What use is that? I'm not being facetious, I'd really rather like to know.
Who or what is the audience for that sort of long form writing? If it's a human, why would they read it? They'd just give it to an LLM and get the salient points back. If the audience is another LLM, why expand it?
The only use case is an audience of humans who still read and understand, and those people aren't going to be interested in a message when it is not apparent that the sender actually understands the message themselves.
That's why there's so many meetings in white collar companies. Because people can't understand what is going on at those documents so they just need to "align".
LLMs are amazing at generating this useless documentation that goes absolutely nowhere.
That's already available today. We don't have to perfect LLM writing.
1. I have a bunch of data or research that I've gathered with a unique hypothesis
2. Having gotten my arms around that pile of information, I believe I have a compelling thesis to put forth
3. I design the narrative arc and of the thesis. The important parts, the necessary but not sufficient scaffolding.
4. An AI helps fill in the story from there. Fact checks each claim, connects the dots, makes it comprehensible.
Who is this for? Well, quite possibly the human who asked for it. It's pretty informative to read back a research brief in full that you helped do the scaffolding.
Also of very clear use is other AI's who did not have the same unique hypothesis and did not gather the supporting evidence. It's an interesting angle for others to build on.
And of course, other humans! Most human written content gets almost zero readers today as it is. And I suppose LLM content probably pulls the asymptote closer to zero, but some pieces of content may be genuinely interesting or useful.
I think this certainly has some value but this claim in and of itself is stated like your hand-wavy step 3. How do they fact check claims and connect the dots?
Maybe LLMs get there but currently they write in an extremely verbose manner, and things that have gotten into the context window that are no longer relevant continue to stick around (just try having it write some code, then work some of it back to simplify the problem - it will insist on writing comments about code that no longer exists).
Right now using an LLM to write documents is like taking a superhighway to travel 100 meters. Yeah you're doing a lot but is all that really necessary?
I won't deny that LLMs will never have a place in writing. But I personally don't think the current form is "the one that actually lands" (!).
I used to say this was the future of advertising (cr sales person prompts “we have some new EV SUVs on the lot”; GPT generates an ad email with a synthetic video, blinking text etc; then the recipient’s spam processor tells them “that dealer has some new SUVs”. I suppose the same could happen with so-called “long form”.
People are terrible at writing. Near universally bad. Even good writers have drafts and editors.
There is a constant refrain here that somehow short messages are more valuable than longer ones. But that assumes it's understandable. Lots of short content is, frankly, awful because the writer cannot put themselves in the position of the reader and explain all the things around the point they're making that the reader really should be told.
You can view writing as translation. From your language to a language your audience speaks. At that level is it so odd if the word count differs from one side to the other?
Look at it the other way, could you take a good longer message you’ve written and make it shorter and less readable for your audience while still making sense to you and containing the key points?
personally, I think there's a time and place for short versus long, just like there's a time and place for a 45mins TV episode versus a 2 hour marathon movie.
My opinion of LLM design review isn't that high - it seems to miss design tweaks that could vastly simplify corner cases. But if your code isn't written for human consumption maybe it doesn't matter. I'm still directly responsible for what I commit, so I can't just offload it to Claude.
It's like watching somebody about to be hit by a bus. You yell, you wave your arms, but they either don't hear you, or they don't believe you. The last thing that goes through their head is a Greyhound's hood ornament.
The most popular programming languages in 2030 will, in fact, be English and Mandarin. Deal with it and get over it.
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Edit, to bcrosby95: Look up the etymology of the word 'computer'. It didn't originally have anything to do with hardware. The first computers were people, who were told what to do ("programmed") without necessarily knowing what they were working on in a big-picture sense.
Calling it gatekeeping is just laughable. That's like saying it's gatekeeping to say that the painter painted their painting, and that the person who commissioned the painting did not paint it. It's wholely absurd.
