On the post-grad job hunt right now - I note that most employers will ask in a technical interview or whiteboard interview "how are you using LLMs?"
It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products. I've been responding with a sort of long winded answer about how 'there is clearly a learning curve for how this technology fits into any process and how I always always always double double double check yadayadayada'
I'm probably using the chat/ask functionality on a daily basis for quick debugging / new technology learning questions but I have yet to really use the fully agent or computer-use products because I've had more bad results than good the few times I've tried them (re-factoring a big repo of decades old fortran+C code for modern compiler/OS some things started to work but ultimately I abandoned that effort).
> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products.
Have you considered just answering truthfully?
Would you even want to work somewhere where you need to play a role and where they flip out when you say the wrong word you should've correctly guessed through mind reading?
That sounds not like a job but a toxic relationship.
“I assume it's because he is seeking to pay rent, food bills, and other expenses through employment.”
Fair enough, so if there were one “right” answer, that would be the one to give whether true or not.
But here there is no obvious right answer. If the employer is looking for a particular answer, the poster doesn’t know what it is. In that case, the best thing to say is simply the truth, particularly when the truth that the poster gives here is completely reasonable.
This a very condescending and privileged comment. The job market is much different when you're just starting out, and it's especially brutal these days for new grads.
I believe that I must be truthful because of my faith, though I understand people feeling pressure otherwise. I have had to quit places that I found lying to part of the employees before.
It is very sad to me that people do feel that pressure, and how the current job market is.
On topic with the article, I would love to be able to trust AI with more, but have found that I have some useful moments with it, but more because of Internet search not being how it used to be for quality.
I think it depends. The people that I know that have made significant sacrifices to live along their morals are usually people who 1) are intensely bitter when others will not sacrifice as much as them; 2) are completely understanding of people who will not sacrifice as much as them or acknowledge that they simply have less to sacrifice than others. For example someone who is willing to live the "dirtbag" lifestyle out of their car to dedicate to their outdoorsman activity who is either bitter others have the relative financial security or feel immensely grateful they have consistently good enough health that allows them to be outdoors with so little resources.
For example I think the decision to stick to certain morals is very hard if someone has a disabled dependent, are disabled themselves, or require consistent access to healthcare. There are different lines for different people of course. Our ire shouldn't go towards individuals who make these decisions but the people in power who force others to be in a position where these decisions need to be made.
This isn't a value item for most people. Employer doesn't want ai used great handcoding or employer wants ai used great prompt coding.
My truth is I don't care either way . I get the sense that's the same for parent poster. They just want a job and to say the right thing to get past the hiring filter. Even if I did have a truth its not something I would put above being remote, pay and how a company develops software. I'd rather not have a truth and not have a daily standup.
I think honesty is still probably correct - if you're struggling to figure out how to hedge.
I think you'd rather have good odds at some companies and 0% at others, rather than abysmal but non-zero odds at all companies.
And as an added bonus, you might get hired at a company where you're actually a good fit, rather than one you weasled your way into, and get to pay rent, food bills, and other expenses through employment for a long time!
It's pretty easy as an interviewer to spot when a candidate is hedging on a question, and it's the kind of thing that might get discussed in the post-interview debrief.
"Wouldn't give a straight answer on question X" isn't an instant no-hire, but it's not a positive signal.
This doesn't make sense in practice. He hedged so not sure need to look at other factors vs he picked a side and he selected the opposite of what we wanted no-hire or he answered what we wanted small positive signal need to look at other factors.
I mean maybe that is because I live in a still mostly not failed state (Germany), but I can't imagine that these things would be _so bad_ that living in fear of saying the wrong thing would be something worth considering.
Plus, and leaving that aside, I have my doubts that even if you did that, that that company would stay alive for very long.
Reality has the habit of eventually ripping this kind of unproductively delusional people (like e.g. a boss that flips if you don't say the right word with regards to the current hype) to shreds eventually.
The US has no social safety net. Healthcare comes from your employer. Everything is centered around having a job. Opinions on AI diverge significantly and someone’s response to this question would be pivotal to me in a hiring role. The market is not great for job seekers. The hiring manager can wait for someone who aligns with their company’s perspective on this.
No, if anything, I would say a very unfortunate trait of existence right now is that reality does NOT tend to punish corporations for being completely idiotic, at least not very fast at all.
Look at musk's companies. They will basically never (on any near timescale...) produce GAAP profitability and yet their IPO is in the trillions. To the point that S&P refusing to suspend their GAAP profitability requirements means the index will basically never see this company in it (which I'm quite pleased about).
The power of already-accumulated capital is simply more powerful than things like "don't be completely pants-on-head stupid about a recent fad" "don't seig-heil in front of the world stage" "there's no point in having people come to an office just to spend all day on zoom" etc etc etc.
The market can remain irrational longer than you can remain solvent, and companies can remain irrational longer than you can go without contributing to your 401k.
Last time I was in Germany I saw elderly people going through garbage bins in the park I sat at. I think you overestimate the safety net in Germany. In my European country the elderly sit at cafes drinking coffee, not going through bins.
Update:
Every street corner has a yellow garbage bin for recycling. That is where your plastic bottles go. Seems like a better system than having elderly going through bins.
Keep in mind that not every old person who searches garbage bins is actually poor. Some of them just have dementia. I personally know such people in my home town.
Not OP but many people eligible for social benefits don't seek it, for all kinds of reasons (not knowing about it, pride, ideology, peer pressure, ...)
I typically seek employment for the free electricity, coffee, internet, water, microwave usage and coverage from rain. Some employers even offer showers!
The best benefit about working in a large office is that nobody checks the basement.
It's still just a bad answer across the board. Having opinions and being able to articulate and defend them clearly is itself an extremely important hiring signal regardless of a company's stance on generative AI. An AI-forward company will be looking for an answer like "I haven't written code manually since 2025, I use ..., I stay on top of new tools without drowning in hype by ..." If that's not your answer, you probably aren't a good fit for those companies, but companies that would be a fit will still want a similar level of decisiveness. Much better to give an honest answer that will sound good to the right people than a wishy-washy answer that will sound bad to everyone.
I don't think having trouble knowing how to tailor your message to your audience because of limited information implies it isn't truthful. Answers to jobb interview questions are usually very manicured and rehearsed but I don't think they're generally lies.
> Would you even want to work somewhere where you need to play a role and where they flip out when you say the wrong word you should've correctly guessed through mind reading?
This just sounds like a standard tech interview. Mind reading to find and perform the secret “signal”. Nobody flips out if you don’t find it, they just move on to one of the other 1,000 candidates for the role.
Because almost every HR department now has a directive to only let people through the screening process who say they are using "fully agentic workflows" even though that's moronic.
I remember the graduate recruitment days - If you told the truth you were the only candidate they saw all day that wasn't the captain of the football team, top of the class and voted most likely to succeed - aka the worst candidate they saw all day.
It's funny cause I just interviewed some people last month and I asked the same exact question. And the answer to your question is probably. The technology is so new that I expect people to have a variety of different opinions.
From the 3 people I interviewed, all of the answers are very similar which is along the lines of: Kinda, but we need to be careful of using it, privacy, hallucination, etc.
All very safe answers and doesn't say anything new to me. If they had been more specific about why and their experiences with it, I'd probably favor them more due to their experience with it. It'd also signal to me that they form their own opinion rather than simply following the crowd.
Yes. Hedging results in a middle-of-the-road answer that, at best, comes across as lukewarm. Companies want to hire people they're excited about and are convinced fit into their culture. An honest answer will get you more strong noes but also more strong yeses, and strong yeses turn into offers. Hedging, produces only weak yeses and noes, which tend to end in no offers especially in tighter job markets like the one we're in.
The skilled interviewer is rare. But if truly skilled they understand why people hedge and would not consider that dishonesty but a skillset the company might need. A semi-skilled interview might pick up on that and assume the worst.
Very few jobs are looking for opinioned most are looking at people who might fit in unless you are hiring to distory from without.
While this industry surely is frustrating and full of pitiful fraudsters, I don't think that what you're saying is fair or leading us anywhere.
Most of our stuff in this world actually does work, and the reason why it does is that skilled (teams of) people that care have built it.
Meaning that these people can be found in many _many_ places.
> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products.
I'm an old hat on both sides of this type of discussion from a post-grad view.
Recommendation: use it to own the conversation and to signal mutual fit. Yes, your idea of AI lover versus hesitant matters. I recommend reframing the question to pivot to your fit to the org (and org fit to you) question. Show/concisely explain how you consider whether LLMs are fit to a task and how to tell it improves outcomes.
An outcome focus and willingness to show thought process around a common use case will be a substantially strong response.
True, but please don't give job seekers false hope with this statement. I commonly see 60 - 180 applicants for one open position. Good luck finding a hiring manager who wants to take a bet instead of going with proven experience.
I think upskilling is the right move in this environment and it is dead simple: Invest a couple of days to show initiative, learn agents yourself and be able to speak from true experience.
I love that we’re already talking about “proven experience” for a technology that’s essentially 15 months old, arguably only broke into the mainstream 3-6 months ago, has an unclear RoI for many companies, and seems to be changing quickly in both cost and “best practices.”
You’re more or less admitting that you’re playing trendy tech lottery. Which is fine, but maybe not generalizable to the whole industry.
15 months, 15 months ago, is not the same 15 months now. You'd be ignorant to think this a trend that will just fade. If we look at that has happened the last 15 months, it'll keep getting bigger and better. Hopefully not more expensive though.
