This shows that everyone in the study (economic experts, ML experts and even developers themselves, even after getting experience) are novices if we look at them from the Dunning-Kruger effect [1] perspective.
[1] https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect
"The Dunning–Kruger effect is a cognitive bias in which people with limited competence in a particular domain overestimate their abilities."
No, they underestimated their own abilities for the most part; the estimates for AI-disallowed tasks were all undershot in terms of real implementation time.
What they overestimated was the ability of LLMs to provide real productivity gains on a given task.
> What they overestimated was the ability of LLMs to provide real productivity gains on a given task.
This is not about the ability of LLM overestimated by developers, this is about the ability of developer interacting with LLM overestimated by developers themselves, economic experts and ML experts.
LLMs are not "able" per se, they are "prompted" to be "able." They are not agents, but behave as agents on someone's behalf - and no one have a clue whether use of LLMs is positive or detrimental, with the bias being "LLM's use is net positive".
The overestimation of LLM's abilities by everyone calls for Dunning-Kruger.
If you are looking for a 0.1% increase in productivity, then 16 is too small.
The methodology contains several fundamental flaws that likely explain this anomalous result.
Most critically, the study examines a highly specific scenario - expert developers working on codebases they've contributed to for years (averaging 1,500 commits over 5 years) - which creates a ceiling effect where AI has minimal room to provide value.
The 30-minute Cursor training for developers, 56% of whom had never used the tool before, is woefully inadequate for learning effective AI pair programming techniques. With only 16 participants and a non-blinded design where developers knew their condition and were paid $150/hour, the study lacks both statistical power and ecological validity.
The restriction to a single tool configuration (Cursor Pro with specific Claude models) and acknowledgment that the tool doesn't optimise token sampling or prompting strategies further limits the findings' applicability to the broader question of AI's impact on developer productivity.
- Concerningly small sample size: Only 16 developers across 246 tasks provides insufficient statistical power for broad generalisations about AI's impact on millions of developers
- Inadequate AI training: 30-minute basic Cursor tutorial for developers where 56% had never used the tool - completely insufficient for developing effective AI collaboration skills
- Selection bias towards ceiling effects: Developers averaged 5 years and 1,500 commits on their repositories, creating an expertise level where AI assistance has minimal value-add potential
- Single tool learning: Study restricted to education Cursor Pro with specific Claude models, not representative of the diverse AI tooling ecosystem (Windsurf, Cline, Roo Code etc.)
- Artificial task constraints: Tasks were acknowledged as "shorter than average" and broken into ≤2 hour chunks, not representative of real development work
- No experimental blinding: Developers knew their condition and were being observed/recorded, potentially affecting natural work patterns
- Suboptimal AI usage: Study acknowledges Cursor doesn't sample sufficient tokens and developers reported overusing AI due to experimental conditions
- Narrow context: All repositories were large (1.1M LoC average), mature (10 years old), with high quality standards - a specific niche not representative of most development
- Self-reported metrics: Time tracking was self-reported with only 29% verified through screen recordings, introducing measurement bias
- Expertise mismatch: Focusing on experts contradicts established findings that AI tools provide greater benefits to less experienced developers
- Tool proficiency confound: No control for varying levels of AI tool proficiency or different prompting strategies between participants
- Limited generalisability: The specific combination of expert developers + familiar codebases + large repositories + short tasks creates an artificial scenario unlike typical development workflows
Even so... I still would be really surprised if there wasn't some systematic error here skewing the results, like the developers deliberately picked "easy" tasks that they already knew how to do, so implementing them themselves was particularly fast.
Seems like they authors had about as good methodology as you can get for something like this. It's just really hard to test stuff like this. I've seen studies proving that code comments don't matter for example... are you going to stop writing comments? No.
We explore this factor in section (C.2.5) - "Trading speed for ease" - in the paper [1]. It's labeled as a factor with an unclear effect, some developers seem to think so, and others don't!
> like the developers deliberately picked "easy" tasks that they already knew how to do
We explore this factor in (C.2.2) - "Unrepresentative task distribution." I think the effect here is unclear; these are certainly real tasks, but they are sampled from the smaller end of tasks developers would work on. I think the relative effect on AI vs. human performance is not super clear...
[1] https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study.pdf
Can someone point me to these 300k/yr jobs?
Ah, there’s your issue. There’s not a developer in human history who hasn’t drastically underestimated how long it would take to complete a task.
The conclusion isn't that "estimates are hard" (they can be), but rather that AI-assistance can lead people to believe they're being more productive than they actually are, because they incorrectly think they've spent less time.
The graphs in the paper tell part of that story; the time that is being reduced is in actual programming time, "Reading & Searching", "Testing & Debugging", but that time is being spent elsewhere, notably in parts specific to LLMs (reviewing output, prompting, waiting for the AI to spit out results).
anyway -AI as the tech currently stand is a new skill to use and takes us humans time to learn, but once we do well, its becomes force multiplier
ie see this: https://claude.ai/public/artifacts/221821f0-0677-409b-8294-3...
And keep in mind that a 5-10x price hike is to be expected if those companies keep spending billions to make millions.
Right now, there is a consistent stream of papers incoming which indicates that AI might be much more of a specialized tool for very particular situations instead of the "solve everything"-tool the hype makes people believe. This is highly significant.
"Just believe me bro" is just not enough.
For intuition on why it's insufficient to consider N alone, I assume e.g. that you'd greatly increase your belief that a coin was unfair long before 16 consecutive heads--as already noted, the size of the effect also matters. That relationship isn't intuitive in general, and attempts to replace the math with feelings tend to fail.
you're addicted to the FEELING of productivity more than actual productivity. even knowing this, even seeing the data, even acknowledging the complete fuckery of it all, you're still gonna use me. i'm still gonna exist. you're all still gonna pretend this helps because the alternative is admitting you spent billions of dollars on spicy autocomplete.
I wouldn't accept someone's copy and pasted code from another project if it were under an incompatible license, let alone something with unknown origin.
(https://github.com/albumentations-team/Albumentations)
15k stars, 5 million monthly downloads
----
It may happen that Cursor in the agentic mode writes code slower than I am. But!
It frees me from being in the IDE 100% of the time.
There is infinite list of educational videos, blog posts, scientific papers, hacker news, twitter, reddit that I want to read and going through them, while agents do their job is ultra convenient.
=> If I think about "productivity" in a broader way => with Cursor + agents, my overall productivity moved to a whole another level.
Cursor’s workflow exposes how differently different people track context. The best ways to work with Cursor may simply not work for some of us.
If Cursor isn’t working for you, I strongly encourage you to try CLI agents like Claude Code.
I'm the one providing the invention, it's transforming my invention into an implementation; sometimes better than others.
My personal hypothesis is that seeing the LLM write _so much_ code may create the feeling that the problems it is solving would take longer to solve by yourself.
We speak (the best we can) to changes in amount of code -- I'll note that this metric is quite messy and hard to reason about!
Such as: do you end up spending more time to find and fix issues, does AI use reduce institutional knowledge, will you be more inclined to start projects over from scratch.
I think personally when i tried tools like Void IDE, I was fighting with Void too much. It is beta software, it is buggy, but also the big one... learning curve of the tool.
I havent had the chance to try cursor but i imagine its going to have a learning curve as a new tool.
So perhaps there is a slowdown at first expected; but later after you get your context and prompting down pat. Asking specifically for what you want. Then you get your speed up.
So I never feel like I'm getting any faster. 90% of my time is still spent in frustration, even when I'm producing twice the code at higher quality
Soon once the tools and how people use them improve AI won’t be a hinderance for advanced tasks like this, and soon after AI will be able to do these prs on their own. It’s inevitable given the rate of improvement even since this study.
Currently AI is like a junior engineer, and if you don't have good experience managing junior engineers, AI isn't going to help you as much.
I feel like a proper study for this would involve following multiple developers over time, tracking how their contribution patterns and social standing changes. For example, take three cohorts of relatively new developers: instruct one to go all in on agentic development, one to freely use AI tools, and one prohibited from AI tools. Then teach these developers open source (like a course off of this book: https://pragprog.com/titles/a-vbopens/forge-your-future-with...) and have them work for a year to become part of a project of their choosing. Then in the end, track a number of metrics such as leadership position in community, coding/non-coding contributions, emotional connection to project, social connections made with community, knowledge of code base, etc.
Personally, my prior probability is that the no-ai group would likely still be ahead overall.
For example, today I asked claude to implement per-user rate-limiting into my nestjs service, then iterated by asking implementing specific unit tests and some refactoring. It one-shot everything. I would say 90% time savings.
Unskilled people ask them ”i have giant problem X solve it” and end up with slop
I already know what I need to write, I just need to get it into the editor. I wouldn’t trade the precision I have with vim macros flying across multiple files for an AI workflow.
I do think AI is a good rubber ducky sometimes tho, but I despise letting it take over editing files.
The developers might feel more productive because they're engaging with their code at a higher level of abstraction, even if it takes longer. This would be consistent with why they maintained positive perceptions despite the slowdown.
Would be interesting (and in fact necessary to derive conclusions from this study) to see aggregate number of tasks completed per developer with AI augmentation. That is, if time per task has gone up by 20% but we clear 2x as many tasks, that is a pretty important caveat to the results published here
Except on "hello world" situations (which I guess is a solid part of the corpus LLMs are trained with) these tools were consistently slower.
Last time was an area where several files were subtly different in a section that essentially does about the same thing, and needed to be aligned and made consistent†.
Time to - begrudgingly - do it manually: 5min
Time to come up with a one-shot shell incantation: 10min
Time to very dumbly manually mark the areas with ===BEGIN=== and ===END=== and come up with a one-shot shell incantation: 3min
Time to do it for the LLM: 45min††; also it required regular petting every 20ish command so zero chance of letting it run and doing something else†††.
Time to review + manually fix the LLM output which missed two sections, left obsolete comments, and modified four files that were entirely unrelated yet clearly declared as out of scope in the prompt: 5min
Consistently, proponents have been telling me "yeah you need to practice more, I'm getting fine results so you're holding it wrong, we can do a session together and I'll show you how to do it", which they do, and then it doesn't work, and they're like "well I'll look into it and circle back" and I never hear from them again.
As for suggestions, for every good completion where I accept saying "oh well, why not", 99 get rejected: the majority are complete hallucinations absolutely unrelated to the surrounding logic, a third are either broken or introduce non-working code, and 1-5 _actively dangerous_ in some way.
The only places where I found LLMs vaguely useful are:
- Asking questions about an unknown codebase. It still hallucinates and misdirects or is excessively repetitive about some things (even with rules) but it can crudely draw a rough "map" and make non-obvious connections about two distant areas, which can be welcome.
- Asking for a quick code review in addition to the one I ask to humans; 70% of such output is laughably useless (although harmless beyond the noise + energy cost), 30% is duplicate of human reviews but I can get it earlier, and sometimes it unearths a good point that has been overlooked.
† No, the specific section cannot+should not be factored out
†† And that's because I interrupted it because it was going about modifying files that it should not have.
††† A bit of a lie because I did the other three ways during that time. Which also is telling because the time to do the other ways would actually be _lower_ because I was interrupted by / had to keep tabs on what the AI agent was doing.
If someone told me “you can have a free junior engineer, but they get swapped out each week for a new person”, I’d say no thanks.
I’m sure someone could figure out a way to make money on in that situation, but it wouldn’t be by building anything I’d be comfortable attaching my name to or or would want to use myself
Plenty of "you're holding it wrong" and "it works for me", but not directly telling someone that their perceptions were inaccurate.
My personal theory is that getting a significant productivity boost from LLM assistance and AI tools has a much steeper learning curve than most people expect.
This study had 16 participants, with a mix of previous exposure to AI tools - 56% of them had never used Cursor before, and the study was mainly about Cursor.
They then had those 16 participants work on issues (about 15 each), where each issue was randomly assigned a "you can use AI" v.s. "you can't use AI" rule.
So each developer worked on a mix of AI-tasks and no-AI-tasks during the study.