Anybody can pick up a book and learn to actually code themselves. Or you can use an LLM to try to make things without bothering with that. But even if the LLM worked perfectly, pretending these are the same thing is silly.
The history of the word computer is obviously irrelevant. Words change, it turns out.
(I’ve heard about some GPU compute nuance meaning that even without randomness injected they still wouldn’t quite be deterministic, but that’s also not core to their nature)
Neither do humans.
Two people can give the exact same prompt to the exact same LLM and get different results.
No one cares.
This is like telling someone else to code something for you.
Exactly.
What are you willing to bet?
To be precise: I will bet that high-level programming languages won't be any less popular as a whole, but the vast majority of code will be written by AI rather than humans, working from specs written in natural language or something very close to it.
What we call "source code" today will be thought of as "object code" by 2030. Something that occasionally needs to be inspected by humans, but rarely authored directly. Anyone not writing code this way had better be doing it as a hobby, because almost no one will pay for it.
Other person probably doesn’t like the idea that LLMs will replace hard earned skills. On the flip side, I bet you’ve seen your skills atrophy at an alarming rate and are trying to justify it.
Both sides come from fear. Just relax and take things as they come. Whatever happens happens.
It's baffling you people are in control of such a strong product when you are obsessed with this intellectual pornography; wow - look at how smart it made my thoughts look (n.b. look, not read). Don't look too close. And certainly don't ask me what it means.
The widespread introduction of LLM code generation is very destructive to that.
Perhaps LLMs can be brought to support human cognition in the same way writing can; but that has yet to be designed and it does not seem to be the way things are heading.
The bottom line is, prompting is definitely writing.
Cognitive burden increases marginally with AI assisted coding.
This is why we haven't seen big projects(think browsers and browser engines) spawning in the past year.
Why are you reviewing AI code in detail? Do you also review the assembly output of GCC line by line?
In the case of writing, it’s like hiring someone to write a book for you vs. hiring someone to translate a book you wrote into another language. In the first case, you didn’t really define the message for readers, whereas in the second case you did, and the translator is converting that same message for another audience to consume.
In the case of LLMs, the behavior is non-deterministic and inconsistent. If I don’t explain how handle an edge case or give a performance constraint, the LLM will still produce code and may do so in different ways, handling edge cases differently and with different performance characteristics. I can’t reason about how the LLM will fill in those gaps, it’s “random.”
Maybe you don’t care about how the LLM handles those edge cases or handles performance, but that’s different than a deterministic abstraction whose implementation details you don’t care about, but whose logic and performance is deterministic and consistent
The closest thing we have to vetting LLMs is “whoa look, it escaped this sandbox, that’s prolly not great but it’s so cool!”
I wouldn't use it for flight control software yet, at least not without careful review, but most software isn't exactly critical. At the same time, I wouldn't trust flight control software that was only reviewed by humans, since AI is so much better at debugging.
We'll probably need humans in the loop for safety critical software for at least a year or two, before AI fully outpaces humans at generating correct code.
So I assume you don’t fly? Or is it only software created after 2025 which must be reviewed by the All Knowing Entity?
And, AI is rapidly getting better than people at both code review and authorship, so a human deeply involved is turning into nothing but a slowdown. The main purpose people have is testing that the specs were, in fact, implemented properly.
The vast majority of properly written software was already plumbing well over a decade ago. The software engineering is making high level decisions based on experience with respect to the existing tools and the needs of the business. If you're not already using LLMs that way, you would have been a similarly bad manager of human devs writing similar inadequate slop. Less code has always been better code.
The line in the sand for these arguments really ought to be whether you think LLMs are better than humans who actually know what they're doing.
If you think LLMs are better, or could get better while continuing to use statistical methods, you automatically lose the argument (delusional/ignorant) and any hope of regaining credibility. That's not dogma. That's the science.
twitch
Gcc makes maybe 1 mistake ever 2 billion emissions. LLMs make 1 mistake ever 3rd emission.
