If users create little interactive toys and shove them into their friends’ feeds, Meta gets more feed inventory, more engagement, more impressions, and more chances to drop sponsored units into the attention stream. You got that part right. But the ad does not have to be literally stapled to the gizmo. The gizmo just has to keep users’ eyeballs fixed in the feed for one more pass through the variable-reward slot machine. Same dopamine-tuned engagement model as TikTok, YouTube, Instagram, and the rest of the scroll-farm economy.

And I would not let the prompt/data angle off the hook, either. FB has already said it can use interactions with generative AI features to personalize content and ad recommendations. So FB’s business model becomes: users generate the bait -> users distribute the bait -> FB monetizes the attention around the bait -> the interaction telemetry improves the next round of bait. Wash, rinse, optimize, repeat. That is not copyright theft. It is much more boring, much more scalable, and much more on-brand for Zuckerberg.

“Effectively theft” is a great way to waste a useful point by stapling it to a strawman.

Meta does not need to own everything users create for this to be a bad deal. In fact, the Pocket terms point straight back to Meta’s standard deal: users retain their IP rights, while Meta gets the license, platform control, engagement data, and AI-improvement exhaust. That is not copyright theft. It is surveillance capitalism with a toy-box UI.

And that distinction matters, because Slashdot is full of pedants with calipers. Say “Meta steals your copyright,” and the thread immediately bogs down in ownership, licenses, assignment, fair use, AI authorship, and whether prompt output is even copyrightable in the first place. Meanwhile, the real criticism quietly escapes through the server-room vent.

The real criticism is simpler and nastier: Pocket turns user creativity into product telemetry. Prompts, revisions, remixes, plays, shares, camera/mic-adjacent interactions where users grant permission, abandonment points, and feed behavior. Meta does not have to pick your Pocket (nice analogy, btw, if a bit misguided) by taking title to your idea. It can just watch millions of people prototype tiny attention traps and then build a behavioral graph from the telemetry. That is the gold they are farming, not IP rights

So yes, distrust Meta. Absolutely. But do not replace threat modeling with pocket puns and call it analysis. Strawmen are bad arguments anywhere; on Slashdot they are worse, because they give the opposition a nice clean target while the actual monster keeps eating the village.

“Malware Gizmos” is a nice scary phrase, but it is doing a lot of unearned work here.

These are not random APKs being passed around the cafeteria with “install unknown sources” turned on. Pocket gizmos are AI-generated interactive experiences running inside Meta’s app/feed, under Meta’s account system, Meta’s Community Standards, Meta’s reporting/removal process, and the mobile OS permission model. That does not make them safe. It does make the “new STD” metaphor sound more like Slashdot drive-by trolling than threat modeling.

The real risk is not that Grandma is going to catch a polymorphic worm from a dancing cat gizmo. The real risk is that Meta now has another low-friction funnel for collecting prompts, edits, remixes, interaction patterns, camera/mic-adjacent behaviors where users grant access, and engagement telemetry. That is the actual trap. Not vibe-coded malware.

By all means, distrust Meta. I do. Meta has earned every ounce of side-eye it gets. But if we are going to criticize the thing, criticize the thing that exists: an AI toy-feed wrapped around data extraction and engagement optimization. Calling it malware because “AI bad” just lets Meta defenders dismiss the valid critique as drive-by Slashdot hysteria.

What Meta is almost certainly farming is user intent.

Prompts. Edits. Remixes. Likes. Abandonment. Which tiny games get played, which ones get shared, which mechanics make people tilt the phone, which ones ask for camera/mic access, which ones train the next interaction model, and which ones can be shoved into a feed until the dopamine lever squeaks. Pocket looks less like vibe coding in the programmer sense and more like TikTok plus Roblox plus a telemetry tap directly into Meta’s AI stack.