Often the hiring manager will have the person to be hired somewhere in his report chain. So if a person can't effectively communicate and can't properly respond to a "I only have 2 minutes, shoot", then I am getting a future liability into the company that will slow down all future communications.
I much rather prefer someone who needs 3 seconds to triage a question and tell me: "This is X, I know this, here is the solution" or "This is Y, I don't know it, but I will get back to you within 24h".
I do absolutely not want a "Well let's think jointly about this for a couple of minutes". There is no jointly with your boss. Let's do a some math of a 1:12 manager to direct report ratio. That means for every hour you have, your boss only has 5 minutes. And if you talk to your boss' boss, they have 25 seconds for every of your hours.
I absolutely do want to work with people who want to think jointly about interesting questions for a couple of minutes. Give me your long-winded (thoughtful!) answers. Let me see how you think. Let me see how well I (and others) can think through things with you. That's what the point of an interview is, IMO. And I've been gainfully employed in tech for 15 years now with that attitude, often in environments with other like-minded folks, often involved in the hiring decisions that have led me to work with those other like-minded folks.
So in the same interest of helping post-grad job seekers, do what you've gotta do to get yourself paid, but maybe don't presume that vibe_that_works speaks for every hiring manager.
That does sound like a bad org tho, sorry to say that.
Not to disagree of course that time is limited, but in my experience, optimizing it this harshly leads to poor results, because eventually, you just get leapfrogged by reality.
Hyper-optimized systems are brittle and can't really adapt to the market changing.
But yeah, I guess they still need developers. Just doesn't sound like a fun job :D
Just trying to fix the misunderstanding: I am not saying that you will have a literal 25 seconds meeting with your boss's boss. I am just making a math argument taking typical orgchart ratios.
So let me take this a step further. You want to meet your boss' boss for 10 minutes to present them something. 10 minutes of his time are an equivalent of more than 20 hours of your time. So if your initial idea was to "take maybe 1-2h" to prepare for this -> You are underprepared by at least one order of magnitude.
I mean I am no expert, but to me it sounds like the org you're describing seems to lean away from the "engineering" side of things and into the "org for the sake of org".
Which might not be ideal, because "orging for the sake of org" to my understanding consumes significant resources not going into building products/marketshare/shareholder value.
But then again, I'm no hiring manager in such a structure, so this is probably just an uninformed take.
> I do absolutely not want a "Well let's think jointly about this for a couple of minutes".
But why?
Most of my most fulfilling experiences in tech have come out sitting down and hashing out a problem with someone else (including with managers/leaders).
It sounds like a miserable org if I am not expected/allowed to have an actual back and forth conversation with my boss. If I'm employed to be on a team working on an aligned common goal, why would I not use that collective skill and experience to my fullest advantage?
You're describing a coding sweatshop. What is the point of any discussion at all then? If the "boss" can't carve out enough time, that's their own problem. Letting that stress propagate to the team is plain bad leadership.
I know you might think some of these candidates don't have other much better choices to find work, but they absolutely do.
Not OP also but it typically signals that you're not confident with your answers. If I am actually curious about it, I'd ask a followup question for them to expand.
Not the OP, but because that’s not usually the answer I’m looking for, and my assumption would be the interviewee is not familiar with the concepts. I’d want to hear about how they use it, what are their pain points, how they’ve automated stuff and etc.
still 10x better than the 'finish this leetcode tweak algorithm in 20 minutes and tell me your thought process along the way, and yes you will never need that skill in the real job but we need find out who had time to cram for the algorithm books in the last few months'
I understand the pressure to get employed from your perspective, but differences in opinion should be voiced out and typically aren't the thing leading to rejection from the company. It's common that engineering leads seek out people with different backgrounds and views to work on the same team. If anything, answering truthfully will make you stand out from others who've responded in a generic, heavily hedged way.
I would hope this is true both in the context of LLMs and more broadly, but I think this is especially not the case for LLMs. It's hard to take the idea that companies are trying to hire people with reservations about LLMs seriously when many companies have LLM use mandates. It is counterproductive in the eyes of the employer to hire employees that will be combative on LLM from day one.
> re-factoring a big repo of decades old fortran+C cod
Having been in academia in the past and now in software I can say with a lot of certainty that this will take a lot more upfront work than otherwise.
Academic code does not have a lot of structure. And usually lacks a lot in terms of tests. While AI is best when it can mimic patterns as well as there are tests to target.
So you will probably need to budget a few weeks to establish good patters, docs as well as testing patterns before you can seriously make it really do what you want it to do.
exactly yeah it was a code base written by atmospheric physicists I assume and I had an idea that maybe copilot could get it working to interface with some more modern software and it just didn't really have what it takes.
Even with 3 weeks I'm just not the Fortran/C programmer to get that job done so I moved on to other things.
Consider using agent mode for some things, you are definitely missing out.
The analogy I've had for myself is that it feels like using a bulldozer to dig rather than a shovel. If you use it to dig archaeological artifacts, it can make things worse than you started. A lot of the work however, is just moving dirt around, so you are wasting time by using a shovel.
Exact same experience. My background is embedded and VLSI so I hedge my bets by saying that LLM are ok for Python scripting, but not there yet for synthesizable Verilog. It is really hard to see if the "how are you using LLMs?" question is for "we are AI Native™" or a form of cheating (like in university).
I personally think "I pretty much use it as a faster and more flexible StackOverflow" is probably the most neutral position you can have on it
That's probably not going to be enough for AI maxxers, but it probably won't be too much of a turn off for anyone but the most extreme AI minners, and everyone in between will probably be fine with it.
Frankly I plan to steer well clear of any "the majority of our code is AI generated" shops for the foreseeable future. Seems like disasters waiting to happen and I'd rather let other people step on those rakes
I've noticed several companies replacing deterministic systems in their support flows with a LLM version that is slower and worse. Many interfaces simply aren't better with AI added
The real best case scenario is using LLMs to help build deterministic systems. Instead of asking an LLM to do some task that you know will be repeated, instead ask the LLM to build a program (Python script or whatever) to do the task.
If it's a one-off script/program that doesn't require additional "domain knowledge", sure. But what if you need to give as context your whole backend repository because you need to take into account a few business rules? Why give anthropic/openai access to my "secret sauce" (e.g., company private repos)?
In that case, it's way better to simply write the code yourself.
Making systems fully deterministic ignores the entire purpose of having agents involved.
IMHO the best of both worlds option is agents working with deterministic CLIs. Where the agent does the reasoning (and text generation) but uses CLIs to carry out all of the actions (issuing refunds, unblocking accounts, or whatever).
It's possible to get very reliable and consistent work out of agents when they're using well written prompts with well designed CLIs.
At some level everything an agent does is through a "programmatic interface" (tool calls).
Some people might use skill-based scripts, MCPs, or some kind of raw access to a database. My point is that well designed CLIs are the optimal programmatic interface, for many reasons.
At Big Tech Company I Work At the LLM is quite happy to make raw API calls. If it thinks the data is big, then it'll write a Python tool to do it.
The reason crafted backing CLIs are useful is you can guide the LLM towards stuff that is immediately useful rather than hoping the nondetermism can separate the wheat from the chaff.
Take CI: is it interesting to know which tests passed? Maybe, but probably not. What is really interesting is what failed. Instead of having the LLM go out and talk directly to the CI system, write an intermediate CLI that filters out less actionable stuff by default, and have a flag that'll deliver the full dump if necessary.
It's a skill to do this stuff, and it's a lot of hard won experience than something I think is easily teachable. You kind of have to feel out your model and how it "thinks" about solving problems.
And then a new model version comes out and you have to learn it all again!
The best case scenario of LLM is transforming input into output where both are languages and accuracy doesn't matter, e.g. "rewrite this poem in pirate speech."
Because typing “code” takes time and significant amounts of it.
We are slowly waking up to the fact, which was always true, that “coding” is just a fanciful preparatory task in order to appease the spirits properly so that we may invoke the spirit of what we are actually after: a live, running process that does useful things. Code is completely useless when separated from that fact.
Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding. Knowing when it does and when it does not have this property is a skill of its own.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
I believe this is the general belief about basically every human skill, that if you stop doing the technical fundamentals you get worse at understanding the activity. The question is whether coding is like sailing a square-rigged wooden ship, which became completely useless knowledge after the invention of the steam engine, or if it's like playing an instrument, which while technically unnecessary after the advent of MIDI and other tools, absolutely hurts your ability to arrange, compose and perform if the skill is neglected.
For my money: I think the AI scenario is more like the latter, but "humans are worse at coding" isn't the consequence I see coming. I worry that in ten years we will be awash in software that's impossible to understand. I don't think that's happened in any human industry ever. Someone has always understood how the machines are built, even if they're very remote from the users of the machine.
The sci-fi novel A Fire in the Deep starts with describing a Software Archeologist, who digs through millennia of strata of layers of indirection and I think we could end up needing that one day.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
Like, perhaps, understanding that it is free of security and functionality bugs.
No serious programmer is regularly bottlenecked by typing speed. Even the ones who type slowly.
If you find yourself writing repetitive code you should consider adding a layer of abstraction. If your language isn't powerful enough you can write a code generator.
It depends on where you're using AI. If you're working on a project for yourself or in a tiny company. Then sure, writing the code probably was your bottleneck. But at mid to large companies writing code is maybe 50% of the job, and the other 50% is the process around it. All those processes are the bottle neck, no matter how fast you can write the code. And this was a bottleneck I was hitting well before AI.
> Can you type a hundred lines a second? If not, then it is.
