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Things that helped me get out of the AI 10x engineer imposter syndrome
- bilsbie 1y agoI’m actually infinity more productive because I wouldn’t start projects without AI. (Just lazy and burned out from the tedium of codin g) but I’m enjoying it if the AI does a lot of the tedium.
- RobKohr 1y agoYep, I feel you. Let me just explain what I want in detail and one little piece at a time, and AI, make my words become code and I will watch you do it to make sure you don't mess up.
- jf22 1y ago> What LLMs produce is often broken, hallucinated, or below codebase standards. With enough rules and good prompting this is not true. The code I generate is usually better than what I'd do by hand. The reason the code is better all the extra polish and gold plating is essentially free. Everything I generate comes out commented great error handling, logging, SOLID, and united tested using established patterns in the code base.
- tobyhinloopen 1y agoI agree. With plenty of prompts (leave them in documents) you can get pretty good results.
- drgiggles 1y agoEasily 99% of comments generated by LLMs are useless.
- micromacrofoot 1y agoThey're often repetitive if you're reading the code, but they're useful context that feeds back into the LLM. Often once the code is clear enough I'll delete them before pushing to production.
- mrugge 1y agodo you have proof of this being useful for llm? wouldn't you rather it re-read the actual code it generated instead of assuming that the potentially wishful thinking or stale comment is going to lead it astray?
- micromacrofoot 1y agoit reads both, so with the comments it more or less parrots the desired outcome I explained... and it sometimes catches the mismatch between code and comment itself before I even mention it I read and understand 100% of the code it outputs, so I'm not so worried about falling too far astray... being too prescriptive about it (like prompting "don't write comments") makes the output worse in my experience
- the__alchemist 1y agoI've noticed this too. They are often restatements of the line in verbal form, or intended for me, the LLM-reader about the prompt, vice a code maintainer.
- exe34 1y agoThey comment on the how, not the why.
- shortrounddev2 1y agoI think its because LLMs are often trained with data from code tutorial sites and forums like stackoverflow, and not always production code
- ggregoire 1y agoThat's how I detect who is using LLMs at work. # loop over the images for filename in images_filenames: # download the image image = download_image(filename) # resize the image resize_image(image) # upload the image upload_image(image)
- rob_c 1y ago99% of comments are not needed as they just re-express what the code below does. I prefer to push for self documenting code anyway, never saw the need for docs other than for an API when I'm calling something like a black box.
- cjfd 1y agoVery often comments generated by humans are also useless. The reason for this are mandated comment policies, e.g., 'every public method should have a comment'. An utterly disgusting practice. One should only have a comment if one has something interesting to say. In a not-overly-complex code base there should maybe be a comment perhaps every 100 lines or so. In many cases it makes more sense to comment the unit tests than the code.
- drgiggles 1y agoI am pretty far to one end of the spectrum on need for comments. Very rarely is a comment useful to help you/another developer decipher the intent and function of a piece of code.
- skydhash 1y agoI think the rules for comments on public method is to use something like doxygen to extract the reference. And most IDE can display them upon hovering. And comments can remind the caller of pre- and post-conditions.
- gddgb 1y ago[dead]
- jf22 1y agoThen tell it to write better comments...
- timmytokyo 1y agoAh, so it's good enough to write code on its own without time-consuming, excessive hand-holding. But it's not good enough to write comments on its own.
- jf22 1y agoIf you put in the work to write rules and give good prompts you get good results just like every other tool created by mankind. How often do you use coding LLMs?
- ewoodrich 1y agoI can't speak to comments rules specifically but I am a heavy user of "agentic" coding and use rules files and while they help they are simply not that reliable. For something like comments that's probably not that big of a deal because some extra bad comments isn't the end of the world. But I have rules that are quite important for successfully completing a task by my standards and it's very frustrating when the LLM randomly ignores them. In a previous comment I explained my experiences in more detail but depending on the circumstances instruction compliance is 9/10 times at best, with some instructions/tasks as poor as 6/10 in the most "demanding" scenarios particularly as the context window fills up during a longer agentic run.
- wglb 1y agoNot what I have found with gemini. What is particularly useful is the comments about reasoning about new code added at my request.
- reactordev 1y agoLet’s talk about rules and docs, shall we? What makes a good rule for AI to keep it on task? What are your setups for docs and attaching them to the context (do you need to? Or just the location?) Let’s boil this down to an easy set of reproducible steps any engineer can take to wrangle some sense from their AI trip.
- rob_c 1y agoAka, let's train people how to use the tool...
- exe34 1y agoLet's check that the claim matches the evidence first!
- reactordev 1y agoYou seem to be against the idea. Yet you yourself were trained. Weird.
- jf22 1y agoThere are tons of these guides around the internet. I'm only using what other people have already published.
- icey 1y agoThe company I work at (https://getunblocked.com https://getunblocked.com) is built to give tools like Claude Code and Cursor context based on all your docs, issues, code, and chat threads from Slack and soon Teams. Happy to give you a demo sometime if you're interested!
- symfrog 1y agoDo you have a link to some of the code that you have produced using this approach? I am yet to see a public or private repo with non-trivial generated code that is not fundamentally flawed.
- rob_c 1y agoThat's disingenuous or naive. Almost nobody decides to expressly highlight the section of code (or whole files generated by ai) they just get on with the job when there's real deadlines and it's not about coding for the sake of the art form...
- symfrog 1y agoI am talking about correctness, not style, coding isn't just about being able to show activity (code produced), but rather producing a system that is correctly performing the intended task
- rob_c 1y agoYes, and frankly you should be spending time writing large integration tests correctly not microscopic tests that forgot how tools interact. It's not about lines of code or quality it's about solving a problem. If the problem creates another problem then it's bad code. If it solves the problem without causing that then great. Move onto the next problem.
- jakelazaroff 1y agoIf the generated implementation is not good, you're trading short-term "getting on with the job" and "real deadlines" for mid-to-long-term slowdown and missed deadlines. In other words, it matters whether the AI is creating technical debt.
- rob_c 1y agoIf you're creating technical debt, you're creating technical debt. That has nothing to do with AI/LLMs. If you can't understand what the tool spits out either; learn, throw it away, or get it to make something you can understand.
- the__alchemist 1y agoI am pattern matching your last statement with what I've seen with my teammates who are more AI-oriented: I suspect this is a matter of making the metrics the goal. I would rather maintain something that is simple, works, and have targeted comments than something messy that meets the metrics you list.
- echelon 1y agoI don't get all the prompt vibe coding going around. I don't use prompts to generate code. I use "tab-tab" auto complete to speed through refactorings and adding new fields / plumbing. It's easily a 3x productivity gain. On a good day it might be 10x. It gets me through boring tedium. It gets strings and method names right for languages that aren't statically typed. For languages that are statically typed, it's still better than the best IDE AST understanding. It won't replace the design and engineering work I do to scope out active-active systems of record, but it'll help me when time comes to build.
- recursive 1y agoI use tab auto complete, and i think it's a 5% productivity gain. On a good day, maybe 10%. I haven't put much effort into optimizing the setup or learning advanced usage patterns or anything. I'm using stock copilot, provided by my employer. If I had to pay for it, I wouldn't be using it, as it doesn't justify the cost.
- Bootvis 1y agoReally, what are you making that a 5% increase in productivity doesn’t justify a Copilot subscription?
- recursive 1y agoThat's not a rigorously measured number. The 5% is an increase in straight-ahead code speed. I spend a small fraction of my time typing code. Smaller than I'd like. And it very well might be an economically rational subscription. For me personally, I'm subscription averse based on the overhead of remembering that I have a subscription and managing it.
- abtinf 1y agoCan you point to an example repo with enough rules and good prompts?
- mrugge 1y agoFirst thing I do is tell llm to stop writing useless docstrings and comments and instead follow clean code principles where each variable is a noun and function call a verb.
- drgiggles 1y ago[flagged]
- deleted 1y ago[deleted]
- mrbungie 1y ago> The code I generate is usually better than what I'd do by hand. I'm always baffled by this. If you can't do it that well by hand, how can you discriminate its quality so confidently? I get there is a artist/art consumer analogy to be made (i.e. you can see a piece is good without knowing how to paint), but I'm not convinced it is transferrable to code. Also, not really my experience when dealing with IaC or (complex) data related code.
- tptacek 1y agoWhat an odd question. For the exact same reason people who write prose professionally usually have someone else edit their work: because editing your own work is harder, and everybody slips up sometimes.
- dingnuts 1y agoehhhhhhh yeah but this is like hiring Reddit to do your prose editing, considering generated code is slightly worse than what you'd find on r/programming
- tptacek 1y agoYou can believe that or not believe that without changing the implication of the previous question, which was that someone who routinely slips while writing code would be incapable of determining whether the LLM got it right. Obviously not.
- mrbungie 1y agoI'm not getting this analogy. Editors can't normally discriminate if the content itself is good (after all, the writer is the SME), but rather, only perfect its form (syntax, grammar, etc). Well-written bullshit in perfect prose is still bullshit.
- halfmatthalfcat 1y agoI didn't find it odd at all and it seems more odd to liken an LLM to a human editor.
- micromacrofoot 1y agoI've been finding actual human-written bugs and correcting them with Claude, so I find the "often broken" claims a load of nonsense... I've been fixing dozens of minor bugs in our codebase that no one's been arsed to fix for years due to bigger priorities (which tbh is generating more features and tech debt). It may change in the future, but AI is without a doubt improving our codebase right now. Maybe not 10X but it can easily 2X as long as you actually understand your codebase enough to explain it in writing.
- intended 1y agoCould you share an example ? These conversations on AI code good, vs AI code bad constantly keep cropping up. I feel we need to build a cultural norm to share examples places of succeeded, and failures, so that we can get to some sort of comparison and categorization. The sharing also has to be made non-contentious, so that we get a multitude of examples. Otherwise we’d get nerd-sniped into arguing the specifics of a single case.
- r3trohack3r 1y agoI do think a lot of the discourse in this space can be summed up as: people are arguing about two non-overlapping segments of a distribution having no idea the other segment even exists; instead they just assume the other side is [hype/pessimistic].
