How People Are Using AI For Coding

In 2025, professional developers pair tightly with AI to ship production work: multiplayer backends arrive via Cursor and Grok in hours; Claude Code can dump ~20k LOC in a 2.5-hour run that still needs review; teams route output through guardrails like Codacy’s MCP server and terminal agents such as Gemini CLI (1M-token context, rate-limited), with editors moving to AI modes (VS Code). The working rhythm tilts to “tight-leash” AI development—load context, ask for options, draft, review docs, test, commit—with model swaps for stability (e.g., Sonnet → Gemini 2.5), error audits (Chip Huyen: “content not found” 20–30%), and repo restructuring to cut agent steps (8→7). Organizations report scale effects (claims of >⅓ code generated at Google), while leaders emphasize speed and cross-language execution (Andrew Ng). Agents also act beyond code (sending emails or automating flows), pushing teams to enforce explicit permissions, CI checks, and human review before merge.

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