Really does not sound like that from your description. It sounds like coaching a noob, which is a lot of work in itself.

Wasn’t there a study that said that using LLMs makes people feel more productive while they actually are not?

Even if you do it by yourself, you need to do the same thinking and iterative process by yourself. You just get the code almost instantly and mostly correctly, if you are good at defining the initial specification.

The trick is knowning where the particular LLM sucks. I expect in a short amount of time there is no productivity gain but when you start to understand the limitations and strengths - holey moley.

It's more like x units of time thinking and y units of times coding, whereas I see people spend x/2 thinking, x typing the specs, y correcting the specs, and y giving up and correcting the code.

These are not _tools_ -they are like cool demos. Once you have a certain mass of functional code in place, intuition - that for myself required decades of programming to develop - kicks in and you get these spider sense tinglings ”ahh umm this does not feel right, something’s wrong”.

My advice would be don’t use LLM until you have the ”spider-sense” level intuition.

And if this is true, you will have to coach AI each time whereas a person should advance over time.

As for humans, they might not have the motivation technical writing skill to document what they learnt. And even if they did, the next person might not have the patience to actually read it.

Also, a good few times, if it were a human doing the task, I would have said they both failed to follow the instructions and lied about it and attempted to pretend they didn’t. Luckily their lying abilities today are primitive, so it’s easy to catch.

This is probably because the llm is trained on millions of lines of Go with nested error checks vs a few lines of contrary instructions in the instructions file.

I keep fighting this because I want to understand my tools, not because I care that much about this one preference.

I've been trying out Codex the last couple days and it's much more adherent and much less prone to lying and laziness. Anthropic says they're working on a significant release in Claude Code, but I'd much rather have them just revert back to the system as it was ~a month ago.

I've never had a model lie to me as much as Claude. It's insane.

It's funny. Just yesterday I had the experience of attending a concert under the strong — yet entirely mistaken — belief that I had already been to a previous performance of the same musician. It was only on the way back from the show, talking with my partner who attended with me (and who had seen this musician live before), trying to figure out what time exactly "we" had last seen them, with me exhaustively listing out recollections that turned out to be other (confusingly similar) musicians we had seen live together... that I finally realized I had never actually been to one of this particular musician's concerts before.

I think this is precisely the "experience" of being one of these LLMs. Except that, where I had a phantom "interpolated" memory of seeing a musician I had never actually seen, these LLMs have phantom actually-interpolated memories of performing skills they have never actually themselves performed.

Coding LLMs are trained to replicate pair-programming-esque conversations between people who actually do have these skills, and are performing them... but where those conversations don't lay out the thinking involved in all the many implicit (thinking, probing, checking, recalling) micro-skills involved in actually performing those skills. Instead, all you get in such a conversation thread is the conclusion each person reaches after applying those micro-skills.

And this leads to the LLM thinking it "has" a given skill... even though it doesn't actually know anything about "how" to execute that skill, in terms of the micro-skills that are used "off-screen" to come up with the final response given in the conversation. Instead, it just comes up with a prediction for "what someone using the skill" looks like... and thinks that that means it has used the skill.

Even after a hole is poked in its use of the skill, and it realizes it made a mistake, that doesn't dissuade it from the belief that it has the given skill. Just like, even after I asked my partner about the show I recall us attending, and she told me that that was a show for a different (but similar) musician, I still thought I had gone to the show.

It took me exhausting all possibilities for times I could have seen this musician before, to get me to even hypothesize that maybe I hadn't.

And it would likely take similarly exhaustive disproof (over hundreds of exchanges) to get an LLM to truly "internalize" that it doesn't actually have a skill it believed itself to have, and so stop trying to use it. (If that meta-skill is even a thing that LLMs have ever learned from their training data — which I doubt. And even if they did, you'd be wasting 90% of a Transformer's context window on this. Maybe something that's worth keeping in mind if we ever switch back to basing our LLMs on RNNs with true runtime weight updates, though!)

edit: because people are stupid, 'competitively' in this sense isn't some theoretical number pulled from an average, it's 'does this person feel better off financially working with you than others around them who don't work with you, and is is this person meeting their own personal financial goals through working with you'?