A quarter of the participants saw increased performance, 3/4 saw reduced performance.
One of the top performers for AI was also someone with the most previous Cursor experience. The paper acknowledges that here:
> However, we see positive speedup for the one developer who has more than 50 hours of Cursor experience, so it's plausible that there is a high skill ceiling for using Cursor, such that developers with significant experience see positive speedup.
My intuition here is that this study mainly demonstrated that the learning curve on AI-assisted development is high enough that asking developers to bake it into their existing workflows reduces their performance while they climb that learing curve.
It's completely normal in development. How many years of programming experience you need for almost any language? How many days/weeks you need to use debuggers effectively? How long from the first contact with version control until you get git?
I think it's the opposite actually - it's common that new classes of tools in tech need experience to use well. Much less if you're moving to something different within the same class.
Is that perhaps because of the nature of the category of 'tech peoduct'. In other domains, this certainly isn't the case. Especially if the goal is to get the best result instead of the optimum output/effort balance.
Musical instruments are a clear case where the best results are down to the user. Most crafts are similar. There is the proverb "A bad craftsman blames his tools" that highlights that there are entire fields where the skill of the user is considered to be the most important thing.
When a product is aimed at as many people as the marketers can find, that focus on individual ability is lost and the product targets the lowest common denominator.
They are easier to use, but less capable at their peak. I think of the state of LLMs analogous to home computing at a stage of development somewhere around Altair to TRS-80 level. These are the first ones on the scene, people are exploring what they are good for, how they work, and sometimes putting them to effective use in new and interesting ways. It's not unreasonable to expect a degree of expertise at this stage.
The LLM equivalent of a Mac will come, plenty of people will attempt to make one before it's ready. There will be a few Apple Newtons along the way that will lead people to say the entire notion was foolhardy. Then someone will make it work. That's when you can expect to use something without expertise. We're not there yet.
Maybe, but it isn't hard to think of developer tools where this is the case. This is the entire history of editor and IDE wars.
Imagine running this same study design with vim. How well would you expect the not-previously-experienced developers to perform in such a study?
Apple's Response to iPhone 4 Antenna Problem: You're Holding It Wrong https://www.wired.com/2010/06/iphone-4-holding-it-wrong/
If my phone keeps crashing or if the browser is slow or clunky then yes, it’s not on me, it’s the phone, but an LLM is a lot more open ended in what it can do. Unlike the phone example above where I expect it to work from a simple input (turning it on) or action (open browser, punch in a url), what an LLM does is more complex and nuanced.
Even the same prompt from different users might result in different output - so there is more onus on the user to craft the right input.
Perhaps that’s why AI is exempt for now.
We have 2 sibling teams, one the genAI devs and the other the regular GPU product devs. It is entirely unsurprising to me that the genAI developers are successfully using coding agents with long-running plans, while the GPU developers are still more at the level of chat-style back-and-forth.
At the same time, everyone sees the potential, and just like other automation movements, are investing in themselves and the code base.
I have the opposite impression! I find it's hard to think of any other tech product where users expect to master it with no training at all. I think people get tricked into believing they need no training because the tool uses natural language as the UI.
You learn how to use a spreadsheet or a word processor, how to drive a car, sail a boat, play a guitar. In the 90s there were courses that spent hours teaching users how to work a mouse and keyboard!
Of course you need to learn how to use a coding assistant as well, it just makes sense.
There has already been a million words written about how to use LLMs from people who don't really know how to use LLM's. Everyone is learning, there is a boom, you can make a fortune selling knowledge about LLM's whether you have that knowledge or not.
My previous employer didn't even allow me to use Vim until I learned it properly so it wouldn't affect my productivity. Why would using a cursor automatically make you better at something if it's just new to you and you are already an elite programmer according to this study?
It's also possible for the user to be not using it right and that not be a value judgement on the user. We all suck at using new tools, that's part of learning.
I think we can be more open minded that an absolutely brand new technology (literally did not exist 3y ago) might require some amount of learning and adjusting, even for people who see themselves as an Einstein if only they wished to apply themselves.
Sorry to be pedantic but this is really common in tech products: vim, emacs, any second-brain app, effectiveness of IDEs depending on learning its features, git, and more.
All take quite an effort to master, until then they might slow one down or outright kill.
Noting a few important points here:
1. Some prior studies that find speedup do so with developers that have similar (or less!) experience with the tools they use. In other words, the "steep learning curve" theory doesn't differentially explain our results vs. other results.
2. Prior to the study, 90+% of developers had reasonable experience prompting LLMs. Before we found slowdown, this was the only concern that most external reviewers had about experience was about prompting -- as prompting was considered the primary skill. In general, the standard wisdom was/is Cursor is very easy to pick up if you're used to VSCode, which most developers used prior to the study.
3. Imagine all these developers had a TON of AI experience. One thing this might do is make them worse programmers when not using AI (relatable, at least for me), which in turn would raise the speedup we find (but not because AI was better, but just because with AI is much worse). In other words, we're sorta in between a rock and a hard place here -- it's just plain hard to figure out what the right baseline should be!
4. We shared information on developer prior experience with expert forecasters. Even with this information, forecasters were still dramatically over-optimistic about speedup.
5. As you say, it's totally possible that there is a long-tail of skills to using these tools -- things you only pick up and realize after hundreds of hours of usage. Our study doesn't really speak to this. I'd be excited for future literature to explore this more.
In general, these results being surprising makes it easy to read the paper, find one factor that resonates, and conclude "ah, this one factor probably just explains slowdown." My guess: there is no one factor -- there's a bunch of factors that contribute to this result -- at least 5 seem likely, and at least 9 we can't rule out (see the factors table on page 11).
I'll also note that one really important takeaway -- that developer self-reports after using AI are overoptimistic to the point of being on the wrong side of speedup/slowdown -- isn't a function of which tool they use. The need for robust, on-the-ground measurements to accurately judge productivity gains is a key takeaway here for me!
(You can see a lot more detail in section C.2.7 of the paper ("Below-average use of AI tools") -- where we explore the points here in more detail.)
4% more idle time 20% more AI interaction time
The 28% less coding/testing/research is why developers reported 20% less work. You might be spending 20% more time overall "working" while you are really idle 5% more time and feel like you've worked less because you were drinking coffee and eating a sandwich between waiting for the AI and reading AI output.
I think the AI skill-boost comes from having work flows that let you shave half that git-ops time, cut an extra 5% off coding, but cut the idle/waiting and do more prompting of parallel agents and a bit more testing then you really are a 2x dev.
The over-optimism is indeed a really important takeaway, and agreed that it's not tool-dependent.
I think one would have to compare the difficulty level of tasks.
I speculate that on easy tasks, LLM's can do a great job based on their training data alone, so you'd experience a speedup regardless of your prompt engineering skill level. But on large codebases and for complex tasks, an LLM cannot stand on it's own legs, and the differentiator becomes the quality of the prompt.
I think you'd need not only expert programmers, but expert programmers who have become expert prompt engineers(you would need some kind of extensive system prompt describing how the large codebase works), and those don't really exist yet, I think.
Very often, I have entire tasks that I can't offload to the Agent. I won't say I'm 20x more productive, it's probably more in the range of 15% to 20% (but I can't measure that obviously).
It’s much more useful getting something off the ground than maintaining a huge codebase.
LLMs have a v. steep and long learning curve as you posit (though note the points from the paper authors in the other reply).
Current LLMs just are not as good as they are sold to be as a programming assistant and people consistently predict and self-report in the wrong direction on how useful they are.
The developer who has experience using cursor saw a productivity increase not because he became better at using cursor, but because he became worse at not using it.
I would argue you don't need the "as a programming assistant" phrase as right now from my experience over the past 2 years, literally every single AI tool is massively oversold as to its utility. I've literally not seen a single one that delivers on what it's billed as capable of.
They're useful, but right now they need a lot of handholding and I don't have time for that. Too much fact checking. If I want a tool I always have to double check, I was born with a memory so I'm already good there. I don't want to have to fact check my fact checker.
LLMs are great at small tasks. The larger the single task is, or the more tasks you try to cram into one session, the worse they fall apart.
One thing that happened here is that they aren't using current LLMs:
> Most issues were completed in February and March 2025, before models like Claude 4 Opus or Gemini 2.5 Pro were released.
That doesn't mean this study is bad! In fact, I'd be very curious to see it done again, but with newer models, to see if that has an impact.
I think an easy measure to help identify why a slow down is happening would be to measure how much refactoring happened on the AI generated code. Often times it seems to be missing stuff like error handling, or adds in unnecessary stuff. Of course this assumes it even had a working solution in the first place.
I recall an adage about work-estimation: As chunks get too big, people unconsciously substitute "how possible does the final outcome feel" with "how long will the work take to do."
People asked "how long did it take" could be substituting something else, such as "how alone did I feel while working on it."
I assume that many large companies have tested efficiency gains and losses of there programmers much more extensively than the authors of this tiny study.
A survey of companies and their evaluation and conclusions would carry more weight—-excluding companies selling AI products, of course.
I totally agree with this. Although also, you can end up in a bad spot even after you've gotten pretty good at getting the AI tools to give you good output, because you fail to learn the code you're producing well.
A developer gets better at the code they're working on over time. An LLM gets worse.
You can use an LLM to write a lot of code fast, but if you don't pay enough attention, you aren't getting any better at the code while the LLM is getting worse. This is why you can get like two months of greenfield work done in a weekend but then hit a brick wall - you didn't learn anything about the code that was written, and while the LLM started out producing reasonable code, it got worse until you have a ball of mud that neither the LLM nor you can effectively work on.
So a really difficult skill in my mind is continually avoiding temptation to vibe. Take a whole week to do a month's worth of features, not a weekend to do two month's worth, and put in the effort to guide the LLM to keep producing clean code, and to be sure you know the code. You do want to know the code and you can't do that without putting in work yourself.
Everything else in your post is so reasonable and then you still somehow ended up suggesting that LLMs should be quadrupling our output
I agree. I have found that I can use agents most effectively by letting it write code in small steps. After each step I do review of the changes and polish it up (either by doing the fixups myself or prompting). I have found that this helps me understanding the code, but also avoids that the model gets in a bad solution space or produces unmaintainable code.
I also think this kind of close-loop is necessary. Like yesterday I let an LLM write a relatively complex data structure. It got the implementation nearly correct, but was stuck, unable to find an off-by-one comparison. In this case it was easy to catch because I let it write property-based tests (which I had to fix up to work properly), but it's easy for things to slip through the cracks if you don't review carefully.
(This is all using Cursor + Claude 4.)
Poor stack overflow, it looks like they are the ones really hurting from all this.
This is my intuition as well. I had a teammate use a pretty good analogy today. He likened vibe coding to vacuuming up a string in four tries when it only takes one try to reach down and pick it up. I thought that aligned well with my experience with LLM assisted coding. We have to vacuum the floor while exercising the "difficult skill [of] continually avoiding temptation to vibe"
My working hypothesis is that people who are fast at scanning lots of text (or code for that matter) have a serious advantage. Being able to dismiss unhelpful suggestions quickly and then iterating to get to helpful assistance is key.
Being fast at scanning code correlates with seniority, but there are also senior developers who can write at a solid pace, but prefer to take their time to read and understand code thoroughly. I wouldn't assume that this kind of developer gains little profit from typical AI coding assistance. There are also juniors who can quickly read text, and possibly these have an advantage.
A similar effect has been around with being able to quickly "Google" something. I wouldn't be surprised if this is the same trait at work.
That said, if the language has GC and other helpers, it makes it easier to scan.
Code and architecture review is an important part of my role and I catch issues that others miss because I spend more time. I did use AI for review (GPT 4.1), but only as an addition, since not reliable enough.
Most people who subscribe to that narrative have some connection to "AI" money, but there might be some misguided believers as well.
> My personal theory is that getting a significant productivity boost from LLM assistance and AI tools has a much steeper learning curve than most people expect.
"History does not repeat, but it rhymes."
If we rhyme "strong types will change the world" with "agentic LLMs will change the world," what do we get?
