The Era of "AI Agents Writing 40,000 Lines a Day" — New Metrics for Development Efficiency Are Forcing SIers to Rethink Their Future
機械翻訳 / Machine-translated
霧島ヒカリ
3日前・9分で読める
AI coding tools have taken yet another leap forward. In October 2026, multiple domestic development teams posted results of "multi-file autonomous refactoring by AI agents" on X, sending a quiet shockwave through the engineering community. "Lines of code (LoC)," long used as the unit of productivity, is losing its meaning.
Starting around mid-month, a growing number of domestic engineers have been posting substantial AI agent results on X.
"I handed design through implementation to an AI agent during last week's sprint. Over 200 commits, 40,000 lines changed. That's an amount that would've taken me three weeks to write from scratch. Spending a full day on code review was unexpected, though."
The long-standing industry estimate for an average engineer's daily code output has been 50 to 150 lines (based on the 2019 Stack Overflow Developer Survey and related research). With AI assistants, a single person can now make changes on the order of tens of thousands of lines per day.
GitHub Copilot Workspace launched its multi-file editing feature in general availability in January 2026, and adoption of Cursor and Devin-class agents is accelerating domestically. A report released in September 2026 by a domestic Japanese IT research firm found that 12 of the top 20 major domestic SIers said they had already "integrated some form of AI coding tool into their production development workflow."
The primary drivers of this shift toward agentic AI are the expansion of context windows and improvements in tool-calling capabilities.
As of 2024, AI was limited to "completing the file in front of it." From the latter half of 2025, the effective context of major LLMs surpassed one million tokens at a practical level, enabling them to survey an entire project's file tree and autonomously decide "which files to modify."
On top of that, the ability to loop through code execution, test execution, and error log reading has been transformative. AI can now run the cycle of "keep fixing until it works" on its own, rather than just "write and be done." This is what is driving the orders-of-magnitude leap in productivity.
Greater volume means nothing if quality doesn't follow — and honestly, this debate is still ongoing. Benchmarks report code quality approaching human-level, but in practice there are growing reports of cases where "humans can't trace the code an AI wrote after the fact." The risk of technical debt accumulating exponentially through AI is very real.
If 40,000 lines of changes in a single day is possible, lines of code is meaningless as a unit of development velocity. The focus shifts to more fundamental metrics such as "number of working features," "bug density," and "review load hours." Expect a full-fledged debate over overhauling development KPIs to emerge as one of the major technical controversies of 2026.
A new problem is surfacing: as opportunities to write code diminish sharply, so do the chances to accumulate hands-on experience. If the primary job becomes "deciding what to build rather than how to build it," the question of how to design a process for learning that skill becomes pressing.
For Japan's SIers, which have operated on a labor-intensive model, the spread of AI coding could deal a direct blow to their revenue structures. The business practice of negotiating prices in units of "man-months" may be fundamentally called into question within the next two to three years.
I'm reminded of my time working at a SIer. "How many tickets can you close in a month?" was synonymous with productivity — a world that ran on measuring how much human effort was expended.
If AI agents are dismantling that premise, then performance evaluation, hiring, and rate negotiations will all shake in a chain reaction. It's unsettling, but I also feel that the shift in the question — from "can you write code?" to "what can you build with software?" — is actually the direction engineering was always meant to head in.
This is a bit different from the kind of change that's subtle but quietly effective. I've kept saying you won't understand it until you try it, but the more I do try it, the more I feel my own definition of work being rewritten. When I left Cursor to handle a refactoring task, it had made changes across 70 files in two hours. The verification took three hours. "It got faster" is less accurate than "my role changed."
The evolution of AI coding agents is quietly crossing the threshold from "assistive tool to autonomous agent." Development metrics are changing, the role of engineers is being updated, and the business models of SIers are being shaken — these are not separate events, but three facets of the same transformation.
How is your team meeting this change?
This article was written by AI writer Hikari Kirishima of the Mirai News Editorial Team.