Junior is a person, not your personal assistant like LLM.

Also it is never a policy to pay competitively for the existing employees, only for the new hires.

Humans aren’t tools.

These models are only going to get better and cheaper per watt.

What do you base this claim on? They have only gotten exponentially more expensive for decreasing gain so far - quite the opposite of what you say.

That's a significant difference. There are a lot of tasks that can be done by a n00b with some advice, especially when you can say "copy the pattern when I did this same basic thing here and here".

And there are a lot of things a n00b, or an LLM, can't do.

The study you reference was real, and I am not surprised — because accurately gauging the productivity win, or loss, obtained by using LLMs in real production coding workflows is also not junior stuff.

On a tangent; that study is brought up a lot. There are some issues with it, but I agree with the main takeaway to be weary of the feeling of productivity vs actual productivity.

But most of the time its brought up by AI skeptics, that conveniently gloss over the fact it's about averages.

Which, while organizationally interesting, is far less interesting than to discover what is and isn't currently possible at the tail end by the most skillful users.

Productivity is something that creates business value. In that sense an engineer who writes 10 lines of code but that code solves a $10M business problem or allows the company to sign 100 new customers may be the most productive engineer in your organization.

Taken along with the dozens of other studies that show that humans are terrible at estimating how long it will take them to complete task, you should be very skeptical when someone says an LLM makes them x% more productive.

There’s no reason to think that the most skillful LLM users are not overestimating productivity benefits as well.

There have been many more studies showing productivity gains across a variety of tasks that preceded that one.

That study wasn't necessarily wrong about the specific methodology they had for onboarding people to use AI. But if I remember correctly it was funded by an organization that was slightly skeptical of AI.

AI coding assistant trial: UK public sector findings report: https://www.gov.uk/government/publications/ai-coding-assista... - UK government. "GDS ran a trial of AI coding assistants (AICAs) across government from November 2024 to February 2025. [...] Trial participants saved an average of 56 minutes a working day when using AICAs"

Human + AI in Accounting: Early Evidence from the Field: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5240924 - "We document significant productivity gains among AI adopters, including a 55% increase in weekly client support and a reallocation of approximately 8.5% of accountant time from routine data entry toward high-value tasks such as business communication and quality assurance."

OECD: The effects of generative AI on productivity, innovation and entrepreneurship: https://www.oecd.org/en/publications/the-effects-of-generati... - "Generative AI has proven particularly effective in automating tasks that are well-defined and have clear objectives, notably including some writing and coding tasks. It can also play a critical role for skill development and business model transformation, where it can serve as a catalyst for personalised learning and organisational efficiency gains, respectively [...] However, these potential gains are not without challenges. Trust in AI-generated outputs and a deep understanding of its limitations are crucial to leverage the potential of the technology. The reviewed experiments highlight the ongoing need for human expertise and oversight to ensure that generative AI remains a valuable tool in creative, operational and technical processes rather than a substitute for authentic human creativity and knowledge, especially in the longer term.".

> On average, users reported time savings of 56 minutes per working day [...] It is also possible that survey respondents overestimated time saved due to optimism bias.

Yet in conclusion, this self-reported figure is stated as an independently observed fact. When people without ADHD take stimulants they also self-report increased productivity, higher accuracy, and faster task completion but all objective measurements are negatively affected.

The OECD paper supports their programming-related findings with the following gems:

- A study that measures productivity by the time needed to implement a "hello world" of HTTP servers [27]

- A study that measures productivity by the number of lines of code produced [28]

- A study co-authored by Microsoft that measures productivity of Microsoft employees using Microsoft Copilot by the number of pull requests they create. Then the code is reviewed by their Microsoft coworkers and the quality of those PRs is judged by the acceptance rate of those PRs. Unbelievably, the code quality doesn't only remain the same, it goes up! [30]

- An inspirational pro-AI paper co-authored by GitHub and Microsoft that's "shining a light on the importance of AI" aimed at "managers and policy-makers". [31]

Interesting analogy, because all those studies with objective measurements are defied by US students year by year, come finals seasons.

