Comment by ditchfieldcaleb
1 month ago
I agree with you on everything you said here except:
> when you know how the thing works and have that mental context, you will always be faster than an AI
That's just plain false, honestly. No one can type at the speed AI can code, even factoring in the time you need to spend to properly write out the spec & design rules the AI needs to follow when implementing your app/feature/whatever. And that gap will only increase as LLMs get more intelligent.
Some of us do actually have intimate knowledge in certain areas where guidance of an AI takes longer than doing it yourself. It's not about typing speed, it's that when you know something really really well the solution/code is already known to you or the very act of thinking about the problem makes the solution known to you in full. When that happens it's less text to write that solution than it is to write a sufficient description of the solution to AI (not even counting the back and forth required of reviewing the AI output and correcting it).
Giving a precise description of what the computer is supposed to do is exactly what programming is.
The more specific your requirements the closer you get to natural language not being useful anymore.
This is actually my biggest gripe with vibecoding. The single best feature of any programming language is that it is precise. And that is what we throw out?! I favor of natural language, of all things?! We're insane!
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I code mostly in APL and J. It’s much faster to type the code than explain everything to AI.
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Maybe a failure to automate?
The volume of people successfully adopting agentic engineering practices suggests this stuff isn't rocket science, but it is a learned skill and takes setup.
A year later into heavy AI coding, my experience is what you're describing should aid in being able to run 5+ agents simultaneously on a project because you know what you're doing, you set it up right, and you know how to tell agents to leverage that properly.
> successfully adopting agentic engineering practices
What's your definition of "successfully"?
More LOC committed per day is probably the only one that's guaranteed when you let spicy autocomplete take the wheel.
I don't think it's at all possible to reason about the other more meaningful metrics in software development, because we simply don't have the context of what each human is working on, and as with the WYSIWYG fad of 3 decades ago, "success" is generally self-reported, by people who don't know what they don't know, and thus they don't know what spicy autocomplete is getting woefully wrong.
"But it {compiles,runs,etc}" isn't a meaningful metric when a large portion of the code in question is dynamic/loosely typed in a non-compiled language (JavaScript, Python, Ruby, PHP, etc).
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Maybe you're the exception and are actually doing it right and actually getting good results, but every time I have heard this, it has been an ignorance-is-bliss scenario where the person saying it is generating massive amounts of code that they don't understand, not because they're incapable but because they don't care to, and immediately wiping their hands of it afterward.
To give an example of where I hear this, it is indistinguishable from the things I hear from my coworkers: "You just need the right setup!" (IMO the actual difference is I need to turn off the part of my brain that cares about what the code actually does or considers edge cases at all) What I actually see, in practice, are constant bugs where nobody ever actually addresses the root cause, and instead just paves over it with a new Claude mass-edit that inevitably introduces another bug where we'll have to repeat the same process when we run into another production issue.
We end up making no actual progress, but boy do we close tickets, push PRs, and move fast and oh man do we break things. We're just doing it all in-place. But at least we're sucking ourselves off for how fast we're moving and how cutting edge we are, I guess.
I dunno, maybe I'm doing it wrong, maybe my team is all doing it wrong. But like I said the things they say are indistinguishable from the common HN comment that insists how this stuff is jet fuel for them, and I see the actual results, not just the volume of output, and there's no way we're occupying the same reality.
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You seem to have missed OP's point: some things are only encoded in our brains when you are sufficiently experienced.
Translating that into code can happen directly by you, or into prompt iterations that need to result in the same/similar coded representation.
In other words, when it matters how something works and it is full of intricate details, you do not need to specify it, you just do it (eg. as an example which is probably not the best is you knowing how to avoid N+1 query performance issue — you do not need a ticket or spec to be explicit, you can just do it at no extra effort — models are probably OK at this as it is such a pervasive gotcha, but there are so many more).
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Yes, there are still many areas where skilled humans are faster than AI (meaning faster coding yourself, than providing so much context and guidance that the AI can do it on its "own").
But in general the statement is really not true anymore, generic projects/problems have a pretty good chance that the AI can one shot a working solution from a lazily typed vague prompt.
