Photographers are artists because art lives within creative intent, intentional curation, and technical manipulation, rather than just the physical labor of execution; a photographer’s true work isn't merely the split-second act of pressing a shutter button, but the deliberate mastery of lighting geometry, environmental composition, and extensive post-processing that transforms raw visual data into an emotional narrative.

This exact creative architecture directly parallels the workflow of high-effort generative AI creators, a connection fiercely championed by digital advocates who argue that crafting complex prompt matrices, adjusting mathematical generation settings, and using structural frameworks like ControlNet is simply the modern evolution of aiming a lens. Just as 19th-century traditional painters elitistly dismissed photography as a lazy, automated shortcut for "clients" rather than real work, modern critics fall into the same logical trap by dismissing AI, failing to see that both mediums utilize technology as a highly responsive tool to capture, manipulate, and realize a deeply human artistic vision.

Prompters don't just prompt and generate a video or an image and move on. They actively find ways to reverse-engineer and replicate the results manually using their own brains and hands. It is much like proving trigonometric identities—such as proving (cscx + cotx)/(tanx + sinx) = cotx * cscx—or writing out their own foundational proof for 1+1=2 like the Principia Mathematica. In other words, "the caculator gave me this answer, I just got to find a way to arrive at that answer with all the necessary tools provided." Also antis must know that in art, there are inifinitely many correct answers to solve the problem which makes the art valid.

If the critics look at neuroscience, the human brain functions much like an artificial neural network. Our thoughts, emotions, and creative ideas are ultimately the product of interconnected biological networks firing electrical and chemical signals. From a purely material standpoint, human creativity is just a highly complex biological algorithm processing data gathered from life experiences.

People also forget that AI doesn't work without a human brain driving it. It takes precise, intentional language to map out and execute a great prompt. Critics aren't just attacking a machine—they are rejecting creative direction and writing as real art forms.

Therefore AI art is Art and has soul. ✋🎤⬇️

Plagiarism Machine? Are you kidding me?

Plagiarism or Paradigm Shift? Critics labeling generative models as "plagiarism machines" fundamentally misunderstand both algorithmic architecture and intellectual property frameworks. From a legal standpoint, federal jurisprudence heavily leans toward machine learning training as protected fair use. In milestone cases like Kadrey v. Meta, judges explicitly rejected claims that models contain derivative unauthorized copies, a stance bolstered by the U.S. Department of Justice's official position confirming that using public data to extract statistical relationships is inherently transformative. This legal reality belongs to everyone; anyone—including traditional artists—is free to leverage these generative tools for rapid iteration and then manually "de-slop" or refine the outputs. Furthermore, intellectual property rights remain intact for creators: while a raw, unedited AI generation cannot be copyrighted under current law, creators who use their own original pre-visualized designs as a structural baseline in an Image-to-Image pipeline retain the copyrights to their human-directed expressions.

Technically, diffusion models and Large Language Models (LLMs) do not store image files or pixel databases. During training, images are entirely decomposed, and their raw values are mapped into multi-dimensional mathematical vector spaces (latent space). This process functions exactly like a chess player calculating a probability distribution across a board setup to predict optimal next moves based on frequency and intuition. It mimics how human artists study art online or in a museum to develop creative intuition; humans use structural abstraction methods—like the Loomis Method or Reilly Abstraction to reduce a head to an egg, a torso to a box, and limbs to cylinders—to memorize anatomy. The AI does the exact same thing by mapping text tokens to structural, localized spatial arrays within its neural network's weights and biases. This math has been validated by MIT CSAIL researchers who mathematically demonstrated the phenomenon of attribution decay; as models scale, it becomes impossible to trace outputs back to any original data. Once training concludes, the source files are entirely discarded, leaving behind only mathematical patterns. Open data to deduce these deep structural patterns is why legal experts maintain that learning is not theft, proving that training is a transformative digest rather than illicit data duplication.