My personal theory is that we will get the same: some people will get modest-to-substantial benefits there, but changes in the world will be small if noticeable at all.
Also, my long experience is that even in PoC phase, using a type system adds almost zero extra time… of course if you know the type system, which should be trivial in any case after you’ve seen a few.
AI has a lot of potential but it's way over-hyped right now. Listen to the people on the ground who are doing real work and building real projects, none of them are over-hyping it. It's mostly those who have tangentially used LLMs.
It's also not surprising that many in this thread are clinging to a basic premise that it's 3 steps backwards to go 5 steps forward. Perhaps that is true but I'll take the study at face value, it seems very plausible to me.
For nasty, legacy codebases there is only so much you can do IMO. With green field (in certain domains), I become more confident every day that coding will be reduced to an AI task. I’m learning how to be a product manager / ideas guy in response
The most useful thing of all would have been to have screen recordings of those 16 developers working on their assigned issues, so they could be reviewed for varying approaches to AI-assisted dev, and we could be done with this absurd debate once and for all.
Definitely. Effective LLM usage is not as straightforward as people believe. Two big things I see a lot of developers do when they share chats:
1. Talk to the LLM like a human. Remember when internet search first came out, and people were literally "Asking Jeeves" in full natural language? Eventually people learned that you don't need to type, "What is the current weather in San Francisco?" because "san francisco weather" gave you the same, or better, results. Now we've come full circle and people talk to LLMs like humans again; not out of any advanced prompt engineering, but just because it's so anthropomorphized it feels natural. But I can assure you that "pandas count unique values column 'Foo'" is just as effective an LLM prompt as "Using pandas, how do I get the count of unique values in the column named 'Foo'?" The LLM is also not insulted by you talking to it like this.
2. Don't know when to stop using the LLM. Rather than let the LLM take you 80% of the way there and then handle the remaining 20% "manually", they'll keep trying to prompt to get the LLM to generate what they want. Sometimes this works, but often it's just a waste of time and it's far more efficient to just take the LLM output and adjust it manually.
Much like so-called Google-fu, LLM usage is a skill and people who don't know what they're doing are going to get substandard results.
IMO 80% is way too much, LLMs are probably good for things that are not your domain knowledge and you can efford to not be 100% correct, like rendering the Mandelbrot set, simple functions like that.
LLMs are not deterministic sometimes they produce correct code and other times they produce wrong code. This means one has to audit LLM generated code and auditing code takes more effort than writing it, especially if you are not the original author of the code being audited.
Code has to be 100% deterministic. As programmers we write code, detailed instructions for the computer (CPU), we have developed allot of tools such as Unit Tests to make sure the computer does exactly what we wrote.
A codebase has allot of context that you gain by writing the code, some things just look wrong and you know exactly why because you wrote the code, there is also allot of context that you should keep in your head as you write the code, context that you miss from simply prompting an LLM.
It is not as straightforward as people are told to believe!
While the results are going to be similar, typing a question in full can help you think about it yourself too, as if the LLM is a rubber duck that can respond back.
I've found myself adjusting and rewriting prompts during the process of writing them before i ask the LLM anything because as i was writing the prompt i was thinking about the problem simultaneously.
Of course for simple queries like "write me a function in C that calculates the length of a 3d vector using vec3 for type" you can write it like "c function vec3 length 3d" or something like that instead and the LLM will give more or less the same response (tried it with Devstral).
But TBH to me that sounds like programmers using Vim claiming they're more productive than users of other editors because they have to use less keystrokes.
I think an approach that I tried recently is to use it as a friction remover instead of a solution provider. I do the programming but use it to remove pebbles such as that small bit of syntax I forgot, basically to keep up the velocity. However, I don't look at the wholesale code it offers. I think keeping the active thinking cap on results in code I actually understand while avoiding skill atrophy.
If you're short on time, I'd recommend just reading the linked blogpost or the announcement thread here [1], rather than the full paper.
> This gap between perception and reality is striking: developers expected AI to speed them up by 24%, and even after experiencing the slowdown, they still believed AI had sped them up by 20%.
I wonder what could explain such large difference between estimation/experience vs reality, any ideas?
Maybe our brains are measuring mental effort and distorting our experience of time?
I feel like there are two challenges causing this. One is that it's difficult to get good data on how long the same person in the same context would have taken to do a task without AI vs with. The other is that it's tempting to time an AI with metrics like how long until the PR was opened or merged. But the AI workflow fundamentally shifts engineering hours so that a greater percentage of time is spent on refactoring, testing, and resolving issues later in the process, including after the code was initially approved and merged. I can see how it's easy for a developer to report that AI completed a task quickly because the PR was opened quickly, discounting the amount of future work that the PR created.
But it's hampered me in the fact that others, uninvited, toss an AI code review tool at some of my open PRs, and that spits out a 2-page document with cute emoji and formatted bullet points going over all aspects of a 30 line PR.
Just adds to the noise, so now I spend time deleting or hiding those comments in PRs, which means I have even _less_ time for actual useful maintenance work. (Not that I have much already.)
Scaled up slightly, we use it to build plenty of internal tooling in our video content production pipeline (syncing between encoding tools and a status dashboard for our non-technical content team).
Using it for anything more than boilerplate code, well-defined but tedious refactors, or quickly demonstrating how to use an unfamiliar API in production code, before a human takes a full pass at everything is something I'm going to be wary of for a long time.
If we studied folks with _no_ AI experience, then we might underestimate speedup, as these folks are learning tools (see a discussion of learning effects in section (C.2.7) - Below-average use of AI tools - in the paper). If we studied folks with _only_ AI experience, then we might overestimate speedup, as perhaps these folks can't really program without AI at all.
In some sense, these are just two separate and interesting questions - I'm excited for future work to really dig in on both!
Wouldn't this be an underestimate, since without ai there'd be no forward progress at all? So ai-assisted is infinite speedup if the outputs are good.
I can use it for parts of code, algorithms, error solving, and maybe sometimes a 'first draft'.
But there is no way I could finish an entire piece of software with AI only.
But isn't the important thing to measure... how long does it take to debug the resulting code at 3AM when you get a PagerDuty alert?
Similarly... how about the quality of this code over time? It's taken a lot of effort to bring some of the code bases I work in into a more portable, less coupled, more concise state through the hard work of
- bringing shared business logic up into shared folders
- working to ensure call chains flow top down towards root then back up through exposed APIs from other modules as opposed to criss-crossing through the directory structure
- working to separate business logic from API logic from display logic
- working to provide encapsulation through the use of wrapper functions creating portability
- using techniques like dependency injection to decouple concepts allowing for easier testing
etc
So, do we end up with better code quality that ends up being more maintainable, extensible, portable, and composable? Or do we just end up with lots of poor quality code that eventually grows to become a tangled mess we spend 50% of our time fighting bugs on?
But, taking a broader view its possible that these initial speed ups are negated by the fact that I never really learn go or helm charts as deeply now that I use claude code. Over time, its possible that my net productiviy is still reduced. Hard to say for sure, especially considering I might not have even considered talking these more difficult go library modifications if I didn't have claude code to hold my hand.
Regardless, these tools are out there, increasing in effectiveness and I do feel like I need to jump on the train before it leaves me at the station.
That said, this comes up often in my office. It's just not giving really good advice in many situations - especially novel ones.
AI is super good at coming up with things that have been written ad nauseam for coding-interview-prep website
One thing that did work in my favor is that, I was clearly creating a failing repro test case, and adding before and after along with PR. That helped getting the PR landed.
There are also a few PRs that never got accepted because the repro is not as strong or clear.
“One shotting” apps, or even cursor and so forth seem like a waste of time. It feels like if you prompt it just right it might help but then it never really does.
For everything else, I think you're right, and actually the dialog-oriented method is way better. If I learn an approach and apply some general example from ChatGPT, but I do the typing and implementation myself so I need to understand what I'm doing, I'm actually leveling up and I know what I'm finished with. If I weren't "experienced", I'd worry about what it was doing to my critical thinking skills, but I know enough about learning on my own at this point to know I'm doing something.
I'm not interested in vibe coding at all--it seems like a one-way process to automate what was already not the hard part of software engineering; generating tutorial-level initial implementations. Just more scaffolding that eventually needs to be cleared away.
We probably need a bit less wishful thinking. Blindly trusting what the AI suggests tends to backfire. The real challenge is figuring out where it actually helps, and where it quietly gets in the way.
You can still do those things.
There's some existing lit about increased contributions to OS repositories after the introduction of AI -- I've also personally heard a fear anecdotes about an increase in the number of low-quality PRs from first time contributors, seemingly as a result of AI making it easier to get started -- ofc, the tradeoff is that making it easier to get started has pros to it too!
[1] https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study.pdf
* 3 weeks to transition from ai pairing to AI Delegation to ai multitasking. So work gains are mostly week 3+. That's 120+ hours in, as someone pretty senior here.
* Speedup is the wrong metric. Think throughput, not latency. Some finite amount of work might take longer, but the volume of work should go up because AI can do more on a task and diff tasks/projects in parallel.
Both perspectives seem consistent with the paper description...
Because one of the big takeaways from this study is that people are bad at predicting and observing their own time spent.
At the same time... that's not why I'm comfortable writing this. It's pretty obvious when you know what good vs bad feels like here and adjust accordingly:
1. Good: You are able to generate a long plan and that plan mostly works. These are big wins _as long as you are multitasking_: you are high throughput, even if the AI is slow. Think running 5-20min at a time for pretty good progress, for just a few minutes of your planning that you'd largely have to do anyways.
2. Bad: You are wasting a lot of attention chatting (so 1-2min runs) and repairing (re-planning from the top, vs progressing). There is no multitasking win.
It's pretty clear what situation you're in, with run duration on its own being a ~10X level difference.
Ex: I'll have ~3 projects going at the same time, and/or whatever else I'm doing. I'm not interacting "much" so I know it's a win. If a project is requiring interaction, well, now I need to jump in, and it's no longer agentic coding IMO, but chat assistant stuff.
At the same time, I power through case #2 in practice because we're investing in AI automation. We're retooling everything to enable long runs, so we'll still do the "hard" tasks via AI to identify & smooth the bumps. Similar to infrastructure-as-code and SDLC tooling, we're investing in automating as much of our stack as we can, so that means we figure out prompt templates, CI tooling, etc to enable the AI to do these so we can benefit later.
Two things that stand out to me are 1. it depends a lot on what kind of task you are having the LLM do. and 2. if the LLM process takes more time, it is very likely your cognitive effort was still way less - for sysadmin kinds of tasks, working with less often accessed systems, LLMs can read --help, man pages, doc sites, all for you, and give you the working command right there (And then run it, and then look at the output and tell you why it failed, or how it worked, and what it did). There is absolutely no question that second part is a big deal. Sticking it onto my large open source project to fix a deep, esoteric issue or write some subtle documentation where it doesnt really "get" what I'm doing, yeah it is not as productive in that realm and you might want to skip it for the thinking part there. I think everyone is trying to figure out this question of "when and how" for LLMs. I think the sweet spot is for tasks involving systems and technologies where you'd otherwise be spending a lot of time googling, stackoverflowing, reading man pages to get just the right parameters into commands and so forth. This is cognitive grunt work and the LLMs can do that part very well.
My week of effort with it was not really "coding on my open source project"; two examples were, 1. running a bunch of ansible playbooks that I wrote years ago on a new host, where OS upgrades had lots of snags; I worked with Claude to debug all the various error messages and places where the newer OS distribution had different packages, missing packages, etc. it was ENORMOUSLY helpful since I never look at these playbooks and I dont even remember what I did, Claude can read it for you and interpret it as well as you can. 2. I got a bugzilla for a fedora package that I packaged years ago, where they have some change to the directives used in specfiles that everyone has to make. I look at fedora packaging workflows once every three years. I told Claude to read the BZ and just do it. IT DID IT. I had to get involved running the "mock" suite as it needed sudo but Claude gave me the commands. zero googling. zero even reading the new format of the specfile (the bz linked to a tool that does the conversion). From bug received to bug closed and I didnt do any typing at all outside of the prompt. Had it done before breakfast since I didnt even need any glucose for mental energy expended. This would have been a painful and frustrating mental effort otherwise.
so the studies have to get more nuanced and survey a lot more than 16 devs I think
I use it like a know-it-all personal assistant that I can ask any question to; even [especially] the embarrassing, "stupid" ones.
