Prompt for Anthropic: “Annotate all the logical fallacies in https://fly.io/blog/youre-all-nuts/“

Logical Fallacies in “My AI Skeptic Friends Are All Nuts”

Ad Hominem Attacks

• “You’re All Nuts” (title) – Directly attacking the character/mental state of opponents rather than their arguments
• “their arguments are unserious” – Dismissing critics’ intelligence rather than addressing substance
• “Are you a vibe coding Youtuber? Can you not read code?” – Personal attack on coding ability
• “what the fuck is wrong with you?” – Direct personal attack
• “skills issue!” – Attacking competence rather than the argument

Straw Man Fallacies

• “If you’re making requests on a ChatGPT page and then pasting the resulting (broken) code” – Misrepresenting how skeptics actually use LLMs
• “Steve Jobs was wrong: we do not need to carve the unseen feet in the sculpture” – Oversimplifying the “craft” argument to something about perfectionism rather than understanding/maintainability
• “Maximum effort on a random unit test?” – Reducing quality concerns to absurd extremes

False Dichotomy/Binary Thinking

• “Things either work or they don’t” – Oversimplifying complex technology adoption into binary terms
• “I buy a fucking table” vs. building one – Presenting only two extreme options (artisan perfectionism vs. pure pragmatism)
• LLM adoption presented as inevitable binary choice – Either embrace fully or be left behind

Appeal to Consequences

• “Extraordinarily talented people are doing work that LLMs already do better, out of spite” – Arguing people should adopt LLMs to avoid negative consequences rather than on merits
• “The folks on my team who aren’t embracing AI? It’s like they’re standing still” – Fear-based argument about being left behind

Appeal to Authority/Bandwagon

• “Some of the smartest people I know” – Using intelligence of friends as credential rather than addressing their specific arguments
• “However you define ‘serious developer’, I qualify” – Establishing credentials to claim authority
• “Kids today don’t just use agents” – Appeal to what young developers are doing
• Citing friend’s success story without providing verifiable data

Hasty Generalization

• “Most code on most projects is tedious” – Broad generalization without supporting evidence
• “LLMs kick ass generating [Go code]” – Generalizing from one language experience to all programming
• “We all write mediocre code” – Universal claim without evidence

Tu Quoque (You Too/Hypocrisy)

• “No profession has demonstrated more contempt for intellectual property” – Deflecting plagiarism concerns by pointing to developers’ past behavior with media piracy
• “Then shut the fuck up about IPR. Reap the whirlwind” – Using alleged hypocrisy to dismiss legitimate concerns

Red Herring

• Long tangent about woodworking and buying tables – Diverting from code quality/understanding concerns to craftsmanship metaphor
• Discussion of travel agents, record stores, darkroom techs – Diverting from specific developer job displacement to general technological unemployment

Begging the Question/Circular Reasoning

• “LLMs already do better” – Assumes conclusion (LLMs are better) as premise for why people should use them
• “But something real is happening” – Using vague assertion as evidence for the importance he’s trying to prove

False Cause/Post Hoc

• “I can feel my blood pressure rising thinking of all the bookkeeping” → “An LLM can be instructed to just figure all that shit out” – Assuming LLMs solve the specific emotional/motivational problems described

Appeal to Emotion

• “I can feel my blood pressure rising” – Using visceral emotional language rather than logical argument
• “That dopamine hit is why I code” – Emotional appeal about coding satisfaction
• Aggressive tone throughout (“what the fuck”, “shove this concern up your ass”) – Using emotional intensity rather than reasoned argument

Equivocation

• “Probabilistic” vs “stochastic” – Playing word games rather than addressing the underlying concern about unpredictability
• “It’s code. It’s not Yacc output” – Conflating different meanings of “code” to dismiss concerns

Survivorship Bias

• Focusing only on success stories and dismissing failures as user error (“skills issue”)
• “Five get merged” – Only highlighting successes without discussing failure rates or costs

Special Pleading

• “I’m discussing only the implications of LLMs for software development. For art, music, and writing? I got nothing” – Exempting his own field from broader criticisms while dismissing concerns in that field

Moving the Goalposts

• Starting with “LLMs can write code” and shifting to “agents can do more than just write code” when challenged
• “Even the most Claude-poisoned serious developers… still own curation, judgement, guidance” – Retreating from stronger claims about LLM capabilities

Anecdotal Evidence

• “‘I’m sipping rocket fuel right now,’ a friend tells me” – Using single anecdote as representative evidence
• Personal experience with incident response – Using one example to make broad claims

No True Scotsman

• “If you were trying and failing to use an LLM for code 6 months ago, you’re not doing what most serious LLM-assisted coders are doing” – Redefining who counts as a “serious” user to exclude counterexamples

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Analysis Summary

The author makes several valid points about LLM capabilities and productivity gains, but undermines their argument through extensive use of logical fallacies. The piece reads more like a persuasive rant than a reasoned argument, relying heavily on:

1. Personal attacks rather than substantive counterarguments
2. Emotional manipulation rather than evidence
3. Misrepresenting opponents’ positions rather than steel-manning them
4. Anecdotal evidence rather than systematic data

A stronger argument would address skeptics’ concerns directly with evidence while acknowledging legitimate limitations and trade-offs.