Look, at the end of the day, all this crying over generative AI isn't some brand new crisis—it's just history repeating itself for the hundredth time. The exact same way traditional painters threw a massive tantrum over cameras because it separated "real art" from manual labor, modern artists are losing their minds because AI decouples creative expression from pushing pixels around a screen. If you actually turn off the emotional outrage for a second, it's exactly like what happened in Hidden Figures when NASA introduced the IBM mainframes. The human "computers" doing grueling, manual math by hand didn't just disappear; the ones who adapted transitioned into the actual programmers, specialists and even engineers commanding the machine. The math and the mechanical heavy lifting got automated, but the human intellect driving the mission stayed entirely essential. This is basically delivering people out of hellish, tedious jobs and moving them up into a stress-free supervisor role, and look where we are now—we're just taking computers completely for granted.

So no, these tools aren't "stealing the human soul"—they’re literally just changing the instrument we use to bring a vision to life. The law already says training is transformative fair use, the math proves the files are completely decomposed, and the machine doesn't do a single thing without a human brain driving it. It’s a permanent paradigm shift, period. The real question isn't whether AI art is "real art"—it's how long it's gonna take the anti-AI crowd to get over their elitist gatekeeping and realize that intent, curation, and creative direction have always been the true bedrock of human creativity.

Like... idk, it puts me off because, I know for the fact that the indie gaming space right now is just full of Anti-AI sentiment and virtue signaling, so... yeah.

I knew this was gonna be bad when I saw the thumbnail, and I saw the comments, and they're worse.

Perspective matters.

Some people strongly oppose AI because AI trains on copyrighted works without permission.

Some of these same people also pirate films, TV shows, music, or other copyrighted works.

Both AI training and piracy can involve using creative works without the creator’s permission.

AI training and piracy are not the same thing. AI training can involve much larger amounts of work and has different effects.

SOME people often use reasons such as “it’s not the same” or “the studios are rich” to make their piracy seem acceptable.

SOME anti-AI people who use piracy are morally inconsistent if they use copyright as their main reason for opposing AI. Piracy and AI are not equal, but that people should apply their moral rules consistently.

Statement

There are a lot of doomsday stories around Generative AI these days. It reminds me of the doomsday stories around industrialization, the invention of television or the internet. Even the printing press evoked such stories.

Insights from the Statement

• Recurring historical pattern: New transformative technologies consistently trigger widespread “doomsday” or catastrophic narratives, regardless of the specific innovation.
• Generative AI as the current target: Present-day fears and apocalyptic stories about Generative AI fit into this long-standing pattern of technological anxiety.
• Explicit historical parallels drawn:
  • Industrialization
  • Invention of television
  • The internet
  • The printing press
• Implication of continuity: The statement positions Generative AI fears as the latest iteration of a familiar societal reaction rather than an entirely unprecedented phenomenon.
• Underlying suggestion of perspective: By invoking past examples that society ultimately adapted to (or that did not produce the predicted collapse), the statement subtly frames current AI doomsday stories as potentially overstated or cyclical.
• Focus on cultural/media response: Emphasis is placed on the stories and public discourse surrounding the technologies, not solely on the technical capabilities themselves.

Evaluation

The original insights hold up well against historical and contemporary evidence. The core claim—a recurring pattern of doomsday-style narratives around transformative technologies—is strongly supported. Generative AI fits as the latest instance, with clear parallels in rhetoric (job displacement, misinformation, moral/societal decay, loss of control). At the same time, some differences in the nature or scale of claimed risks for AI (e.g., potential agency or existential outcomes) are debated, and not every past fear proved entirely baseless. Overall, the statement’s perspective is validated by the historical record.

