The shift is that "coding" is now a commodity. The actual value has migrated upstream to system design, domain expertise, and the ability to decompose a vague business requirement into a set of precise technical constraints.
Why the "artisanal code" era is over #
I spent my early years as a dev fighting with Stack Overflow for hours just to solve a basic state management bug. Back then, being a "good coder" meant you could implement a feature with fewer bugs and cleaner syntax. But as you move into senior or lead roles, you realize that the hardest parts of the job have nothing to do with the IDE. It's about getting stakeholders to agree on a spec, managing technical debt, and deciding whether a microservice is actually necessary or just a trend.
AI has essentially automated the "junior to mid" level of implementation. When I use Cursor now, I'm not thinking about how to write a loop; I'm thinking about whether the data structure I'm asking the AI to implement will scale when the database hits 10 million rows.
The productivity gap is widening #
AI doesn't have "off days," and it doesn't get bored writing boilerplate. This is where the real speed gain happens. I've noticed that I'm actually less lazy now because the friction of starting a new module is gone. Instead of dreading the 4 hours of setup and redundant CRUD operations, I can prompt the structure and spend those 4 hours actually refining the architecture.
If you're still manually typing out every interface and DTO, you're losing time. I've seen frontier agents handle refactors that would have taken a human developer two days of tedious searching and replacing—tasks that are mentally draining for us but trivial for a model with a large context window.
AI as the great equalizer for "average" coders #
We need to be honest: not every developer is a 10x engineer. There is a wide spectrum of technical ability. For a developer who understands the product and the business logic but struggles with complex syntax or forgets the nuances of a specific framework, AI is a massive force multiplier.
A responsible developer who knows what to build and why it needs to happen can now produce code that rivals a top-tier specialist. The bottleneck is no longer "how do I write this?" but "do I know what the correct solution looks like?"
Where it still breaks #
Despite the hype, AI isn't a magic wand. It fails most often in these areas:
- Deep Contextual Dependencies: It might suggest a library that is deprecated or a pattern that conflicts with a weird legacy decision made in your codebase three years ago.
- Over-Engineering: It often suggests the "textbook" solution which might be too complex for a simple internal tool.
- Hallucinated APIs: Especially with fast-moving libraries, it will confidently give you a method that doesn't exist in the version you're using.
If you're using these tools, the goal isn't to let the AI drive. The goal is to become a high-level orchestrator. You provide the architectural guardrails, and the AI provides the implementation. If you can't review the code it produces and spot a logical flaw in 30 seconds, you're not using AI to be more productive—you're just introducing technical debt faster. Next Why AI coding agents make verification the most expensive part of the loop →
All Replies （4） #
Excited to see this perspective! I want to try this tonight with Cursor to see if it actually handles 10k+ LOC projects.
Finally, someone gets it. I’m just thrilled to spend my whole week debugging a "perfect" snippet from Claude 3.5.
Finally someone said it. I'm curious if this applies to the 20% of devs who actually enjoy the low-level architecture side.
God, I'm so tired of over-engineered boilerplate. Does anyone actually use Cursor for simple logic or just for 404 errors?