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Exploring the Future of Software Development with AI Coders

Introduction

Large Language Models (LLMs) like ChatGPT have caused a stir in creative circles with their ability to generate images, text, and code.
While initial results were flawed, these models are steadily improving, challenging the notion that machines cannot think creatively.
The article explores the potential impact of AI on software development and offers frameworks to understand how AI could augment or replace certain development tasks.

Software development involves more than just writing code, encompassing tasks like requirements gathering, design, infrastructure setup, debugging, and deployment.
A framework is proposed to classify the level of AI assistance in software development:
- Level 1: No AI involvement (traditional development)
- Level 2: Developers using AI assistants like ChatGPT or Copilot for tasks like writing tests, boilerplate code, and refactoring (current state)
- Level 3: AI coders capable of handling entire projects, from requirements to deployment (emerging capability, as demonstrated by Devin AI)

The article draws an analogy between AI coders and outsourced software vendors/consultants, with organizations potentially relying on AI coders to varying degrees.
Regardless of the level of AI coder involvement, the importance of having an in-house team to oversee and align the work with long-term organizational goals is emphasized.

Software development is not just about writing code but about effectively modeling and translating complex business problems into digital solutions.
The article questions whether business users could leverage AI coders to create complex workflows without the involvement of software developers.
It argues that for complex problems, human expertise is still required to manage complexity and translate real-world domains into digital models, similar to how civil engineers are needed for complex construction projects.

The article challenges the assumption that the market for software development is fixed, with AI coders simply taking market share away from human developers.
Instead, it suggests that the market for "solving business problems" is much larger than just software development, implying that AI coders could expand the overall market rather than completely replacing human developers.

Business logic must be defined in an unambiguous format, whether it's programming languages, Excel formulas, or low-code flows.
Even if AI coders can generate software products from conversational English instructions, there would likely be an underlying formal definition of the business logic generated in the backend, akin to code.
This formal definition would still require human expertise to understand and modify as needed, necessitating the involvement of software developers.

The article concludes that there will likely be a continued market for software developers in the foreseeable future, although the nature of their work and the tools they use may change significantly.
The role of software developers in overseeing, aligning, and managing complexity will remain essential, even as AI coders become more capable.