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)
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 looks like plenty of hours of fun! Thanks for the link :)
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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 issue of senior-juniors has always been a problem; AI simply means they're losing their hiding spots.
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'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'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.
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.
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.
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.
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.
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.
This persona driven workflow is so weird to me. Feels like stuck in old ways.
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.
In 15 years senior developers will not be needed as well. Anyway no company is obliged to worry about 15 years timescale
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.
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!
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>`
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.
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%.
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.
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.
Was it a real website? No, but it’s a live mockup way better than any Figma mock or rigid demo-ware.
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.
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.
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 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”.
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.
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?
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.
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.
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 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.
> 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.
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.