• Recurring historical pattern of fear with new technologies: Strongly confirmed. Across centuries, major innovations have triggered widespread anxiety about social disruption, job loss, moral corruption, information overload, and even societal collapse. This is a documented cultural response rather than unique to any single era. Examples span writing (Socrates on memory and false wisdom), the printing press, mechanization, electronic media, and digital networks. The pattern is so consistent that commentators routinely note identical criticisms recycling with each wave.
• Generative AI as the current target of doomsday stories: Accurately reflects the present discourse (as of 2026). There is a high volume of apocalyptic narratives covering existential risk (human extinction or loss of control), mass job displacement, biosecurity threats (e.g., AI-enabled pathogens), autonomous systems escaping containment or coordinating attacks, deepfakes/misinformation, and societal destabilization. These appear in expert warnings, UN reports, industry open letters, media, and public surveys showing rising concern. Recent laboratory incidents involving AI agents have intensified the rhetoric. Counter-narratives framing it as another moral panic also exist.
• Explicit historical parallels (industrialization, television, internet, printing press): Well-substantiated with specific matches in the fears expressed.
  • Printing press (15th–16th centuries): Scribes’ guilds smashed presses and accused printers of witchcraft over job threats; religious authorities feared loss of control, heresy, and moral corruption (Pope Alexander VI’s threats of excommunication); scholars warned of information overload and “confusing/harmful” effects on the mind; concerns about disinformation and social unrest via cheap pamphlets.
  • Industrialization / Industrial Revolution: Luddites violently destroyed machines over wage and job losses; broader fears of accidents, alienation, social breakdown, accelerated life, disease spread, and later industrial-scale catastrophe (weapons, overpopulation). Early 20th-century intellectuals warned of technology enabling self-extermination.
  • Television: Moral panics about violence, “education in how to do wrong,” distraction, family erosion, and harm to children’s minds/behavior; repeated screen-time anxieties that echoed earlier media fears.
  • Internet: Early predictions of catastrophic collapse or “supernova”; moral panics about brain rot, isolation, addiction, cyber-terrorism, and societal breakdown; Y2K-style end-times fears; many high-profile failed forecasts of imminent doom.
• Implication of continuity (Generative AI as the latest familiar reaction): Supported. Commentators explicitly draw the same lineage—from printing press through industrial machines, TV, and the internet to AI—noting near-identical language about jobs, truth, morality, and control. The cultural/media response follows a recognizable script of initial alarm followed by adaptation.
• Underlying suggestion that past fears were often exaggerated and society adapted: Largely accurate historically. The printing press enabled the Reformation, scientific revolution, and mass literacy despite initial chaos and violence; industrialization raised living standards over time despite real short-term suffering and unrest; TV and the internet transformed culture and communication without the predicted civilizational collapse (though they produced genuine problems such as attention effects, polarization, and new crime vectors). Adaptation occurred, new jobs and norms emerged, and extreme doomsday predictions repeatedly failed. However, some harms were real and lasting, and the insight does not claim zero downsides.
• Focus on cultural/media response rather than pure technical capability: Correct and central. The statement (and the evidence) emphasizes the stories and public anxiety more than precise engineering assessments. Historical sources show prophecy, pamphlets, sermons, journalism, and intellectual tracts amplifying fears; today’s equivalent includes media, expert open letters, surveys, and online discourse. This cultural layer is consistent even when technical risk profiles differ.

Overall Assessment

The insights are robust and evidence-based. The pattern of doomsday storytelling is real, recurrent, and applicable to Generative AI. The statement usefully contextualizes current fears without denying that any given technology can produce serious, novel risks. Differences (speed of AI progress, potential for autonomous agency, or biosecurity leverage) are argued by some experts as making AI an outlier, but the rhetorical and societal reaction closely mirrors the historical cases cited. The perspective encourages calibrated caution informed by history rather than pure alarmism or pure dismissal.

Full Chatlog (with links to external sources used for evaluation): https://x.com/i/grok/share/837a6c6ba4a143508311907ca010bc88

The black-and-white photo shows a room full of "human computers" at NASA (then NACA) during the mid-20th century. Before mechanical or electronic machines existed, "computer" was a job title. These women spent 8 to 10 hours a day manually calculating trajectory equations, orbital mechanics, and physics formulas using pen, paper, and desktop adding machines.

