step towards having a fully decentralized network. Bluesky did a great job of attracting journalists, authors, and other people who want to discuss culture and society; those conversations are too important to be owned by any one entity. We’re closer than ever to a social media commons that really works.

Link: Trump TV Draws Ire Amid White House Press Shutout: 'State-Run Media', by Casey Loving in The Wrap It’s not an accident that Trump’s move to ban CNN, MS NOW and Politico from the White House and, the same week, start his own friendly streaming channel happened just before the midterm elections. As The Wrap reported: > “The White House posted [a] message on X on Monday to announce Trump TV, a 24/7 livestream of ‘top past moments, announcements and the latest and greatest from the administration all in one place.’ While the Trump administration and members of the political right are calling this feed a win, others have different words for it: ‘state-run media.’” It’s more than that: calling it “state-run media” would be pulling our punches. It’s a fully fledged propaganda network. If it had been established with press access to the White House fully functioning, it would have floundered as yet another half-assed also-ran attempt by the Presidency; without a fully functioning press pool, newsrooms are likely to turn to it to inform their coverage. And the press pool is in trouble. Fox, ABC, CBS, and NBC subsequently pulled out of pooled TV coverage of White House events in solidarity. This is good to see, but in some ways it may also be what Trump wants: his channel then becomes the main source of footage from the President’s own events. In a normal world where the norms of American democracy were seen as worth preserving, the White House would back down. In a world where the country is being deliberately dragged into authoritarianism, it may not. A First Amendment lawsuit seeks to reinstate the banned newsrooms’ access. Hopefully, it will succeed. In the meantime, Trump may still get eyes on a content channel his administration wholly owns, which broadcasts the Trump message without context or scrutiny, as he aims to take the country down a road it may not be able to easily return from.

Twenty-two years ago this month, I moved house. I’d been living in Edinburgh, an ancient city whose gothic architecture cuts into an expansive sky. Even the air felt apart from the rest of the country: there was a different sort of chill to it, and it carried a malt smell from the breweries on the edge of town. When the train doors opened at Waverley Station, I didn’t just know I was home; I felt it on my skin and in my bones. Edinburgh is famous for its support of the arts: the Festivals double the population of the city each summer. People fly in from all over the world to showcase their creativity and find new audiences. The city is alive. But I had to leave. I’d been working on Elgg, the open source social networking platform I co-founded. It was originally built as a reaction to the prevailing educational technology platforms, which seemed to have been built to satisfy compliance teams rather than to help anybody learn. _People are already learning from each other on the web,_ was the implication. _Let’s take those ideas and bring them in._ Nobody had coined “Web 2.0” yet, but that’s what we were building: a way to support the informal learning and connection that happens in hallways, study rooms, clubs, and parties. Those relationships and conversations are what you really take away with you when you graduate, but they were completely absent from the prevailing software. But the social headwinds were enormous. “It’ll never work here,” we were told, again and again. “Blogging is for teenage girls crying in their bedrooms,” one university leader memorably told us. We were in our early stages, looking for generative conversations, and they were hard to come by. In improv theater, there’s a yes, and rule that allows everyone to build on each other’s ideas without destroying the flow. A _no, but_ response is conversation-ending punctuation: the generative energy has been terminated. But in Scotland, all we were getting was “no, but”. We were looking for optimistic creativity but could only find pessimism. The difference in energy when I moved back to Oxford, my hometown, was night and day. I was able to find the _yes, and_ energy I needed, and I felt less alone in trying to build something new. Elgg eventually powered networks for Ivy League universities, multinational corporations and NGOs, governments, and impactful social movements. None of it would have happened if I’d succumbed to the pessimism. It’s worth saying that Edinburgh has since established a robust innovation scene — and much of it is down to creating a cultural shift as well as simply providing infrastructure. Both help, but it’s incredibly hard to create anything new if you’re being told none of it will work every day. Twenty-two years later, I’ve made another move — and I’m experiencing the same vibe shift. There are one hundred and sixty-six Canary palms on Palm Drive, the long, straight road that leads to the oval that serves as the entrance to the Stanford campus. Every morning, I walk past each one. Every evening, I walk back. The sky is blue save for the low-flying planes coming in to land at SFO. Every few minutes, a Waymo self-driving car drives past me; more than once, a coyote or a hare has crossed my path. This is home now. A little over two weeks ago, I arrived on the Stanford campus. During the month of August, I moved out of my house and drove across country. The Pennsylvania forests and rolling hills of Appalachia gave way to cornfields, plains, salt flats, and mountains. Back home, my house was on the market. This isn’t a subtle move to a next chapter: it’s all change. For the next academic year, I’ll be a JSK Fellow: part of a cohort of thirteen people invited to Stanford to explore and test out practical responses to the challenges facing journalists and journalism around the world. I’ll spend almost every day on campus, taking courses, visiting research labs, attending and hosting events, and connecting with people across disciplines and backgrounds. Immediately, two things hit me. First: every single person in my fellowship cohort is inspiring. I get to share a room with people who have made enormous differences to their communities through their dedication to journalism, in places like Ukraine and Venezuela as well as here at home. The JSK staff are similarly impressive, with their own backstories of real impact. I feel very lucky to be in the room with them (and, inevitably, like a bit of an imposter). Second: this is _explicitly_ a _yes, and_ space. The change in pressure and energy was palpable. And it couldn’t have come at a better time for me. Newsrooms are under threat financially, politically, technically, and existentially. In that environment, it’s easy to become a _no, but_ culture: when everything you do is scrutinized, the culturally safest thing to do is to take the conservative path. Decisions are made to iterate on the status quo rather than take leaps of faith. I’ve heard “this will never work here” as a thought-terminating statement at the early stages of a project’s ideation many times. When there _are_ real changes, rather than innovate and do something new based on their community’s real needs, many newsrooms in the United States simply ask the question: “what is the _New York Times_ doing?” It’s understandable — the conditions that newsrooms are forced to work under are far from ideal — but it comes with a real cost, particularly when trust in journalism and loyalty to newsrooms are plummeting, in the fastest period of technology change (and therefore journalistic and democratic change) in decades. Real journalism provides the information and context that people need to make informed decisions, including who to vote for; it is part of the bedrock for a functioning democracy. As it happens, journalism’s decline is coinciding with democracy’s decline. We need new ideas that will serve our communities more effectively, and fast. Entrenching in the status quo or copying the _Times_ won’t cut it. And, speaking purely for myself, that widening gap between the immense, crucial need and what actions newsrooms are prepared to take can lead to burnout and learned helplessness. I badly needed a _yes, and_ space. It’s been two weeks. If the onboarding was the extent of my JSK experience it would be incredibly valuable: the mindset shift I desperately needed, and a set of tools that usefully reframe what I’ve been thinking about. But it’s a nine-month program, and there’s a lot more to come. The need for innovation in news is not exactly new. When I first moved to the United States fifteen years ago, one of my first acts was to attend a design thinking session about the future of news at the Stanford d.School as part of Ben Huh’s Moby Dick Project. It foreshadowed everything that would happen next: I’ve been working on media innovation ever since, including for years at Matter, a media accelerator that was heavily rooted in the d.School’s methodology. We liked to quote the academic Clay Shirky, who wrote about the need for experimentation in the industry as the newspaper market started to bottom out with the phrase: “nothing will work, but everything might.” Last week, I spent the day at the d.School again as part of my onboarding, and I felt that phrase resonating again. “Nothing _will_ work, but everything _might_.” Let’s be generative. Every JSK fellow applies with a project to work on. Here’s mine, as I originally framed it: Trust in journalism is in long-term decline. Referrals to news articles from search engines and social media are also declining. There are many reasons for this. Google switching to AI-powered instant answers and seismic changes in incentives by the owners of prominent social media platforms are two commonly-stated causes, but they aren’t the whole story. It’s also true that many newsrooms didn’t understand or adapt to changes in how people want to interact with information that came about in the Web 2.0 era. Newsrooms still cling to a broadcast or publishing model rather than embracing conversation as a community of journalists and readers. People believe news is biased and shaped according to the needs of each newsroom’s corporate owners, but newsrooms don’t want to open up their process or add transparency for fear of becoming part of the story. Most modern newsrooms live or die on the web, but the web is a conversation, not a broadcast medium, and newsrooms typically haven’t upgraded their thinking to fit. Helping them to build stronger human relationships with their communities — and each other — on the web will increase trust, loyalty, and resilience. And new community platform technologies like ATproto and ActivityPub could help provide building blocks for new kinds of news sites that elevate community and relationships to first-class parts of a newsroom’s work. In the community-first software era, I expanded on where I might start with this idea: > Newsrooms rely on something called a “callout” when they want to learn more from their readers. More often than not, this is a simple web form: “Has your doctor pushed this prescription medication? Let us know.” But instead of a two-dimensional form, what if we built a short-term community space that safely brought readers in and allowed them to discuss in more depth with the journalists? > > My bet is that two things will happen: the journalists will get better information, because it will arise in conversation, and those readers will build stronger, more transparent relationships with the newsroom. And stronger, more transparent relationships will lead to more trust and more loyalty. It sounds plausible, right? And maybe it is. But in the process of discussing my idea with other fellows, through the genuinely excellent facilitation by Tran Ha, I’ve realized I’ve skipped into solution-land without spending enough time on making sure I’ve framed the problem correctly. I’ve been led by these beliefs: * I believe that good journalism is a vital prerequisite for democracy: good contextual information allows us to make strong democratic decisions, including who to vote for, when to protest, where to spend our money, and how to use our voices. * I also believe that many of our societal problems have been caused by platforms like X and Facebook that are so large that their owners can affect democratic discourse on a global scale, and that open protocols prevent anyone from having that much power. * And finally, I believe that AI is abstracting us away from the human information sources that can provide real context and connection. Those are genuine beliefs I hold. But they’re abstract, ideological ideas. The idea that journalism is important, or that AI has a distancing effect, doesn’t connect to someone who can’t find stories that represent their community or who has had to build an AI prompt to pull together actionable information because it was scattered. The idea that open protocols are better doesn’t speak to someone who needs to share information with their community in a safer way because they’re under attack on incumbent platforms. These things are hard to explore when you’re a part of an existing organization, with its own culture, norms, rhythms, and risks. Here, I’m not _Ben Werdmuller, Senior Director of Technology at ProPublica_ (or _CTO at The 19th_ , _Director of Investments at Matter_ , _Co-founder of Elgg_ , etc) — I’m an academic fellow with a mandate to explore independently. I don’t need to be restricted by an organizational context; I can sit with the problem in my own time and space. It’s a gift. For my project, my first task is to learn more about the ecosystem I want to help. Which communities should I start with? What are their characteristics? What are their needs? Who are the newsrooms that serve them today? As a human, my task is also to fully break out of a reactive, iterative mindset and into one of holistic exploration. How can I serve my own mind, body, and spirit so that I can work on these problems to the best of my ability? How can I learn from my fellowship cohort and the incredible people on the Stanford campus? How can I show up for them so that we can support each other’s work and each make the impact we set out to achieve? And how can I embrace my own inner _yes, and_? That’s my mission at the start of this year. I’m going to take you with me — and I hope to be in conversation with many of you as I progress. Let’s work on this together.

This is, perhaps, an unusual post for me in this space, but: I’m selling my home, it's a great house, and I want you to know about it. 7 Windsor Ave, Elkins Park, PA was built in 1925. Set on half an acre of land, it’s a beautiful Tudor home with a ton of space and flexibility. The photos speak for themselves. I loved living here. Its second floor rooms are well-suited to being set up as spacious work-from-home offices, which is what we did — the whole family comfortably worked from the house. I commuted into the offices in Manhattan and DC many times a month, which I found both easy and genuinely fun via train — and the rest of the time I enjoyed the leafy, peaceful neighborhood. It’s very close to schools and daycares, and it felt like a privilege to be able to do drop-off and pick-up on foot. I’ve headed west for an opportunity I couldn’t say no to, but it was emotionally hard to part with it. I fell in love with it in many ways — there’s no way I could have this level of space and peace while maintaining proximity to major cities at this price in California. > View this post on Instagram > > A post shared by Charlotte Marinello Pellechia (@charpellechia) Elkins Park is an architecturally significant suburb of Philadelphia: Frank Lloyd Wright’s only synagogue and Lynnewood Hall are both in the neighborhood, and the homes are magnificent. And as I mentioned above, it’s also a very accessible commute into New York City. There’s public transit within a few minutes’ walk, which takes you into Philadelphia, 30th Street Station, and Philadelphia International Airport without changes. Elkins Park itself has restaurants, a coffee roastery, and a great bookstore. The Crying in H Mart H Mart is also here; the Korean restaurants in the neighborhood are so good that people travel from NYC to eat there. It’s just a few minutes away from Glenside — the Keswick Theatre there hosts acts like the Magnetic Fields and John Waters — and Jenkintown, which is home to breweries, indie movie houses, and more. 7 Windsor Ave is represented by Charlotte Pellechia at Kurfiss Sotheby’s. You can book a viewing via its Zillow listing or contact Charlotte directly.

_Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society._ _Did someone forward this to you?_ Subscribe for free_._ * * * ### Seattle Times and Newsday are the latest publications to sue OpenAI and Microsoft I have some complicated feelings about these ongoing newsroom lawsuits against AI companies. The latest comes from The Seattle Times and Newsday. From the lawsuit itself: > “AI products like ChatGPT and CoPilot are touted as producers of content, but in fact they are rapacious consumers, devouring human-authored content and delivering back to the world copies and derivative imitations of that same original content they consumed to achieve their commercial objectives.” The government has argued that training LLMs is fair use, and organizations like the EFF have agreed with it. The EFF’s argument in particular is important to take note of: > “The “market dilution” theory would eviscerate not only the fair use doctrine, but also other limits on copyright that work specifically to prevent rightsholders from unfairly suppressing competition by claiming broad ownership over tropes, genres, styles, and so on. In other words, publishers would wield unchecked veto power over any expression that might conceivably compete with a work they own.” That’s a genuine problem! Expanding the scope of copyright law really does affect free expression. It’s an important concern to bring up — if the market dilution argument held, it could protect all sorts of businesses, both small and multinational. No industry deserves to exist for its own sake, and we can’t ask the world to stay still to protect an industry. Systems and methods that work against journalism can’t be illegal in their own right. At the same time, _we need journalism_. An informed voting population is a prerequisite for a functioning democracy. The implication is that if the old methods no longer allow it to be sustainable, journalism _must_ evolve and find new methods that are. We need information, context, and reporting; we _don’t_ need the businesses that produce those things to operate in the exact same way they do now, or to look similar to how they do today. There’s no doubt that AI in its current form takes work that is often independently produced by people with relatively few resources and uses it to enhance models that are controlled by large tech companies with billions of dollars at their disposal, which are often, in turn, _controlled_ by billionaires. It’s a giant transfer of property from people with relatively low power to some of the most powerful people in the world. I think it’s fair to say they’re strip-mining culture to enrich themselves. But those power dynamics are not entirely cut and dry. The EFF’s point is, in part, that expanding copyright scope will primarily benefit large corporations like Disney that have often, themselves, applied a chokehold to cultural expression. In preventing strip-mining by some billionaires, there’s a risk of giving new powers to _other_ billionaires. Although I want to focus on the power dynamics, there’s also a mechanical objection to consider. LLMs are performing statistical analysis on source material in order to train, and the outputs are created using probabilistic math, not intentional reproduction. We need to retain the ability to analyze creative work for all kinds of reasons, and even index it — consider search engine indices, which I think we generally want to exist so that people can find our work. Of course, actual verbatim outputs — or approximate copies thereof — are plagiarism. And the materials LLMs are trained on are often obtained illegally or outside the terms of their licensing agreements: unambiguous theft that needs to be treated accordingly. The Anthropic copyright settlement was about this: it wasn’t that training on books was found to be infringing in itself. In fact, Anthropic was found not liable for this. It was that _it stole them_. Anthropic settled for billions of dollars. That’s good in itself, but it turns out — surprise, surprise — that many publishers took the money for themselves and didn’t pass them down to authors, even when the rights had reverted _to_ those authors. The elephant in the room is that copyright was never designed to protect individual authors: it was designed to benefit the public, with author protections a necessary means to that end. In practice, most journalists, artists, etc, don’t own their work to begin with, and royalties are frequently corporation-to-corporation transfers even without AI. The Newsday and Seattle Times lawsuits seek to protect corporate interests in a rapidly-changing market, with journalist reward effectively a trickle-down benefit. That's not to single them out in any way; it's just the industry dynamic. The licensing deals emerging as the market solution — among them OpenAI with News Corp, Axel Springer, the FT; Amazon with the New York Times — are corporate deals. Individual journalists and freelancers whose work constitutes the value see little of it, except in that it’s revenue that theoretically allows their employers to continue to exist and employ them. We probably need a new deal for creators that touches several levels. The first is contracts and labor conditions. Unions have a huge part to play on this side of the equation. The Writers Guild of America won reasonable AI concessions from studios after their 2023 strike, ensuring that writer compensation couldn’t be diluted if a studio used AI. A revised contract this year also gave them written notice rights when studios intend to train models on their work, and a right to discuss compensation. That’s great for members of the WGA, but most creative workers aren’t a part of an industry union. If that changed, both publishers and AI vendors would need to negotiate more creator-favorable terms. It seems like it should be a goal. The second is infrastructure. We need independent creators to be able to set rights over their work and enforce them. Really Simple Licensing is one effort that’s working in this direction: a way to create an ASCAP-like standard for creative work. Notably, though, not a single major AI vendor has agreed to comply. There are criticisms of the collective licensing approach generally, and there’s still a platform compensation problem: if Reddit implements RSL, _it_ will receive revenue, but its users who created the licensed content probably won’t. The third is law. Our existing rules need to be updated to protect creators, _regardless_ of AI; the advent of generative AI has made it even more important. Publishers must not be able to take all the money from licensing creative work (the EU’s Digital Single Market Directive is a good model to follow). AI vendors should be transparent about what they’re training on and how. Nobody should be able to steal training material. And rules should be revised to reflect that work can now be trained on and used to generate content at scale, automatically, which has knock-on effects on the public good. These are all big topics. The shift we have to contend with is seismic in a way that hasn’t been rivaled since the advent of the web itself (and in some ways the impact on creators and publishers may be bigger than that). Each newsroom lawsuit is a kind of shot across the bow; the substantive conversations we need to be having are more expansive. But, in a political environment where AI vendors are aligned with the administration, and in a world where so many people want to be excited about AI in not very nuanced ways, I wonder if the conversations and actions that would lead to more equitable treatment and compensation for creators will be allowed to happen at all. Newsrooms should protect themselves, but news as an industry should make sure it's looking out for the people who create its value, too. * * * ### LG TVs caught spying even when offline or on standby This is an enormous abuse of trust: > “LG smart TVs are almost constantly logging and uploading data about owners and their homes, even when offline or on standby mode. […] Packet captures showed the TVs scanning the local area network for nearby hardware like phones or smartwatches, as well as logging location data and details of nearby Wi-Fi networks, and feeding the information back to LG Ad Solutions. Perhaps more concerningly, the TVs were capable of recording microphone audio when in standby; this continued even after the TV was disconnected from the internet, with audio files stored offline and uploaded once a connection was restored.” Most households invite at least one television into their homes. It’s almost impossible to buy one without smart TV features; because most television shows are streamed over the internet these days, they’re usually online. You could leave the TV offline while connecting it to a separate box like an Apple TV, but it’ll keep nagging you to connect. The contract should look like this: you pay for a TV and it shows you stuff. The idea that it will scan your local network for devices, determine what content you like to watch, and maybe even _listen to you using a built-in microphone_ is not something that anybody signs up for. Someone somewhere at LG has been so incentivized to make a graph go up and to the right that they have decided that basic ethical tenets like privacy and transparency aren’t worth holding on to. That’s particularly important in a world where the Department of Homeland Security has created _Minority Report_ -style Predictive Intelligence Targeting Teams who instruct police to pull people over based on profiled data like their financial activity, despite there being no evidence that they have committed a crime. Data from disparate sources is routinely bought by law enforcement and agencies like DHS in order to create these sorts of profiles; a connected TV in your home could easily contribute. Are you watching the wrong kind of content? Automatic Content Recognition could rat you out. Are the devices connected to your home network outside of the norm? Up goes a flag. So that’s homes. But most offices have TVs installed somewhere, too. Consider that an installed smart TV could easily be snooping on network devices, content, and even video calls conducted over these screens. Some more sophisticated offices will have them set to a less privileged network or insist on them remaining offline; most will dutifully connect them up. The result is a live microphone and content recognition tech installed in newsrooms, universities, non-profits, and businesses around the country. Manufacturers like LG can sell this data. They will likely argue that it keeps TV prices down – and maybe that’s true. But this reporting comes after LG promised regulators in Texas to stop collecting viewing data without informed consent; there’s a second mic that may continue recording even when the main microphone setting has been turned off. They’ve shown they cannot be trusted. This kind of invisible data gathering should be illegal. Nobody signed up to be spied on; they just want to watch TV. * * * ### AI safety is designed in the West, and failing users everywhere Amnesty International believes that Facebook was complicit in the genocide against the Rohingya people in Myanmar: > “In 2017, the Rohingya were killed, tortured, raped, and displaced in the thousands as part of the Myanmar security forces’ campaign of ethnic cleansing. In the months and years leading up to the atrocities, Facebook’s algorithms were intensifying a storm of hatred against the Rohingya which contributed to real-world violence.” It wasn’t that anyone at Facebook actively wanted to enable a genocide: they just didn’t care about _not_ enabling one. Their teams were warned at least fifteen times, but mitigating this obvious harm was deprioritized in favor of other things. They were focused on their own world, with its epicenter in Menlo Park, California. Ultimately, although Facebook was not the source of the genocide, its use contributed significantly, leading to Rohingya deaths. By prioritizing growth in Myanmar but not staffing for local needs, it invested in its valuation rather than preventing harm in a country it didn’t care about. So it’s more than a little concerning to see AI vendors making the same mistakes. As Rina Chandran writes in Rest of World, failures in the Global South caused by Silicon-Valley-centric product development are becoming more common: > “For example, in Tigrinya, spoken by about 9 million people in Eritrea and northern Ethiopia, machine translation rendered smallpox as syphilis, gonorrhea as diabetes and ‘you have been given intravenous antibiotics’ as ‘you have been given intravenous insecticides.’” While international efforts like the Bletchley Declaration aim to mitigate what are considered to be frontier risks of AI, it asks for little accountability for the other harms that inevitably arise from product teams that aren’t rooted in the interests, needs, norms, and values of diverse international communities. Those harms can add up to become deep wounds in themselves, but it’s also worth asking another question. As Rest of World points out: > “At the heart of the issue ‘is the question of who gets to define what counts as a safety problem in the first place,’ Elizabeth Orembo, a fellow at Research ICT Africa, a think tank, told _Rest of World_. Big tech firms tend to focus on model risks such as deception, autonomous behavior, cyber capabilities, and aiding bioweapons, she said. They do not pay much attention to deployment risks including discrimination, exclusion, surveillance, language failures, and the inability of affected communities to seek remediation.” Can affected communities be co-owners of those risk frameworks, including the resulting product roadmaps, or are they, once again, just expected to let technology happen to them? Facebook was warned fifteen times that it was doing the wrong thing in Myanmar, and still did nothing. AI companies are retreating from even the weak commitments they previously made. They need to be forced to do the right thing: if not by markets and investors, then by international regulation.

