Things that helped me get out of the AI 10x engineer imposter syndrome

https://colton.dev/blog/curing-your-ai-10x-engineer-imposter-syndrome/

by coltonv • 12 months ago

949 647 12 months ago

colton.dev

jf229• 12 months 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• 12 months ago

I agree. With plenty of prompts (leave them in documents) you can get pretty good results.

drgiggles2• 12 months ago

Easily 99% of comments generated by LLMs are useless.

micromacrofoot3• 12 months ago

They'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.

the__alchemist2• 12 months ago

I'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.

exe343• 12 months ago

They comment on the how, not the why.

shortrounddev23• 12 months ago

I think its because LLMs are often trained with data from code tutorial sites and forums like stackoverflow, and not always production code

ggregoire• 12 months ago

That'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_c9• 12 months ago

99% 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• 12 months ago

Very 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.

jf229• 12 months ago

Then tell it to write better comments...

wglb2• 12 months ago

Not what I have found with gemini.

What is particularly useful is the comments about reasoning about new code added at my request.

reactordev2• 12 months ago

Let’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_c9• 12 months ago

Aka, let's train people how to use the tool...

jf229• 12 months ago

There are tons of these guides around the internet. I'm only using what other people have already published.

icey• 12 months ago

The company I work at (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!

symfrog2• 12 months ago

Do 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_c9• 12 months ago

That'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...

mavelikara• 12 months ago

Not the OP, not my code. But here is Mitchel Hashimoto showing his workflow and code in Zig, created with AI agent assistance: https://youtu.be/XyQ4ZTS5dGw

micahscopes2• 12 months ago

This one was a huge success:

https://github.com/micahscopes/radix_immutable

I took an existing MIT licensed prefix tree crate and had Claude+Gemini rewrite it to support immutable quickly comparable views. The execution took about one day's work, following two or three weeks thinking about the problem part time. I scoured the prefix tree libraries available in rust, as well as the various existing immutable collections libraries and found that nothing like this existed. I wanted O(1) comparable views into a prefix tree. This implementation has decently comprehensive tests and benchmarks.

No code for the next two but definitely results...

Tabu search guided graph layout:

https://bsky.app/profile/micahscopes.bsky.social/post/3luh4d...

https://bsky.app/profile/micahscopes.bsky.social/post/3luh4s...

Fast Gaussian blue noise with wgpu:

https://bsky.app/profile/micahscopes.bsky.social/post/3ls3bz...

In both these examples, I leaned on Claude to set up the boilerplate, the GUI, etc, which gave me more mental budget for playing with the challenging aspects of the problem. For example, the tabu graph layout is inspired by several papers, but I was able to iterate really quickly with claude on new ideas from my own creative imagination with the problem. A few of them actually turned out really well.

wglb2• 12 months ago

https://github.com/wglb/gemini-chat Almost entirely generated by gemini based on my english language description. Several rounds with me adding requirements.

(edit)

I asked it to generate a changelog: https://github.com/wglb/gemini-chat/blob/main/CHANGELOG.md

the__alchemist2• 12 months ago

I 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.

echelon2• 12 months ago

I 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.

jf229• 12 months ago

The code I generate with LLMs is clean and looks as good if not better than what I'd write by hand.

abtinf• 12 months ago

Can you point to an example repo with enough rules and good prompts?

mrugge2• 12 months ago

First 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.

mrbungie5• 12 months 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.

tptacek19• 12 months ago

What 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.

k1t• 12 months ago

You have misinterpreted GP's "better than what I would do" as "better than what I could do".

wahnfrieden• 12 months ago

You're forgetting that code quality also requires time. Developers make tradeoffs all the time on how much time to invest into improving the quality of what they write, for both new and existing code. When someone claims that LLMs can produce higher-quality code it can include quality levels that may be unjustifiably slow to hand-craft depending on constraints and needs.

Related - agentic LLMs may be slow to produce output but they are parallelizable by an individual unlike hand-written work.

jf229• 12 months ago

I can do it by hand but it takes time.

With AI the extra quality and polish is basically free and instantaneous.

Kiro• 12 months ago

You underestimate how lazy people are. I always take shortcuts and skip taking edge cases into account. LLMs have no problem writing tedious guards and creating abstractions without hacks, which means the code becomes more robust than if I would do it by hand.

micromacrofoot3• 12 months ago

I'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• 12 months ago

Could 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• 12 months ago

I 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].

micahscopes2• 12 months ago

It makes me wince a little

zoeysmithe3• 12 months ago

What 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.

tovej2• 12 months ago

Could 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.

tempodox• 12 months ago

So you're not a developer any more but a tenant who pays rent. Speaking of which, I have a bridge to sell you…

jf229• 12 months ago

I'm not even sure what this is supposed to say.

neutronicus3• 12 months ago

Yeah that fucker Claude is tireless when it comes to checking return types, checking for null, etc etc

nurettin2• 12 months ago

Here'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

NitpickLawyer3• 12 months ago

Sorry 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.

apwell232• 12 months 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

jf229• 12 months ago

It's not magic. The techniques are well established and widely shared.

miggy• 12 months ago

In 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.

lvl1552• 12 months ago

Takes 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.

recursive4• 12 months ago

Would you say they're holding it wrong?

blitzar• 12 months ago

You are meant to hold it upside down with the screen facing away, otherwise you cant possibly expect it to work.

exe343• 12 months ago

It'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.

apwell232• 12 months ago

its an illusion that you have discovered some golden promoting workflows. If you had then you would share it instead of being handwavy and secretive.

komali22• 12 months ago

Do you have an example FOSS project you can share with the requisite AI guardrail files, and example prompts?

rohit89• 12 months ago

Yea. 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.

shortrounddev23• 12 months ago

I 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_c9• 12 months 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.

bpt36• 12 months ago

They 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.

ajaisibsbz• 12 months ago

Is typing speed a bottle neck for people? Because otherwise you’re offloading thinking to the LLM. Unless you can understand code faster than you can write it (which I’ve never experienced - best case scenario I can understand as fast as I read).

As a junior I used to think it was ok to spend much less time on the review than the writing, but unless the author has diligently detailed their entire process a good review often takes nearly as long. And unsurprisingly enough working with an AI effectively requires that detail in a format the AI can understand (which often takes longer than just doing it).

Wilder7977• 12 months ago

That's a very narrow perspective. Will the tool deskill me? Will the tool lower my work quality? Will the tool make a bigger share of my work reviewing vs thinking and creating? Will the tool make the work overall less interesting (which motivates me)? Etc. Etc. This is even assuming that the FOMO is justified. So fat studies don't show this is the case, but things might change.

shortrounddev23• 12 months ago

Writing code is the only part of my job that I like. Im not exhausted by coding because I love coding. Im exhausted by pointless meetings, talking to clients, and trying to bring non technical people up to speed with what im working on. Allowing a machine to write code for me and then manually editing it sounds like a really really miserable way to spend the precious little time I have on this earth

whstl• 12 months ago

Here'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.

DuckOnFire• 12 months ago

Learning 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.

simpaticoder• 12 months ago

The expectations are higher than reality, but LLMs are quite useful in many circumstances. You can characterize their use by "level of zoom", from "vibe coding" on the high end, to "write this function given its arguments and what it should return" at the low end. The more 'zoomed in' you are, the better it works, in my experience.

Plus there are use-cases for LLMs that go beyond augmenting your ability to produce code, especially for learning new technologies. The yield depends on the distribution of tasks you have in your role. For example, if you are in lots of meetings, or have lots of administrative overhead to push code, LLMs will help less. (Although I think applying LLMs to pull request workflow, commit cleanup and reordering, will come soon).

fisherrjd• 12 months ago

Number of people that read the article before counting: 2

bpt36• 12 months ago

I 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.

tptacek19• 12 months ago

This article sets a ludicrous bar ("10x"), then documents the author's own attempt over some indeterminate time to clear that bar. As a result, the author has classified all the AI-supporters in the industry into three categories: (1) people who are wrong in good faith, (2) people who are selling AI tools, and (3) evil bosses trying to find leverage in programmer anxiety.

That aside: I still think complaining about "hallucination" is a pretty big "tell".

coltonv20• 12 months ago

Hi there! I appreciate your comment, and I remember reading your article about AI and some of the counterarguments to it helped me get over the imposter syndrome I was feeling.

To be clear, I did not classify "all the AI-supporters" as being in those three categories, I specifically said the people posting that they are getting 10x improvements thanks to AI.

Can you tell me about what you've done to no longer have any hallucinations? I notice them particularly in a language like Terraform, the LLMs add properties that do not exist. They are less common in languages like Javascript but still happen when you import libraries that are less common (e.g. DrizzleORM).

tptacek19• 12 months ago

Can you help me understand which articles you're referring to? A link to the biggest "AI made me a 10x developer" article you've read would certainly clear this up.

BoxFour• 12 months ago

I still think complaining about "hallucination" is a pretty big "tell".

The conversation around LLMs is so polarized. Either they’re dismissed as entirely useless, or they’re framed as an imminent replacement for software developers altogether.