- micahscopes 1y agoIt makes me wince a little
- zoeysmithe 1y agoWhat a scary time it is for devs. We spent all this time learning this obscure skill and now when I play with claude or even chatgpt it makes really good code. I just asked it to write me a video game and it did it. Perfect godot code. I was stunned it didn't hallucinate and when I asked for clarification on a snippet of code, it perfectly answered. I think its only a matter of time until our roles are commoditized and vibe-coding becomes the norm in most industries. Vibe coding being a dismissive term on developing a new skillset. For example we'll be doing more planning and testing and such instead of writing code. The same way, say, sysadmins just spin up k8s instead of racking servers or car mechanics read diagnosis codes from readers and, often, just replace an electric part instead of hand-tuning carbs or gapping spark plugs and such. That is to say, a level of skill is being abstracted away. I think we just have to see this, most likely, as how things will get done going forward.
- tovej 1y agoCould you at least mention what the video game was, or why it was such a good implementation? Also, what was "perfect" about the code? "Perfect" is not a word I would ever use to describe code. This reads like empty hype to me, and there's more than one claim like this in these threads, where AI magically creates an app, but any description of the app itself is always conspicuously missing.
- zoeysmithe 1y agoYes Im exaggerating and its not writing a AAA game from a prompt but I asked it to make a game like Zelda and it figured it out and walked me through all the aspects of it. That's a lot more than I expected. I'm not a games programmer, so I'm probably a lot more impresse than I should be, but I went from not knowing anything about godot to having a framework up to build a 2d rpg-esque game fairly quickly and me learning as it gave me the code. Note, I used the new chatgpt study mode, so that's may be different than just regular prompts. I fully expected just broken code and random AI musings, but instead I got a very solid implementation of a game, albeit a simple one. Or at least as simple as I asked for, I imagine I can keep building out more with its help. I also have never used godot before, and I was surprised at how well it navigated and taught me the interface as well. At least the horror stories about "all the code is broken and hallucinations" isn't really true for me and my uses so far. If LLM's will succeed anywhere it will be in the overly logical and predictable worlds of programming languages, but that's just a guess on my part, but thus far whenever I reach out for code from LLM's, its been a fairly positive experience.
- tempodox 1y agoSo you're not a developer any more but a tenant who pays rent. Speaking of which, I have a bridge to sell you…
- jf22 1y agoI'm not even sure what this is supposed to say.
- neutronicus 1y agoYeah that fucker Claude is tireless when it comes to checking return types, checking for null, etc etc
- nurettin 1y agoHere's my workflow (if I feel like using claude) Me: Here's the relevant part of the code, add this simple feature. Opus: here's the modified code blah blah bs bs Me: Will this work? Opus: There's a fundamental flaw in blah bleh bs bs here's the fix, but I only generate part of the code, go hunt for the lines to make the changes yourself. Me: did you change anything from the original logic? Opus: I added this part, do you want me to leave it as it was? Me: closes chat
- NitpickLawyer 1y agoSorry to be that guy, but you're using it wrong. The best flows right now are architect -> act -> test. First you have a session in "architect" / "plan" mode (depending on your ide/tool) where you discuss, ask questions, etc. Then, when everything is clear in "chat" mode, you ask the model to make a plan. You verify the plan, and then you tell it to start implementing it. You still get to approve tools, calls, tests, etc. You can also provide feedback on the way if you missed something (i.e. use uv instead of pip, etc). Coding in a chat interface, and expecting the same results as with dedicated tools is ... 1-1.5 years old at this point. It might work, but your results will be subpar.
- nurettin 1y agoNah it's good thanks for your input. I saw people use plan.md and todo.md and ide/commandline for this before. manus.ai demonstrates this via its chat interface as well.
- apwell23 1y ago> With enough rules and good prompting this is not true. There are atleast 10 posts on HN these days with the same discussion in circle. 1. AI sucks at code 2. you are not using my magic prompting technique
- jf22 1y agoIt's not magic. The techniques are well established and widely shared.
- apwell23 1y ago[flagged]
- swader999 1y agoYeah there's so many now it's hard to settle on one. YouTube is littered with them. Agent OS, amp.code, BMAD. I'm probably trying BMAD in earnest next ...
- jf22 1y agoEach of the "tools" does things slightly differently but the techniques to use them effectively are largely the same now (rules, planning, context management, good prompting). You know like when the loom came out there were probably quite a few models but using it was similar. Like cars are now.
- miggy 1y agoIn my experience, unit tests and logging code generated by LLMs tend to be overly verbose, miss meaningful assertions, and often produce boilerplate that looks correct but doesn’t test or log anything useful. It’s easy to get misled by the surface structure.
- lvl155 1y agoTakes on dev-focused AI is so divided right now. Appears some people just don’t understand the workflows that are effective. It actually takes a lot of work to set it up. It’s not as simple as typing in prompts.
- recursive 1y agoWould you say they're holding it wrong?
- deleted 1y ago[deleted]
- blitzar 1y agoYou are meant to hold it upside down with the screen facing away, otherwise you cant possibly expect it to work.
- exe34 1y agoIt's lik monads. You either know how to use it or you don't. Those who claim to know seem unable to explain it so that those who don't can reproduce the success.
- apwell23 1y agoits an illusion that you have discovered some golden promoting workflows. If you had then you would share it instead of being handwavy and secretive.
- komali2 1y agoDo you have an example FOSS project you can share with the requisite AI guardrail files, and example prompts?
- rohit89 1y agoYea. I feel likes its a waste of time and energy to read/argue about it. Either it works or it doesn't. Everyone already has exposure to it and opinions on it. There's no need to convince anyone. Reality will bear out the results.
- shortrounddev2 1y agoI dont use ai code generation tools, I just use claude as a search engine. It hasn't changed the output rate of my code but I believe that its improved the quality of it by exposing me to patterns and features that I otherwise may not have. I used to take a very object oriented approach to code, but when I would ask claude to look at my code and critique it, it would often lead me into more functional patterns, with result type returns and eliminating global state. Ive completely stopped using exceptions and functional programming has GREATLY increased the confidence I have in my code to the point where I write 2000 lines at a time and get a successful first test nearly every time. The credit lies with a more functional style of C++ and typescript (the languages i use for hobbies and work, respectively), but claude has sort of taken me out of the bubble I was brought up in and introduced new ideas to me. However, I've also noticed that LLM products also tend to reinforce your biases. If you dont ask it to critique you or push back, it often tells you what a great job you did and how incredible your code is. You see this with people who have gotten into a kind of psychotic feedback loop with ChatGPT and who now believe they can escape the matrix. I think LLMs are powerful, but only for a handful of use cases. I think the majority of what theyre marketed for right now is techno-solutionism and theres an impending collapse in VC funding for companies that are plugging in chatgpt APIs for everything from insurance claims to medical advice
- rob_c 1y ago> I dont use ai code generation tools Then unfortunately you're leaving yourself at a serious disadvantage. Good for you if you're able to live without a calculator, but frankly the automated tool is faster and leaves you less exhausted so you should be taking advantage of it.
- bpt3 1y agoThey are using them, just in a curated and deliberate way. I use it similar to the parent poster when I am working with an unfamiliar API, in that I will ask for simple examples of functionality that I can easily verify are correct and then build upon them quickly. Also, let me know when your calculator regularly hallucinates. I find it exhausting to have an LLM dump out a "finished" implementation and have to spend more time reviewing it than it would take to complete it myself from scratch.
- DuckOnFire 1y agoLearning to use AI well feels like a whole new job—it's not just coding anymore, it's prompting, testing, and debugging the AI too.
- whstl 1y agoHere's a thesis: Maybe LLMs make you 10x faster at using boilerplate-heavy things like Shadcn/ui or Tanstack. ...which is still only about half as fast as using a sane ecosystem. IMO this is why there's so many diverging opinions about the productivity of AI tools.
- simpaticoder 1y agoThe 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).
- fisherrjd 1y agoNumber of people that read the article before counting: 2
- bpt3 1y agoI did read it, but anyone who didn't isn't missing anything. To summarize, LLM agents are not the silver bullet those promoting them suggest they are. The headline is all that was needed.
- tptacek 1y agoThis 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".
- coltonv 1y agoHi 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 1y agoCan 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 1y agoMy 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.
- adolph 1y agoThose who are not aware of the mythical man month's silver bullet are condemned to rewrite it. The amount of product ideation, story point negotiation, bugfixing, code review, waiting for deployments, testing, and QA in that go into what was traditionally 3 months of work is now getting done in 7 work days? For that to happen each and every one of these bottlenecks has to also seen have 10x productivity gains.
- kirykl 1y agoAI never says it doesn’t know. It’ll always have an answer even if it’s wrong or misleading
- andix 1y agoThis really depends on the prompting. I experienced it multiple times that claude code couldn't figure out how to fix a bug and just gave up. Instead of getting stuck in an infinite loop.
- deleted 1y ago[deleted]
- meroes 1y agoUsually I get the loop, which runs me out of compute, and have to wait till tmrw. Glad there is actually a way to stop it from confidently “fixing” the same issue.
- lightweb 1y agoLLMs still leave something to be desired for DevOps related work; infrastructure code. There is still not really enough context available when crossing the division between the hardware, OS, and software. For Terraform, specifically, Claude 4 can get thrown into infinite recursive loops trying to solve certain issues within the bounds of the language. Claude still tries to add completely invalid procedures into things like templates. It does seem to work a bit better for standard application programming tasks.
- bpt3 1y agoIt's not surprising to me that it struggles with a language where there aren't billions of lines of code available to use as training data. I wonder if that's all it is, or if the lack of context you mention is a more fundamental issue.
- bgwalter 1y agoThe worst of all is that we used to spend time thinking about real issues. Now 70% of thinking, blogging, and (in some companies) programming time is spent on how to make inferior and nondeterministic tools accomplish something. It's like discussing in a gaming guild how to reach the next level. It isn't real.
- 2d8a875f-39a2-4 1y agoOnly vibe-coding influencers were ever talking about 10x multipliers. Internally we expected 15%-25%. A big-3 consultancy told senior leadership "35%-50%" (and then tried to upsell an AI Adoption project). And indeed we are seeing 15%-35% depending on which part of the org you look and how you measure the gains.
- octo888 1y agoWhat are you measuring?
- efields 1y agoIn 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 1y agoIf 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.
