AI Slop Is Changing How Engineers Review Code

What happened

AI coding tools can now generate thousands of lines of code in minutes, helping companies build features, run tests, and fix issues faster. But the flood of AI-generated code still has to be reviewed. Large language models can produce code that looks clean on the surface but conceals sloppy mistakes such as faulty assumptions, security vulnerabilities, or subtle errors that emerge only after deployment.

Synthesia has found as many as 10 versions of the same function, leaving engineers to identify and remove redundant functions. Once that’s done, engineers re-train the AI agent so that doesn’t happen again. ” Some teams are trying to prevent review problems before AI generates a single line of code.

McLaren Stanley, a senior principal engineer at Amazon Stores, says he is using AI to modernize 17 years of code underlying Amazon’s mobile shopping app. His 70-person team supports more than 1,000 developers by maintaining the architectural backbone they need to build features. With AI writing the code, Stanley said, engineers spend more time deciding what it should do before generation begins.

Much of that work involves writing a “specification,” which is a detailed plan for what the AI agent should build and how. Preventing recurring mistakes before generating code can save engineers time later. Stanley recalls how a missing instruction once caused an agent to generate 25,000 lines in the wrong version of the programming language Swift. Switching versions produced 600 errors it could not fix at once.

Stanley discarded the code, updated the specification, and restarted the agent. Fifteen minutes later, it regenerated the code correctly. Once the code exists, specialized AI agents can handle the first round of checks before a person steps in.

Why it matters

Fixing those problems could erase the productivity gains AI promises. Companies are responding to the onslaught of AI code slop by rethinking how they review code. New strategies are emerging: Among other approaches, engineers are scrutinizing plans before AI begins coding, deploying specialized AI agents to catch routine flaws, sending risky changes to human reviewers, or requiring developers to defend the code their agents produce.

The shift comes as the surge in AI-generated code puts new pressure on engineering teams. In a survey of more than 1,100 developers by Sonar, an AI code verification startup, respondents estimated that AI contributed 42 percent of the code they added to shared codebases. Yet while developers found AI useful for explaining and prototyping code, 96 percent did not fully trust its output to work correctly.

Investors see an opportunity in closing that gap. 5 billion valuation, while claiming it performs more than 2 million reviews a week for 17,000 customers, including Nvidia, Indeed, and BMW Group. The new era of code review will determine whether AI can ever provide code that is both faster and more reliable.

It also has some software engineers thinking about the future of their profession: If entry-level engineers spend less time writing code themselves, how will they learn to judge it? AI-written code is shifting the bottleneck from generating software to reviewing it. According to the Sonar study, 38 percent of developers said “more effort” is required to review AI-generated code than code written by their colleagues.

” For Synthesia, an AI video generation platform, code review has become essential to its engineering workflow. In November 2025, Synthesia’s 118 engineers went all-in on AI coding tools like Claude Code. According to Peter Hill, Synthesia’s chief technology officer, the result has been a massive surge in code volume. ” —Peter Hill, Synthesia That code demands close examination.

As of August, the number of pull requests, or proposed changes to a codebase submitted for review, had risen 120 percent year-over-year, Hill says. 95 percent of those requests contain AI-generated code. One recurring problem is duplication. Hill says AI tools may not recognize that code for a task already exists, and they’ll write another version because they have limited context.

What to watch

David Yanacek, a senior principal engineer at Amazon Web Services (AWS), says the company uses agents to test whether code works, check it against the original plan, and look for security flaws before a person reviews it. That first pass becomes more important as AI-generated code volume increases.

At Bonterra, a nonprofit software provider with about 290 engineers, proposed changes tripled within three months of adopting AI, according to CTO Tanuja Korlepra. Code entering review rose tenfold and review times tripled, making it impractical for engineers to inspect every line. ” —Samar Abbas, Temporal Bonterra’s agents compare code with the approved design, security rules, coding standards, and accessibility requirements, then report their confidence in the result. A low score or flagged problem sends the change to a person.