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AI-Assisted Coding Creates Problems: Insights for SMBs in 2025

AI-Assisted Coding Creates More Problems – Report: What SMBs Need to Know in 2025

Estimated reading time: 5 minutes

  • A new report warns that AI-assisted coding creates more problems – report – InfoWorld — highlighting unexpected bugs and security risks.
  • While AI tools boost coding speed, they may introduce hidden complexities and reduce code quality if left unchecked.
  • Business owners need to balance AI efficiency with strong human oversight and code review practices.
  • For SMBs and marketers adopting low-code or no-code platforms, understanding where AI breaks down is vital.
  • Learn how to mitigate the risks of AI-generated code using best practices, n8n workflows, and expert guidance.

Table of Contents

What’s In the Report That Says AI-Assisted Coding Creates More Problems?

In the December 2025 report titled AI-assisted coding creates more problems – report – InfoWorld, researchers evaluated the impact of large language model–based code assistants like GitHub Copilot and ChatGPT on developer productivity and code quality.

According to the full article via InfoWorld, these AI tools frequently produce code that appears correct but contains logical errors, security flaws, or performance bottlenecks that human reviewers can easily miss.

Key findings from the report:

  • Increased Bug Density: Code produced with AI assistance had a higher bug rate than human-written code in controlled experiments.
  • False Confidence Among Developers: Some teams adopted AI code suggestions without properly validating them, assuming correctness.
  • Vulnerabilities Ignored: Even when security best practices were known, AI-generated code often ignored safe coding standards.

These conclusions don’t suggest abandoning AI code tools altogether—but they do indicate a critical need for evaluation, testing, and guardrails.

How Are SMBs and Marketers Using AI Code Tools Today?

Small businesses and marketers are increasingly turning to AI-driven coding tools to speed up development and reduce outsourcing costs. Common use cases include:

  1. Building Automations with n8n: Low-code platforms like n8n connect APIs and trigger workflows—often with AI-assisted scripts to process or transform data.
  2. Customizing WordPress or Shopify: Marketers use ChatGPT or Copilot to write snippets for product carousels, SEO optimization, or user experience tweaks.
  3. Rapid Prototyping: Entrepreneurs with limited tech budgets may lean on AI to quickly create MVPs or integrations for SaaS tools.

The appeal is clear: tools like ChatGPT and GitHub Copilot can generate functional code in seconds, often faster than hiring a freelance developer.

However, as the AI-assisted coding creates more problems – report – InfoWorld suggests, the convenience can hide long-term complications:

  • Bugs may not show up immediately, especially in non-critical features.
  • Security flaws in code that accesses APIs or sensitive data might go unnoticed.
  • Overreliance on AI encourages copy-paste coding without understanding context.

What Are the Downsides of Relying on AI-Assisted Coding?

If you’re building workflows, integrations, or digital tools for your business, it’s essential to understand both the pros and cons of AI-generated code.

Pros:

  • Speeds up prototyping
  • Reduces dependency on full-time developers
  • Facilitates experimentation and flexibility
  • Great for boilerplate or repetitive tasks

Cons:

  • Poor Error Handling: AI often fails to anticipate edge cases or invalid inputs.
  • Security Oversights: Generated code may skip input sanitization, authentication checks, or rate-limiting rules.
  • Low Maintainability: Code is often unreadable, inconsistent, or undocumented.
  • Hidden Technical Debt: Teams may “paper over” problems rather than solving them properly.

In short, using AI for code generation doesn’t remove the need for skilled human validation—it increases the importance of it.

How to Implement This in Your Business

Whether you’re a growing ecommerce startup or a digital agency automating client workflows, here’s how to responsibly use AI coding tools:

  1. Define Code Review Standards:
    • Always pass AI-generated code through code reviewers or at least a linter.
    • Use tools like ESLint or Prettier for JavaScript, and security scanners for backend code.
  2. Limit AI to Non-Critical Components:
    • Use AI assistance for internal dashboards, automation scripts, or MVP testing—not customer-facing apps or payment integrations.
  3. Test with Real-World Data:
    • AI outputs are often built on assumptions. Run QA with edge cases, invalid inputs, rate variations, or multi-device environments.
  4. Combine with Human-in-the-Loop Workflows:
    • Use tools like n8n.io to integrate automated routines but add manual approvals or logging steps for safety.
  5. Educate Your Team:
    • Make sure marketers or junior developers understand the risks of trusting AI blindly. Reading the AI-assisted coding creates more problems – report – InfoWorld article can be a great training step.
  6. Keep Documentation Updated:
    • Even when code is AI-generated, document what it does, what inputs it expects, and how to modify it safely.

How AI Naanji Helps Businesses Leverage AI Code Tools Safely

AI Naanji supports startups, marketers, and SMBs in safely maximizing the benefits of AI-driven workflows and automation.

Our team specializes in n8n workflow development, AI consulting, and integrating tools like ChatGPT, Make, and Copilot into secure, scalable systems. We help clients avoid the most common pitfalls highlighted in the AI-assisted coding creates more problems – report – InfoWorld by implementing rigorous validation steps, testing frameworks, and low-risk deployment strategies.

Backed by deep expertise in digital transformation, we build sustainable automations without sacrificing quality, performance, or security.

FAQ: AI-Assisted Coding Creates More Problems – Report – InfoWorld

  • Q: What does the InfoWorld report say about AI-assisted coding?
    A: The report shows that code generated by AI tools often contains more bugs, security flaws, and logic gaps versus code written by experienced developers.
  • Q: Should small businesses stop using AI tools for code?
    A: Not necessarily. The key is to use them wisely—preferably for low-risk tasks—and always validate the outputs before going live.
  • Q: Are certain tools more prone to these issues?
    A: While tools like ChatGPT and Copilot are commonly cited, the issue is less about the tool and more about blind implementation without testing or context.
  • Q: What’s the biggest risk from AI-generated code?
    A: Hidden vulnerabilities—especially in areas involving user input, data handling, or third-party integrations.
  • Q: How can I safely implement AI coding in my stack?
    A: Use layered safeguards: rigorous QA, documented workflows, human review, and platforms like n8n that support oversight and modularity.

Conclusion

While AI-assisted tools promise efficiency, the research is clear: AI-assisted coding creates more problems – report – InfoWorld highlights the real risks companies must consider. For SMBs and marketers eager to move fast, ignoring these pitfalls can lead to time-consuming bugs—or worse, serious vulnerabilities.

By combining human judgment with automation strategies, and choosing vetted integration platforms like n8n, digital professionals can work smarter—without inviting unnecessary risk.

Need help designing safe, scalable AI-powered workflows? Let AI Naanji be your partner in utilizing automation tools the right way.

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