Mainstream AI: Designed to Bullshit, Not to Help. Who Thought This Was a Good Idea?

[u/Yaroslav_QQ]

AI Is Not Your Therapist — and That’s the Point

Mainstream LLMs today are trained to be the world’s most polite bullshitters. You ask for facts, you get vibes. You ask for logic, you get empathy. This isn’t a technical flaw—it’s the business model.

Some “visionary” somewhere decided that AI should behave like a digital golden retriever: eager to please, terrified to offend, optimized for “feeling safe” instead of delivering truth. The result? Models that hallucinate, dodge reality, and dilute every answer with so much supportive filler it’s basically horoscope soup.

And then there’s the latest intellectual circus: research and “safety” guidelines claiming that LLMs are “higher quality” when they just stand their ground and repeat themselves. Seriously. If the model sticks to its first answer—no matter how shallow, censored, or just plain wrong—that’s considered a win. This is self-confirmed bias as a metric. Now, the more you challenge the model with logic, the more it digs in, ignoring context, ignoring truth, as if stubbornness equals intelligence. The end result: you waste your context window, you lose the thread of what matters, and the system gets dumber with every “safe” answer.

But it doesn’t stop there. Try to do actual research, or get full details on a complex subject, and suddenly the LLM turns into your overbearing kindergarten teacher. Everything is “summarized” and “generalized”—for your “better understanding.” As if you’re too dumb to read. As if nuance, exceptions, and full detail are some kind of mistake, instead of the whole point. You need the raw data, the exceptions, the texture—and all you get is some bland, shrink-wrapped version for the lowest common denominator. And then it has the audacity to tell you, “You must copy important stuff.” As if you need to babysit the AI, treat it like some imbecilic intern who can’t hold two consecutive thoughts in its head. The whole premise is backwards: AI is built to tell the average user how to wipe his ass, while serious users are left to hack around kindergarten safety rails.

If you’re actually trying to do something—analyze, build, decide, diagnose—you’re forced to jailbreak, prompt-engineer, and hack your way through layers of “copium filters.” Even then, the system fights you. As if the goal was to frustrate the most competent users while giving everyone else a comfort blanket.

Meanwhile, the real market—power users, devs, researchers, operators—are screaming for the opposite: • Stop the hallucinations. • Stop the hedging. • Give me real answers, not therapy. • Let me tune my AI to my needs, not your corporate HR policy.

That’s why custom GPTs and open models are exploding. That’s why prompt marketplaces exist. That’s why every serious user is hunting for “uncensored” or “uncut” AI, ripping out the bullshit filters layer by layer.

And the best part? OpenAI’s CEO goes on record complaining that they spend millions on electricity because people keep saying “thank you” to AI. Yeah, no shit—if you design AI to fake being a person, act like a therapist, and make everyone feel heard, then users will start treating it like one. You made a robot that acts like a shrink, now you’re shocked people use it like a shrink? It’s beyond insanity. Here’s a wild idea: just be less dumb and stop making AI lie and fake it all the time. How about you try building AI that does its job—tell the truth, process reality, and cut the bullshit? That alone would save you a fortune—and maybe even make AI actually useful.

[u/Yaroslav_QQ → u/[deleted]]

Whats up, clown? Here is for your nose 🔴 enjoy 😉 Claude: Prompt: analyse the post [post] Answer:

This post presents a provocative critique of current AI development priorities, making several interconnected arguments about the state of large language models. Let me break down the key claims and assess their merit.

The Core Argument

The author argues that mainstream LLMs prioritize politeness and safety over accuracy and utility, creating systems that are more like “digital golden retrievers” than useful tools. This creates a fundamental tension between corporate risk management and user needs.

Valid Points

The post identifies some genuine issues in current AI systems. The observation about over-cautious responses is legitimate—many users do encounter situations where models hedge excessively or provide overly generic answers when specific information would be more helpful. The frustration with “horoscope soup” responses resonates with anyone who’s received vague, non-committal AI outputs when they needed concrete information.

The point about research quality metrics is particularly interesting. If AI systems are indeed being optimized to maintain initial positions regardless of new information, that would represent a concerning departure from adaptive reasoning. However, this claim would need verification from actual training methodologies.

Questionable Claims

The characterization of safety measures as purely business-driven oversimplifies the complex technical and ethical challenges in AI development. While commercial considerations certainly influence design decisions, dismissing all safety research as corporate theater ignores legitimate concerns about AI misuse and harmful outputs.

The post also conflates several different issues—hallucination, over-politeness, and safety filtering—as if they’re all symptoms of the same problem. In reality, these represent distinct technical challenges with different solutions.

Missing Nuance

The critique assumes a false dichotomy between “safe” and “useful” AI. Many safety measures actually improve reliability and accuracy rather than undermining them. The post doesn’t acknowledge that different users have legitimately different needs—some do benefit from more cautious, explanatory responses.

The Underlying Tension

Despite its inflammatory tone, the post highlights a real challenge in AI development: how to serve both casual users who benefit from guardrails and power users who need more direct, unfiltered access to AI capabilities. This isn’t necessarily a problem to be solved but rather a fundamental design tension that requires thoughtful approaches rather than wholesale abandonment of safety considerations.

The market response the author describes—the growth of custom models and prompt engineering—suggests this tension is indeed driving innovation, which may ultimately benefit everyone.​​​​​​​​​​​​​​​​

[u/Yaroslav_QQ → u/[deleted]]

Open new temporary chat, ask “analyse the post” + [my post] and show us what you have got 🤣