Its beneficial for the company for sure at this stage cause most of the people using these LLM tools and agents have built the muscle to be able to review the code so the work gets done faster. These tools are definitely helpful in lot of ways to move faster.

Where it does not helps, software engineering is a field where you have to actively practice it to make sure your mind remembers the trade offs, different paths you have evaluated before, sort of building muscle memory. Using LLMs mean that eventually your debugging skills, ability to understand some one else code would degrade and moving to the next level in terms of software competency may never happen for you.

A concrete example, the ability to remember 10 digit phone numbers was really normal before smart phone, try to push your mind now to see if you are still able to do that?

Lastly, at any given point of time 90% of the engineers were working on managing the existing code base, so career wise with so much code being written the job safety should not be an issue if you have skills to understand and debug some one else code

I'm not necessarily a software engineer, but I do a lot of coding for my cybersecurity projects. I find it's very helpful to have AI generate the first "batch" of code, but I do it in a very unusual manner. I don't use agents, I just use an LLM, and I review the code before I ever hit "copy" so I ensure I'm not outsourcing my thinking to a bot.

Anyway, when it comes time to make a project, I first figure out what I want to build. Then I research the steps I would need to take to build it. For example, if I wanted to make a webapp that has a deliberate SQL injection for a CTF, I would need a database, a login page, and a dashboard to navigate to after logging in. For larger projects, I might use AI to help me figure out the components required.

Then I save those notes and then focus on a single component. Continuing with this example, I'll start with the login page. I'll get AI to generate the code on that. Sometimes it doesn't cooperate and makes the SQL query secure, so I have to manually go in there and fix it myself, rather than ask it to redo it again. I get serious imposter syndrome whenever I use AI to generate code, so I make sure I can understand some of it before I run it, and I try to manually fix bugs before I ask for help.

Once all the components are built, I tie them all together and start testing the program.

> if the AI is writing all the code, what value am I bringing?

What wouldn't an average person off the street be able to do?

I think you're mistaking typing characters on a keyboard for coding. Designing a system is coding. Knowing what refactorings to do to ensure things evolve in the right direction is coding.

Being able to recall all the syntax has lost value due to AI, but designing and leading the feature development hasn't and that can't (yet) be trusted to AI.

I'm not too worried about keeping skills sharp - if I'm ever put in a situation where they become relevant again I'm confident I can just relearn them.

I have the agent grill me/ask me questions about what has been implemented, often on a fairly technical/granular level of the feature is important enough.This is obviously way slower than just vibing the whole thing and ticking off a passing test suite (that the agent also wrote). But I find that doing this has allowed me to retain most of my problem solving and reasoning skills (which have definitely atrophied).

People are kidding themselves thinking they fully understand what is going on at a deep level for 3-4 (or more) parallel tasks constantly context switching. It's also an anxiety-inducing way to work and has led to a proliferation of agent management tools enabling max efficiency.

Focusing on systems thinking/observability/knowing when NOT to build something/using agents to fix/learn new things quickly seem to be where I see devs still bringing the most value compared to any other cowboy.

see you need to manage between using ai to debug code or create code even and reviewing it manually because its the hard truth ai debugging of code is not of good quality and manual debugging is always superior now comming to the part manual debugging being slow for that you have to understand skills are the centre meaning you can use a hybrid model have ai write code in segment meaning the amoung of code which human brain can read by this you can review the code fast enough as well as you'll save your ai also some time while debugging the code and to answer your question what is going to happen in future in future or even now human developer can't be replaced compared with someone with less skill and heavy ai agent knowledge its just about balancing between too hope it helps

I operate at the level where I'm telling the agents what functions, classes, code structure to create

So its still fairly involved and low level but a lot less manual typing

I also don't use auto-mode when they write code, I review the diffs immediately as they come and approve/deny as I find it stops the agents going too far down the wrong path.

I think the term automated programming captures it best

I heavily use browser based LLM for bugs I care about. Code that is close to business logic like SQL is all 'by hand'.

Frontend stuff for me, which I'm bad at, I let agents do all of and I test the end result.

Other things are in-between, but time wise it's probably mostly me writing code. Lines of code is the LLM for sure.

imo the valuable thing we bring as software engineers is and has always been solutions to problems with the contextually correct quality/speed/cost tradeoffs. with agentic coding, these factors all shift and we're all in the process of recalibrating. namely the speed dial can now be shifted way down.

to answer directly, i live in claude code now. and while it's a different shape of daily work, i'm still engineering, still evaluating those 3 factors and figuring out how to triage and solve user-problems much faster than i could 5 years ago.

[flagged]

[flagged]

[dead]