- 9337throwaway 1y ago[dead]
- g3f32r 1y agoI 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.
- generalizations 1y agoIn 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.
- Scarblac 1y agoBut 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.
- lopatin 1y agoNot typing the trivial parts is pretty great though
- skydhash 1y agoI 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.
- deleted 1y ago[deleted]
- AstroBen 1y agoBeing 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 1y agoUntil it spends 10 minutes fucking up the trivial part and then youre 10 minutes down and you still have to do it yourself.
- siva7 1y agoI beg to differ. My diffs are 10x bigger than before though i don't have any more time to review them.
- doc_manhat_3 1y agoThis is the point that the author is making. 10x bigger diff is probably not leading to 10x productivity. [In fact you can sometimes find that 10x bigger diff leads to decreased productivity down the line...]
- ramon156 1y agoSorry if this is grumpy, but I'm tired of seeing so many blogposts making the same conclusion from the dev's side LLMs make writing code quick, that's it. There's nothing more to this. LLMs aren't solutioning nor are they smart. If you know what you want to build, you can build quick. Not good, quick. That said, if managers don't care about code quality (because customer's don't care either) then who am I to judge them. I don't care. I'm on the edge of just blacklisting the word AI from my feed.
- jongjong 1y agoAI is making 10x developers 10x more productive and is making 0.1x devs 0.1x times as productive. When I use Claude Code on my personal projects, it's like it can read my mind. As if my project is coding itself. It's very succinct and consistent. I just write my prompt and then I'm just tapping the enter key; yes, yes, yes, yes. I also used Claude Code on someone else's code and it was not the same experience. It kept trying to implement dirty hacks to fix stuff but couldn't get very far with that approach. I had to keep reminding it "Please address the root cause" or "No hacks" or "Please take a step back and think harder about this problem." There was a lot of back-and-forth where I had to ask it to undo stuff and I had to step in and manually make certain changes. I think part of the issue is that LLMs are better at adding complexity than at removing it. When I was working on the bad codebase, the times I had to manually intervene, the solution usually involved deleting some code or CSS. Sometimes the solution was really simple and just a matter of deleting a couple of lines of CSS but it couldn't figure it out no matter how I wrote the prompt or even if I hinted at the solution; it kept trying to solve problems by adding more code on top.
- bpt3 1y agoIt's making everyone faster. That means that good developers are more productive, and bad developers create more work for everyone else at an very rapid pace.
- simonw 1y agoI 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.
- hyperbovine 1y agoThis 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/ https://www.simplethread.com/the-10x-programmer-myth/).
- Izkata 1y ago10x 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-software-developers-and-teams-the-origin-of-10x/ https://www.construx.com/blog/productivity-variations-among-...
- mgfist 1y agoAll 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.
- Jcampuzano2 1y agoThe only people who get 10x productivity are people who are either: - solo projects - startups with few engineers doing very little intense code review if any at all - people who don't know how to code themselves. Nobody else is realistically able to get 10x multipliers. But that doesn't mean you can't get a 1.5-2x multiplier. I'd say even myself at a large company that moves slow have been able to realize this type of multiplier on my work using cursor/claude code. But as mentioned in the article the real bottleneck becomes processes and reviews. These have not gotten any faster - so in real terms time to ship/deliver isn't much different than before. The only attempt that we should make at minimizing review times is by making them higher priority than development itself. Technically this should already be the case but in my experience almost no engineer outside of really disciplined companies and not in FAANG actually makes reviews a high priority, because unfortunately code reviews are not usually part of someones performance review and slows down your own projects. And usually your project manager couldn't give two shits about someone elses work being slow. Processes are where we can make the biggest dent. Most companies as they get large have processes that get in the way of forward velocity. AI first companies will minimize anything that slows time to ship. Companies simply utilizing AI and expecting 10x engineers without actually putting in the work to rally around AI as a first class citizen will fall behind.
- TacticalCoder 1y ago[dead]
- evantbyrne 1y ago10x has always been an exaggeration, but I know from repeated experience it is possible to complete projects far quicker than 2x the speed of the typical team on a modern web stack. The way you do it is by writing less code. Typically this is done by using mature software as a starting point, rather than screwing around with the hot new thing. Seems fairly obvious when stated plainly, and yet so many teams make the same mistake of building from near scratch. Even worse, what teams come up with is usually slower to iterate with than existing software, because they approach it from the perspective of building a singular app rather than designing something to build generalized solutions upon.
- izzydata 1y agoI don't believe that literal typing of code is the limiting factor in development work. There is the research and planning and figuring out what it is even you need to develop in the first place. By the time you know what questions to even ask an LLM you are not saving much time in my opinion. On top of that you introduce the risk of LLM hallucination when you could have looked it up from a normal web search yourself in slightly more time. Overall it feels negligible too me in its current state.
- danenania 1y agoI think it depends a lot on the task. While you’re right that just typing is rarely a bottleneck, I would say that derivative implementations often are. Things like: build a settings system with org, user, and project level settings, and the UI to edit them. A task like that doesn’t require a lot of thinking and planning, and is well within most developers’ abilities, but it can still take significant time. Maybe you need to create like 10 new files across backend and frontend, choose a couple libraries to help with different aspects, style components for the UI and spend some time getting the UX smooth, make some changes to the webpack config, and so on. None of it is difficult, per se, but it all takes time, and you can run into little problems along the way. A task like that is like 10-20% planning, and 80-90% going through the motions to implement a lot of unoriginal functionality. In my experience, these kinds of tasks are very common, and the speedup LLMs can bring to them, when prompted well, is pretty dramatic.
- 9rx 1y ago> There is the research and planning and figuring out what it is even you need to develop in the first place. This is where I have found LLMs to be most useful. I have never been able to figure out how to get it to write code that isn't a complete unusable disaster zone. But if you throw your problem at it, it can offer great direction in plain English. I have decades of research, planning, and figuring things out under my belt, though. That may give me an advantage in guiding it just the right way, whereas the junior might not be able to get anything practical from it, and thus that might explain their focus on code generation instead?
- megaloblasto 1y agoI 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.
- mentos 1y agoYep same experience here saved me an infinite amount of time so to me that puts me somewhere between 10x and infinity ha
- wwweston 1y agoThis 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 1y agoIndeed. As a (mostly) hobbyist programmer LLMs have been a godsend for those late night coding sessions when the brain fog is thick.
- rel2thr 1y agoIt’s true the traditional software development team structure won’t scale 10x You have to change the organization. - no peer code review, u review the AI output and that’s enough - devs need authority to change code anywhere in the company. No more team A owns service A and team B owns service B - every dev and ops person needs to be colocated, no more waiting for timezones - PMs and engineers are the same role now Will it work for every company? No , if you are building a pacemaker , don’t use AI . Will things break? Yes sometimes but you can roll back. Will things be somewhat chaotic? Yes somewhat but what did you think going 10x would feel like?
- jonathanstrange 1y agoI've tested Gemini Pro 2.5 yesterday with a function I had troubles with. It wasn't something I can't do, just one of those things easy to get wrong that I postponed because I lacked focus that day due to a heat wave. The AI spit out a perfect function with working tests after the first prompt. Now I don't want to sound like a doomsayer but it appears to me that application programming and corresponding software companies are likely to disappear within the next 10 years or so. We're now in a transitional phase were companies who can afford enough AI compute time have an advantage. However, this phase won't last long. Unless there is a principal block to further enhance AI programming, not just simple functions but whole apps can be created with a prompt. However, this is not where it is going to stop. Soon, there will be no need for apps in the traditional sense. End users will use AI to manipulate and visualize data and operating systems will integrate the AI services needed for this. "Apps" can be created on the fly and are constantly adjusted to the users' needs. Creating apps will not remain a profitable business. If there is an app X someone likes, they can prompt their AI to create an app with the same features, but perhaps with these or those small changes, and the AI will create it for them, including thorough tests and quality assurance. Right now, in the transitional phase, senior engineers might feel they are safe because someone has to monitor and check the AI output. But there is no reason why humans would be needed for that step in the long run. It's cheaper to have 3 AIs quality test and improve the outputs of one generating AI. I'm sure many companies are already experimenting with this, and at some point the output of such iterative design procedures will have far less bugs than any code produced by humans. Only safety critical essential features such as operating systems and banking will continue to be supervised by humans, though perhaps mostly for legal reasons. Although I hope it's not but to me the end of software development seems a logical long-term consequence of current AI development. Perhaps I've missed something, I'd be interested in hearing from people who disagree. It's ironic because in my great wisdom I chose to quit my day job in academia recently to fulfill my lifelong dream of bootstrapping a software company. I'll see if I can find a niche, maybe some people appreciate hand-crafted software in the future for its quirks and originality...
- jdefr89 1y agoYou just said we won’t need to develop apps anymore.. What will be the AI need to create at all?
- nimos 1y agoClaude max is $200 a month. Consider a fully loaded cost of 200k for an engineer or $16,666 per month. They only have to be >1.012x engineer for the "AI" to be worth it. Of course that $200 dollars per month is probably VC subsidized right now but there is lots of money on the table for <2x improvement.
- alistairSH 1y agoIs anybody (who has the data) actually claiming use of AI will make a single average engineering 10x faster/better? Or the data showing something else... possibly, a company starts telling engineers to use AI, then RIFs a huge portion, and expects the remaining engineers to pick up the slack. They now claim "we're more efficient!" when they've just asked their employees to work more weekends.
- swader999 1y agoI have points completed over a six week period at 4.3 per day average, similar architecture was 3.5 points per week before Claude code. The average with Claude is slowing though, need a couple of more months to make any conclusions but management won't let me stop now lol.
