Post AI Code Glut Era: Companies Specializing in Code Cleanup See Valuations Surge Tenfold

Anyone who often uses AI to write code knows that it is not difficult to produce a runnable program, but if you are not careful enough, the file will inexplicably become several times larger. I once got a sudden whim to ask AI to.

Before AI programming became popular, writing code was the most time-consuming part of software development. But AI is so powerful now that code that used to take hours or even days to complete can be generated in minutes.

What Happened

However, the problem is that as the pile of code grows, the tedious work of sifting through the messy low-quality code also piles up. AI can write hundreds of lines of code in a minute, but companies cannot directly deploy this code.

• Although it was generated with just a few words, at first glance, it stubbornly output more than 2,000 lines of unmaintainable spaghetti code.

• On August 12, according to TechCrunch, Blacksmith, which provides continuous integration (CI) infrastructure for GitHub Actions, completed a $45 million Series B financing, with a valuation reaching $550 million.

• Less than a year ago, its valuation was only about $60 million.

Key Details

A company that specializes in "flushing the toilet" for AI-generated code has seen its valuation surge nearly ten times as a result. Blacksmith does not invent smarter AI, nor does it help programmers write code.

• In September 2025, when the company announced its Series A financing, it disclosed that excluding the impact of the growing number of developers, the volume of continuous integration tasks run by its existing customers.

• At the end of August 2025, a team of 3 engineers at OpenAI started from a blank code base and used Codex to develop an internal software.

• Five months later, the code base had expanded to about 1 million lines, and about 1,500 Pull Requests (PRs) were completed during the period.

Why It Matters

What it does is the remaining work after AI generates code: running tests, performing builds, and verifying whether these AI-generated codes can be safely integrated into real software. It is not the testing itself that drives up its value, but the ever-increasing.

• OpenAI wrote directly in this experiment that as code throughput increased, the new bottleneck they encountered became "human QA capability".

• But soon a new problem emerged: code is being written so fast that humans can no longer keep up.

What Reports Say

Coverage of the story so far points to:

• Continued reporting by 36Kr as more details emerge