So yes, be suspicious of Meta. That part is just good hygiene. But “AI bad” is the lazy Slashdot version of thinking. The real critique is more specific: Meta is trying to turn user creativity, user attention, and user interaction traces into training data and engagement inventory. The gizmo is the bait. The behavioral graph is Meta's real target.

Basic question since I have not researched Nuclear power in a long time. The last time there was a big push and building of nuclear reactors the main problem was the spent fuel rods. The response back then was we will have developed the tech to deal with it in the future when the rods are used up. Has this happened? or are we still doing the bury it in the earth until the future when we have developed the tech to deal with it?

First, apologies to Motown, Edwin Starr, Norman Whitfield and Barrett Strong. But this is obligatory... :)

  Q-C (What Is It Good For?)

Q-C! (Huh, yeah!)
What is it good for?
Well, it ain't good for nothing!
(Uh-huh!)
Q-C! (Huh, yeah!)
What is it good for?
Just don't hype the scaling!
Listen to me...

Q-C... it ain't nothing but a heartbreaker.
Q-C... friend only to the dilution refrigerator!
Oh, you wanted perfect gates, but Physics gave you error rates,
An RF knife fight with a pipe organ, sealing your fate!
I said...

Q-C! (Huh, yeah!)
What is it good for?
Simulating chemistry!
(Absolutely!)
Q-C! (Huh, yeah!)
What is it good for?
But Physics is the border guard!
Say it again, y'all!

Q-C... I despise,
The impossible roadmap with the confident fonts and lies!
They think it tries every answer all at once, it's true,
But a millikelvin chip ain't a magic GPU!
I said...

Q-C! (Good God, y'all!)
What is it good for?
Well, it ain't good for nothing!
(Listen to me...)

Q-C... has shattered many an engineer's dreams,
Made VC's invest in fault-tolerant schemes.
But amplitude ain't permission, and interference ain't omniscience,
Measurement is where the magic trick meets physics' strict conditions!
Lord knows...

Q-C! (Huh, yeah!)
What is it good for?
Material science!
(Uh-huh!)
Q-C! (Huh, yeah!)
What is it good for?
Just don't hype the scaling!

Q-C... hear me today,
The traveling salesman still has to travel anyway!
The exponential search space is grinning in the tall grass,
While a classical processor kicks your millikelvin ass!
I said, Q-C!

This is just numerology masquerading as evidence. When the opening move is “wild off the cuff guess” and the next move is a percentage, that is not data analysis. That is a vibe cosplaying as rational thought. Training data quality is a real problem. So are filtering, curation, retrieval, evals, domain tuning, and human review. “The web contains garbage” does not prove that models cannot extract useful structure from it. Compilers consume source written by humans too; somehow LLVM survived Stack Overflow.

Now you are just playing bait-and-switch with definitions. For AGI, say AGI. For generative AI, the ship has already left port. And “parrot or person” is the false dichotomy at the center of the whole performance. LLMs do not need to be conscious citizens with library cards to be useful systems. The question in this thread is not whether ChatGPT has a soul. The question is whether particular copying, training, output, and platform design cross copyright lines. Your parrot/person routine is theatrical fog, and when it's this thick and obvious, it is usually because somebody is trying to obscure their bias.

Nice product-market strawman. “Producing content” is not the only use case, nor even the most interesting one. Summarizing 80 pages, interrogating logs, turning vague specs into tests, translating formats, drafting boring boilerplate, extracting facts from PDFs, building code scaffolding, and helping users query systems in natural language are not solved by shouting “more content!” Databases were not pointless because we already had files. When new technology disrupts an ecological niche, the people pushed out of the niche start lining up strawmen in defense. Your argument here is older than the buggy whip makers who whined that automobile makers were killing their art.