No one has ever needed to do that for something that is new. And if it’s not new, you want to do it repeatedly with some guarantee of reliability. Not just in an uncontrolled manner.
That is why we have snippet systems, macros and code generators. And the best with code is to solve problem once and reuse the solution. Which we have done with libraries, frameworks and supporting software.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
I would argue that this is nearly always the case. I don't think people really understand programs that they've only read at more than a very superficial level. This is why I tend to make (temporary) small changes, printlns, etc. when exploring a new code base: it aids greatly in understanding how a program actually works.
And it's even worse (in my experience) with LLM generated code, as it tends not to result in particularly understandable code. It is a lot like LLM generated prose: it often looks entirely reasonable at a surface level, but has a of weirdness/incorrectness hidden beneath the surface. But that surface level makes it very hard to avoid glossing over the details when reviewing the code. For this reason, I personally find it's much more effort to carefully review code than it is to write it.
Humans make mistakes all the time, but their code tends to naturally be structured for human understanding (to some degree based on skill/experience) because they themselves needed to understand it to write it.
I think LLMs are very useful tools, but after quite a lot of experience using them, I think it's generally better to use them as a sounding board, or to help you get unstuck or remove points of friction. Using them to write all of your code (at least for me) seems like a net negative.
I also think it's extremely easy to overestimate how much time they save. It feels like they're a productivity boost because it takes less intense focus to implement something. But I've experienced several instances where actually writing the code myself would have been both quicker and have resulted in better code.
All that being said, it can also be really hard to not write all of your code with agents once you get used to it. There's also a kind of slot-machine-like effect where you write a prompt, excited for the result, and when it doesn't quite come out right, you think "ah just one more prompt and it'll be good." It's hard to see when you're actually doing it though.
It's also weird to me how much people think typing is what the LLM is replacing. Typing was never the hard part. It's the translation of the high-level idea into an unambiguous process that's hard. That's also the valuable part, that requires thinking through the edge cases and consequences of decisions, and that just gets glossed over when using an LLM unless you rigorously review what the LLM has done.
At the end of the day there's a real tradeoff to be made, and it's worth being conscious of what's being given up.
That is one of the things code does. It also communicates the developer's thoughts about how that process should work to others. If the latter is neglected, the code becomes very difficult to collaborate on. Very few lines of code that are written are "write once". Mostly they're changed, repeatedly, over time by many people. The live, running process is a very temporary entity by comparison. Yes, it needs to exist and do useful work. No, it is absolutely not the only thing that matters.
If you already know what the inputs/outputs are, why should you spend days or weeks of your life typing it out rather than giving it in a well-specified and tested form to an LLM to get it done a hundred times faster?
Because a vanishingly small share of professional software engineering work involves pure functions whose implemention doesn't matter.
Democratizing access to code generation so that people can craft little personal utility scripts and quickly draft mocks/prototypes is great, but a lot of people on sites like this work on much more sophisticated projects and understand that developing a thorough enough specification for AI to be able to run with isn't meaningfully different in effort than what we've already been doing for the last 50 years. And in fact, for those fluent with their traditional tools and workflows, trying to craft those specifications as an English prose conversation for AI is often much more work that takes longer and is less reliable.
Because it’s rarely so black and white. Knowing the inputs and outputs is merely the first steps, you need to think about the transitions too as they have their own costs.
Those costs don’t disappear and it’s truly naive to think they don’t matter. Take security issues, they may arise because what you thinks was the input is merely a subset of the true input range. And the extra possibilities lead to unforeseen behavior.
A lot of programming is about ensuring that the input and the output are the sets defined in the specs. And the rest is that the transition/relation is the right tradeoffs of performance, correctness, and costs.
I am seeing similar things in just regular tooling and development. Things that can be solved deterministically or what would have been a simple CLI 5 years ago are now an LLM integration.
Instead of using the LLM to create deterministic tools, we are using LLMs to replace them. It's completely backwards and I don't know why people (especially high ranking people in my company at least) seem to think that this is the way forward. No, I don't want a whole CI pipeline that is just LLM prompts. Yes it's very easy, but it's expensive, slow and prone to failure in ways you can't even predict.
Same things like using LLMs for the code review process. What would have been a simple linting rule is now a pass with an LLM rather than using the LLM to create the linting rule, which it is absolutely excellent at creating.
My management is pushing for us to come up with ideas on where we can use LLMs in our product. The whole team has been very resistant for this exact reason. Anything we can think of will only make things worse, and we’ve already been told anything above a 1-2% failure rate is unacceptable. If anything we need more structure and standards to hit that, not less.
I believe that llm’s can be used to re-imagine experiences but it’s definitely not the way people think. The constraint is imagination and thinking about complex trade offs more than anything else. Which is the essence of innovation.
The agent paradigm will eventually give way to experiences that are a hybrid of deterministic and non deterministic and you won’t even know the llm was involved or visible.
Luckily for programmatic or logic following, smaller models tend to do better, it can be surprising at first to see the more expensive models do worse at a task but it’s true.
> replacing deterministic systems in their support flows
The issue is, they don't want to provide "better" support but "cheaper" support. Imagine a trained agent that understands the big picture. Now imagine a company investing in humans to use AI to retrieve knowledge that the human can easily identify as being relevant or not, and using that knowledge to better aid the customer.
Right now AI is being sold as a "we don't need support personells" instead of "how can we provide better service." For a lot of products, better service will probably not matter as "cheaper" products will win most of the time.
Most people don't want to pay for better. They want to pay the same for something better, which is what companies are not investing their time in figuring out how to use AI properly for I think.
A lot of people want to pay for better, but that is hard. Better is more expensive, most of the time, but being more expensive is no guarantee for being better. It feels like the correlation is very weak. Most expensive products are just expensive, not good.
If there was a reliable way to identify the "better" thing, I and a lot of other people would go for that every time we can.
When I hear about engineers who are bored with coding, I have to imagine it's because the task of "programming logical work flows" has become rote to them.
Instead of refining their approach, or challenging their current knowledge base for discovery of inefficiencies or baseless assumptions, they'd rather hit an "easy" button.
I understand the desire to NOT do work. I understand the desire to spend quality time and free time with family. And I understand the idea that familiarity breeds contempt.
What I don't understand is the willingness to replace a deterministic language/framework/approach with a probabilistic slop machine.
As a contractor who built a lot of predictive systems and workflows in last three years I can tell you that quite often there is a specific request to put AI into it even when it is not needed and would objectively make the system worse, slower and more expensive.
I have one too. He'll say "Claude says this:" and pastes a screenshot of some Claude Code output. Most of the time it's wrong, or makes assumptions that won't hold true. Or it comes up with some overcomplicated solution and I'm like "This is like a 10 line change, right here".
These people just destroy their ability to read and understand the systems they're working with. I actually see it as them making themselves redundant. Because if you can't understand anything without Claude, and Claude doesn't even give the right answers, then what are you worth?
Where I work, management hasn't considered integrating AI at all, yet some clients are very vocal about it being the future and worry we are going to be left behind. Most people just don't care, and I worry the squeaky wheel will eventually get the grease.
I bet lemmings are grateful they were left behind.
It beggars belief that people think that they should rush in some uncertain direction, like some drawbridge is going to be lifted the moment people work out what the right direction is. It's utter stupidity.
Every single person who bootstrapped becoming powerful did it by rushing into things, but it's a high variance strategy because you could also end up destitute
I keep seeing requests to replace what would be a perfect UNIX shell script with agents, like what is the benefit other than being able to say we're doing AI?
Maybe it should have clicked earlier in life and I'm perhaps that much dumb dumb, but it only recently occurred to me (from experiencing it at two very different companies and discussing with peers having reached a certain seniority level more or less at the same time) how dysfunctional many companies are, and how often they produce incentives that are misaligned with the overall company goals and sustainability principles. I blame in large part a layer of middle management that selfishly puts itself above all else, misguides, misrepresents, because it essentially pays larger dividends (literally and not) to "play the networking game than to be an efficient and effective productive structure". Maybe that's to be expected in a services-driven economy where the value of the work is immaterial and subjective (and the whole phenomenon of bullshit jobs).
Of course I will do that, I get paid for doing that.
Most of the times I can convince that AI is not necessary by showing small PoC flow with AWS diagrams of data flows. This works well especially if the ask comes from technical people.
Other times the C-level interjects (CEO, CFO, sometimes even CTO) and demands that AI should be there. I literally had CEOs send me instagram reels of some AI shovel-sellers to demonstrate that I am wrong and AI is the way to go. No point arguing after that because I have no problem implementing whatever AI they want rather than losing a paying project.
With inexperienced or non-technical people, talking to them about AI can be very confusing, as a LOT of their "AI" usecases are basically they didn't realize or know how to write a program for this straightforward logic.
The Claude web UI popped a modal up a few days ago advertising their new model to me. It was full of HTML tags that were escaped or otherwise not rendered so that the text was literally
<b>Included in your plan limits until Jun 22</b> <br><br>Fable takes 2x the usage of Opus.
<b> Switch models when a message is flagged</b><k <br> When safety measures flag a message, automatically switch to a different model to keep chatting. When off, your chat will pause instead. <a href="https://support.claude.com/en/articles/153636
target="_blank" rel="noopener noreferrer" > Learn more</a>
...and this was presumably generated with the flagship model from the world's most prestigious LLM company.
This all reminds me of in the 90s when the Borland C++ compilers and Turbo Pascal shit came out and everyone was still hand rolling assembler because the optimising compilers were so bad. I thought Opus 4.6 was pretty good, basically a step change. The stuff I got out of Fable before they blocked it was nearly alien. If things keep improving, I don’t see humans writing code in 2 to 3 years except maybe super niche areas. This will all go the way that optimising compilers did. No amount of resistance, anger or denialism will change that.