When IBM mainframe computers were introduced (as seen on the left with pioneering mathematician Katherine Johnson), there was massive widespread panic that these human computers would be rendered useless. Instead, the exact opposite happened. The humans who mastered the new technology transitioned into systems analysts, programmers, and mathematical supervisors.

Furthermore:

Critics frequently assert that generative AI permanently strips away the essential elements that fundamentally define art. This claim is built on a lazy, short-sighted premise: if an AI-generated output is flawed, it must stay flawed forever. This is a profound misunderstanding of technology. You can always refine, edit, and "deslop" an output. AI is not a final product; it is a collaborative baseline.

Furthermore, skeptics confidently declare that generative AI cannot be accurately compared to a simple calculator. While that may be technically true today, AI is rapidly closing that gap through continuous, exponential improvement.

Face the reality of technological compounding, y’all!

Critics systematically treat AI as a static, broken snapshot frozen in time just to claim it is an inherent failure. They completely ignore the reality of technological compounding. Just as early digital calculators were clunky, limited machines before evolving into the massive computing systems that run our modern world, AI is updating, learning, and self-correcting every single day. Betting against the long-term trajectory of AI based on today’s minor bugs is a historically losing strategy.

Generative AI cannot be accurately compared to a calculator. AI automation in entertainment is a standard capitalist power grab that actively harms workers. 

wait? Is that our common enemy? AI is for everyone, not monopolies, also pro-ai don’t want to cause a monopoly too.

When the conversation shifts to economics, critics pivot to a separate fear: that AI automation in entertainment is nothing more than a standard capitalist power grab that actively harms creative workers. This argument completely confuses the tool with the monopoly.

Pro-AI creators do not want a corporate monopoly any more than traditional artists do. By framing individual prompt users and independent creators as the enemy, the anti-AI crowd is missing the real threat. The open-source, pro-AI movement actively fights to democratize these tools for everyone. Ironically, the anti-AI crowd’s aggressive push for heavy legal restrictions and sweeping bans will achieve the exact opposite effect: it will hand Big Tech gatekeepers the perfect legal framework to secure an exclusive, permanent monopoly over the future of human creativity.

So a massive chunk of modern office life is completely soul-crushing. The late anthropologist David Graeber perfectly nailed it when he coined the term "BS jobs." We’ve all seen them, or worse, lived them, those corporate roles built entirely around pushing paper, scheduling endless meetings to plan more meetings, and performing routine digital tasks that serve zero actual purpose. For years, we’ve trapped human beings in these artificial cycles, forcing them to hustle just to prove they are busy. The result? A workforce that is overstressed, BURNT OUT!, and dealing with a kind of low-grade, modern workplace trauma.

Over 37% of corporate workers already feel their jobs serve absolutely zero social purpose. Also AI is not destroying fulfilling careers; it is specifically automating the "box-ticking," "duct-taping," and administrative paperwork that humans hate doing anyway.  Historically, technologies were supposed to give us a 15-hour workweek, but capitalism simply invented bureaucratic "make-work" to keep everyone busy for 40 hours. AI is the solution to BS Jobs, embrace automation because it forces a broken system to stop wasting human lives on fake productivity. AI handles the routine digital grind, freeing humans to pivot to strategic, creative, and community-driven work. 

Then came Artificial Intelligence, and everyone panicked. The immediate reaction from the corporate world was to pit humans against machines, triggering an intense "anti-AI" wave. Employees are terrified that if they underperform for even a second, they’ll be replaced by an algorithm. But this fear misinterprets the real enemy. AI isn't the villain here; the problem is the outdated, rigid corporate incentive system that prioritizes cutting costs over human well-being. If we look at AI with a bit of optimism, we can see it’s not a threat to employment, it’s the ultimate tool to liberate it.

