I'm almost 50, and have been writing code professionally since the late 90s. I can pretty much see projects in my head, and know exactly what to build. I also get paid pretty well for what I do. You'd think I'd be the prototype for anti-AI.
I'm not.
I can build anything, but often struggle with getting bogged down with all the basic work. I love AI for speed running through all the boring stuff and getting to the good parts.
I liken AI development to a developer somewhere between junior and mid-level, someone I can given a paragraph or two of thought out instructions and have them bang out an hour of work. (The potential for then stunting the growth of actual juniors into tomorrow's senior developers is a serious concern, but a separate problem to solve)
I love AI for speed running through all the boring stuff and getting to the good parts.
In some cases, especially with the more senior devs in my org, fear of the good parts is why they're against AI. Devs often want the inherent safety of the boring, easy stuff for a while. AI changes the job to be a constant struggle with hard problems. That isn't necessarily a good thing. If you're actually senior by virtue of time rather than skill, you can only take on a limited number of challenging things one after another before you get exhausted.
Companies need to realise that AI to go faster is great, but there's still a cognitive impact on the people. A little respite from the hardcore stuff is genuinely useful sometimes. Taking all of that away will be bad for people.
That said, some devs hate the boring easy bits and will thrive. As with everything, individuals need to be managed as individuals.
That makes me think of https://store.steampowered.com/app/2262930/Bombe/ which is a version of Minesweeper where instead of clicking on squares you define (parametric!) rules that propagate information around the board automatically. Your own rules skip all the easy parts for you. As a result, every challenge you get is by definition a problem that you've never considered before. It's fun, but also exhausting.
I remember listening to a talk about Candy Crush and how they designed the game to have a few easy levels in between the hard ones, to balance feeling like you're improving while also challenging players. If all the levels get progressively harder, then a lot of people lose motivation to keep playing.
That's crazy to me. I solve problems. I'm not a janitor or tradesman, you bring me in to envision and orchestrate solutions that bring bottom line value. I live to crack hard nuts, if I never have to bother with rigging again I'll be so happy.
There's also the fact that, while you're coding the easy stuff, your mind is thinking about the hard stuff, looking things up, seeing how they articulate. If you're spending 100% of your time on hard stuff, you might be hurting these preliminaries.
This makes no sense. Yes, having time to think about the hard part is good, but just because you’re not doing the boilerplate anymore doesn’t mean you can’t do the thinking part anymore! See how absurd it sounds when you actually describe it this way?
I know brilliant people who took up knitting to keep their hands busy while they think over their difficult problems. But that only works if you can knit in your work hours. Sadly, despite clearly improving the productivity of these people, this is a fireable offense in many jobs.
I'm not saying that the only way to think through a hard problem is to work on boilerplate. If you are in a workplace where you can knit, or play table soccer, by all means, and if these help you, by all means, go for it.
What I'm thinking out loud is that if we're dedicating 100% of our time to the hard problems, we'll hit a snag, and that boilerplate may (accidentally) serve as the padding that makes sure we're not at these 100%.
That being said, I'm not going to claim this as a certainty, just an idea.
I don't disagree, but I find a better use of my time is writing. Not code, but essentially a work journal. It's not big thoughts, it's bullet points. It's not documentation, but more of a open mind map: what's been done, what needs to be done, questions that inevitably pop up, etc. I use Obsidian for this, but if I write much more than what would go on a few post-it notes, it's too much.
> Devs often want the inherent safety of the boring, easy stuff for a while
That's matches my experience. In my first job, every time a new webapp project has been starting it was fun. Not because of challenges or design, but simply because of the trivial stuff done for n-th time - user accounts, login, password reset, admin panel. Probably should have been automated at this point, but we got away with reinventing the wheel each time.
> AI changes the job to be a constant struggle with hard problems.
Very true. I think AI (especially Claude Code) forced me to actually think hard about the problem at hand before implementing the solution. And more importantly, write down my thoughts before they fleet away from my feeble mind. A discipline that I wished I had before.
That's strange, I've never thought it can be done this way. Normally I'd read the docs, maybe sketch up some diagrams, then maybe take a walk while thinking on how to solve the problem, and by the time I got back to the screen I'd already have a good idea on how to do it.
These days the only difference is that I feed my ideas to a few different LLMs to have "different opinions". Usually they're crap but sometimes they present something useful that I can implement.
I think you're describing things we already knew long before this era of AI. Less code is better code, and the vast majority of bugs come from the devs who "hate the boring easy bits".
I disagree that this has anything to do with people needing a break. All code eventually has to be reviewed. Regardless of who or what wrote it, writing too much of it is the problem. It's also worth considering how much more code could be eliminated if the business more critically planned what they think they want.
These tensions have existed even before computers and in all professions.
>AI changes the job to be a constant struggle with hard problems
I find this hilarious. From what I've seen watching people do it, it changes the job from deep thought and figuring out a good design to pulling a lever on a slot machine and hoping something good pops out.
The studies that show diminished critical thinking have matched what i saw anecdotally pairing with people who vibe coded. It replaced deep critical thinking with a kind of faith based gambler's mentality ("maybe if i tell it to think really hard it'll do it right next time...").
The only times ive seen a notable productivity improvement is when it was something not novel that didnt particularly matter if what popped out was shit - e.g. a proof of concept, ad hoc app, something that would naturally either work or fail obviously, etc. The buzz people get from these gamblers' highs when it works seems to make them happier than if they didnt use it at all though.
Which was my original point. Not that the outcome is shit. So much of what we write is absolutely low-skill and low-impact, but necessary and labor-intensive. Most of it is so basic and boilerplate you really can't look at it and know if it was machine- or human-generated. Why shouldn't that work get cranked out in seconds instead of hours? Then we can do the actual work we're paid to do.
To pair this with the comment you're responding to, the decline in critical thinking is probably a sign that there's many who aren't as senior as their paycheck suggests. AI will likely lead to us being able to differentiate between who the architects/artisans are, and who the assembly line workers are. Like I said, that's not a new problem, it's just that AI lays that truth bare. That will have an effect generation over generation, but that's been the story of progress in pretty much every industry for time eternal.
> So much of what we write is absolutely low-skill and low-impact, but necessary and labor-intensive. Most of it is so basic and boilerplate you really can't look at it and know if it was machine- or human-generated.
Is it really? Or is it a refusal to do actual software engineering, letting the machine taking care of it (deterministically) and moving up the ladder in terms of abstraction. I've seen people describing things as sludge, but they've never learned awk to write a simple script to take care of the work. Or learned how to use their editor, instead using the same pattern they would have with Notepad.
I think it's better to take a step back and reflect on why we're spending time on basic stuff instead. Instead of praying that the LLM will generate some good basic stuff.
If you're not able to review what it generates, you shouldn't be using it (and arguably are the wrong person to be doing the boilerplate work to begin with)
Put differently, I go back to my original comment, where AI is essentially a junior/mid dev that you can express what needs to be done with enough detail. In either case, AI or dev, you'd review and/or verify it.
> Or is it a refusal to do actual software engineering, letting the machine taking care of it (deterministically) and moving up the ladder in terms of abstraction.
One could say the same of installing packages in most modern programming languages instead of writing the code from first principles.
I think there are two kinds of uses for these tools:
1) you try to explain what you want to get done
2) you try to explain what you want to get done and how to get it done
The first one is gambling, the second one has very small failure rate, at worst, the plan it presents shows it's not getting the solution you want it to do.
The thing is to understand that a model has "priors" which steer how it generates code. If what you're trying to build matches the priors of the model you can basically surf the gradients to working software with no steering using declarative language. If what you want to build isn't well encoded by the models priors it'll constantly drift, and you need to use shorter prompts and specify the how more (imperative).
> In some cases, especially with the more senior devs in my org, fear of the good parts is why they're against AI. Devs often want the inherent safety of the boring, easy stuff for a while. AI changes the job to be a constant struggle with hard problems. That isn't necessarily a good thing. If you're actually senior by virtue of time rather than skill, you can only take on a limited number of challenging things one after another before you get exhausted.
The issue of senior-juniors has always been a problem; AI simply means they're losing their hiding spots.
100% agree. I am interested in seeing how this will change how I work. I'm finding that I'm now more concerned with how I can keep the AI busy and how I can keep the quality of outputs high. I believe it has a lot to do with how my projects are structured and documented. There are also some menial issues (e.g. structuring projects to avoid merge conflicts becoming bottlenecks)
I expect that in a year my relationship with AI will be more like a TL working mostly at the requirements and task definition layer managing the work of several agents across parallel workstreams. I expect new development toolchains to start reflecting this too with less emphasis on IDEs and more emphasis on efficient task and project management.
I think the "missed growth" of junior devs is overblown though. Did the widespread adoption of higher-level really hurt the careers of developers missing out on the days when we had to do explicit memory management? We're just shifting the skillset and removing the unnecessary overhead. We could argue endlessly about technical depth being important, but in my experience this hasn't ever been truly necessary to succeed in your career. We'll mitigate these issues the same way we do with higher-level languages - by first focusing on the properties and invariants of the solutions outside-in.
> I can build anything, but often struggle with getting bogged down with all the basic work. I love AI for speed running through all the boring stuff and getting to the good parts.
I'm in the same boat (granted, 10 years less) but can't really relate with this. By the time any part becomes boring, I start to automate/generalize it, which is very challenging to do well. That leaves me so little boring work that I speed run through it faster by typing it myself than I could prompt it.
The parts in the middle – non-trivial but not big picture – in my experience are the parts where writing the code myself constantly uncovers better ways to improve both the big picture and the automation/generalization. Because of that, there are almost no lines of code that I write that I feel I want to offload. Almost every line of code either improves the future of the software or my skills as a developer.
But perhaps I've been lucky enough to work in the same place for long. If I couldn't bring my code with me and had to constantly start from scratch, I might have a different opinion.
I find it best as a "personal assistant," that I can use to give me information -sometimes, highly focused- at a moment's notice.
> The potential for then stunting the growth of actual juniors into tomorrow's senior developers is a serious concern
I think it's a very real problem. I am watching young folks being frozen out of the industry, at the very beginning of their careers. It is pretty awful.
I suspect that the executives know that AI isn't yet ready to replace senior-levels, but they are confident that it will, soon, so they aren't concerned that there aren't any more seniors being crafted from youngsters.
I have a couple of niche areas of non-coding interest where I'm using AI to code. It is so amazing to write rust and just add `todo!(...)` through out the boiler plate. The AI is miserable at implementing domain knowledge in those niche areas, but now I can focus on describing the domain knowledge (in real rust code because I can't describe it precisely enough in English + pseudo code), and then say "fill in the todos, write some tests make sure it compiles, and passes linting", verify the tests check things properly and I'm done.
I've struggled heavily trying to figure out how to get it to write the exactly correct 10 lines of code that I need for a particularly niche problem, and so I've kind of given up on that, but getting it to write the 100 lines of code around those magic 10 lines saves me so much trouble, and opens me up to so many more projects.
> I can pretty much see projects in my head, and know exactly what to build.
This is where AI actually helps - you have a very precise vision of what you want, but perhaps you've forgotten about the specific names of certain API methods, etc. Maybe you don't want to implement all the cases by hand. Often validating the output can take just seconds when you know what it is you're looking for.
The other part of making the output do what you want is the ability to write a prompt that captures the most essential constraints of your vision. I've noticed the ability to write and articulate ideas well in natural language terms is the actual bottleneck for most developers. It takes just as much practice communicating your ideas as it does anything else to get good at it.
Exactly. I tend to like Hotz, but by his description, every developer is also "a compiler", so it's a useless argument.
My life quality (as a startup cofounder wearing many different hats across the whole stack) would drop significantly if Cursor-like tools [1] were taken away from me, because it takes me a lot of mental effort to push myself to do the boring task, which leads to procrastination, which leads to delays, which leads to frustration. Being able to offload such tasks to AI is incredibly valuable, and since I've been in this space from "day 1", I think I have a very good grasp on what type of task I can trust it to do correctly. Here are some examples:
- Add logging throughout some code
- Turn a set of function calls that have gotten too deep into a nice class with clean interfaces
- Build a Streamlit dashboard that shows some basic stats from some table in the database
- Rewrite this LLM prompt to fix any typos and inconsistencies - yeah, "compiling" English instructions into English code also works great!
- Write all the "create index" lines for this SQL table, so that <insert a bunch of search usecases> perform well.
[1] I'm actually currently back to Copilot Chat, but it doesn't really matter that much.
That's one of the thing that I wouldn't delegate to LLM. Logging is like a report of things that happens. And just like a report, I need relevant information and the most useful information.
...
A lot of these use cases actually describes the what. But the most important questions is always the why. Why is it important to you? Or to the user? That's when things have a purpose and not be just toys.
Code with logging is "self reporting". Adding logging statements is not reporting itself. Adding `logger.error(f"{job} failed")` is not reporting itself, and LLMs are perfectly capable of adding such statements in applicable places.
As to why, it's because I'm building an app with a growing userbase and need to accommodate to their requirements and build new features to stay ahead of the competition. Why you decided I'm describing a toy project is beyond me.
As someone else said: Launch is only the first step. That's when practicality start to matter.
The reason senior engineers are being paid that well is not because they need to type a lot of code to get new features in. It's because they need to figure how to have less code while having more features.
> I love AI for speed running through all the boring stuff and getting to the good parts.
But the issue is some of that speedrunning sometimes takes so much time, it becomes inefficient. It's slowly improving (gpt5 is incredible), but sometimes it get stuck on really mundane issue, and regress endlessly unless I intervene. And I am talking about straightforwars functional code.
That‘s exactly why i like AI too. I even let them play roles like „junior dev“, „product owner“ or „devops engineer“ and orchestrate them, to play together as a team - with guidance from me (usually the „solution architect“ or „investor“)! This „team“ achieves in weeks what we usually needed months for - for 2.40€/h*role!
Yes, unfortunately the boring parts are what junior devs used to do so the senior devs could work on the good stuff. Now that AI is doing the boring stuff nobody has to hire those pesky jr developers anymore. Yay?
The problem is that junior developers are what we make senior developers with— so in 15 years, this is going to be yet another thing that the US used to be really good at, but is no longer capable of doing, just like many important trades in manufacturing. The manufacturers were all only concerned with their own immediate profit and made the basic sustainability of their workforce, let alone the health of the trades that supported their industries, a problem for everyone else to take care of. Well, everyone else did the same thing.
It’s yet another place where we know our own capacity as a society is shrinking and hoping that ??? (Ai? Robots? Fusion?) will fix it before it’s too late. I never thought programming would join elder-care in this category though, that came as a surprise.