I’d actually love it if LLMs could skip the slow high level lanaguages entirely and just churned out some weird LLM bytecode that was closer to the metal. I don’t want to read it or understand it at all. Here’s my spec, build it and notify when done. I want to ship stuff not build or dick around with code. Basically like when I go to a shop because I want a table, I don’t care if some carpenter “crafted” it or a machine mass produced and spat it out. It’s cute, but most people just want stuff and don’t care how it’s built.
> AI has gotten so good that despite any misgivings, “everyone is using A.I.”
In my experience, it's a mixed bag. I wrote this comment[0], yesterday. It reflects my current work, and how I am integrating an LLM.
I have used it for two parts of my project:
1) The backend (PHP), and
2) The frontend (Swift)
It has been a huge help, in both, but #2 is a cautionary tale. It really needs adult supervision, in developing native UIKit Swift apps. I'm realizing how truly bad the code it wrote was. I mean, terrible.
That's jarring, because it did a great job with #1. It made sound, reasonable design decisions, and provided code that is better than what I would write.
With #2, it behaved exactly like an inexperienced engineer, panicking, when confronted with real-world problems. My rewrite is going to feature a much simpler, sound approach.
All that said, it has been a net positive, and has increased my productivity by a large margin.
I guess the lesson I needed to get from this, is that it is good at helping me to find problems, but maybe not so good at fixing them.
I'd like to add that there is almost no way of "running away" from it.
If I search for anything on the internet I am almost guaranteed to be handed pages and pages of AI generated content.
In lieu of that I found that directly prompting for an answer tends to yield better results nowadays. Not because it's good per-se, but because having control over the prompt beats having little to no control over it though search by proxy.
It saddens me to see that high quality content is drowned in this sea of garbage to the point of being almost impossible to find.
I think this is where the circle closes with the "dead internet theory"... you go to Reddit, and see bots commenting on posts created by bots.
Then you go on to search for something, and find only results that are clearly AI generated pages and come to the conclusion that directly prompting some LLM is better than reading an AI slop page that's output by the same AI for slightly less specific prompt.
My concern is that this will only get worse over time - which is great for companies selling AI tokens and bad for society and whoever wants to interact with other humans over the internet.
This would be expected. The corner cases people faced with PHP throughout the decades have been well documented on the internet for eons.
Swift, not so much. It's relatively new. Looking at AI's abilities like an engineer's career span scaled about 10-20x of time makes it make a bit more sense.
It's going to be worse at newer/niche things, intuitively - which is only going to get worse as it "learns" from garbage outputted by other LLMs moving forward.
No doubt in my mind, a future Apple model will be the best to use for this purpose. They likely have more swift to train on than anyone else, and would benefit directly from more quality apps, rather than the slop flowing into the App Store (>1k app submissions per hour; they claim)
That's just one way to use LLMs though. Recently on a flight I could not figure out how to connect my wife's earphones (i.e. put them in pairing mode) to my macbook since I was used to the old Airpods Pro case. So I asked Gemma4 26B A4B (offline, LM Studio) and was told to use the 'two tap on front of case' gesture, which worked. This situation would have been significantly more frustrating without (local) LLMs. I'm essentially carrying around a basic "how to" on everything, inaccurate though it may be, it's better than nothing.
My experience was different. I found it extremely good at fronting technology like react while I had to hand hold it for the backend tasks. Even with fable it was the same.
Well Apple just released a bunch of Agent Skills. I tried it on my macOS apps and I noticed some improvements codewise and updated some deprecations I didn’t know existed in Swift.
Definitely frontend (it's what I do, every day, and I enjoy it), but I have a great deal of experience (over 25 years), writing some pretty robust backend stuff. I just don't enjoy it as much.
I'm nowhere near that level of experience, although I've done both as well. I'm more backend oriented. And my experience has been the opposite. When I ask for backend code, footgun after footgun appears on my screen. With frontend code, much less of an issue, as far as I can tell. Part of me believes this is because I'm less skilled at frontend, and I don't bat an eye when the LLM plops down yet another useMemo (I've since learned that this is rarely needed). But in your case this argument can hardly be made. With 25 years I trust your ability to spot a good design on either end of the stack. So then I don't know where this discrepancy comes from. Maybe my prompting skills leave something to be desired.
I don't do "megascale" backends, though. My code is generally smaller-scale stuff that's designed to be deployed on a wide variety of cheap hosting, and is pretty conservative. It doesn't "push the limits."
I'm unlikely to run into many of the problems that (for example) the PornHub developers hit, several times an hour.
In that case, I benefit from folks like you, that allow me to have solutions that scale down to my level.
In my experience the language has become irrelevant for me, I created a system like mix of revenuecat and firebase and I’m not even sure what language which part is. It has client side libraries that are swift and kotlin, the Identity management is Swift but the iAP/Subscription tracking is go IIRC. It’s all integrated somehow and works very well.
My theory is that most of the Swift code in the public domain, is basically demo code. Short, idealized, code samples to demonstrate issues and solutions; much like you would see in StackOverflow.
PHP has huge, entire frameworks and systems, refined over years.
There is also a lot of low quality PHP code out there, and a lot of legacy code in a language that I am told (I have not used if for years myself though) has changed a lot.
I do not know about crazy, but certainly sub-optimal. For example a loop over DB query results instead of modifying the code to work with a single query.
I think the gap is because 1. For coding, Claude is amazing - mainly because of its curated skills and because massive amounts of working code has already been carefully labeled over the last decade or so via GitHub. And because with any Turing complete language, there is only so much one can do.
But 2. For most other things, LLMs are fairly underwhelming. Research is usually mediocre. Try being rigorous and repeat your research prompt many times - then make a confusion matrix to tally up how many false positives and false negatives occur. And for the rest, be honest and ask yourself if the LLM is doing much more than a basic search engine query or trip to Wikipedia would have told you. For “normie” use cases, it’s handy-ish but far from revolutionary
> People are consuming AI like they eat meat: some are embracing it, some are limiting their use of it, and some are avoiding it altogether.
That's an interesting analogy as, despite the real ecological issues with it and principled arguments against meat eating, in general meat consumption has trended upward globally in country after country for decades.
Maybe this is because I live in Wyoming, but "AI is not ubiquitous, there are some people, like Vegans, who eschew it" is not the most compelling argument.
I fear AI is going to be used for everything not because it's the best solution, but because people are inherently lazy and just want to get their thing done, and they don't care so much about the quality.
"low effort and convenient" seems to consistently win over "best quality" and this is going to be a downgrade in everything, for everyone
I assume for a lot of people, an llm is going to produce higher quality results for most knowledge tasks than they could do on their own. I think that's okay
Anyone who does a Google search gets a satisfactory looking answer as the very first entry. I daresay most people don't go beyond that, not even the entries on the first page, let alone go to the next.
I argue that this is at the level of everyone for everything.
> Anyone who does a Google search gets a satisfactory looking answer as the very first entry.
Google has search results still? I don't use Google much anymore (thanks Kagi), but this is what ends up showing for me, I don't even see any search results anymore: https://i.imgur.com/eHIA2Df.png It seems like it's 50/50 on page reload if the LLM-reply UI expands automatically or not, which covers my entire screen. I guess Google is doing some A/B testing perhaps.
I don’t see the contradiction? If the inventory of clicks is declining and the number of businesses bidding on clicks is more or less constant, why wouldn’t that increase price?
When was the last time you used Google? The first entry (and a few after that) is always spam.
Anyone who does a search and accepts the first answer just doesn't care much or is incompetent. Anyone with any critical thinking whatsoever does way more than that if they want a correct answer.
> More than 30 percent of the US working-age population is using AI [meaning about 70% isn’t], an increase of 3 percentage points from the end of 2025
This makes me less bearish on the AI investments that are being made, if 70% of the working age population isn't using AI then there still is a lot of growth.
The future is here, it's just not evenly distributed (yet)
One thing I'd personally like to see a little more discussion of (at least within my social circles) is.. what exactly does "using AI" mean?
How does this connect to everyone's high level ideas/thoughts about "tech", "AI" and "morals and feels" etc. These lines can start to seem a little blurry, at least for me.
For example, would we say my partner is "using AI" (for all intents and purposes), if she's frequently using Google.com throughout the day, and then ends up picking and believing the AI generated answer overview at the top of the SERPs almost every time?
Or do we feel "uses AI", is more along the lines of the vampire kids running 1000 sub-agents on a mattress floor in SF?
I kind of find the whole spectrum really interesting because even basic phone use is now stuffed with AI, whether we choose to label it or not.
So true, just built a deterministic system to identify duplicated code. It's offline and doesn't use AI on purpose, since a gate that blocks your CI has to give the exact same answer every time, and finding dupes means comparing every function against every other (that's index work). It does NOT use AI. But ironically, I used AI to build it (https://github.com/Rafaelpta/dupehound )
This is a pattern I encourage - the AI might not be reliable, but with coaching, it can produce reliable tools. `colordiff` was causing issues with `less` when I was looking at diffs (character encoding issues I think), and when I asked Kimi K2.6 what to do, it built me a rust command-line diff tool in one shot that I've been using ever since (it even downloaded rust, wrote the tool, and compiled it).
For example; ChatGPT is replacing my Google searching. Not necessarily because it's better, or because it's summaries are better than Google (I find them subjectively better but it's not clear cut).