What a horrible, cold, inhumane world that would be.
If you're not billed for usage, anyway.
Otherwise, for the other 99% of folks, that attitude is of course a pit trap that captures code bases and makes them maintainable only through the providers -- presumably one or few -- with a rich enough model to keep up with the growing mess. Preserving a code base that's legible, organized, and fundamentally maintainable by both humans and trailing commodity models is of imminent concern for anybody who doesn't want their margin strangled by your employer once it's too late to have other options.
As frontier capabilities advance, the details don't matter less; they matter more.
Prices are very competitive and today's SOTA is next to free in half a year.
Whether code is maintainable without AI becomes less and less important.
I couldn’t help myself, replied and asked him for a recipe for delicious apple cobbler and hiking trail recommendations in Glasgow, which “he” immediately provided. Highlight of my career.
I think my core argument is this: I have access to every bit of information your AI does, so if I want an AI answer I’ll get one myself. If that isn’t true, why are you hoarding information? Push it somewhere we can all see it. So the only reason I would send you a message is to access _your_ brain. I have no interest in talking to an AI through a worse interface.
1. I understand fully the code and everything it does 2. You can pick up on mistakes super early and it can adjust the plan is it goes. 3. Faster than writing it by hand but slower than letting the LLM do it.
[0] https://ankursethi.com/blog/prevent-cognitive-debt-by-manual...
I worry about AI Loopidity here though. Think about the similar analogy of email. If my set of ideas is condensable to bullet points, but I use AI to expand the content, then I add no information density and a lot of noise. Other folks then use AI to summarize the content to a list of bullet points, ideally the same but not certainly the same, and thus communication has been only partially successful.
You get pushback for this? I saw an anthropic job post recently, and they wanted you specifically to have claude muck with your resume before applying.
The entire point is what runtime you’re running your code on. A computer with any modern stack requires a lot of text for you to communicate “spin a square around on its center” to it. A human requires only that short string because they have a faster natural language interpreter.
Text meant for a human can communicate “spin a square around its center” much better than any code that mimics it. In some sense, all programming is boilerplate expansion because computers have (until now) been unable to be programmed with anything approaching natural language.
Maybe sometimes, but not always. When you need to actually render the thing you have all kinds of micro decisions, like where to put the square, what color, how fast it spins, etc.
You might not care about the details, but maybe you do. If it spins at 10000 rpm, will you care then?
Natural language, and human communication in general, is ambiguous, and coding is in great part about disambiguation.
Sure, you can use English to disambiguate as much as needed, but wouldn’t you then end up with some yaml-like spec that wasn’t much easier to create in the first place?
You can already do this. And you can build pipelines where AI performs fact checks on what it writes, with citations a human can reference as well.
On the other hand, long form writing for human consumption seems like it may evade LLMs for much, much longer.
So, I'm not sure if it's a question of time at all: if a LLM text contains some piece of information beyond the information that went into the prompt, where does this "extra" information come from? [Note, I'm not thinking about facts which could trivially come from the training corpus, I'm thinking specifically as information in the sense of intended message from sender (author) to receiver (reader)]
[1] cf. this comment where I explain this analogy between LLMs and noise channel in communication theory: https://news.ycombinator.com/item?id=49510244
All of the things you say are very true in the near term for short form writing - a page or two of Claudeslop will probably be much easier to swallow in a year or two than it is now. But I don’t see a path to fully AI-generated novels or long-form investigative journalism becoming mainstream in the next couple of years.
Really huffing your own farts there, huh?
Later
> I wonder if long form writing will go the way of code. You and the AI agree on an outline or other high level representation, then the LLM expands it into a document.
So in the future, it won't be necessary for you to think?
This is just noise generation. If anyone is meant to actually read the document it should be written by you.
No. No it is not. Nobody who actually cares about the quality of their work is letting an LLM just turn out code without reviewing it carefully.