Things that helped me get out of the AI 10x engineer imposter syndrome
468 comments
·August 5, 2025voxleone
torginus
I now understand what artists felt when seeing stable diffusion images - AI code is often just wrong - not in the moral sense, but it contains tons of bugs, weirdness, excess and peculiarities you'd never be happy to see in a real code base. Often getting rid of all of this, takes comparable amount of time as doing the job in the first place.
Now I can always switch to a different model, increase the context, prompt better etc. but I still feel that actual good quality AI code is just out of arms reach, or when something clicks, and the AI magically starts producing exactly what I want, that magic doesn't last.
Like with stable diffusion, people who don't care as much or aren't knowledgeable enough to know better, just don't.
A week ago, I received a bug ticket claiming one of the internal libs i wrote didn't work. I checked out the code, which was full of weird issues (like the debugger not working and the typescript being full of weird squiggles), and my lib crashed somewhere in the middle, in some esoteric minified js.
When I asked the guy who wrote it what's going on, he admitted he vibe coded the entire project.
jdelman
Speaking of irony... did ChatGPT help you write this comment?
galaxyLogic
> They don’t require formal models or diagrams.
Nor do they produce those (do they?). That is what I would like to see. Formal models and diagrams are not needed to produce code. Their point is that they allow us to understand code and to formalize what we want it to do. That's what I'm hoping AI could do for me.
coltonv
Very interesting perspective. Thanks for sharing!
jdefr89
It kills the magic of coding for sure. The thing is. Now with everyone doing it, you get a ton of slop. Computing’s become saturated as hell. We don’t even need more code as it is. Before LLMs you could pretty much find what you needed on github… Now it’s even worse.
simonw
I found myself agreeing with quite a lot of this article.
I'm a pretty huge proponent for AI-assisted development, but I've never found those 10x claims convincing. I've estimated that LLMs make me 2-5x more productive on the parts of my job which involve typing code into a computer, which is itself a small portion of that I do as a software engineer.
That's not too far from this article's assumptions. From the article:
> I wouldn't be surprised to learn AI helps many engineers do certain tasks 20-50% faster, but the nature of software bottlenecks mean this doesn't translate to a 20% productivity increase and certainly not a 10x increase.
I think that's an under-estimation - I suspect engineers that really know how to use this stuff effectively will get more than a 0.2x increase - but I do think all of the other stuff involved in building software makes the 10x thing unrealistic in most cases.
mavamaarten
Yeah. I just need to babysit it too much. Take copilot, it gives good suggestions and blows me away sometimes with a block of code which is exactly what I'd type. But actively letting it code (at least with gpt4.1 or gpt4o) just doesn't work well enough for me. Half of the time it doesn't even compile, and after fixing that it's just not really correctly working either. I'd expect it to work like a very junior programmer, but it works like a very drunk senior programmer that isn't listening to you very well at all.
edm0nd
>I'd expect it to work like a very junior programmer, but it works like a very drunk senior programmer that isn't listening to you very well at all.
This seems to be the current consensus.
A very similar quote from another recent AI article:
One host compares AI chatbots to “a very smart assistant who has a dozen Ph.D.s but is also high on ketamine like 30 percent of the time.”
https://lithub.com/what-happened-when-i-tried-to-replace-mys...
__loam
Even saying it has a dozen PhDs belies the reality that these things have no relationship with the truth
Uehreka
Totally disagree. The current state of coding AIs is “a level 2 product manager who is a world class biker balancing on a unicycle trying to explain a concept in French to a Spanish genius who is only 4 years old.” I’m not going to explain what I mean, but if you’ve used Qwen Code you understand.
mullingitover
> But actively letting it code (at least with gpt4.1 or gpt4o)
It's funny, Github Copilot puts these models in the 'bargin bin' (they are free in 'ask' mode, whereas the other models count against your monthly limit of premium requests) and it's pretty clear why, they seem downright nerfed. They're tolerable for basic questions but you wouldn't use them if price weren't a concern.
Brandwise, I don't think it does OpenAI any favors to have their models be priced as 'worthless' compared to the other models on premium request limits.
ewoodrich
Shhh... the free GPT 4.1 exposed to the VS Code LM API is the only reason I still pay for GitHub Copilot.
hnuser123456
I think there are three factors to this: 1. What to code (longer, more specific prompts are better but take longer to write), and 2. How to code it (specify languages, libraries, APIs, etc.) And if you're trying to write code that uses a newer version of a library that works differently from what's most commonly documented, it's a long uphill battle of constantly reminding the LLM of the new changes.
If you're not specific enough, it will definitely spit out a half-baked pseudocode file where it expects you to fill in the rest. If you don't specify certain libraries, it'll use whatever is featured in the most blogspam. And if you're in an ecosystem that isn't publicly well-documented, it's near useless.
platevoltage
>I'd expect it to work like a very junior programmer, but it works like a very drunk senior programmer that isn't listening to you very well at all.
Best analogy I've ever heard and it's completely accurate. Now, back to work debugging and finishing a vibe coded application I'm being paid to work on.
docmars
With something like Devin, where it integrates directly with your repo and generates documentation based on your project(s), it's much more productive to use as an agent. I can delegate like 4-5 small tasks that would normally take me a full day or two (or three) of context switching and mental preparation, and knock them out in less than a day because it did 50-80% of the work, leaving only a few fixes or small pivot for me to wrap them up.
This alone is where I get a lot of my value. Otherwise, I'm using Cursor to actively solve smaller problems in whatever files I'm currently focused on. Being able to refactor things with only a couple sentences is remarkably fast.
The more you know about your language's features (and their precise names), and about higher-level programming patterns, the better time you'll have with LLMs, because it matches up with real documentation and examples with more precision.
spopejoy
> Being able to refactor things with only a couple sentences is remarkably fast.
