...go hang out there with the other link-spammers trying to monetize anti-AI sentiment.

AI Doesn't Simulate Reality.

Congratulations are in order: you have successfully established that MCP is not a conscious mind.

Neither are HTTP, OAuth, JSON-RPC, load balancers, or any of the other infrastructure actually being discussed here. The MCP update removes the initialization handshake and protocol-level sessions, makes each request self-contained, permits ordinary round-robin load balancing, adds header-based routing and cacheable tool lists, restructures interactive requests, hardens authorization, and formalizes extensions. Those are concrete engineering changes addressing concrete deployment problems. None requires a soul to emerge from a server rack.

It simulates fluency. It calculates text. We are living through the greatest psychological gaslighting in the history of technology.

“It calculates text” is reductionism masquerading as analysis. A compiler calculates symbols. A database rearranges bits. A CPU flips transistors. Describing the mechanism at its lowest level does not establish that the resulting representations are meaningless or unreal.

There is substantial evidence that models trained through next-token prediction can develop internal representations of board states, spatial structure, temporal structure, and even human-interpretable latent concepts. That does not establish consciousness or AGI, but it does dispose of the claim that these systems necessarily produce nothing beyond surface fluency. The resulting representation does not become unreal merely because every step that produced it was computational.

And “gaslighting” is doing some heroic unpaid labor here. Gaslighting requires deliberate psychological manipulation. Disagreeing with your philosophy of mind does not qualify.

Every day, Silicon Valley founders stand on stages and promise that if we just feed enough internet data into a massive GPU cluster, a conscious mind will magically emerge from the server rack.

Perhaps some founders have made extravagant AGI claims. The MCP specification does not.

This is a bait-and-switch. The article describes an interoperability protocol for connecting models to tools and data sources. You have replaced that engineering discussion with the most inflated claim you could find somewhere in Silicon Valley, then demanded that MCP answer for it.

It is the philosophical equivalent of crashing a discussion about an HTTP revision to announce that the Internet has failed to become sentient.

They show us models that can write poetry, pass the bar exam, and simulate empathy, and they tell us we are standing on the brink of Artificial General Intelligence.

Even granting your premise that an LLM merely “simulates fluency,” your conclusion runs backward. If a model cannot maintain a sufficiently accurate representation of reality internally, then giving it controlled access to external tools, databases, and authoritative sources becomes more useful, not less.

“AI does not simulate reality” is not an argument against MCP. It is practically the requirements document.

MCP exists because useful systems need access to information and capabilities beyond whatever is encoded in their parameters. You have mistaken the problem the protocol addresses for an objection to the protocol.

They are lying to you. Not maliciously, but because they have confused the map with the territory.

That is rhetorical packet corruption.

Lying implies deliberate deception. Gaslighting implies it even more strongly. Someone who has confused the map with the territory is mistaken, not lying. Pick an accusation and establish it.

You have not criticized the protocol, its architecture, its security model, or a single change in the new specification. You have simply hung an anti-AI placard on an unrelated technical thread and supplied an X link for anyone who wants the rest of the sermon.

Every time you speak to another human being, two cameras and two directional microphones are already pointed at you. They are called eyes and ears. So the mere presence of sensors in a pair of glasses does not create some unprecedented privacy apocalypse. The real discontinuity is simpler and far more dangerous: carbon forgets; silicon does not. The problem with wearable AI is not that it can see and hear. The problem is that it may never forget.

Human memory is lossy, selective, contextual, and unreliable. Wearable AI can preserve exact language, identify speakers, timestamp encounters, correlate remarks across years, and retrieve a contradiction while the other person is still speaking. What was once an informal conversation can become permanent, searchable evidence.

Keeping the processing local would solve some very serious problems. It could prevent Meta, Apple, OpenAI, advertisers, data brokers, and governments from quietly accumulating everyone’s conversations in centralized databases. That matters. But it would not solve persistence itself. A private archive is still an archive, and the people inside it may never have agreed to become permanent entries in someone else’s searchable memory.

I say that as someone who strongly supports wearable AI and can imagine an immediate use for it. I would love glasses that could recall my previous conversations with someone, compare what they are saying now with what they said before, and help me identify equivocation, rhetorical sleight of hand, or a logical fallacy in real time. Having that capability in a coffee shop or at a political rally would be enormously useful to me.

But I understand the social friction that creates. Human relationships evolved around imperfect memories; introducing a memory system that never forgets creates a profound asymmetry. One participant would be relying on ordinary, fallible human memory. The other would be carrying a complete evidentiary history of the relationship, backed by an LLM capable of searching, comparing, and prosecuting every inconsistency.