I have a similar relation to AI with programming -- and my sense is very many HN readers do as well, evidenced not least by the terrific experience report from antirez [1]. Yet it is rare to see such honest and open statements even here. Instead, HN is full of endless anti-AI submissions on the front page where the discussion underneath is just an echo chamber of ill-substantiated attacks on 'AI hype' and where anything else is down-voted.
It's what is, to me, so bizarre about the present moment: certainly investment is exceptionally high in AI (and of course use), but the dominant position in the media is precisely such a strange 'anti-AI hype' that positions itself as a brave minority position. Obviously, OpenAI/Altman have made some unfortunate statements in self-promotion, but otherwise I genuinely can't think of something I've read that expresses the position attacked by the anti-AI-ers -- even talk of 'AGI' etc comes from the AI-critical camp.
In a sense, the world seems divided into three: obvious self-promotion from AI companies that nobody takes seriously, ever-increasingly fervent 'AI critique', and the people who, mostly silent, have found modern AI with all its warts to be an incomparably useful tool across various dimensions of their life and work. I hope the third camp becomes more vocal so that open conversations about the ways people have found AI to be useful or not can be the norm not the exception.
It’s hard to see how the current rate of progress is compatible with, 30 years from now, it being good business sense to pay human professionals six figure salaries. Opinions then split: the easiest option is pure denial, to assume that the current rate of progress doesn’t exist. Next easiest is to assume that progress will halt soon, then that we will be granted the lifestyle of well paid professionals when unsupervised AI can do our job for cheaper, then that Altman will at least deign to feed us.
Most of my work is for my employer, but the bigger point is that you wouldn't be able to tell my "AI work" from my other work because I primarily use it for the boring stuff that is labor-intensive, while I work on the actual business cases. (Most of my work doesn't fall under the category of "web application", but rather, backend and background-processing intensive work that just happens to have an HTML front-end)
Yes, I wrote my own DSL, complete with BDD testing framework, to write my blog with. In Rust!
GET /hello/:world
|> jq: `{ world: .params.world }`
|> handlebars: `<p>hello, {{world}}</p>`
describe "hello, world"
it "calls the route"
when calling GET /hello/world
then status is 200
and output equals `<p>hello, world</p>`
I've used many different ones, and find the result pretty similar. I've used Copilot in VS Code, Chat GPT stand-alone, Warp.dev's baked in tools, etc. Often it's a matter of what kind of work I'm doing, since it's rarely single-mode.
I don't think thats contrary to the article's claim: the current tools are so bad and tedious to use for repetitive work that AI is helpful with a huge amount of it.
> developer somewhere between junior and mid-level
Why the insistence on anthropomorphizing what is just a tool? It has no agency, does not 'think' in any meaningful manner, it is just pattern matching on a vast corpus of training data. That's not to say it can't be very useful - as you seem to have found - but it is still just a tool.
This post is such a cold take, and is going to age horribly.
Self driving cars fail because of regulatory requirements for five nines reliability, and they're doing inference over a dynamic noisy domain.
Autonomous engineering does not have these issues. Code doesn't need to be five nines correct, and the domain of inference is logical and basically static.
If the AI agent/coding companies didn't have their heads up their collective asses we could have fully spec driven autonomous coding within ~3 years, 100%.
I'm a 100% vibe-coder. AI/CS is not my field. I've made plenty of neat apps that are useful to me. Don't ask me how they work; they just do.
Sure the engineering may be abysmal, but it's good enough to work.
It only takes basic english to produce these results, plus complaining to the AI agent that "The GUI is ugly and overcrowded. Make it look better, and dark mode."
Want specs? "include a specs.md"
This isn't a 20% more productive feeling. It's productivity beyond what I will ever do on my own, given this is not my field.
This is all possible because AI was trained on the outstanding work of CS engineers like ya'll.
But the article is highly opinionated. It's like saying only phD's can be called scientists, or only programmers can be computer hackers. But in reality every human is a scientist and a hacker in the real world. The guy in a street corner in India came up with novel ways to make and sell his product, but never wrote a research paper on it. The guy on his fourth marriage noted a statistical correlation in the outcome when meeting women at a bar vs. at a church. The plant that grew in the crevice of a rock noted sunlight absorption was optimal at an angle of 78.3 degrees and grew in that direction.
I think it's like CMS and page builders enabling people to build their own websites without html and server knowledge. They're not making web developers disappear, instead there are more web developers now because those some of those people would eventually outgrow their page builders and need to hire web developers.
It’s a pretty good solution for creating live mockups. A designer on my team came back eight hours after a meeting with a fully vibe coded, multi-page interface. I was honestly blown away. I had no idea this was the state of what’s possible with these tools.
Was it a real website? No, but it’s a live mockup way better than any Figma mock or rigid demo-ware.
Apps running locally can also be subject to security issues. What you're trying to say is probably "apps not using untrusted input". If an app takes no input all, I guess we could say that security isn't an issue, but there could still be safety issues.
The Internet is awash with people making the same claims you are, but where are the actual results that we can see and use? Where are all these supposed new programs that were only possible to make because of generative ai? The number of new apps in the app store is flat. Still getting the same amount in 2025 as in 2022.
App-store listing is a whole other animal. I don't care to go through all that just to share my app. I also don't care to resolve every technical issue others experience.
Every time I've thought about generating revenue by selling my apps, two thoughts come to mind: my code is not professional-grade, and the field is so competitive than within months a professional will likely create a better app so why pollute the web with something subpar.
The hacker on the street corner isn't distributing his "secret sauce" because it wouldn't meet standards, but it works well for him, and it was cheap/free.
Valid inquiry. In relative terms I'm the Indian on a street corner who hacks things together using tools professionally designed by others. Among the repos I've chosen to publicly share: https://github.com/sm18lr88
I started fully coding with Claude Code. It's not just vibe coding, but rather AI-assisted coding. I've noticed there's a considerable decrease in my understanding of the whole codebase, even though I'm the only one who has been coding this codebase for 2 years. I'm struggling to answer my colleagues' questions.
I am not defending we should drop AI, but we should really measure its effects and take actions accordingly. It's more than just getting more productivity.
I’m experiencing something similar. We have a codebase of about 150k lines of backend code. On one hand, I feel significantly more productive - perhaps 400% more efficient when it comes to actually writing code. I can iterate on the same feature multiple times, refining it until it’s perfect.
However, the challenge has shifted to code review. I now spend the vast majority of my time reading code rather than writing it. You really need to build strong code-reading muscles. My process has become: read, scrap it, rewrite it, read again… and repeat until it’s done. This approach produces good results for me.
The issue is that not everyone has the same discipline to produce well-crafted code when using AI assistance. Many developers are satisfied once the code simply works. Since I review everything manually, I often discover issues that weren’t even mentioned. During reviews, I try to visualize the entire codebase and internalize everything to maintain a comprehensive understanding of the system’s scope.
I'm very surprised you find this workflow more efficient than just writing the code. I find constructing the mental model of the solution and how it fits into existing system and codebase to be 90% of effort, then actually writing the code is 10%. Admittedly, I don't have to write any boilerplate due to the problem domain and tech choices. Coding agents definitely help with the last 10% and also all the adjacent work - one-off scripts where I don't care about code quality.
I wrote a couple python scripts this week to help me with a midi integration project (3 devices with different cable types) and for quick debugging if something fails (yes, I know there are tools out there that do this but I like learning).
I’m could have used an LLM to assist but then I wouldn’t have learned much.
But I did use an LLM to make a
management wrapper to present a menu of options (cli right now) and call the scripts. That probably saved me an hour, easily.
That’s my comfort level for anything even remotely “complicated”.
I keep wanting to go back to using claudecode but I get worried about this issue. How best to use it to complement you, without it rewriting everything behidn the scenes? whats the best protocol? constnat commit requests and reviews?
This is the chief reason I don't use integrations. I just use chat, because I want to physically understand and insert code myself. Else you end up with the code overtaking your understanding of it.
Yes. I'm happy to have a sometimes-wrong expert to hand. Sometimes it provides just what I need, sometimes like with a human (who are also fallible), it helps to spur my own thinking along, clarify, converge on a solution, think laterally, or other productivity boosting effects.
This is a more extreme example of the general hacker news group think about AI.
Geohot is easily a 99.999 percentile developer, and yet he can’t seem to reconcile that the other 99.999 percent are doing something much more basic than he can ever comprehend.
It’s some kind of expert paradox, if everyone was as smart and capable as the experts, then they wouldn’t be experts.
I have come across many developers that behave like the AI. Can’t explain codebases they’ve built, can’t maintain consistency.
It’s like a aerospace engineer not believing that the person that designs the toys in an Kinder egg doesn’t know how fluid sims work.
I think his excellency in his own trade limited his vision for the 99% who just want to get by in the job. How many dev even deal with compiler directly these days? They write some code, fix some red underlines, then push, pray and wait for pipeline pass. LLMs will be gods in this process, and you can even beg another one if your current one does not work best.
I keep seeing people praise this guys, but honestly I never saw anything impressive in anything he's ever done. He does seem to be prolific and with a lot of energy, but I've seen plenty of equally talented people.
You've seen plenty of people who hacked the ps3 and iphone as teenagers and created a low level system analysis tool for doing such system hacks? You've seen plenty of people writing self driving car software a decade ago? Why did you write this when you know nothing?
I actually have seen plenty of people that could have done something like this, but did not because they simply never tried. Being daring by itself is a skill, but we're talking raw technical ability here.
I've actually seen another developer that was probably in the same category write his own self-driving software. It kind of worked, but couldn't have ever been production ready, so it was just an exercise in flexing without any practical application.
So, what product that George built do you actually use?
For the comment above, the more relevant denominator is all humans vs. all developers. If you use all humans as the denominator, he's easily in the top 1% or 0.001% (I haven't followed his work closely, but you'd only have to be a good dev to be in top 1% of the global population).
Thank you, perhaps I worded it harshly, but that was my general feeling. Being a good developer already is a high level. Being able to start impressive-sounding projects that never materialize into anything is a luxury for which most competent developers simply don't have the extra energy.
That METR study gets a lot of traction for its headline; and I doubt many people read the whole thing—it was long—but the data showed a 50% speed up for the one dev with the most experience with Cursor/AI, suggesting a learning curve and also wild statistical variation on a small sample set. An errata later suggested another dev who did not have a speedup had not represented their experience correctly, but still strongly draws into question the significance of the findings.
The specific time sucks measured in the study are addressable with improved technology like faster LLMs and improved methodology like running parallel agents—the study was done in March running Claude 3.7 and before Claude Code.
We also should value the perception of having worked 20% less even if you actually spent more time. Time flies when you’re having fun!
First, the assertion that the best model of "AI coding" is that it is a compiler. Compilers deterministically map a formal language to another under a spec. LLM coding tools are search-based program synthesizers that retrieve, generate, and iteratively edit code under constraints (tests/types/linters/CI). That’s why they can fix issues end-to-end on real repos (e.g., SWE-bench Verified), something a compiler doesn’t do. Benchmarks now show top agents/models resolving large fractions of real GitHub issues, which is evidence of synthesis + tool use, not compilation.
Second, that the "programming language is English". Serious workflows aren’t "just English." They use repo context, unit tests, typed APIs, JSON/function-calling schemas, diffs, and editor tools. The "prompt" is often code + tests + spec, with English as glue. The author attacks the weakest interface, not how people actually ship with these tools.
Third, non-determinism isn't disqualifying. Plenty of effective engineering tools are stochastic (fuzzers, search/optimization, SAT/SMT with heuristics). Determinism comes from external specs: unit/integration tests, type systems, property-based tests, CI gates.
False dichotomy: "LLMs are popular only because languages/libraries are bad."
Languages are improving (e.g. Rust, Typescript), yet LLMs still help because the real bottlenecks are API lookup, cross-repo reading, boilerplate, migrations, test writing, and refactors, the areas where retrieval and synthesis shine. These are complementary forces, not substitutes.
Finally, no constructive alternatives are offered. "Build better compilers/languages" is fine but modern teams already get value by pairing those with AI: spec-first prompts, test-gated edits, typed SDK scaffolds, auto-generated tests, CI-verified refactors, and repo-aware agents.
A much better way to think about AI coding and LLMs is that they aren’t compilers. They’re probabilistic code synthesizers guided by your constraints (types, tests, CI). Treat them like a junior pair-programmer wired into your repo, search, and toolchain. But not like a magical English compiler.
Ofcourse, there is some truth in what you say. But business is desperate for new tech where they can redefine the order (who is big and who is small). There are floating billions which chase short term returns. Fund managers will be fired if they are not jumping on the new fad in the town. CIO's and CEO's will be fired if they are not jumping on AI. It's just nuclear arms race. It's good for none. but the other guy is on it, so you need to be too.
Think about this. Before there were cars on roads, people were just as much happy. Cars came, cities were redesigned for cars with buildings miles apart, and commuting miles became the new norm. You can no longer say cars are useless because the context around them has changed to make the cars a basic need.
AI does same thing. It changes the context in which we work. Everyone expects you use AI (and cars). It becomes a basic need, though a forced one.
To go further, hardly anything produced by science or technology is a basic need for humans. The context got twisted, making them basic needs. Tech solutions create the problems which they claim to solve. The problem did not exist before the solution came around. That's core driving force of business.
I have a boring opinion. A cold take? served straight from the freezer.
He is right, however AI is still darn useful. He hints at why: patterns.
Writing a test suite for a new class when an existing one is in place is a breeze. It even can come up with tests you wouldnt have thought of or would have been too time pressed to check.
It also applies to non-test code too. If you have the structure it can knock a new one out.
You could have some lisp contraption that DRYs all the WETs so there is zero boilerplate. But in reality we are not crafting these perfect cosebases, we make readable, low-magic and boilerplatey code on tbe whole in our jobs.
But what about the tests usefulness? Tests enforce contracts, contracts are about the domain, not the implementation. The number of tests don't actually matter as much as what is being actually verified. If you look at the code to know what to tests, you are doing it wrong.
The usefulness is in saving time boilerplating, plus figuring out tests I may not have thought of.
But I do closely review the code! It turns the usual drudge of writing tests into more of a code review. Last time I did it it had some mistakes I needed to fix for sure.
I think this gets to a fundamental problem with the way the AI labs have been selling and hyping AI. People keep on saying that the AI is actually thinking and it's not just pattern matching. Well, as someone that uses AI tools and develops AI tools, my tools are much more useful when I treat the AI as a pattern matching next-token predictor than an actual intelligence. If I accidentally slip too many details into the context, all of a sudden the AI fails to generalize. That sounds like pattern matching and next token prediction to me.
