The 4 Levels of AI Coding | YK Sugi from Eventual
YK from Eventual has been practicing "agentic coding" for two and a half years, back when most people thought AI coding meant copy-pasting from ChatGPT. His big realization? AI coding and traditional software engineering aren't opposites. They're complementary. But there's a progression most engineers miss.
Level 1: AI Coding (Where Most Engineers Get Stuck)
Let AI "go wild." Great for one-off projects and quick experiments. Terrible for anything you need to maintain. If you're still here after 6 months of AI coding, you're doing it wrong.
Level 2: Agentic Coding with Discipline
Version control enters the chat. You actually understand what each file does and how they connect. Basic security precautions exist. You're no longer flying blind.
Level 3: Software Engineering
Tests get written (and verified). Pre-commit hooks, CI jobs, the whole traditional engineering toolkit. You understand your code at the function and class level, not just the "it works" level.
Level 4: High Quality Software Engineering
The holy grail. Code that's "indistinguishable" from what a staff engineer would write by hand. This requires line-by-line understanding of every single generated line. No shortcuts.
YK's point: Level 1 AI coding is fine for prototypes and throwaway scripts. But if you're building anything that matters, anything large-scale, anything mission-critical, you need to level up. The tools exist to generate higher quality, not just higher quantity.
Most engineers think AI coding means sacrificing quality for speed. YK shows you can have both, but only if you know how to use the levels.
Timestamps
00:00 Intro: AI coding vs traditional engineering are complementary
00:18 Level 1: AI Coding (letting AI "go wild")
00:39 Level 2: Agentic Coding with Discipline
01:03 Level 3: Software Engineering practices
01:31 Level 4: High Quality Software Engineering
02:04 Summary: Quality vs quantity in AI-generated code
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