> The only stupid question is the one we don't ask.
- On an old art teacher's wall
How can you be so sure? Did you compare in a systematic way or read papers by people who did it?
Now I surely get results giving the llm only snippets and keywords, but anything complex, I do notice differences the way I articulate. Not claiming there is a significant difference, but it seems to me this way.
Maybe the LLM doesn't strictly need it, but typing out does bring some clarity for the asker. I've found it helps a lot to catch myself - what am I even wanting from this?
I don't have any studies, but it eems to me reasonable to assume.
(Unlike google, where presumably it actually used keywords anyway)
I very much think that these things are going to wind up being massive amplifiers for people who were already extremely sophisticated and then put massive effort into optimizing them and combining them with other advanced techniques (formal methods, top-to-bottom performance orientation).
I don't think this stuff is going to democratize software engineering at all, I think it's going to take the difficulty level so high that it's like back when Djikstra or Tony Hoare was a fairly typical computer programmer.
I've found that there are a couple of things you need to do to be very efficient.
- Maintain an architecture.md file (with AI assistance) that answers many of the questions and clarifies a lot of the ambiguity in the design and structure of the code.
- A bootstrap.md file(s) is also useful for a lot of tasks.. having the AI read it and start with a correct idea about the subject is useful and a time saver for a variety of kinds of tasks.
- Regularly asking the AI to refactor code, simplify it, modularize it - this is what the experienced dev is for. VIBE coding generally doesn't work as AI's tend to write messy non-modular code unless you tell them otherwise. But if you review code, ask for specific changes.. they happily comply.
- Read the code produced, and carefully review it. And notice and address areas where there are issues, have the AI fix all of these.
- Take over when there are editing tasks you can do more efficiently.
- Structure the solution/architecture in ways that you know the AI will work well with.. things it knows about.. it's general sweet spots.
- Know when to stop using the AI and code it yourself.. particuarly when the AI has entered the confusion doom loop. Wasting time trying to get the AI to figure out what it's never going to is best used just fixing it yourself.
- Know when to just not ever try to use AI. Intuitively you know there's just certain code you can't trust the AI to safely work on. Don't be a fool and break your software.
----
I've found there's no guarantee that AI assistance will speed up any one project (and in some cases slow it down).. but measured cross all tasks and projects, the benefits are pretty substantial. That's probably others experience at this point too.
Same thought came when I was reading the article and glad I am not alone.
Anecdotally, most common productivity boost is coming from cutting down weird slow steps in processes. Write an automation script, campaign previewer for marketing, etc etc.
Coding seems to transform to be a more efficient (again anecdotally) but not entirely faster. You can do a better work on a new feature in the same or slightly smaller time.
Idle time at 4% was interesting. I think this number goes higher the more you use a specific tool and adjust your workflow to that
Could be the case for some, but I also think, that there is not much to climb on the learning curve for AI agents.
In my opinion, its more interesting, that the study also states, that AI capabilities may be comparatively lower on existing code:
> Our results also suggest that AI capabilities may be comparatively lower in settings with very high quality standards, or with many implicit requirements (e.g. relating to documentation, testing coverage, or linting/formatting) that take humans substantial time to learn.
This is consistent with my personal/pear experience. On existing code: You have to do try and error with AI until you get a 'good' result. Or highly modify AI generated code by yourself (which is often slower then writing it yourself from the beginning).
With more coaching from me, which I might end up doing, I think he would get further. But I expected the chatbot to get him further through the process than this. My conclusion so far is that this technology won't meaningfully shift the balance of programmers to non-programmers in the general population.
The study used 246 tasks across 16 developers, for an average of 15 tasks per developer. Divide that further in half because tasks were assigned as AI or not-AI assisted, and the sample size per developer is still relatively small. Someone would have to take the time to review the statistics, but I don’t think this is a case where you can start inferring that the developers who benefited from AI were just better at using AI tools than those who were not.
I do agree that it would be interesting to repeat a similar test on developers who have more AI tool assistance, but then there is a potential confounding effect that AI-enthusiastic developers could actually lose some of their practice in writing code without the tools.
I don't think this is a confounding effect
This is something that we definitely need to measure and be aware of, if there is a risk of it
Yes, and I'll add that there is likely no single "golden workflow" that works for everybody, and everybody needs to figure it out for themselves. It took me months to figure out how to be effective with these tools, and I doubt my approach will transfer over to others' situations.
For instance, I'm working solo on smallish, research-y projects and I had the freedom to structure my code and workflows in a way that works best for me and the AI. Briefly: I follow an ad-hoc, pair-programming paradigm, fluidly switching between manual coding and AI-codegen depending on an instinctive evaluation of whether a prompt would be faster. This rapid manual-vs-prompt assessment is second nature to me now, but it took me a while to build that muscle.
I've not worked with coding agents, but I doubt this approach will transfer over well to them.
I've said it before, but this is technology that behaves like people, and so you have to approach it like working with a colleague, with all their quirks and fallibilities and potentially-unbound capabilities, rather than a deterministic, single-purpose tool.
I'd love to see a follow-up of the study where they let the same developers get more familiar with AI-assisted coding for a few months and repeat the experiment.
Actually, it works well so long as you tell them when you’ve made a change. Claude gets confused if things randomly change underneath it, but it has no trouble so long as you give it a short explanation.
The short version is that devs want to give instructions instead of ask for what outcome they want. When it doesn’t follow the instructions, they double down by being more precise, the worst thing you can do. When non devs don’t get what they want, they add more detail to the description of the desired outcome.
Once you get past the control problem, then you have a second set of issues for devs where the things that should be easy or hard don’t necessarily map to their mental model of what is easy or hard, so they get frustrated with the LLM when it can’t do something “easy.”
Lastly, devs keep a shit load of context in their head - the project, what they are working on, application state, etc. and they need to do that for LLMs too, but you have to repeat themselves often and “be” the external memory for the LLM. Most devs I have taught hate that, they actually would rather have it the other way around where they get help with context and state but want to instruct the computer on their own.
Interestingly, the best AI assisted devs have often moved to management/solution architecture, and they find the AI code tools brought back some of the love of coding. I have a hypothesis they’re wired a bit differently and their role with AI tools is actually closer to management than it is development in a number of ways.
The CTO and VPEng at my company (very small, still do technical work occasionally) both love the agent stuff so much. Part of it for them is that it gives them the opportunity to do technical work again with the limited time they have. Without having to distract an actual dev, or spend a long time reading through the codebase, they can quickly get context for an build small items themselves.
This suggests me though that they are bad at coding, otherwise they would have stayed longer. And I can't find anything in your comment that would corroborate the opposite. So what gives?
I am not saying what you say is untrue, but you didn't give any convincing arguments to us to believe otherwise.
Also, you didn't define the criteria of getting better. Getting better in terms of what exactly???
But for very large stable codebases it is a mixed bag of results. Their selection of candidates is valid but it probably illustrates a worst case scenario for time based measurement.
If an AI code editor cannot make more changes quicker than a dev or cannot provide relevant suggestions quick enough/without being distracting then you lose time.
Are we are still selling the "you are an expert senior developer" meme ? I can completely see how once you are working on a mature codebase LLMs would only slow you down. Especially one that was not created by an LLM and where you are the expert.
I think LLMs shine when you need to write a higher volume of code that extends a proven pattern, quickly explore experiments that require a lot of boilerplate, or have multiple smaller tasks that you can set multiple agents upon to parallelize. I've also had success in using LLMs to do a lot of external documentation research in order to integrate findings into code.
If you are fine-tuning an algorithm or doing domain-expert-level tweaks that require a lot of contextual input-output expert analysis, then you're probably better off just coding on your own.
Context engineering has been mentioned a lot lately, but it's not a meme. It's the real trick to successful LLM agent usage. Good context documentation, guides, and well-defined processes (just like with a human intern) will mean the difference between success and failure.
I've found AI to be quite helpful in pointing me in the right direction when navigating an entirely new code-base.
When it's code I already know like the back of my hand, it's not super helpful, other than maybe doing a few automated tasks like refactoring, where there have already been some good tools for a while.
Nothing new this time except for people who have no vision and no ability to work hard not “getting it” because they don’t have the cognitive capacity to learn
For example I whipped together a Steam API -based tool that gets my game library and enriches it with data available in maybe 30 minutes of active work.
The LLM (Cursor with Gemini Pro + Claude 3.7 at the time IIRC) spent maybe 2-3 hours on it while I watched some shows on my main display and it worked on my second screen with me directing it.
Could I have done it myself from scratch like a proper artisan? Most definitely. Would I have bothered? Nope.
You hit the nail on the head here.
I feel like I’ve seen a lot of people trying to make strong arguments that AI coding assistants aren’t useful. As someone who uses and enjoys AI coding assistants, I don’t find this research angle to be… uh… very grounded in reality?
Like, if you’re using these things, the fact that they are useful is pretty irrefutable. If one thinks there’s some sort of “productivity mirage” going on here, well OK, but to demonstrate that it might be better to start by acknowledging areas where they are useful, and show that your method explains the reality we’re seeing before using that method to show areas where we might be fooling ourselves.
I can maybe buy that AI might not be useful for certain kinds of tasks or contexts. But I keep pushing their boundaries and they keep surprising me with how capable they are, so it feels like it’ll be difficult to prove otherwise in a durable fashion.
I do think you're onto something with getting pebbles out of the road inasmuch as once I know what I need to do AI coding makes the doing much faster. Just yesterday I was playing around with removing things from a List object using the Java streams API and I kept running into ConcurrentOperationsExceptions, which happen when multiple threads are mutating the list object at the same time because no thread can guarantee it has the latest copy of the list unaltered by other threads. I spent about an hour trying to write a method that deep copies the list, makes the change and then returns the copy and running into all sorts of problems til I asked AI to build me a thread-safe list mutation method and it was like "Sure, this is how I'd do it but also the API you're working with already has a method that just....does this." Cases like this are where AI is supremely useful - intricate but well-defined problems.
Most commenters on this paper seem to not respond to the strongest result from it. That is, the developers wrongly thought and felt that using AI had sped up their work. So we need to be super cautious about what we think we know.
I think this may become a long horizon harvest for the rigorous OOP strategy, may Bill Joy be disproved.
Gray goo may not [taste] like steel-cut oatmeal.
Once AI can actually untangle our 14 year old codebase full of hosh-posh code, read every commit message, JIRA ticket, and Slack conversation related to the changes in full context, it's not going to solve a lot of the hard problems at my job.
i don't mean to pick on your usage of this specifically, but i think it's noteworthy that the colloquial definition of "rubber ducking" seems to have expanded to include "using a software tool to generate advice/confirm hunches". I always understood the term to mean a personal process of talking through a problem out loud in order to methodically, explicitly understand a theoretical plan/process and expose gaps.
based on a lot of articles/studies i've seen (admittedly haven't dug into them too deeply) it seems like the use of chatbots to perform this type of task actually has negative cognitive impacts on some groups of users - the opposite of the personal value i thought rubber-ducking was supposed to provide.
This was my pr-AI experience anyway, so getting that first chunk of time back is helpful.
Related: One of the better takes I've seen on AI from an experienced developer was, "90% of my skills just became worthless, and the other 10% just became 1,000 times more valuable." There's some hyperbole there, I but I like the gist.
Otherwise he can shut the fuck up about being 1000x more valuable imo
However, it is _fun_ to go over the barrier if it is chatting with a model to get a quick tutorial and produce working code for a prototype (for your specific needs) where the understanding that you just developed is applied. The alternative (without LLMs) is to first do the ground work of learning via tutorials in text/video form and then do the cognitive mapping of applying the learning to one's prototype. I would make a lot of mistakes that expert/intermediate React developers don't make on this path.
