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If you think your technical skills are becoming useless because AI can write code, I think you're seriously underestimating what's coming.
I've been reading how engineers at top AI companies are working with coding agents. And honestly, it's making me rethink where I should invest my time as an engineer.
Here are 5 things I'm personally focusing on:
1. Getting much better at codebase design. If you can read a 17,000-line file and reduce the input tokens needed for the same AI coding task by 83%. That's fascinating because good architecture now directly affects how efficiently your agents can work.
2. Developing deeper domain expertise, an agent can write a payment integration, but knowing how settlement, refunds, retries and reconciliation should behave is a different matter. You want to understand the business well enough to catch mistakes that perfectly valid code can still make.
3. LGetting better results from smaller models. I know Opus 5.5 makes everyone feel powerful, but here's a more interesting challenge. Can you get a cheaper model like Terra or Haiku to complete the same task reliably? Learning how to achieve that consistently is a skill I'd love to get better at.
4. Making my systems easier for agents to inspect. OpenAI gave its coding agents access to running applications, browser interactions, logs and traces. That way, agents could actually reproduce bugs and verify their fixes. I'm particularly interested in building more of this into my own workflows.
5. Understanding where the next engineering bottleneck appears. Anthropic went from roughly 2,500 to over 30,000 code changes merged daily. Suddenly, source control, builds and CI became major problems. I think there's a huge opportunity for engineers who can identify and solve these problems as AI increases development speed.
The more I read about how these teams are working, the more I realize how much there is for experienced engineers to explore.
Personally, I don't want to spend the next few years just getting faster at prompting AI to write code. We need to understand these systems well enough to make a smaller team capable of doing work that previously needed dozens of engineers.
I'm considering writing a detailed article on these ideas, with real engineering examples and things you can try yourself.
Would you be interested? Let me know in the comments.
I've been reading how engineers at top AI companies are working with coding agents. And honestly, it's making me rethink where I should invest my time as an engineer.
Here are 5 things I'm personally focusing on:
1. Getting much better at codebase design. If you can read a 17,000-line file and reduce the input tokens needed for the same AI coding task by 83%. That's fascinating because good architecture now directly affects how efficiently your agents can work.
2. Developing deeper domain expertise, an agent can write a payment integration, but knowing how settlement, refunds, retries and reconciliation should behave is a different matter. You want to understand the business well enough to catch mistakes that perfectly valid code can still make.
3. LGetting better results from smaller models. I know Opus 5.5 makes everyone feel powerful, but here's a more interesting challenge. Can you get a cheaper model like Terra or Haiku to complete the same task reliably? Learning how to achieve that consistently is a skill I'd love to get better at.
4. Making my systems easier for agents to inspect. OpenAI gave its coding agents access to running applications, browser interactions, logs and traces. That way, agents could actually reproduce bugs and verify their fixes. I'm particularly interested in building more of this into my own workflows.
5. Understanding where the next engineering bottleneck appears. Anthropic went from roughly 2,500 to over 30,000 code changes merged daily. Suddenly, source control, builds and CI became major problems. I think there's a huge opportunity for engineers who can identify and solve these problems as AI increases development speed.
The more I read about how these teams are working, the more I realize how much there is for experienced engineers to explore.
Personally, I don't want to spend the next few years just getting faster at prompting AI to write code. We need to understand these systems well enough to make a smaller team capable of doing work that previously needed dozens of engineers.
I'm considering writing a detailed article on these ideas, with real engineering examples and things you can try yourself.
Would you be interested? Let me know in the comments.
very weird thing happening rnView quoted post on X
technical people are sad, they think their skills are now useless
non-technical people are desperately wishing they were more technical so they could use agents better