- komali2 1y agoWhen my friend bullied me into using Cursor and I got that vscode fork set up with good enough vim bindings to not make me rip my hair out, it was like a first hit of a good drug. I couldn't believe how productive I was, and how much brain power I was saving by chilling at my desk and watching youtube while Cursor agented its way through some code that I would occasionally check in on and tweak. I got new modals done, new scientific charts (that I'd been terrified to implement since it was my job to engineer them, though they were chemistry charts so I didn't really understand them all that well), a full design rewrite, new components, oh man it felt great. Then it came time to make a change to one of the charts. Team members were asking me questions about it. "How can we make this axis display only for existing data rather than range?" I'm scrolling through code in a screenshare that I absolutely reviewed, I remember doing it, I remember clicking the green arrow in Cursor, but I'm panicking because this doesn't look like code I've ever seen, and I'm seeing gaping mistakes and stupid patterns and a ton of duplicated code. Yeah I reviewed it, but bit by bit, never really all at once. I'd never grocked the entire file. They're asking me questions to which I don't have answers, for code "I'd just written." Man it was embarrassing! And then to make the change, the AI completely failed at it. Plotly.js's type definitions are super out of date and the Python library is more fleshed out, so the AI started hallucinating things that exist on Python and not in JS - so now I gotta head to the docs anyway. I had to get much more manual, and the autocomplete of cursor was nice while doing so, but sometimes I'd spend more time tab/backspacing after realizing the thing it recommended was actually wrong, than I'd have spent just quickly typing the entire whatever thing. And just like a hit, now I'm chasing the dragon. I'd love to get that feeling back of entering a new era of programming, where I'm hugely augmented. I'm trying out all the different AI tools, and desperately wishing there was an autocomplete as fast and multi-line and as good as jumping around as Cursor, available in nvim. But they all let me down. Now that I'm paying more attention, I'm realizing the code really isn't good at all. I think it's still very useful to have Claude generate a lot of boilerplate, or come in and make some tedious changes for me, or just write all my tests, but beyond that, I don't know. I think it's improved my productivity maybe 20%, all things considered. Still amazing! I just wish it was good as I thought it was when I first tried it.
- andrewmutz 1y agoThe part I agree about: Software engineering is about more than writing code, so accelerating coding by 10X doesn't accelerate a software engineer by 10X. The part I disagree about: I've never worked at a company that has a 3 month cycle from code-written to code-review-complete. That sounds insane and dysfunctional. AI won't fix an organization like that
- coltonv 1y agoPerhaps I was not clear here. My point isn't to say that one PR gets merged in 3 months. My point is to say that, lets say, 15 PRs from one dev get merged per quarter in the old days, for a 10x productivity boost that means that roughly 15 PRs get merged per 7 business days now. My point is simply that the amount of time that goes into the basic lag cycle involved in code review can't be compressed to 7 days.
- andrewmutz 1y agoI think focusing on end-to-end time confuses things more than it helps. A system can have 10X throughput with the latency being unchanged. You don't need to reduce latency or cycle time to have a 10X increase in throughput. The better argument is that Software Engineers spend a lot of time doing things that aren't writing code and arent being accelerated by any AI code assistant
- paulsutter 1y agoIf you’re writing the same code you would have written 2 years ago, you won’t see much speedup But if your system records internal state in english and generates code while handling requests, complex systems can become much simpler. You can build things that were impossible before
- swader999 1y agoI measured quite carefully on a greenfield project and saw 4.3x for the first three weeks which was incredible. Now it's about 2x, I really need to improve my context wrangling.
- bicx 1y agoI've been using Claude Code professionally for the past 2 months, with limited agent use prior to that (via Windsurf). I would say I've seen a 30% boost in productivity overall, with significant spikes in particular types of work. Where CC has excelled: - New well-defined feature built upon existing conventions (10x+ boost) - Performing similar mid-level changes across multiple files (10x+ boost) - Quickly performing large refactors or architecture changes (10x+ boost) - Performing analysis of existing codebases to help build my personal understanding (x10+ boost) - Correctly configuring UI layouts (makes sense: this is still pattern-matching, but the required patterns can get more complex than a lot of humans can quickly intuit) Where CC has floundered or wasted time: - Anything involving temporal glitches in UI or logic. The feedback loop just can't be accomplished yet with normal tooling. - Fixing state issues in general. Again, the feedback loop is too immature for CC to even understand what to fix unless your tooling or descriptive ability is stellar. - Solving classes of smallish problems that require a lot of trial-and-error, aren't covered by automated tests, or require a steady flow of subjective feedback. Sometimes it's just not worth setting up the context for CC to succeed. - Adhering to unusual or poorly-documented coding/architecture conventions. It's going to fight you the whole way, because it's been trained on conventional approaches. Productivity hacks: - These agents are automated, meaning you can literally have work being performed in parallel. Actual multitasking. This is actually more mentally exhausting, but I've seen my perceived productivity gains increase due to having 2+ projects going at once. CC may not beat a single engineer for many tasks, but it can literally do multiple things at once. I think this is where the real potential comes into play. Monitoring multiple projects and maintaining your own human mental context for each? That's a real challenge. - Invest in good context documents as early as possible, and don't hesitate to ask CC to insert new info and insights in its documents as you go. This is how you can help CC "learn" from its mistakes: document the right way and the wrong way when a mistake occurs. Background: I'm a 16yoe senior fullstack engineer at a startup, working with React/Remix, native iOS (UIKit), native Android (Jetpack Compose), backends in TypeScript/Node, and lots of GraphQL and Postgres. I've also had success using Claude Code to generate Elixir code for my personal projects.
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- neom 1y agoThe last month on our team: https://s.h4x.club/RBuDv0jd https://s.h4x.club/RBuDv0jd The full year is just the more of the above.
- Bukhmanizer 1y agoI’ve mentioned this elsewhere, but I think a better question is: do you find AI makes your coworkers N times better. It makes everyone “produce more code” but your worst dev producing 10X the code is not 10X more productive. There’s also a bit of a dunning Kruger effect where the most careless people are the most likely to YOLO thousands of lines of vibecode into prod. While a more meticulous engineer might take a lot more time to read the changes, figure out where the AI is wrong, and remove unnecessary code. But the second engineer would be seen as much much less productive than the first in this case
- sergiotapia 1y agoProduct guys with no technical experience are getting one-shotted by VC dollars making them think they can create projects themselves. It's an admirable goal but will never happen. Now for senior developers, AI has been tremendous. Example: I'm building a project where I hit the backend in liveview, and internally I have to make N requests to different APIs in parallel and present the results back. My initial version to test the idea had no loading state, waiting for all requests to finish before sending back. I knew that I could use Phoenix Channels, and Elixir Tasks, and websockets to push the results as they came in. But I didn't want to write all that code. I could already taste it and explain it. Why couldn't I just snap my fingers? Well AI did just that. I wrote what I wanted in depth, and bada bing, the solution I would have written is there. Vibe coders are not gonna make it. Engineers are having the time of their lives. It's freeing!
- swader999 1y agoThis was the best insight in the article: Do 10x engineers actually exist? "This debate isn't something I want to weigh in on but I might have to. My answer is sometimes, kinda. When I have had engineers who were 10x as valuable as others it was primarily due to their ability to prevent unnecessary work. Talking a PM down from a task that was never feasible. Getting another engineer to not build that unnecessary microservice. Making developer experience investments that save everyone just a bit of time on every task. Documenting your work so that every future engineer can jump in faster. These things can add up over time to one engineer saving 10x the time company wide than what they took to build it." So true, a lot of value and gains are had when tech leads can effectively negotiate and creatively offer less costly solutions to all aspects of a feature.
- fnands 1y ago> Talking a PM down from a task that was never feasible One of our EMs did this this week. He did a lot of homework: spoke to quite a few experts and pretty soon realised this task was too hard for his team to ever accomplish, if it was even possible. Lobbied the PM and, a VP and a C-level, but managed to stop a lot of wasted work from being done. Sometimes the most important language to know as a dev is English* s/English/YourLanguageOfChoice/g
- necovek 1y agoAn aside, but I am curious: as an old hat today, I now find that using the Perl RE (though some of it lives on through sed) syntax as "we used to do back in the day" in regular communications confuses most people. People are usually unfamiliar with it, so I am slowly phasing it out. What's your experience? And what do the "kids" use these days to indicate alternative options (as above — though for that, I use bash {} syntax too) or to signal "I changed my mind" or "let me fix that for you"?
- newswasboring 1y ago/s/ is kind of a skeuomorph for me. I have never used sed but I understand this syntax.
- Glyptodon 1y agoI 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.
- AdrianB1 1y agoI 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.
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- necovek 1y agoThe 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 :)
- bongodongobob 1y agoNo one said anything about lines of code. I would assume output here means features completed, tickets knocked out, tasks completed etc.
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- jsnell 1y ago> 10x productivity means ten times the outcomes, not ten times the lines of code. This means what you used to ship in a quarter you now ship in a week and a half. Not really? That's defining productivity as latency, but it's at least as valid to define productivity as throughput. And then all the examples that are just about time spent waiting become irrelevant. When blocked waiting on something external, you just work on other things.
- coltonv 1y agoI mean throughput, not latency. As in if you ship 10 meaningful changes in a month before you now ship 100. My point around waiting for things like code review is that it creates a natural time floor, the context switching takes time and slows down other work. If you have 10x as much stuff to get reviewed, all the time loss to context switching is multiplied by 10x.
- dowager_dan99 1y agoI am a dinosaur but still feel strongly enough to post this PSA: please go back and read "No Silver Bullet" (and his follow up) again. You should probably schedule a re-read every 2-5 years, just to keep your sanity in these crazy, exhausting times. I believe his original thesis remains true: "There is no single development, in either technology or management technique, which by itself promises even one order-of-magnitude improvement within a decade in productivity, in reliability, in simplicity." Over the years this has been misrepresented or misinterpreted to suggest it's false but it sure feels like "Agentic Coding" is a single development promising a massive multiplier in improvement that once again is, another accidental tool that can be helpful but is definitely not a silver bullet.
- alliancedamages 1y agoAgreed. Here's one of many HN posts on "No Silver Bullet": https://news.ycombinator.com/item?id=32423356 https://news.ycombinator.com/item?id=32423356
- swader999 1y agoI used to agree with this except for one exception, sitting and working right beside your end user(s). If you can colocate with them it is a silver bullet. I'm not sure about agentic coding. Need another month at it.
- BeetleB 1y agoIsn't this a strawman? I follow the AI coding space, and I've never found anyone claiming it made them 10x as productive.
- jorblumesea 1y agoThis is not a surprising conclusion for anyone that works in the field and uses the current gen of LLMs. The problem is... 1. there is an enormous investment in $$$ produces a too big to fail scenario where extravagant claims will be made regardless 2. leadership has made big promises around productivity and velocity for eng the end result of this is going to be a lot of squinting at the problem, ignoring reality and declaring victory. These AI tools are very useful in automating chore and grunt tasks.