Hallucinations are worth talking about! Just yesterday, for example, Claude 4 Sonnet confidently told me Godbolt was wrong wrt how clang would compile something (it wasn’t). That doesn’t mean I didn’t benefit heavily from the session, just that it’s not a replacement for your own critical thinking.

Like any transformative tool, LLMs can offer a major productivity boost but only if the user can be realistic about the outcome. Hallucinations are real and a reason to be skeptical about what you get back; they don’t make LLMs useless.

To be clear, I’m not suggesting you specifically are blind to this fact. But sometimes it’s warranted to complain about hallucinations!

tptacek19• 12 months ago

That's not what people mean when they bring up "hallucinations". What the author apparently meant was that they had an agent generating Terraform for them, and that Terraform was broken. That's not surprising to me! I'm sure LLMs are helpful for writing Terraform, but I wouldn't expect that agents are at the point of being able to reliably hand off Terraform that actually does anything, because I can't imagine an agent being given permission to iterate Terraform. Now have an agent write Java for you. That problem goes away: you aren't going to be handed code with API calls that literally don't exist (this is what people mean by "hallucination"), because that could wouldn't pass a compile or linter pass.

bgwalter2• 12 months ago

That bar is industry standard in the hype machine. Altman and others have set it:

https://www.windowscentral.com/software-apps/sam-altman-ai-w...

https://brianchristner.io/how-cursor-ai-can-make-developers-...

https://thenewstack.io/the-future-belongs-to-ai-augmented-10...

dontlikeyoueith• 12 months ago

That aside: I still think complaining about "hallucination" is a pretty big "tell".

And I think that sentence is a pretty big tell, so ...

gulbanana• 12 months ago

The embittered tone and obvious bad faith of your comments here make it clear how seriously we should take your opinions about AI.

adolph• 12 months ago

Those 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• 12 months ago

AI never says it doesn’t know. It’ll always have an answer even if it’s wrong or misleading

andix• 12 months ago

This 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.

meroes• 12 months ago

Usually 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• 12 months ago

LLMs 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.

bpt36• 12 months ago

It'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.

bgwalter2• 12 months ago

The 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• 12 months ago

Only 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• 12 months ago

What are you measuring?

efields• 12 months ago

In a week, Claude Code and I have built a PoC Rails App for a significant business use case. I intend to formally demo it for buy-in tomorrow after already doing a short "is this kind of what you're looking for?" walkthrough last week. From here, I intend to "throw it over the fence" for my staff, RoR and full-stack devs, to pick it apart and/or improve what they want to in order to bring it from 80-100% over the next two months. If they want to rewrite it from scratch, that's on the table.

It's not a ground-breaking app, its CRUD and background jobs and CSV/XLSX exports and reporting, but I found that I was able to "wireframe" with real code and thus come up with unanswered questions, new requirements, etc. extremely early in the project.

Does that make me a 10x engineer? Idk. If I wasn't confident working with CC, I would have pushed back on the project in the first place unless management was willing to devote significant resources to this. I.e. "is this really a P1 project or just a nice to have?" If these tools didn't exist I would have written spec's and excalidraw or Sketch/Figma wireframes that would have taken me at least the same amount of time or more, but there'd be less functional code for my team to use as a resource.

OtherShrezzing• 12 months ago

If you think your CC wireframe has taken approx as much time as it'd have taken you with another tool like Figma + spec-writing, and one of your engineering team's options is "rewrite it from scratch" (without a spec), has the use of CC saved your company any time at all?

It reads like this project would have taken your company 9 weeks before, and now will take the company 9 weeks.

g3f32r• 12 months ago

I think the comment was showing that the project takes 9 weeks either way, but coming to that determination was much more confident and convincing with a functional demo versus a hand-wavy figma + guesstimate.

coffeefirst• 12 months ago

Yeah. The prototyping is neat. But in past lives I would literally sketch the "POC" on paper.

I sort of want to get back to that... it was really good at getting ideas across.

generalizations4• 12 months ago

In many ways this feels like average software engineers telling on themselves. If you know the tech you're building, and you're good at splitting up your work, then you know ahead of time where the complexity is and you can tell the AI what level of granularity to build at. AI isn't magic; there is an upper limit to the complexity of a program that e.g. Sonnet 4 can write at once. If you can grok that limit, and you can grok the tech of your project, then you can tell the AI to build individual components that stay below that threshold. That works really well.

Scarblac2• 12 months ago

But the hard part is figuring out the more complex parts. Getting that right is what takes the time, not typing in the more trivial parts.

lopatin• 12 months ago

Not typing the trivial parts is pretty great though

j453• 12 months ago

It can be, but if you're familiar with what you're working with and have experience with other systems that have transferrable knowledge, again, it can be an advantage.

I was surprised with claude code I was able to get a few complex things done that I had anticipated to be a few weeks to uncover, stitch together and get moving.

Instead I pushed Claude to consistently present the correct udnerstanding of the problem, strucutre, approach to solving things, and only after that was OK, was it allowed to propose changes.

True to it's shiny things corpus, it will over complicate things because it hasn't learned that less is more. Maybe that reflects the corpus of the average code.

Looking at how folks are setting up their claude.md and agents can go a long way if you haven't had a chance yet.

alphazard• 12 months ago

The point is that good software engineers are good at doing the "hard part". So good that they have a backlog of "trivial" typing tasks. In a well functioning organization they would hand off the backlog of trivial parts to less experienced engineers, who might be herded by a manager. Now we don't need the less experienced engineers or the manager to herd them.

Fraterkes2• 12 months ago

Is the implication here that you consider yourself an above-average engineer?

generalizations4• 12 months ago

The implication is that I'm finding it straightforward to handle the issues that other people here seem to consider insurmountable. And they seem to be directly related to how good one is at building software.

lnenad• 12 months ago

I agree, and the fact that in their list of scenarios that cause these not actually mentioning some people actually are 10x definitely points to them not being self-aware.

Quarrelsome6• 12 months ago

might it not be the other way round? For all we know its mediocre devs who are relishing the prospect of doing jack shit all day and still being able to submit some auto generated PRs. Being "amazed" at what it produces when someone with higher standards might be less than amazed.

I find it impossible to work out who to trust on the subject, given that I'm not working directly with them, so remain entirely on the fence.

ares623• 12 months ago

I'd be curious how skill atrophy affects engineers who use AI semi-exclusively for these trivial tasks.

hyperadvanced• 12 months ago

I tried doing all of my work for ~1 month with copilot/claude. It didn’t cause a ton of atrophy, because it didn’t work - I couldn’t avoid actually getting involved in rewriting code

theshrike794• 12 months ago

Of course there is an upper limit for AI. There's an upper limit for humans too.

What you need is just boring project management. Have a proper spec, architecture and tasks split into manageable chunks with enough information to implement them.

Then you just start watching TV and say "implement github issue #42" to Claude and it'll get on with it.

But if you say "build me facebook" and expect a shippable product, you'll have a bad time.

pornel2• 12 months ago

This is tautological. If you keep instructions dumbed-down enough for AI to work well, it will work well.

The problem is that AI needs to be spoon-fed overly detailed dos and donts, and even then the output can't be trusted without carefully checking it. It's easy to reach a point where breaking down the problem into pieces small enough for AI to understand takes more work than just writing the code.

AI may save time when it generates the right thing on the first try, but that's a gamble. The code may need multiple rounds of fixups, or end up needing a manual rewrite anyway, after wasting time and effort on instructing the AI. The ceiling of AI capabilities is very uneven and unpredictable.

Even worse, the AI can confidently generate code that looks superficially correct, but has subtle bugs/omissions/misinterpretations that end up costing way more time and effort than the AI saved. It has uncanny ability to write nicely structured, well-commented code that is just wrong.

generalizations4• 12 months ago

I made an STT tool (guess who wrote it for me) and have a bluetooth mic. I spend 10 minutes pacing and telling the AI what I need it to build, and how to build it. Then it goes off and builds it, and meanwhile I go to the next Claude Code instance on a different project, and do the same thing there. Then do the same for a third, and maybe by that time the first is ready for more direction. Depending on how good you are with context switching and quickly designing complex systems and communicating those designs, you can get a whole lot done in parallel. The problems you're describing can be solved, if you're careful and detailed.

It's a brave, weird and crazy new world. "The future is now, old man."

jimbo808• 12 months ago

You didn't read the article

globular-toast2• 12 months ago

Sure, absolutely. But that's the hard part of software engineering. The dream is composing software from small components and adding new features by just writing more small components.

But nobody has ever managed to get there despite decades of research and work done in this area. Look at the work of Gerald Sussman (of SICP fame), for example.

So all you're saying is it makes the easy bit easier if you've already done, and continue to do, the hard bit. This is one of the points made in TFA. You might be able to go 200mph in a straight line, but you always need to slow down for the corners.

siva7• 12 months ago

I beg to differ. My diffs are 10x bigger than before though i don't have any more time to review them.

doc_manhat_32• 12 months ago

This 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• 12 months ago

Sorry 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• 12 months ago

AI 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.

bpt36• 12 months ago

It'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.

simonw8• 12 months ago

I found myself agreeing with quite a lot of this article.