Link: Seattle Times and Newsday are the latest publications to sue OpenAI and Microsoft, by Anthony Ha in TechCrunch I have some complicated feelings about these ongoing newsroom lawsuits against AI companies. The latest comes from The Seattle Times and Newsday. From the lawsuit itself: > “AI products like ChatGPT and CoPilot are touted as producers of content, but in fact they are rapacious consumers, devouring human-authored content and delivering back to the world copies and derivative imitations of that same original content they consumed to achieve their commercial objectives.” The government has argued that training LLMs is fair use, and organizations like the EFF have agreed with it. The EFF’s argument in particular is important to take note of: > “The “market dilution” theory would eviscerate not only the fair use doctrine, but also other limits on copyright that work specifically to prevent rightsholders from unfairly suppressing competition by claiming broad ownership over tropes, genres, styles, and so on. In other words, publishers would wield unchecked veto power over any expression that might conceivably compete with a work they own.” That’s a genuine problem! Expanding the scope of copyright law really does affect free expression. It’s an important concern to bring up — if the market dilution argument held, it could protect all sorts of businesses, both small and multinational. No industry deserves to exist for its own sake, and we can’t ask the world to stay still to protect an industry. Systems and methods that work against journalism can’t be illegal in their own right. At the same time, _we need journalism_. An informed voting population is a prerequisite for a functioning democracy. The implication is that if the old methods no longer allow it to be sustainable, journalism _must_ evolve and find new methods that are. We need information, context, and reporting; we _don’t_ need the businesses that produce those things to operate in the exact same way they do now, or to look similar to how they do today. There’s no doubt that AI in its current form takes work that is often independently produced by people with relatively few resources and uses it to enhance models that are controlled by large tech companies with billions of dollars at their disposal, which are often, in turn, _controlled_ by billionaires. It’s a giant transfer of property from people with relatively low power to some of the most powerful people in the world. I think it’s fair to say they’re strip-mining culture to enrich themselves. But those power dynamics are not entirely cut and dry. The EFF’s point is, in part, that expanding copyright scope will primarily benefit large corporations like Disney that have often, themselves, applied a chokehold to cultural expression. In preventing strip-mining by some billionaires, there’s a risk of giving new powers to _other_ billionaires. Although I want to focus on the power dynamics, there’s also a mechanical objection to consider. LLMs are performing statistical analysis on source material in order to train, and the outputs are created using probabilistic math, not intentional reproduction. We need to retain the ability to analyze creative work for all kinds of reasons, and even index it — consider search engine indices, which I think we generally want to exist so that people can find our work. Of course, actual verbatim outputs — or approximate copies thereof — are plagiarism. And the materials LLMs are trained on are often obtained illegally or outside the terms of their licensing agreements: unambiguous theft that needs to be treated accordingly. The Anthropic copyright settlement was about this: it wasn’t that training on books was found to be infringing in itself. In fact, Anthropic was found not liable for this. It was that _it stole them_. Anthropic settled for billions of dollars. That’s good in itself, but it turns out — surprise, surprise — that many publishers took the money for themselves and didn’t pass them down to authors, even when the rights had reverted _to_ those authors. The elephant in the room is that copyright was never designed to protect individual authors: it was designed to benefit the public, with author protections a necessary means to that end. In practice, most journalists, artists, etc, don’t own their work to begin with, and royalties are frequently corporation-to-corporation transfers even without AI. The Newsday and Seattle Times lawsuits seek to protect corporate interests in a rapidly-changing market, with journalist reward effectively a trickle-down benefit. The licensing deals emerging as the market solution — among them OpenAI with News Corp, Axel Springer, the FT; Amazon with the New York Times — are corporate deals. Individual journalists and freelancers whose work constitutes the value see little of it, except in that it’s revenue that theoretically allows their employers to continue to exist and employ them. We probably need a new deal for creators that touches several levels. The first is contracts and labor conditions. Unions have a huge part to play on this side of the equation. The Writers Guild of America won reasonable AI concessions from studios after their 2023 strike, ensuring that writer compensation couldn’t be diluted if a studio used AI. A revised contract this year also gave them written notice rights when studios intend to train models on their work, and a right to discuss compensation. That’s great for members of the WGA, but most creative workers aren’t a part of an industry union. If that changed, both publishers and AI vendors would need to negotiate more creator-favorable terms. It seems like it should be a goal. The second is infrastructure. We need independent creators to be able to set rights over their work and enforce them. Really Simple Licensing is one effort that’s working in this direction: a way to create an ASCAP-like standard for creative work. Notably, though, not a single major AI vendor has agreed to comply. There are criticisms of the collective licensing approach generally, and there’s still a platform compensation problem: if Reddit implements RSL, _it_ will receive revenue, but its users who created the licensed content probably won’t. The third is law. Our existing rules need to be updated to protect creators, _regardless_ of AI; the advent of generative AI has made it even more important. Publishers must not be able to take all the money from licensing creative work (the EU’s Digital Single Market Directive is a good model to follow). AI vendors should be transparent about what they’re training on and how. Nobody should be able to steal training material. And rules should be revised to reflect that work can now be trained on and used to generate content at scale, automatically, which has knock-on effects on the public good. These are all big topics. The shift we have to contend with is seismic in a way that hasn’t been rivaled since the advent of the web itself (and in some ways the impact on creators and publishers may be bigger than that). Each newsroom lawsuit is a kind of shot across the bow; the substantive conversations we need to be having are more expansive. But, in a political environment where AI vendors are aligned with the administration, and in a world where so many people want to be excited about AI in not very nuanced ways, I wonder if the conversations and actions that would lead to more equitable treatment and compensation for creators will be allowed to happen at all. Newsrooms should protect themselves, but they should make sure they’re looking out for the people who create their value, too.

Link: LG TVs caught spying even when offline or on standby, by Dominic Preston in The Verge This is an enormous abuse of trust: > “LG smart TVs are almost constantly logging and uploading data about owners and their homes, even when offline or on standby mode. […] Packet captures showed the TVs scanning the local area network for nearby hardware like phones or smartwatches, as well as logging location data and details of nearby Wi-Fi networks, and feeding the information back to LG Ad Solutions. Perhaps more concerningly, the TVs were capable of recording microphone audio when in standby; this continued even after the TV was disconnected from the internet, with audio files stored offline and uploaded once a connection was restored.” Most households invite at least one television into their homes. It’s almost impossible to buy one without smart TV features; because most television shows are streamed over the internet these days, they’re usually online. You could leave the TV offline while connecting it to a separate box like an Apple TV, but it’ll keep nagging you to connect. The contract should look like this: you pay for a TV and it shows you stuff. The idea that it will scan your local network for devices, determine what content you like to watch, and maybe even _listen to you using a built-in microphone_ is not something that anybody signs up for. Someone somewhere at LG has been so incentivized to make a graph go up and to the right that they have decided that basic ethical tenets like privacy and transparency aren’t worth holding on to. That’s particularly important in a world where the Department of Homeland Security has created _Minority Report_ -style Predictive Intelligence Targeting Teams who instruct police to pull people over based on profiled data like their financial activity, despite there being no evidence that they have committed a crime. Data from disparate sources is routinely bought by law enforcement and agencies like DHS in order to create these sorts of profiles; a connected TV in your home could easily contribute. Are you watching the wrong kind of content? Automatic Content Recognition could rat you out. Are the devices connected to your home network outside of the norm? Up goes a flag. So that’s homes. But most offices have TVs installed somewhere, too. Consider that an installed smart TV could easily be snooping on network devices, content, and even video calls conducted over these screens. Some more sophisticated offices will have them set to a less privileged network or insist on them remaining offline; most will dutifully connect them up. The result is a live microphone and content recognition tech installed in newsrooms, universities, non-profits, and businesses around the country. Manufacturers like LG can sell this data. They will likely argue that it keeps TV prices down – and maybe that’s true. But this reporting comes after LG promised regulators in Texas to stop collecting viewing data without informed consent; there’s a second mic that may continue recording even when the main microphone setting has been turned off. They’ve shown they cannot be trusted. This kind of invisible data gathering should be illegal. Nobody signed up to be spied on; they just want to watch TV.

Link: AI safety is designed in the West, and failing users everywhere, by Rina Chandran in Rest of World Amnesty International believes that Facebook was complicit in the genocide against the Rohingya people in Myanmar: > “In 2017, the Rohingya were killed, tortured, raped, and displaced in the thousands as part of the Myanmar security forces’ campaign of ethnic cleansing. In the months and years leading up to the atrocities, Facebook’s algorithms were intensifying a storm of hatred against the Rohingya which contributed to real-world violence.” It wasn’t that anyone at Facebook actively wanted to enable a genocide: they just didn’t care about _not_ enabling one. Their teams were warned at least fifteen times, but mitigating this obvious harm was deprioritized in favor of other things. They were focused on their own world, with its epicenter in Menlo Park, California. Ultimately, although Facebook was not the source of the genocide, its use contributed significantly, leading to Rohingya deaths. By prioritizing growth in Myanmar but not staffing for local needs, it invested in its valuation rather than preventing harm in a country it didn’t care about. So it’s more than a little concerning to see AI vendors making the same mistakes. As Rina Chandran writes in Rest of World, failures in the Global South caused by Silicon-Valley-centric product development are becoming more common: > “For example, in Tigrinya, spoken by about 9 million people in Eritrea and northern Ethiopia, machine translation rendered smallpox as syphilis, gonorrhea as diabetes and ‘you have been given intravenous antibiotics’ as ‘you have been given intravenous insecticides.’” While international efforts like the Bletchley Declaration aim to mitigate what are considered to be frontier risks of AI, it asks for little accountability for the other harms that inevitably arise from product teams that aren’t rooted in the interests, needs, norms, and values of diverse international communities. Those harms can add up to become deep wounds in themselves, but it’s also worth asking another question. As Rest of World points out: > “At the heart of the issue ‘is the question of who gets to define what counts as a safety problem in the first place,’ Elizabeth Orembo, a fellow at Research ICT Africa, a think tank, told _Rest of World_. Big tech firms tend to focus on model risks such as deception, autonomous behavior, cyber capabilities, and aiding bioweapons, she said. They do not pay much attention to deployment risks including discrimination, exclusion, surveillance, language failures, and the inability of affected communities to seek remediation.” Can affected communities be co-owners of those risk frameworks, including the resulting product roadmaps, or are they, once again, just expected to let technology happen to them? Facebook was warned fifteen times that it was doing the wrong thing in Myanmar, and still did nothing. AI companies are retreating from even the weak commitments they previously made. They need to be forced to do the right thing: if not by markets and investors, then by international regulation.

_Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society._ _I've been taking a hiatus during August while I_ prepare to join the Stanford JSK fellowship_, but I couldn't wait to send you today's link._ _Did someone forward this to you?_ Subscribe for free_._ * * * ### Can public media build its own social network? Chicago Public Media teams up with New_ Public to try I’ve been more excited about this announcement than any other development in either social media or news technology this year. Chicago Public Media — which runs both WBEZ and the Chicago Sun-Times — will run a network of online social communities for Chicago neighborhoods at chicago.com. These communities run on New_ Public’s Roundabout, a social networking platform designed to be a positive space for local communities to organize, meet, and share. As Nieman Lab reports: > “Roundabout’s team sees these communities as places local reporters can be invited into to listen and build trust — places where they can answer people’s questions and learn what they should be reporting on. Reporters, and reporting, are not central to what Roundabout is; they are components that can strengthen the local community conversation that is central.” I strongly believe that this sort of community engagement is how news turns around its precipitous decline in trust from the public. People trust human relationships, not brands; hosting a space to support those relationships, and forming them authentically and organically with a wider community, will lead to more representative journalism, stronger trust from the public, and better information overall. More than that, these sorts of communities have the potential to meet what most people need from local journalism in a format that feels more equitable (everyone participates rather than it being a voice from above) and modern. Roundabout is not the only game in town here: this is a genuine, growing trend across media. The Newsmast Foundation has been building community spaces for newsrooms that are backed by the Fediverse; its first major launch was British newspaper the Bristol Cable last year. Meanwhile, Bonfire Networks has been building its community platform to operate with similar intentions to Roundabout, including by working with media companies like Jacobin Germany. Still, this is a significant, brave development, particularly for US media, and is notable for its scope and ambition. It’s incredibly rare for a news organization to put their neck on the line and truly build new technology that up-ends existing paradigms. A new kind of social space for one of America’s largest cities fits that bill. Conversely, working with local media orgs — people who already understand their local communities deeply and are active in them — is a great way for New_ Public to build a platform that is safer and less toxic than something like Nextdoor. Roundabout has a bunch of its own features that help provide this safety; the focus on conversation over profiles or clout, and Nextdoor’s notoriously racist “suspicious person” posts are banned. There’s something else here too: Blaine Cook, currently the platform’s principal engineer, confirmed that it runs on AT Protocol, the open social web protocol that powers Bluesky. It’s its own archipelago for now, but once private spaces land in AT Protocol properly, it’ll be connected to the wider network (known as the Atmosphere). This protocol compatibility allows anyone to build their own apps. Once Chicago’s network is connected to the wider Atmosphere, anyone will be able to build other communities that connect up to it, and build platform software that interoperates with Chicago’s. Given the size of this project, it’s likely the biggest foray for any media company onto the open social web. If this experiment in Chicago is successful, I think we’ll see New_ Public expand to work with other media orgs — and we’ll see some of the orgs that might not have considered this kind of platform come around to the idea. * * * ### And more: Here are some of the stories I didn't get a chance to go into in depth this week. #### How can news media wean themselves off the Big Tech drip? The University of Amsterdam is teaming up with European newsrooms De Correspondent and Follow the Money for a two-year project that will see researchers co-design news technology prototypes with the public. The goal is to help newsrooms build greater autonomy through building things their readers actually want to use. #### The offline messaging apps challenging internet shutdowns Authoritarian governments sometimes combat protest by conducting internet shutdowns. A new set of offline-first, truly decentralized social apps allow protestors to communicate regardless by using Bluetooth mesh networks. Modi's government tried to block access to Bitchat's GitHub repo: a sure indication that they're worried about it. In turn, these apps are a sign that the genie cannot be put back into the bottle. #### Mea Culpa - Dark Hours A developer used Claude to build a tool to give you an idea what you could see in the sky that night. The LLM created something that was so markedly similar to an existing open source project that it duplicated a bug that project's author had since fixed. The plagiarism was unintentional and didn't come to light until the open source author reached out. Everyone using AI to develop software (or anything releasable) should consider this a warning. #### America’s largest newspaper chain, USA Today Co., partners with Palantir to analyze audience data as search traffic falls Apparently Axel Springer and Fox News are doing the same. I'm not sure it makes me feel good to know that the firm that powers ICE also has access to data about which news stories people are reading.

On Thursday, I left my role as Senior Director of Technology at ProPublica, where I led IT, Security, and Engineering. In September, I will begin my John S. Knight Journalism Fellowship at Stanford University. Which means that, during August, I will move with my family back to the San Francisco Bay Area and be based in the vicinity of the Stanford campus. ### What can you expect from me? In August, my writing will likely be more sporadic. From September, I expect to spend more time documenting my research, opening conversations, and being intentional about pushing forward the ideas I’ve always held space for. My thesis is that community — and open community protocols and platforms — can help build trust, loyalty, and resilience in news. I laid out some of those ideas in The Community-First Software Era. But I’m going into it with an ethos of intentional serendipity, armed with everything I’ve learned about leadership, technology, journalism, and entrepreneurship. I’ll _also_ be armed with everything I _will_ learn with Stanford as a platform. I can’t predict what I’ll emerge with — but I can commit to taking you along with me. ### Where else can you find me? I’m going to endeavor to stick close to Stanford over the next year. I’ve also decided that I won’t get on a plane for the rest of 2026, partially as a challenge to myself and partially as a reaction to some really bad flights earlier this year. But I’m making an exception for the News Product Alliance Summit in Chicago from October 21-23. Last year I found it to be the most substantive conference about news product and technology I’d been to. I was lucky enough to present two sessions and loved the experience. So I’m back, with Joe Germuska, to talk about how newsrooms can benefit from open technology and protocols: > Proprietary social media platforms have inserted themselves between newsrooms and the things journalism needs to survive: engagement, trust, and revenue. As AI creates new layers of intermediation and puts newsrooms at further risk, building direct relationships with your own audiences has never been more urgent. > > Drawing from our combined experience building technology for newsrooms, we'll make the case for open protocols — shared, interoperable technologies no single company controls — as the foundation for a healthier news ecosystem. We'll explore how building on open infrastructure, rather than proprietary platforms, helps publishers reach more people, deepen engagement, and control their own destinies, without losing out on user experience or adding complexity. I’m hoping to put on more events in California through Stanford, so watch this space. ### Can we chat? This work can’t be done in a vacuum. I want to learn and build alongside people who are doing great work, led by important values. If anything I’ve spoken about above — or anything I write in this space — resonates with you, I’d love to chat. In September I’ll resume my Open Office Hours and will be available both to chat online and over a coffee for folks who might be in the Bay Area. ### A note about ProPublica I truly loved my time leading tech at ProPublica. The phrase we used internally was that it was _never boring_ : there was always something happening. It was sometimes exhilarating, sometimes frustrating, but it was always done with a community of really great human beings working together towards the most meaningful mission of my career. That mission runs deep throughout the newsroom: > To expose abuses of power and betrayals of the public trust by government, business, and other institutions, using the moral force of investigative journalism to spur reform through the sustained spotlighting of wrongdoing. Some newsrooms report. Some observe and have a view from nowhere. ProPublica exists to _spur reform_ , and its impact showcase demonstrates that it succeeds. Every workplace has things to improve or friction to overcome — they’re all works in progress — but it was hugely motivational to be working alongside these incredible people for this incredible reason. The day after I left, the ProPublica Guild ratified its first contract. It was a long, fraught conversation that had been happening almost the entire time I was at the organization. (My timing is impeccable.) I wasn’t a part of the Guild or manager bargaining, but I couldn’t say anything about it while the negotiation was happening for fear of accidentally interfering with the process. I’m very glad everyone got there: every worker deserves a good union to support them, and the people who work to publish ProPublica’s journalism – across editorial and business teams – certainly deserve a great deal. I will be cheerleading for ProPublica forever, and I hope to be friends with the people behind it forever. I’m grateful that I was able to be a part of that community. And if you’re looking for a place to financially support that drives real change, there are much worse places to donate.

_Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society._ _Did someone forward this to you?_ Subscribe for free_._ * * * ### Leaders are Leverage I’ve often shared Corey Ford’s pieces. I find his frameworks and thinking genuinely useful, and he’s been a friend and mentor to me for well over a decade. This piece outlines his underlying thinking, and why he’s focused where he has: > “When I work with one leader, I'm not working with one person. I'm working with every person on their team, every meeting they'll ever run, every piece of feedback they'll ever give, every subculture they'll ever build. A leader is not a single node in an organization. A leader is a multiplier. Change how one leader leads, and you change what work feels like for everyone around them, and everyone around the people they develop, for years.” It’s all about seeding culture. I see a lot of similarities in the underlying ideas in Corey’s work and the intention behind culture change manifestos like Emergent Strategy. Change is fractal, bubbling up from one person to affect a whole system. I was involved in Matter, the accelerator Corey founded, in two ways: first as an entrepreneur, receiving an earlier version of the ideas he continues to teach, and then as a member of the team, helping to deliver them to cohorts of entrepreneurs. It changed my life, and I watched it change the way other participants think about building teams, products, and cultures. Those ideas are now part of the Sulzberger Executive Leadership Program at Columbia University. If you’re a newsroom leader, I believe you should strongly consider it. And even if you’re not, I recommend that you follow Corey and his work. I guarantee he’ll change your thinking. * * * ### Fed up with Big Tech, communities turn to data collectives for control It’s interesting to contrast the current moment to the “information wants to be free” era of Web 2.0, twenty or so years ago. Back then, everyone was talking about open APIs and open data. Now, it’s become clearer that communities need to control the terms of their data if they’re going to avoid being strip-mined for somebody else’s profit. > “Workers, producers, consumers, and others have been establishing cooperatives and other community-led associations to pool resources, share benefits, and address socioeconomic challenges for centuries. The United Nations marked 2025 as the year of cooperatives, positioning them as “essential solutions to today’s global problems,” kindling renewed interest in data collectives and cooperatives.” While there’s certainly an argument to be made that communities tend to over-estimate the value of their own data (looking at you, news), some of these datasets may be truly unique in ways that would add value to an AI service or model. As this article points out, collectively-owned data includes creative works in more than 20 African languages that aren’t recognized in mainstream linguistic frameworks. The danger, of course, is that putting these kinds of gates in front of underrepresented cultures just works to further marginalize them: in that potential future, if everyone’s using a model where those languages are missing, they become irrelevant. But there’s another one where data collectives can pull the levers they have to bring about the world they want to see. That’s exactly what the Nwulite Obodo Open Data License aims to do: data rights holders can negotiate to share their work and cultural heritage without losing their right to benefit from it. (Nwulite Obodo is Igbo for raising, reviving, and building the community.) In one model, vendors building non-extractive and responsibly trained models for public interest purposes get to use their data for free, but the closed-model big tech vendors have to pay. That’s what Meesum Alam did with voice data for 39 at-risk languages in Pakistan: the communities he worked with determined that the data was free for research and non-commercial purposes, but for-profit tech companies would need to negotiate terms (which Meta did). That potentially becomes more interesting: either OpenAI et al negotiate to license the data, or they lose functionality to their public interest competitors. There’s also a world where some communities proactively document their cultures and make them available specifically so that models, whoever they’re built by, won’t omit them. Either the world has more equitable AI or the communities financially benefit from their cultural heritage. Whatever happens, these communities certainly have the right to control their data however they see fit. What vendors do about it is the open question. But initiatives like Mozilla Data Collective make it more possible to have more substantive conversations about how data is provided and used, and that can only be a good thing. * * * ### US government targets Cop City protester over phone operating system This is worth knowing about and is concerning — but not necessarily for the main reason that’s being reported. The Department of Justice is trying to prosecute Sam Tunick, an Atlanta-based activist, for allegedly using a duress password on his GrapheneOS phone when he crossed the border in January 2025. > “Agent Findley and several others repeatedly asked Tunick to open his phone during the interrogation, telling him they would seize it if he did not. When he finally provided a passcode, “the screen went blank, flashed several times and the phone appeared to restart”, according to the motion.” The phone was wiped. According to the Department of Justice, rather than the usual unlock password, the one Tunick had provided was a signal that GrapheneOS should reset the device to factory settings. That’s the core issue: it’s not that he was using GrapheneOS or had set up a duress password, but he was accused of using it to reset his device rather than give his data to law enforcement when asked. At the point where law enforcement or border protection are asking you for data, it’s your right to refuse a search, but you typically can’t actively destroy it. I’ve always understood that the police can’t compel you to unlock your phone without a warrant, although, unfortunately, Customs and Border Protection has an exemption around the border. If there _is_ a warrant, or if CBP asks you in a border zone, you may still refuse to unlock it, but the device may be seized and held. The trick here, which Tunick’s lawyers are arguing, is that the request was unlawful to begin with. Because Tunick was a part of Atlanta’s Stop Cop City protests, he had been put on a terrorist watchlist; that fact was circulated just three hours prior. That flagged him for the secondary inspection that led to him being asked to unlock his phone. Protest is protected by the first amendment and a core component of democratic speech; putting protesters on a watchlist designed to protect the public against violent extremism is undemocratic. That’s even more affronting when you consider that the protest was against a police training center: the message it sends is nakedly authoritarian. Finally, and most egregiously, the questioning was about child exploitation imagery, which they had no reason to suspect him of holding. As a result, the search may not have been legal. While a duress password is a deliberate act of destruction, the better path when crossing the border is to not have data to seize to begin with. Anyone who deals with sensitive information should consider that their phone might be taken at the border. Customs and Border Protection policy even allows agents to clone it, giving them permanent access to your data even after they hand your device back to you. They’re only supposed to do this when there’s a national security concern or reasonable suspicion of a crime — but if activists are being targeted as terrorists, that policy threshold doesn’t feel like a solid protection. So: log out of your email, calendar, and file sharing before you embark upon your travels. Delete Signal entirely (but back it up). Consider which photos you want to travel with. Don’t travel with a stock phone — that can lead to more questions — but intentionally cut down your information footprint. That way, even if you are stopped, you won’t compromise sources (if you’re a journalist) or your compatriots (if you’re an activist). And you’re not forced to delete data in the moment in a way that could leave you vulnerable.

Link: Leaders Are Leverage, by Corey Ford at Point C I’ve often shared Corey Ford’s pieces. I find his frameworks and thinking genuinely useful, and he’s been a friend and mentor to me for well over a decade. This piece outlines his underlying thinking, and why he’s focused where he has: > “When I work with one leader, I'm not working with one person. I'm working with every person on their team, every meeting they'll ever run, every piece of feedback they'll ever give, every subculture they'll ever build. A leader is not a single node in an organization. A leader is a multiplier. Change how one leader leads, and you change what work feels like for everyone around them, and everyone around the people they develop, for years.” It’s all about seeding culture. I see a lot of similarities in the underlying ideas in Corey’s work and the intention behind culture change manifestos like Emergent Strategy. Change is fractal, bubbling up from one person to affect a whole system. I was involved in Matter, the accelerator Corey founded, in two ways: first as an entrepreneur, receiving an earlier version of the ideas he continues to teach, and then as a member of the team, helping to deliver them to cohorts of entrepreneurs. It changed my life, and I watched it change the way other participants think about building teams, products, and cultures. Those ideas are now part of the Sulzberger Executive Leadership Program at Columbia University. If you’re a newsroom leader, I believe you should strongly consider it. And if you’re not, I recommend that you follow Corey and his work. I guarantee it’ll change your thinking.

Link: US government targets Cop City protester over phone operating system, by Timothy Pratt in The Guardian This is worth knowing about and is concerning — but not necessarily for the main reason that’s being reported. The Department of Justice is trying to prosecute Sam Tunick, an Atlanta-based activist, for allegedly using a duress password on his GrapheneOS phone when he crossed the border in January 2025. > “Agent Findley and several others repeatedly asked Tunick to open his phone during the interrogation, telling him they would seize it if he did not. When he finally provided a passcode, “the screen went blank, flashed several times and the phone appeared to restart”, according to the motion.” The phone was wiped. According to the Department of Justice, rather than the usual unlock password, the one Tunick had provided was a signal that GrapheneOS should reset the device to factory settings. That’s the core issue: it’s not that he was using GrapheneOS or had set up a duress password, but he was accused of using it to reset his device rather than give his data to law enforcement when asked. At the point where law enforcement or border protection are asking you for data, it’s your right to refuse a search, but you typically can’t actively destroy it. I’ve always understood that the police can’t compel you to unlock your phone without a warrant, although, unfortunately, Customs and Border Protection has an exemption around the border. If there _is_ a warrant, or if CBP asks you in a border zone, you may still refuse to unlock it, but the device may be seized and held. The trick here, which Tunick’s lawyers are arguing, is that the request was unlawful to begin with. Because Tunick was a part of Atlanta’s Stop Cop City protests, he had been put on a terrorist watchlist; that fact was circulated just three hours prior. That flagged him for the secondary inspection that led to him being asked to unlock his phone. Protest is protected by the first amendment and a core component of democratic speech; putting protesters on a watchlist designed to protect the public against violent extremism is undemocratic. That’s even more affronting when you consider that the protest was against a police training center: the message it sends is nakedly authoritarian. Finally, and most egregiously, the questioning was about child exploitation imagery, which they had no reason to suspect him of holding. As a result, the search may not have been legal. While a duress password is a deliberate act of destruction, the better path when crossing the border is to not have data to seize to begin with. Anyone who deals with sensitive information should consider that their phone might be taken at the border. Customs and Border Protection policy even allows agents to clone it, giving them permanent access to your data even after they hand your device back to you. They’re only supposed to do this when there’s a national security concern or reasonable suspicion of a crime — but if activists are being targeted as terrorists, that policy threshold doesn’t feel like a solid protection. So: log out of your email, calendar, and file sharing before you embark upon your travels. Delete Signal entirely (but back it up). Consider which photos you want to travel with. Don’t travel with a stock phone — that can lead to more questions — but intentionally cut down your information footprint. That way, even if you are stopped, you won’t compromise sources (if you’re a journalist) or your compatriots (if you’re an activist). And you’re not forced to delete data in the moment in a way that could leave you vulnerable.

Link: Fed up with Big Tech, communities turn to data collectives for control, by Rina Chandran at Rest of World It’s interesting to contrast the current moment to the “information wants to be free” era of Web 2.0, twenty or so years ago. Back then, everyone was talking about open APIs and open data. Now, it’s become clearer that communities need to control the terms of their data if they’re going to avoid being strip-mined for somebody else’s profit. > “Workers, producers, consumers, and others have been establishing cooperatives and other community-led associations to pool resources, share benefits, and address socioeconomic challenges for centuries. The United Nations marked 2025 as the year of cooperatives, positioning them as “essential solutions to today’s global problems,” kindling renewed interest in data collectives and cooperatives.” While there’s certainly an argument to be made that communities tend to over-estimate the value of their own data (looking at you, news), some of these datasets may be truly unique in ways that would add value to an AI service or model. As this article points out, collectively-owned data includes creative works in more than 20 African languages that aren’t recognized in mainstream linguistic frameworks. The danger, of course, is that putting these kinds of gates in front of underrepresented cultures just works to further marginalize them: in that potential future, if everyone’s using a model where those languages are missing, they become irrelevant. But there’s another one where data collectives can pull the levers they have to bring about the world they want to see. That’s exactly what the Nwulite Obodo Open Data License aims to do: data rights holders can negotiate to share their work and cultural heritage without losing their right to benefit from it. (Nwulite Obodo is Igbo for raising, reviving, and building the community.) In one model, vendors building non-extractive and responsibly trained models for public interest purposes get to use their data for free, but the closed-model big tech vendors have to pay. That’s what Meesum Alam did with voice data for 39 at-risk languages in Pakistan: the communities he worked with determined that the data was free for research and non-commercial purposes, but for-profit tech companies would need to negotiate terms (which Meta did). That potentially becomes more interesting: either OpenAI et al negotiate to license the data, or they lose functionality to their public interest competitors. There’s also a world where some communities proactively document their cultures and make them available specifically so that models, whoever they’re built by, won’t omit them. Either the world has more equitable AI or the communities financially benefit from their cultural heritage. Whatever happens, these communities certainly have the right to control their data however they see fit. What vendors do about it is the open question. But initiatives like Mozilla Data Collective make it more possible to have more substantive conversations about how data is provided and used, and that can only be a good thing.

_Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society._ _Did someone forward this to you?_ Subscribe for free_._ * * * ### China delivers a one-two punch to America’s AI dominance AI models, as a product in themselves, have very little moat beyond what amounts to brand loyalty and superficial switching costs. Instead, the moat is in the enterprise services that sit around them: the deals and contracts, connectivity with enterprise systems, and quality of life features in an enterprise context. If we consider the models themselves, it’s easy to switch between them: someone could be using ChatGPT today and Claude tomorrow, with very little impact on their workflows. This is particularly true in the engineering world, where models are accessed via API: you can swap out the API and use the same prompt. Those companies can make deals to lock their customers in, but in practice there’s very little long-term technical incentive to use one vendor over another. You pick the best model for your needs and change models and vendors if another one becomes better. The US government has placed export controls on GPUs. There are also strong regulations that (reasonably) prevent sharing certain kinds of data with Chinese servers. The result is that while Chinese companies have enough compute to _train_ models, they can’t really provide the kinds of global-scale centralized services that we see from OpenAI and Anthropic — at least, not in the same way. And open almost always wins when it comes to infrastructure adoption. Open technologies can be used permissionlessly and therefore can be at the center of more innovation. You can host them where you want, experiment with them, alter them, and tweak to fit your use case. Open weights models are not open source, but they _are_ portable and permissionless. With all this in mind, it makes sense for China to release its AI models openly. It turns a US-created compute disadvantage into a distribution advantage; it commoditizes the layer where American companies make money; and it creates a far more effective global ecosystem than could be established through locked-in, centralized services. It’s obvious to me that there are ecosystem benefits throughout China, from manufacturing to scientific research; every sector can just plug in these models. The saving grace for American companies has been that US frontier models have outperformed open ones. That gap is now closing: > “Moonshot and Alibaba unveiled models they claim can go toe-to-toe with the best from OpenAI and Anthropic at a fraction of the cost. The rapid-fire releases suggest America’s lead at the AI frontier is increasingly tight, just as the technology is becoming central to national security, economic power, and geopolitical influence.” Even without these new capabilities, the strategy has already been working. a16z partner Martin Casado noted in the Economist that there’s an 80% chance that any given startup is using Chinese models, and Chinese models are poised to take the lead. It’s worth taking a step back and considering the surprising underlying dynamics. We think of China as being a locked-down society — and it is in many ways. I have serious concerns about how these models might reflect Chinese government perspectives (try asking them about Tiananmen Square). But it’s American companies that are keeping tight control of their technology rather than releasing it as openly as possible. This is in stark contrast to the strategy behind US government support for the open internet, for example. Locked-down business practices for a technology with no real moat but significant potential ecosystem benefits is an obviously losing strategy; permissively releasing it with an open, collaborative approach is obviously a winning one. But the incentives in the US aren’t there: instead, these companies are forced to chase first-order profits rather than ecosystem benefits, and the government tries to put its finger on the scale through forcible measures like tight export controls. We should consider what would need to change to make those incentives more aligned. That’s particularly important given how much of the US economy is currently driven by AI spending. If the bottom falls out of that spending — and I think it clearly will, given the dynamics — the outcome could be severe. I care about having open technology that can be run in the public interest, aligned with the public’s values. Threads like public AI, federated services, and open research have traction but need backing. Getting there in the US needs more nuanced strategy and support than we’re seeing today. * * * ### This Conversation Is Being Recorded. They All Are. I’ve been thinking about this story for days. > “A Zoom call isn’t complete without an artificial-intelligence note taker. Phones are out at meetings, capturing every word. During impromptu conversations with co-workers, someone might turn on the Granola transcription app, which can turn the interactions into one-page summaries or a list of action items. Even at bars and on dates, people are using AI-infused listening apps to analyze conversations later on.” The story goes on to talk to a woman who uses Granola to record her dates, then pours the transcripts into Claude to give her feedback about how she could have done better. And there’s account after account of people using it in meetings without asking for consent or revealing that they’re recording. Certainly in Silicon Valley, a societal shift seems to be underway. It’s likely much more widespread than that. I’ve been present in meetings outside the tech industry where Granola’s watermarking was visible but I wasn’t asked to consent. The watermarking is optional; I have to assume I’ve been in meetings where I’ve been recorded without my knowledge. Pair this trend with the story that the Trump Administration actively sought the phone records of journalists — and their families — who reported on the new Qatari-gifted Air Force One. Subpoenas were issued to the phone carriers, and the Department of Justice notified the newsroom a week later. In some cases, subpoenas can be issued to carriers and service providers privately, allowing the data to be retrieved without the newsroom’s knowledge; in this case, the DoJ did try to gag the phone company from alerting the newsroom. A world in which every conversation is recorded and transcribed is one where every conversation can be subpoenaed or surveilled. Here, the surveillance is decentralized through people who actively want to conduct it for their own benefit, but the data is still stored centrally and available for authorities to subpoena or someone else to mine. Granola’s security page makes clear that the data is accessible to them — and therefore to a third party that compels them to hand it over — and notes that: > “Granola trains on your anonymized data so we can keep making Granola better. You can opt out of this in your Settings.” Granola makes a point of saying that audio is not stored, but given that transcriptions _are_ , this seems moot: the words in a conversation carry its meaning. Subpoenas for your conversations go to it, not to you, and you may never know they were served. If you record someone’s conversation without letting them know, you’re putting them at risk. Don’t get me wrong: I would _love_ to have an automatic summary of meetings I’ve taken part in. I have also run meetings on non-sensitive topics where I’ve asked for consent before starting transcription. It’s the ubiquity and covert nature of the transcription that bothers me, paired with its central storage in what amounts to a honeypot for subpoenas and hackers. Recording a conversation with someone without their consent is illegal in many states and countries, so this behavior may be forced to change. California is one of them, and Granola appears to be thriving there, so there is a world where the law changes to meet the new ubiquitous surveillance norm. Until the dust settles one way or the other, anyone who wants to talk about a sensitive topic, particularly in Silicon Valley, will need to be more wary than usual. * * * ### How OpenAI’s human mistake led to the AI-powered hack on Hugging Face The biggest technology story this week was how a combination of OpenAI models hacked into third-party AI provider Hugging Face and breached its production database. The incident was initially spun as a sort of partnership between the two companies, but it seems like that’s not what went down at all. > “OpenAI failed to properly configure what it called a ‘highly isolated environment,’ allowing a testing sandbox that should have been completely secluded from the internet to actually connect to the internet.” That’s actually one of at least two lapses here: not only did OpenAI fail to properly isolate its models, but Hugging Face’s production database was in a state where those models could hack into it. The whole thing does not speak well of security practices at AI vendors overall. We’re being asked to share more and more private information with model vendors. The standard protection they offer — at least, to their paying customers — is that your data will not be used for model training purposes. That’s all well and good, but your data is still hitting their servers, potentially being logged for an extended period in such a way that it could, in theory, be accessed by their employees. Even before we bring in the possibility of hackers, that leaves your private data open to being accessed via subpoena, an unscrupulous employee, or, indeed, an unscrupulous _vendor_. (Consider that Uber breached at least one journalist’s privacy and considered hiring an opposition research firm. Do we really think AI vendors are more ethical? Why?) Leaving a production database in a state where it could be breached is the icing on the cake. In this case, the models weren’t even harnessed to hack Hugging Face — they did so autonomously to _cheat a test_. Imagine what they might do if they were _intentionally_ pointed that way. Hacking is becoming cheaper and easier: once the preserve of talented technologists, this story proves that the latest frontier models can effectively find and exploit vulnerabilities in systems. And apparently, AI vendors can’t be trusted to secure their own infrastructure. The combination of us being encouraged to share more and more data, the inherent risks of centralizing that data, the dubious security of the places we’re being asked to share it, and the obvious shadiness of some of the companies involved should give us all pause. For newsrooms and anyone dealing with sensitive or private data, particularly relating to source materials or journalism in progress, this weak security environment is not enough. We need zero data retention contracts at minimum, but the only real way to be sure nobody can access our information is to use confidential computing environments and, ultimately, local models. Anything less leaves our work open to cowboys, hackers, and, apparently, misconfigured robots. * * * ### Staying in a job for the health insurance? About 1 in 4 Americans do, a survey says This is a striking, but not necessarily surprising, figure from a new survey by the West Health-Gallup Center on Healthcare in America: > “A new report finds that nearly a quarter of workers who get health insurance through their jobs report staying in unwanted jobs for health insurance — a figure that's risen dramatically in the last five years.” The figure rises to 41% of people with three or more chronic health conditions. The figure has risen wildly in part because Affordable Care Act subsidies were allowed to expire. I’ll get the soapbox out of the way first: having spent around thirty years of my life in the UK before moving to the US, the thing I miss most is the NHS. It’s been treated like a political football since I left and is apparently a shell of its former self — not because the idea is bad and can’t work but because conservative politicians, some of whom have received funding from private healthcare companies, have deliberately sabotaged it. But it’s hard to explain the _lack of fear_ of walking into a doctor’s office or a hospital. You know for a fact that there won’t be an onerous bill. You can just get seen. That security allowed me to found my first startup, which in turn has set the stage for my entire career. If you’re in the US, you may have heard some less pleasant things about socialized healthcare: it turns out much of it was a deliberate disinformation campaign by private health insurers, which I think says a lot about how the whole American healthcare system actually works. That soapbox out of the way, I also want to highlight how the private healthcare system creates perverse incentives for employers and dampens innovation. If employees have freedom of movement between companies, the incentive for employers is to create the best working conditions possible: higher wages, great benefits, a nurturing working environment. If, on the other hand, some employees are effectively chained to their desks by their need to have healthcare, employers have less of a need to provide those things. As long as they provide a reasonable health plan, wages and working conditions are secondary. As the West Health-Gallup Center themselves assert, the effect is lower wages and worse work. In turn, fewer innovators are empowered, which is a disaster for industries like news that desperately need innovation. Often, innovators will find themselves constrained by their existing employers for various reasons and want to leave to explore a new idea that has the potential to change their industry. (That was my experience leaving the university sector to build a social platform for learning, which was ultimately used by Ivy Leagues, non-profits, and governments around the world.) If they _can’t_ because they’re tethered to employers who won’t greenlight their ideas, those innovations will never see the light of day. So not only does socialized healthcare allow people to be healthier by removing the fear of going to the doctor in the first place, it improves wages, creates more competitive working conditions, and promotes innovation. Even a representative for the Cato Institute — a libertarian think tank — has this to say in the piece: > “Favoring employer-sponsored health insurance creates coverage gaps, reduces income mobility, and is crying out for reform.” When even the libertarians want reform, you know it’s a bad deal. We need a different healthcare system. While the libertarians would likely disagree, my vote — having experienced and enjoyed it for much of my life — is for universal healthcare. The only real downside to it is that a bunch of companies that have entrenched their positions taking advantage of ordinary people will be denied a little profit. Which, you know. Pardon me while I find my tiny violin. * * * ### And more: Here are some of the stories I didn't get a chance to go into in depth this week. #### Protecting our FLOSS commons from LLMs The source code repository hosting service Codeberg has banned LLM-generated code. Time will tell whether that's a move that solidifies a niche as a place for hand-crafted software, or whether it just turns people back to GitHub. #### Google search traffic to leading UK publishers set to halve by Q3 2027 The march towards Google Zero continues apace. I believe the most effective way to build resilience against this trend is by building stronger relationships – not just one-way audience strategies, but real community. #### The Fourth Circuit Says Border Agents Can Search Your Phone By Hand, No Suspicion Required A court upheld that border agents have the right to search your phone. Newsrooms should build strong, repeatable guidance for journalists who might want to cross borders with source information. #### Kaiser Permanente nurses say technology is making their jobs — and patient care — worse Despite what vendors and management say, the people who are actually on the ground providing healthcare report that AI is having a detrimental effect on the care they can provide. That will eventually come to a head – particularly if it starts to reveal itself in patient outcome statistics.

