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1019: LGTM, Ship It: The AI Code Review Problem
Where this was said
The Challenges of Code Review in AI Development
At 10:33 · chapter starts 6:24
The question from LGTM Bro hits a raw nerve: an engineering team where individual contributors are merging 60 PRs per release cycle — each spanning 20 to 70 files and thousands of lines of AI-generated code — with little to no meaningful human review. The inevitable results are already appearing: technical debt, recurring bugs, architectural inconsistencies, and a codebase becoming progressively harder to maintain. Wes frames this with two archetypal AI-era team strategies: the 'feature cannon' (ship everything, worry later) versus the more considered approach of using AI's horsepower to finally clear years of accumulated debt. His punchline is blunt: the problems being described are not new, and neither are the solutions. What AI has done is speedrun the same failure modes that previously took six years to manifest — compressing them into months. Scott adds the damning detail that AI actively resists removing code even when instructed to, making the bloat problem self-perpetuating. The hosts don't offer a silver-bullet fix; instead they note that good software engineering principles — tests, linting, shared utilities, meaningful review — are exactly as relevant as they ever were, just more urgent.
One team is merging 60 PRs per developer per week, spanning thousands of lines of AI-generated code with almost no meaningful human review. The result: technical debt, recurring bugs, fragile architecture, and a codebase becoming impossible to maintain.
One team reported individual contributors merging 60 PRs per release cycle (one week), spanning 20-70 files and thousands of lines of AI-generated code.
Every software engineering problem being blamed on AI — duplication, inconsistency, fragile code — has existed for decades. AI didn't create bad practices. It just compresses 6 years of codebase decay into a few months.
Problems that previously took 6 years to manifest — duplicated code, architecture inconsistencies, fragile implementations — are now appearing in months due to AI-generated code velocity.
Scott Tolinski noted that AI tends to add code rather than remove it, even when prompted to, making bloat and technical debt increasingly difficult to manage.
Most people think 'local AI' means the software is on their machine. It doesn't. Tools like OpenCode with DeepSeek still make API calls — to servers in China. True local means the model weights run on your hardware.
