The Product Creep Trap: Why AI Makes You Build Things You Do Not Need
It starts with a simple prompt on a Sunday evening. You are looking at your team's reporting process, and you think to yourself that it is a bit clunky. Five years ago, fixing this would have required a dedicated sprint, a product manager, and two engineers. Today, you have an LLM. You describe the problem, paste some API documentation, and by 11:00 PM, you have a functional internal dashboard that pulls data from Jira and Slack.
It feels like magic. It feels like you just saved the company $50,000 in SaaS fees. You go to bed feeling like a genius.
But two months later, that dashboard breaks because a dependency updated. Your "simple" reporting tool now requires a weekly maintenance window. The tokens are getting expensive. You realize you are spending 10% of your engineering capacity maintaining a tool that is not even what you sell to your customers.
This is the new reality of the AI era. We are no longer limited by how fast we can type or how many lines of code we can write. We are limited by our ability to focus. Because we can build anything, we are increasingly building everything.
Defining the New Enemy: Product Creep
For decades, project managers have battled scope creep. We all know the drill: a project starts with three requirements and ends with thirty. But AI has introduced a more dangerous evolution of this phenomenon. I call it Product Creep.
Scope creep happens within a specific project. Product Creep happens at the organizational level. It is the tendency for a company to drift away from its core value proposition simply because AI makes it "trivial" to build adjacent features and internal utilities.
In the pre-AI world, the high cost of development acted as a natural filter. If a feature was not essential, it did not get built because the ROI was not there. Now, the "cost to build" has plummeted, but the "cost to own" remains stubbornly high. Organizations are stuffing their products with features "just because they can" and building internal CRMs "just to save a few bucks," not realizing they are slowly suffocating their core business.
Research into software maintenance suggests that the initial build is only about 20% of the total cost of a software lifecycle1. AI helps with that first 20%, but it does almost nothing for the remaining 80% of long-term maintenance, security patching, and integration updates.
The Vibe-Coding Mirage
The term "vibe-coding" has gained traction recently to describe the process of generating software through high-level prompts and "vibes" rather than rigorous architecture. While it is an incredible tool for prototyping, it creates a dangerous illusion of competence in areas where a company has no business operating.
We are seeing a surge in companies attempting to vibe-code their own internal infrastructure. Why pay for a premium reporting tool when you can have an AI whip one up? Why use a specialized project management layer when you can build a "simple" custom version on top of your existing database?
The problem is that "simple" tools in production are never simple. A tool that partially automates team reporting needs to handle authentication, data persistence, error logging, and API rate limits. When you vibe-code these things, you are often bypassing the architectural rigor that makes software resilient.
When your custom-built reporting tool stops syncing with One Horizon or your GitHub repo because an API version changed, who fixes it? Not the AI. It is your lead developer, who should be working on your actual product. This is the hidden tax of the AI era.
The Hidden Invoice: What "Building In-House" Actually Costs
The argument for building internal tools usually centers on saving money. "Why pay $20 per user per month when we can build it ourselves for free?" The math almost never works out when you look at the total cost of ownership (TCO).
The Infrastructure Overhead
Even a small internal app requires a place to live. You need hosting, CI/CD pipelines, and secret management. If you are building a mobile component, you are looking at $99 a year for an Apple Developer Account just to keep the app on your internal devices. These are not massive costs individually, but they represent a "death by a thousand cuts" for your operational focus.
The Maintenance Trap
Software is not a static asset; it is a living organism. Libraries get deprecated. Security vulnerabilities are discovered in your dependencies. A 2025 study found that teams using AI to generate code saw a 22% increase in "maintenance debt" because the generated code often included outdated or inefficient patterns that required manual intervention later2.
The Token Tax
AI is not free. While the cost of tokens is dropping, high-volume internal tools that constantly process company data can rack up significant API bills. Often, these costs are buried in a general "AI Research" budget, hiding the fact that your "free" internal CRM is actually costing you hundreds of dollars a month in inference fees.
Sticking to Your Core: The Only Competitive Advantage Left
In a world where everyone can generate code, the only thing that cannot be easily replicated is a deep, obsessive focus on a specific problem.
If your company builds Fintech software, every minute spent "vibe-coding" a custom HR portal is a minute your competitors are spending on perfecting their transaction engine. AI has commoditized the how of software development, which makes the what and the why more valuable than ever.
The most successful companies in the next five years will not be the ones that used AI to build the most things. They will be the ones that used AI to build the right things faster. They will resist Product Creep by ruthlessly auditing every internal tool and every new feature request against their core mission.
The Power of the Ecosystem
One of the biggest mistakes companies make during a Product Creep phase is viewing their existing tools as "replaceable" by AI. They look at their integrations with Slack, Google Calendar, Linear, or Jira and think, "I could build a version of this that is tailored exactly to us."
This ignores the massive value of the ecosystem. These platforms are not just "tools"; they are maintained, secured, and constantly evolving environments. When you use a Jira or GitHub integration, you are not just getting a feature; you are getting a team of thousands of engineers who are ensuring that tool works 24/7.
The goal should be to use AI to bridge the gaps between these world-class tools, not to replace them. The most efficient teams are those that leverage powerful integrations to create a seamless flow of data, allowing them to stay focused on their unique value proposition.
How to Spot Product Creep Before It Sinks You
If you are worried that your team might be falling into the Product Creep trap, look for these red flags:
- The "Weekend Project" Debt: You have three or four internal tools that were built by one person over a weekend and now require "quick fixes" every month.
- Feature Justification: When discussing the roadmap, the primary reason for a feature is "it won't take long to build with AI" rather than "our customers are begging for this."
- The Integration Graveyard: You have abandoned half-finished attempts at automating workflows because the AI couldn't quite handle the edge cases.
- Developer Distraction: Your senior engineers are spending more time prompting for internal utilities than reviewing code for your primary product.
The solution to Product Creep is not to stop using AI. It is to increase the friction of starting new projects. Just because it is easy to build doesn't mean it is worth having.
Regaining Visibility and Focus
The antidote to Product Creep is clarity. You need to be able to see exactly where your team's energy is going. Is it going toward your core product, or is it leaking into "vibe-coded" side projects that offer marginal utility?
This is where the right layer of visibility becomes a "secret weapon." You don't need more tools; you need a way to make sense of the tools you already have. You need to see how your commits, your pull requests, and your discussions actually map back to your high-level goals.
One Horizon acts as this essential layer of truth. Instead of letting your team drift into the "Product Creep" trap, it provides the visibility needed to keep everyone aligned on the core business. By connecting your existing ecosystem - GitHub, Jira, Slack, and more - it reveals the true story of your productivity.
It allows leaders to ask the hard questions: Why are we spending so much time on this internal reporting script? Why has this "simple" AI feature been in the PR queue for three weeks? When you have that level of insight, you stop building "just because you can" and start building because it matters.
The companies that win the AI race won't be the ones that generated the most code. They will be the ones that maintained the most focus. They will be the ones who realized that the most powerful thing you can do with AI is use it to do less, but do it better.
Stop the Creep
Footnotes
-
Software Engineering Institute (2024). "The Long-Tail Cost of Software Ownership in the AI Era." https://www.sei.cmu.edu/documents/6244/2024_Year_in_Review.pdf ↩
-
DORA (2025). "State of DevOps Report: The Impact of Generative AI on Technical Debt." https://cloud.google.com/resources/content/2025-dora-ai-assisted-software-development-report ↩