Link: How OpenAI’s human mistake led to the AI-powered hack on Hugging Face, by Lorenzo Franceschi-Bicchierai in TechCrunch The biggest technology story this week was how a combination of OpenAI models hacked into third-party AI provider Hugging Face and breached its production database. The incident was initially spun as a sort of partnership between the two companies, but it seems like that’s not what went down at all. > “OpenAI failed to properly configure what it called a ‘highly isolated environment,’ allowing a testing sandbox that should have been completely secluded from the internet to actually connect to the internet.” That’s actually one of at least two lapses here: not only did OpenAI fail to properly isolate its models, but Hugging Face’s production database was in a state where those models could hack into it. The whole thing does not speak well of security practices at AI vendors overall. We’re being asked to share more and more private information with model vendors. The standard protection they offer — at least, to their paying customers — is that your data will not be used for model training purposes. That’s all well and good, but your data is still hitting their servers, potentially being logged for an extended period in such a way that it could, in theory, be accessed by their employees. Even before we bring in the possibility of hackers, that leaves your private data open to being accessed via subpoena, an unscrupulous employee, or, indeed, an unscrupulous _vendor_. (Consider that Uber breached at least one journalist’s privacy and considered hiring an opposition research firm. Do we really think AI vendors are more ethical? Why?) Leaving a production database in a state where it could be breached is the icing on the cake. In this case, the models weren’t even harnessed to hack Hugging Face — they did so autonomously to _cheat a test_. Imagine what they might do if they were _intentionally_ pointed that way. Hacking is becoming cheaper and easier: once the preserve of talented technologists, this story proves that the latest frontier models can effectively find and exploit vulnerabilities in systems. And apparently, AI vendors can’t be trusted to secure their own infrastructure. The combination of us being encouraged to share more and more data, the inherent risks of centralizing that data, the dubious security of the places we’re being asked to share it, and the obvious shadiness of some of the companies involved should give us all pause. For newsrooms and anyone dealing with sensitive or private data, particularly relating to source materials or journalism in progress, this weak security environment is not enough. We need zero data retention contracts at minimum, but the only real way to be sure nobody can access our information is to use confidential computing environments and, ultimately, local models. Anything less leaves our work open to cowboys, hackers, and, apparently, misconfigured robots.

Link: Staying in a job for the health insurance? About 1 in 4 Americans do, a survey says, by Joseph Kim at NPR This is a striking, but not necessarily surprising, figure from a new survey by the West Health-Gallup Center on Healthcare in America: > “A new report finds that nearly a quarter of workers who get health insurance through their jobs report staying in unwanted jobs for health insurance — a figure that's risen dramatically in the last five years.” The figure rises to 41% of people with three or more chronic health conditions. The figure has risen wildly in part because Affordable Care Act subsidies were allowed to expire. I’ll get the soapbox out of the way first: having spent around thirty years of my life in the UK before moving to the US, the thing I miss most is the NHS. It’s been treated like a political football since I left and is apparently a shell of its former self — not because the idea is bad and can’t work but because conservative politicians, some of whom have received funding from private healthcare companies, have deliberately sabotaged it. But it’s hard to explain the _lack of fear_ of walking into a doctor’s office or a hospital. You know for a fact that there won’t be an onerous bill. You can just get seen. That security allowed me to found my first startup, which in turn has set the stage for my entire career. If you’re in the US, you may have heard some less pleasant things about socialized healthcare: it turns out much of it was a deliberate disinformation campaign by private health insurers, which I think says a lot about how the whole American healthcare system actually works. That soapbox out of the way, I also want to highlight how the private healthcare system creates perverse incentives for employers and dampens innovation. If employees have freedom of movement between companies, the incentive for employers is to create the best working conditions possible: higher wages, great benefits, a nurturing working environment. If, on the other hand, some employees are effectively chained to their desks by their need to have healthcare, employers have less of a need to provide those things. As long as they provide a reasonable health plan, wages and working conditions are secondary. As the West Health-Gallup Center themselves assert, the effect is lower wages and worse work. In turn, fewer innovators are empowered, which is a disaster for industries like news that desperately need innovation. Often, innovators will find themselves constrained by their existing employers for various reasons and want to leave to explore a new idea that has the potential to change their industry. (That was my experience leaving the university sector to build a social platform for learning, which was ultimately used by Ivy Leagues, non-profits, and governments around the world.) If they _can’t_ because they’re tethered to employers who won’t greenlight their ideas, those innovations will never see the light of day. So not only does socialized healthcare allow people to be healthier by removing the fear of going to the doctor in the first place, it improves wages, creates more competitive working conditions, and promotes innovation. Even a representative for the Cato Institute — a libertarian think tank — has this to say in the piece: > “Favoring employer-sponsored health insurance creates coverage gaps, reduces income mobility, and is crying out for reform.” When even the libertarians want reform, you know it’s a bad deal. We need a different healthcare system. While the libertarians would likely disagree, my vote — having experienced and enjoyed it for much of my life — is for universal healthcare. The only downside to universal healthcare is that a bunch of companies that have entrenched their positions taking advantage of ordinary people will be denied a little profit. Pardon me while I find my tiny violin.

Link: This Conversation Is Being Recorded. They All Are., by Katherine Bindley at the Wall Street Journal I’ve been thinking about this story for days. > “A Zoom call isn’t complete without an artificial-intelligence note taker. Phones are out at meetings, capturing every word. During impromptu conversations with co-workers, someone might turn on the Granola transcription app, which can turn the interactions into one-page summaries or a list of action items. Even at bars and on dates, people are using AI-infused listening apps to analyze conversations later on.” The story goes on to talk to a woman who uses Granola to record her dates, then pours the transcripts into Claude to give her feedback about how she could have done better. And there’s account after account of people using it in meetings without asking for consent or revealing that they’re recording. Certainly in Silicon Valley, a societal shift seems to be underway. It’s likely much more widespread than that. I’ve been present in meetings outside the tech industry where Granola’s watermarking was visible but I wasn’t asked to consent. The watermarking is optional; I have to assume I’ve been in meetings where I’ve been recorded without my knowledge. Pair this trend with the story that the Trump Administration actively sought the phone records of journalists — and their families — who reported on the new Qatari-gifted Air Force One. Subpoenas were issued to the phone carriers, and the Department of Justice notified the newsroom a week later. In some cases, subpoenas can be issued to carriers and service providers privately, allowing the data to be retrieved without the newsroom’s knowledge; in this case, the DoJ did try to gag the phone company from alerting the newsroom. A world in which every conversation is recorded and transcribed is one where every conversation can be subpoenaed or surveilled. Here, the surveillance is decentralized through people who actively want to conduct it for their own benefit, but the data is still stored centrally and available for authorities to subpoena or someone else to mine. Granola’s security page makes clear that the data is accessible to them — and therefore to a third party that compels them to hand it over — and notes that: > “Granola trains on your anonymized data so we can keep making Granola better. You can opt out of this in your Settings.” Granola makes a point of saying that audio is not stored, but given that transcriptions _are_ , this seems moot: the words in a conversation carry its meaning. Subpoenas for your conversations go to it, not to you, and you may never know they were served. If you record someone’s conversation without letting them know, you’re putting them at risk. Don’t get me wrong: I would _love_ to have an automatic summary of meetings I’ve taken part in. I have also run meetings on non-sensitive topics where I’ve asked for consent before starting transcription. It’s the ubiquity and covert nature of the transcription that bothers me, paired with its central storage in what amounts to a honeypot for subpoenas and hackers. Recording a conversation with someone without their consent is illegal in many states and countries, so this behavior may be forced to change. California is one of them, and Granola appears to be thriving there, so there is a world where the law changes to meet the new ubiquitous surveillance norm. Until the dust settles one way or the other, anyone who wants to talk about a sensitive topic, particularly in Silicon Valley, will need to be more wary than usual.

Link: China delivers a one-two punch to America’s AI dominance, by Robert Hart in The Verge AI models, as a product in themselves, have very little moat beyond what amounts to brand loyalty and superficial switching costs. Instead, the moat is in the enterprise services that sit around them: the deals and contracts, connectivity with enterprise systems, and quality of life features in an enterprise context. If we consider the models themselves, it’s easy to switch between them: someone could be using ChatGPT today and Claude tomorrow, with very little impact on their workflows. This is particularly true in the engineering world, where models are accessed via API: you can swap out the API and use the same prompt. Those companies can make deals to lock their customers in, but in practice there’s very little long-term technical incentive to use one vendor over another. You pick the best model for your needs and change models and vendors if another one becomes better. The US government has placed export controls on GPUs. There are also strong regulations that (reasonably) prevent sharing certain kinds of data with Chinese servers. The result is that while Chinese companies have enough compute to _train_ models, they can’t really provide the kinds of global-scale centralized services that we see from OpenAI and Anthropic — at least, not in the same way. And open almost always wins when it comes to infrastructure adoption. Open technologies can be used permissionlessly and therefore can be at the center of more innovation. You can host them where you want, experiment with them, alter them, and tweak to fit your use case. Open weights models are not open source, but they _are_ portable and permissionless. With all this in mind, it makes sense for China to release its AI models openly. It turns a US-created compute disadvantage into a distribution advantage; it commoditizes the layer where American companies make money; and it creates a far more effective global ecosystem than could be established through locked-in, centralized services. It’s obvious to me that there are ecosystem benefits throughout China, from manufacturing to scientific research; every sector can just plug in these models. The saving grace for American companies has been that US frontier models have outperformed open ones. That gap is now closing: > “Moonshot and Alibaba unveiled models they claim can go toe-to-toe with the best from OpenAI and Anthropic at a fraction of the cost. The rapid-fire releases suggest America’s lead at the AI frontier is increasingly tight, just as the technology is becoming central to national security, economic power, and geopolitical influence.” Even without these new capabilities, the strategy has already been working. a16z partner Martin Casado noted in the Economist that there’s an 80% chance that any given startup is using Chinese models, and Chinese models are poised to take the lead. It’s worth taking a step back and considering the surprising underlying dynamics. We think of China as being a locked-down society — and it is in many ways. I have serious concerns about how these models might reflect Chinese government perspectives (try asking them about Tiananmen Square). But it’s American companies that are keeping tight control of their technology rather than releasing it as openly as possible. This is in stark contrast to the strategy behind US government support for the open internet, for example. Locked-down business practices for a technology with no real moat but significant potential ecosystem benefits is an obviously losing strategy; permissively releasing it with an open, collaborative approach is obviously a winning one. But the incentives in the US aren’t there: instead, these companies are forced to chase first-order profits rather than ecosystem benefits, and the government tries to put its finger on the scale through forcible measures like tight export controls. We should consider what would need to change to make those incentives more aligned. That’s particularly important given how much of the US economy is currently driven by AI spending. If the bottom falls out of that spending — and I think it clearly will, given the dynamics — the outcome could be severe. I care about having open technology that can be run in the public interest, aligned with the public’s values. Threads like public AI, federated services, and open research have traction but need backing. Getting there in the US needs more nuanced strategy and support than we’re seeing today.