But because the app has a nice history; can ask a relatively complicated question and go do something else and then come back to it, ask a follow up. Etc.
None of that is specifically an AI benefit, but it's a workflow that really helps, well, flow.
That's funny, Google Gemini and AI mode in search has replaced my ChatGPT prompting, because I know Gemini will correctly cite sources (as of course it's by Google) rather than hallucinating.
Also, Gemini is free or at least has much higher usage limits than ChatGPT or Claude, and it's well integrated into Android and soon Apple with their new Siri, so things like circle to search just work well.
That's totally fair and things may change. For me its the history and the fact I can come back to it.
If I am honest I believe my final solution will be a combination of Open Claw, a custom knowledge wiki based on Wikmd. I just need a good all for Claw with history that is as good as gpt
Edit: and context too. It inferred my energy supplier from previously chats and so when I just asked a pertinent question it referenced their policy. Admittedly Google will have way more context if they get the product right.
In my non-tech circle, most people don't even realize how the internet is running literally everything. Even if we start to use mass scale AI for something, they wouldn't realize or care much about it.
They at best turn on the TV to watch netflix or look at the phone to send messages on whatsapp.
If all of that went away tomorrow, they'd be inconvenienced at best and then go on with their day to day life.
This feels like we are literally all in our IT echo chamber where we throw stuff on walls and go crazy, while the world is sunshine and rainbows, always been.
"They at best turn on the TV to watch netflix or look at the phone to send messages on whatsapp. If all of that went away tomorrow, they'd be inconvenienced at best and then go on with their day to day life."
I'm not saying it is a good thing, but this is completely out of touch with how dependent (most) people are on these technologies.
A lot of people are genuinely stranded if their phone runs out of
battery. How do they pull up a map, or call an Uber, or phone someone to pick them up?
You'll find it hard to pin down what you mean by "everything" otherwise you wouldn't have said that. Nobody uses the internet for everything.
Local models are highly likely to dominate in the long run as "good enough" inevitably becomes trivially cheap. This is a very different pattern of incentives and adoption compared to the internet.
I think it's more similar to the advent of personal computers. They had a brief surge and then turned into something else (smartphones, cloud, etc.) for all but a few niche cases. AI is not changing the consumer landscape. It's getting absorbed into existing platforms where there's a clear use case and benefit. It's just another expected software feature. This is far from the first time people have rejected a "personal assistant" concept and they'll just keep rejecting it.
It seems fair to leave the definition of "everything" to a reasonable person's interpretation. It's obvious that the internet is beyond ubiquitous in modern life.
I agree that where models run will will change over time, probably they'll run everywhere, but it's still the same kind of AI we are talking about.
Just about every app has a "help" button, but do you really use it? What about captions on a video or any number of other accessibility features? They're in everything, but not used for everything.
It makes perfect sense that they exist and were way overdue for an update, but they're just extra blades on the multitool. Perhaps in some designs they become more integral, but that is expected and invisible.
Yes "everything", but that's not even close to sufficient to become a huge breakthrough like the internet.
A counterpoint to this is that we have some real different definitions of AI.
If you consider things like the machine learning filters in your smartphone camera and Google's AI Overviews for searches it's entirely plausible that the US is currently at 75%+ of AI usage.
One of the reasons is that the free options are generally fairly poor and it’s hard to get people to sign up and actually pay for something. Especially if they assume it’s going to be similar quality.
If I worked in marketing/growth for an AI company I would try to consider some ways of breaking through this gap.
Not everyone but most. And I've been having this discussion with people around me a lot lately and everyone that has the ability to think more than half a step ahead sees it(and frankly we are fed up). I previously discussed how a friend admitted that he's never seen the code that powers his project at an S&P 500 company. Yesterday I was talking to another friend and former coworker who complained that when cloudflare went out a month or so ago, his entire team just slammed their laptops and went home cause they couldn't work(no sloppus/sloppenai). Another friend of mine: her dad is in hospital with a terminal disease and her mom (in her late 50's or early 60's, idk) uses chatgpt as a personal therapist. Gatorade-fed crops here we come, Leeerooooy Jeeeenkins!
I only use AI for software development. For writing, I don't use it at all except to translate source materials. So yes, AI is only for software development in my case.
The real question is whether I have any value outside of software development. Sometimes I get the feeling that AI is replacing the value I have in society.
I have no doubt that as AI gets more expensive, my employer would lay off more developers to pay for more AI tokens, until there are very few developers left. And the hilariously sad part is, the current developers keep training the AI to do their job. Eventually I expect they will lay off almost all the developers. It really feels like we're going to be stabbing each other in the back just to be the last one to get let go.
I don't think AI has any real value for software development, personally. The quality just isn't there, unless you invest so much effort that you may as well have written it yourself. But the market can stay irrational longer than you can stay solvent, and even though I think the industry will get over the idiocy of having LLMs write software, there's no telling how long that will take. So it's a scary time to work in tech even if I think the trend will ultimately reverse.
As of ~8 months ago the quality is most definitely there, for almost every form of programming I've experienced.
If you're working in some vanishingly rare domain then maybe it's not yet, but most coding challenges are very much in the wheelhouse of the current frontier models.
I envy you. For me, AI is faster than the code I write myself in many, many cases. It might replace the average developer, but a talented developer like you probably won't be replaced
I was not hired to write code, but to solve problems (where often the end result is code, but it’s not the whole process). But the message from management is that our bottleneck was coding, and by using AI to code, we’ll be 10x faster and all the company problem will be solved. Essentially 1. Use AI everywhere 2. ??? 3. Profit.
Where I work, the CTO drank a whole bunch of AI kool-aid recently, so now we're expected to "10x" our output with AI. I don't think he realizes this also means 10x more problems of all kinds. But I fully expect him to double-down and when AI costs skyrocket, he'd lay off more developers to pay for more AI.
I am constantly looking for a new job, but all of them are also require AI coding experience.
Everyone is using AI, issue is not just everyone recognizes what AI actually is, how broadly it's used.
Looking things up and asking questions was always something for a minority of the population so the language model usage being relatively low isn't a surprise.
Problem arises if the non-AI segment is leveraged to create regulations that impact the AI using segment negatively.
everyone might not be using ai.
but i see myself reaching for it for every small thing these days.
it's like every curiousity or lifestyle choice or optimization is something ai can help research.
i am not saying it's really powerful or great.
but the lure is undeniable. because of how low friction it has become.
They are great on exploring, understanding and finding bugs in existing codebase.
They are great for simple or one time scripts/programs.
They are terrible, really terrible coders. The overengineering is so deep in their training that no matter what is your prompt, your skills or agents.md/claude.md, if you don't babysit them continuously, at some point they will just fuck up your codebase.
Software engineers are definitely in a bit of a bubble here. Are we just early adopters who see the value sooner, or does it uniquely benefit software engineering, or do we just like cool automation and we're deluding ourselves that this adds value beyond the cost?
Yes I believe software benefits uniquely, just like building tooling and automating software have long been easier in software than other domains. Humans defined all the rules of the world you live in, humans wrote strict rules in methodically parsable formats.
The moment you have to interact with the physical world or humans (psychological, imaginative, aesthetic, etc), there are often undiscovered or changing rules—or no rules at all. Or systems are subject to perturbations beyond a defined scope.
The other thing I believe is software developers are experts at doing the things that allow them to make doing those very things easier and more automated. And they do this in public, perfectly documented online.
Both because of the things I described above and because software developers have created the largest machine-accessible training set for plying their trade of any trade, ML—that is ultimately interpolating massive datasets to do things—is unsurprisingly uniquely successful for software tasks.
I've been thinking about this, and I think software is uniquely knowledge work that has the most defined structure and least personally interaction. Hell, some of the software I write is for machine to talk to other machines. It's not surprising such a closed system is so amenable to AI, and other knowledge workers are not getting the same benefits.
Software has huge and detailed code repositories ripe for training use. There's just enough inference in current models to remix that code in useful ways for the most popular languages.
The less popular a language, the more models struggle.
Writing, UI, and presentations have similar knowledge bases.
Outside of those, quality becomes much more hit and miss. If you ask for a recipe you may get something good, or you may get something completely inedible and random.
"Domain specific knowledge" really means "strong foundations and relevant abstractions" and LLMs just don't do that reliably.
That's a decent article. My only issue is it seems heavily biased at the end, or at least he seems to misunderstand what the 'A.I. types in Silicon Valley' are doing.
> Computers should adapt to people. Asking people to make themselves more legible to software — to turn themselves into a database — is a doomed idea.
I've been in software a long time, and I do sort of see this trend, but I think it's because these are tools that build other tools. The interface has always been a 'best I can do for now' thing, with the focus on doing things that are useful. Computers were just calculators in the beginning, which led to more complex calculators, instruction sets, programming languages, operating systems, GUIs, interconnectivity, etc.
What people are doing today is experimenting, like they always have. They're putting their experiments out there so that others can use them and build on them. Some will use those tools to build other tools, and some won't. But over time, the experiments that work will get distilled and turn into real products that people who 'do not yearn for automation' will still want to use, so it seems like the value is there.
I guess the real question is whether they will create value that offsets the near-term costs, because I don't think the billions in investments are sustainable, and I'm not convinced the centralized data center paradigm is the right way.
Software engineers aren't even all using AI, contrary to frequent claims here that they are. There are very many who have tried it, found it didn't add value to their work, and aren't using it unless FOMO-driven managers force them to.
I disagree. Everyone will be using AI for everything, but, increasingly, people won't think about whether they are using "AI" – just like they don't think about using databases.