I'm curious, this is js/ts? Asking because depending on the lang, good old machine refactoring is either amazeballs (Java + IDE) or non-existent (Haskell).
I'm not js/ts so I don't know what the state of machine refactoring is in VS code ... But if it's as good as Java then "a couple of sentences" is quite slow compared to a keystroke or a quick dialog box with completion of symbol names.
skinnymuch
Is work paying for Devin or you are? How pricey is it to delegate the task example you gave?
bcrosby95
It codes like a junior, has the design sense of a mid, while being a savant at algorithms.
oriolid
Make it idiot at algorithms and I believe you.
morpheos137
What does it really mean to know something or understand something. I think AI knows a great deal (associating facts with symbols), confabulates at times when it doesn't know (which is dishonestly called hallucination, implying a conscious agent misperceiving, which AI is not), and understands almost nothing.
The best way to think of chat bot "AI" is as the compendium of human intelligence as recorded in books and online media available to it. It is not intelligent at all on its own and its judgement can't be better than its human sources because it has no biological drive to sythesize and excel. Its best to think of AI as a librarian of human knowledge or an interactive Wikipedia which is designed to seem like an intelligent agent but is actually not.
ffsm8
To date, I've not been able to effectively use Copilot in any projects.
The suggestions were always unusably bad. The /fix were always obviously and straight up false unless it was a super silly issue.
Claude Code with Opus model on the other hand was mind-blowing to me and made me change my mind on almost everything wrt my opinion of LLMs for coding.
You still need to grow the skill of how to build the context and formulate the prompt, but the buildin execution loop is a complete game changer and I didn't realize that until I actually used it effectively on a toy project myself.
MCP in particular was another thing I always thought was massively over hyped, until I actually started to use some in the same toy project.
Frankly, the building blocks already exist at this point to make a vast majority of all jobs redundant (and I'm thinking about all grunt work office jobs, not coding in particular). The tooling still need to be created, so I'm not seeing a short term realization (<2 yrs), but medium term (5+yrs)?
You should expect most companies to let people go at staggering numbers, with only small amounts of highly skilled people left to administer the agents
simonw
> You should expect most companies to let people go at staggering numbers, with only small amounts of highly skilled people left to administer the agents
I don't buy that. The linked article makes a solid argument for why that's not likely to happen: agentic loop coding tools like Claude Code can speed up the "writing code and getting it working" piece, but the software development lifecycle has so much other work before you get to the "and now we let Claude Code go brrrrrrr" phase.
necovek
Thanks for this perspective, but I am a bit confused by some of your takes: you used "Claude Code with Opus model" in "the same toy project" with great success, which led you to conclude that this will "make a vast majority of all jobs redundant".
Toy project viability does not connect with making people redundant in the process (ever, really) — at least not for me. Care to elaborate where do you draw the optimism from?
bluefirebrand
> You should expect most companies to let people go at staggering numbers, with only small amounts of highly skilled people left to administer the agents
I'm gonna pivot to building bomb shelters maybe
Or stockpiling munitions to sell during the troubles
Maybe some kind of protest support saas. Molotov deliveries as a service, you still have to light them and throw them but I guarantee next day delivery and they will be ready to deploy into any data center you want to burn down
What Im trying to say is "companies letting people go in staggering numbers" is a societal failure state not an ideal
samtp
> until I actually started to use some in the same toy project
Thats the key right there. Try to use it in a project that handles PII, needs data to be exact, or has many dependencies/libraries and needs to not break for critical business functions.
leptons
So what happens when someone calls in and the "AI" answers (because the receptionist has been fired and replaced by "AI"), and the caller asks to access some company record that should be private? Will the LLM always deny the request? Hint: no, not always.
There are so many flaws in your plan, I have no doubt that "AI" will ruin some companies that try to replace humans with a "tin can". LLMs are being inserted loosey-goosey into too many places by people that don't really understand the liability problems it creates. Because the LLM doesn't think, it doesn't have a job to protect, it doesn't have a family to feed. It can be gamed. It simply won't care.
The flaws in "AI" are already pretty obvious to anyone paying attention. It will only get more obvious the more LLMs get pushed into places they really do not belong.
__loam
Who buys their crap if you fire everyone?
sureglymop
I do also believe that those who are often looked at or referred to as 10x engineers will maybe only see a marginal productivity increase.
The smartest programmer I know is so impressive mainly for two reasons: first, he seems to have just an otherworldly memory and seems to kind of have absolutely every little feature and detail of the programming languages he uses memorized. Second, his real power is really in cognitive ability, or the ability to always quickly and creatively come up with the smartest and most efficient yet elegant and clean solution to any given problem. Of course somewhat opinionated but in a good way. Funnily he often wouldn't know the academic/common name for some algorithm he arrived at but it just happened to be what made sense to him and he arrived at it independently. Like a talented musician with perfect pitch who can't read notation or doesn't know theory yet is 10x more talented than someone who has studied it all.
When I pair program with him, it's evident that the current iteration of AI tools is not as quick or as sharp. You could arrive at similar solutions but you would have to iterate for a very long time. It would actually slow that person down significantly.
However, there is such a big spectrum of ability in this field that I could actually see this increasing for example my productivity by 10x. My background/profession is not in software engineering but when I do it in my free time the perfectionist tendencies make me work very slowly. So for me these AI tools are actually cool for generating the first crappy proof of concepts for my side projects/ideas, just to get something working quickly.
skadamou
I like the quip that AI raises the floor not the ceiling. I think it helps the bottom 20% perform more like the middle 50% but doesn't do much for people at the top.
finaard
Maybe to get an impression that they'd be performing like them - but not actually performing.
It helps me being lazy because I have a rough expectation of what the outcome should be - and I can directly spot any corner cases or other issues the AI proposed solution has, and can either prompt it to fix that, or (more often) fix those parts myself.
