Reading the code does not mean you understand the code. One lesson that experience in software gave me: I never understood the code. You think it works a certain way, until you find out that it doesn't.
What LLMs make possible is for me to say: find out all the ways this thing works. Analyze the different ways we can run this software, build a fuzzer, build property tests, and run this software in every scenario possible. Log full traces. Log all the outputs. Now, analyze each scenario for bugs. You can't do that by hand.
If we are committed to it, if we put the resources towards it and dedicate the time to it (and we could do this just by saying: it will take half as long as it used to take!), software built by llms in healthcare, finance, automotive, defense, power plans, aviation, manufacturing can all be made MORE reliable and better with LLMs... without ever reading a single line of code. The LLMS are very good at logic, by the way.
Anyway all of this reads like someone who is not actually using LLMs to build software or hasn't tried them in a while. I felt the same way in 2025. I've written 100s of thousands of lines of difficult code. You, the person reading this, has probably interacted with software I've written. For a time you would've interacted with it every time you made a debit card transaction in the united states, for example. I understand code, and care about quality, and that's why I'm all in on LLMs for code.
I agree, I think this false dichotomy between using LLMs and caring about quality/reliability needs to stop. All these mission critical industries listed in the article rely on extensive testing for quality assurance, with human code review being a layer on top of all that, but far from the most critical one.
Interpretability is the same, our abilities to do that have increased rather than decreased. I think a codebase generated by AI is actually more understandable than one generated by humans at this point, and you can ask clarifying questions whenever you get stuck.
TFA's points only make sense if the mental model the author has in mind is someone who writes a prompt then immediately puts an app into production without any thought behind it.
> I agree, I think this false dichotomy between using LLMs and caring about quality/reliability needs to stop
You're not going to get people to stop doing that by arguing on the internet, but in the end it won't matter, because it will stop, naturally.
In the future, you'll just get left behind and not hired if you're building code by hand, it's that simple. Even traditional code reviews are going to go away. It'll be more about the scope and then verifying correctness.
I'm not sure I understand what you're saying? To keep it simple, how much code do you expect will be written by humans in say, two years time?
I don't know about two years' time, but in ten years' time the majority of code will be written by hand. LLMs are like outsourcing to cheap labor was 20 years ago: it's trendy, but when the abysmal quality becomes apparent the pendulum will swing back.
efficax · · focus · HN ↗
What LLMs make possible is for me to say: find out all the ways this thing works. Analyze the different ways we can run this software, build a fuzzer, build property tests, and run this software in every scenario possible. Log full traces. Log all the outputs. Now, analyze each scenario for bugs. You can't do that by hand.
If we are committed to it, if we put the resources towards it and dedicate the time to it (and we could do this just by saying: it will take half as long as it used to take!), software built by llms in healthcare, finance, automotive, defense, power plans, aviation, manufacturing can all be made MORE reliable and better with LLMs... without ever reading a single line of code. The LLMS are very good at logic, by the way.
Anyway all of this reads like someone who is not actually using LLMs to build software or hasn't tried them in a while. I felt the same way in 2025. I've written 100s of thousands of lines of difficult code. You, the person reading this, has probably interacted with software I've written. For a time you would've interacted with it every time you made a debit card transaction in the united states, for example. I understand code, and care about quality, and that's why I'm all in on LLMs for code.
pu_pe · · focus · HN ↗
Interpretability is the same, our abilities to do that have increased rather than decreased. I think a codebase generated by AI is actually more understandable than one generated by humans at this point, and you can ask clarifying questions whenever you get stuck.
TFA's points only make sense if the mental model the author has in mind is someone who writes a prompt then immediately puts an app into production without any thought behind it.
munksbeer · · focus · HN ↗
You're not going to get people to stop doing that by arguing on the internet, but in the end it won't matter, because it will stop, naturally.
In the future, you'll just get left behind and not hired if you're building code by hand, it's that simple. Even traditional code reviews are going to go away. It'll be more about the scope and then verifying correctness.
brazukadev · · focus · HN ↗
I expect the exactly opposite to happen. These are going to be the most requested developers as the last ones that understand how it work.
They would then be convinced to use AI for speed, but vibecoders that just prompt AI are the ones that won't find jobs.
munksbeer · · focus · HN ↗
bigstrat2003 · · focus · HN ↗
munksbeer · · focus · HN ↗