Yeah it’s when you go off the happy path that it gets difficult. Like there’s a weird behaviour in your vibe-coded app that you don’t quite know how to describe succinctly and you end up in some back-and-forth.
But man AI is phenomenal for getting stuff out of your head and working quick.
> Some of us do actually have intimate knowledge in certain areas where guidance of an AI takes longer than doing it yourself.
You speak as if AI development is frozen, and you ignore the poster's point:
> that gap will only increase as LLMs get more intelligent
That doesn't matter. The statement wasn't "faster than AI right now", it was "will always be faster than AI". And that's just nonsense.
Current AI systems are extremely serial, in that very little of the inherent parallelism of the problem is utilized. Current-gen AI systems run at most a few hundreds of thousands of operations in parallel, while for frontier models, billions of operations could be run in parallel. Or in other words, what currently takes AI 8 hours will take it barely long enough for you to perceive the delay after you release the enter key.
For a demo, play around with https://chatjimmy.ai/ , the AI chatbot of Taalas, where they etched the model into silicon in a distributed way, instead of saving it in RAM and sucking it to execution units by a straw. It's a 8B parameter model, so it's unsuitable for complex problems, but the techniques used for it will work for larger models too, and they are working to get there.
And even Taalas is very far from the limits. Modern better quality LLM chatbots operate at ~40 tokens per second. The Taalas chatbot operates at 17000 tokens/s. If you took full advantage of parallelism, you should be able to have a latency of low hundreds of clock cycles per token, or single request throughput of tens of millions of tokens per second. (With a fully pipelined model able to serve one token per clock cycle, from low hundreds of requests.) Why doesn't everyone do it like that right now? Because to do this, you need to etch your model into silicon, which on modern leading edge manufacturing is a very involved process that costs hundreds of millions+ in development and mask costs (we are not talking about single chips here, you can barely fit that 8B model into one), and will take around a year. So long as the models keep improving so much that a year-old model is considered too old to pay back the capital costs, the investment is not justified. But when it will be done, it will not just make AI faster, it will also make it much more energy-efficient per token. Most of the energy costs are caused by moving data around and loading/storing it in memory.
And I want to stress that none of the above is dependent on any kind of new developments or inventions. We know how to do it, it's held back only by the pace of model improvement and economics. When models reach a state of truly "good enough", it will happen. It feels perverse to me that people are treating this situation as "there was a per-AI period that worked like X, now we are in a post-AI period and we have figured out that it will work like Y". No. We are at the very bottom of a very steep curve, and everything will be very different when it's over.
Huh, I have to say that I am impressed with Chat Jimmy. No doubt that the hardware running this model operates faster than any human. If this was possible to scale, (and I'm not saying it isn't, I just don't think it's likely right now) LLM's have a real shot of replacing real-time graphics, frontend UIs, and all sorts of interactive media if the market allows it.
I still think regardless of how fast a model outputs tokens, it still benefits the person responsible for that output to be well informed and knowledgeable about the abstractions they're piling on top of. If you have deep knowledge, you can operate faster than other people, and make those important decisions in a more intelligent manner than any model.
Maybe in the model we do get super intelligence and my point will finally break, but at that time I don't think I'll be worried about being wrong on the internet.
I don't believe this. Either you're lying, or you just haven't caught on with how to use Agentic AI.
Everything I do to interact with my computer is through an agent now.
I don't believe this. Either you're lying, or you just haven't caught on how to use a computer.
Everything I do to interact with my computer is still the same.
See how boring you are?
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Care to explain which particular intimate knowledge allowed you in the last 6-9 months to be faster than AI in certain area?
Honestly, I'm still faster than AI cooking scrambled eggs, but definitely not faster than neither AI (or compiler) in translating stuff into code.
I interpret "faster than AI" to include writing the prompt. For me (scientific computing) it is more often than not faster to write out a simulation or design in a language I know inside out like fortran or mathematica than explicate the requirements to an LLM to request the code. Obviously if someone wrote out a prompt to me and the LLM it would be way faster, but I don't think that's what the commenter had in mind.