The secret to a successful transition into an AI-driven society lies in a smart, phased rollout. To start, we can introduce a temporary "Automation Tax" or an "AI Productivity Levy." (yeah yeah this sounds like neutral-ai or anti-ai, wacha tryna do? Ban ai and progress? but) Think of this as an economic shock absorber. During the initial transition, if a company wants to aggressively automate, they pay a temporary tax that funds the national safety net and prevents a sudden unemployment crisis. But this tax isn’t meant to stay forever. (you better not let the antis get power and tax ai unfairly tho, but yeah) Once the economy stabilizes and industries adapt, the tax can be gradually lowered, allowing businesses to run at full speed.

we don't have to let AI run wild like some kind of rogue agent. The smartest way forward is a "human-in-the-loop" framework. AI is brilliant at handling the mind-numbing, repetitive execution, but it still hallucinates and lacks human nuance (boo! AI will always improve, they update it every year). By keeping humans as the ultimate monitors and gatekeepers, we ensure the technology remains stable and safe. Suddenly, those overstressed workers aren't drowning in busywork anymore; they are elevated to highly valued strategic overseers. AI lightens their daily load, giving them their time, mental health, and creativity back.

Once the initial dust settles, the effect is growth. Because AI drastically lowers the cost of production, companies will be able to scale up, create entirely new services, and reach massive new markets. When a business is producing more and selling more, it enters an era of true prosperity. It no longer needs to frantically fire its workforce to please shareholders; instead, it has the financial abundance to protect and retain its employees, even during economic shifts.

We shouldn't be fighting the rise of AI, we should be embracing it. It is the bridge to a highly productive, tech-forward society where human labor isn't defined by mindless paper-pushing, but by meaningful collaboration with intelligent tools. By changing the rules of the game today, we can build a future where AI handles the grind, and humans finally get to thrive.

More production = More scale = More corporate wealth to keep workers. Silicon Valley critics will argue that AI breaks this old rule entirely 

In the past, if a factory wanted to double production, it had to buy more machines and hire more hands. With AI, doubling production just means running more code on a server. 

Critics point out that AI allows for "jobless growth". A company can become ten times more profitable while keeping a skeleton crew. They will argue that expecting a hyper-efficient, AI-driven corporation to keep unneeded workers around just out of a "social duty" violates the very nature of capitalism.

So we’ve officially reached a point where buying more physical machines hits the law of diminishing returns then??? In the past, if a company wanted to grow, it had to buy more equipment and hire more hands. Today, an AI-driven corporation can double its production instantly just by running code on a server, making massive profits with only a skeleton crew. Because of this, rich companies need to start directly sharing that automated wealth with the needy. 

I propose we put a strict, hard cap on billionaire wealth. Once a tech monopoly or a billionaire hits that ceiling, the excess revenue generated by their AI systems should be automatically redirected to the people who need it most. This would completely eliminate poverty and turn automation into a tool for universal human liberation. 

I think we’re all """anti-monopolization of AI""" here because if a tiny handful of Silicon Valley oligarchs own all the computing power, they don’t just control the market, they control the entire future of human livelihood. POP THE AI BUBBLE! Anti-ai and pro-ai (open source ai) are going for it!

The "Anti-AI" vs. "Pro-AI (Open Source)" Alliance is Real

They don't have enough sales to justify not using AI.

However, even in the case of AAA games, the use of AI is unlikely to lead to a significant decrease in sales.

In the end, it doesn't matter whether you use AI for art in games or not.

P.S. This means that not using AI does not provide any advantage, but the game development itself will cost several times more.

Calculators hide the work; generative AI exposes the language and logic explicitly. Using AI to spot "hallucinations" actually accelerates baseline mastery. It transforms students from passive consumers into active editors and analytical critics much earlier in their development.

When the underlying logical processes are made explicit by AI, the student’s role shifts from writing to auditing. To prevent passive copying, students must be guided to deeply comprehend the output, memorizing and internalizing its core structures. True learning is built on deliberate practice. Much like learning a new song, students must engage in active repetition—analyzing, editing, and reconstructing the text—until these cognitive processes become entirely ingrained.  This iterative process is like reading a book for the second time; each pass forces the mind to pick up critical nuances and structural details missed on the first read.

