> Each of these 'phases' of LLM growth is unlocking a lot more developer productivity, for teams and developers that know how to harness it.
I still find myself incredibly skeptical LLM use is increasing productivity. Because AI reduces cognitive engagement with tasks, it feels to me like AI increases perceptive productivity but actually decreases it in many cases (and this probably compounds as AI-generated code piles up in a codebase, as there isn't an author who can attach context as to why decisions were made).
I realize the author qualified his or her statement with "know how to harness it," which feels like a cop-out I'm seeing an awful lot in recent explorations of AI's relationship with productivity. In my mind, like TikTok or online dating, AI is just another product motion toward comfort maximizing over all things, as cognitive engagement is difficult and not always pleasant. In a nutshell, it is another instant gratification product from tech.
That's not to say that I don't use AI, but I use it primarily as search to see what is out there. If I use it for coding at all, I tend to primarily use it for code review. Even when AI does a good job at implementation of a feature, unless I put in the cognitive engagement I typically put in during code review, its code feels alien to me and I feel uncomfortable merging it (and I employ similar levels of cognitive engagement during code reviews as I do while writing software).
It shifts your cognitive tasks to other things. Like every tool. The tool itself is an abstraction over tedium. We built it for a reason. You will spend less time thinking about some things, and more time thinking about others.
> and this probably compounds as AI-generated code piles up in a codebase, as there isn't an author who can attach context as to why decisions were made
I don't see why. If anything there's more opportunity for documentation because the code was generated from a natural language prompt that documents exactly what was generated, and often why. Recording prompts in source control and linked to the generated code is probably the way to go here.
I use LLMs (like claude-code and codex-cli) the same way accountants use calculators. Without one, you waste all your focus on adding numbers; with one, you just enter values and check if the result makes sense. Programming feels the same—without LLMs, I’m stuck on both big problems (architecture, performance) and small ones (variable names). With LLMs, I type what I want and get code back. I still think about whether it works long-term, but I don’t need to handle every little algorithm detail myself.
Of course there are going to be discussions what is real programming (like I'm sure there were discussions what is "real" accounting with the onset of a calculator)
The moment we stop treating LLMs like people and see them as big calculators, it all clicks.
Talking about a specific set-up they use isn't the goal of the post, so I don't think it's a cop out.
"How to harness it" is very clearly the difference between users right now, and I'd say we're currently bottom heavy with poor users stuck in a 'productivity illusion'.
But there is the question of "what is productivity?"
I'm finding myself (having AI) writing better structured docs and tests to make sure the AI can do what I ask it to.
I suspect that turns into compounding interests (or lack of technical debt).
For an industry where devs have complained, for decades, about productivity metrics being extremely hard or outright bullshit, I see way too many of the same people now waving around studies regarding productivity.
From the article: Anthropic has been suffering from pretty terrible reliability problems.
In the past, factories used to shut down when there was a shortage of coal for steam engines or when the electricity supply failed. In the future, programmers will have factory holidays when their AI-coding language model is down.
I expected to see OpenAI, Google, Anthropic, etc. provide desktop applications with integrated local utility models and sandboxed MCP functionality to reduce unnecessary token and task flow, and I still expect this to occur at some point.
The biggest long-term risk to the AI giant's profitability will be increasingly capable desktop GPU and CPU capability combined with improving performance by local models.
Speed is not a problem for me. I feel they are at the right speed now where I am able to see what it is doing in real time and check it's on the right track.
Honestly if it were any faster I would want a feature to slow it down, as I often intervene if it's going in the wrong direction.
Cursor imo is still one of the only real players in the space. I don’t like the claude code style of coding, I feel too disconnected. Cursor is the right balance for me and it is generally pretty darn quick and I only expect it to get quicker. I hope there are more players that pop up in this space.
Ive used chatGPT to help me learn new stuff about which I know nothing (this is it's best use imo) and also write boilerplatey functions, eg. write a function that does precisely X.
Having it integrated into my IDE sounds like a nightmare though. Even the "intellisense" stuff in visual studio is annoying af and I have to turn it off to stop it auto-wrecking my code (eg. adding tonnes of pointless using statement). I dont know how the integrated llm would actually work, but I defo dont want that.
> "An LLM agent runs tools in a loop to achieve a goal."
This is the magic sauce compared to copy-pasting snippets to a web browser.
It can automatically read code, search through it and modify multiple files automatically. Then it can compile it, run tests, run the actual application to check it works etc.
Multiple contexts is hard, and often counter-productive. It used to be popular on HN to talk about keeping your "flow", and railing against everything that broke a programmer's flow. These slow AI's constantly break flow.
Flow sacredness made sense when we could only do our work serially and juggling tasks just meant switching around one thing at a time. Now we can parallelize more of our activities, so it's time to reevaluate the old wisdom.
The only reason I want these things to be any smarter is because I need them to do more work over longer periods screwing up. The only reason I need them to do more work over long periods is because they are too slow to properly pair with.
If I could have it read more of my project in a single gulp and produce the 10-1000 lines of code I want in a few seconds, I wouldn't need it to go off and write the thousands of lines on its own in the background. But because even trivial changes can take minutes by the time it slurps up the right context and futzes with the linter and types, that ideal pair programmer loop is less attractive.
I'd take quality improvements over speed any day. AI gets you 80% of the way there, but you often spend more time fixing the last 20% than you would have approaching the problem yourself.