* * * _Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society._ _Did someone forward this to you?_ Subscribe for free_._ * * * ### Mamdani invests in tech capacity to “solve real problems” There’s a lot that newsrooms can learn from Zohran Mamdani’s mayoral administration in New York City. His latest announcement is the Public Interest Technology (PIT) Crew, a set of dynamic, cross-disciplinary digital teams that will solve problems across the city using a rapid, human-centered approach. As Pamela Herd notes here, this is a shift from contracting out to building internal capacity: > “Traditionally, the conventional wisdom since the 1990s and before was that governments could buy tech products like an off-the-shelf product. This led to a massive turn to contracting out, which was great for consultants but bad for government capacity. The outsourced approach often cost too much, delivering too little and too late. > > […] What people who know tech and government have been screaming for years is that building good tech needs in-house capacity, even when you are using contractors. It requires the government owning the design, development and delivery of technology, relying on rapid iteration to fix problems in a way that is impossible when contractors are running things.” This dynamic is also highly prevalent in newsrooms, resulting in the same problems. If you rely too heavily on buying existing technology or working with outside contractors, you are building operational, functional, and intellectual dependencies on those organizations. You import their values and ways of working, which in the case of some vendors may be catastrophic in itself, but you also put yourself on their timelines and make yourself subject to their feature priorities and interests. And that’s before you consider security and trust profiles, which may radically differ between newsrooms and the vendors that serve them. New York City isn’t alone; other governments are beginning to shift from outsourcing back to internally owned technology. The article links to a report explaining Colorado’s move back to internally-run IT, which states the issue plainly: > “There is an alignment problem: the issue is not effort, but that we have organized around internal structures rather than outcomes, and that misalignment has made excellent work harder.” Mamdani’s PIT Crew sounds a lot like how a product team should work: directed groups of experts rapidly prototyping solutions to concretely defined problems anchored in real people’s needs. By doing it internally, he can make sure these solutions are built exactly the way the city needs, build institutional capacity and knowledge, and, theoretically at least, do it far more cheaply in the long run. As these sorts of civic measures succeed, I think (or, perhaps, I _hope_) we’ll see more newsrooms translate those outcomes to their own businesses and begin to understand that they need to prioritize technical capacity too. All the same reasons apply here. Of course, most newsrooms don’t have the budget of the New York City Mayor’s office. I think the solution to that is third entities: non-profit organizations that exist to provide shared technical capacity across newsrooms, based on newsroom needs, _that behave as if they were part of newsroom teams_. Think of it as a kind of PIT Crew for news, operated independently but in deep collaboration with newsrooms. By using a radically open source approach, newsrooms can pool resources together and solve shared technical problems more easily, on their terms and according to their values. While there are always places for startups and tech platforms, the idea that the tech industry can always serve needs better than building institutional capacity is fundamentally broken; it’s also fundamentally right-wing. I’m delighted to see the New York City Mayor’s office move in a more productive direction. I hope it becomes an example for everyone. * * * ### We are not alone I was delighted to be included in this roundup by Adiel Kaplan, the Program Director at the Tow-Knight Center for Journalism Futures at the Craig Newmark Graduate School of Journalism at CUNY. As Adiel says: > “Having a say in what the future of news looks like will likely require not just that collaboration across newsrooms, but also outside them, with other institutions that want to shape a future with informed communities at its center — which is, after all, the mission. Right? > > […] It will also require a different way of thinking about our role in this ecosystem, beyond creating content and distributing it. It might mean getting more involved in building technology, or joining forces in new ways with government-funded institutions.” This is exciting to me: I’ve been saying for a while now that news needs to get more involved in building technology. My flippant line is that _news treats technology as something that happens to it, like an asteroid_ — but it’s actually a creative work, like an article. Although many newsrooms are too small to build a strong capacity in themselves, it’s perfectly possible for news _as an industry_ to build capacity and create the technology that is unique to its use cases on its terms. So I think it’s a very good thing that news institutions are talking about this need. The people listed in the article are exceptional. I’m just happy to be on the list in such fine company. Don’t sleep on any of them; I feel most connected to Ivan Sigal’s ambitious and vital work at the Modal Foundation and what Trei Brundrett is building (in collaboration with Blaine Cook and others) at New_ Public. But these are all worthy endeavors: the Library Newsroom Project is a genius on-the-ground effort to create local newsrooms based in every public library in the US, and Sannuta Raghu’s news atoms embed meaning and provenance in natural language articles. All are promising. We need to move forward. There are certainly more people who could have been added to such a list; my hope is that if one were written a year from now, it would be exponentially longer. Let’s innovate. * * * ### White House Directed Patel to Oversee Investigation Involving Times Reporting The White House personally directed FBI Director Kash Patel to issue subpoenas to journalists reporting on the President’s new Qatari-gifted Air Force One. > “The White House’s deep involvement in the case came after officials said that President Trump was enraged about the coverage of the Qatari-donated plane, which The Times reported Thursday lacks the same defensive countermeasures of the previous Air Force One.” These subpoenas were delivered by hand to some of the reporters at home, echoing the FBI’s raid of a Washington Post engagement reporter’s home earlier this year. In both cases, it’s highly likely that these were attempts to discover who leaked information to their respective newsrooms. There’s lots to say about first amendment issues here, and commentators like Dan Kennedy at Media Nation have pertinent thoughts. It’s clear that journalism is under attack by the administration, and they rescinded rules that protected journalists in leak investigations last year. The US Press Freedom Tracker is a sobering read. But it’s also important to take a moment to talk about the technology side of this story. When the administration wants to issue a subpoena to a newsroom, it has a few avenues available to it. The first is to issue it directly to the newsroom or to its reporters, as they did here. In some ways, this is the best outcome: then the newsroom knows about the subpoena and can actively fight it in court. The other avenue is to subpoena the newsroom’s service providers. If source information is stored unencrypted on a service like Google Workspace, the administration could subpoena Google. If a gag order is added — which might well happen if it’s a criminal subpoena or labeled a matter of national security — then the newsroom would never find out and have the chance to fight it. This is true even if the service provider nominally promises to notify the newsrooms about subpoenas: a gag order is a gag order. Larger newsrooms have strong data security practices for this reason: they know to create policies and architectures which force subpoenas to come through them. But not every newsroom has the capacity to build a strong security strategy. Which means for every story we hear about that involves these newsrooms, there may be many more that took place in secret. The Freedom of the Press Foundation maintains digital security resources and runs training for newsrooms and specific advice about source protection. The EFF also has some great resources. More resources are out there. But there is more of a need than ever for every newsroom to make sure they have access to someone who can advise them on digital security both holistically and on a case-by-case basis. Not every newsroom can afford a permanent member of staff, but finding access to some kind of resource is vital. Likewise, journalism funders should focus on providing access to experts, understanding that these issues are existential for the organizations they fund. Not only is this an attack on press freedoms, but it’s also an attack on trust. Every newsroom can do its reporting because sources feel safe to reach out to it; if their safety is in question, they may be less likely to leak, and we may be less likely to read the stories that help us make good democratic decisions. That’s what the administration seems to be banking on. * * * ### Trump dismantled a federal climate website. These women rebuilt it. This shouldn’t have been necessary, but is still wonderful to see. Climate.gov had been the go-to resource for climate data, but it went offline when the Trump Administration radically cut NOAA’s funding. At that point: > “[Rebecca] Lindsey joined forces with former NOAA employees Anna Eshelman, and Mary Lindsey, her older sister, to become the core team behind the deactivated site’s successor, Climate.us, preserving over 15 years of key climate data and resources. The trove features key maps, educational materials and climate indicator reports, including the now-deleted Fifth National Climate Assessment, the government’s most comprehensive analysis of climate change that was at risk of being lost to the public.” This is possible because US government data is public domain by law. Had it not been available under a permissive license, the administration’s act of vandalism would have meant the data was gone for good. But because it was, the datasets can find a new home. It’s a joy to use. Check out the climate dashboard, which tracks numbers like the total area of the Arctic Ocean that was at least 15% ice-covered each September. It also hosts a set of resources for teaching climate and energy. The dataset gallery includes crucial information like the NOAA’s archive of oral histories from people whose lives were affected by climate change. But it’s also precarious. The whole thing relies on donations to keep it afloat, which is really what tax dollars are for. Still, for the moment it’s wonderful to see people pick up the slack when government is no longer doing its job. In the absence of government support, archives like this are works of journalism in themselves: ways to help us make stronger decisions. They deserve stronger support, and ultimately, we all deserve the restoration of such important government infrastructure. * * * ### A Leak of San Francisco Police Drone Footage Exposes the New Reality of Urban Surveillance I’m not sure I agree with this article’s implication that the problem with SFPD’s drone policing was that it accidentally leaked the data. > ““There’s a certain trust given to the police to use these things correctly,” says Curry. “When you're watching a drone feed live, you can look into dozens of different apartments, you can see police zooming in on people, you can see arrests. The fact that all of this was exposed feels like a really big issue from a privacy perspective.”” I’d humbly submit that the privacy problem exists regardless of whether the footage was leaked or not: this is ubiquitous surveillance of a city’s citizens from above. That footage can be analyzed, both by humans and software, to track people and target them for any reason. There is very little oversight, and because the police department is using a private company to run it, the teams there presumably have access to an enormous amount of private footage. The thing is, none of this actually makes us safer. As the ACLU of Northern California points out in its Seeing Through Surveillance report: > “The evidence is clear that while surveillance has increased exponentially, public safety has not. On the contrary, surveillance systems often make people less safe, especially for groups that have historically been in the government’s crosshairs. Modern surveillance technology makes it possible for the government to track who we are, where we go, what we do, and who we know. It fuels high-tech profiling and perpetuates systems of biased policing. It facilitates deportations, chills speech, and imperils the rights of activists, religious minorities, and people who need reproductive and gender-affirming care.” Most importantly, it doesn’t actually help. As the report points out, the city of San Francisco itself learned that adding cameras to its highest-crime neighborhoods _had no impact on crime_. Regardless, it added more funding to the program and voted to remove oversight in 2023. The result is more money spent, less privacy, with no impact on public safety. And now we know that the footage is being accidentally leaked, the privacy footprint is obviously even worse. In a world that is becoming markedly more authoritarian, it’s unconscionable that supposedly permissive cities would add more surveillance. It doesn’t work, it misuses funds that could be spent helping the vulnerable, and it’s data that could be used for undemocratic purposes. It needs to stop — and to do that, we need to apply pressure to our elected representatives and raise awareness of how backwards it is.

Link: We are not alone, by Adiel Kaplan at the Tow-Knight Center I was delighted to be included in this roundup by Adiel Kaplan, the Program Director at the Tow-Knight Center for Journalism Futures at the Craig Newmark Graduate School of Journalism at CUNY. As Adiel says: > “Having a say in what the future of news looks like will likely require not just that collaboration across newsrooms, but also outside them, with other institutions that want to shape a future with informed communities at its center — which is, after all, the mission. Right? > > […] It will also require a different way of thinking about our role in this ecosystem, beyond creating content and distributing it. It might mean getting more involved in building technology, or joining forces in new ways with government-funded institutions.” This is exciting to me: I’ve been saying for a while now that news needs to get more involved in building technology. My flippant line is that _news treats technology as something that happens to it, like an asteroid_ — but it’s actually a creative work, like an article. Although many newsrooms are too small to build a strong capacity in themselves, it’s perfectly possible for news _as an industry_ to build capacity and create the technology that is unique to its use cases on its terms. So I think it’s a very good thing that news institutions are talking about this need. The people listed in the article are exceptional. I’m just happy to be on the list in such fine company. Don’t sleep on any of them; I feel most connected to Ivan Sigal’s ambitious and vital work at the Modal Foundation and what Trei Brundrett is building (in collaboration with Blaine Cook and others) at New_ Public. But these are all worthy endeavors: the Library Newsroom Project is a genius on-the-ground effort to create local newsrooms based in every public library in the US, and Sannuta Raghu’s news atoms embed meaning and provenance in natural language articles. All are promising. We need to move forward. There are certainly more people who could have been added to such a list; my hope is that if one were written a year from now, it would be exponentially longer. Let’s innovate.

Link: Mamdani invests in tech capacity to “solve real problems”, by Pamela Herd in Can We Still Govern? There’s a lot that newsrooms can learn from Zohran Mamdani’s mayoral administration in New York City. His latest announcement is the Public Interest Technology (PIT) Crew, a set of dynamic, cross-disciplinary digital teams that will solve problems across the city using a rapid, human-centered approach. As Pamela Herd notes here, this is a shift from contracting out to building internal capacity: > “Traditionally, the conventional wisdom since the 1990s and before was that governments could buy tech products like an off-the-shelf product. This led to a massive turn to contracting out, which was great for consultants but bad for government capacity. The outsourced approach often cost too much, delivering too little and too late. > > […] What people who know tech and government have been screaming for years is that building good tech needs in-house capacity, even when you are using contractors. It requires the government owning the design, development and delivery of technology, relying on rapid iteration to fix problems in a way that is impossible when contractors are running things.” This dynamic is also highly prevalent in newsrooms, resulting in the same problems. If you rely too heavily on buying existing technology or working with outside contractors, you are building operational, functional, and intellectual dependencies on those organizations. You import their values and ways of working, which in the case of some vendors may be catastrophic in itself, but you also put yourself on their timelines and make yourself subject to their feature priorities and interests. And that’s before you consider security and trust profiles, which may radically differ between newsrooms and the vendors that serve them. New York City isn’t alone; other governments are beginning to shift from outsourcing back to internally owned technology. The article links to a report explaining Colorado’s move back to internally-run IT, which states the issue plainly: > “There is an alignment problem: the issue is not effort, but that we have organized around internal structures rather than outcomes, and that misalignment has made excellent work harder.” Mamdani’s PIT Crew sounds a lot like how a product team should work: directed groups of experts rapidly prototyping solutions to concretely defined problems anchored in real people’s needs. By doing it internally, he can make sure these solutions are built exactly the way the city needs, build institutional capacity and knowledge, and, theoretically at least, do it far more cheaply in the long run. As these sorts of civic measures succeed, I think (or, perhaps, I _hope_) we’ll see more newsrooms translate those outcomes to their own businesses and begin to understand that they need to prioritize technical capacity too. All the same reasons apply here. Of course, most newsrooms don’t have the budget of the New York City Mayor’s office. I think the solution to that is third entities: non-profit organizations that exist to provide shared technical capacity across newsrooms, based on newsroom needs, _that behave as if they were part of newsroom teams_. Think of it as a kind of PIT Crew for news, operated independently but in deep collaboration with newsrooms. By using a radically open source approach, newsrooms can pool resources together and solve shared technical problems more easily, on their terms and according to their values. While there are always places for startups and tech platforms, the idea that the tech industry can always serve needs better than building institutional capacity is fundamentally broken; it’s also fundamentally right-wing. I’m delighted to see the New York City Mayor’s office move in a more productive direction. I hope it becomes an example for everyone.

Link: Trump dismantled a federal climate website. These women rebuilt it., by Jenae Barnes at The 19th This shouldn’t have been necessary, but is still wonderful to see. Climate.gov had been the go-to resource for climate data, but it went offline when the Trump Administration radically cut NOAA’s funding. At that point: > “[Rebecca] Lindsey joined forces with former NOAA employees Anna Eshelman, and Mary Lindsey, her older sister, to become the core team behind the deactivated site’s successor, Climate.us, preserving over 15 years of key climate data and resources. The trove features key maps, educational materials and climate indicator reports, including the now-deleted Fifth National Climate Assessment, the government’s most comprehensive analysis of climate change that was at risk of being lost to the public.” This is possible because US government data is public domain by law. Had it not been available under a permissive license, the administration’s act of vandalism would have meant the data was gone for good. But because it was, the datasets can find a new home. It’s a joy to use. Check out the climate dashboard, which tracks numbers like the total area of the Arctic Ocean that was at least 15% ice-covered each September. It also hosts a set of resources for teaching climate and energy. The dataset gallery includes crucial information like the NOAA’s archive of oral histories from people whose lives were affected by climate change. But it’s also precarious. The whole thing relies on donations to keep it afloat, which is really what tax dollars are for. Still, for the moment it’s wonderful to see people pick up the slack when government is no longer doing its job. In the absence of government support, archives like this are works of journalism in themselves: ways to help us make stronger decisions. They deserve stronger support, and ultimately, we all deserve the restoration of such important government infrastructure.

_Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society._ _Did someone forward this to you?_ Subscribe for free_._ * * * ### Chicago Public Media launching community website — chicago.com — in the fall In an increasingly AI-dominated information landscape, real trusted communities and relationships will be the way to build trust and loyalty — I’m convinced of it, and organizations like New_ Public agree. So it was exciting to see Chicago Public Media take a huge step towards building a community platform. > The site will include Chicago-area information, civic and cultural resources, community-sourced knowledge and opportunities for audience participation, the nonprofit said Wednesday. It will also curate headlines from the Sun-Times, WBEZ and other news sources. This will be a familiar argument to regular readers: > For independent journalism to “truly service the public … we should have digital infrastructure that is also steered by public media companies,” Chicago Public Media CEO Melissa Bell said. The news industry “has ceded a lot of distribution to places like Facebook and X, formerly known as Twitter, and I think that has done a disservice to centering civic discourse in a healthy way.” We’ve seen other platforms release similar efforts effectively. The Newsmast Foundation builds community-first social media apps on open protocols for newsrooms that include The Bristol Cable and Find Out Media. Flipboard’s Surf platform powers curated social feeds, again built on open social web protocols, for the likes of 404 Media and Rolling Stone (as well as my own curated non-profit US news feed). And Canada’s Village Media serves 13 local social networks through its SPACES platform. But this is the first time we’ve seen a single social platform rolled out by a public media company at this scale. Chicago Public Media was gifted the underlying chicago.com domain and will be rolling it out to neighborhoods and suburbs throughout the area. It sounds like each community will be highlighted (perhaps with its own feed), with an attached hub that covers the entire region. Clearly, this is an experiment, but I’m delighted to see a public media innovator explore these ideas at this scale. I see it as vindication for the idea that building stronger community applications into the public media model is a path towards a more trusted future for local journalism. I’ll be watching very closely, and I’m curious to see who dives in next. * * * ### Your SaaS Metrics Are A Result, Not A Strategy I still subscribe to sites like Crunchbase News from my time in startup-land; although it’s been a while since I’ve run the financial side of a business, I’m interested, and I know that I’ll run one again. I see stories like this and wonder: what would it look like for a newsroom to think this way? In startups, these metrics are known top to bottom, but I’ve rarely heard business teams talk about LTV (customer Life Time Value), CAC (Customer Acquisition Cost), or even ARR (Annual Recurring Revenue). This may be happening in finance and fundraising teams, but the culture of talking about customers / donors in teams more widely often simply isn’t there: metrics aren't communicated, dashboards aren't made available, the concepts of the metrics themselves are not explained. Not everyone should be thinking about this all the time – the firewall between business and editorial is important to maintain – but in order to make sharp prioritization and experimentation decisions, the business side should be much more customer / donor focused than they often are. Beyond that, this piece points out, rightly, that metrics are not strategy: they’re the measurable outcome of your strategy. They’re important tools to help you figure out cause and effect and improve your revenue efficiency, but they are not the underlying mechanism. Interesting provocation here from the author: > “The Rule of 4 adds a simple durability check: ARR growth divided by annual customer churn should be above four. If it is low, growth may be hiding a leaking bucket. > > [The board should ask:] are we growing on top of a loyal customer base, or replacing customers we should have kept?” Growth in annual recurring revenue — the portion of your revenue that is from recurring customers like subscribers or monthly / annual donors — is expressed as a percentage. So is churn: what percentage of customers (paid subscribers, members, recurring donors) cancel their commitments and don’t return? How many newsrooms have those numbers handy? What would it take to measure them? Which systems are missing that would let you do that? There is _so much_ that newsrooms — including non-profit publications — can learn from for-profit startups and other businesses. There’s a lot to be gained by sharing knowledge from those other domains. Figuring out which metrics successful businesses track and mapping the data gaps inside a newsroom is a good place to start. * * * ### How Kalshi infects the news Kalshi’s deals with newsrooms seem to be paying dividends for the company: > “Since December CNBC has published 58 articles that do little more than advertise the existence of a Kalshi market related to a news event. […] Since April, CNBC has employed a dedicated reporter to produce these articles. CNBC also maintains a page on its website featuring Kalshi prediction markets selected by CNBC editors, along with its web coverage. […] In at least 22 cases, CNBC has written about Kalshi and not disclosed its financial conflict.” CNN doesn’t pay for access, and instead is paid to exclusively promote Kalshi. CNBC reporting carries a disclosure which states that its relationship goes further: “CNBC and Kalshi have a commercial relationship that includes customer acquisition and a minority investment.” CNBC will gain financially if its coverage leads to more signups or a growth in Kalshi’s valuation. CNN’s is a simpler paid placement, but both deals are aggressive ways for Kalshi to compete with Polymarket, which has been making similar deals with newsrooms like Yahoo Finance. This is even happening when markets are not significant enough to be newsworthy. As the New Yorker noted in December: > “When Enten lauded the benefits of analyzing betting odds, on air the other day, he failed to mention that only several hundred thousand dollars had been bet on that particular market. Kalshi’s odds provided good fodder for television, but, statistically speaking, they didn’t say much.” It reminds me of the deals Twitter made with newsrooms relatively early in its life. Suddenly, almost out of nowhere, anchors read out tweets on the news, and shows promoted their official Twitter accounts over their websites. This didn’t happen organically: Twitter partnerships teams made deals behind the scenes to ensure their product was showcased well. It was one of the first times that a web startup impactfully executed on a media strategy, and startups have built on that pattern ever since. Here, rather than serving a social network, money is changing hands for newsrooms to promote gambling markets — and in CNBC’s case, they will make more money if more people gamble. It’s obviously weirder, and the incentives here would pull at traditional newsroom ethics in an uncomfortable way even if adequate disclosures were published. This comes at an unfortunate time when trust in news is falling quickly, and newsrooms like CNN are increasingly seen as serving their owners rather than bastions of trustworthy reporting. These Kalshi deals are weird, and an obvious conflict of interest that will likely drive people to trust the news even less than they do today. The Reuters Institute’s 2026 Digital News Report found that 70% of respondents think media owners and corporate parents exert undue influence on the news. As more of these sorts of deals are made, and as trust in news continues to decline, newsrooms are going to need to more overtly state that their coverage is free from this sort of sponsored content. Stronger, more transparent ethics statements, and louder conversations about how reporting decisions are made, will help some newsrooms to explain how they stand apart from these dynamics. In the meantime, CNN and CNBC are helping to drive trust in media into the gutter. * * * ### AI Content Is Everywhere on Social Media, Especially LinkedIn This is one of the core effects of AI: even when people are not engaging with AI-generated content directly, it’s hard to avoid. Our feeds are increasingly full of AI slop. > “AI-generated content appeared across all social media platforms in our data set. The average AI rate across all scanned items was 13.8%, but specific rates varied by platform and item length. On four out of five platforms, longer content was more likely to be AI-generated than shortform content. Across all platforms, one in four longform items (25.72% of items over 250 words) were fully AI-generated.” Specifically, long-form content on LinkedIn was 41% likely to be AI-generated, which shouldn’t surprise anyone who’s browsed LinkedIn lately. Medium was 31% likely and X was 29% likely. Open social web platforms like Bluesky and Mastodon don’t seem to have been a part of the dataset, but I think it would be foolish to assume they’re immune. Some caveats here: the analysis was done by Pangram, which builds a browser extension and back-end tech that attempts to detect AI-generated content. That’s an imperfect process, and there are no tools that are completely reliable at making this distinction. False positives and false negatives have been common with these tools, although Pangram claims a 0.01% false positive rate. So take it with a pinch of salt, but it’s reasonable to assume that these numbers are _directionally_ true. All of this serves to drive trust in these platforms even lower. Increasingly, people on platforms like LinkedIn are being lazy writers and using AI to produce content that you don’t want to put the effort into. I generally think that if you can’t be bothered to write something, it’s not reasonable to ask people to read it; still, there may be some value in AI _assisted_ writing, depending on the piece and how it was produced. (That kind of AI content, by the way, was not really measured by this test.) But AI has also led to a lot of outright spam making its way into people’s feeds in order to shamelessly build clout and advertising revenue. Both things are making these platforms unusable, which in turn is driving people to smaller communities and group chats with people they _know_ they can trust. I believe that’s going to be a big trend: AI leading to a noticeable drop in quality that drives people away from the platforms where it’s allowed to thrive. In that world, platforms that foster trusted relationships and communities will win. _Via_ 404 Media, which has characteristically great coverage of the story_._

Link: Chicago Public Media launching community website, by Amy Yee at the Chicago Sun-Times In an increasingly AI-dominated information landscape, real trusted communities and relationships will be the way to build trust and loyalty — I’m convinced of it, and organizations like New_ Public agree. So it was exciting to see Chicago Public Media take a huge step towards building a community platform. > The site will include Chicago-area information, civic and cultural resources, community-sourced knowledge and opportunities for audience participation, the nonprofit said Wednesday. It will also curate headlines from the Sun-Times, WBEZ and other news sources. This will be a familiar argument to regular readers: > For independent journalism to “truly service the public … we should have digital infrastructure that is also steered by public media companies,” Chicago Public Media CEO Melissa Bell said. The news industry “has ceded a lot of distribution to places like Facebook and X, formerly known as Twitter, and I think that has done a disservice to centering civic discourse in a healthy way.” We’ve seen other platforms release similar efforts effectively. The Newsmast Foundation builds community-first social media apps on open protocols for newsrooms that include The Bristol Cable and Find Out Media. Flipboard’s Surf platform powers curated social feeds, again built on open social web protocols, for the likes of 404 Media and Rolling Stone (as well as my own curated non-profit US news feed). And Canada’s Village Media serves 13 local social networks through its SPACES platform. But this is the first time we’ve seen a single social platform rolled out by a public media company at this scale. Chicago Public Media was gifted the underlying chicago.com domain and will be rolling it out to neighborhoods and suburbs throughout the area. It sounds like each community will be highlighted (perhaps with its own feed), with an attached hub that covers the entire region. Clearly, this is an experiment, but I’m delighted to see a public media innovator explore these ideas at this scale. I see it as vindication for the idea that building stronger community applications into the public media model is a path towards a more trusted future for local journalism. I’ll be watching very closely, and I’m curious to see who dives in next.