So...one business model treated as destiny. Treating ad-tech as the only viable business model shows a massive lack of imagination. Google-style ads are not how enterprise software scales, and are not the only way LLMs makes money. Enterprises buy latency reduction, workflow automation, support deflection, developer acceleration, compliance review, document analysis, and boring internal productivity nobody will ever put in a Super Bowl commercial. Your Copilot adoption anecdote may be true in your shop, but an anecdote is a packet capture from one subnet. Generalizing from that to the whole network is bad telemetry.

More false dichotomies, this time it's tool use recast as moral failure. The choice is not AI dross versus independent genius. The choice is bad judgment versus good judgment. Good people already use search engines, spreadsheets, IDEs, compilers, code review, dashboards, staff work, LLMs, and lawyers. A leader who pastes "samey" AI sludge is weak. A leader who uses AI to explore options, pressure-test assumptions, and communicate faster has not been compromised. The bottleneck is still judgment. A Hollywood director who uses AI to storyboard five different versions of a scene is still being creative; he is leveraging the AI samey-ness you are decrying to get a more fine-grained look at the scene before he commits resources to filming it. That is a win for both the audience and the bean counters back at the studio HQ.

Spare us the sermon. These are real issues; nobody denies that trust, provenance, validation cost, confidentiality, copyright, and vendor terms are all real deployment concerns. They belong on the checklist. But welding them to “employees getting dumber” is no different from when my high school principal tried to forbid calculators and had to be taken to court to make him join the twentieth century. That was during the Carter administration, btw -- your argument is fifty years old. We already review junior work, consultant work, SQL migrations, security patches, vendor output, and code changes. The adult answer is measurement, sandboxing, governance, and evals, not declaring every inference engine a cognitive tapeworm.

And this is where the actual legal story matters. The Cox decision makes it harder to pin contributory liability on a provider merely because it knew infringement was happening downstream. Intent matters. Inducement matters. Tailoring matters. That is why NYT is now aiming at Microsoft’s alleged role in building and operating the machinery, not just shouting “AI cloud bad.” That is the real fight. Not parrots. Not personhood. Not whether Bob in accounting wrote a dull memo with Copilot.

“Nope” is not an argument. It is just a vibe with a punctuation mark.

You make the accidental good point. Specialize. Keep sensitive data controlled. Avoid lock-in. Prefer vetted corpora. Use local or open models where they fit. Put APIs behind abstraction layers. Maintain exit ramps. Do human review on high-stakes output. Yes. Absolutely. That is not an anti-AI argument; that is ordinary systems engineering and vendor hygiene with the cloud invoice scotch-taped to the front.

So, in a bucket: you took a narrow copyright-liability story and stapled to it every anti-AI talking point from the break room bulletin board. Some of your concerns survive cross-examination, but most of the big claims you are making die from false dichotomy, anecdote laundering, and category confusion: AGI, GenAI, search, copyright, trust, productivity, and cloud lock-in all blended into one rhetorical smoothie. The law is asking whether Microsoft induced infringement or tailored services to it, and you hijacked that into whether parrots can be people.

True, but only in the narrowest textbook sense. It is also entirely irrelevant to traffic safety research. Unless your standard for causality requires a double-blind trial where researchers randomly assign pedestrians to be struck by different classes of vehicles, observational studies with robust regression controls are the gold standard.

Exactly. Congratz on successfully regurgitating the first paragraph of your Inferential Statistics 101 text book. Which is why the actual researchers didn't do that. The IIHS study didn't just yell "trucks bad" at a spreadsheet. They used NHTSA crash data, fatality data, vehicle measurements, and registration data, applying regression models to control for confounding variables. They looked at nearly 18,000 single-vehicle/single-pedestrian crashes and still found that tall, blunt front ends substantially increased pedestrian fatality risk.

Maybe, indeed. Those are excellent questions. But "maybe" is doing a lot of heavy lifting for you here. If you want to claim that tall hoods have saved tens of thousands of occupant lives to offset the pedestrian fatalities, the burden of proof is on you to provide that data. Hitchens's razor applies: what can be asserted without evidence can be dismissed without evidence. I'm with Hitchens here: Put up, or STFU.