> This isn’t to say “AI” technology won’t lead to some extremely good tools. But I argue this comes from increased amounts of search and optimization and patterns to crib from, not from any magic “the AI is doing the coding”
* I can tell claude code to crank out some basic crud api and it will crank it out in a minute saving me an hour or so.
* I need an implementation of an algorithm that has been coded a million times on github, I ask the AI to do it and it cranks out a correct working implementation.
If I only use the AI in its wheelhouse it works very well, otherwise it sucks.
I think this comes down to levels of intelligence. Not knowledge, I mean intelligence. We often underestimate the amount of thinking/reasoning that goes into a certain task. Sometimes the AI can surprise you and do something very thoughtful, this often feels like magic.
Both CRUD and boilerplate are arguably a tooling issue. But there are also a bunch of things only AI will let you do.
My tests with full trace level logging enabled can get very verbose. It takes serious time for a human to parse where in the 100 lines of text the relevant part is.
Just telling an AI: "Run the tests and identify the root cause" works well enough, that nowadays it is always my first step.
> AI makes you feel 20% more productive but in reality makes you 19% slower. How many more billions are we going to waste on this?
Adderall is similar. It makes people feel a lot more productive, but research on its effectiveness[0] seems to show that, at best, we get only a mild improvement in productivity, and marked deterioration of cognitive abilities.
I’m someone with ADHD who takes prescribed stimulants and they don’t make me work faster or smarter, they just make me work. Without them I’ll languish in an unfocused haze for hours, or zone in on irrelevant details until I realise I have an hour left in the day to get anything done. It could make me 20% less intelligent and it would still be worth it; this is obviously an extreme, but given the choice, I’d rather be an average developer that gets boring, functional code done on time than a dysfunctional genius who keeps missing deadlines and cannot be motivated to work on anything but the most exciting shiny new tech.
I have family that had ADHD, as a kid (they called it “hyperactivity,” back then). He is also dyslexic.
The ADHD was caught early, and treated, but the dyslexia was not. He thought he was a moron, for much of his early life, and his peers and employers did nothing to discourage that self-diagnosis.
Since he learned of his dyslexia, and started treating it, he has been an engineer at Intel, for most of his career (not that I envy him, right now).
You have to realize that ADD meds are meant only for people with ADD, not healthy people at the prime of their life. Excess neurochemicals can have the opposite effect.
Their benefits when used as intended are solidly documented in research literature.
I had a problem client that I ended up firing and giving money back to about 15 years ago. Lot of red flags, but the breaking point was when they offered me adderall so I could "work faster".
That said, I'll leave the conclusions about whether it's valuable for those with ADHD to the mental health professionals.
I’m quite sure that there’s a ton more research on it. The drug’s been around for decades. Lots of time for plenty of studies.
If legitimate research had found it to be drastically better, that study would definitely have been published in a big way.
Unscientifically, I personally know quite a number of folks that sincerely believed that they couldn’t function without it, but have since learned that they do far better on their own. I haven’t met a single one that actually had their productivity decline (after an adjustment period, of course), after giving up Adderall. In fact, I know several, that have had their careers really take off, after giving it up.
My point is that micro-studies like that on a tiny random (or even counter-indicated, "healthy") selection of the general population don't tell you much for drugs that do specific things.
"Antibiotics don't improve your life, but can damage your health" would likely be the outcome on 13 randomly selected healthy individuals. But do the same study on 13 people with a bacterial infection susceptible to antibiotics and your results will be vastly different.
I don't think that it matters, in this context, as a lot of folks here, have their minds made up, already, and won't let anything interfere.
They'll need to learn, the same way I see lots of people learn.
It's been around long enough, though, that all the Adderall-eating people should have established a Gattaca-like "elite," with all us "undermen," scrabbling around at their feet.
It’s interesting how science can become closer to pseudoscience than proper research through paper-milling.
It seems like that with such small groups and effects you could run the same “study” again and again until you get the result that you initially desired.
It's very easy to find studies that prove that Adderall (etc.) improve non-ADHD people's cognition ability. And it's equally easy to find studies that prove otherwise. The parent comment is very spot on. You can find evidence supporting anything nowadays.
AI coding is the one thing that got my back to programming. I got to the point in life, when my ability to focus is reducing, and I prefer to send the remaining energy elsewhere. I kind of gave up on programming, just doing architecture and occasionally doing very small programming tasks. It all changed when I discovered Claude Code and saw that the way it works, is kind of how I work. I also use a lot of grep to find my way through a new codebase, I also debug stuff by adding logs to see the context, I also rely on automated tests to tell me something is broken. I'm still very good at reading code, I'm good at architecture, and with these tools, I feel I can safely delegate the boring bits of writing code and debugging trivial things to AI. Yes, it's slower than if I focused on the task myself, but the point is that I'd not be able to focus on the task myself.
I feel the same way. After 30+ years coding, I know what I want to build, but finding the time to work on it and the focus is harder than it used to be.
Great short read. But this “ It’s why the world wasted $10B+ on self driving car companies that obviously made no sense.”
Not everything should make sense. Playing , trying and failing is crucial to make our world nicer. Not overthinking is key, see later what works and why.
Waymo's driving people around with an injuries-per-mile rate that's lower than having humans do it. I don't see how that reconciles with "obviously made no sense".
Pretty much nailed it. Once you’re at about 40k LOC you can just turn off the autocomplete features and use Claude or GPT to evaluate specific high-level issues. My sense is 40k LOC is the point at which the suggestions are offset by the rabbit holes they sometimes send you down, but, more importantly by obscuring from you the complexity of the thing you’re building—temporarily.
I expect much of this can be solved with better architecture: smaller, more independent components. Break large code bases up into independent libraries, and LLMs can work again because they need much less code in their context.
Suggesting I restructure my code base to work better with a non-deterministic black box feels like an instance of Conway’s law[1] that should be avoided.
There's a lot of complaining about current compilers / languages / codebase in similar posts, but barely any ideas for how to make them better. It doesn't seem surprising that people go for the easier problem (make the current process simpler with LLMs) than for the harder one (change the whole programming landscape to something new and actually make it better).
> Or we could, you know, do the hard work and build better programming languages, compilers, and libraries. But that can’t be hyped up for billions.
100% this. I fear that AI will cause us to be stuck in a local optimum for the next decades where most of the code will be Python or JS because these are the languages best supported by LLMs. Don't get me wrong, Python and JS and mature and productive languages. That's fine. But we could have it so much better if there was more effort put into a next generation of tools that take all the harsh lessons learnt from the tools before and "just" do it better. I acknowledge that we get incremental improvements here and there but some things are just unfixable without breaking existing ecosystems.
I have been working on finding out ways to make use of AI a net-positive in my professional life as opposed to yet another thing I have to work around and have cognitive load of. Some notes so far in getting great benefits out of it on couple projects -
* Getting good results from AI forced me to think through and think clearly - up front and even harder.
* AI almost forces me to structure and break down my thoughts into smaller more manageable chunks - which is a good thing. (You can't just throw a giant project at it - it gets really far off from what you want if you do that.)
* I have to make it a habit of reading what code it has added - so I understand it and point to it some improvements or rarely fixes (Claude)
* Everyone has what they think are uninteresting parts of a project that they have to put effort into to see the bigger project succeed - AI really helps with those mundane, cog in the wheel things - it not only speeds things up, personally it gives me more momentum/energy to work on the parts that I think are important.
* It's really bad at reusability - most humans will automatically know oh I have a function I wrote to do this thing in this project which I can use in that project. At some point they will turn that into a library. With AI that amount of context is a problem. I found that filling in for AI for this is just as much work and I best do that myself upfront before feeding it to AI - then I have a hope of getting it to understand the dependency structure and what does what.
* Domain specific knowledge - I deal with Google Cloud a lot and use Gemini for understanding what features exist in some GCP product and how I can use it to solve a problem - works amazingly well to save me time. At the least optioning the solution is a big part of work it makes easier.
* Your Git habits have to be top notch so you can untangle any mess AI creates - you reach a point where you have iterated over a feature addition using AI and it's a mess and you know it went off the rails after some point. If you just made one or two commits now you have to unwind everything and hope the good parts return or try to get AI to deal with it which can be risky.
People repeating articles or papers. I know myself. I know from my own experiences what the good and bad of practice A or database B is. I don't need to read a conclusion by some Muppet.
Chill. Interesting times. Learn stuff, like always. Iterate. Be mindful and intentional and don't just chase mirrors but be practical.
The takeaway from the paper is you don’t know yourself. It’s one paper, and a small sample size, but attempting to refute its conclusion by stating it’s false doesn’t really get us anywhere.
Vibe coding large projects isn’t feasible yet, but as a developer here’s how I use AI to great effect, to the point where losing the tool greatly decreases my productivity:
- Autocomplete in Cursor. People think of AI agents first when they talk about AI coding but LLM-powered autocomplete is a huge productivity boost. It merges seamlessly with your existing workflow, prompting is just writings comments, it can edit multiple lines at once or redirect you to the appropriate part of the codebase, and if the output isn’t what you need you don’t waste much time because you can just choose to ignore it and write code as you usually do.
- Generating coding examples from documentation. Hallucination is basically a non-problem with Gemini Pro 2.5 especially if you give it the right context. This gets me up to speed on a new library or framework very quickly. Basically a stack overflow replacement.
- Debugging. Not always guaranteed to work, but when I’m stuck at a problem for too long, it can provide a solution, or give me a fresh new perspective.
- Self contained scripts. It’s ideal for this, like making package installers, cmake configurations, data processing, serverless micro services, etc.
- Understanding and brainstorming new solutions.
- Vibe coding parts of the codebase that don’t need deep integration. E.g. create a web component with X and Y feature, a C++ function that does a well defined purpose, or a simple file browser. I do wonder if a functional programming paradigm would be better when working with LLMs since by avoiding side effects you can work around their weaknesses when it comes to large codebases.
Code has a lot of bits of information the compiler users to construct the program. But not all because software needs iteration to get right both in bugs and in solving the intended problem.
The llm prompt has even fewer bits of information specifying the system than code. The model has a lot more bits but still finite. A perfect llm cannot build a perfect app in one shot.
However AIs can research, inquire, and iterate to gain more bits than when you started.
So the comparison to a compiler is not apt because the compiler can’t fix bugs or ask the user for more information about what the program should be.
Most devs are using ai at the autocomplete level which is like this compiler analogy which makes sense in 2025 but that isn’t where we will be in 2030.
What we don’t know is how good the technology will be in the future and how cheap and how fast. But it’s already very different than a compiler.
I use LLM to do things like brainstorm, explaining programming concepts and debug. I will not use it to write code. The output is not good enough, and I feel dumber.
I only see the worst of my programming collegues coding with AI. And the results are actual trash. They have no actual understanding of the code "they" are writing, and they have no idea how to actually debug what "they" made, if LLM is not helpful. I can smell the technical debt.
Even when copy-pasting an entire function from stack overflow, you generally still need to have some understanding of what the inputs and outputs are, even if it remains somewhat of a black box, so that you can plug it into your existing code.
AI removes that need. You don't need to know what the function does at all, so your brain devotes no energy towards remembering or understanding it.
I used to be a bit more open minded on this topic but im increasingly viewing any programmers who use AI for anything other than brainstorming and looking stuff up/explaining it as simply bad at what they do.
These articles are beyond the point of exhausting. Guys, just go use the tools and see if you like them and feel more capable with them. If you do, great, if you don’t, then stop.
The truth will emerge naturally through labor market competition in the long-run. You do you. I will be using these tools extensively. Good luck out there in the arena.
> It’s why the world wasted $10B+ on self driving car companies that obviously made no sense. There’s a much bigger market for truths that pump bags vs truths that don’t.
Yes. He mentions that in passing that saying people will accuse him of hating on it because he didn't profit from it. I think his point of view is that his company's attempt was smaller scale and not part of the $10B+ waste?
In any case I don't fully understand what he's trying to say other than negating the hype (which i generally agree with), but not offering any alternative thoughts of his own other than- we have bad tools and programming language. (why? how are they bad? what needs to change for them to be good?)
Once we realize that what we actually want is turning specifications into software, I think that English will become the base for a new, high level specification language.
We are turning specifications into software precisely because English (and any natural languages) lacks the formality that makes it not reliable (necessary quality for a tool), but great for imagination (the source of invention).
We always start from natural language. RFC, docs, tickets,... are in natural language. But gaining formality (losing ambiguity) is what programming is (software engineering is doing programming well). People that struggled with programming struggle in fact with formality.
Instead of everyone telling their personal anecdote, we should look at the actual research.
Asking ChatGPT to summarize the state of AI coding research that’s not done by one of the providers, and I feel like I got the sanest summary of the actual state of things:
> If you weight independent work more heavily: current best evidence says don’t expect speedups for senior devs in complex, well-known codebases without process changes; expect security pitfalls unless you add guardrails. Gains look more reliable for novices, unfamiliar code, and templated tasks
Seems the most sane PoV on the actual benefits I’ve seen.
Actual link if you want to pick apart GPT-5s summary
These workflows always surprise me. Isn’t this what you’ve been doing with humans all along? Write up a basic project plan likely following some structure the product team likes. Share it with the eng team. One to a few members of the eng team writes up a more specific plan. Everyone gets together again and goes through the specific plan to iron out the kinks. You the implement the plan.
I’ve seen at my workplace people are more willing to help Claude with a plan than their fellow humans. I pointed that out and one engineer replied with “well I know Claude will read the documentation”. It’s a depressing observation but I don’t know if it’s wrong.
I also had a compiler related description come to me after using Copilot. It allows you to partially generate imperative code declaratively, by writing
a comment like
//now I will get rows X, Y, Z from ContentsProvider
then tab tab complete. You can then even tweak the generated code, very useful!
There is some amount of truth on the AI coding claims.
But, what's with the self driving hate? I take Waymos on a regular basis, and he is basing his credibility on the claim that they are not a thing. Makes him sound bitter more than insightful.
George famously started a self driving hardware-as-an-addon company. He started after both claiming self driving was easy, and reportedly taking up an Elon bounty/bet in the seven figure range to make self driving AI for Tesla.
I think some of the “hate” is the hype. We’re all tired of companies announcing ground breaking tech that isn’t readily available a decade later.
I don’t live in California (like most of the population of the planet) - Toronto for 18 years and now the American side of the Great Lakes.
Ice storms, snow, sleet, cold weather 5-6 months out of the year. Batteries suck in the cold, sensors fail or under-perform. Hell, door handles and windows struggle in this weather.
Waymo is not a thing in NY or Chicago or Minneapolis or Philadelphia (I could go on).
And that's just in North America where cities were built for cars and with cars in mind. Autonomous driving in Europe or Asia is likely a magnitude harder due to windy roads, non trivial intersections, complex interactions of humans, bikes, trams and cars etc.