If you're good at SQL, or SQL-like languages like Linq, it might be more efficient precisely writing a reasonably complex query than trying to explain it in detail to an AI.
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Not the parent but I've had this happen when debugging for sure. Sometimes I ask Claude Code to help me debug something and it makes a wrong assumption and just churns in circles burning tokens. While it's doing that I realize the problem and fix it.
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In my experience AI can write _something_ from scratch, but often edge cases won't be handled until I go through and read the results or test it. Usually when I'm writing by hand I will naturally find the majority of edge cases as I go. By the time I've read through the results and fixed said edge cases, I usually would have been faster just doing it myself.
My experience is the opposite: AI takes too many edge cases into account and guard against even the most unlikely thing. The upside is that it often handles edge cases that I either didn't think about or was too lazy to implement.
I can with full confidence say that the code AI writes is more robust and safe than if I would have done it myself. The code definitely becomes more bloated though.
My experience has been that it wraps all the obvious things, and even some obscure things, in error handling. In this sense it is safer.
It also fails to write abstractions unless they're carbon copies of a well established pattern, and when abstractions already exist, it needs babysitting to ensure it will use them appropriately. It won't introspect about its current direction unless forced to by the user or by an error, and when forced it will happily "fix" non-issues just because you pointed them out, since it's a happy little yes-man.
Because of this, code written by a good engineer is more likely to start out broken but converges towards correctness as more abstractions get built, while code written by AI duplicates abstraction layers, leaks between them, and never converges towards anything.
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This has been my experience thus far. Yes, a complete prototype can be made, but.. you don't really know until you read the code and test it. Just yesterday, small things came up in terms of Qt screen focus that wouldn't have come up otherwise save for initial testing.
I think, and I recognize it is mostly against the 'agentic' push, I will stick with slow iteration.
It also loves to add edge case handling where it's not needed and in poorly chosen places
> No one can type at the speed AI can code
Don't we already have a weekly post nowadays explaining, again, that typing isn't the bottleneck?
That is not true in startups, where people are getting work done. Maybe in later stage companies where 'stakeholders' are 'synergizing' in meetings over the Q2 roadmap.
Which is still false and not serious. It's one of the dumbest rationalizations I've seen. AI has many flaws but pretending that it's useless because of that is not it.
It should be “…you will always be faster than someone _without the knowledge_ using an AI”
>No one can type at the speed AI can code
You can definitely be faster than frontier models. The number of tokens per second is not that high and they require a lot of tokens for thinking and navigating things.
Especially if you use auto-complete AI, ironically. You type a few characters, the line fills out in less than a second, as opposed to a reasoning model that takes maybe a second per 2-3 lines it writes out.
as i understood it he's referring to the overall time it takes to build a complete finished piece of software, accounting for the refactoring and bug fixes and all that. cause handn't you understood the tools you're using you would be running into roadblocks and that adds up
if you've never had the experience of handing something off to someone else being more laborious and slower than doing it yourself due to having to set constraints and define success, then you simply haven't held a senior enough position to comment on this with any authority
Also employees who work slower than you (and spend most of their time not actually working).
They probably mean faster to a higher-level goal rather than SLOC. Typing speed and SLOC have never been that useful for measuring productivity.
Plenty of cars can get off the line faster than an F1 car. But around a track, an F1 is by far the fastest in the world.
Going fast isn’t the difficult bit.
Except it's often faster to make the change yourself than explain it to an AI.
Where does this certainty that LLMs will get more intelligent stem from?
They progressed very quickly in the past year. Not just models, but all the harness around them to code.
When they start plateauing, then we can assume they're done progressing.
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> LLMs get more intelligent
The Spicy Autocomplete koolaid club is out in force today I see.
We clearly have different ideas of what the word "intelligent" means.
Explaining your idea of intelligent would have been a better comment than name calling and shallow dismissal.
Your views might carry more weight if the crux of your rebuttal wasn't manufactured outrage that I used a laughably accurate nickname for a type of software.