来源：https://human-in-the-loop.bearblog.dev/llms-are-eroding-my-software-engineering-career-and-i-dont-know-what-to-do/

I'm a software engineer, completing 10 years of professional experience this year. I started my career as a web frontend engineer, but shortly transitioned to (web) backend and never looked back.

Through a series of coincidences, once I stepped into backend development, I ended up working in software development roles in the domains of finance, bookkeeping and payment processing, where I had great autonomy and a close and candid relationship with Product Managers and stakeholders.

I learnt a lot about the domain and how to effectively write programs for it: PCI compliance, double-entry ledgers, escrows, reconciliation, payment lifecycles, bank transfer idempotency, etc.

It was, then, obvious that I should focus my career on becoming an expert on that domain to stand out as a professional and differentiate myself in a field that showed signs of an increasing need for domain specialists.

Last year, I got hired by a company in the finance workspace. That company also embraced AI wholeheartedly, so I got ChatGPT and Claude Enterprise accounts from day one and was encouraged to use them for my research, exploration, and even coding, albeit with a warning that I should still review and own every single line that made it into production.

One of my first projects involved reworking the legacy online payment system, which was a mess. They hired me for my previous experience in building that and trusted me with the task.

Then my manager reached out to me: even though you're delivering code at a good pace, you're taking too long to deliver those Design Docs. Are you using AI? You should use more AI.

And then I started realizing: all the knowledge I have accumulated over the years: the trade-offs between implementations, how acquiring works, how to structure idempotency to prevent double-charges, everything, was becoming useless. Even though the models still needed some steering, they could connect the dots on how to structure such systems, which was the hardest part that only develops in your brain after years of hands-on experience. That was my first shock.

But sure, I thought, they can do that because there's plenty of articles on the web on how that shit works along with all the technical documentation. What the models will never be good at, and that's where humans will shine, is debugging!

Then came the MCPs, the agentic workflows and Claude 4.5 and the sky started to fall.

Claude 4.5, to be honest, wasn't that good. It solved like 60% of the bugs given a stack trace and some context. Sometimes it gave a solution that sounded plausible but was totally wrong.

Then came 4.6, 4.7, GPT 5.5, Opus 4.8 and the DataDog MCP... Now I have CLIs that one-shots bugs across distributed systems for me. Bugs that I couldn't solve in the past. Bugs that would take 2 days of full-time debugging. 90% of the bugs are one-shotted now, including bizarre race conditions, unexpected corner-cases, third-party integration issues, undocumented API edge cases, everything. I hardly have to intervene.

Of course, I'm still employable because someone has to review the code and steer the robot. But I'm just another off-the-shelf engineer now. I have no domain expertise that another Sr. engineer steering an LLM cannot match. All my finance and payment domain expertise, all the debugging intuition and distributed system knowledge earned through hours of sweat and tears, is now promptable.

We were taught that generalists and specialists will always have their roles. But now the market is shaping everyone into becoming a generalist. If everyone is a generalist, the price of a generalist falls if there's no demand to match.

I still have one pillar standing, though: code quality and software architecture - what's now being reduced to being called "taste".

This is the last pillar standing. Except that nobody cares anymore. Agents do a really bad job at keeping codebases organized. But it's now fine. Nobody needs A or B-grade codebases anymore because they're being made for LLMs, not for humans to read.

I spent 10 years getting good at things that are becoming less and less valuable. My last pillar of expertise is now reduced to a "taste" and will probably won't last long.

The only way out for keeping my employability in the long-term now seems to be shifting my domain expertise to something LLMs will not get good at so easily. But what's left? Maybe I should consider transforming my woodworking hobby into a profession...