One could argue that it shortcuts some learning and perhaps the old way results in better retention. But, our field changes so fast... and when it remains static for too long, projects die. I think of all this as accelerant for progress in adoption of new ways of thinking about software and diffusing that more quickly across the developer population globally. Code is always fungible, anyway. The job is about all the other things that one needs to do besides coding.
I just used it to write about 80 lines of new code like that, and there's no question it saves time.
Autocorrect is a scourge of humanity.
I'm the biggest skeptic, but more and more I'm seeing it get me the bulk of the way with very little back-and-forth. If it was even more heavily integrated in my dev environment, it would save me even more time.
Did you measure subjective fatigue as one way to explain the misperception that AI was faster? As a developer-turned-manager I like AI because it's easier when my brain is tired.
TLDR: mixed evidence that developers make it less effortful, from quantitative and qualitative reports. Unclear effect.
If the instruction is just "implement this ticket with AI", then that's very realistic in that it's how management often tries to operate, but it's also likely to be quite suboptimal. There are ways to use AI that help a lot, and other ways that hurt more than it helps.
If your developers had sufficient experience with AI to tell the difference, then they might have compensated for that, but reading the paper I didn't see any indication of that.
That being said, we can't rule out that the experiment drove them to use more AI than they would have outside of the experiment (in a way that made them less productive). You can see more in section "Experimentally driven overuse of AI (C.2.1)" [1]
[1] https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study.pdf
Also, cool work, very happy to see actually good evaluations instead of just vibes or observational stuies that don't account for the Hawthorne effect
We'll be releasing anonymized data and some basic analysis code to replicate core results within the next few weeks (probably next, depending).
Our GitHub is here (http://github.com/METR/) -- or you can follow us (https://x.com/metr_evals) and we'll probably tweet about it.
What if agentic coding sessions are triggering a similar dopamine feedback loop as social media apps? Obviously not to the same degree as social media apps, I mean coding for work is still "work"... but there's maybe some similarity in getting iterative solutions from the agent, triggering something in your brain each time, yes?
If that was the case, wouldn't we expect developers to have an overly positive perception of AI because they're literally becoming addicted to it?
https://softwarecrisis.dev/letters/llmentalist/
Plus there's a gambling mechanic: Push the button, sometimes get things for free.
I wish there was a simple way to measure energy spent instead of time. Maybe nature is just optimizing for something else.
There's no flow state to be achieved with AI tools (at the moment)
My issue with this being a 'negative' thing is that I'm not sure it is. It works off of the same hunting / foraging instincts that keep us alive. If you feel addiction to something positive, it is bad?
Social media is negative because it addicts you to mostly low quality filler content. Content that doesn't challenge you. You are reading shit posts instead of reading a book or doing something with better for you in the long run.
One could argue that's true for AI, but I'm not confident enough to make such a statement.
The "economic experts" and "ml experts" are in many cases effectively the same group-- companies pushing AI coding tools have a vested interest in people believing they're more useful than they are. Executives take this at face value and broadly promise major wins. Economic experts take this at face value and use this for their forecasts.
This propagates further, and now novices and casual individuals begin to believe in the hype. Eventually, as an experienced engineer it moves the "baseline" expectation much higher.
Unfortunately this is very difficult to capture empirically.
- Test subjects consistently report that keyboarding is faster than mousing.
- The stopwatch consistently proves mousing is faster than keyboarding.
This bit I wasn't at all surprised by, because this is _very common_. People who are doing a [magic thing] which they believe in often claim that it is improving things even where it empirically isn't; very, very common with fad diets and exercise regimens, say. You really can't trust subjects' claims of efficacy of something that's being tested on them, or that they're testing on themselves.
And particularly for LLM tools, there is this strong sense amongst many fans that they are The Future, that anyone who doesn't get onboard is being Left Behind, and so forth. I'd assume a lot of users aren't thinking particularly rationally about them.
It was fun to watch, it’s super polished and sci-fi-esque. But after 15 minutes I felt braindead and was bored out of my mind lol
Developers totally spend time totally differently, though, this is a great callout! On page 10 of the paper [1], you can see a breakdown of how developers spend time when they have AI vs. not - in general, when these devs have AI, they spend a smaller % of time writing code, and a larger % of time working with AI (which... makes sense).
[1] https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study.pdf
I'm not making any claim in either direction, the authors themselves recognize the study's limitations, I'm just trying to say that everyone should have far greater error bars. This technology is the weirdest shit I've seen in my lifetime, making deductions about productivity from anecdotes and dubious benchmarks is basically reading tea leaves.
The standard experimental design that solves this is to randomly assign participants to the experiment group (with AI) and the control group (without AI), which is what they did. This isolates the variable (with or without AI), taking into account uncontrollable individual, context, and environmental differences. You don't need to know how the single individual and context would have behaved in the other group. With a large enough sample size and effect size, you can determine statistical significance, and that the with-or-without-AI variable was the only difference.
But Figure 18 shows that time spent actively coding decreased (which might be where the feeling of a speed-up was coming from) and the gains were eaten up by time spent prompting, waiting for and then reviewing the AI output and generally being idle.
So maybe it's not a good idea to use LLMs for tasks that you could've done yourself in under 5 minutes.
Per our website, “To date, April 2025, we have not accepted compensation from AI companies for the evaluations we have conducted.” You can check out the footnote on this page: https://metr.org/donate
Not all payment is cash. Compute credits is still by all means compensation.
As a philosopher who is into epistemology and ontology, I find this to be as abhorrent as religion.
'Science' doesn't matter who publishes it. Science needs to be replicated.
The psychology replication crisis is a prime example of why peer reviews and publishing in a journal matters 0.
Specifically, it works as an example of a specific case where peer review doesn’t help as much. Peer review checks your arguments, not your data collection process (which the reviewer can’t audit for obvious reasons). It works fine in other scenarios.
Peer review is unrelated to replication problems, except to the extent to which confused people expect peer review to fix totally unrelated replication problems.
...Or should I say "were" very important? With the help of today's GenAI every low effort stuff can look high effort without much extra effort.
This is not directed at you, but I am worried that contributors that use AI "exclusively" to contribute to OSS projects are extracting the value (street cred, being seen as part of the project community) without actually contributing anything (by being one more person that knows the codebase and can help steward it).
It's the same thing we've seen out of enshittification of everything. Value extraction without giving back.
Maybe I'm too much of a cynic. Maybe majority of OSS projects don't care. But I know I will be saddened if one of the OSS projects I care about get taken over by such "value extractors".
I've slightly alternate perspective. Imo, using OSS without contributing is the value extraction without giving back.
If someone can fix a bunch of chores (that still take human time), with the use of AI (even though they don't become stewards), I still see it as giving back. Of course, there is a value chain - contributing with AI without understanding code is the bottom of value creation. Like you mentioned, also being a steward is the top of the value chain. Along the way, if the contributor builds some sorta reputation that would help with their career or other outcomes, so be it.
So in that sense, I don't see it as enshittification. AI might make a pathway to resolve a bunch of things which otherwise wouldn't be resolved. In fact, this was the line of thinking for the tool we built. Instead of people making these mindless PRs, can we build an agent that can take care of 'trivial' tasks. I manually created PRs to test that hypothesis.
There is also a natural self selection here. If someone was able to fix something without understanding any code, that is also indicative of how trivial the task is. There is a reverse effect to my argument though. These "AI contributors" can create a ton of PRs that would create a lot of work for maintainers to review them.
In my case, I was being upfront about how I'm raising PRs and requesting permissions if it is OK to work on certain issue. Maintainers are quite open and inviting.
I've been sensing in these cases that i don't have a good enough way to express these problems, and that i actually need to figure that out, or the rest of my team, whether they're using AI or not, are gonna have a real hard time understanding the change i made.
I feel like programming has become increasingly specialized and even before AI tool explosion, it's way more possible to be ignorant of an enormous amount of "computing" than it used to be. I feel like a lot of "full stack" developers only understand things to the margin of their frameworks but above and below it they kind of barely know how a computer works or what different wire protocols actually are or what an OS might actually do at a lower level. Let alone the context in which in application sits beyond let's say, a level above a kubernetes pod and a kind of trial-end-error approach to poking at some YAML templates.
Do we all need to know about processor architectures and microcode and L2 caches and paging and OS distributions and system software and installers and openssl engines and how to make sure you have the one that uses native instructions and TCP packets and envoy and controllers and raft systems and topic partitions and cloud IAM and CDN and DNS? Since that's not the case--nearly everyone has vast areas of ignorance yet still does a bunch of stuff--it's harder to sell the idea that whatever AI tools are doing that we lose skills in will somehow vaguely matter in the future.
I kind of miss when you had to know a little of everything and it also seemed like "a little bit" was a bigger slice of what there was to know. Now you talk to people who use a different framework in your own language and you feel like you're talking to deep specialists whose concerns you can barely understand the existence of, let alone have an opinion on.
Have you used modern software… or just software in general to be honest.
We have had orders of magnitude improvement in hardware performance and much fewer orders of magnitude increase in software performance and features.
May I present the windows start menu as a perfect exhibit, we put a web browser in there and made actually finding the software you want to use harder than ever, even search is completely broken 99% of the time (really try powertoys run or even windows + s for a night and day difference).
We add boundless complexity to things that doesn’t need it, millions of lines of code, then waste millions of cycles running security tools to heuristically prevent malicious actors from exploiting our millions of lines of code that is impossible to know because it is deemed to difficult to learn the underlying semantics of the problem domain.
You brought up Rust, it is fascinating.
The Rust's type system differs from typical Hindle-Milner by having operations that can remove definitions from environment of the scope.
Rust was conceived in 2006.
In 2006 there already were HList papers by Oleg Kiselyov [1] that had shown how to keep type level key-value lists with addition, removal and lookup, and type-level stateful operations like in [2] were already possible, albeit, most probably, not with nice monadic syntax support.
[1] https://okmij.org/ftp/Haskell/HList-ext.pdf
[2] http://blog.sigfpe.com/2009/02/beyond-monads.html
But it was not, Rust was not embedded into Haskell and now it will never get effects (even as weak as monad transformers) and, as a consequence, will never get proper high performance software transactional memory.
So here we are: everything in Haskell's strong type system world that would make Rust better was there at the very beginning of the Rust journey, but had no impact on Rust.
Rhyme that with LLM.
https://www.businessinsider.com/apple-antennagate-scandal-ti...
how much did that uproar and settlement matter?
e.g., Nokia 1600 user guide from 2005 (page 16) [0]
[0] https://www.instructionsmanuals.com/sites/default/files/2019...
This is going to be interesting long-term. Realistically people don't spend anywhere close to 100% of time working and they take breaks after intense periods of work. So the real benefit calculation needs to include: outcome itself, time spent interacting with the app, overlap of tasks while agents are running, time spent doing work over a long period of time, any skill degradation, LLM skills, etc. It's going to take a long time before we have real answers to most of those, much less their interactions.
In practice I have not had any issues getting information out of an LLM when speaking to them like a computer, rather than a human. At least not for factual or code-related information; I'm not sure how it impacts responses for e.g. creative writing, but that's not what I'm using them for anyway.
I like to think of it that instead of having seemingly endless amounts of half thoughts spinning around inside your head, you make an idea or thought more “fully formed” when you express it verbally or with written (or typed) words.
I believe this is part of why therapy can work, by actually expressing our thoughts, we’re kind of forced to face realities and after doing so it’s often much easier to reflect on it. Therapists often recommend personal journals as they can also work for this.
I believe rubber ducking works because in having to explain the problem, it forces you to actually gather your thoughts into something usable from which you can more effectively reflect on.
I see no reason why doing the same thing except in writing to an LLM couldn’t be equally effective.
This is what human language does though, isn't it? Evolves over time, in often weird ways; like how many people "could care less" about something they couldn't care less about.
I would think this could contrast with agentic coding, where the AI keeps generating code, and then you iterate on this process to get the AI to fix its mistakes. With normal human code review, it takes longer to get revisions and can feel like a slog. But with AI that's a much tighter loop, so maybe developers feel extra productive from all these dopamine hits from each interaction with the agent.
