Imagine if naked people were stupider. It turns out, naked models actually are.

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Sorry, not that kind of naked model. But the disappointment you're feeling right now? That's exactly how Kyle Kingsbury feels about LLMs. Kyle Kingsbury is one of the best systems engineers alive. His Jepsen project spent a decade methodically proving that distributed databases didn't work as advertised. That CockroachDB, MongoDB, Redis, and dozens of others made consistency guarantees they couldn't keep. He published the results, the vendors fixed the bugs, and the entire industry got more honest. Jepsen is a masterwork of applied skepticism. Last week he published a 32-page essay called "The Future of Everything is Lies, I Guess: Bullshit About Bullshit Machines." It's beautifully written, deeply researched, genuinely funny, and wrong about the most important thing. His observations are correct. His conclusion is not. A note on scope: this essay addresses Kingsbury's technical claims: that LLMs are unreliable bullshit machines incapable of producing trustworthy output. His broader concerns about labor displacement, information ecology, and cultural impact are real, separate questions that deserve their own essays. I'm not dismissing them by not addressing them here. I'm addressing the architecture question: does model unreliability make useful systems impossible, or does it make them an engineering problem? I think it's the latter. Kingsbury's essay assumes the former. That's where we disagree. Testing the engine on a bench Kingsbury's essay is structured as a catalogue of LLM failures. He asked Gemini to apply materials to a 3D bathroom rendering. It forgot the toilet and changed the room's shape. He asked Claude to do image-to-image transformation. It produced thousands of lines of JavaScript creating an incomprehensible garble of nonsense polygons. He asked ChatGPT to put white patches on a blue shirt. It changed the color, moved the patches, deleted them. He watched a colleague's LLM claim to download stock data and produce a graph of randomly generated number