And about those blind-spot indicators -- they don't solve what is fundamentally a geometry problem compounded by physics. Blind-spot indicators address adjacent-lane awareness; they are not a magic amulet against the front blind zone created by a 45-inch hood. Furthermore, a dash-cam or an AEB sensor does not change the impact mechanics when a collision inevitably occurs. A tall, blunt front end hits higher on the center of mass, driving the pedestrian down under the wheels rather than up onto a deformable hood. You cannot solve a kinetic energy transfer problem with a warning beep.

Righhhhht. Dismissing controlled regression analyses because you prefer a hypothetical "maybe" is certainly a choice, but it isn't a compelling counter-argument.

If I had mod points for this thread, you would be at plus five funny... :)

The off-center driver position is not the bad design choice here. That part is mostly boring old human-factors engineering. You are on the right track when you talk about a centered view with more near visibility. You just need a slightly bigger picture. In right-side traffic countries, the driver sits on the left because that puts the driver’s eyes closer to the centerline of the road, which improves sight lines for oncoming traffic, passing, lane placement, and not shaving the paint off opposing vehicles. The view you are optimizing is not the center line of the vehicle, but the center line of the road it is travelling down. In left-side traffic countries, you just mirror this. The “best” side is whichever side puts the driver closest to the middle of the road.

With that said, to bring this back to your question about tall and long hoods—the defenders of the modern brodozer will inevitably point to two things: physics and the EPA. They aren't entirely wrong. Pushing modern trucks to absurd 15,000-lb towing capacities requires massive radiators, which dictate taller front ends. Furthermore, the EPA's CAFE footprint rules actively incentivized automakers to bloat vehicle dimensions to qualify for laxer fuel economy targets. But while engineering requirements and federal loopholes provided the massive canvas, they don't explain the aggressively hostile design language painted onto it.

The aesthetic decisions behind these front ends flow from a fairly straight-forward psychological model of the truck-buying American public. They are based on a phenomenon that social psychologists call "status signalling compensatory consumption." Study after study show that an economically significant chunk of the male demographic buys status-signaling products to patch perceived deficits in their social power, status, identity, or masculinity. Marketing departments didn't accidentally discover that pickups sell better when wrapped in dominance cosplay, cliff-face grillework, and ad copy that smells faintly of elk musk. These front ends aren't optimized for pedestrian safety, or even aerodynamic efficiency; they are optimized to extract $80,000 from buyers desperate to project the authority they feel they lack.

“Obscuring negative associations” is doing a lot of unpaid labor there. First establish the association. Then establish that reliable sources treat it as significant. Then establish that Wikipedia is suppressing it rather than applying due weight. Until then, this is Russell’s teapot with a swastika armband.

You are right that science has no obligation to give every crank theory equal time. Climate denial does not become a coequal scientific position just because someone demands “balance.” That is exactly what sensationalist media and cable news (looking at you, Fox News) has been doing for decades—fooling low-information viewers into thinking crackpot conspiracies belong on the same stage as rigorous scientific research. On that point, you'll get no argument from me.

But you are smuggling in a false dichotomy. The alternatives are not “scientific truth” on one side and “social-media shouting” on the other. Science is not Twitter with lab coats, but it is still a social process. Peer review, replication, conferences, journals, grant fights, disciplinary norms, consensus formation, and paradigm shifts are not decorative plumbing. They are the machinery.

If you haven't read "The Structure of Scientific Revolutions" by Thomas Kuhn, this a really good point for you to pick it up. If you have read it, then I would suggest you need to re-read it. Science is fundamentally a social process. It was Kuhn’s central insight. He did not show that facts are democratic, or that electrons take a caucus vote before tunneling. He showed that what counts as a good question, a valid method, a decisive anomaly, or an acceptable explanation is mediated through scientific communities operating within paradigms. The objectivity comes from the discipline of that process, not from pretending the process does not exist.