I'm a pretty huge proponent for AI-assisted development, but I've never found those 10x claims convincing. I've estimated that LLMs make me 2-5x more productive on the parts of my job which involve typing code into a computer, which is itself a small portion of that I do as a software engineer.

That's not too far from this article's assumptions. From the article:

I wouldn't be surprised to learn AI helps many engineers do certain tasks 20-50% faster, but the nature of software bottlenecks mean this doesn't translate to a 20% productivity increase and certainly not a 10x increase.

I think that's an under-estimation - I suspect engineers that really know how to use this stuff effectively will get more than a 0.2x increase - but I do think all of the other stuff involved in building software makes the 10x thing unrealistic in most cases.

hyperbovine• 12 months ago

This is (I think) a reference to the 10x engineer, another myth of which I have always been highly dubious (https://www.simplethread.com/the-10x-programmer-myth/).

Izkata• 12 months ago

10x sounds nice which is probably why it stuck, but it came from actual research which found the difference was larger than 10x - but also they were measuring between best and worst, not best and average as it's used nowadays.

https://www.construx.com/blog/productivity-variations-among-...

logicchains• 12 months ago

You don't believe Fabrice Bellard exists?

AdrianB13• 12 months ago

It highly depends on the circumstances. In over 30 years in the industry I met 3 people that were many times more productive than everyone else around them, even more than 10 times. What does this translate to? Well, there are some extraordinary people around, very rare and you cannot count on finding some and, when you find them, it is almost impossible to retain them because management and HR never agree to pay them enough to stay around.

coltonv20• 12 months ago

Thanks for the comment Simon! This is honestly the first one I've read where it feels like someone actually read the article. I'm totally open to the idea that some people, especially those working on the languages/tools that LLMs are good at, are indeed getting a 2x improvement in certain parts of their job.

simonw8• 12 months ago

Something I have realized about Hacked News is that most of the comments on any given article are from people who are responding to the headline without actually clicking through and reading it!

This is particularly true for headlines like this one which stand alone as statements.

mavamaarten• 12 months ago

Yeah. I just need to babysit it too much. Take copilot, it gives good suggestions and blows me away sometimes with a block of code which is exactly what I'd type. But actively letting it code (at least with gpt4.1 or gpt4o) just doesn't work well enough for me. Half of the time it doesn't even compile, and after fixing that it's just not really correctly working either. I'd expect it to work like a very junior programmer, but it works like a very drunk senior programmer that isn't listening to you very well at all.

ffsm85• 12 months ago

To date, I've not been able to effectively use Copilot in any projects.

The suggestions were always unusably bad. The /fix were always obviously and straight up false unless it was a super silly issue.

Claude Code with Opus model on the other hand was mind-blowing to me and made me change my mind on almost everything wrt my opinion of LLMs for coding.

You still need to grow the skill of how to build the context and formulate the prompt, but the buildin execution loop is a complete game changer and I didn't realize that until I actually used it effectively on a toy project myself.

MCP in particular was another thing I always thought was massively over hyped, until I actually started to use some in the same toy project.

Frankly, the building blocks already exist at this point to make a vast majority of all jobs redundant (and I'm thinking about all grunt work office jobs, not coding in particular). The tooling still need to be created, so I'm not seeing a short term realization (<2 yrs), but medium term (5+yrs)?

You should expect most companies to let people go at staggering numbers, with only small amounts of highly skilled people left to administer the agents

edm0nd• 12 months ago

I'd expect it to work like a very junior programmer, but it works like a very drunk senior programmer that isn't listening to you very well at all.

This seems to be the current consensus.

A very similar quote from another recent AI article:

One host compares AI chatbots to “a very smart assistant who has a dozen Ph.D.s but is also high on ketamine like 30 percent of the time.”

https://lithub.com/what-happened-when-i-tried-to-replace-mys...

bcrosby95• 12 months ago

It codes like a junior, has the design sense of a mid, while being a savant at algorithms.

mullingitover• 12 months ago

But actively letting it code (at least with gpt4.1 or gpt4o)

It's funny, Github Copilot puts these models in the 'bargin bin' (they are free in 'ask' mode, whereas the other models count against your monthly limit of premium requests) and it's pretty clear why, they seem downright nerfed. They're tolerable for basic questions but you wouldn't use them if price weren't a concern.

Brandwise, I don't think it does OpenAI any favors to have their models be priced as 'worthless' compared to the other models on premium request limits.

hnuser123456• 12 months ago

I think there are three factors to this: 1. What to code (longer, more specific prompts are better but take longer to write), and 2. How to code it (specify languages, libraries, APIs, etc.) And if you're trying to write code that uses a newer version of a library that works differently from what's most commonly documented, it's a long uphill battle of constantly reminding the LLM of the new changes.

If you're not specific enough, it will definitely spit out a half-baked pseudocode file where it expects you to fill in the rest. If you don't specify certain libraries, it'll use whatever is featured in the most blogspam. And if you're in an ecosystem that isn't publicly well-documented, it's near useless.

docmars3• 12 months ago

With something like Devin, where it integrates directly with your repo and generates documentation based on your project(s), it's much more productive to use as an agent. I can delegate like 4-5 small tasks that would normally take me a full day or two (or three) of context switching and mental preparation, and knock them out in less than a day because it did 50-80% of the work, leaving only a few fixes or small pivot for me to wrap them up.

This alone is where I get a lot of my value. Otherwise, I'm using Cursor to actively solve smaller problems in whatever files I'm currently focused on. Being able to refactor things with only a couple sentences is remarkably fast.

The more you know about your language's features (and their precise names), and about higher-level programming patterns, the better time you'll have with LLMs, because it matches up with real documentation and examples with more precision.

platevoltage• 12 months ago

I'd expect it to work like a very junior programmer, but it works like a very drunk senior programmer that isn't listening to you very well at all.

Best analogy I've ever heard and it's completely accurate. Now, back to work debugging and finishing a vibe coded application I'm being paid to work on.

nudgeOrnurture• 12 months ago

complete coding noob here.

if I want to throw a shuriken abiding to some artificial, magic Magnus force like in the movie wanted, both chatGpt and Claude let me down, using pygame. what if I wanted c-level performance or if I wanted to use zig? burp.

It works like the average Microsoft employee, like some doped version of an orange wig wearer who gets votes because his daddys kept the population as dumb as it gets after the dotcom x Facebook era. in essence, the ones to be disappointed by are the Chan-Zuckerbergs of our time. there was a chance, but there also was what they were primed for

mikepurvis• 12 months ago

Two other observations I've found working with ChatGPT and Copilot:

First, until I can re-learn boundaries, they are a fiasco for work-life balance. It's way too easy to have a "hmm what if X" thought late at night or first thing in the morning, pop off a quick ticket from my phone, assign to Copilot, and then twenty minutes later I'm lying in bed reviewing a PR instead of having a shower, a proper breakfast, and fully entering into work headspace.

And on a similar thread, Copilot's willingness to tolerate infinite bikeshedding and refactoring is a hazard for actually getting stuff merged. Unlike a human colleague who loses patience after a round or two of review, Copilot is happy to keep changing things up and endlessly iterating on minutiae. Copilot code reviews are exhausting to read through because it's just so much text, so much back and forth, every little change with big explanations, acknowledgments, replies, etc.

mgfist5• 12 months ago

My biggest takeaway from using AI is that

(1) for my day job, it doesn't make me super productive with creation, but it does help with discovery, learning, getting myself unstuck, and writing tedious code.

(2) however, the biggest unlock is it makes working on side projects __immensely__ easier. Before AI I was always too tired to spend significant time on side projects. Now, I can see my ideas come to life (albeit with shittier code), with much less mental effort. I also get to improve my AI engineering skills without the constraint of deadlines, data privacy, tool constraints etc..

bluefirebrand8• 12 months ago

(1) for my day job, it doesn't make me super productive with creation, but it does help with discovery, learning, getting myself unstuck, and writing tedious code

I hear this take a lot but does it really make that much of an improvement over what we already had with search engines, online documentation and online Q&A sites?

kkukshtel• 12 months ago

2 heavily resonates with me. Simon Wilson made the point early on that AI makes him more ambitious with his side projects, and I heavily agree. Suddenly lots of things that seemed more or less un-feasible are now not only do-able, but can actually meet or exceed your own assumptions for them.

Being able to sit down after a long way of work and ask an AI model to implement some bug or feature on something while you relax and _not_ type code is a major boon. It is able to immediately get context and be productive even when you are not.

theshrike794• 12 months ago

#2 is the reason I keep paying for Claude Code Pro.

For 20 a month I can get my stupid tool and utility ideas from "it would be cool if I could..." to actual "works well enough for me" -tools in an evening - while I watch my shows at the same time.

After a day at work I don't have the energy to start digging through, say, OpenWeather's latest 3.0 API and its nuances and how I can refactor my old code to use the new API.

Claude did it in maybe one episode of What We Do in the Shadows :D I have a hook that makes my computer beep when Claude is done or pauses for a question, so I can get back, check what it did and poke it forward.

trylist• 12 months ago

#2 I expect to wind up as a huge win professionally as well. It lowers the investment for creating an MVP or experimental/exploratory project from weeks to hours or days. That ability to try things that might have been judged too risky for a team previously will be pretty amazing.