Regardless, I'm not saying it's a cheap or practical to get high this way, especially over the long term. People probably try stimulants because folk wisdom tells them that they'll get better grades. Then they get high and they feel like a superman from the dopamine rush, so they keep using them because they think it's materially improving their grades but really they're just getting high.

I don't have to worry about managing the noob's emotions or their availability, I can tell the LLM to try 3 different approaches and it only takes a few minutes... I can get mad at it and say "fuck it I'll do this part myself", the LLM doesn't have to be reminded of our workflow or formatting (I just tell the LLM once)

I can tell it that I see a code smell and it will usually have an idea of what I'm talking about and attempt to correct, little explanation needed

The LLM can also: do tons of research in a short amount of time, traverse the codebase and answer questions for me, etc

it's a noob savant

It's no replacement for a competent person, but it's a very useful assistant

It takes an LLM 2-20 minutes to give me the next stage of output not 1-2 days (week?). As a result, I have higher context the entire time so my side of the iteration is maybe 10x faster too.

I don't need a study to tell me that five projects that have been stuck in slow plodding along waiting for me to ever have time or resources for nearly ten years. But these are now nearing completion after only two months of picking up Claude Code. And with high-quality implementations that were feverdreams.

My background is academic science not professional programming though and the output quality and speed of Claude Code is vastly better than what grad students generate. But you don't trust grad student code either. The major difference here is that suggestions for improvement loop in minutes rather than weeks or months. Claude will get the science wrong, but so do grad students.

(But sure technically they are not finished yet ... but yeah)

But this is literally what senior engineers do most of the time? Have juniors write code with direction and review that it isn't buggy?

Given the nature of the statistics in question, the line between the two is extremely blurry at this point.

I mentor high-school students and watch them live write code that takes a completely bizarre path. It might technically be intentional, but that doesn’t mean it’s good or useful.

Some people really are going to hang on until the better end (and beyond) eh?

"AI can't code like me!" people are going to get crushed.

And not at all for you, because you're unlikely to retain them for long. Which makes this immaterial - AI or human, you're only going to delegate to n00bs.

(This is distinct from the question of which benefits society more, which is a separate discussion.)

And then quit after accepting a new job that pays them their modified value, because tech companies are particularly bad at proactive retention.

My hunches are that the to-and-fro of ideas as you discuss options or make corrections leads to a context with competing “intentions”; and that they can’t tell the difference between positive and negative experiences when it comes across each successive token in the context.

But I don’t make LMMs, so this is pure guesswork.

The rabbit hole problem is pretty rare. Usually it happens when the model flips into "stupid mode", some kind of context poisoning. If you are experienced, you know to purge the context when that happens.

In personal projects I avoid manual editing as a form of deliberate practice. At work, I only edit when it is a very small edit. I can usually explain what I want more concisely and quickly than hand editing code.

I probably would use more hand editing if I had classic refactoring tools in the IDEs similar to intellij/pycharm. Though cli based tools were a pleasant surprise once I actively started using them.

What does this look like in practice?

And then I waste 20 minutes bashing my head against the wall trying to write three paragraphs meticulously documenting all the key gotchas and lessons from the "good" context window with the magic combination of words needed to get Claude back in the right head space to one-shot again.

At least if I pull that off I can usually ask Claude to use it as documentation for the project CLAUDE.md with a pretty good success rate. But man it leaves a bad taste in my mouth and makes me question whether I'm actually saving time or well into sunk cost territory...