The bottom 20% may not have enough skill to spot that, and they'll produce superficially working code that'll then break in interesting ways. If you're in an organization that tolerates copy and pasting from stack overflow that might be good enough - otherwise the result is not only useless, but as it provides the illusion of providing complete solution you're also closing the path of training junior developers.
Pretty much all AI attributed firings were doing just that: Get rid of the juniors. That'll catch up with us in a decade or so. I shouldn't complain, though - that's probably a nice earning boost just before retirement for me.
jes5199
maybe, but I find that it makes it much faster to do things that _I already know how to do_, and can only slowly, ploddingly get me to places that I don't already have a strong mental model for, as I have to discover mistakes the hard way
Quarrelsome
I think its more effective at lowering the floor. The amount of people that can't code at all but can now slap something together makes it a huge step forward. Albeit one that mostly steps on a pile of dogshit after it hits any sort of production reality.
Its like Wordpress all over again but with people even less able to code. There's going to be vast amounts of opportunities for people to get into the industry via this route but its not going to be a very nice route for many of them. Lots of people who understand software even less than c-suite holding the purse-strings.
eldaisfish
I add to that analogy. AI raises the floor but some of the floor tiles fall away, unpredictably.
threatofrain
AI is strong in different places, and if it keeps on being strong in certain ways then people very soon won't be able to keep up. For example, extreme horizontal knowledge and the ability to digest new information almost instantly. That's not something anyone can do. We don't try to compete against computers on raw calculation, and soon we won't compete on this one either. We simply won't even think to compare.
People keep focusing on general intelligence style capabilities but that is the golden grail. The world could go through multiple revolutions before finding that golden grail, but even before then everything would have changed beyond recognition.
So write an integration over the API docs I just copy-pasted.
mgfist
My biggest takeaway from using AI is that
(1) for my day job, it doesn't make me super productive with creation, but it does help with discovery, learning, getting myself unstuck, and writing tedious code.
(2) however, the biggest unlock is it makes working on side projects __immensely__ easier. Before AI I was always too tired to spend significant time on side projects. Now, I can see my ideas come to life (albeit with shittier code), with much less mental effort. I also get to improve my AI engineering skills without the constraint of deadlines, data privacy, tool constraints etc..
theshrike79
#2 is the reason I keep paying for Claude Code Pro.
For 20 a month I can get my stupid tool and utility ideas from "it would be cool if I could..." to actual "works well enough for me" -tools in an evening - while I watch my shows at the same time.
After a day at work I don't have the energy to start digging through, say, OpenWeather's latest 3.0 API and its nuances and how I can refactor my old code to use the new API.
Claude did it in maybe one episode of What We Do in the Shadows :D I have a hook that makes my computer beep when Claude is done or pauses for a question, so I can get back, check what it did and poke it forward.
kkukshtel
2 heavily resonates with me. Simon Wilson made the point early on that AI makes him more ambitious with his side projects, and I heavily agree. Suddenly lots of things that seemed more or less un-feasible are now not only do-able, but can actually meet or exceed your own assumptions for them.
Being able to sit down after a long way of work and ask an AI model to implement some bug or feature on something while you relax and _not_ type code is a major boon. It is able to immediately get context and be productive even when you are not.
bluefirebrand
> (1) for my day job, it doesn't make me super productive with creation, but it does help with discovery, learning, getting myself unstuck, and writing tedious code
I hear this take a lot but does it really make that much of an improvement over what we already had with search engines, online documentation and online Q&A sites?
iamjackg
It is the best version of fuzzy search I have ever seen: the ultimate "tip of my tongue" assistant. I can ask super vague things like "Hey, I remember seeing a tool that allows you to put actual code in your files to do codegen, what could it be?" and it instantly gives me a list of possible answers, including the thing I'm looking for: Cog.
I know that a whole bunch of people will respond with the exact set of words that will make it show up right away on Google, but that's not the point: I couldn't remember what language it used, or any other detail beyond what I wrote and that it had been shared on Hacker News at some point, and the first couple Google searches returned a million other similar but incorrect things. With an LLM I found it right away.
hathawsh
Yes.
Me, typing into a search engine, a few years ago: "Postgres CTE tutorial"
Me, typing into any AI engine, in 2025: "Here is my schema and query; optimize the query using CTEs and anything else you think might improve performance and readability"
simonw
Those things don't really help with getting unstuck, especially if the reason you are struck is that there tedious code that you anticipate writing and don't want to deal with.
mgfist
Yes. It's so dramatically better it's not even funny. It's not that information doesn't exist out there, it's more that an LLM can give it to you in a few seconds and it's tailored to your specific situation. The second part is especially helpful if the internet answer is 95% correct but is missing something specific to you that ends up taking you 20 minutes to figure out.
antonvs
Are you boycotting AI or something?
If you try it yourself you'll soon find out that the answer is a very obvious yes.
You don't need a paid plan to benefit from that kind of assistance, either.
j45
Yes, it can.
null
hyperbovine
This is (I think) a reference to the 10x engineer, another myth of which I have always been highly dubious (https://www.simplethread.com/the-10x-programmer-myth/).
Izkata
10x sounds nice which is probably why it stuck, but it came from actual research which found the difference was larger than 10x - but also they were measuring between best and worst, not best and average as it's used nowadays.
https://www.construx.com/blog/productivity-variations-among-...
mgfist
All of this is hard to quantify. How much better than the average engineer is John Carmack, or Rob Pike or Linus? I consider myself average-ish and I don't think there's any world in which I could do what those guys did no matter how much time you gave me (especially without the hindsight knowledge of the creations). So I'd say they're all infinitely better than me.
zahlman
>but also they were measuring between best and worst, not best and average as it's used nowadays.
Depending on the environment, I can imagine the worst devs being net negative.
