2025 Retrospective: How AI Changed the Way I Engineer

As 2025 comes to a close, I’ve been reflecting on how much the landscape of software and data engineering has shifted in just twelve months. If 2023 was the year of AI hype and 2024 was the year of experimentation, 2025 has been the year of integration.

Things have changed significantly. It is no longer just about using ChatGPT on the side; companies are now actively encouraging employees to leverage AI in their daily workflows. The shift has transformed how I approach coding, testing, and debugging—but it has also highlighted exactly where the human engineer is still irreplaceable.

Here is my reflection on the good, the efficient, and the limitations I encountered this year.

1. The Shift to High-Level Logic and New Horizons

The most immediate change has been the encouragement from leadership to use AI for coding and debugging. This hasn’t just made me faster; it has changed what I focus on.

I used to spend a significant chunk of my day writing boilerplate code. Now, I leverage LLMs to handle the setup, allowing me to focus on the critical tasks—specifically, unit testing and system architecture.

Furthermore, the barrier to entry for new technologies has lowered effectively to zero. In the past, picking up a new language or framework required days of tutorial reading. Now, I can dive into a framework I’ve never used before, ask the AI to scaffold the structure, and explain the syntax as we go. It has turned “learning on the job” into “executing while learning.”

2. Death to the “Tedious Task”

One of my favorite changes in 2025 is the elimination of “mindless” coding.

Previously, if I had to map a list of 50 fields from a source to a destination, I would rely on multi-line cursor tricks in my IDE to be efficient. It was faster than manual typing but still required mechanical effort.

Now, that workflow is obsolete. I simply provide the AI with a list of items and a prompt: “Map these fields to a struct, infer the correct data types, and handle nullable fields.”

The AI is smart enough to figure out that “created_at” should be a Timestamp and “price” should be a Decimal, adjusting the syntax perfectly. It turns a 20-minute formatting task into a 20-second review task.

3. The Reality Check: Why AI Can’t Replace Us (Yet)

Despite the efficiency gains, 2025 also taught me that AI is nowhere near ready to fully replace a human engineer. The biggest hurdle? Context and dependencies.

AI is fantastic at isolated logic, but it struggles immensely when things get messy in the integration layer.

I recently spent an hour debugging a code generation process involving protoc (Protocol Buffers). The error was obscure, burying itself deep in the dependency chain. I pasted the logs and code into OpenAI, then Claude, and finally Gemini.

I spent an hour cycling through their suggestions. They all hallucinated flags that didn’t exist or suggested fixes for the wrong library versions. None of them could “see” the full environment or understand the subtle incompatibility between specific versions of the protobuf compiler.

Eventually, I had to stop asking the AI and start acting like an engineer. I dug into the release notes, relied on my intuition, and realized I needed to manually reconfigure protoc to a lower version.

The Verdict

The AI had the code, but it didn’t have the context. It couldn’t replicate the intuition gained from years of dealing with dependency hell.

As we move into 2026, I view AI not as a replacement but as a hyper-efficient junior developer who types at lightning speed but needs a senior engineer to review the architecture and solve the hardest, deepest bugs.

The tools have changed, but the problem-solving mindset is more valuable than ever.