That was a really great read. Not saying I agree with it all, I’m maybe more in the camp that believes AI assisted coding is a time-saver but it’s refreshing (and overdue) to have a counterpoint to the deafening and repetitive drumbeat of the VC-backed hype machine.
Even though I don’t buy that LLMs are going to replace developers and quite agree with what is said, this is more of a critique of LLMs as English-to-code translators. LLMs are very useful for many other things.
Researching concepts, for one, has become so much easier, especially for things where you don’t know anything yet and would have a hard time to even formulate a search engine query.
I agree. I think a better analogy than a compiler is a search engine that has an excellent grasp of semantics but is also drunk and schizophrenic.
LLMs are really valuable for finding information that you aren't able to formulate a proper search query for.
To get the most out of them, ask them to point you to reliable sources instead of explaining directly. Even then, it pays to be very critical of where they're leading you to. To make an LLM the primary window through which you seek new information is extremely precarious epistemologically. Personally, I'd use it as a last resort.
It seems to speed up feature development but requires one to have a good understanding of the codebase to guide it and be aware of edge cases it missed.
Also, it doesn't seem to be able to take advantage of latest information or new SDK features unless deliberately informed. Not sure if I'm doing it right, but I resorted to feeding it documentation when it can't seem to something right.
The only thing I'm still unsure is the context management with /compact and /clear
I do agree with many points in the article, but not about the last part, namely that coding with AI assist makes you slower.
Personal experience (data points count = 1), as a somewhat seasoned dev (>30yrs of coding), it makes me WAY faster. I confess to not read the code produced at each iteration other than skimming through it for obvious architectural code smell, but I do read the final version line by line and make a few changes until I'm happy.
Long story short: things that would take me a week to put together now take a couple of hours. The vast bulk of the time saved is not having to identify the libraries I need, and not to have to rummage through API documentation.
> Personal experience (data points count = 1), as a somewhat seasoned dev (>30yrs of coding), it makes me WAY faster.
> Long story short: things that would take me a week to put together now take a couple of hours. The vast bulk of the time saved is not having to identify the libraries I need, and not to have to rummage through API documentation.
One of these is not true.
With libraries, it's either you HAVE to use it, so you spend time being acquainted with it (usually a couple hours to make sense of its design, the rest will come on a needed basis) or you are evaluating multiple ones (and that task is much quicker).
> you are evaluating multiple ones (and that task is much quicker).
Of course the latter. And of course I ask the AI to help me select a libray/module/project/whatever that provides what I need. And I ask the AI to classify them by popularity/robustness. And then I apply whatever little/much I know about the space to refine the choice.
may go as far as looking at examples that use the API. And maybe rummage through the code being the API to see if I like what I see.
The whole thing is altogether still way faster than having to pick what I need by hand with my rather limited data ingestion capabilities.
And then, once I've picked one, connecting to the API's is a no-brainer with an LLM, goes super fast.
It takes time. There are cycles of “Oh wow!” “Oh wait...” “What if?” and “Aha!” Each of those has made me more effective and resulted in reliable benefits with less zig zagging back and forth.
That's the point - it's a higher level of abstraction.
>highly non-deterministic
...not unlike say a boss telling a junior to change something?
The bet here isn't that AI can be as precise as something hand coded but rather that you can move up a step in the abstraction layer. To use his compiler example...I don't care what the resulting assembly instructions look like, just whether it works. It's the same thing here just one level higher
> That's the point - it's a higher level of abstraction.
That's not what abstraction is. When I type `echo Hello, World`, I don't have to deal with graphic drivers and test rendering to have the text on the screen. And I don't have to worry that "Goodbye" will appear instead.
> Not unlike say a boss telling a junior to change something?
Junior don't stay junior for long. And bosses usually give juniors less time to grow than people are allocating AI tools to actually prove themselves. Github copilot was more than 3 years ago. Today a new hire is expected to be productive on day one.
I agree that most natural languages are a very poor tool to write code specification in.
Specifically, natural language is:
- ambiguous (LLMs solve this to a certain extent)
- extremely verbose
- doesn't lend itself well to refactoring
- the same thing can be expressed in way too many different ways, which leads to instability in specs -> code -> specs -> code -> specs loops (and these are essential to do incremental work)
Having something at our disposal that you can write code specs in, that is as easy as natural language yet, more concise, easy to learn and most of all not so anal/rigid as typical code languages are would be fantastic.
nice misspelling (or a joke?), related to all the lawsuits around LLMs.
Joking aside, it’s already there in a sense. Several times I started with a brief outline of what the prototype should do (an HTML/CSS/JS app), and sure enough, refinements and corrections followed. When the final version worked more or less as expected, I asked the LLM to create a specification (a reproducing prompt) of everything we made together. Even if the vibe-coded prototype is dropped, the time wasn’t wasted, I probably would never have come to the same bullet list specification without having an actual working app at my disposal to test and evaluate. So paradoxically this specification even might be used by a human later
> It’s why the world wasted $10B+ on self driving car companies that obviously made no sense. There’s a much bigger market for truths that pump bags vs truths that don’t.
This reeks of bias-dismissing massive investments as ‘obvious’ nonsense while hyping its own tinygrad as the ‘truth’ in AI coding.
Author is allowed to claim ‘most people do not care to find the truth’ but it’s hypocritical when the post ignores counterpoints, like PyTorch’s dominance in efficient coding benchmarks.
Author doesn’t seem to care about finding the full truth either, just the version that pumps its bag.
LLMs are a tool to help match human thought to what computers can do. People would like them to have exact reproducible results, but they're on the other end of the spectrum, more like people than tools. George correctly points out there is a vast space to explore closer to the compute hardware that might profitably be explored. Thanks to the same LLMs, that's about to get a whole lot easier. If you marveled at the instant response of Turbo Pascal and IDEs, you're in for a whole lot more.
--- (that was the tl;dr, here's how I got there) ---
As a mapper[3], I tend to bounce all the things I know against each new bit of knowledge I acquire. Here's what happens when that coincides with GeoHot's observation about LLMs vs Compilers. I'm sorry this is so long, right now it's mostly just stream of thought with some editing. It's an exploration of bouncing the idea of impedance matching against the things that have helped advance programming and computer science.
--
I've got a cognitive hammer that I tend to over-use, and that is seeing the world through the lens of a Ham Radio operator, and impedance matching[2]. In a normal circuit, maximum power flows when the source of power and the load being driven have the same effective resistance. In radio frequency circuits (and actually any AC circuit) there's another aspect, reactance. It's a time shifted form of current. This is trickier there are now 2 dimensions to consider instead of one, but most of the time, a single value, VSWR is sufficient to tell how well things are matched.
VSWR is adequate to know if a transmitter is going to work, or power bouncing back from the antenna might destroy equipment, but making sure it will work across a wide range of frequencies, yields at least a 3rd dimension. As time progresses, if you actually work with those additional dimensions, it slowly sinks in what works, and how, and what had previously seemed like magic, becomes engineering.
For example, vacuum tube based transmitters have higher resistances that almost any antenna, transformers and coupling through elements that shift power back and forth between the two dimensions allow optimum transfer without losses at the cost of complexity.
On the other hand, semiconductor based transmitters tend to have the opposite problem, their impedances are lower, so different patters work for them, but most people still just see it as "antenna matching", and focus on the single number, ignoring the complexity.
{{Wow... this is a book, not an answer on HN... it'll get shorter after a few edits, I hope, NOPE... it's getting longer...}}
Recently, a tool that used to cost thousands of dollars, the Vector Network Analyzer, has become available at less than $100. It allows for measuring resistance, reactance, and gain simultaneously across frequency. It's like compilers, making things manageable in scope that otherwise seemed too complex. It only took a few times playing with a NanoVNA to understand things that previously would have been some intense EE classwork with Smith Charts.
Similarly, tools like Software Defined Radios for $30, and GNU Radio (for $0.00) allowed understanding digital signal processing in ways that would have been equally difficult without professional instruction. With these tools, you can build a signal flow graph in an interactive window, and in moments have a working radio for FM, AM, Sideband, or any other thing you can imagine. It's magic!
-- back to computing and HN --
In the Beginning was ENIAC, a computer that took days to set up and get working on a given problem by a team with some experience. Then John Von Neumann came along, and added the idea of stored programs, which involved sacrificing the inherently parallel nature of the machine, losing 70% of its performance, but making it possible to set it up for a task simply by loading a "program" onto it's back of instruction switches.
Then came cards and paper tape storage, further increasing the speed at which data and programs could be handled.
It seems to me that compilers were like one of the above tools, they made it possible for humans to do things that only Alan Turing or others similarly skilled could do in the beginning of programming.
Interactive programming increased the availability of compute, and make machines that were much faster that programmers, more easily distributed among teams of programmers.
IDEs were another. Turbo Pascal allowed compile, linking, and execution to happen almost instantly. It widely opened the space for experimentation by reducing the time required to get feedback from minutes to almost zero.
I've done programming on and off through 4 decades of work. Most of my contemplation is as an enthusiast, instead of professional. As far as compilers and the broader areas of Computer Science I haven't formally studied, it seems to me that LLMS, especially the latest "agentic" versions, will allow me to explore things far easier than I might have otherwise done. LLMs have helped me to match my own thoughts across a much wider cognitive impedance landscape. (There's that analogy/hammer in use...)
Compilers are an impedance matching mechanism. Allowing a higher level of abstraction gives flexibility. One of the ideas I've had in the past for helping with better interaction between people and compilers is to allow compilers that also work backwards.[1] I'm beginning to suspect that with LLMs, I might actually be able to attempt to build this system, it's always seemed out of reach because of the levels of complexity involved.
I have several other ideas that might warrant a new attempt, now that I'm out of the job market, and have the required free time and attention.
{{Sorry this turned out to be an essay... I'm not sure how to condense it back down right now}}
ok boomer. its silly to read such generalizations. ai is a tool, and as every other tool it needs the right job and the right user to be useful and productive.
How do we resolve the observable tension here with the fact that self-driving cars are operating right now, relatively successfully, in ten or so major American cities?
Not a billion dollar business yet, maybe, but 300 cars generating five or six figures revenue per year each isn't far off.
(And I say this as someone who is skeptical that totally autonomous cars worldwide will ever be a thing, but you can get to £10Bn far, far before that point. Become the dominant mode of transport in just ONE major American city and you're most of the way there).
> How do we resolve the observable tension here with the fact that self-driving cars are operating right now, relatively successfully, in ten or so major American cities?
Because geo fenced driving in a few select cities with very favourable conditions is not what was promised. That's the crux. They promised us that we have self drive anywhere at anytime at the press of a button.
> Not a billion dollar business yet, maybe, but 300 cars generating five or six figures revenue per year each isn't far off.
I'm not sure how you get to 6 figures revenue. Assuming the car makes $100 per hour for 24x7 52 weeks a year we still fall short of 1 million. But let's assume you're right $300M revenue (not profit, are they even operating at a plus even disregarding R&D costs?) on investment of >10 billion (probably more like 100), seems like the definition of hype.
> (And I say this as someone who is skeptical that totally autonomous cars worldwide will ever be a thing, but you can get to £10Bn far, far before that point. Become the dominant mode of transport in just ONE major American city and you're most of the way there).
What I don't understand with this argument, how are you proposing they become the dominant mode of transport. These services are competing with taxis, what do they offer over taxis that people suddenly switch on mass to self driving taxis? They need to become cost competitive (and convenience competitive) with driving your own car, which would significantly drive down revenue. Secondly if robotaxi companies take over transport, why would the public continue to build their infrastructure and not demand that these robotaxi companies start to finance the infrastructure they exclusively use?
I got to six figures by assuming that a human taxi driver makes maybe $30-40k at a guess, and an autonomous car can work 24/7. 6 figures is $100k minimum.
So yeah, right now they'd have to be at ten cities x 300 cars each to hit 300M revenue, but there's still plenty of room for growth. Or should be, assuming the Waymo model isn't maxed out supporting the current handful of cities.
But I'm not convinced they have to hit cost parity with personal cars, because the huge advantage is you can work and drive (or be driven). If NYC and LA rush-hour congestion time becomes productive time, there's your billions.
I drive but prefer to take transit for this reason - some of my colleagues are able to join work calls effectively while driving, but for whatever reason my brain doesn't allow that. Just paying attention to calls is enough, you want me to pay attention to the road AND the call?
I'm almost 50, and have been writing code professionally since the late 90s. I can pretty much see projects in my head, and know exactly what to build. I also get paid pretty well for what I do. You'd think I'd be the prototype for anti-AI.
I'm not.
I can build anything, but often struggle with getting bogged down with all the basic work. I love AI for speed running through all the boring stuff and getting to the good parts.
I liken AI development to a developer somewhere between junior and mid-level, someone I can given a paragraph or two of thought out instructions and have them bang out an hour of work. (The potential for then stunting the growth of actual juniors into tomorrow's senior developers is a serious concern, but a separate problem to solve)
I love AI for speed running through all the boring stuff and getting to the good parts.
In some cases, especially with the more senior devs in my org, fear of the good parts is why they're against AI. Devs often want the inherent safety of the boring, easy stuff for a while. AI changes the job to be a constant struggle with hard problems. That isn't necessarily a good thing. If you're actually senior by virtue of time rather than skill, you can only take on a limited number of challenging things one after another before you get exhausted.
Companies need to realise that AI to go faster is great, but there's still a cognitive impact on the people. A little respite from the hardcore stuff is genuinely useful sometimes. Taking all of that away will be bad for people.
That said, some devs hate the boring easy bits and will thrive. As with everything, individuals need to be managed as individuals.
That makes me think of https://store.steampowered.com/app/2262930/Bombe/ which is a version of Minesweeper where instead of clicking on squares you define (parametric!) rules that propagate information around the board automatically. Your own rules skip all the easy parts for you. As a result, every challenge you get is by definition a problem that you've never considered before. It's fun, but also exhausting.
I remember listening to a talk about Candy Crush and how they designed the game to have a few easy levels in between the hard ones, to balance feeling like you're improving while also challenging players. If all the levels get progressively harder, then a lot of people lose motivation to keep playing.
Oooohhh....
That looks like plenty of hours of fun! Thanks for the link :)
That's crazy to me. I solve problems. I'm not a janitor or tradesman, you bring me in to envision and orchestrate solutions that bring bottom line value. I live to crack hard nuts, if I never have to bother with rigging again I'll be so happy.
Interesting point.
There's also the fact that, while you're coding the easy stuff, your mind is thinking about the hard stuff, looking things up, seeing how they articulate. If you're spending 100% of your time on hard stuff, you might be hurting these preliminaries.