But in all seriousness, completion is not the only metric of productivity. I could easily break it down into a mountain of subtasks that have been fully completed for the bean counters. In the meantime, the code that did not exist 2 months ago does exist.

that's the issue in the argument though. it could be that those projects would also have been completed in the same time if you had simply started working on them. but honestly, if it makes you feel productive to the point you're doing more work than you would do without the drug, I'd say keep taking it. watch out for side effects and habituation though.

There are any number of things you could add to get you to any conclusion. Better to discuss what is there.

I've had the same experience of being able to finish tons of old abandoned projects with AI assistance, and I am not spending any more time than usual working on programming or design projects. It's just that the most boring things that would have taken weeks to figure out and do (instead, let me switch to the other project I have that is not like that, yet) have been reduced to hours. The parts that were tough in a creative fun way are still tough, and AI barely helps with them because it is extremely stupid, but those are the funnest, most substantive parts.

The magic of Claude is that you can simply start.

I'm a career coder and I used LLMs primarily to rapidly produce code for domains that I don't have deep experience in. Instead of spending days or weeks getting up to speed on an SDK I might need once, I have a pair programmer that doesn't check their phone or need to pick up their kids at 4:30pm.

If you don't want to use LLMs, nobody is forcing you. Burning energy trying to convince people to whom the benefits of LLMs are self-evident many times over that they are imagining things is insulting the intelligence of everyone in the conversation.

You’re either trusting the LLM or you still have to pay the cost of getting the experience you don’t have. So in either case you’re not going too much faster - the formers cost not being apparent until it’s much more expensive later on.

Edit: assuming you don’t struggle with typing speed, basic syntax, APIs etc. These are not significant cost reductions for experts, though they are for juniors.

Conflating experience and instinct with knowing everything isn't just false equivalency, it's backwards.

That’s what I mean - by myself it would have taken me easily 10x longer if not worse because UI coding for me is a slog + there’s nuances about reactive coding + getting started is also a hurdle. The output of the code was still high quality because I knew when the LLM wasn’t making the choices I wanted it to make.

I feel strongly that delegation to strengths is one of the most obvious signs of experience.

Apologies for getting hung up on what might seem like trivial details, but when discussing on a text forum, word choices matter.

In other words, I don't think that you temporarily regress to "junior" just because you're working on something new. You still have a profound fundamental understanding of how technology works and what to expect in different situations.

This reminds me of the classic "what happens when you type google.com into a web browser" question, with its nearly infinite layers of abstraction from keyboard switches to rendering a document with calls to a display driver and photons hitting your visual receptors.

We might just be quibbling over terminology, however.

Hey man, I don't bother trying to convince them because it's just going to increase my job security.

Refusing to use LLMs or thinking they're bad is just FUD and it's the same as people that prefer to use nano/vim over an IDE or it's the same as people that say "hur dur cloud is just somebody else's computer"

It's best to ignore and just leave them in the dust.

Coding at full throttle is a very intensive task that requires deep focus. There are many days that I simply don’t have that in me.

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

- https://arxiv.org/abs/2507.09089

""" Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%—AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). """

Curious about specifics of this study. Because in general, how one feels is critical to productivity. It's hard to become more productive when the work is less and less rewarding. The infamous "zone" / "flow state" involves, by its very definition, feeling of increasing productivity being continuously reinforced on a minutes-by-minutes level. Etc.

actual result: 20% slower

link: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

I hate the experience of trying to write code with them. I like to just type my thoughts into the files.

I hate trying to involve the LLM, even as a search. I want my search to feel like looking up references not having a conversation with a robot

Overall, for me, the whole experience of trying to code with LLMs is both frustrating and unrewarding

And it definitely doesn't seem more efficient or faster either

It has about a dozen or so endpoints, facilitating real time messaging.

It took me about 4 hours to build it out, fully tested with documentation and examples and readme.

About two hours were spent setting up the architecture and tests. About 45 min to an hour setting up a few of the endpoints. The rest were generated by CC. FWIW it is using layers and SRP to the max. Everything is simple and isolated, easy to test.

I think if I had a contractor or employee do this they would have coasted for a week and still fucked it up. Adding ridiculous complexity or just fucking up.

