I have ADHD and I’ve been pair programming with LLMs for a while now. At some point I realized the way they fail felt weirdly familiar. Confidently making stuff up, losing context mid conversation, brilliant lateral connections then botching basic sequential logic. That’s just… my Tuesday. So I went into the cognitive science literature. Found six parallels backed by independent research groups who weren’t even looking at this connection. Associative processing. In ADHD the Default Mode Network bleeds into task-positive networks (Castellanos et al., JAMA Psychiatry). Transformer attention computes weighted associations across all tokens with no strong relevance gate. Both are association machines with high creative connectivity and random irrelevant intrusions. Confabulation. Adults with ADHD produce significantly more false memories that feel true (Soliman & Elfar, 2017, d=0.69+). A 2023 PLOS Digital Health paper argues LLM errors should be called confabulation not hallucination. A 2024 ACL paper found LLM confabulations share measurable characteristics with human confabulation (Millward et al.). Neither system is lying. Both fill gaps with plausible pattern-completed stuff. Context window is working memory. Working memory deficits are among the most replicated ADHD findings (d=0.69-0.74 across meta-analyses). An LLM’s context window is literally its working memory. Fixed size, stuff falls off the end, earlier info gets fuzzy. And the compensation strategies mirror each other. We use planners and external systems. LLMs use system prompts, CLAUDE.md files, RAG. Same function. Pattern completion over precision. ADHD means better divergent thinking, worse convergent thinking (Hoogman et al., 2020). LLMs are the same. Great at pattern matching and creative completion, bad at precise multi-step reasoning. Both optimized for “what fits the pattern” not “what is logically correct in sequence.” Structure as force multiplier. Structured environments significantly improve ADHD performance (Frontiers in Psychology, 2025). Same with LLMs. Good system prompt with clear constraints equals dramatically better output. Remove the structure, get rambling unfocused garbage. Works the same way in both systems. Interest-driven persistence vs thread continuity. Sustained focused engagement on one thread produces compounding quality in both cases. Break the thread and you lose everything. Same as someone interrupting deep focus and you have zero idea where you were. The practical takeaway is that people who’ve spent years managing ADHD brains have already been training the skills that matter for AI collaboration. External scaffolding, pattern-first thinking, iterating without frustration. I wrote up the full research with all citations at thecreativeprogrammer.dev if anyone wants to go deeper. What’s your experience? Have you noticed parallels between how LLMs fail and how your own thinking works? submitted by /u/bystanderInnen
Originally posted by u/bystanderInnen on r/ClaudeCode