- pgt 1y agoI could not have built EACL[^1] without AI. We use EACL at work at now, so arguably AI has made me 10x more productive, but only because I know how to write specs for AI to get what I want. Context: EACL is an embedded ReBAC authorization library based on SpiceDB-compatible*, built in Clojure and backed by Datomic. [^1]: https://github.com/theronic/eacl https://github.com/theronic/eacl
- dclowd9901 1y ago> Thus, AI's best use case for me remains writing one-off scripts. Especially when I have no interest in learning deeper fundamentals for a single script, like when writing a custom ESLint rule. Perfectly put. I've been using a lot of AI for shell scripting. Granted I should probably have better knowledge of shell but frankly I think it's a terrible language and only use it because it enjoys wide system support and is important for pipelining. I prefer TS (and will try to write scripts and such in it if I can) and for that I don't use AI almost at all.
- msgodel 1y agoIf you wanted to make me 10x as productive in the past the best thing you could have done is quit forcing use of shared services and infrastructure I don't have control over. Any codebase that's difficult for me to read would be way too large to use an LLM on.
- OutputRiff 1y agoI find myself largely agreeing with this post. In some cases, LLMs can be a real speed boost. Most of the time, that has to do with writing boilerplate and prototyping a new "thing" I want to try out. Inevitably, if I like the prototype, I end up re-writing large swaths of it to make it even half way productizable. Fundamentally, LLMs are bad at keeping an end goal in mind while working on a specific feature and it's terrible at holding enough context to avoid code duplication and spaghetti. I'd like to see them get better and better, but they really are limited to whatever code they can ingest on the internet. A LOT of important code is just not open for consumption in sufficient quantities for it to learn. For this reason, I suspect LLMs will really never be all that good for non-web based engineering. Wheres all the training data gonna come from?
- kasey_junk 1y agoI largely agree with the gist of this article but its calculation about productivity is very flawed as it doesn’t account for the time things sat on the backlog, or the things that wouldn’t have been done at all. Where I see major productivity gains are on small, tech debt like tasks, that I could not justify before. Things that I can start with an async agent, let sit until I’ve got some downtime on my main tasks (the ones that involve all that coordination). Then I can take the time to clean them up and shepherd them through. The very best case of these are things where I can move a class of problem from manually verified to automatically verified as that kick starts a virtuous cycle that makes the ai system more productive. But many of them are boring refactors that are just beyond what a traditional refactoring tool can do.
- rapind 1y agoIt's allowed me to be even more of a perfectionist. I'm quite enjoying it, and I review and revise everything that's produced line by line. I doubt that's the commonly desired outcome, but it is what I want! If AI gets too expensive overnight (say 100x), then I'll be able to keep chugging along. I would miss it (claude-code), but I'm betting that by then a second tier AI would fit my process nearly as well. I think the same class of programmers that yak shave about their editor, will also yak shave about their AI. For me, it's just augmenting how I like to work, which is probably different than most other people like to work. IMO just make it fit your personal work style... although I guess that's problematic for a large team... look, even more reasons not to have a large team!
- lifesucks1 1y agoCouldn't agree more. So fed up of these stupid marketers talking about how they built a SAAS solution in days, solved all their technical problems. AI puts everything magically together. All you need is rules files. I cant even build a consistent backend I am ok with any technology it likes. Once you hit a bug it struggles to fix it without breaking 10 other things. I hope some technique comes in that can help you manage what it outputs. In its current state I would be very careful with what is being pushed to production.
- rufius 1y agoI don’t think AI makes me 10x more productive. It does make me close to 10x less bored though. Much of production software engineering is writing boiler plate, building out test matrices and harnesses, scaffolding structure. And often, it’s for very similarly shaped problems at their core regardless of the company, organization, or product. AI lets me get a lot of that out of the way and focus on more interesting work. One might argue that’s a failure of tools or even my own technique. That might be true, but it doesn’t change the fact that I’m less bored than I used to be.
- coltonv 1y agoI'm happy to hear that! I hope you felt seen by this line from the article: > Oh, and this exact argument works in reverse. If you feel good doing AI coding, just do it. If you feel so excited that you code more than ever before, that's awesome. I want everyone to feel that way, regardless of how they get there.
- te_chris 1y ago100%. It's made me like dev again because my head can be used for things other than remembering arcania - this may be a curse of using languages like Ruby and Elixir which mostly don't have great tooling. I enjoyed the article, fwiw. Twitter was insufferable before Elon bought it, but the AI bro scene is just...wow. An entire scene who only communicate in histrionics.
- epolanski 1y ago> It's not good at keeping up with the standards and utilities of your codebase. Not my experience. You can instruct Claude Code to respect standards and practices of your codebase. In fact I noticed that Claude Code has forced me to make few genuinely important things like documenting more, writing more E2E tests and tracking architectural and style changes. Not only I am forcing myself to a consistent (and well thought styling), but I also need it later to feed it to the AI itself. Seriously, I don't want to offend no one, but if you believe that AI doesn't make you more productive you've got skill issues in adopting and using new tools at what they are good at.
- swader999 1y agoYes, I'm forced to be a real senior dev now, rigid specs, documentation, enforced coverage. Was easier before just hiring really smart people that didn't have to have everything spelled out for them.
- j45 1y agoAI is making experineced developers with architecture experience quite a bit faster. Ingesting legacy code, understanding it, looking at potential ways to rework it, and then putting in place the axioms to first work with it yourself, and then for others to join in has been able to get down from months to weeks and days. Developing green field from scratch, statically typed languages seem to work a bit better than not. Putting enough information around the requirements and how to structure undertake them is critical or it can turn into cowboy coding pretty easily, or default AI is leaning towards the average of it's corpus, not the best. That's where the developer comes in.
- paulhodge 1y agoI've had days where it really does feel like 5x or 10x... Here's what the 5x to 10x flow looks like: 1. Plan out the tasks (maybe with the help of AI) 2. Open a Git worktree, launch Claude Code in the worktree, give it the task, let it work. It gets instructions to push to a Github pull request when it's done. Claude gets to work. It has access to a whole bunch of local tools, test suites, and lots of documentation. 3. While that terminal is running, I go start more tasks. Ideally there are 3 to 5 tasks running at a time. 4. Periodically check on the tabs to make sure they're not stuck or lost their minds. 5. Finally, review the finished pull requests and merge them when they are ready. If they have issues then go back to the related chat and tell it to work on it some more. With that flow it's reasonable to merge 10 to 20 pull requests every day. I'm sure someone will respond "oh just because there are a lot of pull requests, doesn't mean you are productive!" I don't know how to prove to you that the PRs are productive other than just say that they are each basically equivalent to what one human does in one small PR. A few notes about the flow: - For the AI to work independently, it really needs tasks that are easy to medium difficulty. There are definitely 'hard' tasks that need a lot of human attention in order to get done successfully. - This does take a lot of initial investment in tooling and documentation. Basically every "best practice" or code pattern that you want to use use in the project must be written down. And the tests must be as extensive as possible. Anyway the linked article talks about the time it takes to review pull requests. I don't think it needs to take that long, because you can automate a lot.. - Code style issues are fully automated by the linter. - Other checks like unit test coverage can be checked in the PR as well. - When you have a ton of automated tests that are checked in the PR, that also reduces how much you need to worry about as a code reviewer. With all those checks in place, I think it can pretty fast to review a PR. As the human you just need to scan for really bad code patterns, and maybe zoom in on highly critical areas, but most of the code can be eyeballed pretty quickly.
- samtp 1y agoWhat type of software are you building with this workflow? Does it handle PII, need data to be exact, or have any security implications? Because I might just not have a great imagination, but it's very hard for me to see how you basically automate the review process on anything that is business critical or has legal risks.
- zoechi 1y agoFor me the main benefit is that AI chat provides at least 10x better results than Google ~search~ Ad results
- recursive 1y agoAd-free LLM output won't last. Or at least you'll pay a premium for it. Personally, I do pay for a search engine subscription (Kagi) in an attempt to align my interests with my search results.
- yodon 1y agoAnytime you start talking about massive speedups, it's important to go re-read Amdahl's law. The key isn't how much you can speed up the scalable/parallelizable portions, it's how limited you are by the non-scalable/parallelizable aspects.
- lordnacho 1y agoI'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.
- hnroo99 1y agoSide-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?)
- gspencley 1y ago> 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.
- neals 1y agoGoogle became Expert exchange become stackoverflow became Google again became Chatgpt became Google again. Every time, so much more faster to get your boilerplate.
- stillpointlab 1y agoI think this is a strawman argument that is conflating uses for AI. I posted a video not long ago where Andrew Ng makes the claim to the AI Startup school that in testing they are seeing ~10x improvement for greenfield prototypes and 30%-50% improvement in existing production code bases. So two groups are talking past one another. Someone has a completely new idea, starts with nothing and vibe codes a barely working MVP. They claim they were able to go from 0 to MVP ~10x faster than if they had written the code themselves. Then some seasoned programmer hears that claim, scoffs and takes the agent into a legacy code base. They run `/init` and make 0 changes to the auto-generated CLAUDE.md. They add no additional context files or rules about the project. They ask completely unstructured questions and prompt the first thing that comes into their minds. After 1 or 2 days of getting terrible results they don't change their usage or try to find a better way, they instead write a long blog post claiming AI hype is unfounded. What they ignore is that even the maximalists are stating: 30%-50% improvement on legacy code bases. And that is if you use the tool well. This author gets terrible results and then says: "Dark warnings that if I didn't start using AI now I'd be hopelessly behind proved unfounded. Using AI to code is not hard to learn." How sure is the author that they actually learned to use it? "A competent engineer will figure this stuff out in less than a week of moderate AI usage." One of the most interesting things about learning are those things that are easy to learn and hard to master. You can teach a child chess, it is easy to learn but it is hard to master.
- adocomplete 1y agoAI is making me 100x productive in some tasks, and 2-4x in others, and 0x in some. Knowing which tasks AI is great at and delegating is like 95% of the battle.