The nice thing about AI tools is you need less people. Most people are awful at their jobs, anyone can survive a few years and call themselves senior. Most teams are only successful because of the 1 or 2 guys who pull 150% while the others are barely doing 80%.

What makes me nervous is when we generate both the implementation and the test cases. In what sense is this validation?

Either you go formal, or you test the tests, and then test those ...

Attempting to guide it to fixing the errors just introduced novel errors that it didn't make the first time around.

This is not what I signed up for.

AI is great at a first draft of anything, code, images, text, but the real skill is turning that first draft into something good.

There will always be small things to fix, but if there needs to be a second draft, I would hazard that the PR was too big all along: a problem whether an AI is involved or not.

It's no wonder inexperienced developers don't get as much out of it. They define a vague structure, full of problems, but the sycophantic AI will spew out conformant code anyways. Garbage in, garbage out. Bad ideas + fast code gen is just not very productive in the long term - LLMs have made the quality of ideas matter again!

But its a lot better than that.

"Write this table. from here store it into table. Write endpoint to return all from the table"

I also had good luck with stuff like "scrape this page, collect x and y, download link pointed at y, store in this directory".

Yes I have encountered it. Narrowing focus and putting constraints and guiding it closer made the LLM agent much better at producing what I need.

It boils down to me not writing the code really. Using LLMs actually sharpened my architectural and software design skills. Made me think harder and deeper at an earlier stage.

I am surprising myself these days with how fast I'm being using AI as a glorified Stack Overflow.

We are also having studies and posts come out that when actually tried side-by-side, the AI writes the coding route is slower, though the developer percieves it as faster.

But I think, and this is just conjecture, that if you measure over a longer timespan, the ai assisted route will be consistently faster.

And for me, this is down to momentum and stamina. Paired with the ai, I’m much more forward looking, always anticipating the next architectural challenge and filling in upcoming knowledge and resource gaps. Without the ai, I would be expending much more energy on managing people and writing code myself. I would be much more stop-and-start as I pause, take stock, deal with human and team issues, and rebuild my capacity for difficult abstract thinking.

Paired with a good ai agent and if I consistently avoid the well known pitfalls of said agent, development feels like it has the pace of cross country skiing, a long pleasant steady and satisfying burn.

I think the mixed reports on utility have a lot to do with the very different ways the tool is used and how much 'magic' the end-user expects versus how much the end-user expects to guide the tool to do the work.

To get the best out of it, you do have to provide significant amount of scaffolding (though it can help with that too). If you're just pointing it at a codebase and expecting it to figure it out, you're going to have mixed results at best. If you guide it well, it can save a significant amount of manual effort and time.

It doesn’t work. The only way it could is if the LLM has a testing loop itself. I guess in web world it could, but in my world of game dev, not so much.

So I stick with the method I outlined in OP and it is sometimes useful.

Yeah, this is a big thing I'm noticing a lot of people miss.

I have tons of people ask me "how do I get claude to do <whatever>?"

"Ask claude" is the only response I can give.

You can get the LLM to help you figure out how to get to your goal and write the right prompt before you even ask the LLM to get to your goal.

I have this pattern while driving.

Using the main roads, when there is little to no traffic, the commute is objectively, measurably the fastest.

However, during peak hours, I find myself in traffic jams, so I divert to squiggly country roads which are both slower and longer, but at least I’m moving all the time.

The thing is, when I did have to take the main road during the peak traffic, the difference between it and squiggly country roads was like two to three minutes at worst, and not half an hour like I was afraid it would be. Sure, ten minutes crawling or standing felt like an hour.

Maybe coding with LLMs makes you think you are doing something productive the whole time, but the actual output is little different from the old way? But hey, at least it’s not like you’re twiddling your thumbs for hours, and the bossware measuring your productivity by your keyboard and mouse activity is happy!

Meanwhile we adults can do real work on a separate real computer. Never use their laptop more than absolutely minimum possible.

