AI in Coding Careers: Myths vs Realities

Table of Contents

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• Myth 1: AI is replacing Software Engineers 
• Reality: AI is now part of the daily workflow for software engineers as a support tool. 
• Myth 2: AI tools are only used by large companies. 
• Reality: Software engineers at companies of all sizes are using AI. 
• Myth 3: Only data professionals need to understand how artificial intelligence works. 
• Reality: Software engineers should understand the basics of AI. 
• Becoming a Developer with Fullstack Academy in 2025

In the rapidly evolving world of software engineering, AI is becoming an essential support tool rather than a replacement for human developers. Aaron Katz, a software engineer with a decade of experience and an instructor at Fullstack Academy, debunks three common myths about AI in software development. He discusses how AI enhances productivity, the reality of AI adoption across companies of all sizes, and why software engineers—regardless of their specialization—should understand AI fundamentals. Aaron also shares how Fullstack Academy prepares students for an AI-driven workforce.

Do you feel AI is replacing software engineers right now?

Bill Gates announced the three fields that will not be replaced by AI, and the number one was software engineering. He said software engineers will have to evolve to work with AI, but that AI is far from operating independently of software engineers. Remember that Open AI — an AI company — is hiring full-stack and front-end developers! 

“AI is a great tool and it's changing the field,” said Katz, software engineers are going to be writing more code, spending less time on research because of ChatGPT and Gemini — and all the great tools to help with research — but they are not going to replace software engineers.”

How is AI being integrated into the workflow for software engineers?

AI is helping with product completion. Project managers and software engineers sometimes have differing priorities when it comes to developing and releasing a product. A project manager’s job is to get the product as quickly as possible, whereas a software engineer's job is to release the best code possible. “As a software engineer, you're taught that when you get a work item, you should be honest with yourself about how long it will take and then double it and that's what the project manager has to go with,” shared Katz. A project manager knows that software engineers are padding the amount of time they need on a project, explained Katz. Now with Gemini, ChatGPT, and GitHub Copilot, software engineers and project managers have the tools to quickly do the research and minimize the time needed for project completion.  

To Katz, software engineers need to understand how to work with AI. For example, you have conversations in ChatGPT about the best approaches and feedback to writing code like you would with a coworker. You can ask it for feedback on a schema, and if you don’t like its response, you’ll follow up with another idea. It’s through these interactions that you can get what you’re looking for. According to Katz, he’s been able to learn a lot and get really solid code, but if he just accepted what ChatGPT gave him, there would be a lot of code that would be problematic.

What are the AI tools that software engineers rely on?

ChatGPT, Gemini, and GitHub Copilot.

What are the pitfalls of software engineers relying on AI tools?

Personally, Katz likes writing his own code. “I have my own unique style that I write code and I think it very much symbolizes, if someone looks at my code, what kind of person I am, what kind of developer I am.” As you grow as a software developer, you will have your unique style of writing code and your preferences. For that reason, Katz is not a fan of GitHub Copilot because he feels, while functional, it writes generic or repetitive code. You can tell just by looking at the code that someone wrote it using Copilot. 

Katz explained, if you're a hiring manager and realize the developer you’re interviewing heavily relies on Copilot, you may wonder if they can solve basic problems. This is becoming one of the big problems when hiring junior developers — new developers don't have the basic problem-solving skills they had five to ten years ago. Before AI tools, when you got an algorithm, even if you looked at the solution, you had to reverse engineer it and figure out how it works. An AI tool, like Gemini, Copilot, and ChatGPT, tells you what to do and they break it down in such a way that you don't need to analyze it. It stunts your growth as a software developer when your first instinct is to look something up instead of solving it yourself. Developers, even with AI tools, still need to have the skills to finesse their code to get it to work for a particular situation.

Do you feel AI tools are being limited to the big companies or if it can be used at any level?

Companies of all sizes will want you to use AI tools on the job, said Katz.

At larger companies, that may mean using their proprietary AI. For example, if you work for Google, you’ll use Gemini. This can be limiting because a large company wants you to use their proprietary products and sometimes they do that at the expense of their own productivity. 

“If you're working for a small company, any AI tool that you want you'll probably be able to use. Obviously check with the company, don't just start downloading things on your company computer,” explained Katz. Small businesses and startups have this get-it-done mentality so they may be open to a variety of AI tools, he expanded.

Some companies say they have AI when they just have a bunch of “if states.” Years ago, Katz shared, he worked at a company that said it was using AI when it didn’t. He wrote the back end code, and there was no artificial intelligence in it — it was just a simple switch statement, a decision tree.

Do you feel like software engineers should have any understanding of machine learning, Large Language Models, or AI?

Developers need to understand the pitfalls of using AI/machine learning, encouraged Katz. Remember that AI was fed human data, so it’s far from perfect! You should know how to use AI to write better code. Learning how to use a Large Language Model (LLM) as a full-stack developer will give you an advantage over other developers. A lot of companies want developers who are interested in learning and integrating AI.

At Fullstack Academy, you learn how to build a full-stack application. You learn how to learn tech, which is very difficult. From Katz’s experience, students during the first couple weeks are so nervous about making a mistake and don't understand how to read documentation. By the end of the bootcamp, they understand how to look up documentation and how to tinker with code. What they've learned is also how to learn tech, which is crucial since software engineering, AI engineering, and data engineering are constantly growing and evolving fields. You need to learn how to stay up with modern tech. 

Becoming a Developer with Fullstack Academy in 2025

How is Fullstack Academy preparing 2025 cohorts for today’s tech workforce?

According to Katz, Fullstack Academy has evolved the software engineering bootcamp curriculum and other programs to include AI. 

“I model [prompt engineering] for students — how to have conversations with it [AI], how to understand what it's saying, how to get the best possible answer to it because part of it is you have to be a prompt engineer. You have to know how to interact [with AI],” shared Katz. Fullstack Academy and Katz encourage students to be adaptable and be continuous learners.

Are you seeing your students incorporating AI when building their bootcamp projects?

Katz explained he has had students interact with AI and create animations for their projects. “I had a group of students who, for their final capstone project, created a gambling website,” said Katz. “The roulette wheel really spun, they had a slot machine, and they had craps where you threw dice. It was really amazing, and they did a lot of this with no prior knowledge, and it was through tinkering with AI and looking at instructions out there, because figuring out that amount of styling, especially, is very difficult and time-consuming. AI saved them time and made them more productive, so there are good parts to it.” 

Why do you think employers still like hiring Fullstack Academy bootcamp graduates for today’s software engineering roles? 

Fullstack Academy attracts very talented, serious students. “Students are ready to put in the effort, and they come up with amazing capstones [projects],” Katz says,”Companies like hiring junior developers who are hungry. They appreciate that Fullstack Academy graduates have perseverance.” 

Find out more and read Fullstack Academy reviews on Course Report. This article was produced by the Course Report team in partnership with Fullstack Academy.