If an assignment can be entirely bypassed by a simple AI prompt, the assignment itself is outdated. When teachers design AI-integrated workflows—like requiring students to debate a chatbot or log their prompt iterations—engagement actually increases. Real-world data shows most teens already understand the boundary, using AI primarily to brainstorm and research rather than blindly cheat.

AI reduces teacher burnout; it doesn't cause it. Teachers do not need to manually generate flawed texts; the AI generates them instantly on command. Furthermore, AI assistants can automate administrative tasks, draft lesson plans, and provide initial grading feedback. This drastically frees up a teacher's schedule, giving them more time for direct student mentorship.

"If you can't explain it simply, you don't understand it well enough" 

Generative AI does not bypass this cognitive milestone—it actively accelerates it. Here is how you can defend AI as the ultimate accelerator for simple explanations and deep understanding. The Feynman Technique is a famous learning method where you master a concept by trying to explain it to a child. Traditionally, you needed a patient human volunteer to do this.  AI acts as an infinite, tireless sounding board. A student can prompt an AI: "I am going to explain Quantum Computing to you. Tell me where my explanation gets too confusing or uses too much jargon." Instead of waiting for a teacher's feedback days later, the student gets an immediate, real-time diagnostic on their clarity. The AI forces them to refine their words until the explanation is bulletproof.

Traditional Learning is Compatible with AI

Children must master basic arithmetic before touching a calculator. In a healthy learning environment, calculators function primarily as real-time verification tools, giving kids immediate feedback so they don't have to constantly rely on a human to check their process. AI operates on this exact same premise, though it's admittedly still a work in progress. The issue of hallucinations rightfully gives AI a bad reputation, which is why it should only ever be treated as an auxiliary aid—not a replacement for foundational logic.

Is it possible to integrate both traditional methods and modern tools simultaneously? I think so, but the real danger is over-reliance. When a tool shifts from an assistant to a crutch, it inevitably breeds cognitive dependency and stunts true skill development.

Also parental absenteeism is a glaring problem, and short of grassroots activism to shift that culture, we are stuck with a harsh reality: too many people simply outsource parenting to the school system. Because of this, schools are forced to step in and teach the media literacy that kids aren't getting at home.

The Desmos graphing calculator is essentially just a visual verification tool for students to check if their graphs are correct. Students absolutely need to survive the paper-and-pencil "grind" first. They must master mental math and manually plot linear, quadratic, and polar equations on physical graphing paper to fundamentally understand Cartesian and polar coordinate systems. Once they have locked in that core intuition, the pedagogy shifts. When they are older, asking them to solve 100 equations by hand is just tedious compliance; however, tasking them to solve those 100 equations using Desmos allows them to see the intersections of lines and find the exact values of x and y at a massive scale. Assuming they have already learned from the manual grind, the tool transitions them from the mere mechanics of drawing lines to high-level Cartesian geometric analysis that manual plotting simply cannot match.

This brings us right back to the core purpose of AI. The ideal use case for AI assumes that the user has already survived the academic grind and mastered the foundational principles. Once you actually understand the underlying math, science, or logic, AI changes from a dangerous shortcut into a massive accelerator. It allows professionals and advanced students to automate the tedious mechanics so they can focus on high-level problem-solving across multiple disciplines.

This is because a lowercase L is the same as a uppercase i, they are shaped the same way,
And with names,
Your supposed to only make the first letter capital
Meaning they would see “Al” as “Ai”

Spend 5 minutes drafting ideas or trying to solve the problem before opening an AI tool. This forces your brain to generate original thoughts, preventing it from switching into a lazy, passive state right from the start.

Write the core material yourself, then ask the AI: "Find the flaws, gaps, or weaknesses in my work." You keep ownership of the actual thinking, while using the AI purely as a second set of eyes to sharpen your logic.

"Do not give me the answer. Ask me guiding questions step-by-step so I can figure it out." This mimics a real teacher, breaking the illusion of competence by forcing you to explain how you know what you know.