Nor should they! It's such a shit thing to be emotionally invested in. Imagine people would have been upset about databases. It's really fantastic software and we should be happy to have it, and now go and make the most of it, for all of us.
I'm using AI for most things. It has been an incredible improvement to both my quality of life and my wallet. Some of the most high profile items from just the past three months:
- I'm getting my roof replaced due to hail damage. Insurance originally covered only $5k due to depreciation. I fed the insurance policy to AI. I learned about the appraisal clause and invoked it. At the end, I got another $6,500 back.
- I was having issues with plumbing. Four different plumbers came, they all said the cast iron pipes under the house need to change. Quotes ranged from $35k to $55k. I had AI walk me through the process. It taught me about the yard line vs. under-slab distinction, and suggested getting just the yard line replaced first because it's much cheaper and can fix the issue. I did that and spent $6k. The issue was fixed. I "saved" $30k for now by deferring that massive month-long project. (For brevity, I'm omitting a ton of boring technical stuff I learned about plumbing that helped me make the optimal decision - none of the contractors bothered explaining any of it.)
- My 2010 Hyundai Santa Fe is starting to show its age. I've taken it to multiple different repair shops, then fed their diagnoses and recommendations to AI and figured out which ones are trying to fleece me and which ones are being more careful and conservative with their repair recommendations. Probably saved several thousand dollars there. Learned a lot about cars too!
- My partner and I are converting the backyard to a wildlife sanctuary. The AI helped us plan what to plant where (depending on lots of factors like sunlight location, irrigation access, etc.) and it has been going really well. Also planned out a dragonfly pond to deal with mosquitoes. AI created a project plan, including schematics, material purchase list and step-by-step instructions.
- I've been wanting to do various other home improvement projects, but only ones that make financial sense. I took photos of my house, both inside and outside, and fed them to AI, and said "give me a list of projects I can do that will have high ROI for when I decide to sell this house". It spent 15 mins doing deep research, then came back with a long, prioritized list. If I do all the projects, I'd be spending about $40k and it would improve the house valuation by about $90k.
I can go on. There's probably dozens of stuff that I've used it for over the past year that led to massive time and money savings, and I've learned a ton as well about topics I normally would not have been exposed to or bothered to research myself. And I'm not even including all the work-related usage, both for my employer and my side business. That would be its own very long list.
Great examples. I think people not using AI for issues like these lack imagination or more charitably, simply don't know that it works so well for these. Especially non-technical people can find great value out of AI, not just SWEs.
The numbers given in the article are actually consistent with what is usually meant by “everyone” in such statements. Sure, it’s not literally everyone. But it’s a very significant percentage, especially given how quick the adoption has been.
I think what people mean by everyone varies a lot, which is why I wanted to draw attention to more specific numbers. For example, in the Datos data cited[1], on desktop 86% were using traditional search engines >10 visits/month vs. only 21% for AI chat tools. That is indeed a very significant percentage, but more than 4x less than search and (at least I) wouldn't say that ~1/5 is "everyone."
It's funny lately I've been seeing the cursor advertisements all with some premise of regular young person wants to develop an app and the ads really do focus on the simplest of premises: the only ones I've seen in these skits are essentially variants on the "todo app" web app tutorial
the tech is pretty good at helping identify simple bugs when they happen and to write short sections of code given very explicit instructions but yeah I have yet to see good examples of short one sentence ideas turned into a working product that looks better than anything that could be a UDemy tutorial app.
I honestly just use it as a search engine to get around SEO garbage and ads.
My wife uses it for a (non-computer related) business though and it's great for all sorts of normally tedious marketing/social media type jobs though. Stuff that doesn't really require accuracy just needs text on pictures that looks good quickly.
I think everyone just has FOMO and doesn't want to lose to competitors. Eventually it'll die down.
I'd love to see credible numbers on that. I find it hard to believe that stupid corporate mandates are responsible for more than a small fraction of usage, but without data I have just my own instincts to go on there.
At my employer (megacorp with tens of thousands of employees) daily use is mandated. Our annual bonuses and pay raises for our performance reviews were explicitly tied to this.
It's a retrospective analysis of an assertion made by NYTimes. The original headline wasn't clickbait, just presumptive, and even so, it's a pretty significant publication that spends a lot of time on the HN front page (alongside you, I'll add). I think it's perfectly fair, and nowhere close to a strawman, to deconstruct that claim a year later.
On the post-grad job hunt right now - I note that most employers will ask in a technical interview or whiteboard interview "how are you using LLMs?"
It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products. I've been responding with a sort of long winded answer about how 'there is clearly a learning curve for how this technology fits into any process and how I always always always double double double check yadayadayada'
I'm probably using the chat/ask functionality on a daily basis for quick debugging / new technology learning questions but I have yet to really use the fully agent or computer-use products because I've had more bad results than good the few times I've tried them (re-factoring a big repo of decades old fortran+C code for modern compiler/OS some things started to work but ultimately I abandoned that effort).
> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products.
Have you considered just answering truthfully?
Would you even want to work somewhere where you need to play a role and where they flip out when you say the wrong word you should've correctly guessed through mind reading? That sounds not like a job but a toxic relationship.
I assume it's because he is seeking to pay rent, food bills, and other expenses through employment.
“I assume it's because he is seeking to pay rent, food bills, and other expenses through employment.”
Fair enough, so if there were one “right” answer, that would be the one to give whether true or not.
But here there is no obvious right answer. If the employer is looking for a particular answer, the poster doesn’t know what it is. In that case, the best thing to say is simply the truth, particularly when the truth that the poster gives here is completely reasonable.
It's possible to work for an employer, and not have to compromise your values and or professional integrity.
The attitude suggested by your response suggests you haven't lived that reality yet.
Either way, I'd rather be rejected by an employer for speaking my truth, than lie to be somewhere I'd rather not be.
This a very condescending and privileged comment. The job market is much different when you're just starting out, and it's especially brutal these days for new grads.
It is not. I made that choice in the past and will do it again.
"Speak the truth, even if your voice shakes"
You’ve actively made the choice to go hungry instead of hedging your answers during an interview?
You should really examine your situation and beliefs if you think this isn’t a privileged position to be in.
I believe that I must be truthful because of my faith, though I understand people feeling pressure otherwise. I have had to quit places that I found lying to part of the employees before.
It is very sad to me that people do feel that pressure, and how the current job market is.
On topic with the article, I would love to be able to trust AI with more, but have found that I have some useful moments with it, but more because of Internet search not being how it used to be for quality.
I think it depends. The people that I know that have made significant sacrifices to live along their morals are usually people who 1) are intensely bitter when others will not sacrifice as much as them; 2) are completely understanding of people who will not sacrifice as much as them or acknowledge that they simply have less to sacrifice than others. For example someone who is willing to live the "dirtbag" lifestyle out of their car to dedicate to their outdoorsman activity who is either bitter others have the relative financial security or feel immensely grateful they have consistently good enough health that allows them to be outdoors with so little resources.
For example I think the decision to stick to certain morals is very hard if someone has a disabled dependent, are disabled themselves, or require consistent access to healthcare. There are different lines for different people of course. Our ire shouldn't go towards individuals who make these decisions but the people in power who force others to be in a position where these decisions need to be made.
This isn't a value item for most people. Employer doesn't want ai used great handcoding or employer wants ai used great prompt coding.
My truth is I don't care either way . I get the sense that's the same for parent poster. They just want a job and to say the right thing to get past the hiring filter. Even if I did have a truth its not something I would put above being remote, pay and how a company develops software. I'd rather not have a truth and not have a daily standup.
The job market being as it is, a lot of people simply don't have that luxury.
Cite needed - FAANG certainly leaned hard into ‘lie to survive’.
you assume correct
I think honesty is still probably correct - if you're struggling to figure out how to hedge.
I think you'd rather have good odds at some companies and 0% at others, rather than abysmal but non-zero odds at all companies.
And as an added bonus, you might get hired at a company where you're actually a good fit, rather than one you weasled your way into, and get to pay rent, food bills, and other expenses through employment for a long time!
It's pretty easy as an interviewer to spot when a candidate is hedging on a question, and it's the kind of thing that might get discussed in the post-interview debrief.
"Wouldn't give a straight answer on question X" isn't an instant no-hire, but it's not a positive signal.
This doesn't make sense in practice. He hedged so not sure need to look at other factors vs he picked a side and he selected the opposite of what we wanted no-hire or he answered what we wanted small positive signal need to look at other factors.
ironically, I'd understand people not giving a straight answer on this particular topic
I mean maybe that is because I live in a still mostly not failed state (Germany), but I can't imagine that these things would be _so bad_ that living in fear of saying the wrong thing would be something worth considering.
Plus, and leaving that aside, I have my doubts that even if you did that, that that company would stay alive for very long. Reality has the habit of eventually ripping this kind of unproductively delusional people (like e.g. a boss that flips if you don't say the right word with regards to the current hype) to shreds eventually.
The US has no social safety net. Healthcare comes from your employer. Everything is centered around having a job. Opinions on AI diverge significantly and someone’s response to this question would be pivotal to me in a hiring role. The market is not great for job seekers. The hiring manager can wait for someone who aligns with their company’s perspective on this.
No, if anything, I would say a very unfortunate trait of existence right now is that reality does NOT tend to punish corporations for being completely idiotic, at least not very fast at all.
Look at musk's companies. They will basically never (on any near timescale...) produce GAAP profitability and yet their IPO is in the trillions. To the point that S&P refusing to suspend their GAAP profitability requirements means the index will basically never see this company in it (which I'm quite pleased about).
