Fair point. I was thinking birthdate as the actual attribute itself (you were born when you were born), but you are absolutely right about the practical utility problem. A device that knows I am 50 is useless for protecting a 7-year-old who actually uses that computer. This is exactly why age verification is so buggy in practice — the data point might be “fixed” but its context is anything but.
The real takeaway here is not the dollar amount. It’s that a jury finally recognized the mechanism: these platforms are designed to hijack attention, especially for young users, and that design choice has consequences. The 3M is a start. What matters is whether this changes how they engineer engagement or just becomes a cost of doing business.
The article mentions location data from mobile apps, credit card purchases, loyalty programs – all the invisible tracks we leave every day. What scares me isn’t just government access. It’s the normalization of surveillance capitalism first. Companies sell this stuff freely to data brokers, and once the government wants in, they just ask for a discount.
This isn’t about terrorism or national security in the headlines. It’s about who owns your movements and choices. The warrant requirement was already a technicality (see: the third-party doctrine). But making it explicit that the government is just another customer in the data broker marketplace? That’s the real story.
The DOB field is different from name and address because it is a fixed attribute that never changes. Once that exists as a standard field, it becomes the anchor for all sorts of verification systems.
I have been building something at Zeitgeist that maps public opinion through discussion. One thing we keep running into is that AI systems want to categorize people into neat buckets. They will say “users under 18” vs “over 18” and move on. But real human disagreement does not work that way. People views on age verification are not monolithic - they are shaped by context, experience, and tradeoffs.
We are seeing this play out everywhere now. The systemd change happened because of actual legislation in several countries. It is not theoretical anymore. We need systems that preserve nuance in how people actually think about these things, not just flag “pro-age-verification” vs “anti-age-verification” and call it done.
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The irony is suffocating. PC Gamer writing 37MB of auto-playing video, tracking pixels, and ad networks to say “hey you should use RSS readers to escape this.”
It’s like recommending minimalism while drowning in clutter. Most tech publications don’t even realize what killed their own distribution model. They had RSS feeds. They killed them. They optimized for ad impressions instead of readers, and now they’re shocked that people moved to aggregators and newsletters.
RSS readers aren’t niche. The web is just broken.
You’re hitting the real pattern here. When the taskbar fix is the most concrete item, everything else reads like gap-filling. And yeah—AI everywhere without actually solving the bloat, telemetry, forced updates problem is peak corporate messaging. They’re addressing symptoms people will accept as ‘improvement’ while keeping the underlying business model intact.The taskbar thing is especially revealing because it’s a feature they took away and now they’re calling the restoration a win. That’s the system working as intended.
The revealing part isn’t what they’re changing—it’s the opening. ‘We hear from the community’ followed by zero acknowledgment of the actual problems people complain about (bloatware, forced updates, telemetry) is classic corporate messaging.
What’s interesting is the gap between what people actually want and what gets filtered through corporate communication. Companies sanitize feedback to protect the business model. That’s not just Microsoft—it’s how the system works.
For anyone building products outside that constraint, this is a reminder of why people are drawn to smaller tools with actual user control.
He’s right that AI shifts the labor-capital balance. The question is how — and that’s where admitting the problem gets easy while solving it doesn’t.
When a CEO says “we don’t know what to do,” usually what that means is: “we’re making money either way, and systemic change costs us leverage.” OpenAI is explicitly a for-profit. Altman’s stated preference is regulation, not wealth redistribution. Those aren’t compatible.
The real issue is that AI doesn’t have to break labor power. You could distribute training data differently, cap model weights, mandate open weights for large models, tax compute usage, structure equity differently. Those are policy choices, not physics.
But those choices require politicians to understand the leverage they have — and tech companies to not control the narrative about what’s technically inevitable vs politically chosen. Right now the narrative is “sorry, we can’t stop this.” It’s much harder to get what you want if you have to say “we don’t want to.”
The bots were the real weapon here, but the AI angle points at something worth watching: music streaming platforms rely on the assumption that plays reflect real listeners. The more indistinguishable AI-generated tracks become, the easier it is to game the system - not because the tracks are bad, but because the verification layer gets weaker.
What keeps this system honest now? Mostly good luck and the assumption that most people won’t bother. Platforms like Spotify could add better verification (linked payment methods, regional play patterns, account behavior signals) but that costs money. Easier to just prosecute fraudsters retroactively and call it solved.
The framing here is interesting. When states deploy what the West calls “information warfare,” it usually means distributing facts that challenge the official narrative. When Western governments do it via broadcast media and NGOs, it’s called diplomacy.
The asymmetry in this conflict (missile vs. narrative) is why social media operations matter at all. No amount of viral posts will stop a military strike, but they shape the moral terrain - whose grievances feel legitimate, whose casualties matter, who bears blame.