AdrianB1
It highly depends on the circumstances. In over 30 years in the industry I met 3 people that were many times more productive than everyone else around them, even more than 10 times. What does this translate to? Well, there are some extraordinary people around, very rare and you cannot count on finding some and, when you find them, it is almost impossible to retain them because management and HR never agree to pay them enough to stay around.
logicchains
You don't believe Fabrice Bellard exists?
gopher_space
He doesn't believe there are hundreds of Fabrice Bellard clones who think working at your company wouldn't be a waste of their time. The myth might be that thinking about 10X is useful in any sense. You can't plan around one gracing you with their presence and you won't be able to retain them when they do.
Thinking about it personally, a 10X label means I'm supposedly the smartest person in the room and that I'm earning 1/10th what I should be. Both of those are huge negatives.
coltonv
Thanks for the comment Simon! This is honestly the first one I've read where it feels like someone actually read the article. I'm totally open to the idea that some people, especially those working on the languages/tools that LLMs are good at, are indeed getting a 2x improvement in certain parts of their job.
simonw
Something I have realized about Hacked News is that most of the comments on any given article are from people who are responding to the headline without actually clicking through and reading it!
This is particularly true for headlines like this one which stand alone as statements.
yen223
Humans have been hallucinating responses given a prompt long before chatgpt was a thing!
coltonv
Perhaps that's my fault for making the title almost clickbaity. My goal was to get people who felt anxious about AI turning them into dinosaurs not feel like they are missing some secret sauce, so hopefully the reach this is getting contributes that.
Again, appreciate your thoughts, I have a huge amount of respect for your work. I hope you have a good one!
zahlman
> most of the comments on any given article are from people who are responding to the headline without actually clicking through and reading it!
Well, the people who quote from TFA have usually at least read the part they quoted ;)
pmnord
This is a truism across the entire web
cleak
I’ve found I do get small bursts of 10x productivity when trying to prototype an idea - much of the research on frameworks and such just goes away. Of course that’s usually followed by struggling to make a seemingly small change for an hour or two. It seems like the 10x number is just classic engineers underestimating tasks - making estimates based on peak productivity that never materializes.
I have found for myself it helps motivate me, resulting in net productivity gain from that alone. Even when it generates bad ideas, it can get me out of a rut and give me a bias towards action. It also keeps me from procrastinating on icky legacy codebases.
thomascgalvin
My experience with GenAI is that it's a significant improvement to Stack Overflow, and generally as capable as someone hired right out of college.
If I'm using it to remember the syntax or library for something I used to know how to do, it's great.
If I'm using it to explore something I haven't done before, it makes me faster, but sometimes it lies to me. Which was also true of Stack Overflow.
But when I ask it to so something fairly complex on it's own, it usually tips over. I've tried a bunch of tests with a bunch of models, and it never quite gets it right. Sometimes it's minor stuff that I can fix if I bang on it long enough, and sometimes it's a steaming pile that I end up tossing in the garbage.
For example, I've asked it to code me a web-based calculator, or a 3D model of the solar system using WebGL, and none of the models I've tried have been able to do either.
didibus
> engineers that really know how to use this stuff effectively
I guess this is still the "caveat" that can keep the hype hopes going. But I've found at a team velocity level, with our teams, where everyone is actively using agentic coding like Claude Code on the daily, we actually didn't see an increase in team velocity yet.
I'm curious to hear anecdotal from other teams, has your team seen velocity increase since it adopted agentic AI?
jan_g
Same here. I have a colleague that is completely enamored with these agents. Uses them for everything he can, not just coding. Commit messages, opening PRs, Linear tickets, etc. Basically, he uses agents for everything he can. But the productivity gain is just not there. He's about as fast or rather as slow as he was before. And to a degree I think this goes for the whole team. It's the oxymoron of AI: more code, more documentation, more text, more of everything generated than ever, but the effect is that this means more complexity, more PRs to review, more bugs, more stuff to know and understand, ... We are all still learning how to use these agents effectively. And the particular developer's effect can and does multiply as everything else with GenAI. Was he a bit sloppy before, not covering various edge-cases and used quick-and-dirty shortcuts? Then this remains true for the code he produces using agents. And to those, who claim that "by using more agents I will gain 10x productivity" I say please read a certain book about how just adding developers to a project makes it even more delayed. The resemblance of team/project leadership -> developers dynamic is truly uncanny.
generalizations
In many ways this feels like average software engineers telling on themselves. If you know the tech you're building, and you're good at splitting up your work, then you know ahead of time where the complexity is and you can tell the AI what level of granularity to build at. AI isn't magic; there is an upper limit to the complexity of a program that e.g. Sonnet 4 can write at once. If you can grok that limit, and you can grok the tech of your project, then you can tell the AI to build individual components that stay below that threshold. That works really well.
theshrike79
Of course there is an upper limit for AI. There's an upper limit for humans too.
What you need is just boring project management. Have a proper spec, architecture and tasks split into manageable chunks with enough information to implement them.
Then you just start watching TV and say "implement github issue #42" to Claude and it'll get on with it.
But if you say "build me facebook" and expect a shippable product, you'll have a bad time.
Scarblac
But the hard part is figuring out the more complex parts. Getting that right is what takes the time, not typing in the more trivial parts.
alphazard
The point is that good software engineers are good at doing the "hard part". So good that they have a backlog of "trivial" typing tasks. In a well functioning organization they would hand off the backlog of trivial parts to less experienced engineers, who might be herded by a manager. Now we don't need the less experienced engineers or the manager to herd them.
lopatin
Not typing the trivial parts is pretty great though
skydhash
I think most developers bypass the typing of the trivial part by just using a library or a framework. And sometimes typing trivial things can be relaxing, especially after an intense bout with a complex thing.
