Posted on: 25 02 2026.

AI coding assistants are great for MVPs and demos – shipping production code still belongs to experienced engineers

At Comtrade 360, we actively use a set of AI coding assistants and „vibe coding“ tools in real-world projects – primarily GitHub Copilot (including CreateMVPs), Cursor, and Lovable AI. We apply them most often across the following levels and typical use cases:

• Greenfield MVPs (new products): building from more- or less-detailed PRDs
• Prototypes for new features (existing products): extending the current solution and codebase with feature prototypes
• Repository onboarding & exploration: „explain this codebase“, navigation hints, and architecture overviews
• Extending modules (methods, tasks): incremental enhancements within the existing repository
• Defect triage & fixing: diagnosing issues and proposing targeted corrections

Over time – through sustained, hands-on use – we’ve consolidated our practical experience and perspective on where these tools add the most value and how to apply them responsibly.

AI coding assistants excel at speed – up to a point

AI tools promise less boilerplate, fewer documentation detours, and faster iteration. In practice, they deliver – especially for MVPs, new feature prototypes, and personal projects. Beyond those use cases, however, the picture shifts: velocity may feel higher, but getting code production-ready often takes longer than expected.

At first glance, it feels faster

Teams frequently report perceived speed-ups, yet measured outcomes tell a more nuanced story: once reviews, fixes, and cleanup are factored in, experienced developers are often slower on average. Assistants reduce keystrokes but introduce code that must be read, verified, and untangled – less typing, more archaeology.

Quality declines as context expands

Speed is one dimension; quality is another. During long sessions, many engineers observe „context drift“: as more context accumulates, assistants begin pulling in stale or irrelevant details, and accuracy drops. While useful for developing a deep, intuitive understanding of how a codebase works, these tools are not safe to trust blindly-seasoned engineers remain cautious.

The myth of 6× higher productivity

Claims of „6× productivity“ do not withstand basic scrutiny. Turning a three-month effort into two weeks is unrealistic for complex software systems. Real bottlenecks are not typing speed; they are design decisions, PR queues, flaky tests, context switching, and deployment gates.

In controlled tasks, assistants excel at scaffolding – particularly for less experienced developers. However, in mature codebases, minutes saved on boilerplate are often offset by additional reviews, test repairs, and refactoring required to meet production standards.

Where the gains truly appear

Developers with AI access tend to complete more small tasks, with the greatest benefits when they:

• Lack prior context and need rapid scaffolding
• Replace documentation searches with targeted questions
• Build short-lived prototypes

Senior engineers already familiar with the stack often see modest gains – or even slowdowns – because they spend more time validating output than generating it.

Security: the clearest gap

Security risks can compound quickly. Earlier studies linked over-trusting AI output to increased vulnerabilities; newer analyses are even more direct: AI-generated changes often ship faster and with more flaws (privilege-escalation paths, design weaknesses, hard-coded secrets), while review fatigue increases.

Without stronger gates – secret scanning, policy checks, guarded merges – assistants can accelerate the path of least resistance into production. They also expand the attack surface through added plugins, runtimes, and permissions that must be patched, logged, and governed.

The missing gap to production-ready code

AI often gets you to roughly 70%. The final 30% – the difference between a demo and production – is the hardest part: edge cases, failure modes, performance budgets, comprehensive testing, observability, and the architectural rigor that keeps systems stable 24/7.

A demo needs to run once. Production needs to run continuously without breaking.

A pragmatic stance

AI assistants are excellent for:

• MVPs and demos: scaffolds, UIs, glue code, throwaway prototypes
• Exploration: „explain this repo“, „show me the call graph“, „draft tests“
• Boilerplate: configuration, simple CRUD operations, SDK usage, typed DTOs

Shipping to production still belongs to experienced engineers who:

• Design contracts and boundaries before code generation
• Treat AI output like a junior developer’s PR – reviewed, tested, and instrumented
• Enforce hygiene gates (secret scans, license checks, SAST/DAST, CI policies)
• Demand observability (logs/metrics/traces) and maintain realistic performance and accessibility budgets
• Insist on security reviews and fix-forward plans

Bottom line: Use AI to close the demo and prototype gap quickly. Then rely on human engineering discipline to cross the production chasm – where reliability, security, and maintainability truly live. That’s not anti-AI; it’s pro-software.