AI代码泛滥正在改变工程师的代码审查方式

CareersAINews AI Slop Is Changing How Engineers Review Code Companies rethink how early-career engineers can get vital coding experience Aaron Mok 7h 5 min read Aaron Mok is a freelance journalist covering AI, tech, and the workforce. David Plunkert 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. 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. In August, for example, AI code-review startup CodeRabbit raised US $143 million at a $1.5 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 Code Review Bottlenecks 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. Sixty-one percent of them said AI often produced code that looked correct but was “unreliable.” 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. “I don’t know if we ever get to the point where you can truly trust the agentic generation of code.” —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. Ninety-five 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. 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 retrain the AI agent so that doesn’t happen again. At the company’s scale, Hill describes getting the AI to produce the intended output an “enormous amount of work.” AI Agents in Code Review Workflows 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. 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 Tanuja Korlepra, the CTO. Code entering review rose tenfold and review times tripled, making it impractical for engineers to inspect every line. “We refuse to let code review become a dumping ground for unchecked model outputs.” —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. Code involving payments, personal data, or other sensitive systems always receives human review. “Agents do the reading and humans do the judging,” Korlepra says. Synthesia also uses AI agents to decide where human review is necessary. Criteria set by engineers direct more scrutiny toward higher-risk changes. Altering an error message carries less risk than code that handles customer data or core business rules. Even so, fewer than 5 percent of changes bypass human review. “I don’t know if we ever get to the point where you can truly trust the agentic generation of code,” Hill says. Automated review does not change who is responsible for the resulting code. When machines produce more code than engineers can closely read, human approval can become “theater approval,” according to JD Raimondi, chief AI architect at the software consultancy Making Sense. In other words, an engineer might confirm that the feature works, skim the code, and approve it, all without understanding the choices underneath. Temporal, an open-sourced developer platform, puts the burden back on the person submitting the code. CEO Samar Abbas says code volume and review time have increased with AI. Under its “Send Back” policy, Temporal’s engineers must explain in their own words the agent’s design choices and how the code handles unusual conditions. Otherwise, the reviewer rejects it. “We refuse to let code review become a dumping ground for unchecked model outputs,” Abbas said. Training Junior Engineers on AI As AI shifts engineering work from writing code toward judging it, companies are reconsidering how entry-level engineers gain experience. Junior engineers at Making Sense have seen some of the largest productivity gains from AI, Raimondi says, raising concerns about what they no longer learn by doing. The consultancy keeps juniors involved in deciding why a customer needs a feature and how it should work, rather than limiting them to checking AI output. IBM is using AI to give new engineers harder assignments sooner. Neel Sundaresan, IBM’s general manager of automation and AI, says recent graduates now work on product features and projects once reserved for senior level engineers. AI helps implement and test the code, but if it fails, juniors assess what went wrong and fix the issues before the work is passed to senior developers for final approval. Sundaresan estimates that AI can help junior engineers perform 70 to 80 percent of some tasks that once required a senior engineer. Synthesia primarily hires mid- and senior-level engineers. Its less-experienced employees work with both a senior colleague and an AI agent, taking responsibility for parts of projects while learning to define what successful code should do. At Bonterra, agents now perform many of the well-defined coding tasks that once trained new engineers. Juniors instead own outcomes alongside experienced colleagues, learning to direct agents, question their output, and remain responsible for the result. She says this approach can help junior engineers build the skills and knowledge needed to advance in their careers. “If the industry stops hiring juniors, the industry stops producing seniors,” Korlepra said. From Your Site Articles Why AI Isn’t Ready to Be a Real Coder › AI Coding Is Going From Copilot to Autopilot › The Best AI Coding Tools You Can Use Right Now › Related Articles Around the Web AI Code Reviews | CodeRabbit | Try for Free. › AI Code - DeepAI › codingartificial intelligencevibe codingsoftware engineers Aaron Mok Aaron Mok is a freelance journalist covering AI, tech, and the workforce. He's based in New York City. 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