Wikipedia is even more obviously social. It is not a laboratory and it is not a journal. It is a consensus-driven encyclopedia that summarizes reliable sources under rules like verifiability, due weight, and no false balance. That means the climate denial article should not pretend denialism has the same standing as climate science. But it also means “Wikipedia doesn’t debate” is an absurd claim. Wikipedia is practically made of talk pages, RfCs, noticeboards, policy arguments, and edit wars with footnotes.

Truth is not democratic. Fine. But Wikipedia's neutrality is negotiated. If that negotiation is disciplined by good sources and due weight, it can work. If it is captured by factional editing, rule-gaming, or selective enforcement, then calling it “science” does not cleanse the problem. It just gives the hive mind a lab coat.

Fair enough. Then we're not really discussing creativity or artistic merit, are we? We're discussing labor economics. You can't have it both ways, friend.

History suggests they'll absolutely try. Capital has been replacing expensive labor with cheaper tools since the first accountant discovered the abacus. But that's a separate argument. You've constructed a false dichotomy where AI is either worthless "prompt-generated garbage" or a tool for eliminating artists. Those are not the only two possibilities. A24 exploring a third option: AI as a collaborator and creative amplifier.

Let go of your bias for a moment. A storyboard artist who can explore fifty concepts in a day instead of five is still creating. A screenwriter who can rapidly test dialogue variations is still writing. A director who can visualize scenes before committing a budget is still directing.

The tool changes. The creative process remains. You seem to be willfully ignoring this reality in your argument. There is way more to being creative than just coming up with an original thought or vision.

I think you are missing some history here. The modern storyboard was popularized at Disney in the early 1930s when Webb Smith started pinning sketches to a board so directors could experiment with sequences before spending time and money animating them. Drawing fifty sketches was cheaper than animating fifty scenes. It was literally a labor-saving innovation that allowed creative people to iterate faster. It was such a useful hack that it became the industry standard very, very quickly.

And other technologies followed the exact same trajectory -- non-linear digital editing systems, CGI, even Photoshop. Every generation of creative tooling is greeted by predictions that creativity itself is under attack. But being creative is more than just coming up with an entertaining idea. It also includes getting it out there so that an audience can appreciate it. That is what directors do. If you want to monetize it at the same time, fine -- that is what studios are for. You have a very narrow definition of creative, if it doesn't include all the scaffolding that creatives actually need to produce a work that can be shared, for profit or otherwise.

The real question is not whether AI can contribute to the creative process. It demonstrably can. The real question is: When an AI is inserted into the process, who captures the productivity gains? That's a debate worth having. You are welcome to join, if you can stop pretending the only possible outcomes are "AI garbage" or "artist unemployment". Until then, you are missing a much more interesting and relevant discussion.

The interesting part here is not that there are suddenly "responsible AI" groups on both sides of the AI policy binary, but that that everyone with a stake in the debate around AI has discovered "guardrails" as the new magic word.

Look at ARIAM. It is not a grassroots creators' revolt. It is a coalition of incumbent content companies, publishers, and mission-aligned tech firms trying to shape the legal environment around AI. Copyright, attribution, liability, and provenance are real issues; I'm fairly certain Disney, Adobe, the New York Times, Conde Nast, Wiley, the BBC, et al. have not wandered into this debate as disinterested philosophers of human creativity. They have assets to defend, licensing markets to create, and future tollbooths to position. You can bet they are trying to figure out how to plant a cop and a tollbooth between creators who see AI as a collaborator and tool, and the vast catalogs of old media that Big Tech AI companies are already pillaging for training sets.

