Titus Winters, Former Head of Google's Readability Program: AI's Value to Engineering Lies Not in Accelerating Old Work but in Launching New Projects
Titus Winters, Senior Principal Scientist at Adobe and former head of Google's readability program, argues in a podcast that measuring AI's contribution to software engineering by code output and feature delivery counts is asking the wrong question. He observes that while multiple companies have seen a significant rise in code output and an approximate 30% increase in incidents, the number of features delivered remains flat, appearing as if they are treading water. However, he believes AI's true value lies in making projects previously unfeasible due to cost or complexity viable.
In a podcast, Titus Winters described a picture he repeatedly heard from engineer peers at various companies: compared to a year ago, code output has risen significantly, incident rates have also increased by about 30%, but the number of delivered features has remained basically unchanged. Interpreted through traditional developer productivity metrics, this looks like treading water—or even leaning negative because of the rise in incidents.
Winters believes the problem lies with the metrics themselves. He says people are caring about the wrong things. Traditional productivity measures—feature delivery counts, iteration speed, lines of code—measure throughput on known types of work. AI's unique contribution may not lie in this dimension, but rather in lowering the feasibility threshold for projects: making projects that were previously unworthy to start due to labor costs, complexity, or timespan realistic.
This judgment has the background of his own engineering experience. In 2010, shortly after joining Google, he participated in a large-scale refactoring covering approximately 250 million lines of C++ code. At the time, the entire codebase was maintained by about 10,000 engineers, while the team executing the refactoring consisted of only twelve people. Without large language models, they relied on MapReduce to parse the abstract syntax trees of the entire codebase, match call patterns, automatically generate edits, shard and distribute them, lock down old patterns, and finally delete old APIs. This specialized toolchain later gave birth to clang-format. Winters' analogical logic is: having twelve people complete a migration involving 250 million lines of code did not rely on making each person write faster, but rather on how the toolchain changed the boundaries of feasibility for this task.