- greatwhitenorth 1y agoYou're doing it wrong. AI is making me 2375600x productive in all tasks.
- willismichael 1y agoIncluding writing comments on HN.
- zahlman 1y ago>and 0x in some As in, it's now completely preventing you from doing things you could have before?
- adocomplete 1y agoNot preventing, but def wasting time trying to do something that I think it's able to do, but isn't.
- zaking17 1y agoThe other day I asked chatgpt (o3) to help me compare a bunch of task orchestration systems and arrange them according to some variables I care about (popularity, feature richness, durability, whether can be self-hosted, etc.). I ended up using https://www.inngest.com/ https://www.inngest.com/ -- which was new to me -- and that single tool sped up my particular task by at least 10x for the week. That was a one-off project, so it won't generalize in a clean way, but I keep finding individual cases where the particular strengths of LLMs can save me a whole bunch of time. (another example: creating and evaluating responses to technical interview questions). I don't expect that these are easy to quantify, but they are significant. This is not to disagree with the OP, but to point out that, even for engineers, the speedups might not appear where you expect. [EDIT I see like 4 other comments making the same point :)]
- jedberg 1y agoHi there, I was wondering if you'd be willing to share that ChatGPT chat with me (or everyone). I'm the CEO of a competing product (DBOS) and I'm just curious what your question and responses were that let you elsewhere. Thanks!
- d4rkn0d3z 1y agoThe central theme is very hard to disgree with; claims of productivity increase being self-reported are oftem misleading. The way forward is math and meaningful measurement, this bears repeating. I find that getting from zero to 80-90% functionality on just about anything software these days is exceedingly easy. So, I wonder if AI just rides that wave. Software development is maturing now such that making software with or without AI feels 10-100x faster. I suspect it is partially due to the profound leap that has been made with collaborative tools, compilers, languages, and open source methodology, etc..
- bananapub 1y agohopefully we all remember Amdahl's law and reflect on how much time a software engineer actually spends on the "typing code" part of the job of "delivering software that solves some business need".
- dawnerd 1y agoI’ve noticed it slowing our developers down and causing some brain rot when they have to work unassisted. It’s frustrating and sad to see.
- plandis 1y agoThis mostly mimics my own experience. I’ve mostly gotten value out of handing off planned/scoped coding tasks to LLMs. It’s faster to have the LLM generate code and the quality is usually fine if the task is properly scoped. Actually writing software was only like 15-20% of my time though so the efficiency wins from having an LLM write the code is somewhat limited. It’s still another tool that makes me more productive but I’ve not figured out a way to really multiplicatively increase my productivity.
- thephyber 1y agoThe important things to remember about these claims/articles is that LLMs are useful for a wide variety of tasks. An engineer doesn’t only code, but also has to learn, search, gather / define requirements, write tests, troubleshoot, read/review other people's code, deal with project management tools, document (both for developers and for customers). Also, one underestimated aspect is that LLMs don’t get writer’s block or get tired (so long as you can pay to keep the tokens flowing). Also, one of the more useful benefits of coding with LLMs is that you are explicitly defining the requirements/specs in English before coding. This effectively means LLM-first code is likely written via Behavior Driven Development, so it is easier to review, troubleshoot, upgrade. This leads to lower total cost of ownership compared to code which is just cowboyed/YOLOed into existence.
- ajuc 1y agoIt's optimizing the part that is easy at the cost of the part that is hard.
- zahlman 1y ago> When I have had engineers who were 10x as valuable as others it was primarily due to their ability to prevent unnecessary work. Talking a PM down from a task that was never feasible. Getting another engineer to not build that unnecessary microservice. Making developer experience investments that save everyone just a bit of time on every task. Documenting your work so that every future engineer can jump in faster. These things can add up over time to one engineer saving 10x the time company wide than what they took to build it. What about just noticing that coworkers are repeatedly doing something that could easily be automated?
- hirvi74 1y ago> When I have had engineers who were 10x as valuable as others it was primarily due to their ability to prevent unnecessary work. Interesting observation. I am inclined to agree with this myself. I'm more of a 10^0 kind of developer though.
- nashashmi 1y agoI was making a VB script for excel for merging individual workbooks to single workbook . Normally I would design the script myself. But this time I used copilot to do it. It would take me 30-min to 1 hour normally. But with copilot it took 15 minutes and a lot less brain cells. And less skill. It is not making us 10x productive. It is making it 10x easier.
- Anon84 1y agoWords of wisdom: There is no secret herbal medicine that prevents all disease sitting out in the open if you just follow the right Facebook groups. There is no AI coding revolution available if you just start vibing. You are not missing anything. Trust yourself. You are enough. Oh, and don't scroll LinkedIn. Or Twitter. Ever.
- jdefr89 1y agoOr shit you know what… Hacker News for that matter…
- HarHarVeryFunny 1y agoThere was a recent study concluding that AI made experienced developers 20% SLOWER to complete tasks rather than any faster ! https://arxiv.org/abs/2507.09089 https://arxiv.org/abs/2507.09089 Obviously it depends on what you are using the AI to do, and how good a job you do of creating/providing all the context to give it the best chance of being successful in what you are asking. Maybe a bit like someone using a leaf blower to blow a couple of leaves back and forth across the driveway for 30 sec rather than just bending down to pick them up.... It seems people find LLMs interesting, and want to report success in using them, so they'll spend a ton of time trying over and over to tweak the context and fix up what the AI generated, then report how great it was, even though it'd have been quicker to do it themselves. I think agentic AI may also lead to this illusion of, or reported, AI productivity ... you task an agent to do something and it goes off and 30 min later creates what you could have done in 20 min while you are chilling and talking to your workmates about how amazing this new AI is ...
- varispeed 1y agoDepending how they used them. You can say similar thing about having junior developers in the team that you have to delegate tasks to. It takes time to explain to them what needs to be done, nudge into right solution, check etc. But maybe another thing is not considered - while things may take longer, they ease cognitive load. If you have to write a lot of boilerplate or you have a task to do, but there are too many ways to do it, you can ask AI to play it out for you. What benefit I can see the most is that I no longer use Google and things like Stack Overflow, but actual books and LLMs instead.
- HarHarVeryFunny 1y agoI don't think the junior developer comparison holds up too well ... 1) The junior developer is able to learn from experience and feedback, and has a whole brain to use for this purpose. You may have to provide multiple pointers, and it may take them a while to settle into the team and get productive, but sooner or later they will get it, and at least provide a workable solution if not what you may have come up with yourself (how much that matters depends on how wisely you've delegated tasks to them). The LLM can't learn from one day to the next - it's groundhog day every day, and if you have to give up with the LLM after 20 attempts it'd be the exact same thing tomorrow if you were so foolish to try again. Companies like Anthropic apparently aren't even addressing the need for continual learning, since they think that a larger context with context compression will work as an alternative, which it won't ... memory isn't the same thing as learning to do a task (learning to predict the actions that will lead to a given outcome). 2) The junior developer, even if they are only marginally useful to begin with, will learn and become proficient, and the next generation of senior developer. It's a good investment training junior developers, both for your own team and for the industry in general.
- TrackerFF 1y agoSay 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 1y agoHowever, 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 1y agoDepends 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.
- dclowd9901 1y agoI'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?
- CER10TY 1y agoThe 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.
- noahdutch 1y agoAI reduces the time required to complete certain tasks, but that time is then re-allocated to additional validation than would not have been supposed necessary otherwise. It also increases the quantity and rapidity of output expected in a set amount of time and makes me ``lazier", i.e. I sit and watch the code get produced instead of divert my attention elsewhere.
- ath3nd 1y agoI thought AI is making actual experienced developers 19% less productive? https://www.businessinsider.com/ai-coding-tools-may-decrease-productivity-experienced-software-engineers-study-2025-7 https://www.businessinsider.com/ai-coding-tools-may-decrease...
- nathan_compton 1y agoI use AI all the time. Usually I'm a curmudgeon but I decided to go all in on LLM AI stuff and have used ChatGPT and other models extensively to write code. Having thought about it a lot, I think the magic here is that AI combines three things: 1. googling stuff about how APIs work 2. writing boilerplate 3. typing syntax correctly These three things combined make up a huge amount of programming. But when real cognition is required I find I'm still thinking just as hard in basically the same ways I've always thought about programming: identifying appropriate abstractions, minimizing dependencies between things, pulling pieces together towards a long term goal. As far as I can tell, AI still isn't really capable of helping much with this. It can even get in the way, because writing a lot of code before key abstractions are clearly understood can be counterproductive and AI tends to have a monolithic rather than decoupled understanding of how to program. But if you use it right it can make certain tasks less boring and maybe a little faster.
- exabrial 1y ago....But all of these so called "green" companies are using thousands of gallons of water and sucking down clean energy that could have been used to take coal plants offline.
- FrustratedMonky 1y agoMaybe 1x engineer is not going to 10x. But is a 10x going to 100x?
- magicmicah85 1y ago>Oh, and don't scroll LinkedIn. Or Twitter. Ever. This is all you have to takeaway from this article. Social media is a cesspool of engagement farmers dropping BS takes to get you to engage out of FOMO or anger. Every time I'm on there, I am instantly reminded why I quit going there. It's not genuine and it's designed to capture your attention away from more important things. I've been using LLMs on my own for the past few years and we just recently started our own first party model that we can now use for work. I'm starting to get into agentic actions where I can integrate with confluence, github, jira, etc. It's a learning curve for sure but I can see where it will lead to some productivity gains but the road blocks are still real, especially when working with other teams. Whether you're waiting for feedback or a ticket to be worked on, the LLM might speed run you to a solution but you better be ready with the next thing and the next thing while you're waiting.
- hintymad 1y agoI think AI is going to make senior engineers at big tech companies 10x more productive. A lot of senior engineers in the big tech companies spend most of their time in meetings. They're still brilliant. For instance, they read papers and map out the core ideas, but they haven't been in the weeds for a long time. They don't necessarily know all the day-to-day stuff anymore. Things like: which config service is standard now? What's the right Terraform template to use? How do I write that gnarly PromQL query? How do I spin up a new service that talks to 20 different systems? Or in general, how do I map my idea to deployable and testable code in the company's environment? They used to have to grab a junior engineer to handle all that boilerplate and operational work. Now, they can just use an AI to bridge that gap and build it themselves.