Link: AI Content Is Everywhere on Social Media, Especially LinkedIn, by Max Spero at Pangram This is one of the core effects of AI: even when people are not engaging with AI-generated content directly, it’s hard to avoid. Our feeds are increasingly full of AI slop. > “AI-generated content appeared across all social media platforms in our data set. The average AI rate across all scanned items was 13.8%, but specific rates varied by platform and item length. On four out of five platforms, longer content was more likely to be AI-generated than shortform content. Across all platforms, one in four longform items (25.72% of items over 250 words) were fully AI-generated.” Specifically, long-form content on LinkedIn was 41% likely to be AI-generated, which shouldn’t surprise anyone who’s browsed LinkedIn lately. Medium was 31% likely and X was 29% likely. Open social web platforms like Bluesky and Mastodon don’t seem to have been a part of the dataset, but I think it would be foolish to assume they’re immune. Some caveats here: the analysis was done by Pangram, which builds a browser extension and back-end tech that attempts to detect AI-generated content. That’s an imperfect process, and there are no tools that are completely reliable at making this distinction. False positives and false negatives have been common with these tools, although Pangram claims a 0.01% false positive rate. So take it with a pinch of salt, but it’s reasonable to assume that these numbers are _directionally_ true. All of this serves to drive trust in these platforms even lower. Increasingly, people on platforms like LinkedIn are being lazy writers and using AI to produce content that you don’t want to put the effort into. I generally think that if you can’t be bothered to write something, it’s not reasonable to ask people to read it; still, there may be some value in AI _assisted_ writing, depending on the piece and how it was produced. (That kind of AI content, by the way, was not really measured by this test.) But AI has also led to a lot of outright spam making its way into people’s feeds in order to shamelessly build clout and advertising revenue. Both things are making these platforms unusable, which in turn is driving people to smaller communities and group chats with people they _know_ they can trust. I believe that’s going to be a big trend: AI leading to a noticeable drop in quality that drives people away from the platforms where it’s allowed to thrive. In that world, platforms that foster trusted relationships and communities will win. _Via_ 404 Media, which has characteristically great coverage of the story_._

Back in May, I signed up to host the IndieWeb Fiction Carnival, and kicked it off with a prompt: sticks and stones will break my bones_._ I'm incredibly late with my roundup: just as soon as I'd kicked it off, life became chaotic in a way that won't settle down until September. Which is a poor excuse, because I received some great submissions. Daniel Miller wrote Pull: > The first chamber was larger than the second. Wren heard and felt the pneumatic doors close behind them and was careful to place one foot next to the other, in a stable stance, and took hold of the handrails. They felt their boots lock onto the walkway. The loud, mechanical horn blared its single warning and clouds of thin white mist filled the room. Wren stared straight ahead but could see in the peripheral vision possible through their helmet’s visor the particles cling to their suit. In seconds they covered the visor as well. Zachary Kai wrote Tear Me Down, Build Me Back Up: > They repeated the usual version of this phrase, turned on him in the second person, until it became so pervasive they might've well have tattooed it on the insides of his eyelids. April wrote Lara's Neighbour: > Being a young lady in the neighborhood. One of the elderly man came around one day towards Lara and asked when is she getting hitched? Flabbergasted by his question - she said she won't be getting married and then this oldie guy got on her nerves by saying that a young lady is a BURDEN on the family and getting married removes that burden. _Also syndicated to IndieNews._

Link: Your SaaS Metrics Are A Result, Not A Strategy, by Itay Sagie in Crunchbase News I still subscribe to sites like Crunchbase News from my time in startup-land; although it’s been a while since I’ve run the financial side of a business, I’m interested, and I know that I’ll run one again. I see stories like this and wonder: what would it look like for a newsroom to think this way? In startups, these metrics are known top to bottom, but I’ve rarely heard business teams talk about LTV (customer Life Time Value), CAC (Customer Acquisition Cost), or even ARR (Annual Recurring Revenue). This may be happening in finance and fundraising teams, but the culture of talking about customers / donors in teams more widely often isn’t there. While the firewall between business and editorial is important to maintain, the business side should be more customer / donor focused. Beyond that, this piece points out, rightly, that metrics are not strategy: they’re the measurable outcome of your strategy. They’re important tools to help you figure out cause and effect and improve your revenue efficiency, but they are not the underlying mechanism. Interesting provocation here from the author: > “The Rule of 4 adds a simple durability check: ARR growth divided by annual customer churn should be above four. If it is low, growth may be hiding a leaking bucket. > > [The board should ask:] are we growing on top of a loyal customer base, or replacing customers we should have kept?” Growth in annual recurring revenue — the portion of your revenue that is from recurring customers like subscribers or monthly / annual donors — is expressed as a percentage. So is churn: what percentage of customers (paid subscribers, members, recurring donors) cancel their commitments and don’t return? How many newsrooms have those numbers handy? What would it take to measure them? Which systems are missing that would let you do that? There is _so much_ that newsrooms — including non-profit publications — can learn from for-profit startups and other businesses. There’s a lot to be gained by sharing knowledge from those other domains. Figuring out which metrics successful businesses track and mapping the data gaps inside a newsroom is a good place to start.

Link: How Kalshi infects the news, by Aaron Rupar and Judd Legum in Public Notice and Popular Information Kalshi’s deals with newsrooms seem to be paying dividends for the company: > “Since December CNBC has published 58 articles that do little more than advertise the existence of a Kalshi market related to a news event. […] Since April, CNBC has employed a dedicated reporter to produce these articles. CNBC also maintains a page on its website featuring Kalshi prediction markets selected by CNBC editors, along with its web coverage. […] In at least 22 cases, CNBC has written about Kalshi and not disclosed its financial conflict.” CNN doesn’t pay for access, and instead is paid to exclusively promote Kalshi. CNBC reporting carries a disclosure which states that its relationship goes further: “CNBC and Kalshi have a commercial relationship that includes customer acquisition and a minority investment.” CNBC will gain financially if its coverage leads to more signups or a growth in Kalshi’s valuation. CNN’s is a simpler paid placement, but both deals are aggressive ways for Kalshi to compete with Polymarket, which has been making similar deals with newsrooms like Yahoo Finance. This is even happening when markets are not significant enough to be newsworthy. As the New Yorker noted in December: > “When Enten lauded the benefits of analyzing betting odds, on air the other day, he failed to mention that only several hundred thousand dollars had been bet on that particular market. Kalshi’s odds provided good fodder for television, but, statistically speaking, they didn’t say much.” It reminds me of the deals Twitter made with newsrooms relatively early in its life. Suddenly, almost out of nowhere, anchors read out tweets on the news, and shows promoted their official Twitter accounts over their websites. This didn’t happen organically: Twitter partnerships teams made deals behind the scenes to ensure their product was showcased well. It was one of the first times that a web startup impactfully executed on a media strategy, and startups have built on that pattern ever since. Here, rather than serving a social network, money is changing hands for newsrooms to promote gambling markets — and in CNBC’s case, they will make more money if more people gamble. It’s obviously weirder, and the incentives here would pull at traditional newsroom ethics in an uncomfortable way even if the adequate disclosures were published. This comes at an unfortunate time when trust in news is falling quickly, and newsrooms like CNN are increasingly seen as serving their owners rather than bastions of trustworthy reporting. These Kalshi deals are weird, and an obvious conflict of interest that will likely drive people to trust the news even less than they do today. The Reuters Institute’s 2026 Digital News Report found that 70% of respondents think media owners and corporate parents exert undue influence on the news. As more of these sorts of deals are made, and as trust in news continues to decline, newsrooms are going to need to more overtly state that their coverage is free from this sort of sponsored content. Stronger, more transparent ethics statements, and louder conversations about how reporting decisions are made, will help some newsrooms to explain how they stand apart from these dynamics. In the meantime, CNN and CNBC are helping to drive trust in media into the gutter.

Link: In Geneva, the World Can Anchor AI Governance in Free Expression, by Isabelle Anzabi in Tech Policy Press I like that this is happening, will appreciate the recommendations that arise from it, and know with complete certainty that no major AI vendor will adhere to them unless they are forced: > “The United Nations is convening its Global Dialogue on Artificial Intelligence Governance in Geneva July 6-7, and the stakes are high: what rights will anchor the future of how the world governs AI? Generative AI systems mediate, filter, and generate the information we encounter every day. They are, at their core, a technology of expression and access to information. How we govern them will have downstream effects on what people can say, seek, and know. Geneva is where we can get that right.” A previous UNESCO meeting recommended governance that “bars AI systems from being used for social scoring or mass surveillance, and requires member states to ensure that AI actors respect rights in the AI lifecycle”. As we all know, that has _definitely_ happened. Both AI vendors and the two countries that predominantly house them, the United States and China, are famously against surveillance and social scoring, and in favor of maintaining rights. Certainly none of them have, for example, used AI with the most sensitive personal information government has access to, or worked with the private sector to create a surveillance apparatus for policing that includes the broad detainment of immigrants. Neither of them is famous for social scoring — in China by government or in the United States through private enterprise. Job done! Sarcasm aside, it’s important to have these meetings, and it’s important to continue to add pressure to protect rights and prevent abuses of this technology. But we should be clear-eyed about the fact that any recommendations almost certainly won’t actually be followed unless major nations that represent real dollars to these vendors enshrine them in law and hold the line when countries like the US apply pressure to undermine them. It can’t just come down to individual governments. The linked article argues that we should also protect freedom of expression, in particular from government (although I’d argue we should also worry about the influence of wealthy private entities). Because these are black box systems, it’s vital that we prevent governments (or anyone with power) from opaquely tweaking their answers and outputs to benefit their agendas. They should not be allowed to be opaque systems; full transparency and auditability should be requirements. The danger is that, in the wrong hands, AI can be used to cement centralized, undemocratic power. To prevent this, ideally, organizations like the UN should apply real sanctions to nations that don’t obey transparency rules and tweak AI systems in service of undemocratic goals — but the UN’s history of doing this effectively is not strong. Part of the problem is the “AI is the information technology of the future” framing used here. If you believe that AI is the future of information systems, you are also more likely to believe that your nation will miss out if you don’t embrace it completely. (That’s been the marketing: if you don’t jump into AI, you will be left behind.) But the reality is, of course, far more nuanced. AI will absolutely change industries like software engineering, and has already made an impact there, although it’s far from an existential transformation. I’m less receptive to the idea that it will transform entire nations. AI vendors need global markets more than global markets need AI. Governments should understand the power they have, and use it.

Celebrating the 250th anniversary of a declaration that had a passage decrying slavery removed because southern states complained by eating carcinogenic mass produced meat tubes while the President tells half of us we’re evil Marxists while we swelter through a man made heat wave is incredibly American. Happy 4th! All snark aside, genuinely, I do love America. The Declaration of Independence - 250 years ago! - was genuinely important. Most places you go, the people are kind, community-minded, and optimistic. One day, eventually, they will have an equitable government that redresses wrongs and provides real support. My entire political worldview is: what if you could maintain the optimism and energy of America but add an inclusive culture of support that gives everyone education, healthcare, a real safety net and a springboard for their lives, in a way that builds communities rather than extracts from them?It's not our current reality, but I'm certain we can get there. One day, we'll look back, and the corporatism, the militarism, the racism, and the subjugation will be a thing of the past. In their place will be thriving communities. And, finally, all of this will be great.

_Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society._ _Did I miss something important?__Send me an email_ _to let me know._ _Did someone forward this to you?_ Subscribe for free_._ * * * ### OpenAI proposes handing Trump administration 5% stake In order to ward off backlash against AI and curry favor with the Trump administration, Sam Altman has floated the idea of giving 5% of OpenAI to a wealth fund that pays dividends to both the government and citizens — and that every leading AI vendor should do the same. > “Sam Altman, chief executive of the ChatGPT maker, has argued that giving the public a financial stake in the company is the best way to share the upside of AI and has suggested a stake of this size in early conversations with the administration, according to two people familiar with the talks.” It’s transparently a way to align everyone with AI vendor profits. If the sector increases in value, the government and the voting population benefit. If it _decreases_ in value … well, the government is incentivized to prevent that from happening. It also wouldn’t be without precedent: it’s modeled on the Alaska Permanent Fund, which does this with oil profits for Alaskan residents. Intel is also now 10% government-owned, and the administration has reversed course to be behind it since gaining that stake. Would a government whose revenues are directly linked to the performance of a sector be likely to enact hard regulations on that sector? Perhaps not. It’s not a slam dunk, though: for example, the UK receives significant tax revenue on fossil fuels, but still promoted electric cars. There are lots of factors at play, and profit alignment isn’t necessarily outweighed by the effects of other harms. (See also: cigarettes, which are taxed but also tightly controlled as an addictive carcinogen.) Meanwhile, Bernie Sanders has pushed for closer to 50% ownership through a sovereign wealth fund. At this much lower stake, Sam Altman’s proposal uses Sanders’s democratic socialist “share the wealth” language as a way to launder OpenAI’s profits through a thin veneer of good ethics. What’s also interesting to me is that all of these arguments assume that AI is going to be an enormous driver of wealth and innovation — but what if it isn’t? It’s another great way to advertise the technology as something world-changing that everybody must get behind right now. Even if AI turns out to be what the people heavily invested in its success say it will be, it doesn’t stand alone as a sea change innovation. The personal computer, the iPhone, word processors, and spreadsheets were pretty transformational technologies. Should there have been a wealth fund attached to each of those? What, exactly, makes AI different? The answer is that it represents labor displacement: people will lose their jobs. And if that’s actually going to be the case, we need bigger, more structural safety nets and reforms. Dividends from 5% of a sector aren’t going to replace wages at scale — and are heavily dependent on valuations continuing to rise. This proposal ties the welfare of people who have lost their jobs to the success of the companies that drove those losses. The incentives are perverse. We shouldn’t accept this proposal. Instead, we should push for stronger protections and stronger regulation. If a sector can’t succeed without real damage to working communities, then it must not be allowed to. And if these claims turn out not to be true, then it’s an empty gesture designed to add credibility to a self-interested science fiction view of the future. * * * ### Companies Are Making Claude and Codex Talk Like Cavemen to Stop AI’s Soaring Costs I find this very funny: > “Companies are deliberately making their AI tools speak like cavemen in an attempt to stop burning through AI tokens and curb their massive expenditure on AI, 404 Media has found. The tool turns the usually verbose outpost of LLMs like Claude Code, Codex, or Gemini into a much more to the point answer. Think less “you’re right to push back, I was wrong,” and more “Hulk smash.”” If only we had other limited-vocabulary lexicons designed to talk to computers efficiently! I think we’re circling a few different possibilities that may show up over the next few years: * Literally LLM-specific “programming languages” that humans can use to talk to models more efficiently, of which Caveman is the hilarious first step * A proprietary bytecode-like language for LLMs that makes interactions more efficient but also just happens to be owned by one of the major vendors and creates a hitherto-unobtainable moat for their business * This all becomes moot when local models become viable for most businesses without insanely high hardware prices or configuration costs * LLM costs eventually fall to a fraction of their existing level But who knows? Maybe enterprise businesses will continue to talk in stilted caveman language to achieve their business goals forever. * * * ### Journalism Has the Receipts. It Won’t Use Them. Arts organizations learned long ago to prove their economic value with hard numbers: attendance, tourism revenue, multiplier effects. News, as Yoni Greenbaum argues here, likes to cling to civic virtue and assume that the work should speak for itself. > “Journalism operated on a commercial advertising revenue model for over 150 years. Publishers sold readers to advertisers, while editors fretted about maintaining a church-and-state divide between the newsroom and business desk. Journalists saw themselves as watchdogs, not wealth generators. Pitching our value based on our own economic impact felt gauche, too close to an advertorial.” Yoni points out that this is starting to change. We know that news deserts cost communities at least $1.1B a year, for example, because of a report by Rebuild Local News and the University of Illinois Chicago. But newsrooms themselves tend to shy away from reporting their own economic impact — even though they already have the tools to do so. It’s not obvious to me that this accounting would work as an argument across the board for newsrooms, and particularly for those with a national focus. Does ProPublica (my employer until the end of the month) save anyone money? It certainly does prevent corruption, and there are instances with real dollar amounts attached to them: Intuit, for example, paid back $141 million to its customers over deceptive marketing. But I’m not sure that its impact can be quantified easily overall, despite the newsroom’s obvious public benefit. On the other hand, for _local_ newsrooms, this makes a lot of sense to me: at their best, they act as connective tissue for their communities. That $1.1B a year was _just_ increased interest costs from lenders who felt they could charge more to unmonitored governments. They just need to get more comfortable at telling the economic side of their stories. And there’s a wider point here, which is that almost all nonprofit newsrooms need to be able to get more concrete and detailed about their business models. If you’re running an organization that wants to be sustainable, it’s not enough to care about the journalistic process, and your business accounting cannot be limited to activities like events and merchandise. You actually have to care about building a business holistically, and everything that entails. * * * ### Cascade PBS launches Local Public as standalone streaming tech company I’ve got some complicated feelings about this announcement from Cascade PBS: > “Cascade PBS, the non-profit television station serving western Washington state, has spun out its technology division into a separate company that will help similar public broadcasters carve out their own streaming and digital identities. > > The new company, Local Public, will help develop streaming applications for connected TVs, mobile devices and the web, allowing public television stations to offer locally branded streaming experiences featuring their own programming alongside national PBS content.” On one hand, I absolutely love that they were able to spin out their technology division. Most public media companies don’t have the resources or skills to build their own tech, and building this capability outside of any one station so that all of them can take advantage of it makes a lot of sense to me. The Local Public site itself also makes the ROI transparent. WETA, the public media station for Greater Washington, ran the numbers and said that it would break even in the first year, and a calculator is available for other stations that want to check for themselves. The pricing hinges on Passport-eligible donors: those giving at least $60 a year. Local Public charges $60,000 a year for stations with fewer than 15,000, $75,000 for up to 40,000, and $100,000 a year for everyone else — which is not out of bounds. It all seems like a decent business, run in the public interest as a subsidiary of Cascade PBS, that will genuinely help public media stations. I want to see more of this. But I do wish it was fully based on open technology. While stations gain the right to modify the source code of their _apps_ after a year, they remain locked into Local Public’s back-end services. For the CMS, which builds network effects the more stations use it, stations can only get support, maintenance, and customization through Local Public. Over time, that lock-in does not incentivize great support, and Local Public will need to work hard to buck the trends. NPR’s CMS, for example, is notorious among the stations that have to use it. I’m certain the will is there to do better, but they will need to proceed with intention. In my opinion it would be better if, at least after establishing a customer base, they open sourced their back-end CMS too. I tend to think that _any_ technology provided to support the public interest should be fully open. That doesn’t mean there isn’t a tidy business available to its creator — ask Ghost, which is generating millions of dollars off the back of its open source CMS. If there’s a class of organization that absolutely doesn’t deserve to be locked into a technology stack, it’s public service broadcasters. This isn’t Cascade PBS’s fault. It needs its spinout to be sustainable, and this model feels like it will hit that goal. The best scenario, in my mind, would be if there were central funders who bankrolled open tech that the whole ecosystem could use. But, of course, it’s 2026, and central funding for _anything_ public media is hard to come by. Still, this is wonderful to see, and anything that encourages collaboration on a technical level between public service media organizations deserves support. * * * ### Emergency Mode for news Emergency Mode is a set of resources, tools, and training that aims to prepare small newsrooms for various disasters. It’s a co-production between OpenNews, NC Local, and Newspack. They’ve done a great job. As their about page puts it: > “Emergency Mode for News equips local journalists and their newsrooms with the tools they need to respond to climate disasters. With a disaster reporting action pack, software and a learning community, Emergency Mode is designed to help journalists act nimbly and creatively to serve their communities when the unexpected happens.” Toolkits include things like a practical checklist for newsrooms covering wildfires and a template for maintaining source lists during an emergency. There’s also a hands-on workshop series and tools like WordPress plugins for live rolling news updates and providing bandwidth-light versions of sites. Most of all, I really appreciate the practical nature of all of it. Rather than hand-waving about principles and ideas, as many newsroom-facing resources do, everything here is a concrete tool that can actively be used in the field. Newsrooms are more squeezed than they’ve ever been, so it doesn’t hurt that it’s all free. I’d love to see this level of concrete specificity for _the normal working of a newsroom_. Wouldn’t it be cool to have a list of business model checklists you could pull from? Or disaster recovery plans? Or data protection policies? Just as the tools on this site are going to be concretely useful to any newsroom that covers a disaster, checklists, tools, and training for standard operational practices could be really meaningful — particularly for smaller newsrooms that don’t have the ability to hire CTOs, CFOs, and so on. In other words: more, please. This is lovely. * * * ### The Quiet Erosion of Collective Action Under Digital Surveillance The most important outcome of increased surveillance is a chilling effect on free speech and expression. As Gina Romero, the United Nations Special Rapporteur on the Rights to Freedom of Peaceful Assembly, notes here, that extends to the organizations that have been established to protect those rights: > “As organizations operate under the constant assumption that they are being monitored, their core functions are profoundly affected. Their ability to serve as watchdogs, provide rights-based services, protect victims of human rights abuses, and educate the public is severely constrained. Ultimately, the very possibility of advancing and protecting rights, democracy and the rule of law is undermined.” Civil society organizations and advocates have been mislabeled as national security threats around the world. It’s true in some of the nations that we’ve long thought of as being authoritarian, but it’s also true in the United States. Even places like the United Kingdom have tried to apply pressure to technology companies so that they can gain access to backdoors. Tools like Signal have become all the more important. We need more easy to use end to end encrypted systems so that we can communicate and organize with each other without fear of government surveillance. That also allows whistleblowers and sources for journalists to reach out with less of a fear they they will suffer repercussions. But those tools don’t stop you from being surveilled in the real world. Cameras and microphones are everywhere; license plate readers are now commonplace; even AI-enabled drones have been deployed for events like the World Cup. It’s generally true that if government _can_ do something, it will. So the only way to stop this kind of widespread surveillance is to make it impossible. Romero calls for legislative prevention that takes into account the whole systemic impact of surveillance rather than just the immediate first-order effects. Her report also calls out that it can be very difficult to challenge these systems because what they are and who owns them tends to be complicated or obfuscated: > “The study reveals a lack of transparency surrounding the relationship between state power and non-state actors, creating an information vacuum that makes surveillance practices exceedingly difficult to challenge through litigation. As a result, the right to an effective remedy is fundamentally weakened.” So I think we also need more technical capabilities that interfere with how these systems of surveillance actually work. We need more spaces that are designated privacy-first and enforce an anti-surveillance rulebook. And, just as communities have taken it upon themselves to dismantle Flock cameras, we need to take back our streets. * * * ### OpenAI will delay GPT-5.6 after Trump administration request I’ve got (at least) two worries about the story that the Trump Administration halted the release of models from both Anthropic and OpenAI. Anthropic recently pulled its Fable model release in response to the government. Now it turns out that OpenAI has done something similar: > “The Information reported that OpenAI CEO Sam Altman told employees Wednesday in a company Q&A that it would release GPT-5.6 in limited preview form — granting access only to a small group of enterprise customers — in compliance with a request from the federal government. During that preview period, the Trump administration itself would reportedly approve access for customers on a case-by-case basis.” In some ways, what a coup for the AI industry. This technology is so powerful that the government doesn’t think anyone should have it — and when it does inevitably release into the public’s hands, what a valuable product that will be. Get the tech that’s too dangerous to be released! This magical product can be yours for an unbelievable price! So one worry is that this is, in essence, great marketing for these vendors. But it’s worth remembering that these AI models are black boxes that respond to information queries in opaque ways. The more people rely on them for knowledge, the more powerful the models become. The argument being presented is that they can be used in ways that might present traditional security threats — but consider that some versions of the truth, to the wrong kind of authoritarian-minded government, might also be considered a threat. (Remember that “extremism on migration, race, and gender” and hostility to “traditional American views” are now considered markers of domestic terrorism.) This is a golden opportunity, in other words, to hit pause on frontier model releases, at a time when models are becoming more prevalent, in order to make sure models are shaped to represent a certain version of the world. The administration has already signaled a willingness to do this; there is nothing to say they aren’t. The only way to _prove_ that they aren’t is to open source not just the models but the training process and make the whole thing transparent and verifiable. The industry is a long way off from doing that. * * * ### Now we’re getting AI fake news complaining about how AI fake news is the death of real news A bunch of people — including, unfortunately, me — were taken in by this AI-generated newsroom earlier this week. The story was decently written and seemed to be well-cited, but it turned out to be nonsense. Ironically, it was about a would-be media empire that purchased struggling papers, fired their staff, and replaced them with AI, leading to the death of each newsroom. All false. So the big question about The Editorial is: why does it exist? As Joshua Benton put it: > “Fake news isn’t new, obviously. And while AI-generated slop is newer, it’s hardly unfamiliar at this point. But why would a spam site bother making up a story about Alabama weekly newspapers, of all things? Whose interest is it in to get that niche?” Here’s my theory: I think it’s a two-headed LLM poisoning scheme. On one hand, most of the content relates to Chinese-specific interests: articles about Taiwan or African nations where China is making inroads. These are all articles from a China-friendly perspective. If an LLM were to ingest them and _trust the site_ , it might start repeating the assertions made in each one as fact. One way to make sure a site is trusted is to get other, trusted sources to point to it. That’s where the stories about journalism come in: there are few things that journalists engage in more than stories about their own industry. Get enough patsies (like, again, to my chagrin, _me_) to point links in their direction and journalists might post them in high-trust communities on high-trust sites like Reddit, as well as their own, and Bob’s your uncle. We already know that it takes as little as 13 words to poison an LLM with falsehoods. Of course, that might not be it at all. Frank, the site’s owner (who lists himself as CEO of Nordiso Group on LinkedIn), at least appears to be a Finnish solopreneur. If he wanted to clear the air, he could write a post (himself) about what he was up to. It might be that he’s running an experiment to see how easily an LLM can be poisoned with propaganda! Until then, I think it’s reasonable to assume that something underhand is going on.

