The velocity trap: Why AI speed doesn’t always benefit users

The rise of agentic artificial intelligence tools has introduced a new benchmark in software development: speed. Platforms promise to generate applications in minutes, positioning velocity as the ultimate advantage. But this focus on rapid code generation may be misleading. When speed becomes the primary goal, user outcomes are sometimes subpar.

Agentic AI tools—those that autonomously generate entire features or applications—prioritize immediate functionality. Without proper foresight, this approach can risk creating systems that are difficult to maintain, adapt or evolve.

The importance of thoughtful engineering

User outcomes are not the byproduct of fast code—they are the result of thoughtful engineering. This means understanding what users are trying to accomplish, designing systems that evolve with their needs and writing code that teams can build on.

Agentic AI tools sometimes lack the contextual awareness to anticipate user needs or weigh long-term consequences. They excel at pattern matching and short-term problem-solving, but they often struggle to design for adaptability, maintainability and human experience.

Evaluating AI-generated development through a human-centred design lens reveals how velocity can undermine value. Five key concepts illustrate this tension:

• Time to first value: AI may deliver a prototype quickly, but building thoughtfully to truly serve users can require patience.
• Cognitive load ratio: Reviewing and refactoring AI-generated code can increase overhead, pulling engineers away from user-focused problem-solving.
• Code comprehension rate: Code that simply functions isn’t always easy to understand or modify. Lack of clarity slows future development.
• User journey coverage: AI lacks the context to design for the subtle, human experiences that define quality.
• Change readiness: AI-generated code tends to resist change. Tight coupling and lack of abstraction make it harder to evolve with user needs.

The AI paradox

AI supports experienced engineers—but it may mislead others. Senior engineers benefit from AI because they use it within a framework of user-centred thinking. They know when to accept, reject or refactor AI output. Junior engineers, lacking that framework, may rely on AI too much—leading to brittle implementations that collapse under real-world use.

This dynamic reveals a deeper truth: AI amplifies existing capabilities. It doesn’t replace engineering wisdom. Without a strong foundation in user-centred design, AI-generated speed can become a liability.

A false choice

The current narrative presents a binary: embrace AI speed or fall behind. But this framing is flawed. Sustainable development speed and user outcomes are aligned. Generation speed and user outcomes often are not.

Sustainable speed comes from:

• Building systems that are easy to understand and modify
• Catching user experience issues early
• Creating foundations for rapid iteration
• Maintaining code quality that enables confident change

Generation speed creates the illusion of progress while increasing long-term risk—the future belongs to teams that recognize this distinction.

The takeaway

AI can accelerate implementation—but it cannot replace human understanding of what should be implemented. When user outcomes remain the guiding principle, AI becomes a powerful tool in service of thoughtful engineering.