When manually coding and in flow state I'd think it's a more consistent level of arousal, less spiky. Probably varies by person and coding style though, which might also explain why some people love TDD and others can't stand it?
So far, Fred Brook’s “No Silver Bullet” remains undefeated.
I don't like it. I know it is the way it is because it's supposed to support all the cursed weird stuff you can do in JS, but to me as a fullstack developer who's never really taken the time to deep dive and learn TS properly it often feels more like an obstacle. For my own code it's fine, but when I have to work with third party libraries it can be really confusing. It's definitely a skill issue though.
My original point was about history and about how can we extract possible outcome from it.
My other comment tries to amplify that too. Type systems were strong enough for several decades now, had everything Rust needed and more years before Rust began, yet they have little penetration into real world, example being that fancy dandy Rust language.
Define schemas, interfaces, and perhaps some base classes that define the attributes I'm thinking about.
Research libraries that support my cause, and include them.
Reference patterns I have established in other parts of the codebase; internal tooling for database, HTTP services, etc.
Instruct the agent to come up with a plan for a first pass at execution in markdown format. Iterate on this plan; "what about X?"
Splat a bunch of code down that supports the structure I'm looking for. Iterate. Cleanup. Iterate. Implement unit tests and get them to pass.
Go back through everything manually and adjust it to suit my personal style, while at the same time fully understanding what's being done and why.
I use STT a lot to have conversations with the agent as we go, and very rarely allow it to make sequential edits without reviewing first; this is a great opportunity to go back and forth and refine what's being written.
To be clear it wasn't $75k each.
Paper is here: https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study.pdf
* Setup: I did a whole cloth clean room rewrite at production-grade an MCP server that was previously prototyped by the AI head. I intentionally went all-in for this "first serious" agentic coding project: ultimately < 100 lines of code manually edited, while the final AI-generated PR stack was big.
* Baseline: If both of us did manually, I had already estimated similar times to completion due to differences in task scope naturally matching differences in proficiency for those tasks. Despite being new to agentic coding at the time, key aspects were in my favor: senior dev, 2 years of ~daily prompt engineering experience (tactics), PhD in code synthesis (strategy), and the repo setup with various lint/type/test guardrails.
* Result: About the same 1-2 weeks as a junior vibes coder vs the manual coder
* Experience: It was clear the first 1-2 weeks were slow due to onboarding myself and the codebase to agentic coding. That first week was especially rough. While I could get long runs going during it, I was doing the more confidently week 2, and switching to figuring out how to do parallel agents. Near the end, I was switching to doing multiple long runs in parallel on different tasks, where I could maintain maybe 2-4, but managing more gets exhausting, and especially when any are shorter runs.
Separately, we have a genAI team and a GPU Python team both switching to agentic coding. The natural difference in prompt engineering experience across teams seems to have individuals on one team picking up faster than the other, when gauged by the ability to do long runs
The initial experiment is part of why I view current agentic coding being more about overall coding throughput, and not latency within any specific task. If someone can reliably trigger long runs, do multiple in parallel, and doesn't waste time in interactive chatting, the difference is stark.
Likewise, reinforced by both of the above cases, looking for throughput improvements in the first 2-3w seems aggressive. A lot of blogposts seem to be from people new to high-quality prompting, tackling repos/tasks not setup for agents, and as limited nights & weekend efforts. To get to clear throughput multipliers from multiple agents working in parallel on good long runs.. I'd expect that to hit at month 2 or 3 when someone isn't as all in as I was able to be. It's more like a skill with setup, so takes investment before you reap the rewards.
No one would call one a noob for not using Vim or Emacs. But they might for a different reason.
If someone blindly rejects even the notion of these tools without attempting to understand the underlying ideas behind them, that certainly suggests the dilettante nature of the person making the argument.
The idea of vim-motions is a beautiful, elegant, pragmatic model. Thinking that it is somehow outdated is a misapprehension. It is timeless just like musical notation - similarly it provides compositional grammar and universal language, and leads to developing muscle memory; and just like it, it can be intimidating but rewarding.
Emacs is grounded on another amazing idea - one of the greatest ideas in computer science, the idea of Lisp. And Lisp is just as everlasting, like math notation or molecular formulas — it has rigid structural rules and uniform syntax, there's compositional clarity, meta-reasoning and universal readability.
These tools remain in use today despite the abundance of "brand new technology" because time and again these concepts have proven to be highly practical. Nothing prevents vim from being integrated into new tools, and the flexibility of Lisp allows for seamless integration of new tools within the old-school engine.
I'm sure nobody really reject the notion of LLMs but sure as hell do like to moan if the new technology doesn't absolutely perfect fit their own way of working. Does that make them any different than people wanting an editor which is intuitive to use? Nobody will ever know.
I don't know, people change their opinions all the time. I wasn't convinced about many ideas throughout my career, but I'm glad I found convincing arguments for some of them later.
> wanting an editor which is intuitive to use
Are you implying that Vim and Emacs are not?
Intuitive != Familiar. What feels unintuitive is often just unfamiliar. Vim's model actually feels pretty intuitive after the initial introduction. Emacs is pretty intuitive for someone who grokked Lisp basics - structural editing and REPL-driven development. The point is also subjective, for some people "intuitive editor" means "works like MS Word", but that's just one design philosophy, not an objective standard.
Tools that survive 30+ years and maintain passionate user bases must be doing something right, no?
> the new technology doesn't absolutely perfect fit their own way of working.
Emacs is extremely flexible, and thanks to that, I've rarely complained about new things not fitting my ways. I bend tools to fit my workflow if they don't align naturally — that's just the normal approach for a programmer.
TLDR: over the first 8 issues, developers do not appear to get majorly less slowed down.
[1] https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study.pdf
It’s also not a sample size of 1, it’s a sample size of however many tasks you do because you don’t care about measuring the effect AI has on anyone but yourself if you’re trying to discern how it impacts you.
You’ve been given a dubiously capable genie that can write code without you having to do it! If this thing can build first drafts of those side projects you always think about and never get around to, that in and of itself is useful! If it can do the yak-shaving required to set up those e2e tests you know you should have but never have time for it is useful!
Have it try out all the dumb ideas you have that might be cool but don’t feel worth your time to boilerplate out!
I like to think we’re a bunch of creative people here! Stop thinking about how it can make you money and use it for fun!
Took me a week to build those tools. Its much more reliable (and flexible) than any LLM and cost me nothing.
It comes with secure Auth, email, admin, ect ect.. Doesn't cost me a dime and almost never has a common vulnerability.
Best part about it. I know how my side project runs.
Developers' own skills might atrophy, when they don't write that much code themselves, relying on AI instead.
And now when comparing with/without AI they're faster with. But a year ago they might have been that fast or faster without an AI.
I'm not saying that that's how things are. Just pointing out another way to interpret what GP said
A much simpler explanation is what your parent offered. And to many behavioralists it is actually the same explanation, as to a true scotsm... [cough] behavioralist personality is simply learned habits, so—by Occam’s razor—you should omit personality from your model.
Became worse in 50 hours? Super unlikely
This is visible under extreme time pressure of producing a working game in 72 hours (our team scores consistenly top 100 in Ludum Dare which is a somewhat high standard).
We use a popular Unity game engine all LLMs have wealth of experience (as in game development in general), but the output is 80% so strangely "almost correct but not usable" that I cannot take the luxury of letting it figure it out, and use it as fancy autocomplete. And I also still check docs and Stackoverflow-style forums a lot, because of stuff it plainly mades up.
One of the reasons is maybe our game mechanics often is a bit off the beaten road, though the last game we made was literally a platformer with rope physics (LLM could not produce a good idea how to make stable and simple rope physics under our constraints codeable in 3 hours time).
It’s been a majority of my projects for the past two months. Not because work changed, but because I’ve written a dozen tiny, personalised tools that I wouldn’t have written at all if I didn’t have Claude to do it.
Most of them were completed in less than an hour, to give you an idea of the size. Though it would have easily been a day on my own.
It'll also apply to isolated-enough features, which is still a small amount of someone's work (not often something you'd work on for a full month straight), but more people will have experience with this.
No, but I didn't need to read scientific papers to figure how to use Google effectively, either. I'm just using a results-based analysis after a lot of LLM usage.
How do we get beyond that?
LLMs have made the distinction ambiguous because their capabilities are so poorly understood. When I say "you should talk to an LLM like it's a computer", that's a workflow statement; it's a more efficient way to accomplish the same goal. You can try it for yourself and see if you agree. I personally liken people who talk to LLMs in full, proper English, capitalization and all, to boomers who still type in full sentences when running a Google query. Is there anything strictly wrong with it? Not really. Do I believe it's a more efficient workflow to just type the keywords that will give you the same result? Yes.
Workflow efficiencies can't really be scientifically evaluated. Some people still prefer to have desktop icons for programs on Windows; my workflow is pressing winkey -> typing the first few characters of the program -> enter. Is one of these methods scientifically more correct? Not really.
So, yeah -- eventually you'll either find your own workflow or copy the workflow of someone you see who is using LLMs effectively. It really is "just trust me, bro."
It’s just a fun geeky thing to use with a lot of zany customizations. And after two hellish years of memory muscling enough keyboard bindings to finally be productive, you earned it! It’s a badge of pride!
But we all know you’re still fat fingering ggdG on occasion and silently cursing to yourself.
LLMs have given me a whole new love of coding, getting rid of the dull grind and letting me write code an order of magnitude quicker than before.
Or they care about producing value, not just the code, and realized they had more leverage and impact in other roles.
> And I can't find anything in your comment that would corroborate the opposite.
I didn’t try and corroborate the opposite.
Honestly, I don’t care about the “best coders.” I care about people who do their job well, sometimes that is writing amazing code but most of the time it isn’t. I don’t have any devs in my company who work in a magical vacuum where they are handed perfectly written tasks, they complete them, and then they do the next one.
If I did, I could replace them with AI faster.
> Also, you didn't define the criteria of getting better. Getting better in terms of what exactly?
Delivery velocity - bug fixes, features, etc. that pass testing/QA and goes to prod.
Sure they are - or at least were, unitl the last couple years. Same thing with Emacs.
It's hard to claim this now, because the entire industry shifted towards webshit and cloud-based practices across the board, and the classical editors just can't keep up with VS Code. Despite the latter introducing LSP, which leveled the playing field wrt. code intelligence itself, the surrounding development process and the ecosystem increasingly demands you use web-based or web-derived tools and practices, which all see a browser engine as a basic building block. Classical editors can't match the UX/DX on that, plus the whole thing breaks basic assumptions about UI that were the source of the "10x perf gains" in vim and Emacs.
Ironically, a lot of the perf gains from AI come from letting you avoid dealing with the brokenness of the current tools and processes, that vim and Emacs are not equipped to handle.
It’s just not comparable to the LLM crazy hype train.
And to belabor your other point, I have treesitter, lsp, and GitHub Copilot agent all working flawlessly in neovim. Ts and lsp are neovim builtins now. And it’s custom built for exactly how I want it to be, and none of that blinking shit or nagging dialog boxes all over VSCode.
I have VScode and vim open to the same files all day quite literally side by side, because I work at Microsoft, share my screen often, and there are still people that have violent allergic reactions to a terminal and vim. Vim can do everything VSCode does and it’s not dogshit slow.
You'd be hard-pressed to find a popular editor without vim bindings.
You clearly don't have a slightest idea of what you're talking about.
Emacs is actually still amazing in the LLM era. Language is all about plain text. Plain text remains crucial and will remain important because it's human-readable, machine-parsable, version-control friendly, lightweight and fast, platform-independent, and resistant to obsolescence. Even when analyzing huge amounts of complex data - images, videos, audio-recordings, etc., we often have to reduce it to text representation.
And there's simply no tool better than Emacs today that is well-suited for dealing with plain text. Nothing even comes close to what you can do with text in Emacs.
