Software Ownership Cost: AI Won’t Make It Free

A working prototype hides the long tail

Most software looks straightforward when you inspect the happy path. A retry mechanism, object mapper, messaging abstraction, or assertion library can seem small enough to recreate.

But the comparison isn't "your first implementation vs. the dependency's first implementation." It's "your implementation over its expected lifetime vs. a project that has years of production feedback and fixes."

That long tail often includes questions like:

• What happens when a network request times out after the remote service has already processed it?
• How do retries interact with cancellation, backoff, and idempotency?
• Which behavior is guaranteed, and which is just how the library happens to work today?
• What happens when the dependency changes its API?
• How does the system behave under partial failure or weird input?
• When production breaks and it looks related to the replacement, who investigates?

None of this is an argument against building. It's just a reminder that a short "how hard can this be?" estimate and a long-term "what will we be stuck with?" estimate are not interchangeable.

Dependency changes are a business decision, too

The discussions around projects such as Polly, AutoMapper, MediatR, Fluent Assertions, and MassTransit point to the same broader reality: widely used software can change direction, including licensing and commercial models. The details vary by project and release, so teams should check the current license, terms, and documentation instead of relying on a headline or an old assumption. The original discussion is available here.

It also helps to remember that this isn't just "open source vs paid." The options might include evaluating a commercial alternative, staying on an eligible version, migrating to another package, reducing how much you depend on a feature, or maintaining a limited internal implementation. Each option has its own cost profile and risk.

1. Implementation and review

This is the cost most teams can estimate up front: design, coding, testing, and code review. AI can cut down typing and help generate an initial draft. It doesn't remove the need to define the required behavior or verify that the generated code matches your application.

A realistic estimate also includes integration work, tests for failure cases, review by someone who actually understands the system, and a plan for deployment and rollback. Code that compiles is progress, not proof that the behavior matches what you need in production.

2. Maintenance and security

Dependencies change. Runtime environments change. New vulnerabilities show up. Your product changes around the component.

If you own the replacement, you also need someone to track relevant issues, assess severity, produce fixes, and confirm that fixes don't break existing behavior. Security ownership also includes knowing what the component does and where it's used. If an issue appears, can you quickly identify which services are affected? Can you ship a patch without waiting on the original author or a vendor?

A small custom package can be easier to inspect - but only if someone is responsible for keeping that understanding current. Otherwise, you're just transferring knowledge debt to your future selves.

3. Compatibility and migration

Compatibility is often where the "replacement" gets more expensive than expected. Consumers can rely on behavior that was never formally documented - because the existing library made it dependable in practice. Internal teams will do the same thing, even if you never planned to "support" the replacement like a product.

Then there's migration. If a homegrown abstraction spreads across dozens of services, changing its API can require coordinated updates, testing, and release planning. The more teams depend on it, the more it starts to act like a product, whether you intended it to or not.

4. Support and operational responsibility

Once something breaks, the question shifts from "who wrote this?" to "who will get it working again?" That can mean reproducing a bug, explaining behavior to another team, reviewing a proposed change, or joining an incident call outside normal hours.

A commercial product may include support and defined maintenance commitments, depending on its terms. An open-source project might offer community help, paid support, or neither. An internal replacement gives you control - but it also means your organization owns the queue of problems.

None of these options is automatically better. The key is to compare what responsibility transfers and what stays on your team.

A simple total-cost model

For planning, estimate ownership over a relevant period - often three to five years for infrastructure decisions - rather than comparing only initial build effort with an annual license fee.

Total ownership cost = initial implementation or purchase + recurring maintenance + security response + integration and migration + operational support + opportunity cost.

Opportunity cost matters because the same engineers could be working on features customers actually feel. If two developers spend weeks recreating and maintaining commodity functionality, that work has a cost even if payroll doesn't change.

The first version is cheaper; confidence still has a price

Generated code still has to be checked against your product's real requirements: expected behavior, failure modes, performance constraints, data handling, and how everything interacts with the rest of the system. An AI assistant can suggest tests, but someone still has to decide whether those tests actually cover the risks that matter.

This becomes especially important when the code touches retries, concurrency, authentication, serialization, financial logic, or user data. In these areas, it's easy to ship something plausible that fails in ways that are rare - but expensive. You need evidence: review, testing, monitoring, and a clear understanding of what the code guarantees.

AI can help you explore designs and produce narrow replacements. It doesn't magically make ambiguous requirements become clear, and it doesn't take operational responsibility after release.