InfinityByTen• 12 months ago

If my work involves doing a bit of tooling and improve the testing and documenting them, I find myself having much lesser resistance and I'm rather happy to give it off to an AI agent.

I haven't begun doing side projects or projects for self, yet. But I did go down the road of finding out what would be needed to do something I wished existed. It was much easier to explore and understand the components and I might have a decent chance at a prototype.

The alternative to this would have been to ask people around or formulate extensively researched questions for online forums, where I'd expect to get half cryptic answers (and a jibe at my ignorance every now and then) at a pace that I would take years before I had something ready.

I see the point for AI as a prototyping and brainstorming tool. But I doubt we are at a point where I would be comfortable pushing changes to a production environment without giving 3x the effort in reviewing. Since there's a chance of the system hallucinating, I have a genuine fear that it would seem accurate, but what it would do is something really really stupid.

thomascgalvin• 12 months ago

My experience with GenAI is that it's a significant improvement to Stack Overflow, and generally as capable as someone hired right out of college.

If I'm using it to remember the syntax or library for something I used to know how to do, it's great.

If I'm using it to explore something I haven't done before, it makes me faster, but sometimes it lies to me. Which was also true of Stack Overflow.

But when I ask it to so something fairly complex on it's own, it usually tips over. I've tried a bunch of tests with a bunch of models, and it never quite gets it right. Sometimes it's minor stuff that I can fix if I bang on it long enough, and sometimes it's a steaming pile that I end up tossing in the garbage.

For example, I've asked it to code me a web-based calculator, or a 3D model of the solar system using WebGL, and none of the models I've tried have been able to do either.

doc_manhat_32• 12 months ago

I think even claims of 2-5x are highly suspect. It would imply that if your team is using AI then all else equal they accomplish 2-5 times as much in a quarter. I don't know about you but I'm certainly not seeing this and most people on my team use AI.

[And to those saying we're using it wrong... well I can't argue with something that's not falsifiable]

__loam3• 12 months ago

There's a bunch of open source projects where we can observe that not happening in real time.

coffeebeqn3• 12 months ago

My company is all in on LLMs and honestly the improvement seems to be like 0.9x to 1.2x depending on the project. None of them are moving at break neck speed and many projects are just as bogged down by complexity as ever. Pretty big (3000+) company with a large mature codebase in multiple languages. For god knows how much money spent on it

paulcole4• 12 months ago

but I've never found those 10x claims convincing

Who's making these claims?

WD-424• 12 months ago

There was a YC video just a few months ago where a bunch of jergoffs sat in a circle and talked about engineers being 10 to 100x as effective as before. Im sure google will bring it up.

mkozlows• 12 months ago

The thing that I keep wondering about: If the coding part is 2-5x more productive for you, but the stuff around the coding doesn't change... at some point, it'll have to, right? The cost/benefit of a lot of practices (this article talks about code review, which is a big one) changes a lot if coding becomes significantly easier relative to other tasks.

coffeebeqn3• 12 months ago

Why would it have to? Most of the job is trying to understand what people really want and that’s not any easier than it was 10 or 30 years ago

simonw8• 12 months ago

Yes, absolutely. Code used to be more expensive to write, which meant that a lot of features weren't sensible to build - the incremental value they provided wasn't worth the implementation effort.

Now when I'm designing software there are all sorts of things where I'm much less likely to think "nah, that will take too long to type the code for".

cleak• 12 months ago

I’ve found I do get small bursts of 10x productivity when trying to prototype an idea - much of the research on frameworks and such just goes away. Of course that’s usually followed by struggling to make a seemingly small change for an hour or two. It seems like the 10x number is just classic engineers underestimating tasks - making estimates based on peak productivity that never materializes.

I have found for myself it helps motivate me, resulting in net productivity gain from that alone. Even when it generates bad ideas, it can get me out of a rut and give me a bias towards action. It also keeps me from procrastinating on icky legacy codebases.

sureglymop• 12 months ago

I do also believe that those who are often looked at or referred to as 10x engineers will maybe only see a marginal productivity increase.

The smartest programmer I know is so impressive mainly for two reasons: first, he seems to have just an otherworldly memory and seems to kind of have absolutely every little feature and detail of the programming languages he uses memorized. Second, his real power is really in cognitive ability, or the ability to always quickly and creatively come up with the smartest and most efficient yet elegant and clean solution to any given problem. Of course somewhat opinionated but in a good way. Funnily he often wouldn't know the academic/common name for some algorithm he arrived at but it just happened to be what made sense to him and he arrived at it independently. Like a talented musician with perfect pitch who can't read notation or doesn't know theory yet is 10x more talented than someone who has studied it all.

When I pair program with him, it's evident that the current iteration of AI tools is not as quick or as sharp. You could arrive at similar solutions but you would have to iterate for a very long time. It would actually slow that person down significantly.

However, there is such a big spectrum of ability in this field that I could actually see this increasing for example my productivity by 10x. My background/profession is not in software engineering but when I do it in my free time the perfectionist tendencies make me work very slowly. So for me these AI tools are actually cool for generating the first crappy proof of concepts for my side projects/ideas, just to get something working quickly.

skadamou• 12 months ago

I like the quip that AI raises the floor not the ceiling. I think it helps the bottom 20% perform more like the middle 50% but doesn't do much for people at the top.

threatofrain• 12 months ago

AI is strong in different places, and if it keeps on being strong in certain ways then people very soon won't be able to keep up. For example, extreme horizontal knowledge and the ability to digest new information almost instantly. That's not something anyone can do. We don't try to compete against computers on raw calculation, and soon we won't compete on this one either. We simply won't even think to compare.

People keep focusing on general intelligence style capabilities but that is the golden grail. The world could go through multiple revolutions before finding that golden grail, but even before then everything would have changed beyond recognition.

So write an integration over the API docs I just copy-pasted.

tmaly• 12 months ago

I wonder if a better metric would be developer happiness? Instead of being 2x or 5x more productive, what if we looked at what a developer enjoyed doing and figured out how to use AI for everything else?

WD-424• 12 months ago

If the 10x thing was true we’d all be on iOS 24 and the typescript Go rewrite would have been done months ago.

But of course that’s ridiculous.

9rx2• 12 months ago

and the typescript Go rewrite would have been done months ago.

10x is intended to symbolize a multiplier. As Microsoft fired that guy, 10 × 0 is still 0.

fulafel• 12 months ago

Speed of shipping software and pace of writing code are different things. Shipping software like iOS has a <50% component of programming so Amdahl's law caps the end-to-end improvement rather low, assuming other parts of the process stay the same.

GeoAtreides• 12 months ago

0.2x increase

1.2x increase

didibus2• 12 months ago

engineers that really know how to use this stuff effectively

I guess this is still the "caveat" that can keep the hype hopes going. But I've found at a team velocity level, with our teams, where everyone is actively using agentic coding like Claude Code on the daily, we actually didn't see an increase in team velocity yet.

I'm curious to hear anecdotal from other teams, has your team seen velocity increase since it adopted agentic AI?

jan_g• 12 months ago

Same here. I have a colleague that is completely enamored with these agents. Uses them for everything he can, not just coding. Commit messages, opening PRs, Linear tickets, etc. Basically, he uses agents for everything he can. But the productivity gain is just not there. He's about as fast or rather as slow as he was before. And to a degree I think this goes for the whole team. It's the oxymoron of AI: more code, more documentation, more text, more of everything generated than ever, but the effect is that this means more complexity, more PRs to review, more bugs, more stuff to know and understand, ... We are all still learning how to use these agents effectively. And the particular developer's effect can and does multiply as everything else with GenAI. Was he a bit sloppy before, not covering various edge-cases and used quick-and-dirty shortcuts? Then this remains true for the code he produces using agents. And to those, who claim that "by using more agents I will gain 10x productivity" I say please read a certain book about how just adding developers to a project makes it even more delayed. The resemblance of team/project leadership -> developers dynamic is truly uncanny.

nicknow• 12 months ago

Completely agree.

What will happen is over time this will become the new baseline for developing software.

It will mean we can deliver software faster. Maybe more so than other advances, but it won't fundamentally change the fact that software takes real effort and that effort will not go away, since that effort is much more than just coding this or that function.

I could create a huge list of things that have made developing and deploying quality software easier: linters, static type checkers, code formatters, hot reload, intelligent code completion, distributed version control (i.e., Git), unit testing frameworks, inference schema tools, code from schema, etc. I'm sure others can add dozens of items to that list. And yet there seems to be an unending amount of software to be built, limited only by the people available to build it and an organizations funding to hire those people.

In my personal work, I've found AI-assisted development to make me faster (not sure I have a good estimate for how much faster.) What I've also found is that it makes it much easier to tackle novel problems within an existing solution base. And I believe this is likely to be a big part of the dev productivity gain.

Just an example, lets say we want to use the strangler pattern as part of our modernization approach for a legacy enterprise app that has seen better days. Unless you have some senior devs who are both experienced with that pattern AND experienced with your code base, it can take a lot of trial and error to figure out how to make it work. (As you said, most of our work isn't actually typing code.)