This makes no sense. Yes, having time to think about the hard part is good, but just because you’re not doing the boilerplate anymore doesn’t mean you can’t do the thinking part anymore! See how absurd it sounds when you actually describe it this way?
Let me rephrase.
I know brilliant people who took up knitting to keep their hands busy while they think over their difficult problems. But that only works if you can knit in your work hours. Sadly, despite clearly improving the productivity of these people, this is a fireable offense in many jobs.
I'm not saying that the only way to think through a hard problem is to work on boilerplate. If you are in a workplace where you can knit, or play table soccer, by all means, and if these help you, by all means, go for it.
What I'm thinking out loud is that if we're dedicating 100% of our time to the hard problems, we'll hit a snag, and that boilerplate may (accidentally) serve as the padding that makes sure we're not at these 100%.
That being said, I'm not going to claim this as a certainty, just an idea.
I don't disagree, but I find a better use of my time is writing. Not code, but essentially a work journal. It's not big thoughts, it's bullet points. It's not documentation, but more of a open mind map: what's been done, what needs to be done, questions that inevitably pop up, etc. I use Obsidian for this, but if I write much more than what would go on a few post-it notes, it's too much.
> Devs often want the inherent safety of the boring, easy stuff for a while
That's matches my experience. In my first job, every time a new webapp project has been starting it was fun. Not because of challenges or design, but simply because of the trivial stuff done for n-th time - user accounts, login, password reset, admin panel. Probably should have been automated at this point, but we got away with reinventing the wheel each time.
> AI changes the job to be a constant struggle with hard problems.
Very true. I think AI (especially Claude Code) forced me to actually think hard about the problem at hand before implementing the solution. And more importantly, write down my thoughts before they fleet away from my feeble mind. A discipline that I wished I had before.
That's strange, I've never thought it can be done this way. Normally I'd read the docs, maybe sketch up some diagrams, then maybe take a walk while thinking on how to solve the problem, and by the time I got back to the screen I'd already have a good idea on how to do it.
These days the only difference is that I feed my ideas to a few different LLMs to have "different opinions". Usually they're crap but sometimes they present something useful that I can implement.
This is exactly why people hate AI. It disrupts the comfort of easy coding.
I think you're describing things we already knew long before this era of AI. Less code is better code, and the vast majority of bugs come from the devs who "hate the boring easy bits".
I disagree that this has anything to do with people needing a break. All code eventually has to be reviewed. Regardless of who or what wrote it, writing too much of it is the problem. It's also worth considering how much more code could be eliminated if the business more critically planned what they think they want.
These tensions have existed even before computers and in all professions.
>AI changes the job to be a constant struggle with hard problems
I find this hilarious. From what I've seen watching people do it, it changes the job from deep thought and figuring out a good design to pulling a lever on a slot machine and hoping something good pops out.
The studies that show diminished critical thinking have matched what i saw anecdotally pairing with people who vibe coded. It replaced deep critical thinking with a kind of faith based gambler's mentality ("maybe if i tell it to think really hard it'll do it right next time...").
The only times ive seen a notable productivity improvement is when it was something not novel that didnt particularly matter if what popped out was shit - e.g. a proof of concept, ad hoc app, something that would naturally either work or fail obviously, etc. The buzz people get from these gamblers' highs when it works seems to make them happier than if they didnt use it at all though.
Which was my original point. Not that the outcome is shit. So much of what we write is absolutely low-skill and low-impact, but necessary and labor-intensive. Most of it is so basic and boilerplate you really can't look at it and know if it was machine- or human-generated. Why shouldn't that work get cranked out in seconds instead of hours? Then we can do the actual work we're paid to do.
To pair this with the comment you're responding to, the decline in critical thinking is probably a sign that there's many who aren't as senior as their paycheck suggests. AI will likely lead to us being able to differentiate between who the architects/artisans are, and who the assembly line workers are. Like I said, that's not a new problem, it's just that AI lays that truth bare. That will have an effect generation over generation, but that's been the story of progress in pretty much every industry for time eternal.
> So much of what we write is absolutely low-skill and low-impact, but necessary and labor-intensive. Most of it is so basic and boilerplate you really can't look at it and know if it was machine- or human-generated.
Is it really? Or is it a refusal to do actual software engineering, letting the machine taking care of it (deterministically) and moving up the ladder in terms of abstraction. I've seen people describing things as sludge, but they've never learned awk to write a simple script to take care of the work. Or learned how to use their editor, instead using the same pattern they would have with Notepad.
I think it's better to take a step back and reflect on why we're spending time on basic stuff instead. Instead of praying that the LLM will generate some good basic stuff.
If you're not able to review what it generates, you shouldn't be using it (and arguably are the wrong person to be doing the boilerplate work to begin with)
Put differently, I go back to my original comment, where AI is essentially a junior/mid dev that you can express what needs to be done with enough detail. In either case, AI or dev, you'd review and/or verify it.
> Or is it a refusal to do actual software engineering, letting the machine taking care of it (deterministically) and moving up the ladder in terms of abstraction.
One could say the same of installing packages in most modern programming languages instead of writing the code from first principles.
I think there are two kinds of uses for these tools:
1) you try to explain what you want to get done
2) you try to explain what you want to get done and how to get it done
The first one is gambling, the second one has very small failure rate, at worst, the plan it presents shows it's not getting the solution you want it to do.
The thing is to understand that a model has "priors" which steer how it generates code. If what you're trying to build matches the priors of the model you can basically surf the gradients to working software with no steering using declarative language. If what you want to build isn't well encoded by the models priors it'll constantly drift, and you need to use shorter prompts and specify the how more (imperative).
> In some cases, especially with the more senior devs in my org, fear of the good parts is why they're against AI. Devs often want the inherent safety of the boring, easy stuff for a while. AI changes the job to be a constant struggle with hard problems. That isn't necessarily a good thing. If you're actually senior by virtue of time rather than skill, you can only take on a limited number of challenging things one after another before you get exhausted.
The issue of senior-juniors has always been a problem; AI simply means they're losing their hiding spots.
100% agree. I am interested in seeing how this will change how I work. I'm finding that I'm now more concerned with how I can keep the AI busy and how I can keep the quality of outputs high. I believe it has a lot to do with how my projects are structured and documented. There are also some menial issues (e.g. structuring projects to avoid merge conflicts becoming bottlenecks)
I expect that in a year my relationship with AI will be more like a TL working mostly at the requirements and task definition layer managing the work of several agents across parallel workstreams. I expect new development toolchains to start reflecting this too with less emphasis on IDEs and more emphasis on efficient task and project management.
I think the "missed growth" of junior devs is overblown though. Did the widespread adoption of higher-level really hurt the careers of developers missing out on the days when we had to do explicit memory management? We're just shifting the skillset and removing the unnecessary overhead. We could argue endlessly about technical depth being important, but in my experience this hasn't ever been truly necessary to succeed in your career. We'll mitigate these issues the same way we do with higher-level languages - by first focusing on the properties and invariants of the solutions outside-in.
> I can build anything, but often struggle with getting bogged down with all the basic work. I love AI for speed running through all the boring stuff and getting to the good parts.
I'm in the same boat (granted, 10 years less) but can't really relate with this. By the time any part becomes boring, I start to automate/generalize it, which is very challenging to do well. That leaves me so little boring work that I speed run through it faster by typing it myself than I could prompt it.
The parts in the middle – non-trivial but not big picture – in my experience are the parts where writing the code myself constantly uncovers better ways to improve both the big picture and the automation/generalization. Because of that, there are almost no lines of code that I write that I feel I want to offload. Almost every line of code either improves the future of the software or my skills as a developer.
But perhaps I've been lucky enough to work in the same place for long. If I couldn't bring my code with me and had to constantly start from scratch, I might have a different opinion.
I have a similar view of AI.
I find it best as a "personal assistant," that I can use to give me information -sometimes, highly focused- at a moment's notice.
> The potential for then stunting the growth of actual juniors into tomorrow's senior developers is a serious concern
I think it's a very real problem. I am watching young folks being frozen out of the industry, at the very beginning of their careers. It is pretty awful.
I suspect that the executives know that AI isn't yet ready to replace senior-levels, but they are confident that it will, soon, so they aren't concerned that there aren't any more seniors being crafted from youngsters.
I have a couple of niche areas of non-coding interest where I'm using AI to code. It is so amazing to write rust and just add `todo!(...)` through out the boiler plate. The AI is miserable at implementing domain knowledge in those niche areas, but now I can focus on describing the domain knowledge (in real rust code because I can't describe it precisely enough in English + pseudo code), and then say "fill in the todos, write some tests make sure it compiles, and passes linting", verify the tests check things properly and I'm done.
I've struggled heavily trying to figure out how to get it to write the exactly correct 10 lines of code that I need for a particularly niche problem, and so I've kind of given up on that, but getting it to write the 100 lines of code around those magic 10 lines saves me so much trouble, and opens me up to so many more projects.
> I can pretty much see projects in my head, and know exactly what to build.
This is where AI actually helps - you have a very precise vision of what you want, but perhaps you've forgotten about the specific names of certain API methods, etc. Maybe you don't want to implement all the cases by hand. Often validating the output can take just seconds when you know what it is you're looking for.
The other part of making the output do what you want is the ability to write a prompt that captures the most essential constraints of your vision. I've noticed the ability to write and articulate ideas well in natural language terms is the actual bottleneck for most developers. It takes just as much practice communicating your ideas as it does anything else to get good at it.
Exactly. I tend to like Hotz, but by his description, every developer is also "a compiler", so it's a useless argument.
My life quality (as a startup cofounder wearing many different hats across the whole stack) would drop significantly if Cursor-like tools [1] were taken away from me, because it takes me a lot of mental effort to push myself to do the boring task, which leads to procrastination, which leads to delays, which leads to frustration. Being able to offload such tasks to AI is incredibly valuable, and since I've been in this space from "day 1", I think I have a very good grasp on what type of task I can trust it to do correctly. Here are some examples:
- Add logging throughout some code
- Turn a set of function calls that have gotten too deep into a nice class with clean interfaces
- Build a Streamlit dashboard that shows some basic stats from some table in the database
- Rewrite this LLM prompt to fix any typos and inconsistencies - yeah, "compiling" English instructions into English code also works great!
- Write all the "create index" lines for this SQL table, so that <insert a bunch of search usecases> perform well.
[1] I'm actually currently back to Copilot Chat, but it doesn't really matter that much.
> Add logging throughout some code
That's one of the thing that I wouldn't delegate to LLM. Logging is like a report of things that happens. And just like a report, I need relevant information and the most useful information.
...
A lot of these use cases actually describes the what. But the most important questions is always the why. Why is it important to you? Or to the user? That's when things have a purpose and not be just toys.
Code with logging is "self reporting". Adding logging statements is not reporting itself. Adding `logger.error(f"{job} failed")` is not reporting itself, and LLMs are perfectly capable of adding such statements in applicable places.
As to why, it's because I'm building an app with a growing userbase and need to accommodate to their requirements and build new features to stay ahead of the competition. Why you decided I'm describing a toy project is beyond me.
As someone else said: Launch is only the first step. That's when practicality start to matter.
The reason senior engineers are being paid that well is not because they need to type a lot of code to get new features in. It's because they need to figure how to have less code while having more features.
> I love AI for speed running through all the boring stuff and getting to the good parts.
But the issue is some of that speedrunning sometimes takes so much time, it becomes inefficient. It's slowly improving (gpt5 is incredible), but sometimes it get stuck on really mundane issue, and regress endlessly unless I intervene. And I am talking about straightforwars functional code.
That‘s exactly why i like AI too. I even let them play roles like „junior dev“, „product owner“ or „devops engineer“ and orchestrate them, to play together as a team - with guidance from me (usually the „solution architect“ or „investor“)! This „team“ achieves in weeks what we usually needed months for - for 2.40€/h*role!
I can't tell if you are being sarcastic but this sounds absurd. Why let the AI be junior, why not an expert?
This persona driven workflow is so weird to me. Feels like stuck in old ways.
Yes, unfortunately the boring parts are what junior devs used to do so the senior devs could work on the good stuff. Now that AI is doing the boring stuff nobody has to hire those pesky jr developers anymore. Yay?
The problem is that junior developers are what we make senior developers with— so in 15 years, this is going to be yet another thing that the US used to be really good at, but is no longer capable of doing, just like many important trades in manufacturing. The manufacturers were all only concerned with their own immediate profit and made the basic sustainability of their workforce, let alone the health of the trades that supported their industries, a problem for everyone else to take care of. Well, everyone else did the same thing.
> The problem is that junior developers are what we make senior developers with— so in 15 years
In 15 years senior developers will not be needed as well. Anyway no company is obliged to worry about 15 years timescale
It’s yet another place where we know our own capacity as a society is shrinking and hoping that ??? (Ai? Robots? Fusion?) will fix it before it’s too late. I never thought programming would join elder-care in this category though, that came as a surprise.
I have a similar relation to AI with programming -- and my sense is very many HN readers do as well, evidenced not least by the terrific experience report from antirez [1]. Yet it is rare to see such honest and open statements even here. Instead, HN is full of endless anti-AI submissions on the front page where the discussion underneath is just an echo chamber of ill-substantiated attacks on 'AI hype' and where anything else is down-voted.
It's what is, to me, so bizarre about the present moment: certainly investment is exceptionally high in AI (and of course use), but the dominant position in the media is precisely such a strange 'anti-AI hype' that positions itself as a brave minority position. Obviously, OpenAI/Altman have made some unfortunate statements in self-promotion, but otherwise I genuinely can't think of something I've read that expresses the position attacked by the anti-AI-ers -- even talk of 'AGI' etc comes from the AI-critical camp.
In a sense, the world seems divided into three: obvious self-promotion from AI companies that nobody takes seriously, ever-increasingly fervent 'AI critique', and the people who, mostly silent, have found modern AI with all its warts to be an incomparably useful tool across various dimensions of their life and work. I hope the third camp becomes more vocal so that open conversations about the ways people have found AI to be useful or not can be the norm not the exception.
[1] https://antirez.com/news/154
It’s hard to see how the current rate of progress is compatible with, 30 years from now, it being good business sense to pay human professionals six figure salaries. Opinions then split: the easiest option is pure denial, to assume that the current rate of progress doesn’t exist. Next easiest is to assume that progress will halt soon, then that we will be granted the lifestyle of well paid professionals when unsupervised AI can do our job for cheaper, then that Altman will at least deign to feed us.
Would love to see a project you built with the help of AI, can you share any links?