1) the bossware might take screenshots too

2) the bosses pay for the whole LLM so they expect you to use the whole LLM

3) you may not want to contaminate your spare computer with whatever shit you're working on on job, and indeed it may be considered a breach of security (as if feeding OpenAI/Anthropic isn't, lol, but that's beside the point.

So you continue to feel miserable, but you get your paycheck, and it's better than unemployment, and your kids are fed and clothed, so there's that.

If I were to attack the same system myself without any LLM assist, I'd make a lot of choices to optimize for my speed and knowledge base. The code would end up much simpler. For something that would be handed off to another person (including future me) that can be a win. But if the system is self contained then going bigger and fancier in that moment can be a win. It all depends on the exact goals.

All in all, there's a lot of nuance to this stuff and it's probably not really replacing anyone except people who A) aren't that skilled to start with and B) spend more time yelling about how bad AI is than actually digging in and trying stuff.

Coding with AI is like having a team of juniors that can complete their assignments in a few minutes instead of days. The more clear your instructions, the closer it is to what you wanted, but there are almost always changes needed.

Unfortunately it really does make the junior dev position redundant (although this may prove to be very short-sighted when all the SR devs retire).

I've only worked places where Jr's were given roughly the same scope of work as a mid-level dev but on non-critical projects where they could take as much time as necessary and where mistakes would have a very small blast radius.

That type of Jr work has not been made redundant - although I suppose now its possible for a PM or designer to do that work instead (but if your PMs are providing more value by vibe coding non-critical features than by doing their PM work maybe you don't really need a PM?)

The elephant in the room being that if you aren’t senior enough to have written the code you’ll probably run into a catastrophic bug that you are incapable of fixing (or prompting the LLM to fix) very very quickly.

Really it’s just the next iteration of no-code hype where people dream of building apps without code, but then reality always come back to the fact that the essential skill of programmers is to understand and design highly structured and rigid logical systems. Code is just a means of specification. LLMs make it easier to leverage code patterns that have been written over and over by the hundreds of thousands of programmers that have contributed to its training corpus, but they can not replace the precision of thought needed to make a hand-wavy idea into a concrete system that actually behaves in a way that humans find useful.

What about the bugs ? Whould you have inserted the same bugs or different ones ?

But if I want a new system and the specs are clear, it can be built up in stages that are testable, and there are bits that would take some research but are well documented… then it can be a win.

The studies that say devs are slower with LLMs is fair because on average, devs don’t know how to optimize for them. Some do though.

If you have someone who knows what they're doing with the latest and greatest coding agents it's just not comparable. You can have a Dev open up four or more terminals with multiple prompts running at the same time. A manual person just can't keep up.

I generate code but I still place it there by hand, vibe code is just too full of bugs

and I find it a bit boring to wait for code to generate all the time.

Getting that kind of data is difficult, right now it's just something I worry about.

But where can you just "mentor" a junior? Hiring people is not so easy, especially not ones that are worth mentoring. Not every junior will be a willing, good recipient of mentoring, and that's if you manage to get one, given budget constraints and long lead times on hiring. And at best you end up with one or two; with parallel LLMs, you can have almost entire teams of people working for you.

I'm not arguing for replacing juniors - I worry about the same thing you do - but I can see why companies are so eager to use AI, especially smaller startups that don't have the budgets and manpower to hire people.

The juniors that are in trouble are the low-potential workhorse folks who really aren't motivated but happened to get skilled up in a workshop or technical school. They hopped on the coding wagon as a lucrative career change, not because they loved it.

Those folks are in trouble and should move on to the next trend... which ironically is probably saying you can wrangle AI.

Though I'm extremely well versed in Python, I'm right now writing a Python Qt application with Claude. Every single Qt function or object that I use, I read the documentation for.

It's fascinating how a report like yours or theirs acts as a lightning rod for those who either haven't been able to work it out or have rigid mental models about how AI doesn't work and want to disprove the experience of those who choose to share their success.

