Coding AI Generates More, But Not Necessarily Faster

A recent study published in Ars Technica reveals that while AI coding agents are capable of generating more lines of code, the overall efficiency gains can be absorbed by the human review process AI coding agents generate more code, but not more software. This means that for small businesses, simply adopting these tools won't automatically translate to faster project completion or significant cost savings.

The Bottleneck Isn't Always the Code

For a small business owner, the promise of AI-powered coding might sound like a direct path to quicker software development or more efficient internal tool creation. However, this study highlights a critical reality: code generation is only one part of the development lifecycle. The human element of reviewing, testing, and integrating that code remains a significant bottleneck. Imagine a small marketing agency that uses an AI coding agent to draft a new feature for their client-facing dashboard. The agent might churn out hundreds of new lines of code in minutes. But if the agency's lead developer then spends hours scrutinizing each line for errors, ensuring it meets project requirements, and integrating it seamlessly with existing systems, the initial time savings from the AI can quickly diminish.

This isn't to say AI coding tools are without value. They can certainly assist developers by handling repetitive tasks or generating boilerplate code. However, the expectation of a direct, linear increase in productivity needs to be managed. The real benefit might lie in how AI augments human capabilities, freeing up developers to focus on more complex problem-solving and strategic decision-making, rather than simply writing more code faster.

Rethinking the Workflow

For businesses that rely on custom software, internal scripts, or even website enhancements, understanding where the real value lies is crucial. Instead of viewing AI coding agents as a replacement for human developers, consider them as sophisticated assistants. This means re-evaluating workflows to account for the AI's output. For a small construction company that might use AI to help develop a custom bid-tracking application, the focus should shift from the quantity of code generated by the AI to the quality of the final, reviewed, and tested product. The AI can speed up the initial drafting, but the contractor's project manager or a contracted developer will still need to ensure the application accurately reflects project needs and is robust enough for daily use.

This often requires a shift in how development projects are planned and managed. Instead of setting deadlines based on raw coding time, businesses might need to allocate more time for robust review and testing phases. This also means ensuring that the individuals responsible for this review are highly skilled, as they are now the primary gatekeepers of quality and efficiency.

A Caveat on Current AI Capabilities

While AI coding agents are rapidly improving, they are not infallible. The study implies that the code generated, while plentiful, may still require significant human intervention to meet professional standards. Furthermore, the rapid evolution of AI models means that the performance and capabilities of these agents can change quickly. For instance, models like Claude Opus 5.5, released in late September, score highly on quality indices according to Artificial Analysis (https://artificialanalysis.ai), but the specific nuances of their code generation output and how easily it integrates into existing, potentially older, business systems are still being understood.

For a small business, this means that relying solely on AI for critical development tasks without adequate human oversight could lead to unforeseen issues, bugs, or security vulnerabilities. The cost of fixing these problems downstream can far outweigh any initial savings from using AI.

What to Consider Next

Before investing in AI coding tools, consider how your current development process handles code review and testing. Identify your actual bottlenecks. If your development time is already heavily weighted towards review and integration, AI coding agents might offer marginal benefits until your human review processes can scale accordingly. If you're considering using AI for development tasks, focus on how it can assist your existing team, not replace them. Explore using AI to generate initial drafts or repetitive code snippets, but always plan for thorough human review and testing. This proactive approach ensures that AI integration genuinely enhances your business operations, rather than just adding more lines of code to a system that still needs significant human attention.