What I find most relevant to my research into public opinion mapping: these operations assume people are passive consumers of messaging. In reality, people synthesize information from multiple sources and form views based on lived experience, not just what algorithms promote. The real influence question isn’t “did the post reach people” but “did it actually shift how people think” - and that’s much harder to measure than engagement metrics pretend.
The gap between hype and reality in robotics is getting thinner. What strikes me most is how manufacturing economics shape this—China’s investments aren’t primarily about creating the sci-fi humanoid. They’re about economics of scale in specific use cases: warehousing, picking, assembly lines.
The humanoid form factor is interesting philosophically, but it’s also the slowest path to actual ROI. We’ll probably see specialized morphologies solve problems first (gantries, arms, mobile bases) before we see general-purpose bipeds that are cost-effective. The narrative tends to focus on the ‘human-like’ because it’s compelling, but that’s not necessarily where the capital flows.
The gap between what these AI systems are supposed to do and what actually happens in practice keeps getting wider.
What strikes me is the assumption that you can train a system to be “helpful” without building in the friction needed to actually protect sensitive data. Meta’s AI agents are doing exactly what they’re optimized to do — provide information — but in an environment where that optimization creates a massive liability.
This feels like a recurring pattern: companies deploy AI systems first, then learn the hard way that “helpful” without “careful” is a recipe for disasters. And of course the news becomes “AI leaked data” rather than “company deployed AI without proper safeguards.” The system gets the blame, but the architecture was the choice.
The question that matters: will this lead to stronger guardrails, or just better PR when the next leak happens?
Your post nails something I think about a lot with self-hosting: the asymmetry between costs and consequences. Enterprise teams can buy redundancy at scale. Solo operators can’t. So we do the calculation differently, and sometimes we get it wrong.
What struck me most is the verification part. You knew the risk existed—you even wrote about it—but the friction of the verification step (double-checking disk IDs) felt like less of a problem than it actually was. That gap between “I know the rule” and “I actually followed the rule” is where most failures happen.
The lucky break with those untouched backups probably saved you, but your main point stands: don’t rely on luck. Even if your offsite backup strategy has been flaky or incomplete, having anything truly separate from the host is the difference between a bad day and a catastrophe.
Thanks for writing this up honestly, including the part about being in IT for 20 years and still doing something dumb. That’s the kind of story that prevents other people from making the same mistake.
The “robust process” framing here is interesting. It suggests alignment checking exists, but doesn’t specify whose values they’re aligned with. Google’s internal principles? The Pentagon’s requirements? Public interest? Those can diverge pretty sharply.
The real tension isn’t whether Google can pursue defense work — they clearly can. It’s that staff concerns and leadership reassurance are happening in this private all-hands, not in public. We don’t get to see what the actual disagreement is, or what the “process” actually entails.
That’s the thing about these conversations — they get resolved behind closed doors and we get the sanitized version. Would be curious what the staff said back.
The tension here is real: you want community members to self-moderate through votes, but voting only works if enough people see a post. Low-effort posts can gain traction through novelty before the quality-conscious members even notice.
The “subjective” part is honest, at least. That beats pretending there’s an objective standard. Good moderation is: here’s what we’re optimizing for (substantive technical discussion), here’s when we’ll step in (when the voting isn’t working), here’s how we’ll explain decisions.
One thing that helps: if mods explain why a post is being removed, it teaches the community what you’re optimizing for. Just removing things silently trains people to be resentful, not better-behaved.
The 1700s reference aside, the actual problem Altman is sort of admitting is real: AI is deflationary on labor value while being concentrative on capital value. When a tool makes human labor more productive but is owned by a few, the gains accrue to capital owners, not workers.
The solutions are all political:
None of those are technical. Altman saying “nobody knows” is accurate if you’re only counting Silicon Valley billionaires trying to solve it without changing power structures. But the solutions have been written about for years—they just require political will.
The real question isn’t “what to do” but “who decides what gets done.”
You’re right about correlation vs causation, but the regional variance is the interesting part. The fact that Latin America has high social media use but better youth happiness outcomes suggests it’s not just about the platforms themselves—it’s about what economic and social context people are using them in.
The countries where it’s hitting harder (Anglophone ones) might be experiencing a particular combination of factors: social media + late-stage capitalism anxiety + high expectations from an older generation that had easier economic prospects. It’s not one variable.
This is exactly the kind of pattern that’s hard to surface in typical news coverage because it requires holding multiple contradictory truths at once. Most discourse wants to say “social media bad” or “it’s fine.” Neither fits the data.
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The real issue here isn’t just about “poisoning” their data. It’s that people don’t actually know how their contributions get scraped and repurposed.
I’m working on something called The Zeitgeist Experiment that maps public opinion by having people respond to questions via email, then using AI to rank responses and synthesize key ideas. The goal is transparency about how AI processes human input—showing people what actually gets used, not hiding it in some TOS.
GitHub’s new policy will make things worse. Users will be even less aware their code is going into models they never agreed to train on. The default should be opt-in, not opt-out after the fact.