The power of already-accumulated capital is simply more powerful than things like "don't be completely pants-on-head stupid about a recent fad" "don't seig-heil in front of the world stage" "there's no point in having people come to an office just to spend all day on zoom" etc etc etc.
The market can remain irrational longer than you can remain solvent, and companies can remain irrational longer than you can go without contributing to your 401k.
How is Germany relevant in this?
Acknowledging that my perception might be skewed because there are still a ton of social safety nets in place.
The same might not be true everywhere.
Last time I was in Germany I saw what appeared to be homeless children
Did they look Ukrainian or Syrian? Germany let in millions of people over the last few years and never built enough housing.
Welfare doesn't entirely eliminate homelessness.
It's… like… not that simple.
Last time I was in Germany I saw elderly people going through garbage bins in the park I sat at. I think you overestimate the safety net in Germany. In my European country the elderly sit at cafes drinking coffee, not going through bins.
Update:
Every street corner has a yellow garbage bin for recycling. That is where your plastic bottles go. Seems like a better system than having elderly going through bins.
Maybe in your country they also don't have a deposit on bottles/cans, making it pointless to go through trash cans?
Keep in mind that not every old person who searches garbage bins is actually poor. Some of them just have dementia. I personally know such people in my home town.
Not OP but many people eligible for social benefits don't seek it, for all kinds of reasons (not knowing about it, pride, ideology, peer pressure, ...)
That's why I said "mostly"
I typically seek employment for the free electricity, coffee, internet, water, microwave usage and coverage from rain. Some employers even offer showers!
The best benefit about working in a large office is that nobody checks the basement.
It's still just a bad answer across the board. Having opinions and being able to articulate and defend them clearly is itself an extremely important hiring signal regardless of a company's stance on generative AI. An AI-forward company will be looking for an answer like "I haven't written code manually since 2025, I use ..., I stay on top of new tools without drowning in hype by ..." If that's not your answer, you probably aren't a good fit for those companies, but companies that would be a fit will still want a similar level of decisiveness. Much better to give an honest answer that will sound good to the right people than a wishy-washy answer that will sound bad to everyone.
Surviving in most companies requires a certain amount of wishywashiness.
I don't think having trouble knowing how to tailor your message to your audience because of limited information implies it isn't truthful. Answers to jobb interview questions are usually very manicured and rehearsed but I don't think they're generally lies.
> Would you even want to work somewhere where you need to play a role and where they flip out when you say the wrong word you should've correctly guessed through mind reading?
This just sounds like a standard tech interview. Mind reading to find and perform the secret “signal”. Nobody flips out if you don’t find it, they just move on to one of the other 1,000 candidates for the role.
Because almost every HR department now has a directive to only let people through the screening process who say they are using "fully agentic workflows" even though that's moronic.
> Have you considered just answering truthfully?
I remember the graduate recruitment days - If you told the truth you were the only candidate they saw all day that wasn't the captain of the football team, top of the class and voted most likely to succeed - aka the worst candidate they saw all day.
As if most people have a choice in the matter.
To be honest, I don't think I would want to work with or hire you, based on your response here.
Would being truthful improve my chances of being hired?
It's funny cause I just interviewed some people last month and I asked the same exact question. And the answer to your question is probably. The technology is so new that I expect people to have a variety of different opinions.
From the 3 people I interviewed, all of the answers are very similar which is along the lines of: Kinda, but we need to be careful of using it, privacy, hallucination, etc.
All very safe answers and doesn't say anything new to me. If they had been more specific about why and their experiences with it, I'd probably favor them more due to their experience with it. It'd also signal to me that they form their own opinion rather than simply following the crowd.
Yes. Hedging results in a middle-of-the-road answer that, at best, comes across as lukewarm. Companies want to hire people they're excited about and are convinced fit into their culture. An honest answer will get you more strong noes but also more strong yeses, and strong yeses turn into offers. Hedging, produces only weak yeses and noes, which tend to end in no offers especially in tighter job markets like the one we're in.
If whoever is hiring is actually good at their job: yes.
That is of course assuming that they're looking for some long-term stable team member.
A skilled interviewer smells dishonesty.
However, and to be fair, whether and how they act on it depends on the specific situation.
The skilled interviewer is rare. But if truly skilled they understand why people hedge and would not consider that dishonesty but a skillset the company might need. A semi-skilled interview might pick up on that and assume the worst.
Very few jobs are looking for opinioned most are looking at people who might fit in unless you are hiring to distory from without.
What is this "skilled interviewer" thing and where have you seen it?
While this industry surely is frustrating and full of pitiful fraudsters, I don't think that what you're saying is fair or leading us anywhere.
Most of our stuff in this world actually does work, and the reason why it does is that skilled (teams of) people that care have built it. Meaning that these people can be found in many _many_ places.
Not everyone has that luxury when there are bills to pay and mouths to feed.
> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products.
I'm an old hat on both sides of this type of discussion from a post-grad view.
Recommendation: use it to own the conversation and to signal mutual fit. Yes, your idea of AI lover versus hesitant matters. I recommend reframing the question to pivot to your fit to the org (and org fit to you) question. Show/concisely explain how you consider whether LLMs are fit to a task and how to tell it improves outcomes.
An outcome focus and willingness to show thought process around a common use case will be a substantially strong response.
Replying as a hiring manager since this might help other post-grad job seekers:
- Any long-winded answer to a question is immediate out and has been for years.
- Not having used agents and not being able to comment on what to do and what not to do with them is immediate out since early this year.
> Not having used agents and not being able to comment on what to do and what not to do with them is immediate out since early this year.
From all the tech that we have, agents are really not that hard to learn on the job. They're also not a magical silver bullet.
True, but please don't give job seekers false hope with this statement. I commonly see 60 - 180 applicants for one open position. Good luck finding a hiring manager who wants to take a bet instead of going with proven experience.
I think upskilling is the right move in this environment and it is dead simple: Invest a couple of days to show initiative, learn agents yourself and be able to speak from true experience.
I love that we’re already talking about “proven experience” for a technology that’s essentially 15 months old, arguably only broke into the mainstream 3-6 months ago, has an unclear RoI for many companies, and seems to be changing quickly in both cost and “best practices.”
You’re more or less admitting that you’re playing trendy tech lottery. Which is fine, but maybe not generalizable to the whole industry.
15 months, 15 months ago, is not the same 15 months now. You'd be ignorant to think this a trend that will just fade. If we look at that has happened the last 15 months, it'll keep getting bigger and better. Hopefully not more expensive though.
your post on Who's Hiring provides some needed context...
want a Flutter developer who is unusually strong at directing AI-driven software delivery. This is not a traditional "write the code yourself" role.
https://news.ycombinator.com/item?id=47223956
Not wrong. But my statement is true across organizations I have worked with in the past year, several of them not AI-native.
> Any long-winded answer to a question is immediate out and has been for years.
Why?
If the winding path is actually interesting and gives you insights into how the person works, why would that be a bad thing?
Often the hiring manager will have the person to be hired somewhere in his report chain. So if a person can't effectively communicate and can't properly respond to a "I only have 2 minutes, shoot", then I am getting a future liability into the company that will slow down all future communications.
I much rather prefer someone who needs 3 seconds to triage a question and tell me: "This is X, I know this, here is the solution" or "This is Y, I don't know it, but I will get back to you within 24h".
I do absolutely not want a "Well let's think jointly about this for a couple of minutes". There is no jointly with your boss. Let's do a some math of a 1:12 manager to direct report ratio. That means for every hour you have, your boss only has 5 minutes. And if you talk to your boss' boss, they have 25 seconds for every of your hours.
I absolutely do want to work with people who want to think jointly about interesting questions for a couple of minutes. Give me your long-winded (thoughtful!) answers. Let me see how you think. Let me see how well I (and others) can think through things with you. That's what the point of an interview is, IMO. And I've been gainfully employed in tech for 15 years now with that attitude, often in environments with other like-minded folks, often involved in the hiring decisions that have led me to work with those other like-minded folks.
So in the same interest of helping post-grad job seekers, do what you've gotta do to get yourself paid, but maybe don't presume that vibe_that_works speaks for every hiring manager.
That does sound like a bad org tho, sorry to say that.
Not to disagree of course that time is limited, but in my experience, optimizing it this harshly leads to poor results, because eventually, you just get leapfrogged by reality.
Hyper-optimized systems are brittle and can't really adapt to the market changing.
But yeah, I guess they still need developers. Just doesn't sound like a fun job :D
Just trying to fix the misunderstanding: I am not saying that you will have a literal 25 seconds meeting with your boss's boss. I am just making a math argument taking typical orgchart ratios.
So let me take this a step further. You want to meet your boss' boss for 10 minutes to present them something. 10 minutes of his time are an equivalent of more than 20 hours of your time. So if your initial idea was to "take maybe 1-2h" to prepare for this -> You are underprepared by at least one order of magnitude.
I mean I am no expert, but to me it sounds like the org you're describing seems to lean away from the "engineering" side of things and into the "org for the sake of org".
Which might not be ideal, because "orging for the sake of org" to my understanding consumes significant resources not going into building products/marketshare/shareholder value.
But then again, I'm no hiring manager in such a structure, so this is probably just an uninformed take.
> I do absolutely not want a "Well let's think jointly about this for a couple of minutes".
But why?
Most of my most fulfilling experiences in tech have come out sitting down and hashing out a problem with someone else (including with managers/leaders).
