AI Code Review Reality Check: Sridhar Vembu Says LLMs Excel at ‘Glue Code’ But Humans Are Still in Charge

AI code review: Vembu says AI coding improves with LLMs; human oversight still vital

The hype around AI writing code for us is deafening. But what happens when you put it under the microscope of a seasoned engineer? Zoho founder Sridhar Vembu just did exactly that, and his verdict is a masterclass in balanced, real-world perspective on the state of AI code review.

Vembu recently led a tech town hall at Zoho where his team performed a detailed code review of C++ generated by Anthropic’s latest and most powerful model, Claude Opus 4.5 . The results were both impressive and illuminating, offering a crucial reality check for an industry racing headlong into an AI-driven future.

Table of Contents

What is AI Code Review and Why It Matters

AI code review refers to the process of using artificial intelligence to analyze source code for bugs, security vulnerabilities, performance issues, and adherence to coding standards. It’s a critical step in modern software development, acting as a safety net before code goes live. With the rise of Large Language Models (LLMs) like Claude and GitHub Copilot, this process is evolving from simple static analysis to intelligent, context-aware suggestions and even full code generation .

However, as Vembu’s experience shows, the output from these models isn’t a finished product—it’s a starting point that demands expert scrutiny.

Sridhar Vembu’s Hands-On AI Code Review Experience

Vembu, who was previously skeptical about the quality of AI-generated code, admitted that Claude Opus 4.5 marked a significant turning point . During their internal review, the team found the model’s output to be remarkably competent at a specific task: creating what Vembu calls “glue code” .

“They are able to stitch together systems well, taking data from one system and putting it into another,” he noted . This ability to handle the often tedious and boilerplate-heavy integration work between different software components is where modern LLMs truly shine, freeing up human developers for more creative and complex problem-solving.

The Rise of Claude Opus 4.5: A Game-Changer for Coding?

Anthropic’s Claude Opus 4.5 is being hailed as its most capable model yet, with early benchmarks showing it outperforming competitors like Google’s Gemini 3 Pro in real-world coding evaluations . Its strength lies not just in writing syntactically correct code, but in understanding complex instructions and powering sophisticated, multi-step “agentic” workflows that can interact with a developer’s environment .

This leap in capability is what changed Vembu’s mind. One of his co-workers, also previously doubtful, found that Opus 4.5 “dramatically accelerated experimentation, iteration, and” the overall development process . The speed at which developers can now prototype and test ideas has increased exponentially.

Where AI Shines: Glue Code and Rapid Iteration

The practical value of AI in coding today is clear in these specific areas:

  • Boilerplate Generation: Automatically creating repetitive code structures.
  • API Integration: Writing the code needed to connect to and use third-party services.
  • Rapid Prototyping: Quickly building a working model of a feature to test a concept.
  • Code Translation: Converting code from one language to another.

For these tasks, an LLM like Claude Opus 4.5 acts as a powerful co-pilot, handling the mundane so the human engineer can focus on architecture, logic, and innovation.

The Non-Negotiable Role of Human Oversight

Despite the impressive progress, Vembu issued a strong warning: it is “unwise to copy paste” AI-generated code directly into a production system . His core message is that human orchestration remains vital for producing usable, secure, and efficient software .

AI models can produce code that looks correct but may contain subtle logic errors, security flaws, or inefficiencies that only a skilled human reviewer can spot. Vembu emphasized that engineers must “refine and simplify AI-generated code before deployment” . This human-in-the-loop approach is not a temporary phase; it’s the essential workflow for the foreseeable future.

Furthermore, AI lacks the contextual business understanding and long-term architectural vision that a human developer brings to a project. It can write a function, but it can’t decide if that’s the best way to solve the problem within the larger system.

Practical Tips for Developers Using AI Coding Tools

Based on Vembu’s insights and industry best practices, here’s how to effectively integrate AI into your workflow:

  1. Treat AI as a Junior Developer: Its code needs to be reviewed, tested, and approved just like any other team member’s work.
  2. Never Skip Security & Compliance Checks: Always run a full round of review for privacy, security, and regulatory compliance on AI-generated code .
  3. Focus on the ‘Why’: Use AI for the ‘how,’ but make sure you, the human, are always in charge of the ‘why’ behind the code.
  4. Keep Learning: Understanding the fundamentals of programming is more important than ever to effectively guide and critique your AI partner.

Conclusion: The Future of Coding is a Partnership

Sridhar Vembu’s candid assessment of the AI code review process cuts through the hype. While models like Claude Opus 4.5 represent a massive leap forward, they are tools, not replacements. The future of software development belongs to the powerful partnership between human ingenuity and artificial intelligence. The human provides the vision, the critical thinking, and the ethical judgment, while the AI handles the heavy lifting of routine coding tasks. This synergy, not blind automation, is the true path to building better, faster, and more secure software.

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