Notable links: July 3, 2026
Most Fridays, I share a handful of pieces that caught my eye at the intersection of technology, media, and society.
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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.
Discussion in the ATmosphere