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.
>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 don't really buy it actually. There isn't really anything meaningful you can learn with how to use LLMs/agents that has a half-life greater than a few months at this point, so you can just start doing it at any point in the future and not be meaningfully left behind. On the other hand years of letting your actual engineering skills atrophy will have a negative effect on you. I've been witnessing the effects of this. Going back to more coding by hand with AI-assistance circa the 2023 era as a happy medium. I think this is the sweet spot. Full agentic engineering has nasty failure modes and in the long-term is kind of a bad option for basically everyone. I say this after having done it for almost a year at this point, and transitioning away from it now.
> I don't really buy it actually. There isn't really anything meaningful you can learn with how to use LLMs/agents that has a half-life greater than a few months at this point, so you can just start doing it at any point in the future and not be meaningfully left behind.
Right, remember how in ~2023 "prompt engineering" was ostensibly the critical thing everybody "needs to learn now or get left behind"?
I feel these tools—or at least the business-model behind them—have a pattern: Basic adoption is easy, while protecting yourself against their flaws is a shifting target where expertise goes stale.
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.
latentsea · · focus · HN ↗
I don't really buy it actually. There isn't really anything meaningful you can learn with how to use LLMs/agents that has a half-life greater than a few months at this point, so you can just start doing it at any point in the future and not be meaningfully left behind. On the other hand years of letting your actual engineering skills atrophy will have a negative effect on you. I've been witnessing the effects of this. Going back to more coding by hand with AI-assistance circa the 2023 era as a happy medium. I think this is the sweet spot. Full agentic engineering has nasty failure modes and in the long-term is kind of a bad option for basically everyone. I say this after having done it for almost a year at this point, and transitioning away from it now.
Terr_ · · focus · HN ↗
Right, remember how in ~2023 "prompt engineering" was ostensibly the critical thing everybody "needs to learn now or get left behind"?
I feel these tools—or at least the business-model behind them—have a pattern: Basic adoption is easy, while protecting yourself against their flaws is a shifting target where expertise goes stale.