Perfect retrieval is not perfect understanding. People change their minds, test ideas, speak imprecisely, joke, and respond to different contexts. LLMs can also hallucinate, misread context, and state questionable conclusions with unnerving confidence. That is why they remain tools rather than replacements for human judgment. I can conduct a smell test of anything an LLM regurgitates. Until an LLM can reliably do the same, it remains a really smart, really fast intern that still makes intern-level mistakes.

And that capability cannot be legislated away or penned up in some corporate patent stable. The technology will not remain confined to whatever boundaries governments or corporations draw around it. Once the cameras, microphones, radios, storage, and local models exist in a commodity device, technically capable users will repurpose them.

I was a CS undergrad when Phil Zimmermann gave the world PGP. It was just f'ing funny when the US government discovered that mathematics does not climb back into the box when ordered. Wearable AI will follow the same general pattern. The hardware may be harder to copy than software, but the capability will diffuse across the planet, the tools will improve, and LLM-assisted development will continue lowering the expertise required to stitch the pieces together.

I think Stanley Kubrick understood this before most of us had computers on our desks. The bone in 2001 was not merely a tool for obtaining dinner. Almost immediately, it became a weapon, a status amplifier, and a means of domination. The lesson was not that tools are evil; it was that any technology which extends human capability also extends human intention.

Perfect recall of every conversation is not merely a memory aid. It is immense social leverage. That leverage can be weaponized by bad actors or converted into a predatory business model by corporations. The question is not how we shove the technology back into the box, but how we mitigate those intentions without crippling the capability itself.

In a bucket, I do not think the answer is to reject wearable AI. That is neither realistic nor desirable. The answer is to regulate the things that can still be regulated: make persistent retention an explicit and disclosed act rather than the invisible default, require unmistakable notification, attach serious penalties to covert misuse, protect spaces where recording is prohibited, and prevent manufacturers from quietly converting ambient life into corporate training data.

Privacy law needs to distinguish between a machine that remembers what its wearer witnessed and a machine that allows its wearer to perceive anything at all. A blind person’s camera may be functioning as an eye, and a deaf person’s microphone may be functioning as an ear. We need to regulate retention, identification, transmission, and misuse without legislating those people back into darkness or silence.

We probably cannot make persistent memory technically impossible. What we can do is make it socially visible, legally consequential, and commercially abnormal, even if we cannot prevent every abuse.

So take Torvalds' advice and walk away, friend. The immediate subject here is Sashiko, an AI system that reviews patches submitted to the kernel. It does not write patches, merge patches, or waive the kernel's standards. You have silently replaced that subject with an imaginary “slopped Linux kernel,” then left reality behind. Sashiko's own documentation describes a multistage review system whose final product is an ordinary LKML review message, not code smuggled into the tree.

I wouldn't say pragmatic, no.

“No” is not an argument. Torvalds evaluated a tool, found that it produces useful results, acknowledged its failure modes, and concluded that the answer is to manage those failure modes rather than prohibit the tool. That is almost a dictionary illustration of pragmatism. You may dislike his conclusion. You do not get to redefine the word merely because it reached a destination you dislike.

He's tried it, found it works, and now won't listen to the people pointing out the numerous problems with it.

Torvalds explicitly mentions maintainer workload, bad output, imperfect results, economic uncertainty, and the embarrassment of having tools uncover bugs humans missed. His stated objective is to make these tools help maintainers rather than burden them. So your evidence that he “won't listen” is a post in which he explicitly lists the problems and discusses what should be done about them? You are impeaching him with evidence that contradicts your own assertion.

The slopped Linux kernel may well be a violation of the copyrights of several unknowns in Europe, for example.

“Slopped” assumes the conclusion before you have identified a single defective patch. “May well” supplies insinuation without assuming the inconvenience of evidence. “Several unknowns in Europe” supplies neither a copyrighted work, an allegedly infringing kernel passage, a claimant, a contributor, nor even a coherent theory connecting Sashiko's review comments to infringement.

Sashiko is reviewing human-submitted code. You have switched from “an LLM commented on this patch” to “the kernel contains copied LLM output.” Even where AI-assisted code is submitted, kernel policy does not confer some mystical robot exemption. A human must review it, ensure license compliance, sign the Developer Certificate of Origin, and accept responsibility for the contribution. Maintainers can apply extra scrutiny or reject it outright. The DCO is not a magic anti-lawsuit talisman, but it demolishes your pretense that Torvalds has ordered the gates opened and dismissed provenance as somebody else's problem. Bring us an actual patch and an actual infringement allegation. Until then, “unknown Europeans may object” is pure insinuation.