Most of my work is for my employer, but the bigger point is that you wouldn't be able to tell my "AI work" from my other work because I primarily use it for the boring stuff that is labor-intensive, while I work on the actual business cases. (Most of my work doesn't fall under the category of "web application", but rather, backend and background-processing intensive work that just happens to have an HTML front-end)
https://github.com/williamcotton/webpipe
Shhh, WIP blog post (on webpipe powered blog)
https://williamcotton.com/articles/introducing-web-pipe
Yes, I wrote my own DSL, complete with BDD testing framework, to write my blog with. In Rust!
My blog source code written in webpipe:http://github.com/williamcotton/williamcotton.com
What’s the tooling you’re using, and the workflow you find yourself drawn to that boosts productivity?
I've used many different ones, and find the result pretty similar. I've used Copilot in VS Code, Chat GPT stand-alone, Warp.dev's baked in tools, etc. Often it's a matter of what kind of work I'm doing, since it's rarely single-mode.
I don't think thats contrary to the article's claim: the current tools are so bad and tedious to use for repetitive work that AI is helpful with a huge amount of it.
> developer somewhere between junior and mid-level
Why the insistence on anthropomorphizing what is just a tool? It has no agency, does not 'think' in any meaningful manner, it is just pattern matching on a vast corpus of training data. That's not to say it can't be very useful - as you seem to have found - but it is still just a tool.
It's not anthropomorphising though, is it? It's just a comparison of the tool's ability. Like talking about the horsepower of an engine.
This post is such a cold take, and is going to age horribly.
Self driving cars fail because of regulatory requirements for five nines reliability, and they're doing inference over a dynamic noisy domain.
Autonomous engineering does not have these issues. Code doesn't need to be five nines correct, and the domain of inference is logical and basically static.
If the AI agent/coding companies didn't have their heads up their collective asses we could have fully spec driven autonomous coding within ~3 years, 100%.
I'm a 100% vibe-coder. AI/CS is not my field. I've made plenty of neat apps that are useful to me. Don't ask me how they work; they just do.
Sure the engineering may be abysmal, but it's good enough to work.
It only takes basic english to produce these results, plus complaining to the AI agent that "The GUI is ugly and overcrowded. Make it look better, and dark mode."
Want specs? "include a specs.md"
This isn't a 20% more productive feeling. It's productivity beyond what I will ever do on my own, given this is not my field.
This is all possible because AI was trained on the outstanding work of CS engineers like ya'll.
But the article is highly opinionated. It's like saying only phD's can be called scientists, or only programmers can be computer hackers. But in reality every human is a scientist and a hacker in the real world. The guy in a street corner in India came up with novel ways to make and sell his product, but never wrote a research paper on it. The guy on his fourth marriage noted a statistical correlation in the outcome when meeting women at a bar vs. at a church. The plant that grew in the crevice of a rock noted sunlight absorption was optimal at an angle of 78.3 degrees and grew in that direction.
You made a forest hut and you are calling out people who build skyscrapers - gatekeepers.
No. I'm just saying: "yes, AI can code."
I think it's like CMS and page builders enabling people to build their own websites without html and server knowledge. They're not making web developers disappear, instead there are more web developers now because those some of those people would eventually outgrow their page builders and need to hire web developers.
The crucial part is security.
If the apps runs locally it doesn’t matter, if it‘s connected to the net it could be the seed for the next Mirai bot network.
It’s a pretty good solution for creating live mockups. A designer on my team came back eight hours after a meeting with a fully vibe coded, multi-page interface. I was honestly blown away. I had no idea this was the state of what’s possible with these tools.
Was it a real website? No, but it’s a live mockup way better than any Figma mock or rigid demo-ware.
Apps running locally can also be subject to security issues. What you're trying to say is probably "apps not using untrusted input". If an app takes no input all, I guess we could say that security isn't an issue, but there could still be safety issues.
Oh I'd never argue that. Cloud stuff is truly beyond the complexity I'd get involved with at the moment.
What have you actually made?
What's the intent behind your question?
The Internet is awash with people making the same claims you are, but where are the actual results that we can see and use? Where are all these supposed new programs that were only possible to make because of generative ai? The number of new apps in the app store is flat. Still getting the same amount in 2025 as in 2022.
App-store listing is a whole other animal. I don't care to go through all that just to share my app. I also don't care to resolve every technical issue others experience. Every time I've thought about generating revenue by selling my apps, two thoughts come to mind: my code is not professional-grade, and the field is so competitive than within months a professional will likely create a better app so why pollute the web with something subpar.
The hacker on the street corner isn't distributing his "secret sauce" because it wouldn't meet standards, but it works well for him, and it was cheap/free.
Evaluating your empirical experience by judging the complexity you're impressed by.
Valid inquiry. In relative terms I'm the Indian on a street corner who hacks things together using tools professionally designed by others. Among the repos I've chosen to publicly share: https://github.com/sm18lr88
I started fully coding with Claude Code. It's not just vibe coding, but rather AI-assisted coding. I've noticed there's a considerable decrease in my understanding of the whole codebase, even though I'm the only one who has been coding this codebase for 2 years. I'm struggling to answer my colleagues' questions.
I am not defending we should drop AI, but we should really measure its effects and take actions accordingly. It's more than just getting more productivity.
I’m experiencing something similar. We have a codebase of about 150k lines of backend code. On one hand, I feel significantly more productive - perhaps 400% more efficient when it comes to actually writing code. I can iterate on the same feature multiple times, refining it until it’s perfect.
However, the challenge has shifted to code review. I now spend the vast majority of my time reading code rather than writing it. You really need to build strong code-reading muscles. My process has become: read, scrap it, rewrite it, read again… and repeat until it’s done. This approach produces good results for me.
The issue is that not everyone has the same discipline to produce well-crafted code when using AI assistance. Many developers are satisfied once the code simply works. Since I review everything manually, I often discover issues that weren’t even mentioned. During reviews, I try to visualize the entire codebase and internalize everything to maintain a comprehensive understanding of the system’s scope.
I'm very surprised you find this workflow more efficient than just writing the code. I find constructing the mental model of the solution and how it fits into existing system and codebase to be 90% of effort, then actually writing the code is 10%. Admittedly, I don't have to write any boilerplate due to the problem domain and tech choices. Coding agents definitely help with the last 10% and also all the adjacent work - one-off scripts where I don't care about code quality.
I wrote a couple python scripts this week to help me with a midi integration project (3 devices with different cable types) and for quick debugging if something fails (yes, I know there are tools out there that do this but I like learning).
I’m could have used an LLM to assist but then I wouldn’t have learned much.
But I did use an LLM to make a management wrapper to present a menu of options (cli right now) and call the scripts. That probably saved me an hour, easily.
That’s my comfort level for anything even remotely “complicated”.
I keep wanting to go back to using claudecode but I get worried about this issue. How best to use it to complement you, without it rewriting everything behidn the scenes? whats the best protocol? constnat commit requests and reviews?
This is the chief reason I don't use integrations. I just use chat, because I want to physically understand and insert code myself. Else you end up with the code overtaking your understanding of it.
Yes. I'm happy to have a sometimes-wrong expert to hand. Sometimes it provides just what I need, sometimes like with a human (who are also fallible), it helps to spur my own thinking along, clarify, converge on a solution, think laterally, or other productivity boosting effects.
This is a more extreme example of the general hacker news group think about AI.
Geohot is easily a 99.999 percentile developer, and yet he can’t seem to reconcile that the other 99.999 percent are doing something much more basic than he can ever comprehend.
It’s some kind of expert paradox, if everyone was as smart and capable as the experts, then they wouldn’t be experts.
I have come across many developers that behave like the AI. Can’t explain codebases they’ve built, can’t maintain consistency.
It’s like a aerospace engineer not believing that the person that designs the toys in an Kinder egg doesn’t know how fluid sims work.
This.
I think his excellency in his own trade limited his vision for the 99% who just want to get by in the job. How many dev even deal with compiler directly these days? They write some code, fix some red underlines, then push, pray and wait for pipeline pass. LLMs will be gods in this process, and you can even beg another one if your current one does not work best.
> Geohot is easily a 99.999 percentile developer
I keep seeing people praise this guys, but honestly I never saw anything impressive in anything he's ever done. He does seem to be prolific and with a lot of energy, but I've seen plenty of equally talented people.
You've seen plenty of people who hacked the ps3 and iphone as teenagers and created a low level system analysis tool for doing such system hacks? You've seen plenty of people writing self driving car software a decade ago? Why did you write this when you know nothing?
I actually have seen plenty of people that could have done something like this, but did not because they simply never tried. Being daring by itself is a skill, but we're talking raw technical ability here.
I've actually seen another developer that was probably in the same category write his own self-driving software. It kind of worked, but couldn't have ever been production ready, so it was just an exercise in flexing without any practical application.
So, what product that George built do you actually use?
For the comment above, the more relevant denominator is all humans vs. all developers. If you use all humans as the denominator, he's easily in the top 1% or 0.001% (I haven't followed his work closely, but you'd only have to be a good dev to be in top 1% of the global population).
Thank you, perhaps I worded it harshly, but that was my general feeling. Being a good developer already is a high level. Being able to start impressive-sounding projects that never materialize into anything is a luxury for which most competent developers simply don't have the extra energy.
That METR study gets a lot of traction for its headline; and I doubt many people read the whole thing—it was long—but the data showed a 50% speed up for the one dev with the most experience with Cursor/AI, suggesting a learning curve and also wild statistical variation on a small sample set. An errata later suggested another dev who did not have a speedup had not represented their experience correctly, but still strongly draws into question the significance of the findings.
The specific time sucks measured in the study are addressable with improved technology like faster LLMs and improved methodology like running parallel agents—the study was done in March running Claude 3.7 and before Claude Code.
We also should value the perception of having worked 20% less even if you actually spent more time. Time flies when you’re having fun!
So I have all kinds of problems with this post.
First, the assertion that the best model of "AI coding" is that it is a compiler. Compilers deterministically map a formal language to another under a spec. LLM coding tools are search-based program synthesizers that retrieve, generate, and iteratively edit code under constraints (tests/types/linters/CI). That’s why they can fix issues end-to-end on real repos (e.g., SWE-bench Verified), something a compiler doesn’t do. Benchmarks now show top agents/models resolving large fractions of real GitHub issues, which is evidence of synthesis + tool use, not compilation.
Second, that the "programming language is English". Serious workflows aren’t "just English." They use repo context, unit tests, typed APIs, JSON/function-calling schemas, diffs, and editor tools. The "prompt" is often code + tests + spec, with English as glue. The author attacks the weakest interface, not how people actually ship with these tools.
Third, non-determinism isn't disqualifying. Plenty of effective engineering tools are stochastic (fuzzers, search/optimization, SAT/SMT with heuristics). Determinism comes from external specs: unit/integration tests, type systems, property-based tests, CI gates.
False dichotomy: "LLMs are popular only because languages/libraries are bad." Languages are improving (e.g. Rust, Typescript), yet LLMs still help because the real bottlenecks are API lookup, cross-repo reading, boilerplate, migrations, test writing, and refactors, the areas where retrieval and synthesis shine. These are complementary forces, not substitutes.
Finally, no constructive alternatives are offered. "Build better compilers/languages" is fine but modern teams already get value by pairing those with AI: spec-first prompts, test-gated edits, typed SDK scaffolds, auto-generated tests, CI-verified refactors, and repo-aware agents.
A much better way to think about AI coding and LLMs is that they aren’t compilers. They’re probabilistic code synthesizers guided by your constraints (types, tests, CI). Treat them like a junior pair-programmer wired into your repo, search, and toolchain. But not like a magical English compiler.
It's not surprising that you're finding problems with the article. It's written by George Hotz aka Geohot.
Excellent response, completely agree.
Ofcourse, there is some truth in what you say. But business is desperate for new tech where they can redefine the order (who is big and who is small). There are floating billions which chase short term returns. Fund managers will be fired if they are not jumping on the new fad in the town. CIO's and CEO's will be fired if they are not jumping on AI. It's just nuclear arms race. It's good for none. but the other guy is on it, so you need to be too.
Think about this. Before there were cars on roads, people were just as much happy. Cars came, cities were redesigned for cars with buildings miles apart, and commuting miles became the new norm. You can no longer say cars are useless because the context around them has changed to make the cars a basic need.
AI does same thing. It changes the context in which we work. Everyone expects you use AI (and cars). It becomes a basic need, though a forced one.
To go further, hardly anything produced by science or technology is a basic need for humans. The context got twisted, making them basic needs. Tech solutions create the problems which they claim to solve. The problem did not exist before the solution came around. That's core driving force of business.
I have a boring opinion. A cold take? served straight from the freezer.
He is right, however AI is still darn useful. He hints at why: patterns.
Writing a test suite for a new class when an existing one is in place is a breeze. It even can come up with tests you wouldnt have thought of or would have been too time pressed to check.
It also applies to non-test code too. If you have the structure it can knock a new one out.
You could have some lisp contraption that DRYs all the WETs so there is zero boilerplate. But in reality we are not crafting these perfect cosebases, we make readable, low-magic and boilerplatey code on tbe whole in our jobs.
But what about the tests usefulness? Tests enforce contracts, contracts are about the domain, not the implementation. The number of tests don't actually matter as much as what is being actually verified. If you look at the code to know what to tests, you are doing it wrong.
The usefulness is in saving time boilerplating, plus figuring out tests I may not have thought of.
But I do closely review the code! It turns the usual drudge of writing tests into more of a code review. Last time I did it it had some mistakes I needed to fix for sure.
There shouldn't be boilerplate in test code. It should be refactored into harnesses, utils, and fixtures instead.
I think this gets to a fundamental problem with the way the AI labs have been selling and hyping AI. People keep on saying that the AI is actually thinking and it's not just pattern matching. Well, as someone that uses AI tools and develops AI tools, my tools are much more useful when I treat the AI as a pattern matching next-token predictor than an actual intelligence. If I accidentally slip too many details into the context, all of a sudden the AI fails to generalize. That sounds like pattern matching and next token prediction to me.
> This isn’t to say “AI” technology won’t lead to some extremely good tools. But I argue this comes from increased amounts of search and optimization and patterns to crib from, not from any magic “the AI is doing the coding”
* I can tell claude code to crank out some basic crud api and it will crank it out in a minute saving me an hour or so.
* I need an implementation of an algorithm that has been coded a million times on github, I ask the AI to do it and it cranks out a correct working implementation.
If I only use the AI in its wheelhouse it works very well, otherwise it sucks.
I think this comes down to levels of intelligence. Not knowledge, I mean intelligence. We often underestimate the amount of thinking/reasoning that goes into a certain task. Sometimes the AI can surprise you and do something very thoughtful, this often feels like magic.
Both CRUD and boilerplate are arguably a tooling issue. But there are also a bunch of things only AI will let you do.
My tests with full trace level logging enabled can get very verbose. It takes serious time for a human to parse where in the 100 lines of text the relevant part is.