The Guardrails Alliance has the same problem from the political side. Calling this Super PAC "grassroots" is doing a lot of semantic cardio. A Super PAC aiming to convert the discontent of tech workers into cash donations, launched by political operatives (not tech workers!), with millions already in the barrel, is not exactly a grassroots movement. Again, that does not make its policy goals wrong. But it does mean the "ordinary workers vs Big Tech" framing deserves the same skepticism we would apply if the labels were reversed. Guardrails has not filed their first FEC report, so we don't know (yet) where that $5M in seed financing came from. I'm going to bet it wasn't from a collection of disgruntled coders and studio artists worried that they were being asked to train their LLM-based replacements. I wouldn't be surprised if this is just the AI version of every grievance PAC that apparatchiks on both sides of the political divide have been farming low-information voters with since Citizens United made that kind of grift legal. I could be wrong, but I doubt it.

I think the pattern that is emerging is pretty straightforward. AI policy is not a binary anymore (if it ever was.) AI policy is a multi-sided auction. One side, Big Tech incumbents like OpenAI, Google, Meta, and Anthropic, want broad freedom to scrape, train, deploy, and preempt regulation. Another side wants safety rules that may also conveniently raise barriers to entry and protect existing jobs. A third side wants copyright, licensing, and liability rules that let them safely monetize their media catalogs, old and new, and charge rent for anything they control the IP for. And in the back row of the auction house are the indie devs, open-weight model tinkerers, and local-inference gurus trying to run useful AI on hardware they actually own. At least they are not jumping on the guardrails bandwagon. Yet. :)

Everybody can say "we want guardrails around AI." Everybody can invoke democracy, safety, creators, workers, children, innovation, or national competitiveness. The question is not which faction has the prettiest noun pile. The question is who gets paid, who gets regulated out of the market, and who gets to write the definitions of what LLMs are allowed and not allowed to do.

Let's put the guardrails where they really need to be. Before buying any of the rhetoric, I want to see the donor lists, the advisers, the vendors, the affiliated nonprofits, and the model legislation. "Guardrails" can mean public safety. It can also mean a velvet rope around somebody else’s cash register, or a visit from the copyright police because your ChatGPT prompt created a token string that Disney or the BBC or NYT says belongs to them.

I agree with that much. I am not arguing that NASA historically had a secret government rocket foundry staffed by civil servants in short-sleeve shirts and pocket protectors. Apollo, Shuttle, SLS/Artemis, Orion...all of it leaned heavily on private contractors. That is not my objection.

The distinction I am drawing is between “NASA contracts with industry to build hardware under fairly explicit procurement rules” and “a billionaire-controlled company with no orbital launch history gets folded into a public-private Mars mission whose money flow, data rights, ownership structure, and downstream commercial benefits are not very clear from the public announcement.”

COTS is actually a good comparison point, because SpaceX at least had to hit milestones in a program whose purpose was openly to develop commercial cargo transportation. That was the deal: NASA wanted commercial ISS logistics, SpaceX wanted a launch/services business, and the milestones were the meat grinder that Musk had to sausage his way through.

Here, Relativity has not yet put a rocket into orbit, Terran R still has to prove itself, and now the company is talking about a 2028 Mars orbiter, with NASA instruments riding along, while Schmidt is also interested in orbital data centers and private space observatories. Maybe this is brilliant. Maybe NASA gets a bargain. Maybe Schmidt eats most of the risk.

But that is exactly why I want the numbers and terms visible, and why I am more than a little concerned that they are using a 68 year old paragraph in the congressional act that created NASA to hide those numbers and terms, all under the watchful eye of a billionaire appointed to run NASA by a billionaire President.

If the answer is “NASA pays little, the data is public, Relativity carries the performance risk, and the public gets useful science,” great. Put that on the table. If the answer is “NASA supplies credibility, instruments, mission value, and validation while Schmidt’s company gets heritage for a future private infrastructure stack,” then that is Dr Evil wearing a NASA mission patch.

With that said, I agree with your second point completely: picking a company that has not yet reached orbit for an interplanetary transport job on a 2028 timeline is not exactly a low-pucker-factor decision.