AstroBen
Being forced to type in trivial boilerplate means you're very motivated to abstract it. Not saying this'll offset anything but I can see AI making codebases much more verbose
pydry
Until it spends 10 minutes fucking up the trivial part and then youre 10 minutes down and you still have to do it yourself.
j45
It can be, but if you're familiar with what you're working with and have experience with other systems that have transferrable knowledge, again, it can be an advantage.
I was surprised with claude code I was able to get a few complex things done that I had anticipated to be a few weeks to uncover, stitch together and get moving.
Instead I pushed Claude to consistently present the correct udnerstanding of the problem, strucutre, approach to solving things, and only after that was OK, was it allowed to propose changes.
True to it's shiny things corpus, it will over complicate things because it hasn't learned that less is more. Maybe that reflects the corpus of the average code.
Looking at how folks are setting up their claude.md and agents can go a long way if you haven't had a chance yet.
Quarrelsome
might it not be the other way round? For all we know its mediocre devs who are relishing the prospect of doing jack shit all day and still being able to submit some auto generated PRs. Being "amazed" at what it produces when someone with higher standards might be less than amazed.
I find it impossible to work out who to trust on the subject, given that I'm not working directly with them, so remain entirely on the fence.
ares623
I'd be curious how skill atrophy affects engineers who use AI semi-exclusively for these trivial tasks.
lnenad
I agree, and the fact that in their list of scenarios that cause these not actually mentioning some people actually are 10x definitely points to them not being self-aware.
megaloblasto
I thought this would be another AI hate article, but it made some great points.
One thing that AI has helped me with is finding pesky bugs. I mainly work on numerical simulations. At one point I was stuck for almost a week trying to figure out why my simulation was acting so strange. Finally I pulled up chatgpt, put some of my files into the context and wrote a prompt explaining the strange behavior and what I thought might be happening. In a few seconds it figured out that I had improperly scaled one of my equations. It came down to a couple missing parentheses, and once I fixed it the simulation ran perfectly.
This has happened a few times where AI was easily able to see something I was overlooking. Am I a 10x developer now that I use AI? No... but when used well, AI can have a hugely positive impact on what I am able to get done.
wwweston
This is my experience. Code generation is OK if uneven, but debugging can be a big boost.
It’s a rubber duck that’s pretty educated and talks back.
Gud
Indeed. As a (mostly) hobbyist programmer LLMs have been a godsend for those late night coding sessions when the brain fog is thick.
mentos
Yep same experience here saved me an infinite amount of time so to me that puts me somewhere between 10x and infinity ha
TrackerFF
Say you want to create a web app, but you don't know any web dev. You spend a couple of months reading front-end and back-end dev, incrementally create something, and after half a year you've made a web app you like. Say you spent 4 hours a day, 5 days a week, for 6 weeks, going from zero to a functional web app. So you spent 120 hours in total.
Now let's say you use Claude code, or whatever, and you're able to create the same web app over a weekend. You spend 6 hours a day on Saturday and Sunday, in total 12 hours.
That's 10x increase in productivity right there. Did it make you a 10x better programmer? Nope, probably not. But your productivity went up by a tenfold.
And at least to me, that's sort of how it has worked. Things I didn't have motivation or energy to get into before, I can get into over a weekend.
nxpnsv
However, in the first case you learned something which probably is useful when you want to change said app in any way or make another project...
theshrike79
Depends on how you learn.
For me it's 50-50 reading other people's code and getting a feel for the patterns and actually writing the code.
breakingwalls
This, I agree to this 100%. I was able to get at least 2 apps, 2 SAAS products out by pairing up with AI. I was able to learn this as I go and get an app running in the matter of hours than months. Great for prototype to production. -> learn-> fix -> ship -> learn more -> fix things -> ship more.
That being said, I am a generalist with 10+ years of experience and can spot the good parts from bad parts and can wear many hats. Sure, I do not know everything, but, hey did I know everything when AI was not there? I took help from SO, Reddit and other places. Now, I go to AI, see if it makes sense, apply the fix, learn and move on.
CER10TY
The issue is that it‘ll absolutely _suck_. If I tell Claude Code to scaffold a web app from 0 outside of React it‘s terrible.
So no, imho people with no app dev skills cannot just build something over a weekend, at least something that won‘t break when the first user logs in.
Quarrelsome
you're going to push that straight to production? Cmon man, its not the same thing, not by a long shot. That's a crap measure. I don't think we can even reliably measure 1x developer output which makes multiplying it even more nonsensical.
dclowd9901
I'm not sure that math makes sense over the long run. Sure, at first you scaffold together an app from scratch, but I suspect over time, the LLM's capability of maintaining it precipitously drops. At some point, you will like reach a productivity level of zero, as now your application has become too complex to fit in a context window and you have no idea how it actually works. So what is the productivity multiplier then?
samtp
But at the same time you basically outsourced your brain and any learning that would come from the exercise. So while you now have an app, you've experienced close to 0 learning or growth along the way.
Glyptodon
I don't consider myself a 10x engineer. The number one thing that I've realized makes me more productive than other engineers at my company is thinking through system design and business needs with patterns that don't take badly written product tickets literally.
What I've seen with AI is that it does not save my coworkers from the pain of overcomplicating simple things that they don't really think through clearly. AI does not seem to solve this.
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AdrianB1
I don't consider myself a 2x engineer; my company tells me that by not paying me 2x vs my colleagues, even if I know (and others believe that too) I deliver more than 2x their output.
Using AI will change nothing in this context.
lvl155
Counter: you are looking at it wrong. You can get work done in 1/2 of the time it used to. Now you got 1/2 of the day to just mess around. Socialize or network. It’s not necessarily that you’re producing 2x.
theshrike79
[delayed]
bumblehean
> You can get work done in 1/2 of the time it used to. Now you got 1/2 of the day to just mess around. Socialize or network.
This has never been the case in any company I've ever worked at. Even if you can finish your day's work in, say, 4 hours, you can't just dip out for the other 4 hours of the day.
