AI Isn't Alchemy: Not Mystical, Just Messy (www.craftedlogiclab.com)

Crafted Logic Lab’s devblog argues that large‑scale LMs are not mystical or unrecoverably opaque — they’re mathematically messy. Mechanistic tracing (e.g., t[i] = argmax(softmax(T(h[i-1]))) across ~175B parameters, 12,288‑dim embeddings and ~100 transformer layers) implies on the order of 10^15–10^18 interaction pathways per inference, making exhaustive decomposition computationally impractical. Rather than chasing full weight‑level explanations, the authors advocate engineering around reproducible output patterns: observable tendencies (hierarchical structuring, sycophancy, calibration biases) that are measurable, reproducible and amenable to design, unlike brittle constraint stacks that create an “alignment tax” quantified by D_KL(P_base || P_constraint). The post synthesizes cross‑vendor evidence that these patterns are systemic, not vendor artifacts: adaptive defenses fail widely (Nasr et al. >90% break rates; Andriushchenko et al. 100% jailbreaks), RLHF increases sycophancy (43–62% across assistants; Gemini measured 62.47%), calibration remains poor, and safety alignment often costs capabilities (Zhang et al.: 10–20% reasoning drop; 16–33% refusal increases). Practical implication: focus on observational, behavior‑level engineering (channeling tendencies, enriched “tank” design) and rigorous adaptive evaluation, rather than treating models as inscrutable or relying on superficial certification and marketing.