- jonas21 1y ago> You can't compress the back and forth of 3 months of code review into 1.5 weeks. If your organization is routinely spending 3 months on a code review, it sounds like there's probably a 10 to 100x improvement you can extract from fixing your process before you even start using AI.
- coltonv 1y agoI think I may have worded this poorly. I mean the total amount of code review time that goes into 3 months of work (likely on hundreds of PRs) can't be compressed into 1.5 weeks at the same portion of time being allocated to code review. Each code review has a "floor" time, a minimum amount of time loss due to context switching, reading, writing, etc.
- sitzkrieg 1y agoif you are working in a domain you know well, ai will not save you any time. if you are not, you will have to carefully review the ai while not building any intuition or background sources anyway so you end up not saving time and developing a new dependency. but every company is going to enshittify everything they can to pidgeonhole ai use to justify the grifters costs i look forward to years out when these companies trying to save money at any cost have to pay senior developers to rip all this garbage out
- msukkarieh 1y ago> When you write code, how much of your time do you truly spend pushing buttons on the keyboard? It's probably less than you think. Much of your prime coding time is actually reading and thinking Totally agree, IMO there's a lot of potential for these tools to help with code understanding and not just generation. Shameless plug for a code understanding tool we've been working on that helps with this: https://github.com/sourcebot-dev/sourcebot https://github.com/sourcebot-dev/sourcebot
- crvdgc 1y ago> 10x productivity means ten times the outcomes, not ten times the lines of code. This means what you used to ship in a quarter you now ship in a week and a half. This assumes the acceleration happens on all tasks. Amdahl's law states that the overall acceleration is constrained by the portion of the accelerated work. Probably it's just unclear if the "engineer" or "productivity" means the programming part or the overall process.
- dumbfounder 1y agoDoes it need to enable 10x productivity? Part of the job of a developer is the constant pursuit of new tools to make you more efficient. The developers who do not evolve all the time are eventually passed by a younger generation. If it makes you more productive you should use it. Obviously there is going to be a ton of hype, just ignore it.
- stpedgwdgfhgdd 1y agoBased on my own experience and reading a ton of HN posts, i would summarize it as: - vibe coding is fun, but not production-ready software engineering - LLMs like CC today moderately boost your performance. A lot of attention is still needed. - some TDD style is needed for the AI tool to converge - based on the growth of the last few months, it is quite likely that these tools will increase IC productivity substantially - fully autonomous agentic coding will take more time as the error rate needs to decline significantly
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- outime 1y agoNitpick (since I couldn't leave a comment there, hopefully author reads it): > It tends to struggle with languages like Terraform The language is called HCL (HashiCorp Configuration Language).
- itissid 1y agoA 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 1y agoExtremely 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.
- itissid 1y agoHuman goals are more important. I think conceptually the idea should always be strong goals set by humans and then sub goals each with a praticularly well defined *plan* for meeting them. This needs to be the conceptual basis, if you are having to plan for 50% or 75%(gasp) of the time for a feature and then AI just writes code, that is not intelligence much less a 10x engineer. My use case is not for a 10x engineer but instead for *cognitive load sharing*. I use AI in a "non-linear" fashion. Do you? Here is what that means: 1. Brainstorm an idea and write down detailed enough plan. Like tell me how I might implement something or here is what I am thinking can you critique and compare it with other approaches. Then I quickly meet with 2 more devs and make a design decision for which one to use. 2. Start manual coding and let AI "fill the gaps": Write these test for my code or follow this already existing API and create the routes from this new spec. This is non-linear because I would complete 50-75% of the feature and let the rest be completed by AI. 3. I am tired and about to end my shift and there is this last bug, I go read the docs but I also ask AI to read my screen and come up with some hypothesis to come up with. I decide which hypothesis are most promising after some reading and then ask the AI to just test that(not fix it on auto mode). 4. Voice mode: I have a shortcut that triggers claude code and uses it like a quick "lookup/search" in my code base. This avoids context switching.
- lilerjee 1y agoAgree with a lot of arguments of the author. When you're not sure if what someone says makes sense， trust common sense, your own experience, and your thinking.
- socalgal2 1y agoI often wonder how much of 10x engineers is circumstance vs talent/skill. Separate from the issue of LLMs. If I can write blue sky / green field, code. Brand new code in a new repo, no rules just write code, I can write tons of code. What bogs me down are things like tests. It can take more time to write tests than the code itself in my actual work project. Of course I know the tests are important and maybe the LLM can help here. I'm just saying that they slow me down. Waiting for code reviews slows me down. Again, they're super useful but coming from a place where the first 20-25 years of my career I didn't have them they are a drag on my performance. Another is just the size of the project I'm on. > 500 programmers on my current large project. Assume it's an OS. It's just hard to make progress on such a large project compared to a small one. And yet another which is part of the first, other people's code. If I write the whole thing or most of it, then I know exactly what to change. I've written features in code I know in days that someone who was not familiar with the code I believe would have taken months. But, me editing someone else's code without the entire state of the code base in my head is 10x slower. That's a long way of saying, many 10xers might just be in the right circumstance to provide 10x. You're then compared against them but you're not in the same circumstance so you get different results.
- IshKebab 1y agoNah, I've worked with maybe 2 people I'd say were "10x", or at least 5x, and it was definitely skill. I used to not really believe people like that existed but it turned out they're just rare enough that I hadn't worked with any yet. You could definitely go a whole career without ever working with any 10x engineers. And also it's not like they're actually necessary for a project to succeed. They're very good but it's extremely unlikely that a project will succeed on the back of one or two very good engineers. The project I worked with them on failed for reasons nothing to do with us.
- bhopro 1y agoWhile I agree with some components of this blog, I also think that the author is speaking from a specific vantage point. If you are working at a large company on a pre-existing codebase, you likely have to deal with complexity that has compounded over many product cycles, pull requests, and engineer turnover. From my experience, AI has increased my performance roughly by 20%. This is primarily due to LLMs bypassing much of the human slop that has accumulated over the years on Google. For newer languages, packages, and hardware-specific code, I have yet to use a single frontier model that has not slowed me down by 50%. It is clear to me that LLMs are regurgitating machines, and no amount of thinking will save the fact that the transformer architecture (all ML really) poorly extrapolates beyond what is in the training canon. However, on zero-to-one projects that are unconstrained by my mag-seven employer, I am absolutely 10x faster. I can churn through boilerplate code, have faster iterations across system design, and generally move extremely fast. I don't use agentic coding tools as I have had bad experiences in how the complexity scales, but it is clear to me that startups will be able to move at lightning pace relative to the large tech behemoths.
- namuol 1y agoIt turns out, everyone was already a 10-100x engineer in a greenfield project.
- quaintdev 1y agoInteresting that title of this post was changed. I think I have seen this happening 2nd time now. It seems Hacker News does not favor AI negative narratives.
- coltonv 1y agoHas happened to me before. It seems they change anything that has a negative connotation to try to take something more positive out of it. I don't love that they do that without asking or confirming with the author. But this title is also fine with me. I actually thought about naming it "Curing your AI 10x Imposter Syndrome", but it felt like a stretch that someone would understand what the content would be about.
- AtNightWeCode 1y agoWhen I asked ChatGPT about this topic it claimed that AI can make a software developer up to about 50% more productive on average. Sounds more reasonable to me. I often write custom tools to generate code. Sometimes when stars are aligned I get that 100x feeling. And sometimes I regret it so hard a couple of years later.
- rideontime 1y agoWas pleased to see Austen Allred catching strays in this article. May he never live it down.
- abelanger 1y agoOne thing I've been wondering recently: has the experience of using software (specifically web apps) been getting better? It seems like a natural extension of significantly increased productivity would lead to fewer buggy websites and apps, more intuitive UIs, etc. Linear was a very early-stage product I tested a few months after their launch where I was genuinely blown away by the polish and experience relative to their team size. That was in 2020, pre-LLMs. I have yet to see an equally polished and impressive early-stage product in the past few years, despite claims of 10x productivity.
- austinwade 1y agoReally enjoyed this post. I think this is the best mindset to have around the future of AI programming.
- voxleone 1y agoThere’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?
- coltonv 1y agoVery interesting perspective. Thanks for sharing!
- kazinator 1y agoLet's connect on HackedIn!
- galaxyLogic 1y ago> 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.
- gjadi 1y agoHave a look at https://zfhuang99.github.io/tla+/pluscal/chatgpt/2023/09/24/TLA-made-simple-with-chatgpt.html https://zfhuang99.github.io/tla+/pluscal/chatgpt/2023/09/24/...
- jdefr89 1y agoas someone who’s been coding most of his entire life I have to admit.. LLMs kind of killed the magic of programming for me… It’s not as cool when I create something with the LLM.. It just feels like you had someone else do the work for you… It’s sad kind of…
- patrakov 1y agoWhat 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.
- techpineapple 1y agoHas any company done their yearly release - like Apple Google I/O where they released 10x as many products or the releases were 10x as ambitious?
- abhinuvpitale 1y agoSuch an insightful article. The tools are allowing us to 10x-100x productivity in shorter bursts, which makes total sense. There's a lot more to software engineering beyond those bits, and that's why the 10x engineer imposter syndrome.
- gwbas1c 1y ago> If listening to a 70 year old disk makes you happier, just do it. You'll listen to more music if you do that than you would by forcing yourself to use the more "productive" streaming service. Ironically, when I listen to vinyl instead of streaming, I listen to less music. If I'm in the zone, I will often go minutes between flipping the record or choosing another one; even though my record player is right next to me.
- skydhash 1y agoListening to a good album is an immersive experience. I often don't have the urge to directly play another one. If I do, they often similar thematically or by the same artist.
- gwbas1c 1y ago> Listening to a good album is an immersive experience. That's when/if you're giving it your full attention. I used to do that when I was younger, but much less frequently now. That being said, there's something hypnotic about watching a record spin, and seeing the needle in the groove. I don't do it now that I'm older, but my kids used to specifically ask me to play a record just so they could see it spin.