Just telling an AI: "Run the tests and identify the root cause" works well enough, that nowadays it is always my first step.
> AI makes you feel 20% more productive but in reality makes you 19% slower. How many more billions are we going to waste on this?
Adderall is similar. It makes people feel a lot more productive, but research on its effectiveness[0] seems to show that, at best, we get only a mild improvement in productivity, and marked deterioration of cognitive abilities.
[0] https://pmc.ncbi.nlm.nih.gov/articles/PMC6165228/
I’m someone with ADHD who takes prescribed stimulants and they don’t make me work faster or smarter, they just make me work. Without them I’ll languish in an unfocused haze for hours, or zone in on irrelevant details until I realise I have an hour left in the day to get anything done. It could make me 20% less intelligent and it would still be worth it; this is obviously an extreme, but given the choice, I’d rather be an average developer that gets boring, functional code done on time than a dysfunctional genius who keeps missing deadlines and cannot be motivated to work on anything but the most exciting shiny new tech.
I have family that had ADHD, as a kid (they called it “hyperactivity,” back then). He is also dyslexic.
The ADHD was caught early, and treated, but the dyslexia was not. He thought he was a moron, for much of his early life, and his peers and employers did nothing to discourage that self-diagnosis.
Since he learned of his dyslexia, and started treating it, he has been an engineer at Intel, for most of his career (not that I envy him, right now).
You have to realize that ADD meds are meant only for people with ADD, not healthy people at the prime of their life. Excess neurochemicals can have the opposite effect.
Their benefits when used as intended are solidly documented in research literature.
Note that the study is just n=13 and on subjects without ADHD.
That’s the deal.
People without ADHD take it, believing that it makes them “super[wo]men.”
I had a problem client that I ended up firing and giving money back to about 15 years ago. Lot of red flags, but the breaking point was when they offered me adderall so I could "work faster".
That said, I'll leave the conclusions about whether it's valuable for those with ADHD to the mental health professionals.
Thanks again, diarrhea
Research on _13_ people, that's a very important caveat when evaluating something like adderal.
I’m quite sure that there’s a ton more research on it. The drug’s been around for decades. Lots of time for plenty of studies.
If legitimate research had found it to be drastically better, that study would definitely have been published in a big way.
Unscientifically, I personally know quite a number of folks that sincerely believed that they couldn’t function without it, but have since learned that they do far better on their own. I haven’t met a single one that actually had their productivity decline (after an adjustment period, of course), after giving up Adderall. In fact, I know several, that have had their careers really take off, after giving it up.
My point is that micro-studies like that on a tiny random (or even counter-indicated, "healthy") selection of the general population don't tell you much for drugs that do specific things.
"Antibiotics don't improve your life, but can damage your health" would likely be the outcome on 13 randomly selected healthy individuals. But do the same study on 13 people with a bacterial infection susceptible to antibiotics and your results will be vastly different.
I don't think that it matters, in this context, as a lot of folks here, have their minds made up, already, and won't let anything interfere.
They'll need to learn, the same way I see lots of people learn.
It's been around long enough, though, that all the Adderall-eating people should have established a Gattaca-like "elite," with all us "undermen," scrabbling around at their feet.
Not sure why that never happened...
It’s interesting how science can become closer to pseudoscience than proper research through paper-milling.
It seems like that with such small groups and effects you could run the same “study” again and again until you get the result that you initially desired.
So it should be easy to find studies that prove that non-ADHD people that take it, have dramatically improved productivity.
It's very easy to find studies that prove that Adderall (etc.) improve non-ADHD people's cognition ability. And it's equally easy to find studies that prove otherwise. The parent comment is very spot on. You can find evidence supporting anything nowadays.
https://pmc.ncbi.nlm.nih.gov/articles/PMC3489818/table/tbl1/
AI coding is the one thing that got my back to programming. I got to the point in life, when my ability to focus is reducing, and I prefer to send the remaining energy elsewhere. I kind of gave up on programming, just doing architecture and occasionally doing very small programming tasks. It all changed when I discovered Claude Code and saw that the way it works, is kind of how I work. I also use a lot of grep to find my way through a new codebase, I also debug stuff by adding logs to see the context, I also rely on automated tests to tell me something is broken. I'm still very good at reading code, I'm good at architecture, and with these tools, I feel I can safely delegate the boring bits of writing code and debugging trivial things to AI. Yes, it's slower than if I focused on the task myself, but the point is that I'd not be able to focus on the task myself.
I feel the same way. After 30+ years coding, I know what I want to build, but finding the time to work on it and the focus is harder than it used to be.
Great short read. But this “ It’s why the world wasted $10B+ on self driving car companies that obviously made no sense.”
Not everything should make sense. Playing , trying and failing is crucial to make our world nicer. Not overthinking is key, see later what works and why.
"that obviously made no sense" is bizarre.
Waymo's driving people around with an injuries-per-mile rate that's lower than having humans do it. I don't see how that reconciles with "obviously made no sense".
> Playing , trying and failing is crucial to make our world nicer. Not overthinking is key, see later what works and why.
It would be, if there weren't actual important works that needs funding.
Pretty much nailed it. Once you’re at about 40k LOC you can just turn off the autocomplete features and use Claude or GPT to evaluate specific high-level issues. My sense is 40k LOC is the point at which the suggestions are offset by the rabbit holes they sometimes send you down, but, more importantly by obscuring from you the complexity of the thing you’re building—temporarily.
I expect much of this can be solved with better architecture: smaller, more independent components. Break large code bases up into independent libraries, and LLMs can work again because they need much less code in their context.
Suggesting I restructure my code base to work better with a non-deterministic black box feels like an instance of Conway’s law[1] that should be avoided.
https://en.m.wikipedia.org/wiki/Conway%27s_law
There's a lot of complaining about current compilers / languages / codebase in similar posts, but barely any ideas for how to make them better. It doesn't seem surprising that people go for the easier problem (make the current process simpler with LLMs) than for the harder one (change the whole programming landscape to something new and actually make it better).
> Or we could, you know, do the hard work and build better programming languages, compilers, and libraries. But that can’t be hyped up for billions.
100% this. I fear that AI will cause us to be stuck in a local optimum for the next decades where most of the code will be Python or JS because these are the languages best supported by LLMs. Don't get me wrong, Python and JS and mature and productive languages. That's fine. But we could have it so much better if there was more effort put into a next generation of tools that take all the harsh lessons learnt from the tools before and "just" do it better. I acknowledge that we get incremental improvements here and there but some things are just unfixable without breaking existing ecosystems.
I have been working on finding out ways to make use of AI a net-positive in my professional life as opposed to yet another thing I have to work around and have cognitive load of. Some notes so far in getting great benefits out of it on couple projects -
* Getting good results from AI forced me to think through and think clearly - up front and even harder.
* AI almost forces me to structure and break down my thoughts into smaller more manageable chunks - which is a good thing. (You can't just throw a giant project at it - it gets really far off from what you want if you do that.)
* I have to make it a habit of reading what code it has added - so I understand it and point to it some improvements or rarely fixes (Claude)
* Everyone has what they think are uninteresting parts of a project that they have to put effort into to see the bigger project succeed - AI really helps with those mundane, cog in the wheel things - it not only speeds things up, personally it gives me more momentum/energy to work on the parts that I think are important.
* It's really bad at reusability - most humans will automatically know oh I have a function I wrote to do this thing in this project which I can use in that project. At some point they will turn that into a library. With AI that amount of context is a problem. I found that filling in for AI for this is just as much work and I best do that myself upfront before feeding it to AI - then I have a hope of getting it to understand the dependency structure and what does what.
* Domain specific knowledge - I deal with Google Cloud a lot and use Gemini for understanding what features exist in some GCP product and how I can use it to solve a problem - works amazingly well to save me time. At the least optioning the solution is a big part of work it makes easier.
* Your Git habits have to be top notch so you can untangle any mess AI creates - you reach a point where you have iterated over a feature addition using AI and it's a mess and you know it went off the rails after some point. If you just made one or two commits now you have to unwind everything and hope the good parts return or try to get AI to deal with it which can be risky.
> AI makes you feel 20% more productive but in reality makes you 19% slower. How many more billions are we going to waste on this?
True in the long run. Like a car with a high acceleration but low top speed.
AI makes you start fast, but regret later because you don't have the top speed.
This is shown in figure 5 of the paper. https://arxiv.org/pdf/2507.09089
People repeating articles or papers. I know myself. I know from my own experiences what the good and bad of practice A or database B is. I don't need to read a conclusion by some Muppet.
Chill. Interesting times. Learn stuff, like always. Iterate. Be mindful and intentional and don't just chase mirrors but be practical.
The rest is fluff. You know yourself.
The takeaway from the paper is you don’t know yourself. It’s one paper, and a small sample size, but attempting to refute its conclusion by stating it’s false doesn’t really get us anywhere.
Vibe coding large projects isn’t feasible yet, but as a developer here’s how I use AI to great effect, to the point where losing the tool greatly decreases my productivity:
- Autocomplete in Cursor. People think of AI agents first when they talk about AI coding but LLM-powered autocomplete is a huge productivity boost. It merges seamlessly with your existing workflow, prompting is just writings comments, it can edit multiple lines at once or redirect you to the appropriate part of the codebase, and if the output isn’t what you need you don’t waste much time because you can just choose to ignore it and write code as you usually do.
- Generating coding examples from documentation. Hallucination is basically a non-problem with Gemini Pro 2.5 especially if you give it the right context. This gets me up to speed on a new library or framework very quickly. Basically a stack overflow replacement.
- Debugging. Not always guaranteed to work, but when I’m stuck at a problem for too long, it can provide a solution, or give me a fresh new perspective.
- Self contained scripts. It’s ideal for this, like making package installers, cmake configurations, data processing, serverless micro services, etc.
- Understanding and brainstorming new solutions.
- Vibe coding parts of the codebase that don’t need deep integration. E.g. create a web component with X and Y feature, a C++ function that does a well defined purpose, or a simple file browser. I do wonder if a functional programming paradigm would be better when working with LLMs since by avoiding side effects you can work around their weaknesses when it comes to large codebases.
Code has a lot of bits of information the compiler users to construct the program. But not all because software needs iteration to get right both in bugs and in solving the intended problem.
The llm prompt has even fewer bits of information specifying the system than code. The model has a lot more bits but still finite. A perfect llm cannot build a perfect app in one shot.
However AIs can research, inquire, and iterate to gain more bits than when you started.
So the comparison to a compiler is not apt because the compiler can’t fix bugs or ask the user for more information about what the program should be.
Most devs are using ai at the autocomplete level which is like this compiler analogy which makes sense in 2025 but that isn’t where we will be in 2030.
What we don’t know is how good the technology will be in the future and how cheap and how fast. But it’s already very different than a compiler.
I agree.
I use LLM to do things like brainstorm, explaining programming concepts and debug. I will not use it to write code. The output is not good enough, and I feel dumber.
I only see the worst of my programming collegues coding with AI. And the results are actual trash. They have no actual understanding of the code "they" are writing, and they have no idea how to actually debug what "they" made, if LLM is not helpful. I can smell the technical debt.
Is this fundamentally different from them copy pasting code from StackOverflow or random blog posts, without understanding it?
You know, aside from AI making it super easy and fast to generate this tech debt in whatever amounts they desire?
Even when copy-pasting an entire function from stack overflow, you generally still need to have some understanding of what the inputs and outputs are, even if it remains somewhat of a black box, so that you can plug it into your existing code.
AI removes that need. You don't need to know what the function does at all, so your brain devotes no energy towards remembering or understanding it.
Me too.
I used to be a bit more open minded on this topic but im increasingly viewing any programmers who use AI for anything other than brainstorming and looking stuff up/explaining it as simply bad at what they do.
These articles are beyond the point of exhausting. Guys, just go use the tools and see if you like them and feel more capable with them. If you do, great, if you don’t, then stop.
The truth will emerge naturally through labor market competition in the long-run. You do you. I will be using these tools extensively. Good luck out there in the arena.
> It’s why the world wasted $10B+ on self driving car companies that obviously made no sense. There’s a much bigger market for truths that pump bags vs truths that don’t.
Did geohot not found one of these?
Yes. He mentions that in passing that saying people will accuse him of hating on it because he didn't profit from it. I think his point of view is that his company's attempt was smaller scale and not part of the $10B+ waste?
In any case I don't fully understand what he's trying to say other than negating the hype (which i generally agree with), but not offering any alternative thoughts of his own other than- we have bad tools and programming language. (why? how are they bad? what needs to change for them to be good?)
Well, he’s currently running a startup aimed at making better tooling for the space. So, he’s putting his time where mouth is.
This pre-AI article makes a very similar argument: https://mortoray.com/programming-wont-be-automated-or-it-alr...
Once we realize that what we actually want is turning specifications into software, I think that English will become the base for a new, high level specification language.
We are turning specifications into software precisely because English (and any natural languages) lacks the formality that makes it not reliable (necessary quality for a tool), but great for imagination (the source of invention).
We always start from natural language. RFC, docs, tickets,... are in natural language. But gaining formality (losing ambiguity) is what programming is (software engineering is doing programming well). People that struggled with programming struggle in fact with formality.
Instead of everyone telling their personal anecdote, we should look at the actual research.
Asking ChatGPT to summarize the state of AI coding research that’s not done by one of the providers, and I feel like I got the sanest summary of the actual state of things:
> If you weight independent work more heavily: current best evidence says don’t expect speedups for senior devs in complex, well-known codebases without process changes; expect security pitfalls unless you add guardrails. Gains look more reliable for novices, unfamiliar code, and templated tasks
Seems the most sane PoV on the actual benefits I’ve seen.
Actual link if you want to pick apart GPT-5s summary
https://chatgpt.com/share/68c56214-1038-8004-b1ed-359393eb15...
AI coding is working really good for us.
My teammate shared 3 phase workflow we are using on our team to deliver project at rapid phase.
It's shared on ClaudeCode subreddit https://www.reddit.com/r/ClaudeCode/s/iy058fH4sZ
I've been using it for months with great success
These workflows always surprise me. Isn’t this what you’ve been doing with humans all along? Write up a basic project plan likely following some structure the product team likes. Share it with the eng team. One to a few members of the eng team writes up a more specific plan. Everyone gets together again and goes through the specific plan to iron out the kinks. You the implement the plan.
I’ve seen at my workplace people are more willing to help Claude with a plan than their fellow humans. I pointed that out and one engineer replied with “well I know Claude will read the documentation”. It’s a depressing observation but I don’t know if it’s wrong.
I can implement 20 such features on my own each day with no input from others though. That's why i am using this method in the first place.