More output can create more review work

The economics aren't always "less code, less work." Faster generation can increase the volume of proposed changes. If review capacity, test coverage, and architectural understanding don't keep up, the bottleneck just moves.

A better metric than "how quickly someone can generate code" is "how quickly the team can safely deliver a change and understand its consequences." In practice, that means budgeting for:

• time spent verifying generated code;
• tests to validate edge cases and regression risk;
• maintenance ownership after the original author moves on;
• documentation for the next engineer or team;
• monitoring and rollback plans for production changes.

If AI saves four days on the initial build but the team ends up spending months supporting an unbounded internal fork, you didn't really make it cheaper - you just shifted the expense.

AI makes some replacements more viable, not automatically wise

There are situations where AI genuinely improves the build option. The required feature set may be narrow, the interface stable, the team already familiar with the domain, and the replacement may help you avoid a dependency that's otherwise costly or hard to operate.

In those conditions, a faster implementation can make a controlled replacement worth considering.

But it's the opposite when the library contains complex behavior refined by broad production use - or when a failure would hit core revenue or customer data. A generated first draft doesn't reproduce years of operational feedback. It can be a useful starting point, but it shouldn't be treated as equivalent to a mature implementation without evidence.

So the real question isn't "Can AI write this?" It's "Can our team validate, maintain, and support this replacement at an acceptable cost and risk?"

Step 1: Define the actual requirement

Write down what you use today, then separate essential behavior from convenience. A library may expose dozens of features while your product depends on only a few. Conversely, what looks like a small integration can rest on subtle guarantees you've stopped noticing because the dependency already made them "just work."

Be specific. "We need retries" isn't enough. Define which failures are retryable, how many attempts you allow, how backoff works, what cancellation means, and how duplicate requests are prevented. The more precise the requirement, the more meaningful the implementation estimate becomes.

Step 2: Compare all realistic options

Don't collapse the decision into "pay or rewrite." Depending on the project and license, you might have options like:

• staying on the current version under the applicable terms;
• purchasing a commercial license or support plan;
• migrating to a different maintained project;
• removing or simplifying the feature;
• implementing a deliberately limited internal alternative;
• contributing upstream or negotiating terms when it makes sense.

Verify current project documentation and legal terms. Licensing questions can depend on how the software is used, distributed, and incorporated - so bring in qualified counsel when the business impact is real. A blog post or a quick team discussion isn't a substitute for reviewing the actual terms.

Step 3: Estimate lifetime ownership, not just build time

For each option, estimate the initial work and the responsibilities that follow. Include time for maintaining tests, handling upgrades, responding to vulnerabilities, supporting consumers, and planning future migration.

If you're uncertain, use ranges - and write down the assumptions so you can revisit them later.

A simple comparison table can help:

This isn't a scoring formula. It forces the team to name who carries each responsibility - and what happens if the chosen option stops working for the business.

Step 4: Check whether the team can own the boundary

A replacement is easier to manage when its purpose is narrow and its interface is controlled. Keep the internal abstraction small. Don't try to recreate every feature from the original library if your product only needs a subset.

Add tests that describe the behavior you require, document unsupported cases, and assign a team to review changes.

This is also where architecture matters. If the package is isolated behind an adapter, you can swap it later with less disruption. If its types and assumptions leak throughout the application, even a technically successful replacement can create expensive coupling.

Step 5: Set a review point and an exit plan

Every option should include a moment when you revisit the decision. Commercial agreements can change. Open-source projects can go inactive. Internal implementations can quietly grow beyond their original scope.

Define the signals that trigger review: a major price change, a security concern, a support gap, a new product requirement, or rising maintenance effort.

Also write down how you'd leave. That might mean keeping data in a portable format, limiting use of vendor-specific APIs, or maintaining a migration test suite. You don't need to build the exit today, but you should know whether it's feasible and what it would cost.

A decision rule that works in practice

Building is a reasonable option when the scope is genuinely narrow, the behavior is well understood, the dependency creates a real business or technical problem, and you have an accountable team to maintain the result.

Buying or adopting an existing project is often the better route when the feature is complex, widely depended on, security-sensitive, or outside your organization's core expertise.

In many cases, the best answer isn't a full rewrite or unquestioned adoption. It's paying for support, isolating the dependency, using only a smaller subset, or deferring a migration until there's a concrete reason.

The right choice is the one whose ongoing responsibilities your organization can actually carry.