How AI Is Changing Development in Big Tech and What It Means for UI Developers 👨‍💻

Hi! Hope everyone is back in the work rhythm. I want to share some thoughts on how AI is changing developer requirements and how vibe coding blurs specialization boundaries.

I’ll try to answer a few questions:

1. How to grow your career and skills on the technical track
2. What distinguishes a strong developer from a vibe monkey
3. Why vibe coding makes strong developers even stronger
4. Should we resist the trend?

Blurring of specialization boundaries.

“Jack of all trades”

1. Horizontal growth is now a must 🧑‍🎓 Developers aren’t yet expected to be strong in all stacks, but AI tools already let you work beyond your usual context. Managers understand this: if you get $2 out of $1, requirements will change. I think in the next 2–3 years the separation between stacks will weakenand the baseline will be the ability to work almost everywhere. Everyone will gradually become more full-stack. This has long happened in startups and now reached big companies.

Conclusion: learn a secondary specialization and supporting skills. That can be any backend language and framework (Java, Kotlin, Go, Rust), at least one cloud provider (AWS, GCP, Azure), basic database knowledge, and a simple CI/CD pipeline 🚀

2. Expertise is the main thing that distinguishes a strong developer.
When everyone can “do everything,” the value shifts to depth of understanding. With the rise of LLMs, many developers, especially at the Junior–Mid level, stop really understanding the code, lose profiling skills, and rely entirely on AI.

My recent case. The problem was: why does a query over 10K elements sometimes fail with Unexpected Exception: null?
We have our own ORM on top of a graph database.

What the developer did:

1. Found the crash point from the stack trace
2. Asked an LLM, tried a few options, opened a PR (which was rejected)
3. The problem was not solved
4. Concluded it was an ORM bug and handed it over to another team
5. The task was stuck for a couple of weeks

After escalation, it was given to a staff engineer, who did the following:

1. Found the crash point
2. Generated the EQL (our pre-SQL layer)
3. Ran it in debug mode
4. Found broken entities after a third-party migration
5. Fixed them with a manual script and identified the authors

Problem solved. 0 lines of code. The issue was not in the query at all.

Conclusion: don’t be lazy about debugging manually, profiling code, reading documentation, and truly understanding the system. This is exactly what will distinguish a strong developer from a vibe monkey.

3. Follow the trend and find joy in it.
Big Tech is moving toward maximum integration of AI into all processes. There will be metrics that measure how effectively you use AI. The healthiest path is to accept the trend, find interest in it, and use it as a multiplier for your skills. Right now companies give free access to Claude, ChatGPT, Codex, and other tools—you can even use them for personal tasks.

Life for Senior+ engineers has become easier: less routine, more time for architecture and real engineering problems.

- Writing technical documentation is much more pleasant. An LLM can quickly prepare a Google Doc from your context, add code pointers, and draw diagrams. You still make the architectural decisions, but you get rid of the manual writing.
- Onboarding into new projects is easier. You can ask Claude to read a dozen Google Docs, generate a summary, link project folders to the spec, and draw an architecture diagram. This takes 30–40 minutes and saves a lot of time, especially when you work in multiple contexts.

Conclusion: learn to use AI tools effectively, especially on someone else’s budget—burn those tokens!. Learning new things is now much faster with AI and LLMs. The key is: think first, then vibe-code.

Finally, a couple of interesting articles:
1. Predictions on how software engineering will evolve in the near future
2. Profiling and its future development. BTW, the author of that channel has many great posts about profiling!