# The LLM Critics Are Right. I Use LLMs Anyway

[[theocharis-blog|Jeremy Theocharis]] wrote this after Local-First Conf in Berlin (July 2026), where he noticed a dissonance he shares: rooms full of engineers with Claude Code open, applauding speakers who say critical things about LLMs. He agrees with almost every anti-LLM argument and uses LLMs heavily anyway — his June 2026 token spend was $9,838 (Opus 4.8 $5,042, Fable 5 $4,179, Sonnet 4.6 $452). The piece is an attempt to describe the reconciliation rather than resolve it.

## The concessions

Theocharis grants the critics' points up front and doesn't relitigate them: copyrighted training data, environmental cost, the NVIDIA/OpenAI circular-financing setup, the bubble that will burst. Where he adds detail is the effects on software culture:

- **Slop kills the trust OSS ran on.** A proper PR used to cost human time, which filtered out trolls and signaled the author had spent hours. That signal is gone — a maintainer can't tell a carefully-built PR (Claude used only for the description) from an [[build-your-own-openclaw|OpenClaw]] machine acting on its own. This is the same collapse [[i-dont-want-your-prs]] and [[agent-principal-agent-problem]] describe. His speculative fix: only let a small set of verified people contribute, verification via a real-life meetup. He thinks bans like Zig's and Gentoo's ([[simonw-zig-anti-ai]], [[llm-enshittification]]) can't work because you can't reliably tell.
- **The junior-engineer pipeline breaks two ways.** (a) A senior reviewing a junior's code no longer knows if it was vibe-coded in 10 minutes or genuinely struggled over for hours. (b) The teaching incentive disappears — mundane tasks that used to be the junior's growth path now go to the LLM, so why hire juniors? This is the apprenticeship-loss argument [[programming-still-sucks]] names.
- **Geopolitical cutoff risk is not hypothetical.** He cites Anthropic being forced to disable Fable 5 and Mythos 5 for all customers in June 2026 under a US export-control directive suspending access by any foreign national. Quotes Martin Kleppmann: the probability of a Europe/US conflict is still very low, "but last year, it was zero."
- **Silent opinion drift.** LLMs sneak in the majority view of their training data (or the model-makers' convictions). Like two people talking, opinions merge over time — except one participant isn't human.

## Why he uses them anyway

His central claim: **LLMs amplify what you already have; they don't supply what you don't.** "If you have thoughts, they come out sharper and faster. If you have nothing, nothing comes out, very fluently." They're good at brainstorming, grammar, alternatives, rubber-ducking, devil's advocate — and good at producing large volumes of plausible content nobody would read aloud. The value for him is making *fewer things higher quality* (he cites Nolan Lawson's "using AI to write better code more slowly"), spending an extreme number of tokens to prepare a few sentences for a human. This is the inverse of the [[llm-as-average-democratizer]] failure mode — using amplification deliberately instead of settling for the cheap average.

On open-weights models: they keep the large vendors honest when subsidies end, can't be cut off overnight by a government, and survive the bubble bursting. Programmers can fall back on them. This is the [[titit-local-ai]] / [[lucumr-local-models]] argument, arrived at from the resilience angle.

## The credibility test

The load-bearing idea. Amplified bullshit sounds like genius, so from the outside you can't tell "I use AI to think better" (Theocharis) from the identical sentence out of an AI tech-bro. All that's left is trust, which is hard to gain and easy to lose — a single em dash can invalidate a whole piece. His personal test for whether text is slop: **would you stand in front of an audience and read it out, word for word, unashamed?** If your answer is "well, I'd explain what was meant" — it's slop. This is [[credibility-as-slop-test]].