Managers and teammates expect you to be available at the drop of a hat for meetings, incidents, random questions, "emergencies", etc.
Most jobs I've worked at eventually devolve into something like "Well, I've finished what I wanted to finish today. I could either stare at my monitor for the rest of the day waiting for something to happen, or I could go find some other work to do. Guess I'll go find some other work to do since that's slightly less miserable".
You also have to delicately "hide" the fact that you can finish your work significantly faster than expected. Otherwise the expectations of you change and you just get assigned more work to do.
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necovek
The first red flag there is "2x their output". You can find many an anecdote where a good engineer produced better solution in fewer lines of code (or sometimes, by removing code — the holy grail).
So always aim for outcomes, not output :)
At my company, we did promote people quickly enough that they are now close to double their salaries when they started a year or so ago, due to their added value as engineers in the team. It gets tougher as they get into senior roles, but even there, there's quite a bit of room for differentiation.
Additionally, since this is a market, you should not even expect to be paid twice for 2x value provided — then it makes no difference to a company if they get two 1x engineers instead, and you are really not that special if you are double the cost. So really, the "fair" value is somewhere in between: 1.5x to equally reward both parties, or leaning one way or the other :)
AdrianB1
When I go to buy 2 bottles of milk I am never offered to get it for 1.x the price of one bottle. I don't see any way it is fair to deliver double and get just 1.5x, in a hypothetical scenario just for the sake of the discussion. The suggestion to work 50% of the time and relax, socialize and network the other 50% is way more reasonable, when possible (not in my case).
bongodongobob
No one said anything about lines of code. I would assume output here means features completed, tickets knocked out, tasks completed etc.
tptacek
This article sets a ludicrous bar ("10x"), then documents the author's own attempt over some indeterminate time to clear that bar. As a result, the author has classified all the AI-supporters in the industry into three categories: (1) people who are wrong in good faith, (2) people who are selling AI tools, and (3) evil bosses trying to find leverage in programmer anxiety.
That aside: I still think complaining about "hallucination" is a pretty big "tell".
BoxFour
> I still think complaining about "hallucination" is a pretty big "tell".
The conversation around LLMs is so polarized. Either they’re dismissed as entirely useless, or they’re framed as an imminent replacement for software developers altogether.
Hallucinations are worth talking about! Just yesterday, for example, Claude 4 Sonnet confidently told me Godbolt was wrong wrt how clang would compile something (it wasn’t). That doesn’t mean I didn’t benefit heavily from the session, just that it’s not a replacement for your own critical thinking.
Like any transformative tool, LLMs can offer a major productivity boost but only if the user can be realistic about the outcome. Hallucinations are real and a reason to be skeptical about what you get back; they don’t make LLMs useless.
To be clear, I’m not suggesting you specifically are blind to this fact. But sometimes it’s warranted to complain about hallucinations!
tptacek
That's not what people mean when they bring up "hallucinations". What the author apparently meant was that they had an agent generating Terraform for them, and that Terraform was broken. That's not surprising to me! I'm sure LLMs are helpful for writing Terraform, but I wouldn't expect that agents are at the point of being able to reliably hand off Terraform that actually does anything, because I can't imagine an agent being given permission to iterate Terraform. Now have an agent write Java for you. That problem goes away: you aren't going to be handed code with API calls that literally don't exist (this is what people mean by "hallucination"), because that could wouldn't pass a compile or linter pass.
JoshuaDavid
Are we using the same LLMs? I absolutely see cases of "hallucination" behavior when I'm invoking an LLM (usually sonnet 4) in a loop of "1 generate code, 2 run linter, 3 run tests, 4 goto 1 if 2 or 3 failed".
Usually, such a loop just works. In the cases where it doesn't, often it's because the LLM decided that it would be convenient if some method existed, and therefore that method exists, and then the LLM tries to call that method and fails in the linting step, decides that it is the linter that is wrong, and changes the linter configuration (or fails in the test step, and updates the tests). If in this loop I automatically revert all test and linter config changes before running tests, the LLM will receive the test output and report that the tests passed, and end the loop if it has control (or get caught in a failure spiral if the scaffold automatically continues until tests pass).
It's not an extremely common failure mode, as it generally only happens when you give the LLM a problem where it's both automatically verifiable and too hard for that LLM. But it does happen, and I do think "hallucination" is an adequate term for the phenomenon (though perhaps "confabulation" would be better).
Aside:
> I can't imagine an agent being given permission to iterate Terraform
Localstack is great and I have absolutely given an LLM free rein over terraform config pointed at localstack. It has generally worked fine and written the same tf I would have written, but much faster.
coltonv
With terraform, using a property or a resource that doesn't exist is effectively the same as an API call that does not exist. It's almost exactly the same really, because under the hood terraform will try to make a gcloud/aws API call with your param and it will not work because it doesn't exist. You are making a distinction without a difference. Just because it can be caught at runtime doesn't make it insignificant.
Anyway, I still see hallucinations in all languages, even javascript, attempting to use libraries or APIs that do not exist. Could you elaborate on how you have solved this problem?
timmytokyo
As if a compiler or linter is the sole arbiter of correctness.
coltonv
Hi there! I appreciate your comment, and I remember reading your article about AI and some of the counterarguments to it helped me get over the imposter syndrome I was feeling.
To be clear, I did not classify "all the AI-supporters" as being in those three categories, I specifically said the people posting that they are getting 10x improvements thanks to AI.
Can you tell me about what you've done to no longer have any hallucinations? I notice them particularly in a language like Terraform, the LLMs add properties that do not exist. They are less common in languages like Javascript but still happen when you import libraries that are less common (e.g. DrizzleORM).
tptacek
Can you help me understand which articles you're referring to? A link to the biggest "AI made me a 10x developer" article you've read would certainly clear this up.
coltonv
My goal here was not to publicly call out any specific individual or article. I don't want to make enemies and I don't want to be cast as dunking on someone. I get that that opens me up to criticism that I'm fighting a strawman, I accept that.
