And the discipline gets attacked even more when all you read is LLM text. For example in code reviews there is no noticeable value in answering LLM generated review comments. I remember taking great care answering human-written reviews comments with much thought to either further build on their suggestions or to forward knowledge about the system when I had to defend a particular change.
Now, some bot will read my carefully written response and forgets about it. There is no value anymore.
No wonder that our pull request comments feel like the dead internet theory right now.
I am absolutely pro AI assisted coding. But communication between humans HAS to stay human. I am currently experiencing the opposite and it’s bad.
While I think your argument has merit, I would be careful to discount any human skill based on people being too lazy to do it.
But I don’t know where to draw the line yet. It’s difficult to decide what parts you are offloading and what parts you keep for your own brain. Because theoretically you can offload nearly everything when you get a well-defined ticket and just give it to an agent.
Maybe it’s also just me who is having a hard time staring at LLM generated texts all day and missing the past brainstorming, pair programming or “just talking about the project” sessions with colleagues and managers.
I fear that we are moving in the direction of silos again with lots of gate keeping as everyone has their own little rubber duck to talk to now.
Systems from today (if not worked on with discipline) will be no more than an agglomeration of features glued together. But as you said, guess our fight to fight will be to be that disciplined person.
s/time/effort
Maybe that's the key ingredient that's missing from LLM-generated text. Even when there is actual content there, there's this lack of surprise / uncertainty in its delivery that we require to stay engaged. We may not know what will be communicated, but knowing exactly how it will be communicated just kills our innate interest.
Communication expresses an understanding. LLMs arent communicating.
LLMs generate code well enough. Code has no natural language semantics. Programming's condition of satisfaction is that the machine executes correctly. The condition of satisfaction of communication is that the communication expresses what there is to be communicated.
In the limit where an LLM is trained to repoduce an exmplar of communication, it seems as if it does so. In that limit, LLMs can reporduce anything.
If frontier labs wanted to improve "LLMs for communication" they would pay for reasoning traces to be produced which reproduced the thought process of writers/speakers thinking about their audiences. Rarely do such traces exist in prior datasets .
While I don't like LLM voice either, I'd strongly disagree with this. LLM writing can be very tight these days -- sentences do not "go on" and are not "joined irrelevantly". I'm not sure when you last used an LLM, but it must have been a very long time ago; this hasn't been the case since late 2024 for frontier models, in my experience.
Obviously there's no underlying consciousness behind it all, but the verisimilitude is good enough for stimulating conversations on the recent research literature in my field, which I appreciate. For me, at least, the communication level is absolutely fine.
(YMMV, of course, and perhaps my bar for this is lower than yours.)
They seem to be getting better at the O(paragraph) level reporting on technical updates.
They're not yet at O(pages), and certainly no where near O(book).
For foreign text, sure. But I never was never able to read New York Times articles about some invention that started with Lucy leaving her home at seven o'clock.
If you have a specific question, reading LLM text can be more interesting than anything in the world. Even Wikipedia.
I've noticed this as well. When I get a bot to generate something I am happy to read it (although I often skim read it). But god forbid I have to read someone's LLM generated report! Come to think of it, if someone marks the text as LLM generated, especially after they have plonked a hand typed summary or sentence at the top, I am quite happy to read it. Maybe my beef is being expected to read LLM text the same way I would read high effort human prose? I certainly read LLM text differently to human text - you know the first sentence is going to glaze you and the second paragraph will just repeat the first paragraph so you can skim those annoyances.
I came to think of it, LLM output is not worth to be read like human written text. It's best to just skim it and move on. The problem is when this LLM output finds its ways everywhere on the internet. Being able to skim text requires knowing the context beforehand and knowing that you're dealing with LLM generated output. If LLM output that is published was labeled as such it'd probably make us less frustrated.
These aren’t deep AI hustle bro hacks, it’s literally just editing some text. If a big part of your day is reading AI-generated words, slap something together to make it do words gooder.
I find that it often isn't just too low, but also too high at the same time. It has a lot of information that is irrelevant, burying far too little relevant information for the work needed to extract it. I wonder if this is a side effect of attention mechanism, where the AI's ability to track information in a document is far less hampered by other information, leading to it organizing information to be both dense per word but any specific concept to be sparsely spread throughout the writing. I also wonder if this is why the order feels off, because the AI doesn't need for the information to be well ordered to the same extent that a human does, so the order of storing information is often overlooked when storing it into a document (and even information that needs to be ordered might have the ordering information separate from how it is ordered in the document).
Whereas any human would say "I think your file format is missing a key/value pair for foo'ing. Let's trace through the code, and see if/where m_foo is set...."
It happens everywhere. Docstring type comments for a function endlessly just lists information about specific variables, implementation details, etc, and often never really even says what the function does. Then maybe a comment in the body might actually describe what it is for.
These days I've learned when facing these walls of text to check the last sentence first; it is often enough to provide the context for the text dump.
I have instructions to this effect in my claude.md. It doesn't help much on responses, but it give me a way to quickly say "reread claude.md and then rewrite that last prompt" and that can tend to go better.
Someone else mentioned model of mind while writing. The LLM has no model of mind; it does not consider how to present information, what you might already know or not know. We work in a huge mono-repo - it'll blithely just start listing variable names and I have no idea what file that is in, what it is referring to, how it interacts with 5 other classes it hasn't mentioned, or mentions out of order. It doesn't matter to the LLM. It can hold millions of facts in 'mind' at once; humans stall out at 5-7. If I replay saying it is leaving out info, many times it turned out it did dump all the facts out, spread across multiple prompts, often with key facts interspersed with all the vagueisms. No human mind can cope.