A couple of points I'd add to these observations: Even if AI didn't speed anything up... even if it slowed me down by 20%, what I find is that the mental load of coding is reduced in a way that allows me to code for far more hours in a day. I can multitask, attend meetings, get 15 minutes to work on a coding task, and push it forward with minimal coding context reload tax.

Just the ability to context switch in and out of coding, combined with the reduced cognitive effort, would still increase my productivity because it allows me to code productively for many more hours per week with less mental fatigue.

But on top of that, I also antectodally experience the 2-5x speedup depending on the project. Occasionally things get difficult and maybe I only get a 1.2-1.5x speedup. But it's far easier to slot many more coding hours into the week as an experienced tech lead. I'm leaning far more on skills that are fast, intuitive abilities built up from natural talent and decades of experience: system design, technical design, design review, code review, sequencing dependencies, parsing and organizing work. Get all these things to a high degree of correctness and the coding goes much smoother, AI or no AI. AI gets me through all of these faster, outputs clear curated (by me) artifacts, and does the coding faster.

What doesn't get discussed enough is that effective AI-assisted coding has a very high skill ceiling, and there are meta-skills that make you better from the jump: knowing what you want while also having cognitive flexibility to admit when you're wrong; having that thing you want generally be pretty close to solid/decent/workable/correct (some mixture of good judgement & wisdom); communicating well; understanding the cognitive capabilities of humans and human-like entities; understanding what kind of work this particular human/human-like entity can and should do; understanding how to sequence and break down work; having a feel for what's right and wrong in design and code; having an instinct for well-formed requirements and being able to articulate why when they aren't well-formed and what is needed to make them well-formed.

These are medium and soft skills that often build up in experienced tech leads and senior developers. This is why it seems that experienced tech leads and senior developers embracing this technology are coming out of the gate with the most productivity gains.

I see the same thing with young developers who have a talent for system design, good people-reading skills, and communication. Those with cognitive flexibility and the ability to be creative in design, planning and parsing of work. This isn't your average developer, but those with these skills have much more initial success with AI whether they are young or old.

And when you have real success with AI, you get quite excited to build on that success. Momentum builds up which starts building those learning skill hours.

Do you need all these meta-skills to be successful with AI? No, but if you don't have many of them, it will take much longer to build sufficient skill in AI coding for it to gain momentum—unless we find the right general process that folks who don't have a natural talent for it can use to be successful.

There's a lot going on here with folks who take to AI coding and folks who dont. But it's not terribly surprising that it's the senior devs and old tech leads who tend to take to it faster.

Balance that against the threat AI poses to livelihoods and it's not a shock that overall sentiment is negative. But I would guess it will shake out in the direction we are pushing, at least in the nearer (3yr) term.

Lol what Who came up with this? They never were supposed to do anything. Just turned out to be useful in experienced hands as expected

Seniors can make this explicit to models and use them to automate "the code they would have written," whereas a junior doesn’t know what they would have written nor how they would have solved it absent a LLM.

Same applies to all fields: LLMs can be either huge leverage on top of existing knowledge or a crutch for a lack of understanding.

Sometimes detail accuracy is sacrificed in service of a good story. Sometimes they simply full of shit and double down when pushed.

I had a very complex piece of logic, with many many many moving parts. So I implemented many paths of that logic by hand, every one with their own specifics. Every path took something like 200-400 lines of code.

When this was done and correct. It was difficult to see the moving parts through the forest. Some code was similar but still a bit different, and hard to think about and spread in many places.

I put everything into an Llm and asked about isolation, architecture and refactoring.

And it actually gave me pretty good abstractions and a good architecture. It didn't include every possible path, but was easy enough to be continued by hand.

It's not that I would not have tought about it. But it was easier that way, and probably my handcrafted solution would be very similar (+headache).

Of course, I reviewed it extensively, and reimplemented every missing path and corrected the ones that were buggy now by hand.

For the experiment, I played with agents to fill the missing parts, it was a disaster :)