Your article does not specifically say 10x, but it does say this:
> Kids today don’t just use agents; they use asynchronous agents. They wake up, free-associate 13 different things for their LLMs to work on, make coffee, fill out a TPS report, drive to the Mars Cheese Castle, and then check their notifications. They’ve got 13 PRs to review. Three get tossed and re-prompted. Five of them get the same feedback a junior dev gets. And five get merged.
> “I’m sipping rocket fuel right now,” a friend tells me. “The folks on my team who aren’t embracing AI? It’s like they’re standing still.” He’s not bullshitting me. He doesn’t work in SFBA. He’s got no reason to lie.
That's not quantifying it specifically enough to say "10x", but it is saying no uncertain terms that AI engineers are moving fast and everyone else is standing still by comparison. Your article was indeed one of the ones I specifically wanted to respond to as the language directly contributed to the anxiety I described here. It made me worry that maybe I was standing still. To me, the engineer you described as sipping rocket fuel is an example both of the "degrees of separation" concept (it confuses me you are pointing to a third party and saying they are trustworthy, why not simply describe your workflow?), and the idea that a quick burst of productivity can feel huge but it just doesn't scale in my experience.
Again, can you tell me about what you've done to no longer have any hallucinations? I'm fully open to learning here. As I stated in the article, I did my best to give full AI agent coding a try, I'm open to being proven wrong and adjusting my approach.
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claytongulick
A cursory scroll on X, LinkedIn, etc... will show you.
That seemed to me be to be the author's point.
His article resonated with me. After 30 years of development and dealing with hype cycles, offshoring, no-code "platforms", endless framework churn (this next version will make everything better!), coder tribes ("if you don't do typescript, you're incompetent and should be fired"), endless bickering, improper tech adopting following the FANGs (your startup with 0 users needs kubernetes?) and a gazillion other annoyances we're all familiar with, this AI stuff might be the thing that makes me retire.
To be clear: it's not AI that I have a problem with. I'm actually deeply interested in it and actively researching it from a math's up approach.
I'm also a big believer in it, I've implemented it in a few different projects that have had remarkable efficiency gains for my users, things like automatically extracting values from a PDF to create a structured record. It is a wonderful way to eliminate a whole class of drudgery based tasks.
No, the thing that has me on the verge of throwing in the towel is the wholesale rush towards devaluing human expertise.
I'm not just talking about developers, I'm talking about healthcare providers, artists, lawyers, etc...
Highly skilled professionals that have, in some cases, spent their entire lives developing mastery of their craft. They demand a compensation rate commensurate to that value, and in response society gleefully says "meh, I think you can be replaced with this gizmo for a fraction of the cost."
It's an insult. It would be one thing if it were true - my objection could safely be dismissed as the grumbling of a buggy whip manufacturer, however this is objectively, measurably wrong.
Most of the energy of the people pushing the AI hype goes towards obscuring this. When objective reality is presented to them in irrefutable ways, the response is inevitably: "but the next version will!"
It won't. Not with the current approach. The stochastic parrot will never learn to think.
That doesn't mean it's not useful. It demonstrably is, it's an incredibly valuable tool for entire classes of problems, but using it as a cheap replacement for skilled professionals is madness.
What will the world be left with when we drive those professionals out?
Do you want an AI deciding your healthcare? Do you want a codebase that you've invested your life savings into written by an AI that can't think?
How will we innovate? Who will be able to do fundamental research and create new things? Why would you bother going into the profession at all? So we're left with AIs training on increasingly polluted data, and relying on them to push us forward. It's a farce.
I've been seriously considering hanging up my spurs and munching popcorn through the inevitable chaos that will come if we don't course correct.
dontlikeyoueith
> That aside: I still think complaining about "hallucination" is a pretty big "tell".
And I think that sentence is a pretty big tell, so ...
bgwalter
That bar is industry standard in the hype machine. Altman and others have set it:
https://www.windowscentral.com/software-apps/sam-altman-ai-w...
https://brianchristner.io/how-cursor-ai-can-make-developers-...
https://thenewstack.io/the-future-belongs-to-ai-augmented-10...
lordnacho
I'm getting a lot of side-quest productivity out of AI. There's always a bunch of things I could do, but they are tedious. Yet they are still things I wish I could get done. Those kinds of things AI is fantastic at. Building a mock, making tests, abstracting a few things into libraries, documentation.
So it's not like I'm delivering features in one day that would have taken two weeks. But I am delivering features in two weeks that have a bunch of extra niceties attached to them. Reality being what it is, we often release things before they are perfect. Now things are a bit closer to perfect when they are released.
I hope some of that extra work that's done reduces future bug-finding sessions.
gspencley
> making tests
What I'm about to discuss is about me, not you. I have no idea what kind of systems you build, what your codebase looks like, use case, business requirements etc. etc. etc. So it is possible writing tests is a great application for LLMs for you.
In my day to day work... I wish that developers where I work would stop using LLMs to write tests.
The most typical problem with LLM-generated tests on the codebase where I work is that the test code is almost extremely tightly coupled to the implementation code. Heavy use of test spies is a common anti-pattern. The result is a test suite that is testing implementation details, rather than "user-facing" behaviour (user could be a code-level consumer of the thing you are testing).
The problem with that type of a test is that is a fragile test. One of the key benefits of automated tests is that they give you a safety net to refactor implementation to your heart's content without fear of having broken something. If you change an implementation detail, and the "user-facing" behaviour does not change, your tests should pass. When tests are tightly coupled to implementation, they will fail and now your tests, in the worst of cases, might actually be creating negative value for you ... since you every code change now requires you to keep tests up to date even when what you actually care about testing "is this thing working correctly?" hasn't changed.