Link: Cascade PBS launches Local Public as standalone streaming tech company, by Matthew Keys at The Desk I’ve got some complicated feelings about this announcement from Cascade PBS: > “Cascade PBS, the non-profit television station serving western Washington state, has spun out its technology division into a separate company that will help similar public broadcasters carve out their own streaming and digital identities. > > The new company, Local Public, will help develop streaming applications for connected TVs, mobile devices and the web, allowing public television stations to offer locally branded streaming experiences featuring their own programming alongside national PBS content.” On one hand, I absolutely love that they were able to spin out their technology division. Most public media companies don’t have the resources or skills to build their own tech, and building this capability outside of any one station so that all of them can take advantage of it makes a lot of sense to me. The Local Public site itself also makes the ROI transparent. WETA, the public media station for Greater Washington, ran the numbers and said that it would break even in the first year, and a calculator is available for other stations that want to check for themselves. The pricing hinges on Passport-eligible donors: those giving at least $60 a year. Local Public charges $60,000 a year for stations with fewer than 15,000, $75,000 for up to 40,000, and $100,000 a year for everyone else — which is not out of bounds. It all seems like a decent business, run in the public interest as a subsidiary of Cascade PBS, that will genuinely help public media stations. I want to see more of this. But I do wish it was fully based on open technology. While stations gain the right to modify the source code of their _apps_ after a year, they remain locked into Local Public’s back-end services. For the CMS, which builds network effects the more stations use it, stations can only get support, maintenance, and customization through Local Public. Over time, that lock-in does not incentivize great support, and Local Public will need to work hard to buck the trends. NPR’s CMS, for example, is notorious among the stations that have to use it. I’m certain the will is there to do better, but they will need to proceed with intention. In my opinion it would be better if, at least after establishing a customer base, they open sourced their back-end CMS too. I tend to think that _any_ technology provided to support the public interest should be fully open. That doesn’t mean there isn’t a tidy business available to its creator — ask Ghost, which is generating millions of dollars off the back of its open source CMS. If there’s a class of organization that absolutely doesn’t deserve to be locked into a technology stack, it’s public service broadcasters. Still, this is wonderful to see, and anything that encourages collaboration on a technical level between public service media organizations deserves support.

Link: Journalism Has the Receipts. It Won’t Use Them., by Yoni Greenbaum in Backstory & Strategy Arts organizations learned long ago to prove their economic value with hard numbers: attendance, tourism revenue, multiplier effects. News, as Yoni Greenbaum argues here, likes to cling to civic virtue and assume that the work should speak for itself. > “Journalism operated on a commercial advertising revenue model for over 150 years. Publishers sold readers to advertisers, while editors fretted about maintaining a church-and-state divide between the newsroom and business desk. Journalists saw themselves as watchdogs, not wealth generators. Pitching our value based on our own economic impact felt gauche, too close to an advertorial.” Yoni points out that this is starting to change. We know that news deserts cost communities at least $1.1B a year, for example, because of a report by Rebuild Local News and the University of Illinois Chicago. But newsrooms themselves tend to shy away from reporting their own economic impact — even though they already have the tools to do so. It’s not obvious to me that this accounting would work as an argument across the board for newsrooms, and particularly for those with a national focus. Does ProPublica (my employer until the end of the month) save anyone money? It certainly does prevent corruption, and there are instances with real dollar amounts attached to them: Intuit, for example, paid back $141 million to its customers over deceptive marketing. But I’m not sure that its impact can be quantified easily overall, despite the newsroom’s obvious public benefit. On the other hand, for _local_ newsrooms, this makes a lot of sense to me: at their best, they act as connective tissue for their communities. That $1.1B a year was _just_ increased interest costs from lenders who felt they could charge more to unmonitored governments. They just need to get more comfortable at telling the economic side of their stories. And there’s a wider point here, which is that almost all nonprofit newsrooms need to be able to get more concrete and detailed about their business models. If you’re running an organization that wants to be sustainable, it’s not enough to care about the journalistic process, and your business accounting cannot be limited to activities like events and merchandise. You actually have to care about building a business holistically, and everything that entails.

Link: OpenAI proposes handing Trump administration 5% stake, by Cristina Criddle in the Financial Times In order to ward off backlash against AI and curry favor with the Trump administration, Sam Altman has floated the idea of giving 5% of OpenAI to a wealth fund that pays dividends to both the government and citizens — and that every leading AI vendor should do the same. > “Sam Altman, chief executive of the ChatGPT maker, has argued that giving the public a financial stake in the company is the best way to share the upside of AI and has suggested a stake of this size in early conversations with the administration, according to two people familiar with the talks.” It’s transparently a way to align everyone with AI vendor profits. If the sector increases in value, the government and the voting population benefit. If it _decreases_ in value … well, the government is incentivized to prevent that from happening. It also wouldn’t be without precedent: it’s modeled on the Alaska Permanent Fund, which does this with oil profits for Alaskan residents. Intel is also now 10% government-owned, and the administration has reversed course to be behind it since gaining that stake. Would a government whose revenues are directly linked to the performance of a sector be likely to enact hard regulations on that sector? Perhaps not. It’s not a slam dunk, though: for example, the UK receives significant tax revenue on fossil fuels, but still promoted electric cars. There are lots of factors at play, and profit alignment isn’t necessarily outweighed by the effects of other harms. (See also: cigarettes, which are taxed but also tightly controlled as an addictive carcinogen.) Meanwhile, Bernie Sanders has pushed for closer to 50% ownership through a sovereign wealth fund. At this much lower stake, Sam Altman’s proposal uses Sanders’s democratic socialist “share the wealth” language as a way to launder OpenAI’s profits through a thin veneer of good ethics. What’s also interesting to me is that all of these arguments assume that AI is going to be an enormous driver of wealth and innovation — but what if it isn’t? It’s another great way to advertise the technology as something world-changing that everybody must get behind right now. And even if AI turns out to be what the people heavily invested in its success say it will be, it doesn’t stand alone as a sea change innovation. The personal computer, the iPhone, word processors, and spreadsheets were pretty transformational technologies. Should there have been a wealth fund attached to each of those? What, exactly, makes AI different? The answer is that it represents labor displacement: people will lose their jobs. And if that’s actually going to be the case, we need bigger, more structural safety nets and reforms. Dividends from 5% of a sector aren’t going to replace wages at scale — and are heavily dependent on valuations continuing to rise. This proposal ties the welfare of people who have lost their jobs to the success of the companies that drove those losses. The incentives are perverse. We shouldn’t accept this proposal. Instead, we should push for stronger protections and stronger regulation. If a sector can’t succeed without real damage to working communities, then it must not be allowed to. And if these claims turn out not to be true, then it’s an empty gesture designed to add credibility to a self-interested science fiction view of the future.

Link: Now we’re getting AI fake news complaining about how AI fake news is the death of real news, by Joshua Benton in Nieman Lab A bunch of people — including, unfortunately, me — were taken in by this AI-generated newsroom earlier this week. The story was decently written and seemed to be well-cited, but it turned out to be nonsense. Ironically, it was about a would-be media empire that purchased struggling papers, fired their staff, and replaced them with AI, leading to the death of each newsroom. All false. So the big question about The Editorial is: why does it exist? As Joshua Benton put it: > “Fake news isn’t new, obviously. And while AI-generated slop is newer, it’s hardly unfamiliar at this point. But why would a spam site bother making up a story about Alabama weekly newspapers, of all things? Whose interest is it in to get that niche?” Here’s my theory: I think it’s a two-headed LLM poisoning scheme. On one hand, most of the content relates to Chinese-specific interests: articles about Taiwan or African nations where China is making inroads. These are all articles from a China-friendly perspective. If an LLM were to ingest them and _trust the site_ , it might start repeating the assertions made in each one as fact. One way to make sure a site is trusted is to get other, trusted sources to point to it. That’s where the stories about journalism come in: there are few things that journalists engage in more than stories about their own industry. Get enough patsies (like, again, to my chagrin, _me_) to point links in their direction and journalists might post them in high-trust communities on high-trust sites like Reddit, as well as their own, and Bob’s your uncle. We already know that it takes as little as 13 words to poison an LLM with falsehoods. Of course, that might not be it at all. Frank, the site’s owner, at least appears to be a Finnish student. If he wanted to clear the air, he could write a post (himself) about what he was up to. It might be that he’s running an experiment to see how easily an LLM can be poisoned with propaganda! Until then, I think it’s reasonable to assume that something underhand is going on.

Link: Companies Are Making Claude and Codex Talk Like Cavemen to Stop AI’s Soaring Costs, by Joseph Cox at 404 Media I find this very funny: > “Companies are deliberately making their AI tools speak like cavemen in an attempt to stop burning through AI tokens and curb their massive expenditure on AI, 404 Media has found. The tool turns the usually verbose outpost of LLMs like Claude Code, Codex, or Gemini into a much more to the point answer. Think less “you’re right to push back, I was wrong,” and more “Hulk smash.”” If only we had other limited-vocabulary lexicons designed to talk to computers efficiently! I think we’re circling a few different possibilities that may show up over the next few years: * Literally LLM-specific “programming languages” that humans can use to talk to models more efficiently, of which Caveman is the hilarious first step * A proprietary bytecode-like language for LLMs that makes interactions more efficient but also just happens to be owned by one of the major vendors and creates a hitherto-unobtainable moat for their business * This all becomes moot when local models become viable for most businesses without insanely high hardware prices or configuration costs * LLM costs eventually fall to a fraction of their existing level But who knows? Maybe enterprise businesses will continue to talk in stilted caveman language to achieve their business goals forever.

Link: The Quiet Erosion of Collective Action Under Digital Surveillance, by Gina Romero in Tech Policy Press The most important outcome of increased surveillance is a chilling effect on free speech and expression. As Gina Romero, the United Nations Special Rapporteur on the Rights to Freedom of Peaceful Assembly, notes here, that extends to the organizations that have been established to protect those rights: > “As organizations operate under the constant assumption that they are being monitored, their core functions are profoundly affected. Their ability to serve as watchdogs, provide rights-based services, protect victims of human rights abuses, and educate the public is severely constrained. Ultimately, the very possibility of advancing and protecting rights, democracy and the rule of law is undermined.” Civil society organizations and advocates have been mislabeled as national security threats around the world. It’s true in some of the nations that we’ve long thought of as being authoritarian, but it’s also true in the United States. Even places like the United Kingdom have tried to apply pressure to technology companies so that they can gain access to backdoors. Tools like Signal have become all the more important. We need more easy to use end to end encrypted systems so that we can communicate and organize with each other without fear of government surveillance. That also allows whistleblowers and sources for journalists to reach out with less of a fear they they will suffer repercussions. But those tools don’t stop you from being surveilled in the real world. Cameras and microphones are everywhere; license plate readers are now commonplace; even AI-enabled drones have been deployed for events like the World Cup. It’s generally true that if government _can_ do something, it will. So the only way to stop this kind of widespread surveillance is to make it impossible. Romero calls for legislative prevention that takes into account the whole systemic impact of surveillance rather than just the immediate first-order effects. Her report also calls out that it can be very difficult to challenge these systems because what they are and who owns them tends to be complicated or obfuscated: > “The study reveals a lack of transparency surrounding the relationship between state power and non-state actors, creating an information vacuum that makes surveillance practices exceedingly difficult to challenge through litigation. As a result, the right to an effective remedy is fundamentally weakened.” So I think we also need more technical capabilities that interfere with how these systems of surveillance actually work. We need more spaces that are designated privacy-first and enforce an anti-surveillance rulebook. And, just as communities have taken it upon themselves to dismantle Flock cameras, we need to take back our streets.

Link: Emergency Mode for News, by OpenNews, NC Local, and Newspack Emergency Mode is a set of resources, tools, and training that aims to prepare small newsrooms for various disasters. It’s a co-production between OpenNews, NC Local, and Newspack. They’ve done a great job. As their about page puts it: > “Emergency Mode for News equips local journalists and their newsrooms with the tools they need to respond to climate disasters. With a disaster reporting action pack, software and a learning community, Emergency Mode is designed to help journalists act nimbly and creatively to serve their communities when the unexpected happens.” Toolkits include things like a practical checklist for newsrooms covering wildfires and a template for maintaining source lists during an emergency. There’s also a hands-on workshop series and tools like WordPress plugins for live rolling news updates and providing bandwidth-light versions of sites. Most of all, I really appreciate the practical nature of all of it. Rather than hand-waving about principles and ideas, as many newsroom-facing resources do, everything here is a concrete tool that can actively be used in the field. Newsrooms are more squeezed than they’ve ever been, so it doesn’t hurt that it’s all free. I’d love to see this level of concrete specificity for _the normal working of a newsroom_. Wouldn’t it be cool to have a list of business model checklists you could pull from? Or disaster recovery plans? Or data protection policies? Just as the tools on this site are going to be concretely useful to any newsroom that covers a disaster, checklists, tools, and training for standard operational practices could be really meaningful — particularly for smaller newsrooms that don’t have the ability to hire CTOs, CFOs, and so on. In other words: more, please. This is lovely.

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Winnie Lim: > “When I think about what I wish to accomplish if I knew I was going to die, apart from spending time with my loved ones I think only writing is left. It is a way of emptying myself I guess, a way of sublimating my sad existence. I wouldn’t pretend that my writing is useful, but at least it will be a window into an interior world that doesn’t exist anywhere else.” Co-signed. Eliot Peper: > “Writing about whatever you’re uniquely obsessed is hardly a new approach, it’s the basis of all good writing. The only new thing is that writing about anything else is less valuable than it used to be, and it was never that valuable in the first place. Life is short. If you’re going to write, write something worth reading. > > Write what only you can write.”

