AI-assisted coding tools have become a hot topic in the software development world, promising to boost developer productivity and reduce costs. At Software Co, many of our clients ask whether these tools mean projects should now be faster and cheaper. While AI has made significant advancements, the reality is more nuanced. This article explores what clients really need to know about the use of AI in coding—its benefits, limitations, and why human expertise, architecture, and best practices remain essential for building secure, scalable, and high-quality software.
1. Does AI Reduce Software Development Costs? The Full Picture A July 2025 study by METR found that experienced developers using AI tools like Cursor actually took 19% longer to complete tasks compared to those without AI. Why? Because while AI is good at writing boilerplate or generating quick code snippets, it often requires extensive prompting, validation, and rewriting.
These tools can increase efficiency in some isolated cases (e.g., unfamiliar syntax or API integration), but they frequently slow down experienced engineers working within complex, real-world environments. In our projects at Software Co, we use AI to support—not replace—our expert developers.
Key Insight: AI can assist, but not eliminate, human input. The cost savings are marginal and often offset by increased review and testing effort.
2. AI-Created Code Often Compromises Long-Term Quality A GitClear report studying over 211 million lines of code found that AI-assisted developers wrote 4x more duplicate code and fewer clean refactors. This creates “technical debt” that inflates maintenance costs and reduces system agility.
Moreover, AI suggestions are often accepted without full comprehension of context, resulting in brittle code that’s harder to extend or debug. These shortcuts compromise the long-term quality of the product.
Key Insight: AI-generated code can look right, but under the hood, it often lacks the robustness needed for enterprise-grade applications.
3. Security Vulnerabilities Are More Common with AI-Generated Code Multiple studies, including those by Stanford, NYU, and Snyk, confirm that developers using AI are more likely to introduce security flaws. Common examples include weak cryptography, improper input validation, and unsafe dependencies.
In one Snyk survey, 56% of engineering leaders observed insecure AI-generated code regularly. Despite this, fewer than 10% had reliable automated safeguards in place.
Key Insight: AI doesn’t understand threat models or compliance requirements. Human oversight is essential to ensure security.
4. Intellectual Property and Licensing Risks AI models can unintentionally generate code snippets that resemble open-source software with restrictive licenses. The ongoing GitHub Copilot lawsuit illustrates the complexity of AI and intellectual property rights. If such code ends up in a commercial product, businesses could face serious legal and financial consequences.
At Software Co, we implement strict review processes and license-compliance tools to ensure all code—AI-assisted or human-written—is safe to use and distribute.
Key Insight: Clients must demand strong IP governance when AI tools are involved in development.
5. Why Architecture and Human Expertise Still Reign Supreme AI excels at generating short code segments, but it has no conceptual understanding of:
- Microservices architecture and latency budgets
- Scalability and redundancy patterns
- Resilience strategies like circuit breakers or fallback mechanisms
- Regulatory requirements such as data sovereignty or privacy-by-design
These are precisely the areas where software projects succeed or fail. At Software Co, our senior engineers bring decades of expertise in designing systems that grow with your business and comply with local and international standards.
Key Insight: AI doesn’t design systems—humans do. Sound architecture is what makes or breaks a software platform.
6. Software Co’s Approach: AI-Supported, Not AI-Dependent We embrace AI where it adds value—for example, generating unit tests, writing boilerplate, or summarising documentation. However, every AI-generated line is vetted by our developers, passed through security scans, and reviewed for performance, readability, and maintainability.
Our process includes:
- Architecture-first planning
- Domain-Driven Design workshops
- Human-led development supported by AI tools
- Secure coding practices and CI/CD pipelines
- Continuous testing and performance monitoring
Key Insight: The best outcomes come from combining intelligent tools with expert minds and proven engineering discipline.
Smarter, Not Cheaper The arrival of AI-assisted coding tools is a step forward for the software industry, but it doesn’t mean software development should now cost less or be rushed. On the contrary, the best outcomes still rely on human expertise, well-structured processes, and a commitment to long-term quality and security.
At Software Co, we use AI responsibly—as a supporting tool, not a crutch. Our clients benefit from faster iterations in the early stages, without sacrificing the robustness and scalability required for long-term success.
Need a software partner that builds with AI and best practices? Contact us to start your next project with confidence.