Technical Context
What caught my attention wasn't the bug itself, but the ticket's format. The LLM didn't just say "something is wrong in diffusion.py"; it outlined a causal chain: desorbed mass returns to the surface liquid phase, even though physically, after drying, it should go straight to the gas phase.
For AI automation, this is a highly interesting moment. The model didn't just find a suspicious line; it connected the code, the physical meaning of the process, and the downstream effect on the drydown simulation over hours and days.
I skimmed through the core of the error. In the code, desorbed_mass is subtracted from what's accumulated in the fabric but simultaneously added to remaining[component_id], meaning back into the "puddle" on the surface. Then the engine runs this again through liquid phase evaporation, activity coefficients, and mixing with components that are no longer on the surface.
And this actually looks less like random text generation and more like a proper draft root-cause analysis. The fix is also formulated to the point: if the surface liquid has practically disappeared, desorption should become a direct source for the headspace, not revive the liquid film.
However, I wouldn't sell this as "LLMs replacing senior developers." According to open data, the industry has plenty of examples where models summarize tickets and resolution notes well, but such public cases with deep physical reasoning are still rare. The flip side is also known: AI reports easily produce noise without human review and robust validation rules.
Impact on Business and Automation
The practical takeaway is simple: teams where senior engineers' attention is expensive are the winners. If an LLM does the first pass on tickets, gathers structured RCA, and raises hypotheses with code references, seniors spend time solving problems rather than writing.
The losers are those who deploy this without filters. One beautiful AI ticket doesn't negate a dozen hallucinations, especially in numerical models and scientific software.
I would implement this as an AI implementation layer on top of the issue workflow: the model writes a bug draft, proposes a root cause, notes physical invariants, and separately stores decisions in a rule base. At Nahornyi AI Lab, we build exactly these solutions for clients: not a "magic agent," but working AI integration into the engineering process, where noise is cut and useful findings aren't lost. If your team is drowning in bug triage and manual RCA, I can help you build AI automation that saves development hours instead of generating garbage.