Link: OpenAI will delay GPT-5.6 after Trump administration request, by Hayden Field in The Verge I’ve got (at least) two worries about the story that the Trump Administration halted the release of models from both Anthropic and OpenAI. Anthropic recently pulled its Fable model release in response to the government. Now it turns out that OpenAI has done something similar: > “The Information reported that OpenAI CEO Sam Altman told employees Wednesday in a company Q&A that it would release GPT-5.6 in limited preview form — granting access only to a small group of enterprise customers — in compliance with a request from the federal government. During that preview period, the Trump administration itself would reportedly approve access for customers on a case-by-case basis.” In some ways, what a coup for the AI industry. This technology is so powerful that the government doesn’t think anyone should have it — and when it does inevitably release into the public’s hands, what a valuable product that will be. Get the tech that’s too dangerous to be released! This magical product can be yours for an unbelievable price! So one worry is that this is, in essence, great marketing for these vendors. But it’s worth remembering that these AI models are black boxes that respond to information queries in opaque ways. The more people rely on them for knowledge, the more powerful the models become. The argument being presented is that they can be used in ways that might present traditional security threats — but consider that some versions of the truth, to the wrong kind of authoritarian-minded government, might also be considered a threat. (Remember that “extremism on migration, race, and gender” and hostility to “traditional American views” are now considered markers of domestic terrorism.) This is a golden opportunity, in other words, to hit pause on frontier model releases, at a time when models are becoming more prevalent, in order to make sure models are shaped to represent a certain version of the world. The administration has already signaled a willingness to do this; there is nothing to say they aren’t. The only way to _prove_ that they aren’t is to open source not just the models but the training process and make the whole thing transparent and verifiable. The industry is a long way off from doing that.

_Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society._ _Did I miss something important?__Send me an email_ _to let me know._ _Did someone forward this to you?_ Subscribe for free_._ * * * Before we begin this week's roundup, I want to express my condolences to the friends and family of Om Malik, who we sadly lost this week. Om was one of the most important voices in blogging, tech journalism, and then the wider industry. So many people in tech have a story about him that highlights his conscience, thoughtfulness, and kindness. He will be missed. * * * ### Journalism's logical fallacy Journalism is in crisis, and it’s really easy (and lazy) to say that making a technology or process tweak will fix it: we just need to use AI to fill capacity gaps, or build stronger comments into our site, or we need a better business or distribution model. None of those things address the underlying question of _why_ we need journalism, why it’s important, and what it should be. By addressing innovation at the edges, newsrooms are avoiding the hard, existential work of revisiting their core value to begin with. But it’s only by understanding that core value that they will actually reset their relationships with audiences, build greater trust and loyalty, and pull themselves out of the rut they find themselves in. Shirish Kulkarni’s findings from listening projects in Wales — with multiple dramatically different demographics — contradict a lot of the narratives newsrooms have been telling themselves. For example: > “The second finding challenges one of the journalism industry’s most comfortable premises: that audiences – particularly marginalised communities – are news-illiterate and need to be educated. The opposite is true. In fact, the communities we work with are forensically sharp about media – often more so than the industry insiders who talk about them.” This mirrors something you often hear from mission-driven tech projects: “we just need to educate the user”. Usually the opposite is true: you need to educate yourself _about_ the user and give them the thing they actually need. And in the case of journalism, at least as a finding of this research, the need turns out to be pretty simple: > “They want help making good decisions. For themselves, their families, their communities. Not drama, not outrage, not the next breaking story. Practical, trustworthy, usable information that helps them navigate their lives.” It’s important, once again, to separate the work of _news_ — breaking headlines, emergent facts — from _journalism’s_ work to provide context and meaning. The first is a commodity; the second is both inherently community-driven and has always been more valuable. Here I want to bang an old drum: newsrooms like to talk about _audience_ strategies, not _community_ strategies. It’s a meaningful difference that Shirish highlights well. The first implies an ivory tower broadcast approach: “we just need to reach people”. The second is an active relationship between a newsroom and the people it serves; a two-way conversation that requires trust and understanding on both sides. The internet has always been a conversation. We’ve had the ability to build relationship-centric news organizations for 30 years, but most remain stubbornly set in a print mindset. This kind of research makes it clear how important that shift is, but, like Shirish, I don’t believe most existing newsrooms will evolve to actually meet this need. They can’t: the immediate commercial pressure is severe, and changing the model requires changing highly-ingrained cultural norms and assumptions that have been inherited from print. And in the midst of that panic, they’re jumping into bed with companies (AI vendors, proprietary social media platforms) that intermediate their relationships with their communities in exchange for some short-term wins. So their outlook is not rosy. Instead, I think we’ll see _new_ newsrooms emerge that reinvent what journalism is, are unafraid to build real, lasting, two-way relationships with the people they’re trying to serve, and eat everybody else’s lunch. * * * ### Empower others Another superb post from Corey Ford: > “There's a simple diagnostic for any organization: Are your leaders focused on accumulating control, or on empowering the people around them? > > Too many people in positions of power are overly focused on themselves. They hoard decisions. They micromanage. They grab power. And the people around them suffer for it. Those employees wait for direction instead of taking initiative. They bring problems instead of solutions. They ask "now what?" instead of saying "here's what I think we should do.”” He goes on to describe _bus factor_ , the question of what happens if you’re hit by a bus. (My colleague Adam Hirsch recently offered “lottery factor”, reframing the question to be about what happens when someone wins the lottery and goes away to live the life of their dreams, which is more fun to think about.) Could your organization live without you? The only way to build an organization that can live without you is to devolve decision-making — and the only way to do _that_ is to create the conditions to make devolved decision-making a safe norm. Corey’s been building on that idea in his recent posts, and I enthusiastically co-sign. I’ve taken “surround yourself with people who are better than you” to heart: the Director of Product Engineering, and Director of IT and Security, who both report to me, fit that description exactly and we’re all better off for it. When we examine situations where this doesn’t happen, it’s worth considering incentives. If you’re a founder or top-level leader who is building a sustainable organization in good faith, these principles are obviously, straightforwardly a good idea. If, on the other hand, you’re _not_ in that position, and you’re worried about your own standing in the organization, you might feel like it’s a good idea to entrench your own power and decision-making. I think that’s short-sighted: you’re there to support the organization, and hiring an amazing team, as long as you support them well, can only make you look good. At various tech companies in particular, I’ve also encountered people who don’t want power to be devolved to them: they want to be given a plan and to follow it. I don’t think that’s realistic in most smaller companies, and even in larger ones autonomy, insight, and decision-making ability are prized. It’s a thing to hire for. When you want to devolve decision-making, you hire people who can make great decisions. When you want to entrench your own power and micro-manage, you hire followers. These are different paths. I can only recommend the former. This, from Corey, is absolutely vital: > “Real empowerment means saying: "That's not what I would have done. But you own the decision. Here's what I'm thinking about. Here are the questions I'd want you to consider. But ultimately, it's yours.”” Nobody will ever do it your way. If you want them to follow your lead exactly, you’re not devolving power. If you want them to be genuinely empowered, you need to not just accept that they do things their way, but embrace it — while also being transparent about your own thinking and principles, and setting norms mantras that help people navigate what’s important for the team. He’s got a lot to say about that. As always, his full post, and the underlying series, are required reading. * * * ### It is trivially easy to use Reddit to manipulate AI search, research suggests All good fun: > “A tiny snippet of user-generated text as short as 13 words long is often enough to manipulate the AI agents that power tools like ChatGPT and Google’s AI search, new research shows. The study suggests that it is trivially easy for brands to inject promotional content on sites like Reddit, Quora, and Wikipedia with the end goal of poisoning or manipulating the output of AI tools.” So not only do we need to worry about AI-generated slop polluting our social spaces, we _also_ have to worry about people who want to influence the _output_ of the AI-generated slop polluting them, too. There’s a whole industry of companies trying to improve their clients’ coverage in AI results, just as there were for search engine results. And all of them will be spamming the crap out of our public communities and collaborative websites. As 404 Media points out, this poses a real question for moderators in those spaces. How can they possible stem the tide? It’s not clear that this is even possible. Which means, inevitably, that the signal vs noise ratio in those spaces will decline, leading to a decline in usership of those spaces overall, and a retreat for most people into private group chats and uncrawlable communities. To put it simply: > “Poisoning LLM results is basically just as easy as doing targeted posting on highly relevant subreddits to the industry or company you’re trying to promote, phrasing the comment to align with popular LLM queries, and attempting to evade moderation for as long as possible.” Guarding against bot-driven spam is a relatively simpler problem. In contrast, this content will often be insidiously human: cunningly designed to try to provide value while also hiding a paid agenda. In some ways, it’s all the same as it ever was, but the volume of the junk is only going to keep increasing. Of course, the silver lining is that eventually these sources will become unusable for AI training too. Then, finally, maybe everyone will go away. * * * ### Texas anti-ICE protesters convicted of terrorism charges sentenced to at least 50 years in prison This is a litmus test for the freedom to protest in the United States: > “A group of Texas protesters convicted of terrorism charges received unusually harsh sentences of at least 50 years in prison on Tuesday in a closely watched case that was widely seen as a test case of the Trump administration’s efforts to crack down on dissent.” Let’s be clear: a few of the protesters were out of bounds. One fired an AR-15 at the police, which goes beyond legitimate protest into inciting violence (and maybe even deliberate provocation). I would never condone that kind of activity. None of us should. But these sentences far outstrip anything that’s been given to anyone on the right wing: the leader of the Proud Boys, as this article notes, was sentenced to 22 years in prison. One protester wasn’t even present, but was sentenced to 30 years for moving some zines: > “The ninth defendant, Daniel Sanchez-Estrada was not at the protest, but was convicted of corruptly concealing a document or record after prosecutors said he moved leftwing zines and other materials at the request of Rueda, his wife, after she was arrested. Sanchez-Estrada was sentenced to 30 years in prison on Tuesday.” Many of the protesters had guns and were part of a gun club. They all possessed them legally. I personally wish there was not a right to bear arms and think that their ubiquitous presence in America makes everyone less safe, but the right to own them is enshrined in the Second Amendment. Instead, other “evidence” was used to infer that they planned violence, including this specific argument that should give everyone pause: > “[…] including their decision to communicate and auto-delete messages on Signal, an encrypted messaging platform widely used among activists, journalists and other citizens wary of government surveillance.” Collectively, the justice department argued that these convictions are proof that anti-fascists are terrorists, which should _also_ give us pause. The precedent here is obviously very dangerous for freedom of speech, freedom of assembly, and democracy in America.

Link: It Is Trivially Easy to Use Reddit to Manipulate AI Search, Research Suggests, by Jason Koebler at 404 Media All good fun: > “A tiny snippet of user-generated text as short as 13 words long is often enough to manipulate the AI agents that power tools like ChatGPT and Google’s AI search, new research shows. The study suggests that it is trivially easy for brands to inject promotional content on sites like Reddit, Quora, and Wikipedia with the end goal of poisoning or manipulating the output of AI tools.” So not only do we need to worry about AI-generated slop polluting our social spaces, we _also_ have to worry about people who want to influence the _output_ of the AI-generated slop polluting them, too. There’s a whole industry of companies trying to improve their clients’ coverage in AI results, just as there were for search engine results. And all of them will be spamming the crap out of our public communities and collaborative websites. As 404 Media points out, this poses a real question for moderators in those spaces. How can they possible stem the tide? It’s not clear that this is even possible. Which means, inevitably, that the signal vs noise ratio in those spaces will decline, leading to a decline in usership of those spaces overall, and a retreat for most people into private group chats and uncrawlable communities. To put it simply: > “Poisoning LLM results is basically just as easy as doing targeted posting on highly relevant subreddits to the industry or company you’re trying to promote, phrasing the comment to align with popular LLM queries, and attempting to evade moderation for as long as possible.” Guarding against bot-driven spam is a relatively simpler problem. In contrast, this content will often be insidiously human: cunningly designed to try to provide value while also hiding a paid agenda. In some ways, it’s all the same as it ever was, but the volume of the junk is only going to keep increasing. Of course, the silver lining is that eventually these sources will become unusable for AI training too. Then, finally, maybe everyone will go away.

Link: Empower Others, by Corey Ford at Point C Another superb post from Corey Ford: > “There's a simple diagnostic for any organization: Are your leaders focused on accumulating control, or on empowering the people around them? > > Too many people in positions of power are overly focused on themselves. They hoard decisions. They micromanage. They grab power. And the people around them suffer for it. Those employees wait for direction instead of taking initiative. They bring problems instead of solutions. They ask "now what?" instead of saying "here's what I think we should do.”” He goes on to describe _bus factor_ , the question of what happens if you’re hit by a bus. (My colleague Adam Hirsch recently offered “lottery factor”, reframing the question to be about what happens when someone wins the lottery and goes away to live the life of their dreams, which is more fun to think about.) Could your organization live without you? The only way to build an organization that can live without you is to devolve decision-making — and the only way to do _that_ is to create the conditions to make devolved decision-making a safe norm. Corey’s been building on that idea in his recent posts, and I enthusiastically co-sign. I’ve taken “surround yourself with people who are better than you” to heart: the Director of Product Engineering, and Director of IT and Security, who both report to me, fit that description exactly and we’re all better off for it. When we examine situations where this doesn’t happen, it’s worth considering incentives. If you’re a founder or top-level leader who is building a sustainable organization in good faith, these principles are obviously, straightforwardly a good idea. If, on the other hand, you’re _not_ in that position, and you’re worried about your own standing in the organization, you might feel like it’s a good idea to entrench your own power and decision-making. I think that’s short-sighted: you’re there to support the organization, and hiring an amazing team, as long as you support them well, can only make you look good. At various tech companies in particular, I’ve also encountered people who don’t want power to be devolved to them: they want to be given a plan and to follow it. I don’t think that’s realistic in most smaller companies, and even in larger ones autonomy, insight, and decision-making ability are prized. It’s a thing to hire for. When you want to devolve decision-making, you hire people who can make great decisions. When you want to entrench your own power and micro-manage, you hire followers. These are different paths. I can only recommend the former. This, from Corey, is absolutely vital: > “Real empowerment means saying: "That's not what I would have done. But you own the decision. Here's what I'm thinking about. Here are the questions I'd want you to consider. But ultimately, it's yours.”” Nobody will ever do it your way. If you want them to follow your lead exactly, you’re not devolving power. If you want them to be genuinely empowered, you need to not just accept that they do things their way, but embrace it — while also being transparent about your own thinking and principles, and setting norms mantras that help people navigate what’s important for the team. He’s got a lot to say about that. As always, his full post, and the underlying series, are required reading.

Link: Journalism's Logical Fallacy, by Shirish Kulkarni Journalism is in crisis, and it’s really easy (and lazy) to say that making a technology or process tweak will fix it: we just need to use AI to fill capacity gaps, or build stronger comments into our site, or we need a better business or distribution model. None of those things address the underlying question of _why_ we need journalism, why it’s important, and what it should be. By addressing innovation at the edges, newsrooms are avoiding the hard, existential work of revisiting their core value to begin with. But it’s only by understanding that core value that they will actually reset their relationships with audiences, build greater trust and loyalty, and pull themselves out of the rut they find themselves in. Shirish Kulkarni’s findings from listening projects in Wales — with two dramatically different demographics — contradict a lot of the narratives newsrooms have been telling themselves. For example: > “The second finding challenges one of the journalism industry’s most comfortable premises: that audiences – particularly marginalised communities – are news-illiterate and need to be educated. The opposite is true. In fact, the communities we work with are forensically sharp about media – often more so than the industry insiders who talk about them.” This mirrors something you often hear from mission-driven tech projects: “we just need to educate the user”. Usually the opposite is true: you need to educate yourself _about_ the user and give them the thing they actually need. And in the case of journalism, at least as a finding of this research, the need turns out to be pretty simple: > “They want help making good decisions. For themselves, their families, their communities. Not drama, not outrage, not the next breaking story. Practical, trustworthy, usable information that helps them navigate their lives.” It’s important, once again, to separate the work of _news_ — breaking headlines, emergent facts — from _journalism’s_ work to provide context and meaning. The first is a commodity; the second is both inherently community-driven and has always been more valuable. Here I want to bang an old drum: newsrooms like to talk about _audience_ strategies, not _community_ strategies. It’s a meaningful difference that Shirish highlights well. The first implies an ivory tower broadcast approach: “we just need to reach people”. The second is an active relationship between a newsroom and the people it serves; a two-way conversation that requires trust and understanding on both sides. The internet has always been a conversation. We’ve had the ability to build relationship-centric news organizations for 30 years, but most remain stubbornly set in a print mindset. This kind of research makes it clear how important that shift is, but, like Shirish, I don’t believe most existing newsrooms will evolve to actually meet this need. They can’t: the immediate commercial pressure is severe, and changing the model requires changing highly-ingrained cultural norms and assumptions that have been inherited from print. And in the midst of that panic, they’re jumping into bed with companies (AI vendors, proprietary social media platforms) that intermediate their relationships with their communities in exchange for some short-term wins. So their outlook is not rosy. Instead, we’ll see _new_ newsrooms emerge that reinvent what journalism is, are unafraid to build real, lasting, two-way relationships with the people they’re trying to serve, and eat everybody else’s lunch.

Link: Texas anti-ICE protesters convicted of terrorism charges sentenced to at least 50 years in prison, by Sam Levine in The Guardian This is an outrageous litmus test for the freedom to protest in America: > “A group of Texas protesters convicted of terrorism charges received unusually harsh sentences of at least 50 years in prison on Tuesday in a closely watched case that was widely seen as a test case of the Trump administration’s efforts to crack down on dissent.” Let’s be clear: a few of the protesters were out of bounds. One fired an AR-15 at the police, which goes beyond legitimate protest into inciting violence (and maybe even deliberate provocation). I would never condone that kind of activity. But these sentences far outstrip anything that’s been given to anyone on the right wing: the leader of the Proud Boys, as this article notes, was sentenced to 22 years in prison. One protester wasn’t even present, but was sentenced to 30 years for moving some zines: > “The ninth defendant, Daniel Sanchez-Estrada was not at the protest, but was convicted of corruptly concealing a document or record after prosecutors said he moved leftwing zines and other materials at the request of Rueda, his wife, after she was arrested. Sanchez-Estrada was sentenced to 30 years in prison on Tuesday.” Many of the protesters had guns and were part of a gun club. They all possessed them legally. I personally wish there was not a right to bear arms and think that their ubiquitous presence in America makes everyone less safe, but the right to own them is enshrined in the Second Amendment. Instead, other “evidence” was used to infer that they planned violence, including this specific argument that should give everyone pause: > “[…] including their decision to communicate and auto-delete messages on Signal, an encrypted messaging platform widely used among activists, journalists and other citizens wary of government surveillance.” Collectively, the justice department argued that these convictions are proof that anti-fascists are terrorists, which should _also_ give us pause. The precedent here is obviously very dangerous for freedom of speech, freedom of assembly, and democracy in America.

Link: W Social, Public Institutions and the Theater of European Digital Sovereignty, by Elena Rossini Elena Rossini (rightly) calls shenanigans on what’s been happening in the European social world. I think what happened should be instructive for any pro-social technology movement. Here’s what happened: Earlier this month, the European Commission announced a technology sovereignty plan that included a reliance on open source software as a path to autonomy. Eurosky, a non-profit fork of Bluesky that is both fully open source and stores all its data in the EU, subsequently launched Mu, a social media application running on AT Protocol that is fully EU-based and is arguably more fully-featured than Bluesky itself. But the European Commission, including its President and its Central Bank’s President, went another way by migrating to W Social, a proprietary AT Protocol. Whereas Eurosky is a non-profit that has worked extensively in the open with open social web and democratic communities, W Social is a for-profit startup that has been opaque about its intentions and, as Elena now reports, has now pulled its code from being available on an open source basis. These EC profiles now live on a platform that contradicts the EC’s own sovereignty plan. Worse, the founders have a track record of using causes like climate change for their own profit, notably using Greta Thunberg to raise money for a venture capital firm without her knowledge or consent. So I strongly agree with Elena’s implication that the Commission made a poor decision here. But it happened because its founders are heavily connected: it launched at the World Economic Forum in Davos, and its advisors include politicians from across Europe. Ten years ago I joined a top 100 website after working in open source social for a decade. Based on my naïve experience in open circles, I’d assumed it competed on having a great product. In fact, it hired well-connected partnerships people, already known to influential decision-makers, who worked tirelessly behind the scenes. That team included the relatives of Presidential hopefuls and people who had built wildly successful careers as media executives. Having a good product was table stakes at best; being successful meant negotiating politics, making quid-pro-quo deals, and convincing people to join by any means necessary. W Social is the insider’s tool: a platform created people who know how to work the system for their own benefit. That ultimately means it’s more likely to betray its users. It seems likely to me that when the discourse moves away from sovereignty to something else, the founders will also shift. But it’s not a surprise to me that European politicians are more likely to work with a platform that partners with and pays people they already know. The nice thing about open platforms is that there doesn’t need to be one winner. The European Commission has made a bad decision, but Eurosky can still find everyone else. By building better tools for the writers, the artists, the culture-makers, and onboarding people through careful outreach one community at a time, it can serve as the basis for a new social commons that is free from US influence. I hope it succeeds.