This is where an AI/LLM tool can go to work on understanding the code base and understanding the pattern to create a reference implementation approach and tests. That can save a team of devs many weeks of trial & error (and stress) not to mention guidance on where they will run into roadblocks deep into the code base.

And, in my opinion, this is where a huge portion of the AI-assisted dev savings will come from - not so much writing the code (although that's helpful) but helping devs get to the details of a solution much faster.

It's that googling has always gotten us to generic references and AI gets us those references fit for our solution.

baxtr• 12 months ago

The problem is, if you don’t achieve 10x the venture case won’t work out properly.

SkyPuncher• 12 months ago

I've basically come to the same 2x to 5x conclusion as you. Problem is that "5x productivity" is really only a small portion of my actual job.

The hardest part of my job is actually understanding the problem space and making sure we're applying the correct solution. Actual coding is probably about 30% of my job.

That means, I'm only looking at something like 30% productivity gain by being 5x as effective at coding.

QuantumGood• 12 months ago

It lets me try things I couldn't commit the time to in the past, like quickly cobbling together a keystroke macro. I can also put together the outline of a plan in a few minutes. So much more can be 'touched' upon usefully.

prisenco• 12 months ago

| I think that's an under-estimation

I'm not sure it is and I'll take it a step further:

Over the course of development, efficiency gains trend towards zero.

AI has a better case for increasing surface area (what an engineer is capable of working on) and effectiveness, but efficiency is a mirage.

jprokay13• 12 months ago

At first I thought becoming “10x” meant outputting 10x as much code. Now that I’m using Claude more as an expensive rubber duck, I’m hoping that I spend more time defining the fundamentals correctly that will lead to a large improvement in outcomes in the long run.

giantg2• 12 months ago

I completely agree. I saw the claims about 30% increase in dev productivity a while ago and thought how is that possible when most of my job consists of meetings, SARs, threat modeling, etc.

EGreg• 12 months ago

“10x? That’s crazy talk. I’m only 5x more productive, let’s be accurate sirs”

Waterluvian• 12 months ago

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.

This feels exactly right and is what I’ve thought since this all began.

But it also makes me think maybe there are those that A.I. helps 10x, but more because that code input is actually a very large part of their job. Some coders aren’t doing much design or engineering, just assembly.

simonw8• 12 months ago

Yeah, I hadn't thought about that. If you really are a programmer who gets all of their work assigned to them as detailed specifications maybe you are seeing a 10x boost.

I don't think I've encountered programmer like that in my own career, but I guess they might exist somewhere!

belter3• 12 months ago

Looking forward to those 20x most productive days out of an LLMs. And what are those most productive days? The ones when you can simplify and delete hundreds of lines of code... :-)

edanm• 12 months ago

I agree. I'm a big fan/proponent of AI assisted development (though nowhere near your amount of experience with it). And I think that 2x-10x speed up can be true, depending on what you mean exactly and what your task is exactly.

This article thinks that most people who say 10x productivity are claiming 10x speedup on end-to-end delivering features. If that's indeed what someone is saying, they're most of the time quite simply wrong (or lying).

But I think some people (like me) aren't claiming that. Of course the end to end product process includes a lot more work than just the pure coding aspect, and indeed none of those other parts are getting a 10x speedup right now.

That said, there are a few cases where this 10x end-to-end is possible. E.g. when working alone, especially on new things but not only - you're skipping a lot of this overhead. That's why smaller teams, even solo teams, are suddenly super interesting - because they are getting a bigger speedup comparatively speaking, and possibly enough of one to be able to rival larger teams.

jolt42• 12 months ago

Programmers are notoriously bad about making estimates. Sure it sped something up 10x, but did you consider those 10 tries using AI that didn't pan out? You're not even breaking even, you are losing time.

moron4hire• 12 months ago

If 10x could be believed, we're long enough into having AI-coding assist that any such company that had gone all in would be head and shoulders above their competitors by now.

And we're not seeing that at all. The companies whose software I use that did announce big AI initiatives 6 months ago, if they really had gotten 10x productivity gain, that'd be 60 months—5 years—worth of "productivity". And yet somehow all of their software has gotten worse.

motorest• 12 months ago

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.

I think that the key realization is that there are tasks where LLMs excel and might even buy you 10x productivity, whereas some tasks their contribution might even be net negative.

LLM are largely excellent at writing and refactoring unit tests, mainly because their context is very limited (i.e., write a method in a class that calls this specific method of this specific class a specific way and check the output) and their output is very repetitive (i.e., write isolated methods in standalone classes without output that are not called anywhere). They also seem helpful when prompted to add logging. LLMs are also effective in creating greenfield projects, serving as glorified template engines. But when lightly pressed on specific tasks like implementing a cross-domain feature... Their output starts to be at best a big ball of mud.

buu700• 12 months ago

I don't doubt that some people are mistaken or dishonest in their self-reports as the article asserts, but my personal experience at least is a firm counterexample.

I've been heavily leaning on AI for an engagement that would otherwise have been impossible for me to deliver to the same parameters and under the same constraints. Without AI, I simply wouldn't have been able to fit the project into my schedule, and would have turned it down. Instead, not only did I accept and fit it into my schedule, I was able to deliver on all stretch goals, put in much more polish and automated testing than originally planned, and accommodate a reasonable amount of scope creep. With AI, I'm now finding myself evaluating other projects to fit into my schedule going forward that I couldn't have considered otherwise.

I'm not going to specifically claim that I'm an "AI 10x engineer", because I don't have hard metrics to back that up, but I'd guesstimate that I've experienced a ballpark 10x speedup for the first 80% of the project and maybe 3 - 5x+ thereafter depending on the specific task. That being said, there was one instance where I realized halfway through typing a short prompt that it would have been faster to make those particular changes by hand, so I also understand where some people's skepticism is coming from if their impression is shaped by experiences like that.

I believe the discrepancy we're seeing across the industry is that prompt-based engineering and traditional software engineering are overlapping but distinct skill sets. Speaking for myself, prompt-based engineering has come naturally due to strong written communication skills, strong code review skills (e.g. participating in security audits), and otherwise being what I'd describe as a strong "jack of all trades, master of some" in software development across the stack. On the other hand, for example, I could easily see someone who's super 1337 at programming high-performance algorithms and mid at most everything else finding that AI insufficiently enhances their core competency while also being difficult to effectively manage for anything outside of that.

As to how I actually approach this:

* Gemini Pro is essentially my senior engineer. I use Gemini to perform codebase-wide analyses, write documentation, and prepare detailed sprint plans with granular todo lists. Particularly for early stages of the project or major new features, I'll spend a several hours at a time meta-prompting and meta-meta-prompting with Gemini just to get a collection of prompts, documents, and JSON todo lists that encapsulate all of my technical requirements and feedback loops. This is actually harder than manual programming because I don't get the "break" of performing out all the trivial and boilerplate parts of coding; my prompts here are much more information-dense than code.

* Claude Sonnet is my coding agent. For Gemini-assisted sprints, I'll fire Claude off with a series of pre-programmed prompts and let it run for hours overnight. For smaller things, I'll pair program with Claude directly and multitask while it codes, or if I really need a break I'll take breaks in between prompting.

* More recently, Grok 4 through the Grok chat service is my Stack Overflow. I can't rave enough about it. Asking it questions and/or pasting in code diffs for feedback gets incredible results. Sometimes I'll just act as a middleman pasting things back and forth between Grok and Claude/Gemini while multitasking on other things, and find that they've collaboratively resolved the issue. Occasionally, I've landed on the correct solution on my own within the 2 - 3 minutes it took for Grok to respond, but even then the second opinion was useful validation. o3 is good at this too, but Grok 4 has been on another level in my experience; its information is usually up to date, and its answers are usually either correct or at least on the right track.

* I've heard from other comments here (possibly from you, Simon, though I'm not sure) that o3 is great at calling out anti-patterns in Claude output, e.g. its obnoxious tendency to default to keeping old internal APIs and marking them as "legacy" or "for backwards compatibility" instead of just removing them and fixing the resulting build errors. I'll be giving this a shot during tech debt cleanup.

As you can see, my process is very different from vibe coding. Vibe coding is fine for prototyping, on for non-engineers with no other options, but it's not how I would advise anyone to build a serious product for critical use cases.

One neat thing I was able to do, with a couple days' notice, was add a script to generate a super polished product walkthrough slide deck with a total of like 80 pages of screenshots and captions covering different user stories, with each story having its own zoomed out overview of a diagram of thumbnails linking to the actual slides. It looked way better than any other product overview deck I've put together by hand in the past, with the bonus that we've regenerated it on demand any time an up-to-date deck showing the latest iteration of the product was needed. This honestly could be a pretty useful product in itself. Without AI, we would've been stuck putting together a much worse deck by hand, and it would've gotten stale immediately. (I've been in the position of having to give disclaimers about product materials being outdated when sharing them, and it's not fun.)

