A widely shared developer account describes a problem many small software teams are beginning to encounter: an AI-assisted coding workflow that produced thousands of lines of code quickly, but not a working feature.
The author says a business partner submitted a 5,236-line pull request for a payments-backend module in a single day. The PR, they contend, included repetitive AI-style documentation, omitted practical setup information such as where to obtain API keys, and—most importantly—had not been tested by calling the endpoints. According to the post, the endpoints failed when the author tried them.
It is one person’s account, not an audit of a particular tool or development team. But the underlying management lesson is broader than the dispute between two collaborators: code generation is not the same thing as software delivery.
The bottleneck has moved
AI coding tools can reduce the time required to draft routes, integrations, tests and documentation. That can be valuable, particularly for repetitive implementation work. But when generation gets cheaper, the scarce work becomes more visible:
- turning requirements into clear acceptance criteria;
- reviewing architecture and security implications;
- validating integrations against real services and credentials;
- checking that documentation helps a new user complete setup; and
- assigning a human owner to the outcome.
Payments code makes this especially consequential. Even a basic integration can involve secret handling, authentication, error behavior, idempotency, retries, webhooks and environment configuration. A pull request that looks complete in a diff may still be unusable—or create risk—if those paths have not been exercised.
The reported failure was not that an AI tool wrote imperfect code. Software has always required debugging. The operational failure was the apparent absence of a minimal verification loop before review. The author says the relevant endpoints could have been run in minutes.
Treat generated output as an untrusted first draft
Teams adopting AI assistance should avoid treating a large PR as evidence of progress. A better standard is demonstrable behavior.
For each change, require a concise definition of done: the expected user flow, commands or environments used to test it, evidence of successful execution, and known limitations. For APIs, that can be as simple as an example request and response, prerequisites for authentication, and a repeatable test command. For payment-related work, include failure scenarios as well as the happy path.
Review practices may need to change, too. Large, generated diffs are harder—not easier—to inspect. Smaller, incremental PRs make it more practical to review design decisions and isolate regressions. If a change is too large for a reviewer to understand, it is usually too large to approve on the basis of superficial plausibility.
Accountability cannot be delegated
The post also points to a collaboration problem. The author says they had asked to work jointly on the module and that the resulting implementation did not reflect the needs of the person expected to use it. AI can accelerate solitary production, but it does not replace the conversations that resolve product intent, interfaces and operational ownership.
For founders and engineering leaders, the practical question is not whether developers use AI. It is whether the organization measures delivery by generated volume or by verified outcomes. The teams that benefit most will pair fast generation with enforceable tests, clear review boundaries and explicit responsibility for production behavior.
What to watch next
Expect more pressure for AI-specific engineering controls: PR templates that capture validation evidence, automated checks that run before review, tighter limits on generated change size, and policies for declaring AI-assisted code. The tools may keep making code cheaper. The discipline to test, understand and own it remains the differentiator.