If you're not planning to build a Linux-based product in Europe, not a big issue I guess.

You have not established a European violation, so you cannot derive business advice from one. Geographical hand-waving does not transmute “perhaps some unknown person owns something somewhere” into evidence. The word “Europe” is not an incantation that relieves you of identifying what was copied.

Not an issue in the US.

One sentence ago, the law was apparently so subtle that everyone else was irresponsibly overlooking international complexity. Now the copyright status of an unspecified patch, containing unspecified material from an unspecified source, has been conclusively resolved for the entire United States in one sweeping sentence. That is jurisdictional Mad Libs, not a coherent argument.

But it's amazing how many people think US copyright law is the only type of copyright law.

Who said that? Torvalds did not. The Sashiko devs did not. The GP did not.You manufactured a provincially ignorant opponent because refuting that strawman is easier than demonstrating infringement in the Linux kernel. Announcing that foreign laws exist is not a substitute for applying one of them to an identifiable set of facts.

And that's before we get to changing the entire nature of Linux so it's no longer a project understood by human beings.

An AI reviewer does not make the reviewed C code incomprehensible. It produces comments about a diff. A human still submits the patch and accepts responsibility for it; humans still review it, humans still test it, and humans still decide whether it enters the tree. Kernel guidance is explicit: submitters are expected to understand and defend everything they submit. If they cannot, maintainers are entitled to reject the series without detailed review.

As for your prophecy that Linux will cease to be understood by human beings, Hitchens’s razor applies: what can be asserted without evidence can be dismissed without evidence. Consider it etched in silicon.

A compiler does not make C unknowable. The kernel already uses Coccinelle semantic patches to detect problematic patterns and generate complex, tree-wide changes. Nobody claims the resulting code has ceased to be understandable by humans merely because software helped produce the patch. And the kernel does not become occult because one of its reviewers is silicon. “Humans might stop understanding Linux” is a legitimate risk to watch for. It is not evidence that this has happened, and certainly not evidence that an automated reviewer caused it.

This is Bitkeeper all over again.

BitKeeper was proprietary source-control infrastructure used to manage kernel history. Sashiko is an optional review tool that comments on proposed patches. BitKeeper occupied the repository-management layer. Sashiko supplies another opinion during review. BitKeeper's continued availability depended upon a commercial vendor's license. Sashiko's code is Apache-licensed and supports multiple LLM providers. If one provider disappears, Sashiko can be pointed at another; the kernel development infrastructure remains exactly where it was.

The only overlap between these two different tools is that “Torvalds approved a tool.”

That was the last time Torvalds made a "pragmatic" decision, effectively locking out a large number of kernel devs from kernel development, until he was forced to build Git to replace it.

That assertion is so reality-warping it has a Schwarzschild radius. Kernel contributors were not required to use BitKeeper. Contemporary kernel documentation explicitly stated that no developer was required to use BitKeeper to contribute. And the official Git history says Git followed the breakdown of the vendor relationship and revocation of BitKeeper's free-of-charge status, not some mass expulsion of non-BitKeeper developers. I was there; I watched it happen in real-time. The arrangement ended when BitKeeper's free-of-charge availability was withdrawn after the relationship with its vendor broke down. Torvalds and the community then created Git using lessons learned from BitKeeper. The official Git history describes exactly that sequence, completely contradicting your revisionism. Furthermore, calling this “the last time Torvalds made a pragmatic decision” requires us to believe that the top-level maintainer of the kernel has made no pragmatic technical decisions since 2005. That claim collapses under its own theatrical weight.

If anything, BitKeeper demonstrates the opposite of your intended lesson: Torvalds adopted a tool because it improved development, tolerated ideological criticism while it remained technically useful, and replaced it when the dependency became untenable. The result was Git. That is not an indictment of pragmatism. That is pragmatism successfully handling both adoption and failure.

Ultimately, you have not demonstrated slop, infringement, developer lockout, or the disappearance of human comprehension. You have substituted an epithet for a quality analysis, hypothetical claimants for an infringement case, a slippery slope for evidence, and BitKeeper for a relevant analogy.

Torvalds did not say that AI output gets a pass. He said AI is a tool, and that contributions will continue to stand or fall on technical merit. That is the whole point you are trying to obscure. If the code meets the kernel's standards, it is not “slop.” If it does not, reject the patch. That is the standard we use for human work, compiler-generated work, transformation tools, static-analysis fixes, and every other contribution.

Demanding that one disliked tool be judged by tribal affiliation instead of its output is not a tenable position. Your whole post is an attempt to import your anti-AI ideology into the review process, despite Torvalds's explicit rejection of ideological vetoes over technical merit.

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.

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.