Anyway, I don't know if any of this will convince anyone to take my word for it, but hopefully some of my techniques can at least be helpful to someone. The only real metric I have to share offhand is that the project has over 4000 (largely non-trivial) commits made substantially solo across 2.5 months on a part-time schedule juggled with other commitments, two vacations, and time spent on aspects of the engagement other than development. I realize that's a bit vague, but I promise that it's a fairly complex project which I feel pretty confident I wouldn't have been capable of delivering in the same form on the same schedule without AI. The founders and other stakeholders have been extremely satisfied with the end result. I'd post it here for you all to judge, but unfortunately it's currently in a soft launch status that we don't want a lot of attention on just yet.

Cthulhu_• 12 months ago

The other thing is that I don't believe software developers actually "do their best" when writing the code itself, that is, optimize the speed of writing code. Nor do they need to; they know writing the code doesn't take up time, waiting for CI and a code review and that iteration cycle does.

And does an AI agent doing a code review actually reduce that time too? I have doubts. Caveat, I haven't seen it in practice yet.

Jcampuzano2• 12 months ago

The 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.

evantbyrne• 12 months ago

10x 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• 12 months ago

I 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• 12 months ago

I 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.

9rx2• 12 months 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• 12 months ago

I thought this would be another AI hate article, but it made some great points.

One thing that AI has helped me with is finding pesky bugs. I mainly work on numerical simulations. At one point I was stuck for almost a week trying to figure out why my simulation was acting so strange. Finally I pulled up chatgpt, put some of my files into the context and wrote a prompt explaining the strange behavior and what I thought might be happening. In a few seconds it figured out that I had improperly scaled one of my equations. It came down to a couple missing parentheses, and once I fixed it the simulation ran perfectly.

This has happened a few times where AI was easily able to see something I was overlooking. Am I a 10x developer now that I use AI? No... but when used well, AI can have a hugely positive impact on what I am able to get done.

mentos• 12 months ago

Yep same experience here saved me an infinite amount of time so to me that puts me somewhere between 10x and infinity ha

wwweston• 12 months ago

This is my experience. Code generation is OK if uneven, but debugging can be a big boost.

It’s a rubber duck that’s pretty educated and talks back.

Gud• 12 months ago

Indeed. As a (mostly) hobbyist programmer LLMs have been a godsend for those late night coding sessions when the brain fog is thick.

rel2thr• 12 months ago

It’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?

jonathanstrange2• 12 months ago

I'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...

jdefr894• 12 months ago

You just said we won’t need to develop apps anymore.. What will be the AI need to create at all?

jonathanstrange2• 12 months ago

Ephemeral data processing and visualization programs. Nothing that needs to be installed or downloaded or runs on some web server. The concept is pretty simple. The operating system+AI will create those on the fly.

nimos• 12 months ago

Claude 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.

coltonv20• 12 months ago

This is precisely what I suggest, companies should pay for team plans like the one you are describing and see what comes of it.

swader9998• 12 months ago

My MacBook depreciates at higher than $200/month.

alistairSH• 12 months ago

Is 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.

swader9998• 12 months ago

I 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.

komali22• 12 months ago

When 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.

andrewmutz2• 12 months ago

The 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

coltonv20• 12 months ago

Perhaps 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.

andrewmutz2• 12 months ago

I 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• 12 months ago

If 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

swader9998• 12 months ago

I 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• 12 months ago

I'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.

bilsbie• 12 months ago

I’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• 12 months ago

Yep, 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.

neom• 12 months ago

The last month on our team: https://s.h4x.club/RBuDv0jd

The full year is just the more of the above.

Bukhmanizer• 12 months ago

I’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• 12 months ago

Product 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!

swader9998• 12 months ago

This 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• 12 months 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

necovek8• 12 months ago

An 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"?

zhivota• 12 months ago

The existence of 10x engineers is something no one believes until they meet one, and they are extremely rare so I can believe many people have never met one.

The co-founder of a company I worked at was one for a period (he is not a 10xer anymore - I don't think someone can maintain that output forever with life constraints). He literally wrote the bulk of a multi-million line system, most of the code is still running today without much change and powering a unicorn level business.

I literally wouldn't believe it, but I was there for it when it happened.

Ran into one more who I thought might be one, but he left the company too early to really tell.

I don't think AI is going to produce any 10x engineers because what made that co-founder so great was he had some kind of sixth sense for architecture, that for most of us mortals we need to take more time or learn by trial and error how to do. For him, he was just writing code and writing code and it came out right on the first try, so to speak. Truly something unique. AI can produce well specified code, but it can't do the specifying very well today, and it can't reason about large architectures and keep that reasoning in its context through the implementation of hundreds of features.

didibus2• 12 months ago

He literally wrote the bulk of a multi-million line system, most of the code is still running today without much change and powering a unicorn level business

I've been a bit of that engineer (though not at the same scale), like say wrote 70% of a 50k+ loc greenfield service. But I'm not sure it really means I'm 10x. Sometime this comes from just being the person allowed to do it, that doesn't get questioned in it's design choices, decisions of how to structure and write the code, that doesn't get any push back on having massive PRs where others almost just paper stamp it.

And you can really only do this at the greenfield phase, when things are not yet in production, and there's so much baseline stuff that's needed in the code.

But it ends up being the 80/20 rule, I did the 80% of the work in 20% of the time it'll take to go to prod, because that 20% remaining will eat up 80% of the time.

Balgair• 12 months ago

The way that I have seen 10x engineers in my career is as kind like this:

Junior: 100 total lines of code a day

Senior: 10,000 total lines of code a day

Guru: -100 total lines of code a day

CollinEMac• 12 months ago

No one is writing 10,000 lines of code every day nor should they be.

jp57• 12 months ago

The Fred Brooks insight about 10x was that the best programmers were 10x as productive as the worst programmers, not the average programmer.

I guess this leaves open question about the distribution of productivity across programmers and the difference between the min and the mean. Is productivity normally distributed? Log normal? Some kind of power law?

mrbombastic• 12 months ago

The worst programmers i have worked with have negative productivity in that they leave a mess of work for everyone else, so 10x programmers must be the reason any of us are employed!

sbochins• 12 months ago

I’m not sure if 10x engineers exist, but I do know 0.1x engineers exist. Being on a team with them makes a typical engineer seem like they’re driving 10x the expected impact.

codr7• 12 months ago

Totally agree, the best work isn't very visible.

Glyptodon• 12 months ago

I don't consider myself a 10x engineer. The number one thing that I've realized makes me more productive than other engineers at my company is thinking through system design and business needs with patterns that don't take badly written product tickets literally.

What I've seen with AI is that it does not save my coworkers from the pain of overcomplicating simple things that they don't really think through clearly. AI does not seem to solve this.

AdrianB13• 12 months ago

I don't consider myself a 2x engineer; my company tells me that by not paying me 2x vs my colleagues, even if I know (and others believe that too) I deliver more than 2x their output.

Using AI will change nothing in this context.

necovek8• 12 months ago

The first red flag there is "2x their output". You can find many an anecdote where a good engineer produced better solution in fewer lines of code (or sometimes, by removing code — the holy grail).

So always aim for outcomes, not output :)

At my company, we did promote people quickly enough that they are now close to double their salaries when they started a year or so ago, due to their added value as engineers in the team. It gets tougher as they get into senior roles, but even there, there's quite a bit of room for differentiation.

Additionally, since this is a market, you should not even expect to be paid twice for 2x value provided — then it makes no difference to a company if they get two 1x engineers instead, and you are really not that special if you are double the cost. So really, the "fair" value is somewhere in between: 1.5x to equally reward both parties, or leaning one way or the other :)

lvl1552• 12 months ago

Counter: you are looking at it wrong. You can get work done in 1/2 of the time it used to. Now you got 1/2 of the day to just mess around. Socialize or network. It’s not necessarily that you’re producing 2x.

1penny42cents• 12 months ago

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.

100%. The biggest challenge with software is not that it’s too hard to write, but that it’s too easy to write.

jsnell• 12 months 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.

coltonv20• 12 months ago

I 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• 12 months ago

I 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• 12 months ago

Agreed. Here's one of many HN posts on "No Silver Bullet": https://news.ycombinator.com/item?id=32423356

swader9998• 12 months ago

I 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.

jorblumesea• 12 months ago

This 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.

BeetleB• 12 months ago

Isn't this a strawman? I follow the AI coding space, and I've never found anyone claiming it made them 10x as productive.

pgt• 12 months ago

I 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

dclowd99012• 12 months 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.

OutputRiff• 12 months ago

I 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?

msgodel• 12 months ago

If 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.

kasey_junk8• 12 months ago

I 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• 12 months ago

It'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• 12 months ago

Couldn'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• 12 months ago

I 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.

coltonv20• 12 months ago

I'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• 12 months ago

100%. 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• 12 months 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.

swader9998• 12 months ago

Yes, 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.

j453• 12 months ago

AI 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.

paulhodge3• 12 months ago

I'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.

samtp11• 12 months ago

What 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.

paulhodge3• 12 months ago

Mainly working on a dev tool / SaaS app right now. The PII is user names & email.

On the security layer, I wrote that code mostly by hand, with some 'pair programming' with Claude to get the Oauth handling working.

