It's so weird that so many people are ignoring the massive accuracy issues of LLMs and have this misguided idea that you can just trust the output of a computer because... well, it's a computer.
What’s actually weird is pretending anyone in AI development is saying “just trust the computer.” Nobody is advocating blind trust—we’re advocating tool use. You know, like how compilers don’t write perfect code, but we still use them. Or how your IDE doesn’t understand your architecture, but it still catches your syntax errors.
Even weirder? Watching people whose jobs are 40% boilerplate and 60% Googling suddenly develop deep philosophical concerns about epistemology. Everyone who isn’t rage-posting their insecurity over imminent obsolescence is treating LLMs like any other fallible-but-useful tool—except this one just happens to be better at cranking out working code than half of GitHub.
You're not warning us about trust. You're panicking because the tool is starting to do your job—and it doesn’t need caffeine, compliments, or a pull request approval.
It's literally using random numbers in its text generation algorithm.
Translation: randomness isn’t the problem. It’s your discomfort with why it still works anyway that has your knickers in a twist.
That sentence is doing a lot of work to sound like it understands probabilistic modeling. Spoiler: it doesn’t. Claiming LLMs are invalid because they use randomness is like claiming Monte Carlo methods in physics or finance are junk science. Randomness isn’t failure—it’s how we explore probability spaces, discover novel solutions, and generate diverse, coherent outputs.
If you actually understood beam search, temperature, or top-k sampling, you’d know “random” here means controlled variation, not “magic 8-ball with delusions of grammar.” Controlled randomness is what lets LLMs generate plausible alternatives—something you’d know if you’d ever tuned a sampler instead of just rage-posting from the shallow end of the AI pool.
If your job is threatened by a model that uses weighted randomness, I have bad news: your stackoverflow-to-clipboard ratio was higher than you thought. Time to read the GPT-text on the wall and start plotting your next career pivot.
Why not just use astrology?
Because astrology never passed the bar exam, defeated a world-class Go champion, debugged a microservice, or explained value vs. reference semantics in C without making it worse. LLMs have. Hell, you probably leaned on one the last time your regex failed and you didn’t want to ask in the group chat.
But sure—let’s pretend astrology is the same as a transformer architecture trained on hundreds of billions of tokens and fine-tuned across dozens of domains to produce results you can’t replicate without five open tabs and a panic attack.
Want to know the real difference? Nobody ever replaced a software engineer because they wanted a Capricorn instead of an Aquarius.
You’re not mad because LLMs are inaccurate. You’re mad because they’re accurate enough, cheap enough, and scalable enough for management to finally put a price tag on your replaceability.
The AI doomsday clock for coders is ticking. And you just realized it's set to UTC.