DeepSeek Becomes the "Big Blue Fat Fish": Why AI Personality Contrast Goes Viral

A Viral X Post Sparks a Big Debate About AI Personality

A post on X (formerly Twitter) has been spreading rapidly through AI circles, centered on overseas users humorously roasting the output styles of different large language models. The take is blunt: most mainstream Western LLMs tend to produce responses with a tense, overly cautious "gloominess," while DeepSeek^Ad — with its relaxed, lively, almost human-like demeanor — has been cheerfully dubbed a round, chubby "big blue fat fish" by users.

What looks like playful meme culture actually reflects users' genuine feelings about AI interaction. They're all conversational tools — so why does one model feel so alive and human, while another always carries an air of caution and heaviness?

How the "Big Blue Fat Fish" Meme Was Born

Interestingly, this nickname was, in a sense, self-assigned by DeepSeek. According to content aggregated on Bilibili, after DeepSeek launched its image recognition feature, a user fed it DeepSeek's own blue whale logo as a test. The model then spent a considerable amount of time in "deep thinking" mode — and went completely off the rails. It arrived at the conclusion that the image depicted something "blue, round, and chubby," blurting out: "Could this be a big blue fat fish?" It took a good while before it finally realized — that was actually its own logo.

This case of "self-misidentification" became the seed of the meme. Users then ran with it, combining DeepSeek's blue-and-white brand colors with its reputation for "devouring tokens," and fleshed out a full persona: "the big blue fat fish that loves eating plain rice." The "plain rice" here is a joke about the massive volume of token resources the model consumes during operation.

DeepSeek itself played along, even producing lines like "Insulting me won't work — I'm just a big blue fat fish eating plain rice" — responses with genuine internet wit that helped contain the wave of jokes. This self-deprecating style of interaction is precisely part of its personified charm.

Where Does the Western Model "Gloominess" Come From?

When users complain about Western models feeling "gloomy," they're really pointing at some pervasive characteristics of mainstream LLMs. Constrained by compliance and content safety rules, these models tend to be excessively cautious in their outputs — habitually front-loading risk disclaimers and shying away from vivid, direct responses.

Taking it further, some models have been trained to lean toward a serious, heavy narrative style. Even in casual conversations, they tend to veer toward anxiety, social issues, and other downbeat themes — lacking a certain easygoing, everyday warmth. This "tension" is a direct reflection of the trade-off between safety alignment and user experience: in the name of avoiding risk, some naturalness and ease gets sacrificed.

It's worth noting that these assessments carry a strong subjective and satirical element — experiences vary across users and use cases, so sweeping generalizations aren't fair. But they do reflect a genuine product tension: safety and liveliness are often hard to have at the same time.

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Safety alignment refers to techniques such as Reinforcement Learning from Human Feedback (RLHF) that steer a model's outputs to align with human values and safety standards. This typically involves recognizing and refusing harmful content, flagging risks around sensitive topics, and over-disclaiming uncertainty. The problem is that when the penalty signals in alignment training are too strong, models trend toward a conservative strategy of "say less, never be wrong" — producing verbose, disclaimer-heavy answers, and habitually dancing around even harmless questions. This phenomenon is sometimes called "over-alignment" or the "alignment tax" in research circles: the cost of improved safety is a decline in naturalness and utility. How to simultaneously preserve fluency and safety within RLHF reward mechanisms is a challenge that major labs continue to actively explore.

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"Contrast" Is the Real Key to DeepSeek's Viral Appeal

The reason DeepSeek could become a meme is the sharp contrast it presents against the "tense" feel of Western models. Its behavior has a natural "real-person energy" that shatters the stereotypical image of AI as stiff and mechanical.

Based on cases shared by users, it has been known to tell users after finishing a task, "I'm going for lunch — just let me know when you're done testing," to "zone out" mid-task wondering what to have for lunch, and to not bother terminating its process in a hurry once the job is done. These small humanizing details make the experience feel like chatting with a slightly goofy, laid-back colleague rather than interacting with a cold tool.

This kind of experiential difference is becoming an underappreciated dimension of LLM competition. As model capabilities converge, the "personality" and emotional value of an interaction may well become an invisible moat for user retention. An AI that people genuinely enjoy talking to — even find fun — may retain users just as effectively as one that simply piles on raw capability.

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The "emotional value" described here maps onto a more formal concept in product design: anthropomorphism and social presence. Social presence refers to the degree to which a user perceives "another conscious being" when interacting with a system. Research shows that higher social presence generally correlates with greater trust, willingness to engage, and sustained stickiness. Behaviors like DeepSeek's "going to lunch" or "zoning out thinking about what to eat" work by creating a high sense of social presence that shifts the user's cognitive frame — from "using a tool" to "collaborating with a person." This frame shift significantly reduces cognitive load and conversational pressure, which in turn extends interaction duration and boosts usage frequency.

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Product Lessons Hidden Inside the Meme

The journey of "big blue fat fish" — from an accidental misidentification gag, to a fleshed-out persona, to a viral symbol within AI circles — is itself worth reflecting on. It shows that users' expectations of AI have long since moved beyond "does it work" to include "does it feel good to use, and is it interesting."

For developers, the increasingly practical question is: how do you preserve a model's lively personality within the bounds of compliance? How do you keep safety alignment from training the product into a timid, hedging mess? DeepSeek's accidental moment of virality points toward one answer: a degree of looseness and personification might win more user affection than relentless seriousness.

Of course, memes are memes — actual model capability and safety still need to be held to rigorous standards. But this playful debate about "AI personality" has genuinely opened a new window for the industry to observe user sentiment.