When I have the agent working on tasks independently, it's usually working on feature-specific business logic in the API and frontend. For that work it has a lot of standard helper functions to read/write data for the current authenticated user. With that scaffolding it's harder (not impossible) for the bot to mess up.

It's definitely a concern though, I've been brainstorming some creative ways to add extra tests and more auditing to look out for security issues. Overall I think the key for extremely fast development is to have an extremely good testing strategy.

zoechi• 12 months ago

For me the main benefit is that AI chat provides at least 10x better results than Google ~search~ Ad results

recursive4• 12 months ago

Ad-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• 12 months ago

Anytime 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• 12 months ago

I'm getting a lot of side-quest productivity out of AI. There's always a bunch of things I could do, but they are tedious. Yet they are still things I wish I could get done. Those kinds of things AI is fantastic at. Building a mock, making tests, abstracting a few things into libraries, documentation.

So it's not like I'm delivering features in one day that would have taken two weeks. But I am delivering features in two weeks that have a bunch of extra niceties attached to them. Reality being what it is, we often release things before they are perfect. Now things are a bit closer to perfect when they are released.

I hope some of that extra work that's done reduces future bug-finding sessions.

hnroo99• 12 months ago

Side-quest productivity is a great way to put it... It does feel like AI effectively enables the opposite of "death by a thousand cuts" (life by a thousand bandaids?)

gspencley2• 12 months 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.

JoshuaDavid2• 12 months ago

There are some patterns you can use that help a bit with this problem. Lowest hanging fruit is to tell the LLM that its tests should test only through public interfaces where possible. Next after that is to add a "check if any non-public interfaces were used in places where a public interface exposes the same functionality the not-yet-committed tests - if so, rewrite tests to use only publicly exposed interfaces" step to the workflow. You could likely also add linter rules, though sometimes you genuinely need to test something like error conditions that can't reasonably be tested only through public interfaces.

mccoyb• 12 months ago

Force the LLM to write property-based tests (depends on the language you use whether or not good libraries are available -- but if they are available 100% make use of them). Iterate with the LLM on the invariants.

Forcing the discussion of invariants, and property-based testing -- seems to improve on the issues you're mentioning (when using e.g. Opus 4), especially when combined with the "use the public API" or interface abstractions.

arrowsmith• 12 months ago

I like that "side quests" framing.

For much of what I build with AI, I'm not saving two weeks. I'm saving infinity weeks — if LLMs didn't exist I would have never built this tool in the first place.

neals• 12 months ago

Google became Expert exchange become stackoverflow became Google again became Chatgpt became Google again. Every time, so much more faster to get your boilerplate.

stillpointlab• 12 months ago

I 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.

adocomplete2• 12 months ago

AI 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• 12 months ago

You're doing it wrong. AI is making me 2375600x productive in all tasks.

willismichael• 12 months ago

Including writing comments on HN.

zahlman5• 12 months ago

and 0x in some

As in, it's now completely preventing you from doing things you could have before?

adocomplete2• 12 months ago

Not preventing, but def wasting time trying to do something that I think it's able to do, but isn't.

zaking17• 12 months ago

The 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/ -- 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• 12 months ago

Hi 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• 12 months ago

The 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• 12 months ago

hopefully 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• 12 months ago

I’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• 12 months ago

This 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• 12 months ago

The 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• 12 months ago

It's optimizing the part that is easy at the cost of the part that is hard.

zahlman5• 12 months 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?

coltonv20• 12 months ago

I'd call that "Making developer experience investments that save everyone just a bit of time on every task."

zahlman5• 12 months ago

Ah, I guess I missed your meaning, then.

hirvi74• 12 months 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• 12 months ago

I 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• 12 months ago

Words 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.

jdefr894• 12 months ago

Or shit you know what… Hacker News for that matter…

HarHarVeryFunny3• 12 months ago

There 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

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 ...

varispeed2• 12 months ago

Depending 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.

HarHarVeryFunny3• 12 months ago

I 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• 12 months ago

Say you want to create a web app, but you don't know any web dev. You spend a couple of months reading front-end and back-end dev, incrementally create something, and after half a year you've made a web app you like. Say you spent 4 hours a day, 5 days a week, for 6 weeks, going from zero to a functional web app. So you spent 120 hours in total.

Now let's say you use Claude code, or whatever, and you're able to create the same web app over a weekend. You spend 6 hours a day on Saturday and Sunday, in total 12 hours.

That's 10x increase in productivity right there. Did it make you a 10x better programmer? Nope, probably not. But your productivity went up by a tenfold.

And at least to me, that's sort of how it has worked. Things I didn't have motivation or energy to get into before, I can get into over a weekend.

nxpnsv• 12 months ago

However, in the first case you learned something which probably is useful when you want to change said app in any way or make another project...

theshrike794• 12 months ago

Depends on how you learn.

For me it's 50-50 reading other people's code and getting a feel for the patterns and actually writing the code.

dclowd99012• 12 months ago

I'm not sure that math makes sense over the long run. Sure, at first you scaffold together an app from scratch, but I suspect over time, the LLM's capability of maintaining it precipitously drops. At some point, you will like reach a productivity level of zero, as now your application has become too complex to fit in a context window and you have no idea how it actually works. So what is the productivity multiplier then?

CER10TY3• 12 months ago

The issue is that it‘ll absolutely _suck_. If I tell Claude Code to scaffold a web app from 0 outside of React it‘s terrible.

So no, imho people with no app dev skills cannot just build something over a weekend, at least something that won‘t break when the first user logs in.

SchemaLoad• 12 months ago

They will build it, deploy it, get hacked and leak user data.

breakingwalls• 12 months ago

This, I agree to this 100%. I was able to get at least 2 apps, 2 SAAS products out by pairing up with AI. I was able to learn this as I go and get an app running in the matter of hours than months. Great for prototype to production. -> learn-> fix -> ship -> learn more -> fix things -> ship more.

That being said, I am a generalist with 10+ years of experience and can spot the good parts from bad parts and can wear many hats. Sure, I do not know everything, but, hey did I know everything when AI was not there? I took help from SO, Reddit and other places. Now, I go to AI, see if it makes sense, apply the fix, learn and move on.

samtp11• 12 months ago

But at the same time you basically outsourced your brain and any learning that would come from the exercise. So while you now have an app, you've experienced close to 0 learning or growth along the way.

Quarrelsome6• 12 months ago

you're going to push that straight to production? Cmon man, its not the same thing, not by a long shot. That's a crap measure. I don't think we can even reliably measure 1x developer output which makes multiplying it even more nonsensical.

misja1112• 12 months ago

This is true, for small projects, one-offs and prototypes AI is great, it will save you loads of time.

However most paid jobs don't fall into this category.

axi0m2• 12 months ago

You're talking about a neophyte trying to accomplish an engineering job. So yes, in such case, LLMs dramatically change the game, at least for entry-level tasks. Nowadays, anyone can litteraly pretend to be good/average in any field.

The article relates about actual, experienced engineers trying to get even better. That's a completely different matter.

noahdutch• 12 months ago

AI 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• 12 months ago

I thought AI is making actual experienced developers 19% less productive?

https://www.businessinsider.com/ai-coding-tools-may-decrease...

nathan_compton• 12 months ago

I 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• 12 months 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• 12 months ago

Maybe 1x engineer is not going to 10x.

But is a 10x going to 100x?

magicmicah85• 12 months 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• 12 months ago

I 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.

jonas212• 12 months 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.

coltonv20• 12 months ago

I 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• 12 months ago

if 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• 12 months 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

crvdgc• 12 months 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• 12 months ago

Does 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• 12 months ago

Based 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

outime• 12 months ago

Nitpick (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).

itissid2• 12 months ago

A few things need to happen very soon(if the signs are not here already):

1. Tech Company's should be able to accelerate and supplant the FAANGs of this world. Like even if 10x was discounted to 5x. It would mean that 10 human years of work would be shrunk down to 2 to make multi-billion dollar companies. This is not happening right now. If this does not start happening with the current series of model, murphy's law (e.g. interest rate spike at some point) or just damn show me the money brutal questions would tell people if it is "working".

2. I think Anthropic's honcho did a back of the envelope number of 600$ for every human in the US(I think just it was just the US) was necessary to justify Nvidia's market Cap. This should play out by the end of this year or in Q3 report.

gmm1990• 12 months ago

Extremely anecdotal but all I keep seeing is relatively stable services (the google one comes to mind) having major outages. I assume its not AI related or directly ai related at least, but you'd think these outages would be less common if AI was adding so much value.

itissid2• 12 months ago

Human 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• 12 months ago

Agree 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• 12 months ago

I 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• 12 months ago

Nah, 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• 12 months ago

While 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• 12 months ago

It turns out, everyone was already a 10-100x engineer in a greenfield project.

quaintdev• 12 months ago

Interesting 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.

coltonv20• 12 months ago

Has 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• 12 months ago

When 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• 12 months ago

Was pleased to see Austen Allred catching strays in this article. May he never live it down.

abelanger• 12 months ago

One 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• 12 months ago

Really enjoyed this post. I think this is the best mindset to have around the future of AI programming.

voxleone2• 12 months ago

There’s something ironic here. For decades, we dreamed of semi-automating software development. CASE tools, UML, and IDEs all promised higher-level abstractions that would "let us focus on the real logic."

