The Honest Assessment
AI coding tools (GitHub Copilot, Cursor, Claude Code, Codex) have genuinely changed software development economics. But the change is more nuanced than the hype suggests, and the way it affects the cost of building your specific app depends on what kind of work your project primarily involves.
Here is a grounded analysis.
Where AI Coding Tools Genuinely Reduce Cost
Routine implementation
Boilerplate code (CRUD endpoints, form validation, data models, utility functions) is where AI coding tools deliver real productivity gains. A senior engineer with Cursor generates this kind of code 40–70% faster than without it. This directly reduces the cost and time of building standard functionality.
First-draft generation
AI tools are excellent at producing a first draft that engineers then refine. This matters most for features the engineer has done before in a different context. "Build a Stripe webhook handler" takes 20 minutes with AI assistance vs. 2 hours without. The cognitive cost of remembering API details and standard patterns is offloaded.
Documentation and testing
Writing tests and documentation for existing code is one of the highest-impact AI use cases. Tests that engineers would defer (because testing is less satisfying than building) are now 50–70% faster to write with AI assistance. Better test coverage reduces the cost of bugs caught in production.
Junior-level acceleration
AI tools significantly raise the output floor for junior engineers. A junior with AI assistance can produce quality closer to a mid-level engineer for well-defined tasks. This changes the economics of hiring and team composition.
Where AI Coding Tools Do Not Reduce Cost
System design and architecture
No AI tool replaces the judgment required to design a system that will scale, remain maintainable, and evolve correctly. Architectural decisions (how to model multi-tenancy, how to structure the API, how to handle real-time state) require human expertise and domain understanding. Bad architecture chosen quickly (with or without AI) is expensive to fix.
Novel problem-solving
When an engineer faces a genuinely novel problem (a complex integration with a poorly documented API, a performance problem requiring deep profiling, a security vulnerability in a custom implementation), AI tools help at the margins. The core intellectual work is still human.
Product judgment
Building the right feature, in the right way, for the right reason is not an AI problem. "What should we build next?" and "Is this feature correct for our users?" are human questions.
Debugging complex systems
AI tools can suggest fixes for common bugs. For complex, multi-system failures (race conditions, distributed system issues, data corruption bugs), experienced human debugging remains essential.
What This Means for Founders
Building software is cheaper and faster than it was two years ago, primarily for well-scoped work with clear requirements. A project that might have cost $40,000 in 2022 might cost $25,000–$30,000 today, with the same quality and a faster timeline.
But the cost driver for your project is probably not coding speed. It's scope, decision latency, and revision cycles. Fixing those has a larger impact on budget than AI tooling alone.
At Kastling, AI coding tools are standard in our stack. The efficiency gains go into more feature depth and tighter engineering for the same scope, not lower headline numbers. You get better outcomes for the same spend.
FAQ
Q: Can AI tools replace a software engineer entirely?
Not in 2025 for production-quality applications. AI tools are force multipliers for skilled engineers, not substitutes. A non-technical user can generate code with AI tools; they cannot reliably build production software without engineering expertise to evaluate, debug, and integrate what's generated.
Q: Should I hire engineers who use AI tools?
Yes, strongly prefer them. An engineer who uses AI coding tools effectively ships 30–50% more code with equivalent quality. In 2025, declining to use AI tools is a meaningful productivity disadvantage.
Q: Do AI-generated codebases have more bugs?
When AI output is reviewed by skilled engineers, no. When AI output is shipped without review, yes, significantly more bugs. The review step is not optional.
Q: Will AI tools make it free to build apps eventually?
Tools will continue to improve and the cost of routine development will decline. But the cost of system design, product judgment, and architectural expertise will remain real. "Free to build" would require AI that can make product decisions, which is further out than most people predict.