You've probably seen the headlines:  

• Mark Zuckerberg telling Joe Rogan he is going to replace midlevel coders with AI. 
• Amazon Cloud warning its developers to upskill now because AI is advancing fast.
• Salesforce announcing a hiring freeze on developers and turning to AI instead.
• People vibe coding.

They’re scary headlines, but are they true? Is AI really going to replace developers? And is it really going to happen in the next year or so? 

Yeah, probably not. 

There's a lot of conjecture and anxiety in the headlines about AI replacing dev teams. Read a little deeper and you’ll see that the actual story is more nuanced than that. AI is certainly being used more and more for coding work, but its use doesn’t mean that this is the end of dev teams. What it truly means is that dev teams’ work will change. 

In that vein, it’s probably a good idea to take an honest look at how AI is actually being used in the software development process. 

AI in coding and software development: a quick overview 

For the most part, AI’s role in coding is limited to Generative AI coding assistants, like GitHub Copilot, Google Gemini, and Amazon Q. These tools aren’t as new as you might think: the first GenAI code completion tools were launched in 2018. Early GenAI code assistants included Tabnine and IntelliCode. After that, several coding assistants were launched. 

GitHub’s Copilot, one of the most widely-used coding assistants, was launched in 2021 as a tool intended to improve developer job satisfaction, productivity and speed.

But can it replace engineers? Let’s look at what AI can do — and more importantly, what it can’t do — in the software development cycle. 

What AI can do: automation and augmentation

Machines are very good at certain tasks that human brains aren’t so great at. A machine can process large amounts of data, find patterns in that data quickly, and make recommendations based on that information. It doesn’t get tired or bored with repetitive tasks. For the most part, AI’s role in development has been consistent with those specific strengths: 

• Automation: Copilot and other code-assist tools are designed to automate the more tedious and repetitive coding tasks. This can mean automatically generating boilerplate code, generating wireframes, or writing basic functions. When basic tasks are automated, there’s less chance of mistakes caused by fatigue. 
• Code completion: AI code assistants are also able to analyze code that’s being written and generate snippets that the developer can use. Code suggestions are a way to speed up developers’ work so they can get through a project more efficiently. 
• Documentation: It’s important to provide complete and accurate documentation for your projects, but when it’s done manually, there’s always the chance of human error. AI can automate SBOMs and other documentation. This both saves time and reduces the likelihood of mistakes. 
• Debugging: AI can scan for bugs, make recommendations about how to fix them, and automate some debugging tasks.
• Test data: By generating test data, Gen AI can save dev teams time.
• Translation: Code assistants can translate from one coding language to another, or translate between formats.

What AI can’t do: creativity and higher-order thinking

Although AI has many strengths, it lacks several important skills that humans have. Human brains are problem-solvers. We are creative, and able to make complex decisions based on our experience, knowledge, and emotional intelligence. While AI can supply us with suggestions based on data, only humans can use their critical thinking skills to solve problems — which is why developers are necessary. So what can humans do that AI can’t? 

• Be creative: GenAI is great at mixing and remixing known patterns, but that’s not creativity. Humans invent, subvert, and adapt — a human developer might invent a new algorithm, or turn an old way of thinking on its head. There is no substitute for that kind of problem-solving. 
• Understand ambiguity: Not all problems are clear-cut or well-defined. AI needs clear input and structure, but the world is often vague and squishy. Humans can work with vague or rapidly changing inputs, adapting to them in order to solve a problem. 
• Align with business goals: Your GenAI doesn’t understand your business, your customers, or the needs of your end users. It doesn’t care why something is being built; it only knows the code. Developers have that context and can make decisions and changes based on the needs of an organization. 
• Design systems: Humans are able to think holistically about systems in ways machines cannot. This makes us able to design scalable, maintainable architectures that meet the needs of a project or a team.
• Exercise judgement and ethics: Humans understand the ethical implications of our decisions. This keeps us from building biased, unsafe, or manipulative features and from running afoul of the law or compliance requirements. 
• Write complex code: All of the above goes into the human ability to write complex code that meets the needs of users, solves human problems and abides by legal or industry requirements. 

But what about LLMs and coding? 

While code-assist tools are used by professional dev teams, there are a number of people who have been usIing LLMs to generate code. You may see posts about individuals who aren’t coders, but who describe the app they want to write to a tool like ChatGPT or Claude.  

This is called “vibe coding.” Coined in February of 2025, the term describes using an LLM to  transform plain-language requests into working computer code. Will this replace dev teams? 

Probably not. While there are some success stories about vibe coders, writing an app by talking to ChatGPT is unlikely to replace a team of programmers anytime soon. Vibe-coded apps can be very buggy, and even if that weren’t the case, all the above points are still true: only human developers can exercise judgment, design systems, problem-solve, and cope with vague inputs. 

So what will the future of development look like? 

Well, we don’t have a crystal ball (and if we did we would not admit to it) but we think that human developers will always be necessary. 

The anxiety around AI replacing devs is very understandable. Whenever a new technology comes along, people naturally worry that it is going to take their jobs. In many cases what it actually does is change jobs. Developers’ roles will change as AI develops further. 

AI is a new tool, designed to free developers up for more creative and complex work by taking care of the boring stuff. This means that dev teams might become smaller, but they won’t disappear: junior devs will focus more on orchestration and prompt engineering, while senior devs will shift toward architecture and strategy. 

It’s also important to note that AI seems to have been welcomed by developers: one survey found that 60% of CTOs and engineering leaders have actively been rolling out AI coding assistants to their software teams.

Interested in measuring the impact of AI tools within your own organization? Opsera simplifies DevOps measurement, showing you how your team is using AI and delivering insights into AI’s  impact on your developers’ happiness, productivity, and speed. By connecting all your DevOps tools across the entire software delivery process, Opsera gives your team a complete view of your software delivery process, allowing you to analyze the impact AI has on your development cycle and business goals. 

Learn more about the Opsera and the organizational benefits of a unified view of DevOps.