Now that LLMs have actually fulfilled that dream — albeit by totally different means — many devs feel anxious, even threatened. Why? Because LLMs don’t just autocomplete. They generate. And in doing so, they challenge our identity, not just our workflows.

I think Colton’s article nails the emotional side of this: imposter syndrome isn’t about the actual 10x productivity (which mostly isn't real), it’s about the perception that you’re falling behind. Meanwhile, this perception is fueled by a shift in what “software engineering” looks like.

LLMs are effectively the ultimate CASE tools — but they arrived faster, messier, and more disruptively than expected. They don’t require formal models or diagrams. They leap straight from natural language to executable code. That’s exciting and unnerving. It collapses the old rites of passage. It gives power to people who don’t speak the “sacred language” of software. And it forces a lot of engineers to ask: What am I actually doing now?

coltonv20• 12 months ago

Very interesting perspective. Thanks for sharing!

kazinator2• 12 months ago

Let's connect on HackedIn!

galaxyLogic• 12 months 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• 12 months ago

Have a look at https://zfhuang99.github.io/tla+/pluscal/chatgpt/2023/09/24/...

jdefr894• 12 months ago

It kills the magic of coding for sure. The thing is. Now with everyone doing it, you get a ton of slop. Computing’s become saturated as hell. We don’t even need more code as it is. Before LLMs you could pretty much find what you needed on github… Now it’s even worse.

torginus• 12 months ago

I now understand what artists felt when seeing stable diffusion images - AI code is often just wrong - not in the moral sense, but it contains tons of bugs, weirdness, excess and peculiarities you'd never be happy to see in a real code base. Often getting rid of all of this, takes comparable amount of time as doing the job in the first place.

Now I can always switch to a different model, increase the context, prompt better etc. but I still feel that actual good quality AI code is just out of arms reach, or when something clicks, and the AI magically starts producing exactly what I want, that magic doesn't last.

Like with stable diffusion, people who don't care as much or aren't knowledgeable enough to know better, just don't get what's wrong with this.

A week ago, I received a bug ticket claiming one of the internal libs i wrote didn't work. I checked out the reporter's code, which was full of weird issues (like the debugger not working and the typescript being full of red squiggles), and my lib crashed somewhere in the middle, in some esoteric minified js.

When I asked the guy who wrote it what's going on, he admitted he vibe coded the entire project.

rhines• 12 months ago

The comparison to art is apt. Generated art gets the job done for most people. It's good enough. Maybe it's derivative, maybe there are small inaccuracies, but it is available instantly for free and that's what matters most. Same with code, to many people.

And the knock-on effect is that there is less menial work. Artists are commissioned less for the local fair, their friend's D&D character portrait, etc. Programmers find less work building websites for small businesses, fixing broken widgets, etc.

I wonder if this will result in fewer experts, or less capable ones. As we lose the jobs that were previously used to hone our skills will people go out of their way to train themselves for free or will we just regress?

anytime5704• 12 months ago

When I asked the guy who wrote it what's going on, he admitted he vibe coded the entire project.

This really irritates me. I’ve had the same experience with teammates’ pull requests they ask me to review. They can’t be bothered to understand the thing, but then expect you to do it for them. Really disrespectful.

melagonster• 12 months ago

At same time, there's also a huge of annoying Tech-brothers constantly shouting at artists something like, 'Your work was never valuable to begin with; why can't I copy your style? You're nothing but another matrix.'

jdelman2• 12 months ago

Speaking of irony... did ChatGPT help you write this comment?

dehugger• 12 months ago

It doesn't read to me like AI content. It's also against HN guidelines to randomly suggest comments are AI generated.

mikestorrent• 12 months ago

Doesn't read like it to me. It has "reddit spacing", but enough information density and colloquialism that it's probably a person.

Wowfunhappy2• 12 months ago

Please stop assuming that every comment that includes an em dash is AI. Em dashes are very useful!

jjmarr2• 12 months ago

"Software engineering" will become vibe fixing.

There's many jobs that can be eliminated with software, but haven't because managers don't want to hire SWEs without proven value. I don't think HN realizes how big that market is.

With AI, the managers will replace their employees with a bunch of code they don't understand, watch that code fail in 3 years, and have to hire SWEs to fix it.

I'd bet those jobs will outnumber the ones initially eliminated by having non-technical people deliver the first iteration.

Many of those jobs will be high-skill/impact because they are necessarily focused on fixing stuff AI can't understand.

zdragnar• 12 months ago

I try using an LLM for coding now and then, and tried again today with giving a model dedicated to coding a rather straight forward prompt and task.

The names all looked right, the comments were descriptive, it has test cases demonstrating the code work. It looks like something I'd expect a skilled junior or a senior to write.

The thing is, the code didn't work right, and the reasons it didn't work were quite subtle. Nobody would have fixed it without knowing how to have done it in the first place, and it took me nearly as long to figure out why as if I'd just written it myself in the first place.

I could see it being useful to a junior who hasn't solved a particular problem before and wanted to get a starting point, but I can't imagine using it as-is.

belter3• 12 months ago

You miss the fundamental constraint. The bottleneck in software development was never typing speed or generation, but verification and understanding.

Even if LLMs worked perfectly without hallucinations (they don't and might never), a conscientious developer must still comprehend every line before shipping it. You can't review and understand code 10x faster just because an LLM generated it.

In fact, reviewing generated code often takes longer because you're reverse-engineering implicit assumptions rather than implementing explicit intentions.

The "10x productivity" narrative only works if you either:

- Are not actually reviewing the output properly

- Are working on trivial code where correctness doesn't matter.

Real software engineering, where bugs have consequences, remains bottlenecked by human cognitive bandwidth, not code generation speed. LLMs shifted the work from writing to reviewing, and that's often a net negative for productivity.

kaonwarb2• 12 months ago

Even if LLMs worked perfectly without hallucinations (they don't and might never), a conscientious developer must still comprehend every line before shipping it.

This seems excessive to me. Do you comprehend the machine code output of a compiler?

wikibob• 12 months ago

AI slop.

- it’s not just X, it’s Y

- emdashes everywhere

zaptheimpaler2• 12 months ago

I think if you're paying any attention to the state of the world, you can see labor is getting destroyed by capital - bad wages, worse working conditions including more surveillance, metrics everywhere, immoral companies, short contracts and unstable companies/career paths, increasing monopolization and consolidation of power. We were so insulated from this for so long that it's easy to not really grasp how bad things are for most workers. Now the precarity of our situation is dawning on us.

shlant• 12 months ago

omg not everything is late stage capitalism

_dain_2• 12 months ago

lmao well done. can't tell if it's the topvoted reply because people got the joke, or because they didn't.

joe_the_user• 12 months ago

People object to CASE tools also I think.

And while I don't categorically object to AI tools, I think your selling objections to them short.

It's completely legitimate to want an explainable/comprehendable/limited-and-defined tool rather than a "it just works" tool. Ideally, this puts one in an "I know its right" position rather than a "I scanned it and it looks generally right and seems to work" position.

jdefr894• 12 months ago

as 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• 12 months ago

What makes this "AI will replace you with a 10x AI-based engineer" narrative a non-starter is actually having a 10x non-AI engineer on the team.

techpineapple• 12 months ago

Has 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• 12 months ago

Such 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.

gwbas1c2• 12 months 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.

skydhash5• 12 months ago

Listening 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.

gwbas1c2• 12 months 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• 12 months ago

things 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

lemonwaterlime• 12 months ago

I'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• 12 months 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• 12 months ago

They 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• 12 months ago

It is finally starting to feel like the craze and hype bubble is being rightfully questioned left and right.

jwrallie• 12 months ago

The 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• 12 months ago

Whatever productivity gains I get is usually replacing documentation discovery and lookup rather than typing code

fjlijwliej• 12 months 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• 12 months ago

If 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• 12 months ago

Thanks colton. Man, you just made me feel 10x better :) And ahh yes I said 10x. :P

joshdavham• 12 months ago

Personally, he made me feel 100x better!

coltonv20• 12 months ago

I'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• 12 months ago

There 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• 12 months ago

Don't tell that to the investors investing hundreds of billions in AI

dash2• 12 months ago

I 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/ 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• 12 months ago

The 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• 12 months ago

This 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• 12 months ago

Funnily, 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).

misja1112• 12 months ago

The 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• 12 months ago

It 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-toast2• 12 months ago

I 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• 12 months ago

I'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• 12 months ago

Olympic 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• 12 months ago

I 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• 12 months ago

I 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• 12 months ago

Oceoss• 12 months ago

ufff, 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• 12 months ago

The author focuses too much on the strawman of 10x engineer. If an engineer is even 2x more productive overall, that's a huge deal.

deterministic2• 12 months ago

Customer 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.

axi0m2• 12 months ago

Great 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.

Things that helped me get out of the AI 10x engineer imposter syndrome

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