I also had a compiler related description come to me after using Copilot. It allows you to partially generate imperative code declaratively, by writing a comment like
//now I will get rows X, Y, Z from ContentsProvider
then tab tab complete. You can then even tweak the generated code, very useful!
There is some amount of truth on the AI coding claims.
But, what's with the self driving hate? I take Waymos on a regular basis, and he is basing his credibility on the claim that they are not a thing. Makes him sound bitter more than insightful.
George famously started a self driving hardware-as-an-addon company. He started after both claiming self driving was easy, and reportedly taking up an Elon bounty/bet in the seven figure range to make self driving AI for Tesla.
I think some of the “hate” is the hype. We’re all tired of companies announcing ground breaking tech that isn’t readily available a decade later.
I don’t live in California (like most of the population of the planet) - Toronto for 18 years and now the American side of the Great Lakes.
Ice storms, snow, sleet, cold weather 5-6 months out of the year. Batteries suck in the cold, sensors fail or under-perform. Hell, door handles and windows struggle in this weather.
Waymo is not a thing in NY or Chicago or Minneapolis or Philadelphia (I could go on).
And that's just in North America where cities were built for cars and with cars in mind. Autonomous driving in Europe or Asia is likely a magnitude harder due to windy roads, non trivial intersections, complex interactions of humans, bikes, trams and cars etc.
That was a really great read. Not saying I agree with it all, I’m maybe more in the camp that believes AI assisted coding is a time-saver but it’s refreshing (and overdue) to have a counterpoint to the deafening and repetitive drumbeat of the VC-backed hype machine.
Even though I don’t buy that LLMs are going to replace developers and quite agree with what is said, this is more of a critique of LLMs as English-to-code translators. LLMs are very useful for many other things.
Researching concepts, for one, has become so much easier, especially for things where you don’t know anything yet and would have a hard time to even formulate a search engine query.
I’ve found that ChatGPT and Perplexity are great tools for finding “that article I skimmed a year ago that talked about…”.
I agree. I think a better analogy than a compiler is a search engine that has an excellent grasp of semantics but is also drunk and schizophrenic.
LLMs are really valuable for finding information that you aren't able to formulate a proper search query for.
To get the most out of them, ask them to point you to reliable sources instead of explaining directly. Even then, it pays to be very critical of where they're leading you to. To make an LLM the primary window through which you seek new information is extremely precarious epistemologically. Personally, I'd use it as a last resort.
Just started using Claude Code recently.
It seems to speed up feature development but requires one to have a good understanding of the codebase to guide it and be aware of edge cases it missed.
Also, it doesn't seem to be able to take advantage of latest information or new SDK features unless deliberately informed. Not sure if I'm doing it right, but I resorted to feeding it documentation when it can't seem to something right.
The only thing I'm still unsure is the context management with /compact and /clear
I do agree with many points in the article, but not about the last part, namely that coding with AI assist makes you slower.
Personal experience (data points count = 1), as a somewhat seasoned dev (>30yrs of coding), it makes me WAY faster. I confess to not read the code produced at each iteration other than skimming through it for obvious architectural code smell, but I do read the final version line by line and make a few changes until I'm happy.
Long story short: things that would take me a week to put together now take a couple of hours. The vast bulk of the time saved is not having to identify the libraries I need, and not to have to rummage through API documentation.
> Personal experience (data points count = 1), as a somewhat seasoned dev (>30yrs of coding), it makes me WAY faster.
> Long story short: things that would take me a week to put together now take a couple of hours. The vast bulk of the time saved is not having to identify the libraries I need, and not to have to rummage through API documentation.
One of these is not true.
With libraries, it's either you HAVE to use it, so you spend time being acquainted with it (usually a couple hours to make sense of its design, the rest will come on a needed basis) or you are evaluating multiple ones (and that task is much quicker).
> you are evaluating multiple ones (and that task is much quicker).
Of course the latter. And of course I ask the AI to help me select a libray/module/project/whatever that provides what I need. And I ask the AI to classify them by popularity/robustness. And then I apply whatever little/much I know about the space to refine the choice.
may go as far as looking at examples that use the API. And maybe rummage through the code being the API to see if I like what I see.
The whole thing is altogether still way faster than having to pick what I need by hand with my rather limited data ingestion capabilities.
And then, once I've picked one, connecting to the API's is a no-brainer with an LLM, goes super fast.
Altogether major time saved.
It takes time. There are cycles of “Oh wow!” “Oh wait...” “What if?” and “Aha!” Each of those has made me more effective and resulted in reliable benefits with less zig zagging back and forth.
>It’s not precise in specifying things.
That's the point - it's a higher level of abstraction.
>highly non-deterministic
...not unlike say a boss telling a junior to change something?
The bet here isn't that AI can be as precise as something hand coded but rather that you can move up a step in the abstraction layer. To use his compiler example...I don't care what the resulting assembly instructions look like, just whether it works. It's the same thing here just one level higher
> That's the point - it's a higher level of abstraction.
That's not what abstraction is. When I type `echo Hello, World`, I don't have to deal with graphic drivers and test rendering to have the text on the screen. And I don't have to worry that "Goodbye" will appear instead.
> Not unlike say a boss telling a junior to change something?
Junior don't stay junior for long. And bosses usually give juniors less time to grow than people are allocating AI tools to actually prove themselves. Github copilot was more than 3 years ago. Today a new hire is expected to be productive on day one.
> It’s why the world wasted $10B+ on self driving car companies that obviously made no sense.
Obviously... in what way? I feel the anti-ai pattern is clear.
Self-driving cars don't work in my city so the whole concept is a hoax. LLMs don't code my proprietary language so it's a bubble.
> From this study (https://arxiv.org/abs/2507.09089)
I can tell this is going to be the most misquoted study in blogs and pop-sci books after the 10,000-hour mastery study. And it's just a preprint!
I agree that most natural languages are a very poor tool to write code specification in.
Specifically, natural language is:
Having something at our disposal that you can write code specs in, that is as easy as natural language yet, more concise, easy to learn and most of all not so anal/rigid as typical code languages are would be fantastic.Maybe LLMs can be sued to design such a thing ?
> Maybe LLMs can be sued to design such a thing
nice misspelling (or a joke?), related to all the lawsuits around LLMs.
Joking aside, it’s already there in a sense. Several times I started with a brief outline of what the prototype should do (an HTML/CSS/JS app), and sure enough, refinements and corrections followed. When the final version worked more or less as expected, I asked the LLM to create a specification (a reproducing prompt) of everything we made together. Even if the vibe-coded prototype is dropped, the time wasn’t wasted, I probably would never have come to the same bullet list specification without having an actual working app at my disposal to test and evaluate. So paradoxically this specification even might be used by a human later
he lagged behind that's why.
I stopped reading at this point:
> It’s why the world wasted $10B+ on self driving car companies that obviously made no sense. There’s a much bigger market for truths that pump bags vs truths that don’t.
This reeks of bias-dismissing massive investments as ‘obvious’ nonsense while hyping its own tinygrad as the ‘truth’ in AI coding.
Author is allowed to claim ‘most people do not care to find the truth’ but it’s hypocritical when the post ignores counterpoints, like PyTorch’s dominance in efficient coding benchmarks.
Author doesn’t seem to care about finding the full truth either, just the version that pumps its bag.
LLMs are a tool to help match human thought to what computers can do. People would like them to have exact reproducible results, but they're on the other end of the spectrum, more like people than tools. George correctly points out there is a vast space to explore closer to the compute hardware that might profitably be explored. Thanks to the same LLMs, that's about to get a whole lot easier. If you marveled at the instant response of Turbo Pascal and IDEs, you're in for a whole lot more.
--- (that was the tl;dr, here's how I got there) ---
As a mapper[3], I tend to bounce all the things I know against each new bit of knowledge I acquire. Here's what happens when that coincides with GeoHot's observation about LLMs vs Compilers. I'm sorry this is so long, right now it's mostly just stream of thought with some editing. It's an exploration of bouncing the idea of impedance matching against the things that have helped advance programming and computer science.
--
I've got a cognitive hammer that I tend to over-use, and that is seeing the world through the lens of a Ham Radio operator, and impedance matching[2]. In a normal circuit, maximum power flows when the source of power and the load being driven have the same effective resistance. In radio frequency circuits (and actually any AC circuit) there's another aspect, reactance. It's a time shifted form of current. This is trickier there are now 2 dimensions to consider instead of one, but most of the time, a single value, VSWR is sufficient to tell how well things are matched.
VSWR is adequate to know if a transmitter is going to work, or power bouncing back from the antenna might destroy equipment, but making sure it will work across a wide range of frequencies, yields at least a 3rd dimension. As time progresses, if you actually work with those additional dimensions, it slowly sinks in what works, and how, and what had previously seemed like magic, becomes engineering.
For example, vacuum tube based transmitters have higher resistances that almost any antenna, transformers and coupling through elements that shift power back and forth between the two dimensions allow optimum transfer without losses at the cost of complexity.
On the other hand, semiconductor based transmitters tend to have the opposite problem, their impedances are lower, so different patters work for them, but most people still just see it as "antenna matching", and focus on the single number, ignoring the complexity.
{{Wow... this is a book, not an answer on HN... it'll get shorter after a few edits, I hope, NOPE... it's getting longer...}}
Recently, a tool that used to cost thousands of dollars, the Vector Network Analyzer, has become available at less than $100. It allows for measuring resistance, reactance, and gain simultaneously across frequency. It's like compilers, making things manageable in scope that otherwise seemed too complex. It only took a few times playing with a NanoVNA to understand things that previously would have been some intense EE classwork with Smith Charts.
Similarly, tools like Software Defined Radios for $30, and GNU Radio (for $0.00) allowed understanding digital signal processing in ways that would have been equally difficult without professional instruction. With these tools, you can build a signal flow graph in an interactive window, and in moments have a working radio for FM, AM, Sideband, or any other thing you can imagine. It's magic!
-- back to computing and HN --
In the Beginning was ENIAC, a computer that took days to set up and get working on a given problem by a team with some experience. Then John Von Neumann came along, and added the idea of stored programs, which involved sacrificing the inherently parallel nature of the machine, losing 70% of its performance, but making it possible to set it up for a task simply by loading a "program" onto it's back of instruction switches.
Then came cards and paper tape storage, further increasing the speed at which data and programs could be handled.
It seems to me that compilers were like one of the above tools, they made it possible for humans to do things that only Alan Turing or others similarly skilled could do in the beginning of programming.
Interactive programming increased the availability of compute, and make machines that were much faster that programmers, more easily distributed among teams of programmers.
IDEs were another. Turbo Pascal allowed compile, linking, and execution to happen almost instantly. It widely opened the space for experimentation by reducing the time required to get feedback from minutes to almost zero.
I've done programming on and off through 4 decades of work. Most of my contemplation is as an enthusiast, instead of professional. As far as compilers and the broader areas of Computer Science I haven't formally studied, it seems to me that LLMS, especially the latest "agentic" versions, will allow me to explore things far easier than I might have otherwise done. LLMs have helped me to match my own thoughts across a much wider cognitive impedance landscape. (There's that analogy/hammer in use...)
Compilers are an impedance matching mechanism. Allowing a higher level of abstraction gives flexibility. One of the ideas I've had in the past for helping with better interaction between people and compilers is to allow compilers that also work backwards.[1] I'm beginning to suspect that with LLMs, I might actually be able to attempt to build this system, it's always seemed out of reach because of the levels of complexity involved.
I have several other ideas that might warrant a new attempt, now that I'm out of the job market, and have the required free time and attention.
{{Sorry this turned out to be an essay... I'm not sure how to condense it back down right now}}
[1] https://wiki.c2.com/?BidirectionalCompiler
[2] https://en.wikipedia.org/wiki/Impedance_matching
[3] https://garden.joehallenbeck.com/container/mappers-and-packe...
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ok boomer. its silly to read such generalizations. ai is a tool, and as every other tool it needs the right job and the right user to be useful and productive.
i am not agree with your opinions @realGeorgeHotz
if you know the fundamentals really well. AI coding actually speeds up development process.
source to the article: https://geohot.github.io//blog/jekyll/update/2025/09/12/ai-c...
are you planning to stream it? @ThePrimeagen
X.com: https://x.com/abdibrokhim/status/1966815369441542381
How do we resolve the observable tension here with the fact that self-driving cars are operating right now, relatively successfully, in ten or so major American cities?
Not a billion dollar business yet, maybe, but 300 cars generating five or six figures revenue per year each isn't far off.
(And I say this as someone who is skeptical that totally autonomous cars worldwide will ever be a thing, but you can get to £10Bn far, far before that point. Become the dominant mode of transport in just ONE major American city and you're most of the way there).
> How do we resolve the observable tension here with the fact that self-driving cars are operating right now, relatively successfully, in ten or so major American cities?
Because geo fenced driving in a few select cities with very favourable conditions is not what was promised. That's the crux. They promised us that we have self drive anywhere at anytime at the press of a button.
> Not a billion dollar business yet, maybe, but 300 cars generating five or six figures revenue per year each isn't far off.
I'm not sure how you get to 6 figures revenue. Assuming the car makes $100 per hour for 24x7 52 weeks a year we still fall short of 1 million. But let's assume you're right $300M revenue (not profit, are they even operating at a plus even disregarding R&D costs?) on investment of >10 billion (probably more like 100), seems like the definition of hype.
> (And I say this as someone who is skeptical that totally autonomous cars worldwide will ever be a thing, but you can get to £10Bn far, far before that point. Become the dominant mode of transport in just ONE major American city and you're most of the way there).
What I don't understand with this argument, how are you proposing they become the dominant mode of transport. These services are competing with taxis, what do they offer over taxis that people suddenly switch on mass to self driving taxis? They need to become cost competitive (and convenience competitive) with driving your own car, which would significantly drive down revenue. Secondly if robotaxi companies take over transport, why would the public continue to build their infrastructure and not demand that these robotaxi companies start to finance the infrastructure they exclusively use?
I got to six figures by assuming that a human taxi driver makes maybe $30-40k at a guess, and an autonomous car can work 24/7. 6 figures is $100k minimum.
So yeah, right now they'd have to be at ten cities x 300 cars each to hit 300M revenue, but there's still plenty of room for growth. Or should be, assuming the Waymo model isn't maxed out supporting the current handful of cities.
But I'm not convinced they have to hit cost parity with personal cars, because the huge advantage is you can work and drive (or be driven). If NYC and LA rush-hour congestion time becomes productive time, there's your billions.
I drive but prefer to take transit for this reason - some of my colleagues are able to join work calls effectively while driving, but for whatever reason my brain doesn't allow that. Just paying attention to calls is enough, you want me to pay attention to the road AND the call?