## The patterns

The workflow half of the piece — concrete techniques that counter LLM agreeableness:

- **/grill-me** (adapted from Matt Pocock): a short skill that tells the LLM to interview you relentlessly, one question at a time, resolving the decision tree branch by branch, looking up facts itself but putting every *decision* to you, and not acting until you confirm shared understanding. Forces you to form your own thoughts. He now uses this for everything, including writing this article sentence by sentence.
- **Basecamp "Pitch" discipline.** For any code change he writes a three-sentence Problem / What we're shipping / What we're not shipping. The constraint works *because* it was designed for humans — three sentences are short enough that he actually reads them, so another human can too. Analogy to code review: 1,000-line PR gets "LGTM"; 100-line PR gets 15 comments. He skims most LLM output but fact-checks the three-sentence problem statement hard.
- **Adversarial subagents** (the [Ralph Wiggum loop](https://ghuntley.com/ralph/) / Claude's ultracode): lock the LLM to a plan, then keep spawning fresh-context subagents whose only job is to rip it apart, until they're forced to hallucinate problems. When they hallucinate, the weakness becomes a signal — they want to agree there are problems but can't find real ones.
- **Intuition probe** (from Anselm Eickhoff): let a blind LLM hallucinate the API/UX it expects *before* seeing the real thing. Whatever it guesses is probably what most humans would guess, so it's a cheap test of whether your design matches expectations. He built it into a skill.

## The precondition

Every pattern depends on one thing: he has to be able to tell whether the result is good. The more he uses LLMs, the more he ends up in fields he doesn't know — and then he needs experts, because "I can only program things if I understand them." Delegation to an LLM works like delegation to a teammate: only for tasks where you understand the basics and know what good looks like.

He splits learning by whether correctness is cleanly checkable. Where there's a hard check — code compiles or not, tests green or not, protocol decodes or not — you can point the LLM at it and learn together (his example: someone reverse-engineering binaries with Opus 4.6, needing only basic RE knowledge, because the patched binary either worked or bricked the device). In opinion-heavy fields like programming, the LLM just returns the most popular technique, which may be wrong for your case — there you need humans to give you good starting opinions. He recounts a team argument about "AI slop" that turned out to be a disagreement about TDD, not about AI at all: LLMs amplify, so a fight over LLM output was really a fight over human opinions.

## Where it sits

This is the reconciliation slot the [[anti-llm-discourse]] map was missing — someone who accepts the critics' premises and neither refuses (Moser, [[i-am-an-ai-hater]]) nor routes around them via career strategy (Verna, [[elena-verna-job-2027]]), but tries to describe a disciplined middle. Closest neighbors: [[simonw-vibe-coding-agentic]] (Willison conceding the vibe-coding/engineering line collapsed in his own practice) and [[ai-assisted-workflow]] (planning-before-code discipline). The load-bearing move that keeps it out of tech-bro territory is the same one [[cult-of-vibe-coding]] makes from the other direction: you must be able to evaluate the output, or you're just running a slop factory.

## Interesting from the discussion

The Lobsters thread pushed on the piece's blind spot:

- **The output is bad writing, and the post assumes it isn't.** The top comment (Corbin): the author lists copyright, ethics, slop, and economics as the critics' concerns but "completely fails to notice that the output of Transformer-driven chatbot harnesses is *bad writing*." The post only reads as coherent if you assume chatbots are good writers; treating that as almost-agreement is "a dire misreading of a neighboring culture." A reply notes the author literally says "let me go through the biggest ones one by one" and then discusses none of them.
- **Amplification is not augmentation.** "Just because you can yell louder doesn't mean what you have is worth yelling about." LLMs replicate patterns humans read as high quality; there's no guarantee the quality is real, only that it appears so — the appears-to-be vs proves-to-be distinction.
- **Friction is a gating function.** The sharpest counter to "if you have thoughts they come out sharper and faster": friction forces you to decide which ideas are worth fleshing out, adopting, and defending. A system that turns every shower thought into a multi-page essay removes that and produces "cognitive diarrhea" — and the arguments you get out are uniformly same-y, literally the same text everyone else gets. Proposed test: do *you* have any AI-written blogs you love and await the next post from? Probably a short list.