Like, check this out - I am right now transcribing my audio notes into .srt (subtitle) files. There's subed-mode where you can read through subtitles, and even play the audio, karaoke style, while following the text. I can do so many different things from here - extract the summaries, search through things, gather analytics - e.g., how often have I said 'fuck' on Wednesdays, etc.
I can similarly play YouTube videos in mpv, while controlling the playback, volume, speed, etc. from Emacs; I can extract subtitles for a given video and search through them, play the vid from the exact place in the subs.
I very often grab a selected region of screen during Zoom sessions to OCR and extract text within it and put it in my notes - yes, I do it in Emacs.
I can probably examine images, analyze their elements, create comprehensive summaries, and formulate expert artistic evaluation and critique and even ask Emacs to read it aloud back to me - the possibilities are virtually limitless.
It allows you to engage with vast array of LLM models from anywhere. I can ask a question in the midst of typing a Slack reply or reading HN comments or when composing a git commit; I can fact-check my own assumptions. I can also use tools to analyze and refactor existing codebases and vibe-code new stuff.
Anything like that even five years ago seemed like a dream; today it is possible. We can now reduce any complex digital data to plain text. And that feels miraculous.
If anything, the LLM era has made Emacs an extremely compelling choice. To be honest, for me - it's not even a choice, it's the only seriously viable option I have - despite all its drawbacks. Everything else doesn't even come close - other options either lacking critical features or have merely promising ones. Emacs is absolutely, hands-down, one of the best tools we humans have ever produced to deal with plain text. Anyone who thinks it's an opinion and not a fact simply hasn't grokked Emacs or has no clue what you can do with it.
I agree with you that AI dev tools are overhyped at the moment. But IDEs were, in fact, overhyped (to a lesser degree) in the past.
> Interestingly, the best AI assisted devs have often moved to management/solution architecture
Is it just me? Or does it seem to others as well that you pretty much rank these people even at the moment and your first comment contradicts your second comment? Especially when you admit that you rank them based on velocity.
I am not saying you shouldn't do that, but it feels to me like rating road construction workers on the number of potholes fixed, even though it's very possible that the potholes are caused by the sloppy work to begin with.
Not what I would want to do.
I think you are reading what you want to read and not what I said, so yes it is you. The most productive, valuable people with developer titles in my organizations are not the ones who write the cleanest, most beautiful, most perfect code. They do all of the other parts of the job well and write solid code.
Following the introduction of AI tools, many of the people in my organization who most effectively learned to use those tools are people who previously chose to move to manager and SA roles.
Not only are these not contradictory, they fit quite well together. People who do the things around coding well, but maybe have to work hard at writing the actual code, are better at using the AI tools than exceptional coders. For my organization, the former are generally more valuable than the latter without AI, and that is increasing as a result of AI.
> I am not saying you shouldn't do that, but it feels to me like rating road construction workers on the number of potholes fixed, even though it's very possible that the potholes are caused by the sloppy work to begin with.
Not if your measurement includes quality testing the pothole repairs, which mine does, as I explicitly called out. I work in industries with extensive, long testing cycles, we are (imperfectly, of course) able to measure productivity based on things which make it through those cycles.
You are trying very hard to find ways to ignore what I am saying. It is fine if you don’t want to believe me, but these things have been true based on our observations:
A. Great “coders” have a much harder time picking up AI dev tools and using them effectively, and when they see how others use them they will admit that isn’t how they use them. They will revert to their previous habits and give up on the tools.
B. The productivity gains for the people who are good at using the tools, as measured by velocity with a minimum bar for quality (with substantial QA), are very high.
C. We have measured these things to thoroughly understand the ROI and we are accelerating our investment in AI coding tools as a result.
Some caveats I am absolutely willing to make - we are not working on bleeding edge tech doing things no one has ever done before.
We failed to effectively use AI many times before we started to get it right.
There are developers who are slower with the AI code tools than without it.
You're extrapolating, it's not saying this anywhere.
> It's no more "compensation" than a chemistry researcher being "compensated" with test tubes.
Yes, that's compensation too. Thanks for contributing another example. Here's another one: it's no more compensation than a software engineer being compensated with a new computer.
Actually the situation here is way worse than your example. Unless the chemistry researcher is commissioned by Big Test Tube Corp. to conduct research on the outcome of using their test tubes, there's no conflict of interest here. But there is an obvious conflict of interest on AI research being financed by credits given by AI companies to use their own AI tools.
Such companies spit out "credits" all over the place in order to gain traction and enstablish themselves. I remember when cloud providers gave vps credits to startups like they were peanuts. To me, it really means absolutelly nothing.
In-kind compensation is still compensation.
Well, yes? I use compute for some personal projects so I would be absolutely fine if a part of my compensation was in compute credits.
As a company, even more so.
If what you write was true, then the rate of bugs of those incredible devs would simply fall to zero at one point, and at that point they would become a legend who we all would have heard of by now. So the whole story sounds too fishy to my taste.
It's OK if you want to manage your team this way. Everyone needs some external feedback to confirm their own bias. It seems you found yours and it works for you.
It's just not a good argument in support of AI or AI assisted development.
It's too anecdotal.
And since you are the one who are telling me that you are right, and not others, it makes me even more skeptical about the whole story.
Nobody is denying that people have personalities btw. Not even true behavioralists do that, they simply argue from reductionism that personality can be explained with learning contingencies and the reinforcement history. Very few people are true behavioralists these days though, but within the behavior sciences, scientists are much more likely to borrow missing factors (i.e. things that learning contingencies fail to explain) from fields such as cognitive science (or even further to neuroscience) and (less often) social science.
What I am arguing here, however, is that the appeal to personality is unnecessary when explaining behavior.
As for figuring out what personality is, that is still within the realm of philosophy. Maybe cognitive science will do a better job at explaining it than psychometricians have done for the past century. I certainly hope so, it would be nice to have a better model of human behavior. But I think even if we could explain personality, it still wouldn’t help us here. At best we would be in a similar situation as physics, where one model can explain things traveling at the speed of light, while another model can explain things at the sub-atomic scale, but the two models cannot be applied together.
I’ve also noticed that, generally, nobody likes maintaining old systems.
so where does this leave us as software engineers? Should I be excited that it’s easy to spin up a bunch of code that I don’t deeply understand at the beginning of my project, while removing the fun parts of the project?
I’m still grappling with what this means for our industry in 5-10 years…
Cognitive science was able to explain stuff like biases, pattern recognition, language, etc. which behavioral science thought they could explain, but couldn’t. In the 1950s it was really the only game in town (except for psychometrics which failed in a way much more complete—albeit less spectacular—way then behaviorism), so understandably scientists (and philosophers) went a little overboard with it (kind of like evolutionary biology did in the 1920s).
I think a more fair viewpoint is to claim that behaviorism’s heyday in the 1950s has passed, but it still provides an excellent theoretical framework for some of human behavior, and along with cognitive science, is able to explain most of what we know about human behavior.
Its not that I don't like tinkering. I really enjoy tinkering with config files but I never could understand nvim personally since I usually want a lsp / good enough experience that nvim or any lunarvim etc. couldn't provide without me installing additional software.
So if the claim is that I can get everything I have out of vim, most importantly being unbeatably fast text buffers, and I don’t need a suitcase full of config files, that’s very compelling.
Is that the promise of zed?
But nothing will make them stick to the one API version I use.
Models trained for tool use can do that. When I use Codex for some Rust stuff for example, it can grep from source files in the directory dependencies are stored, so looking up the current APIs is trivial for them. Same works for JavaScript and a bunch of other languages too, as long as it's accessible somewhere via the tools they have available.
I’m so glad we’re past that now and can join forces against a common enemy.
Thank you brother.
Emacs veterans simply rejected the entire concept of modality, without even trying to understand what it is about. Emacs is inherently a modal editor. Key-chords are stateful, Transient menus (i.e. Magit) are modals, completion is a modal, isearch, dired, calc, C-u (universal argument), recursive editing — these are all modals. What the idea of vim-motions offers is a universal, simplified, structured language to deal with modality, that's all.
Vim users on the other hand keep saying "there's no such thing as vim-mode". And to a certain degree they are right — no vim plugin outside of vim/neovim implements all the features — IdeaVim, VSCode vim plugins, Sublime, etc. - all of them are full of holes and glaring deficiencies. With one notable exception — Evil-mode in Emacs. It is so wonderfully implemented, you wouldn't even notice that it is a plugin, an afterthought. It really does feel like a baked-in, native feature of the editor.
There are no "wars" in our industry — pretty much only misunderstanding, misinterpretation and misuse of certain ideas. It's not even technological — who knows, maybe it's not even sociotechnological. People simply like talking past each other, defending different values without acknowledging they're optimizing for different things.
It's not Vim's, Emacs' or VSCode's fault that we suffer from identity investment - we spend hundreds of hours using one so it becomes our identity. We suffer from simplification impulse — we just love binary choices, we constantly have the nagging "which is better?" question, even when it makes little sense. We're predisposed to tribal belonging — having a common enemy creates in-group cohesion.
But real, experienced craftspeople... they just use whatever works best for them in a given context. That's what we all should strive for — discover old and new ideas, study them, identify good ones, borrow them, shelve the bad ones (who knows, maybe in a different context they may still prove useful). Most importantly, use whatever makes you and your teammates happy. It's far more important than being more productive or being decisively right. If thy stupid thing works, perhaps it ain't that stupid?
The OP qualifies how the marketing cycle for this product is beyond extreme, and its own category.
Normal people are being told to worry about AI ending the world, or all jobs disappearing.
Simply saying “the problem is the user”, without acknowledging the degree of hype, and expectation setting, the is irresponsible.
I can appreciate that it’s other users who are saying it’s wrong, but that doesn’t escape the point on ignoring the context.
Moreover, it’s unhelpful communication. Its gives up acknowledging a mutually shared context, the natural confusion that would arise from the ambiguous, high level hype, and the actual down to earth reality.
Even if you have found a way to make it work, having someone understand your workflow can’t happen without connecting the dots between their frame of reference and yours.
That's pretty extreme.
No, it's the non-coding managers who vibe-coded a half-working prototype, not other users. And here, the Dunning-Kruger effect is at play - those non-coding types do not understand that AI is not working for them either.
Full disclosure: I do rely on vibe-coded jq lines in one-off scripts that will definitely not process more data after the single intended use, and this is where AI saves my time.
Therefore, classical marketing is less dominant, although more present at down-stream sellers.
A good debugger is very easy to use. I remember the Visual Studio debugger or the C++ debugger on Windows were a piece of cake 20 years ago, while gdb is still painful today. Java and .NET had excellent integrated debuggers while golang had a crap debugging story for so long that I don’t even use a debugger with it. In fact I almost never use debuggers any more.
Version control - same story. CVS for all its problems I had learned to use almost immediately and it had a GUI that was straightforward. git I still have to look up commands for in some cases. Literally all the good git UIs cost a non-trivial amount of money.
Programming languages are notoriously full of unnecessary complexity. Personal pet peeve: Rust lifetime management. If this is what it takes, just use GC (and I am - golang).
I believe that this is okay. One does not need to know the details about every specific git command in order to be able to use it efficiently most of the time.
It is the same with a programming language. Most people are unfamiliar with every peculiarity of every standard library function that the language offers. And that is okay. It does not prevent them from using language efficiently most of the time.
Also in other aspects of life, it is unnecessary to know everything by memory. For example, one does not need to know how to e.g. replace a blade on a lawn mower. But that is okay. It does not prevent them from using it efficiently most of the time.
The point is that if something is done less often, it is unnecessary to remember the specifics of it. It is fine to look it up when needed.
The main difference I see is that LLMs are flaky, getting better over time, but still more so than traditional tooling like debuggers.
> Programming languages are notoriously full of unnecessary complexity. Personal pet peeve: Rust lifetime management. If this is what it takes, just use GC (and I am - golang).
Lifetime management is an inherently hard problem, especially if you need to be able to reason about it at compile time. I think there are some arguments to be made about tooling or syntax making reasoning about lifetimes easier, but not trivial. And in certain contexts (e.g., microcontrollers) garbage collectors are out of the question.
