Two Ways to Build With AI: Runtime Dependency vs. Build Tool
When someone says they “built something with AI,” that phrase does not say much on its own. It might mean the finished product calls an AI service every time it runs and cannot work without it. Or it might mean AI was used during development to write or build the software, and what actually got deployed has no AI dependency at all. These are not two versions of the same thing. One describes a characteristic of the running software. The other describes how something was made. The distinction rarely gets named, and in developer circles it tends to be taken for granted, but for anyone paying for software development, managing a technical team, or planning a product, it is worth understanding.
AI as a Runtime Dependency
Some software is built so that a live AI service is a required part of how it functions. Every time a user interacts with it, the application reaches out to an external AI provider and waits for a response. Without that connection, the feature does not work. Chatbots, AI-powered search, dynamic content generators, and real-time recommendation engines are common examples.
From a cost standpoint, this means the bill never stops. Usage drives cost, and usage is rarely predictable. A traffic spike is also a cost spike. Beyond that, many current business plans are being built around today’s AI pricing, which has been kept low as providers compete for market share. That pricing will not stay where it is indefinitely, and any product with a runtime AI dependency will feel increases directly.
The output is also random by nature. AI models are non-deterministic, meaning the same question or request can produce a different answer each time. That is not a flaw in the software but a fundamental property of how these systems work. It is why AI-powered products tend to include disclaimers, and why the people building them have to plan for responses that may be wrong or inconsistent. The feature is only as reliable as the AI service behind it, and neither the output nor the uptime of that service is within the developer’s control.
Large companies have run into this. Starbucks deployed an AI-powered inventory counting tool across its North American stores in September 2025. Nine months later, the company scrapped it. Store employees had flagged the system as unreliable, and Starbucks reverted to manual counts. The inventory problem the tool was meant to solve remained unsolved. Around the same time, a Pizza Hut franchisee filed a $100 million lawsuit against the chain after a mandatory AI delivery management system was deployed across its 111 locations. Before the rollout, more than 90 percent of deliveries arrived within 30 minutes. After it, delivery times lengthened, customer satisfaction fell, and the franchisee’s year-over-year sales growth in New York City flipped from positive 10 percent to negative 10 percent. These are two examples from the restaurant industry alone in a single month, and they illustrate a recurring pattern: introducing randomness into operations that require precision can become very expensive, and may not be the right solution in the end.
None of this means a runtime AI dependency is always the wrong call. Some features genuinely require it. But the cost and reliability profile is worth a clear look before committing to it.
AI as a Build Tool
AI can also play a role that never shows up in the finished product at all. Developers use AI tooling to write code, work through technical problems, build out datasets, or move faster through parts of a project that would otherwise take longer. Once the software ships, the AI involvement is over. What runs is plain software with predictable costs and consistent behavior.
A useful way to think about it: a development team might use AI tooling to help build a feature backed by a database. Databases are stable, well-understood infrastructure. When something goes wrong with a database, it is almost always traceable to a specific human decision. They do not produce random output. The finished feature runs on that database with no ongoing AI connection, and it behaves the same way every time someone uses it. The AI was part of how it got built, not part of how it works.
The tradeoff is that the quality of what gets built still depends on the people doing the building. AI coding tools have made it easier to produce an application quickly, but they have also made it easier to produce one with security gaps, poor structure, or features that fail under real conditions. There is a growing body of software that looks functional on the surface but was built without the expertise to make it reliable or safe. AI accelerates development. It does not replace the judgment required to build something worth deploying.
What This Means in Practice
For anyone commissioning software or evaluating a development plan, the right question is not just whether AI is involved, but where. A feature that depends on a live AI service carries an ongoing and variable cost tied to an external provider, plus the challenge of managing output that may not always be correct. Software where AI was only involved in the build process is just software, with the cost structure and reliability of software. The same product can include features that depend on a live AI service at runtime alongside features that were built with AI but run entirely on their own. Understanding which is which is part of understanding what you are actually buying or building.