AI is not going to take developer jobs; it is getting rid of entry-level grunt jobs. GitHub Copilot has become a part of everyday life at AlgoCademy, but it is not able to design our adaptive learning algorithms or figure out why students fail to grasp certain concepts. The change is radical. We are shifting away to code writers, to system architects and to problem solvers. I use 60 percent less boilerplate code and 200 percent more algorithmic design decisions. Our recruitment policy is entirely different: instead of having to memorize syntax, we are now looking at people that can debug code written by an AI and be able to comprehend trade-offs within a system. Ideal AI applications: auto test generation and reviews of code. Our AI is more thorough than I am and also makes up comprehensive unit tests. Badest use cases: anything that needs domain knowledge. AI will not be able to create our own learning paths since it is not aware of cognitive load theory. The largest error in my view? New companies laying off old engineers because they believe that AI is more than experience. One of the competitors fired an architecture team and wasted six months trying to debug the spaghetti code produced by AI. This is the truth: AI takes good developers to a new level and reveals mediocre developers. Teams that use human intelligence and the efficiency of AI deliver products 40 percent quicker. The ones which solely depend on AI become technical debt nightmares.
I think there are some places where AI is replacing software developers. Some companies might even have an explicit goal for that. But, I don't think that as a whole, software developers are going to become unimportant. I think they will continue to be vital - their work might just change a bit. They may be instructed to use AI in certain ways, like with testing. Or, their roles might be entirely transitioned into new ones like "AI developer." Even with those changes, I still think there is a place and a necessity for software developers/engineers with skills outside of AI.
At Magic Hour, we've found AI is most powerful when augmenting our developers' capabilities - it's like having a really smart junior developer who can handle repetitive tasks but needs supervision. Just last month, our team used AI pair programming to build our video transformation pipeline in half the usual time, though we learned the hard way that we still need human developers to verify the AI's output and maintain code quality.
Sharing responses on behalf of our Senior Software Engineer and Team Lead at Techstack, who specializes in building complex enterprise solutions and AI integration. Q: Is AI replacing developers? AI replaces part of the developer's job — coding. To be honest, developers have been relying on code generators for a long time — IDE autocomplete, checks, formatting. While IDEs didn't use AI before, they relied on simple pre-defined instructions. Now, these tools have become more and more intelligent, helping developers produce code faster and leaving more time for the creative part of the job — architecture design, customer use cases, technical brainstorms. So from my point of view, business will require developers to become technical PMs and turn customer requirements into strict technical instructions for AI to work with. Q: What can't AI do (yet)? E.g. Design system architecture from scratch, understand product vision or customer pain, replace collaborative decision-making As for me, AI tools feel like a developer who knows everything but lacks intuition. They know how to build solutions by the book—based on successful projects and popular guides—but they don't understand human pain, needs, or patterns. For example, if you ask AI to make the infrastructure as cheap as possible, it might forget that it also needs to be available and consistent, while a human would understand the balance. So, it's valuable to ask AI for advice when building architecture, but it's crucial for an architect or developer to turn that advice into a practical plan.
In my experience, AI development tools are not as useful as they're cracked up to be. They can quickly generate a lot of code, but between debugging and documentation, we have to spend just as many person-hours optimizing that AI code as it would have taken us to just write it from scratch.