- internet_points 1y agothings that might actually make me several x faster: * if my Github actions ran 10x faster, so I don't start reading about "ai" on hackernews while waiting to test my deployment and not noticing the workflow was done an hour ago * if the Google cloud console deployment page had 1 instead of 10 vertical scroll bars and wasn't so slow and janky in Firefox * if people started answering my peculiar but well-researched stackoverflow questions instead of nitpicking and discussing whether they belong on superuser vs unix vs ubuntu vs hermeneutics vs serverfault * if MS Teams died anyway, nice to see others having the same feeling about llm's
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- lemonwaterlime 1y agoI'm consistently baffled at why software engineering is the only engineering to obsess over a mythical "10x" contributor. Mechanical, electrical, civil, and chemical engineers do not have this concept. What makes an excellent engineer is risk mitigation and designing systems under a variety of possible constraints. This design is performed using models of the domains involved and understanding when and where these models hold and where they break down. There's no "10x". There is just being accountable for designing excellent systems to perform as desired. If there were a "10x" software engineer, such an engineer would prevent data breaches from occurring, which is a common failure mode in software to the detriment of society. I want to see 10x less of that.
- mhuffman 1y ago>Mechanical, electrical, civil, and chemical engineers do not have this concept. >What makes an excellent engineer is risk mitigation and designing systems under a variety of possible constraints. I take it that those fields also don't live by the "move fast and break things" motto?
- YZF 1y agoThey might not have that concept but they absolutely have those people. I worked with many mechanical and electrical engineers building complex machines. Some people are just much better than others. This might not happen with more cookie-cutter work but any creative work in these domains absolute has 10x or even 100x engineers. That said collaboration really helps here, i.e. the good engineer can help others solve problems and be more productive then they'd be on their own. In software this seems to be harder for various reasons, one of which is that it's easier to demonstrate/see a good solution in the other domains but software tends to be a lot fuzzier.
- theappsecguy 1y agoIt is finally starting to feel like the craze and hype bubble is being rightfully questioned left and right.
- jwrallie 1y agoThe problem with the 10x engineer myth is that there is no baseline to what an engineer is, as much as there is no baseline for what a human is. Any tool can be shown to increase performance in closed conditions and within specific environments, but when you try to generalize things do not behave consistently. Regardless, I would always argue that trying new tech / tools / workflows is always better than being stiff in your ways, regardless of the productivity results. I do like holding up on new things until things mature down a bit before trying though.
- darepublic 1y agoWhatever productivity gains I get is usually replacing documentation discovery and lookup rather than typing code
- fjlijwliej 1y ago> 10x productivity means ten times the outcomes, not ten times the lines of code. This means what you used to ship in a quarter you now ship in a week and a half. Exactly. I spend less than 20% of my time writing code. If LLMs 1,000,000,000-x'd my code writing, it would make me 1.25x as efficient overall, not 10x as efficient. It's all influencer hype nonsense, just like pair programming and microservices and no-code companies and blockchain.
- cadamsdotcom 1y agoIf writing code - or unfamiliar tasks - is the constraint (often true in greenfield dev).. congratulations, with AI that constraint is gone. Because AI gets you to the next constraint even faster :)
- ameyv 1y agoThanks colton. Man, you just made me feel 10x better :) And ahh yes I said 10x. :P
- joshdavham 1y agoPersonally, he made me feel 100x better!
- coltonv 1y agoI'm happy to hear that! A lot of people posting their hot takes here about how AI is actually great or actually awful, but I was hoping to have more conversations like this in the comments. I'm glad I can help people feel better.
- heisenbit 1y agoThere is no doubt in my mind that AI makes me more productive and gets me back at least to the level Google did when it still worked.
- bloppe 1y agoDon't tell that to the investors investing hundreds of billions in AI
- dash2 1y agoI wonder if the gap between perception and reality, from that recent study, is because AIs are still so slow? The modern equivalent of https://xkcd.com/303/ https://xkcd.com/303/ might be "Waiting for the agent to complete!" So then you know you're more productive (and can spend more ten minute chunks of time reading HN) but your boss doesn't see it....
- amichayg 1y agoThe core value of LLMs is simple: sometimes you need to write code, but what you really want is to design, experiment, or just get something usable. Even when you do write code, you often only care about specific aspects—you just want to automate the rest. This is hard to reconcile with modern business models. If you tell someone that a software engineer can also design, they’ll just fire the designer and pile more work on the engineer. But it doesn’t change the underlying truth: a single engineer who can touch many parts of the software with low cognitive friction is simply a better kind of engineer.
- clauderoux 1y agoThis article nails it. The claim 10x is in my opinion one of these tactics used by large corporations to force engineers into submission. The idea that you could be replaced with an AI is frightening enough to keep people in check, when negotiating your salary. AI is a wonderful tool that I use everyday, and I have been able to implement stuff that I would have considered too cumbersome to even start working on. But, it doesn't make you a 10x more efficient engineer. It gives you an edge when you start a new project, which is already a lot. But don't expect your whole project of 100,000 lines to be handled by the machine. It won't happen any time soon.
- dandanua 1y agoFunnily, you probably won't see in news the idea that 10x increase in productivity should lead to 10x increase in compensation (with the exception of CEOs and very top engineers, that get even bigger multiplier).
- misja111 1y agoThe article is spot on, however, who is claiming a 10x speed up from AI? I have heard many crazy claims so far but nothing that bad. In addition to the article, I'd like to add that most DEV jobs I have been in had me coding only 50% of my time at most. The rest of the time was spent in meetings, gathering requirements and investigating Prod issues.
- lmeyerov 1y agoIt took me a month , despite having done prompt engineering for work the 2 years prior, to hit the real starting line of Claude code productivity Basically, the ability to order my thoughts into a task list long & clear enough for the LLM to follow that I can be working on 3 or so of these in parallel, and maybe email. Any individual run may be faster or slower than I can do it manually, but critically, they take less total human time / attention. No individual technique is fundamentally tricky here, but it is still a real skill. If you read the article, the author is simply not there, and sees what they know as only 1 weeks worth of knowledge. So for their learning rate .. maybe they need 3x longer of learning & experience?
- globular-toast 1y agoI love days where someone else has written down what I wish I could. This is a brilliantly written and sober look into using LLMs as a software engineer.
- allanmacgregor 1y agoI'm skeptical of the 10x claims for different reasons than the author focuses on. The productivity gains might be real for individual tasks, but they're being measured wrong. Most of the AI productivity stories I hear sound like they're optimizing for the wrong metric. Writing code faster doesn't necessarily mean shipping better products faster. In my experience, the bottleneck is rarely "how quickly can we type characters into an editor" - it's usually clarity around requirements, decision-making overhead, or technical debt from the last time someone optimized for speed over maintainability. The author mentions that real 10x engineers prevent unnecessary work rather than just code faster. That rings true to me. I've seen more productivity gains from saying "no" to features or talking teams out of premature microservices(or adopting Kafka :D) than from any coding tool. What worries me more is the team dynamic this creates. When half your engineers feel like they're supposed to be 10x more productive and aren't, that's a morale problem that compounds. The engineers who are getting solid 20-30% gains from AI (which seems realistic) start questioning if they're doing it wrong. Has anyone actually measured this stuff properly in a production environment with consistent teams over 6+ months? Most of the data I see is either anecdotal or from artificial coding challenges.
- joshuak 1y agoOlympic athletes don't exist because no one at my gym runs that fast. You are right that typing speed isn't the bottleneck, but wrong about what AI actually accelerates. The 10x engineers aren't typing faster they're exploring 10 different architectural approaches in the time it used to take to try one, validating ideas through rapid prototyping, automating the boring parts to focus on the hard decisions. You can't evaluate a small sample size of people who are not exploiting the benefits well and come to an accurate assessment of the utility of a new technology. Skill is always a factor.
- naasking 1y agoI recently used Google's Gemini to review and debug some code that was performing some runtime code generation in .NET. It pointed out some issues I was aware of, some cases I hadn't considered but would have eventually hit with some testing, and then helped debug why some tests were failing. Pretty impressive actually. Probably wasn't a 10x savings, but definitely 2x or more in some cases, and some of the tasks it eliminated were the tedious ones, which is a big help for motivation.
- Fuhrmanator 1y agoI recently experimented with Gemini on Colab for building a discrete simulation in Python—initially started with ChatGPT, then moved platforms due to free-tier limits. Gemini was responsive in analyzing graph outputs and made quick progress with rapid prototyping. However, when I shifted focus to refactoring and improving code structure, e.g., extracting classes and encapsulating behavior, it defaulted to a weird hybrid class/functional approach, often placing logic outside domain objects rather than applying polymorphism. Even after I explicitly mentioned principles like "Tell, don’t ask," I had to insist before it adjusted its design choices accordingly. I asked why those principles are NOT there by default, and it said basically most coders don't use them and it seeks direct solutions. While Gemini performed well in tweaking visualizations (it even understood the output of matplotlib) and responding to direct prompts, it struggled with debugging and multi-step refactorings, occasionally failing with generic error messages. My takeaway is that these tools are incredibly productive for greenfield coding with minimal constraints, but when it comes to making code reusable or architecturally sound, they still require significant human guidance. The AI doesn’t prioritize long-term code quality unless you actively steer it in that direction.
- amai 1y agoSee also : https://the-decoder.com/ai-coding-can-make-developers-slower-even-if-they-feel-faster/ https://the-decoder.com/ai-coding-can-make-developers-slower...
- Oceoss 1y agoufff, this resonated with me "It's okay to sacrifice some productivity to make work enjoyable. More than okay, it's essential in our field. If you force yourself to work in a way you hate, you're just going to burn out. "
- subtlesoftware 1y agoThe author focuses too much on the strawman of 10x engineer. If an engineer is even 2x more productive overall, that's a huge deal.
- deterministic 1y agoCustomer code generators have made me 10x more productive (generating 90% of a typical client/server biz application from a declarative spec). AI's so far haven't been able to beat that. However I have found AI's to be great when working with unfamiliar tools. Where the effort involve in reading the docs etc. far outweigh the benefits. In my case using AI's to generate JasperReports .jrxml files made me more productive.
- axi0m 1y agoGreat post. The author precisely described what I have been experiencing for the last months of digging deep into that field (with quite a lot of anxiety at first, hearing here and there what a miracle it was). As long as we don't solve context window limitations and self-improving/continuous learning problem, human engineers still have a long way ahead of them.