The Economics of Building a 128,000 LOC iPhone App with AI

A transparent financial breakdown of building Ahimo, a production-grade iOS health app, using AI-driven development - achieving 97-99% cost reduction compared to traditional development.

While the narrative around AI-driven development often focuses on speed and novelty, the actual economics—the granular costs of tokens, subscription tiers, and the price of scaling through rate limits—remain largely opaque.

I recently completed the development of Ahimo, a production-grade health and well-being application for iOS. The project was built from the ground up with zero manual coding, using Claude Code (transitioning through GPT-4, Sonnet, and Opus).

The scale of the project is significant:

  • Lines of Code: 128,659 (verified)
  • Swift Files: 336
  • Total Commits: 2,818
  • Estimated Tokens Burned: 80–150 Million
  • Core Features: SwiftUI, HealthKit, WeatherKit, CoreData, On-device AI
  • Localization: English, Spanish, German

In the interest of transparency for the developer and founder community, here is the full financial breakdown of what it costs to “hire” an AI to build a 128k LOC application over 13 months.

The Financial Breakdown

A common critique of AI development is the lack of clarity regarding operational costs. Below is the itemized expenditure from December 2024 to December 2025, reflecting the transition from standard usage to high-intensity development.

MonthCostCommitsPhase
Dec 2024$200.000Tooling Discovery
Jan 2025$20.0027Architectural Foundation
Feb 2025$20.00372Architectural Foundation
Mar 2025$20.00305Architectural Foundation
Apr 2025$20.00384Architectural Foundation
May 2025$20.00251Architectural Foundation
Jun 2025$97.42227Development Sprint
Jul 2025$21.62194Maintenance & Refinement
Aug 2025$21.62154Maintenance & Refinement
Sep 2025$91.71166The Final Sprint
Oct 2025$108.10213The Final Sprint
Nov 2025$125.35211The Final Sprint
Dec 2025$108.10314The Final Sprint

Total Cumulative Cost: $873.92

Overcoming the “Rate Limit” Bottleneck

The data shows a clear correlation between expenditure and velocity. The baseline $20/month subscription is sufficient for learning or small modules. However, as the codebase surpassed 50,000 lines, the “cost of context” became the primary factor.

The spikes to ~$100/month represent a strategic decision to bypass rate limits and provide the model with massive context windows (entire view hierarchies and data models). For a professional project, paying 5x the standard subscription to reduce a three-day debugging cycle to twenty minutes is a high-yield investment.

The Time and Expertise Investment

Although I wrote zero lines of code, the role of “Product Owner” in an AI-driven workflow is rigorous. Success required:

  1. Requirement Precision: The AI is only as capable as the instructions it receives.
  2. Continuous QA: Every feature was tested on-device via TestFlight immediately after implementation to identify UI/UX regressions.
  3. Context Management: Managing 2,818 commits meant ensuring the AI adhered to a strict “constitution” (CLAUDE.md) to prevent architectural drift.

ROI: AI vs. Traditional Development

The financial implications of this model are transformative.

  • Agency Build (Estimated): $50,000 - $150,000 (Based on 128k LOC and complex integrations).
  • Freelance (Estimated): $25,000 (Assuming 500 hours at a $50/hr rate).
  • AI Build (Actual): $874.

The cost reduction is approximately 97-99%.

The Market Impact: Democratization of Custom Software

This experiment demonstrates a fundamental shift in the software market. Historically, custom mobile apps, sophisticated web platforms, and multi-language interfaces were luxury assets reserved for venture-backed startups or established corporations.

Now, small businesses and entrepreneurs can deploy bespoke software solutions for under $1,000. This capability is not limited to mobile apps; it applies equally to web applications and corporate websites.

Global Reach by Default

A frequent question I receive is, “How did you build the website in three languages?”

The answer is simple: I asked the AI to do it.

In traditional development, localization is a costly, multi-step process involving translators and engineers. In an AI-driven workflow, it is a single prompt. This means even the smallest project can be “global by default,” removing language barriers that previously limited market reach for small entrepreneurs.

Conclusion

We are entering an era where software development is no longer gated by the ability to write syntax, but by the ability to architect systems and define user experiences. For less than $900, I was able to build an application that, five years ago, would have required a small team of engineers and a six-figure budget.

Ahimo is currently in beta. You can explore the results of this experiment on TestFlight: Ahimo.app