{
  "id": 8963127,
  "title": "Why AI hasn’t replaced software engineers, and won’t",
  "url": "https://urgent.news/2026/09/21/why-ai-hasnt-replaced-software-engineers-and-wont",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-21T16:47:24.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/heyabdullahbruh/why-ai-hasnt-replaced-software-engineers-and-wont-2cpj"
  },
  "original_language": "en",
  "account": "In the realm of software development, there are instances when a problem appears more daunting than it truly is. The writer recently encountered such a scenario while working on a hospitality management software project. The server-side work had been successfully completed, and the team moved into testing. An authentication flow issue surfaced during testing, which initially seemed perplexing. The team reviewed the implementation, examined various components of the flow, and experimented with different methods to identify the root cause. They even employed other Large Language Model (LLM) tools during the investigation, yet none provided a clear indication of the actual issue. Eventually, the writer revisited the authentication implementation independently and discovered a minute discrepancy. A specific keyword had been utilized in a variation that did not align with the project's requirements. This minor difference was sufficient to trigger the authentication flow failure. Once corrected, the authentication flow began functioning as intended. The writer was not solely interested in the fact that the bug was resolved; rather, they were intrigued by the fact that a small problem could remain concealed within an otherwise coherent implementation. The experience prompted the writer to contemplate the appropriate utilization of AI in real-world software projects. While AI coding assistants prove beneficial during development by assisting developers in exploring ideas, navigating unfamiliar areas, investigating possibilities, and streamlining certain aspects of development, the writer emphasized the distinction between using AI as part of an engineering workflow and relying on it to make critical engineering decisions on their behalf. Before commencing implementation, their team invests considerable time in comprehending the project thoroughly. They engage in discussions about the product, its requirements, workflows, architecture, and the interconnections between different system components. The aim is to establish a shared comprehension of the project's purpose and expected behavior before relying on implementation tools to construct it. This distinction is crucial. The team desires to understand the system's rationale, the connections among its parts, and the anticipated behavior before accepting generated code as is. This approach becomes particularly pertinent during the testing phase. During testing, when the authentication issue manifested, the server-side development had already been finalized, and the team commenced testing the system. It was at this juncture that the authentication problem arose. Some sections of the authentication implementation had been crafted with AI assistance by one of the team members. She had reviewed the code and found it reasonable at first glance. However, debugging can be challenging because a problem may not manifest as a problem initially. The implementation might appear logical, the surrounding structure may seem fine, and the flow may seem consistent with expectations. Despite this, the software may still behave incorrectly. The writer's team also investigated the authentication problem. They explored different approaches, examined various parts of the flow, and even consulted other LLMs for assistance. Although these efforts yielded suggestions and potential avenues to explore, the true cause remained elusive. The problem was not a lack of scrutiny; rather, the actual issue was minuscule enough to go unnoticed while the team was focusing on larger segments of the system. The writer noted that when a software system comprises multiple interconnected parts, developers instinctively tend to look for problems at the most significant level initially. Authentication represents a major component of the system. Consequently, when authentication fails, it is natural to scrutinize the broader authentication flow, surrounding logic, and related parts of the application. This is precisely what the writer's team did. However, debugging does not always reward those who examine the largest components first. Sometimes, the root cause is nestled within a single assumption, a subtle implementation divergence, or a minor detail that seemed insignificant during review. This was the case in the writer's scenario. Despite previous consideration of multiple possibilities, thorough reviews, involvement of additional team members, and the utilization of supplementary LLM tools, the problem persisted. Debugging can be particularly arduous for even experienced developers due to the aforementioned factors. The visible behavior can make the problem appear extensive while the underlying cause is minuscule. A minor mismatch can give rise to a much larger symptom. After returning to the authentication flow, the writer decided to revisit the implementation more diligently, driven by the realization that the team's prior investigation was thorough. Upon closer examination, the writer identified a minute implementation detail. A particular keyword had been employed in a different variation than mandated by the project. This single discrepancy was the culprit behind the authentication flow failure. The writer corrected the discrepancy, and the authentication flow resumed functioning correctly. For the writer, this aspect of the experience proved more valuable than the bug itself, as it highlighted the ease with which one might spend time searching for a convoluted explanation while the genuine issue lies in a detail that initially appears insignificant. The writer's reflection on the AI-assisted coding experience did not lead them to abandon the use of AI altogether. Instead, it prompted them to approach its utilization with greater caution. When code is generated with AI assistance, it must still adhere to the specific requirements of the project. In other words, the generated code must align with the actual project's needs. Therefore, even when employing AI coding tools, the resulting code must undergo thorough review and adaptation to ensure it fits seamlessly within the project's framework.",
  "summary": "(TL;DR) There are moments in software development when a problem looks much larger than it actually is. We recently had one of those moments while working on a hospitality management software project. Our server-side work was completed, and we had moved into testing. The authentication flow was not behaving as expected. We reviewed the implementation, looked through different parts of the flow,…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}