Countering the "quality over quantity" mantra in software engineering, Robinhood's internal data reveals a positive correlation between the number of code lines contributed and the quality of that code. This suggests that top-performing engineers excel in both volume and craftsmanship.
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To prevent engineers from gaming output-based pay, 10X assigns a "Technical Strategist" to each project. The engineer is paid for output, but the strategist is incentivized by client retention and account growth (NRR), creating a healthy tension that ensures high-quality work is delivered.
The trend of 'vibe coding'—casually using prompts to generate code without rigor—is creating low-quality, unmaintainable software. The AI engineering community has reached its limit with this approach and is actively searching for a new development paradigm that marries AI's speed with traditional engineering's craft and reliability.
Block's CTO argues that engineers mistakenly equate code quality with product success. He uses the example of early YouTube, which had a famously poor architecture but became wildly successful, while the technically superior Google Video failed. The focus should be on solving a user problem, not on perfect code.
AI coding assistants won't make fundamental skills obsolete. Instead, they act as a force multiplier that separates engineers. Great engineers use AI to become exceptional by augmenting their deep understanding, while mediocre engineers who rely on it blindly will fall further behind.
AI acts as a massive force multiplier for software development. By using AI agents for coding and code review, with humans providing high-level direction and final approval, a two-person team can achieve the output of a much larger engineering organization.
Data on AI tool adoption among engineers is conflicting. One A/B test showed that the highest-performing senior engineers gained the biggest productivity boost. However, other companies report that opinionated senior engineers are the most resistant to using AI tools, viewing their output as subpar.
AI tools can generate vast amounts of verbose code on command, making metrics like 'lines of code' easily gameable and meaningless for measuring true engineering productivity. This practice introduces complexity and technical debt rather than indicating progress.
As AI generates more code, the core engineering task evolves from writing to reviewing. Developers will spend significantly more time evaluating AI-generated code for correctness, style, and reliability, fundamentally changing daily workflows and skill requirements.
The era of winning with merely functional software is over. As technology, especially AI, makes baseline functionality easier to build, the key differentiator becomes design excellence and superior craft. Mediocre, 'good enough' products will lose to those that are exceptionally well-designed.