The root of this problem isn't even the LLM, it's just that the LLM makes it a million times worse. Developers often feel like writing tests are a menial chore that needs to be done after the fact to satisfy code coverage policy. Few developers, at many organizations, have ever truly worked TDD or learned testing best practices, how to write easy to test implementation code etc.
JoshuaDavid
There are some patterns you can use that help a bit with this problem. Lowest hanging fruit is to tell the LLM that its tests should test only through public interfaces where possible. Next after that is to add a "check if any non-public interfaces were used in places where a public interface exposes the same functionality the not-yet-committed tests - if so, rewrite tests to use only publicly exposed interfaces" step to the workflow. You could likely also add linter rules, though sometimes you genuinely need to test something like error conditions that can't reasonably be tested only through public interfaces.
jbryu
Side-quest productivity is a great way to put it... It does feel like AI effectively enables the opposite of "death by a thousand cuts" (life by a thousand bandaids?)
arrowsmith
I like that "side quests" framing.
For much of what I build with AI, I'm not saving two weeks. I'm saving infinity weeks — if LLMs didn't exist I would have never built this tool in the first place.
simpaticoder
The expectations are higher than reality, but LLMs are quite useful in many circumstances. You can characterize their use by "level of zoom", from "vibe coding" on the high end, to "write this function given its arguments and what it should return" at the low end. The more 'zoomed in' you are, the better it works, in my experience.
Plus there are use-cases for LLMs that go beyond augmenting your ability to produce code, especially for learning new technologies. The yield depends on the distribution of tasks you have in your role. For example, if you are in lots of meetings, or have lots of administrative overhead to push code, LLMs will help less. (Although I think applying LLMs to pull request workflow, commit cleanup and reordering, will come soon).
efields
In a week, Claude Code and I have built a PoC Rails App for a significant business use case. I intend to formally demo it for buy-in tomorrow after already doing a short "is this kind of what you're looking for?" walkthrough last week. From here, I intend to "throw it over the fence" for my staff, RoR and full-stack devs, to pick it apart and/or improve what they want to in order to bring it from 80-100% over the next two months. If they want to rewrite it from scratch, that's on the table.
It's not a ground-breaking app, its CRUD and background jobs and CSV/XLSX exports and reporting, but I found that I was able to "wireframe" with real code and thus come up with unanswered questions, new requirements, etc. extremely early in the project.
Does that make me a 10x engineer? Idk. If I wasn't confident working with CC, I would have pushed back on the project in the first place unless management was willing to devote significant resources to this. I.e. "is this really a P1 project or just a nice to have?" If these tools didn't exist I would have written spec's and excalidraw or Sketch/Figma wireframes that would have taken me at least the same amount of time or more, but there'd be less functional code for my team to use as a resource.
OtherShrezzing
If you think your CC wireframe has taken approx as much time as it'd have taken you with another tool like Figma + spec-writing, and one of your engineering team's options is "rewrite it from scratch" (without a spec), has the use of CC saved your company any time at all?
It reads like this project would have taken your company 9 weeks before, and now will take the company 9 weeks.
g3f32r
I think the comment was showing that the project takes 9 weeks either way, but coming to that determination was much more confident and convincing with a functional demo versus a hand-wavy figma + guesstimate.
re-thc
> was much more confident and convincing with a functional demo versus a hand-wavy figma + guesstimate.
Except it also blurs the lines and sets incorrect expectations.
Management often see code being developed quickly (without full understanding of the fine line between PoC and production ready) and soon they expect it to be done with CC in 1/2 the time or less.
Figma on the other hand makes it very clear it is not code.
coffeefirst
Yeah. The prototyping is neat. But in past lives I would literally sketch the "POC" on paper.
I sort of want to get back to that... it was really good at getting ideas across.
9337throwaway
[dead]
patrakov
What makes this "AI will replace you with a 10x AI-based engineer" narrative a non-starter is actually having a 10x non-AI engineer on the team.
itissid
A few things need to happen very soon(if the signs are not here already):
1. Tech Company's should be able to accelerate and supplant the FAANGs of this world. Like even if 10x was discounted to 5x. It would mean that 10 human years of work would be shrunk down to 2 to make multi-billion dollar companies. This is not happening right now. If this does not start happening with the current series of model, murphy's law (e.g. interest rate spike at some point) or just damn show me the money brutal questions would tell people if it is "working".
2. I think Anthropic's honcho did a back of the envelope number of 600$ for every human in the US(I think just it was just the US) was necessary to justify Nvidia's market Cap. This should play out by the end of this year or in Q3 report.
gmm1990
Extremely anecdotal but all I keep seeing is relatively stable services (the google one comes to mind) having major outages. I assume its not AI related or directly ai related at least, but you'd think these outages would be less common if AI was adding so much value.
There’s something ironic here. For decades, we dreamed of semi-automating software development. CASE tools, UML, and IDEs all promised higher-level abstractions that would "let us focus on the real logic."
Now that LLMs have actually fulfilled that dream — albeit by totally different means — many devs feel anxious, even threatened. Why? Because LLMs don’t just autocomplete. They generate. And in doing so, they challenge our identity, not just our workflows.
I think Colton’s article nails the emotional side of this: imposter syndrome isn’t about the actual 10x productivity (which mostly isn't real), it’s about the perception that you’re falling behind. Meanwhile, this perception is fueled by a shift in what “software engineering” looks like.
LLMs are effectively the ultimate CASE tools — but they arrived faster, messier, and more disruptively than expected. They don’t require formal models or diagrams. They leap straight from natural language to executable code. That’s exciting and unnerving. It collapses the old rites of passage. It gives power to people who don’t speak the “sacred language” of software. And it forces a lot of engineers to ask: What am I actually doing now?