Same can be said for version control and programming.
we retroactively assume that everyone just obviously adopts new technology, yet im sure there were tons and tons of people that retired rather than learning how computers worked when the PC revolution was happening.
I understand your point, but would counter with: gdb isn't marketed as a cuddly tool that can let anyone do anything.
I've been hearing this for 2 years now
the previous model retroactively becomes total dogshit the moment a new one is released
convenient, isn't it?
If you pay attention to who says it, you'll find that people have different personal thresholds for finding llms useful, not that any given person like steveklabnik above keeps flip-flopping on their view.
This is a variant on the goomba fallacy: https://englishinprogress.net/gen-z-slang/goomba-fallacy-exp...
Even then though, “technology gets better over time” shouldn’t be surprising, as it’s pretty common.
For context, I've been using AI, a mix of OpenAi + Claude, mainly for bashing out quick React stuff. For over a year now. Anything else it's generally rubbish and slower than working without. Though I still use it to rubber duck, so I'm still seeing the level of quality for backend.
I'd say they're only marginally better today than they were even 2 years ago.
Every time a new model comes out you get a bunch of people raving how great the new one is and I honestly can't really tell the difference. The only real difference is reasoning models actually slowed everything down, but now I see its reasoning. It's only useful because I often spot it leaving out important stuff from the final answer.
I do not program for my day job and I vibe coded two different web projects. One in twenty mins as a test with cloudflare deployment having never used cloudflare and one in a week over vacation (and then fixed a deep safari bug two weeks later by hammering the LLM). These tools massively raise the capabilities for sub-average people like me and decrease the time / brain requirements significantly.
I had to make a little update to reset the KV store on cloudflare and the LLM did it in 20s after failing the syntax twice. I would’ve spent at least a few minutes looking it up otherwise.
It's been a very noticeable uptick in power, and although there have been some nice increases with past model releases, this has been both the largest and the one that has unlocked the most real value since I've been following the tech.
Yes, it might make a difference, but it is a little tiresome that there's always a “this is based on a model that is x months old!” comment, because it will always be true: an academic study does not get funded, executed, written up, and published in less time.
Of course it's possible that at some point you get to a model that really works, irrespective of the history of false claims from the zealots, but it does mean you should take their comments with a grain of salt.
Sure you may end up missing out on a good thing and then having to come late to the party, but coming early to the party too many times and the beer is watered down and the food has grubs is apt to make you cynical the next time a party announcement comes your way.
Like the boy who cried wolf, it'll eventually be true with enough time... But we should stop giving them the benefit of the doubt.
_____
Jan 2025: "Ignore last month's models, they aren't good enough to show a marked increase in human productivity, test with this month's models and the benefits are obvious."
Feb 2025: "Ignore last month's models, they aren't good enough to show a marked increase in human productivity, test with this month's models and the benefits are obvious."
Mar 2025: "Ignore last month's models, they aren't good enough to show a marked increase in human productivity, test with this month's models and the benefits are obvious."
Apr 2025: [Ad nauseam, you get the idea]
Keep writing your code manually, nobody cares.
More generally, the phenomenon this is quite simply explained and nothing surprising: New things improve, quickly. That does not mean that something is good or valuable but it's how new tech gets introduced every single time, and readily explains changing sentiment.
We're in a hype cycle, and it means we should be extra critical when evaluating the tech so we don't get taken in by exaggerated claims.
> By early 2030, the robot economy has filled up the old SEZs, the new SEZs, and large parts of the ocean. The only place left to go is the human-controlled areas. [...]
> The new decade dawns with Consensus-1’s robot servitors spreading throughout the solar system. By 2035, trillions of tons of planetary material have been launched into space and turned into rings of satellites orbiting the sun. The surface of the Earth has been reshaped into Agent-4’s version of utopia: datacenters, laboratories, particle colliders, and many other wondrous constructions doing enormously successful and impressive research.
This scenario prediction, which is co-authored by a former OpenAI researcher (now at Future of Humanity Institute), received almost 1 thousand upvotes here on HN and the attention of the NYT and other large media outlets.
If you read that and still don't believe the AI hype is _extreme_ then I really don't know what else to tell you.
--
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Worries forgotten,
The treadmill doesn't run,
Robots are working,
Humans have fun!More generally, these execs are talking their book as they're in a low margin capital intensive businesses whose future is entirely dependent on raising a bunch more money, so hype and insane claims are necessary for funding.
Now, maybe they do sortof believe it, but if so, why do they keep hiring software engineers and other staff?
(Unless one believes the most grandiose prophecies of a technological-singularity apocalypse, that is.)
Sure they may get even more useful in the future but that doesn’t change my present.
The people not buying into the hype, on the other hands, are actually the ones that have a very good reason to be invested, because if they turn out to be wrong they might face some very uncomfortable adjustments in the job landscape and a lot of the skills that they worked so hard to gain and believed to be valuable.
As always, be weary of any claims, but the tension here is very much the reverse of crypto and I don't think that's very appreciated.
Also, I guess you're saying I'm a paid shill, or have otherwise been brainwashed by marketing of the vendors, and therefore my positive experiences with LLMs are a lie? :).
I mean, you probably didn't mean that, but part of my point is that you see those positive reports here on HN too, from real people who've been in this community for a while and are not anonymous Internet users - you can't just dismiss that as "grassroot marketing".
Correct, I think you've read too much into it. Grassroots marketing is not a pejorative term, either. Its strategy is to trigger positive reviews about your product, ideally by independent, credible community members, indeed.
That implies that those community members have motivations other than being paid. Ideologies and shared beliefs can be some of them. Being happy about the product is a prerequisite, whatever that means for the individual user.
In contrast, what do I care if you believe in code generation AI? If you do, you are probably driving up pricing. I mean, I am sure that there are people that care very much, but there is little inherent value for me in you doing so, as long as the people who are building the AI are making enough profit to keep it running.
With regards to the VCs, well, how many VCs are there in the world? How many of the people who have something good to say about AI are likely VCs? I might be off by an order of magnitude, but even then it would really not be driving the discussion.
Every hype cycle feels like this, and some of them are nonsense and some of them are real. We’ll see.
The steam-powered loom was not good for the luddites either. Good for society at large in the long term but all the negative points that a 40 year old knitter in 1810 could make against the steam-powered loom would have been perfectly reasonable and accurate judged on that individual's perspective.
Generally, I do a couple of edits for clarity after posting and reading again. Sometimes that involves removing something that I feel could have been said better. If it does not work, I will just delete the comment. Whatever it was must not have been a super huge deal (to me).
Right.
> except that that is the same thing the same people say for every model release,
I did not say that, no.
I am sure you can find someone who is in a Groundhog Day about this, but it’s just simpler than that: as tools improve, more people find them useful than before. You’re not talking to the same people, you are talking to new people each time who now have had their threshold crossed.
no, it's the same names, again and again
That sounds like a claim you could back up with a little bit of time spent using Hacker News search or similar.
(I might try to get a tool like o3 to run those searches for me.)
An LLM that can test the code it is writing and then iterate to fix the bugs turns out to be a huge step forward from LLMs that just write code without trying to then exercise it.
The jump has been massive.
It's not showing its reasoning. "Reasoning" models are trained to output more tokens in the hope that more tokens means less hallucinations.
It's just a marketing trick and there is no evidence this sort of fake ""reasoning"" actually gives any benefit.
As with anything, your miles may vary: I’m not here to tell anyone that thinks they still suck that their experience is invalid, but to me it’s been a pretty big swing.
Same. For me the turning point was VS Code’s Copilot Agent mode in April. That changed everything about how I work, though it had a lot of drawbacks due to its glitches (many of these were fixed within 6 or so weeks).
When Claude Sonnet 4 came out in May, I could immediately tell it was a step-function increase in capability. It was the first time an AI, faced with ambiguous and complicated situations, would be willing to answer a question with a definitive and confident “No”.
After a few weeks, it became clear that VS Code’s interface and usage limits were becoming the bottleneck. I went to my boss, bullet points in hand, and easily got approval for the Claude Max $200 plan. Boom, another step-function increase.
We’re living in an incredibly exciting time to be a skilled developer. I understand the need to stay skeptical and measure the real benefits, but I feel like a lot of people are getting caught up in the culture war aspect and are missing out on something truly wonderful.
So are you using Claude Code via the max plan, Cursor, or what?
I think I'd definitely hit AI news exhaustion and was viewing people raving about this agentic stuff as yet more AI fanbois. I'd just continued using the AI separate as setting up a new IDE seemed like too much work for the fractional gains I'd been seeing.
There is a skill gap, like, I think of it like vim: at first it slows you down, but then as you learn it, you end up speeding up. So you may also find that it doesn't really vibe with the way you work, even if I am having a good time with it. I know people who are great engineers who still don't like this stuff, just like I know ones that do too.
I asked it two implement two bicubic filters, a high pass filter and a high shelf filter. Some context, using the gemini webapp it would split out the exact code I need with the interfaces I require one shot because this is truly trivial C++ code to write.
15 million tokens and an hour and a half later I now had a project that could not build, the filters were not implemented and my trust in AI agentic workflows broken.
It cost me nothing, I just reset the repo and I was watching youtube videos for that hour and a half.
Your mileage may vary and I’m very sure if this was golang or typescript it might have done significantly better, but even compared to the exact same model in a chat interface my experience was horrible.
I’m sticking to the slightly “worse” experience of using the chat interface which does give me significant improvements in productivity vs letting the agent burn money and time and not produce working code.
im using claude + vscode's cline extension for the most part, but where it tends to excel is helping you write documentation, and then using that documentation to write reasonable code.
if you're 3/4 of the way done, a lot of the docs of what it wants to work well are gonna be missing, and so a lot of your intentions about why you did or didnt make certain choices will be missing. if you've got good docs, make sure to feed those in as context.
the agentic tool on its own is still kinda meh, if you only try to write code directly from it. definitely better than the non-agentic stuff, but if you start with trying to get it to document stuff, and ask you questions about what it should know in order to make the change its pretty good.
even if you dont get perfect code, or it spins in a feedback loop where its lost the plot, those questions it asks can be super handy in terms of code patterns that you havent thought about that apply to your code, and things that would usually be undefined behaviour.
my raving is that i get to leave behind useful docs in my code packages, and my team members get access to and use those docs, without the usual discoverability problems, and i get those docs for... somewhat slower than i could have written the code myself, but much much faster than if i also had to write those docs
Just two years ago, this failed.
> Me: What language is this: "esto está escrito en inglés"
> LLM: English
Gemini and Opus have solved questions that took me weeks to solve myself. And I'll feed some complex code into each new iteration and it will catch a race condition I missed even with testing and line by line scrutiny.
Consider how many more years of experience you need as a software engineer to catch hard race conditions just from reading code than someone who couldn't do it after trying 100 times. We take it for granted already since we see it as "it caught it or it didn't", but these are massive jumps in capability.
"No, the 2.8 release is the first good one. It massively improves workflows"
Then, 6 months later, the study comes out.
"Ah man, 2.8 was useless, 3.0 really crossed the threshold on value add"
At some point, you roll your eyes and assume it is just snake oil sales
* the release of agentic workflow tools
* the release of MCPs
* the release of new models, Claude 4 and Gemini 2.5 in particular
* subagents
* asynchronous agents
All or any of these could have made for a big or small impact. For example, I’m big on agentic tools, skeptical of MCPs, and don’t think we yet understand subagents. That’s different from those who, for example, think MCPs are the future.
> At some point, you roll your eyes and assume it is just snake oil sales
No, you have to realize you’re talking to a population of people, and not necessarily the same person. Opinions are going to vary, they’re not literally the same person each time.
There are surely snake oil salesman, but you can’t buy anything from me.
I pointed this out in my post for a reason. I get it. But even given a different person is saying the same thing every time a new release comes out - the effect on my prior is the same.
That's not a tradeoff that I like
[0]: https://marketplace.visualstudio.com/items?itemName=anthropi...