This article mentions Cerebras, I tried it out and was 1) disappointed and 2) they started a subscription but gave me no way of cancelling, their billing page is broken :/
I rate the value of LLM based AI on how it improves me personally. Positive experiences include being able to more easily. read scientific papers with an AI filling in details for embedded math, and after a vibe coding session when I feel like I understand the problem better because I was engaged with the process and I understand the resulting code.
Negative experiences include times when I am lazy, turn off my brain and just accept LLM output. I am also a little skeptical about automating email handling, etc. that is cool technology but how useful is it, really? I can imagine insiders talking between themselves saying "feel the bubble!" but when talking with reporters they talk like "feel the AGI" or "oh no, our AI tech is so strong it will take over the world" - excellent strategies for pumping stock prices and valuations.
Well, I seen one suggestion for probable nearest future of AI, to stop on GPT-4 level, but distill and use optimizations like switch to FP8 for faster speed.
So basically idea, model with very same capabilities, but distilled and optimized to have less size and faster inference.
I cannot promise 100x speedup, but I think 10x is very real.
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[ 3.3 ms ] story [ 53.9 ms ] threadI still find myself incredibly skeptical LLM use is increasing productivity. Because AI reduces cognitive engagement with tasks, it feels to me like AI increases perceptive productivity but actually decreases it in many cases (and this probably compounds as AI-generated code piles up in a codebase, as there isn't an author who can attach context as to why decisions were made).
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
I realize the author qualified his or her statement with "know how to harness it," which feels like a cop-out I'm seeing an awful lot in recent explorations of AI's relationship with productivity. In my mind, like TikTok or online dating, AI is just another product motion toward comfort maximizing over all things, as cognitive engagement is difficult and not always pleasant. In a nutshell, it is another instant gratification product from tech.
That's not to say that I don't use AI, but I use it primarily as search to see what is out there. If I use it for coding at all, I tend to primarily use it for code review. Even when AI does a good job at implementation of a feature, unless I put in the cognitive engagement I typically put in during code review, its code feels alien to me and I feel uncomfortable merging it (and I employ similar levels of cognitive engagement during code reviews as I do while writing software).
> AI systems do not currently speed up many or most software developers
> AI systems in the near future will not speed up developers in our exact setting
> There are not ways of using existing AI systems more effectively to achieve positive speedup in our exact setting
In that regard, nothing will change.
I don't see why. If anything there's more opportunity for documentation because the code was generated from a natural language prompt that documents exactly what was generated, and often why. Recording prompts in source control and linked to the generated code is probably the way to go here.
increases the perception of productivity?
Of course there are going to be discussions what is real programming (like I'm sure there were discussions what is "real" accounting with the onset of a calculator)
The moment we stop treating LLMs like people and see them as big calculators, it all clicks.
"How to harness it" is very clearly the difference between users right now, and I'd say we're currently bottom heavy with poor users stuck in a 'productivity illusion'.
But there is the question of "what is productivity?"
I'm finding myself (having AI) writing better structured docs and tests to make sure the AI can do what I ask it to.
I suspect that turns into compounding interests (or lack of technical debt).
For an industry where devs have complained, for decades, about productivity metrics being extremely hard or outright bullshit, I see way too many of the same people now waving around studies regarding productivity.
In the past, factories used to shut down when there was a shortage of coal for steam engines or when the electricity supply failed. In the future, programmers will have factory holidays when their AI-coding language model is down.
The biggest long-term risk to the AI giant's profitability will be increasingly capable desktop GPU and CPU capability combined with improving performance by local models.
I they don't see a viable path forward without specialty hardware
Honestly if it were any faster I would want a feature to slow it down, as I often intervene if it's going in the wrong direction.
Having it integrated into my IDE sounds like a nightmare though. Even the "intellisense" stuff in visual studio is annoying af and I have to turn it off to stop it auto-wrecking my code (eg. adding tonnes of pointless using statement). I dont know how the integrated llm would actually work, but I defo dont want that.
But when it doesn’t output what you want you spend mental energy and extra time reorienting to get back on track.
About 50% of the time it works every time.
This is the magic sauce compared to copy-pasting snippets to a web browser.
It can automatically read code, search through it and modify multiple files automatically. Then it can compile it, run tests, run the actual application to check it works etc.
https://openrouter.ai/rankings
It says "Grok Code Fast 1" is ranked first in token usage? That's surprising. Maybe it's just OpenRouter bias or how the LLM is used.
I would have assumed Claude would be #1
If you want better speeds, your coding agent might perform better outside US office hours :)
If I could have it read more of my project in a single gulp and produce the 10-1000 lines of code I want in a few seconds, I wouldn't need it to go off and write the thousands of lines on its own in the background. But because even trivial changes can take minutes by the time it slurps up the right context and futzes with the linter and types, that ideal pair programmer loop is less attractive.
These things are somewhat useful, but it doesn't come for free, and I'm talking externalities here, which are not priced in.
Negative experiences include times when I am lazy, turn off my brain and just accept LLM output. I am also a little skeptical about automating email handling, etc. that is cool technology but how useful is it, really? I can imagine insiders talking between themselves saying "feel the bubble!" but when talking with reporters they talk like "feel the AGI" or "oh no, our AI tech is so strong it will take over the world" - excellent strategies for pumping stock prices and valuations.
So basically idea, model with very same capabilities, but distilled and optimized to have less size and faster inference.
I cannot promise 100x speedup, but I think 10x is very real.