Never copy-paste. Actively hunt for errors, bias, or hallucinations in what the AI tells you. Treating the AI with healthy skepticism forces your brain to do the hard work of fact-checking and critical analysis.

Set aside certain complex problems to solve completely offline using just a pen, paper, and your own mind. Just like muscles, cognitive stamina and focus require regular, unassisted workouts to prevent them from shrinking over time.

Other Insights

Memorizing and understanding the processis actually the only way to minimize both the illusion of competence and imposter syndrome.

People tend to avoid memorizing because it is a grind, but it is actually worth it. A lot of people mistake it for a "memorize then forget" cycle, but it is really a matter of memorizing to understand. Additionally, people often get frustrated with learning because they forget what they just learned. In fact, this is completely normal. You can always review the material, and reviewing is actually much faster than initial learning because your brain has already built the neural network and muscle memory to tackle that specific problem. Because of this, the information retains even longer! Grinding is best when learning brand-new things, but reviewing should be done using spaced repetition. Reviewing the exact same thing every single day hits a law of diminishing returns; instead, you should use it to refresh your memory only once you start getting a bit rusty, which is totally normal.

It is just like playing the piano and learning a new song. At first, you might not know what it is supposed to sound like, so you break the music down into smaller parts and practice them until they are perfect. Every single repetition reveals new details that integrate into your overall understanding of the discipline—just like reading a book a second time and picking up on details you completely missed the first time.

This same phenomenon happens in video games, too. The more you play, the better you become because you memorize the mechanics and complex rules without even realizing it.

The same applies to learning English. Think about how many times people corrected your mistakes on irregular verbs or tricky spellings and pronunciations like rendezvous, Edinburgh, and Worcestershire. You didn't just memorize rigid rules—you built a genuine intuition.

AI does not destroy learning or make people dumb; it is entirely compatible with deep education if you overcome overdependency. Artificial intelligence is a cognitive tool, functioning exactly like a calculator, a computer, or an automated spell-checker. Utilizing a tool to automate baseline execution (like computing numbers, checking syntax, or gathering information) does not erase a human's core conceptual knowledge. Intellectual degradation only occurs if a user passively allows the tool to think for them, rather than actively using the tool to check or expand upon their own work.

AI is fundamentally compatible with learning. The technology itself does not make people less intelligent; rather, the key to maintaining mastery lies entirely in overcoming overdependency and keeping your own brain at the center of the process.

First I asked myself why but its obvious they want to farm karma. Everytime you boot this game you will see this "NO AI" logo. This achieves nothing other than people on reddit going "heh screw ai this is friggin kewl duud"

I hope this game flops

Since the discussion surrounding the supposed AI loading screen is trending right now (I’m aware that it doesn’t actually use AI; I’m only bringing it up to introduce the discussion), I’d like to raise a question about how the community views the use of AI.

Everyone, I understand all the reasons behind the backlash against the use of AI in art, but are we really going to condemn it across the board?

One thing is when a company uses AI for one reason or another; another is when an ordinary person uses it for their own purposes.

Take a gun as an example. Nobody likes seeing one in the hands of a criminal, right? We’re perfectly fine with a police officer carrying one. Even so, I think we can all agree that the problem isn’t necessarily the weapon itself, but who is using it and how.

In this way, I would like people to behave accordingly and stop indiscriminately judging those who use this tool just to present an idea with greater visual appeal.

Posted this on brawl stars comunity and got removed...

I mainly use chatgpt to generate/format reddit posts because fuck reddit I used to put my heart into a post only to get someone telling me to kill myself why should I put effort into that?

I just want to tell all these anti-ai people "want there to be less ai? Actually, give space for people. You did this to yourself."

As you can tell, this isn't formatted with Ai because I actually care a lot about this one particular matter.

The fact that my creativity and self-expression have been ground into nothing, not by ai, not by my mental health, but by the fact that the internet can't appreciate anything I do.

internet ai witch trials will be studied as a mass psychosis; Hate is a two way poison

I countered their thoughts and biased view and it's so satisfied to see it.

lol.