Executive summary
The belief that artificial intelligence would replace most software developers has shifted from bold prediction to uncomfortable reality check. AI coding tools have delivered genuine gains in speed and convenience, but organisations that moved too quickly to substitute human engineers with AI are now facing an unintended outcome: rising technical debt, increased risk and a greater dependence on senior developers than before.
Rather than removing engineering effort, AI has relocated it. Time once spent writing code is now spent reviewing, debugging, securing and unpicking it. At the same time, a new pattern of so called vibe coding has emerged, where teams use AI to rapidly produce proofs of concept and minimum viable products to test ideas and impress investors. Used deliberately and treated as disposable, this can be powerful for early validation and storytelling, but it becomes dangerous when thrown together prototypes are quietly promoted into production systems.
The organisations seeing the best results are no longer asking how AI can replace developers, but how it can support experienced teams, accelerate experimentation and speed up learning without damaging quality, accountability or long term resilience.
The promise that sparked the rush
Over the last few years, AI driven development tools have been promoted as capable of automating a large share of software engineering work. Some forecasts suggested that 40 to 80 per cent of coding tasks could be automated or heavily assisted. For executives, the message was compelling: faster delivery, smaller teams and significant cost reduction.
On the surface, the logic appeared sound. AI can generate code in seconds. Boilerplate disappears. Prototypes arrive almost instantly. Early experiments with generative coding assistants showed that developers, particularly less experienced ones, could complete tasks more quickly and report feeling less frustrated and more satisfied with their work. For many organisations, this created pressure to reduce engineering headcount and rely heavily on tools to carry the workload.
What followed was predictable.
Where reality diverged from the hype
AI does not understand systems. It predicts patterns. This distinction matters.
AI can generate code that looks correct, but it often lacks architectural awareness, security context, domain understanding and appreciation of long term trade offs. The result is code that compiles and may work in the short term, but quietly introduces fragility into production environments.
Teams began to experience:
- Growing volumes of code with declining maintainability
- Defects that surfaced weeks or months later, often in complex edge cases
- Security weaknesses introduced through repeated insecure patterns or copied examples
- Senior engineers spending more time reviewing and correcting AI output than delivering new capability
Instead of reducing workload, AI increased the need for oversight and deep technical judgement.
The technical debt multiplier effect
One of the most underestimated consequences of AI generated code is the speed at which technical debt accumulates.
AI optimises for plausible output, not durability. It does not feel the impact of outages, regulatory scrutiny or customer harm. Humans do. In many teams, the volume of code produced has increased faster than the organisation’s ability to review, test and refactor it. This shows up as higher code churn, more rework and a growing backlog of clean up work that never quite gets prioritised.
Organisations that reduced engineering capacity too early discovered that:
- Fixing AI generated code often costs more than writing it correctly in the first place, once review, refactoring and defect remediation are included
- Architectural shortcuts compound rapidly when AI produces large amounts of code that bypass established patterns and guardrails
- Senior engineers become bottlenecks, spending more time triaging and redesigning AI created components than shaping new capabilities
In regulated industries such as financial services, payments and healthcare, these risks are magnified by compliance, audit and security obligations. AI tools can introduce vulnerabilities, mishandle sensitive data or conflict with internal coding standards in ways that create regulatory exposure if not tightly governed.
Where vibe coding is genuinely useful
There is one place where so called vibe coding with AI can be strategically valuable: very early stage product work where the goal is to explore ideas, not to build production systems.
In proof of concept and minimum viable product work, leaders are optimising for speed of learning, investor storytelling and stakeholder engagement rather than robustness. AI assisted coding can help small teams quickly generate functional mock ups, clickable demos and thin vertical slices that show end to end value, even if the underlying code is messy and disposable. Used deliberately, this lets product and commercial leaders test propositions with customers and investors in weeks instead of months, while keeping experienced engineers focused on core platforms and long term architecture. The key is to treat this code as throwaway experiment tooling, not as the foundation for a production system or a substitute for disciplined engineering.
Why senior engineers became more valuable, not less
Ironically, AI has increased the value of experienced engineers.
Senior developers remain essential for:
- Making architectural decisions that balance speed, resilience and compliance
- Identifying subtle security and data protection risks in AI suggested patterns and libraries
- Understanding system wide impacts across legacy platforms, microservices and external integrations
- Taking accountability when failures occur, leading incident response and guiding long term remediation
Studies of AI assisted development show that productivity gains are strongest for less experienced developers, while senior engineers are often pulled into additional review and supervision work. On paper, junior staff appear more productive because AI helps them produce more lines of code and complete tasks faster. Without strong senior oversight, however, that speed can conceal deeper structural weaknesses and future reliability issues.
This dynamic has created a widening gap. Organisations that tried to replace senior engineers with AI discovered that they actually needed more senior capability, not less, to manage the volume and complexity of AI generated changes.
What smart organisations are doing differently
The organisations gaining real value from AI are not treating it as a replacement strategy. They are treating it as a capability enhancer.
Common approaches include:
- Clear policies and standards defining where and how AI tools are used, including data protection and secure use guidelines
- Mandatory human review and ownership, with AI generated code treated as untrusted until it passes the same quality and security checks as human written code
- Using AI for repetitive and lower risk tasks such as boilerplate, test generation, documentation and routine refactoring, rather than core architecture or safety critical components
- Treating AI output as a starting point, not a final decision, and encouraging engineers to adapt, challenge and simplify generated solutions
- Explicitly distinguishing between disposable vibe coded artefacts for proofs of concept and investor demos, and durable code that will underpin production systems
Leadership expectations are evolving as a result. AI is increasingly framed not as a simple cost cutting shortcut, but as a productivity amplifier when paired with the right skills, guardrails and measures of success. The focus is shifting from raw output volume to reliability, maintainability, risk reduction and speed of validated learning in early product work.
Conclusion
AI is not failing software development. The failure lies in how it has been positioned and governed.
Replacing developers with AI was always the wrong question. The right question is how to combine machine speed with human judgement, experience and accountability, and when it is appropriate to use fast but disposable vibe coded prototypes to explore ideas and tell a compelling investor story. Organisations that understand this are building systems that are faster, safer and more resilient because they deliberately pair AI assistance with strong engineering leadership, robust governance and a clear separation between experimental and production code. Those that do not are quietly accumulating risk in their codebases, security posture and operational stability, turning short term vibe coded experiments into long term liabilities.
The future of software development is not AI alone and it is not humans alone. It is AI supported, human led and accountability driven, with deliberate use of rapid AI powered experimentation at the edges and disciplined engineering at the core. That distinction will separate sustainable organisations from those that learn very expensive lessons the hard way.
References:
- BIS – “Generative AI and labour productivity: a field experiment on software developers”
https://www.bis.org/publ/work1208.htm - SoftwareSeni – “What the Research Actually Shows About AI Coding Assistant Productivity”
https://www.softwareseni.com/what-the-research-actually-shows-about-ai-coding-assistant-productivity/ - Okoone – “Why AI generated code is creating a technical debt nightmare”
https://www.okoone.com/spark/technology-innovation/why-ai-generated-code-is-creating-a-technical-debt-nightmare/ - LeadDev – “How AI generated code compounds technical debt”
https://leaddev.com/technical-direction/how-ai-generated-code-accelerates-technical-debt - GitGuardian – “GitHub Copilot Security Vulnerabilities: Risks and Best Practices”
https://blog.gitguardian.com/crappy-code-crappy-copilot/
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