It sounds like a miserable org if I am not expected/allowed to have an actual back and forth conversation with my boss. If I'm employed to be on a team working on an aligned common goal, why would I not use that collective skill and experience to my fullest advantage?
> There is no jointly with your boss
You're describing a coding sweatshop. What is the point of any discussion at all then? If the "boss" can't carve out enough time, that's their own problem. Letting that stress propagate to the team is plain bad leadership.
I know you might think some of these candidates don't have other much better choices to find work, but they absolutely do.
Not OP also but it typically signals that you're not confident with your answers. If I am actually curious about it, I'd ask a followup question for them to expand.
Not the OP, but because that’s not usually the answer I’m looking for, and my assumption would be the interviewee is not familiar with the concepts. I’d want to hear about how they use it, what are their pain points, how they’ve automated stuff and etc.
Okay I see thank you.
But that sounds more like "evasive" is the problematic attribute and not "long winding".
Which does show up at the same time often, true. But not always.
What does "use agents" mean from your perspective? Just Claude Code with some MCPs? Or like a full on GasTown type setup?
still 10x better than the 'finish this leetcode tweak algorithm in 20 minutes and tell me your thought process along the way, and yes you will never need that skill in the real job but we need find out who had time to cram for the algorithm books in the last few months'
How are the technical interviews these days? Do they still ask Leetcode style questions or is it getting deprecated?
I understand the pressure to get employed from your perspective, but differences in opinion should be voiced out and typically aren't the thing leading to rejection from the company. It's common that engineering leads seek out people with different backgrounds and views to work on the same team. If anything, answering truthfully will make you stand out from others who've responded in a generic, heavily hedged way.
I would hope this is true both in the context of LLMs and more broadly, but I think this is especially not the case for LLMs. It's hard to take the idea that companies are trying to hire people with reservations about LLMs seriously when many companies have LLM use mandates. It is counterproductive in the eyes of the employer to hire employees that will be combative on LLM from day one.
You should find out during the screener what kinds of view the executives have on LLMs. don’t wait until you’re midway through the third round.
> re-factoring a big repo of decades old fortran+C cod
Having been in academia in the past and now in software I can say with a lot of certainty that this will take a lot more upfront work than otherwise.
Academic code does not have a lot of structure. And usually lacks a lot in terms of tests. While AI is best when it can mimic patterns as well as there are tests to target.
So you will probably need to budget a few weeks to establish good patters, docs as well as testing patterns before you can seriously make it really do what you want it to do.
exactly yeah it was a code base written by atmospheric physicists I assume and I had an idea that maybe copilot could get it working to interface with some more modern software and it just didn't really have what it takes.
Even with 3 weeks I'm just not the Fortran/C programmer to get that job done so I moved on to other things.
Consider using agent mode for some things, you are definitely missing out.
The analogy I've had for myself is that it feels like using a bulldozer to dig rather than a shovel. If you use it to dig archaeological artifacts, it can make things worse than you started. A lot of the work however, is just moving dirt around, so you are wasting time by using a shovel.
A balanced answer that’s often true these days is: you’ve found that LLMs are impressively useful in some cases but fall dramatically short in others.
Exact same experience. My background is embedded and VLSI so I hedge my bets by saying that LLM are ok for Python scripting, but not there yet for synthesizable Verilog. It is really hard to see if the "how are you using LLMs?" question is for "we are AI Native™" or a form of cheating (like in university).
Just answer honestly, and include a note that you intend to fully comply with the companies AI policies. Thats the best answer anyone can give.
I personally think "I pretty much use it as a faster and more flexible StackOverflow" is probably the most neutral position you can have on it
That's probably not going to be enough for AI maxxers, but it probably won't be too much of a turn off for anyone but the most extreme AI minners, and everyone in between will probably be fine with it.
Frankly I plan to steer well clear of any "the majority of our code is AI generated" shops for the foreseeable future. Seems like disasters waiting to happen and I'd rather let other people step on those rakes
The disaster isn’t even waiting to happen. It’s actively happening.
Look at the uptime and incident rate of all the big tech companies that have gone all in on AI generated code
"for entertainment value, when i'd like to see how an enthusiastic 5-year-old would react to the task."
Your 5 year old is going to a heck of a kindergarten.
I've noticed several companies replacing deterministic systems in their support flows with a LLM version that is slower and worse. Many interfaces simply aren't better with AI added
The real best case scenario is using LLMs to help build deterministic systems. Instead of asking an LLM to do some task that you know will be repeated, instead ask the LLM to build a program (Python script or whatever) to do the task.
If it's a one-off script/program that doesn't require additional "domain knowledge", sure. But what if you need to give as context your whole backend repository because you need to take into account a few business rules? Why give anthropic/openai access to my "secret sauce" (e.g., company private repos)?
In that case, it's way better to simply write the code yourself.
Making systems fully deterministic ignores the entire purpose of having agents involved.
IMHO the best of both worlds option is agents working with deterministic CLIs. Where the agent does the reasoning (and text generation) but uses CLIs to carry out all of the actions (issuing refunds, unblocking accounts, or whatever).
It's possible to get very reliable and consistent work out of agents when they're using well written prompts with well designed CLIs.
Isn't this how we end up with things like: https://www.reuters.com/legal/government/high-profile-meta-a...
Yes: https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/
Although you can certainly do a better-and-worse job of preventing these kinds of issues.
How else would anyone do something like issue a refund if not through a programmatic interface?
Direct access to the database, and create the "refund program" on the fly. Yes, stuff of nightmares.
yes... ha ha ha... yes!
Right thats just head cannon though. Unless of course you believe the lies you read on the Internet.
At some level everything an agent does is through a "programmatic interface" (tool calls).
Some people might use skill-based scripts, MCPs, or some kind of raw access to a database. My point is that well designed CLIs are the optimal programmatic interface, for many reasons.
Sorry what other option is there? Is it going to create an API call from scratch every time after reading a page of documentation?
Wait raw access to the database? That’s one of the options for issuing a refund?
Yes, it can do.
At Big Tech Company I Work At the LLM is quite happy to make raw API calls. If it thinks the data is big, then it'll write a Python tool to do it.
The reason crafted backing CLIs are useful is you can guide the LLM towards stuff that is immediately useful rather than hoping the nondetermism can separate the wheat from the chaff.
Take CI: is it interesting to know which tests passed? Maybe, but probably not. What is really interesting is what failed. Instead of having the LLM go out and talk directly to the CI system, write an intermediate CLI that filters out less actionable stuff by default, and have a flag that'll deliver the full dump if necessary.
It's a skill to do this stuff, and it's a lot of hard won experience than something I think is easily teachable. You kind of have to feel out your model and how it "thinks" about solving problems.
And then a new model version comes out and you have to learn it all again!
You can certainly have an agent write code on the fly to issue refunds, or do almost anything else, and some people do things like this.
Some systems do support issuing refunds, among many other actions, by creating an appropriate row in a database.
The best case scenario of LLM is transforming input into output where both are languages and accuracy doesn't matter, e.g. "rewrite this poem in pirate speech."
But that's not worth trillions of dollars...
Or just write it yourself?
Because typing “code” takes time and significant amounts of it.
We are slowly waking up to the fact, which was always true, that “coding” is just a fanciful preparatory task in order to appease the spirits properly so that we may invoke the spirit of what we are actually after: a live, running process that does useful things. Code is completely useless when separated from that fact.
Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding. Knowing when it does and when it does not have this property is a skill of its own.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
I believe this is the general belief about basically every human skill, that if you stop doing the technical fundamentals you get worse at understanding the activity. The question is whether coding is like sailing a square-rigged wooden ship, which became completely useless knowledge after the invention of the steam engine, or if it's like playing an instrument, which while technically unnecessary after the advent of MIDI and other tools, absolutely hurts your ability to arrange, compose and perform if the skill is neglected.
For my money: I think the AI scenario is more like the latter, but "humans are worse at coding" isn't the consequence I see coming. I worry that in ten years we will be awash in software that's impossible to understand. I don't think that's happened in any human industry ever. Someone has always understood how the machines are built, even if they're very remote from the users of the machine.
The sci-fi novel A Fire in the Deep starts with describing a Software Archeologist, who digs through millennia of strata of layers of indirection and I think we could end up needing that one day.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
Like, perhaps, understanding that it is free of security and functionality bugs.
No serious programmer is regularly bottlenecked by typing speed. Even the ones who type slowly.
If you find yourself writing repetitive code you should consider adding a layer of abstraction. If your language isn't powerful enough you can write a code generator.
The typing was never the bottleneck.
Based on what I'm using AI for these days, seems like it always was.
It depends on where you're using AI. If you're working on a project for yourself or in a tiny company. Then sure, writing the code probably was your bottleneck. But at mid to large companies writing code is maybe 50% of the job, and the other 50% is the process around it. All those processes are the bottle neck, no matter how fast you can write the code. And this was a bottleneck I was hitting well before AI.
Can you type a hundred lines a second? If not, then it is.
Code is obscenely low level.
> Can you type a hundred lines a second? If not, then it is.
No one has ever needed to do that for something that is new. And if it’s not new, you want to do it repeatedly with some guarantee of reliability. Not just in an uncontrolled manner.
That is why we have snippet systems, macros and code generators. And the best with code is to solve problem once and reuse the solution. Which we have done with libraries, frameworks and supporting software.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
I would argue that this is nearly always the case. I don't think people really understand programs that they've only read at more than a very superficial level. This is why I tend to make (temporary) small changes, printlns, etc. when exploring a new code base: it aids greatly in understanding how a program actually works.
And it's even worse (in my experience) with LLM generated code, as it tends not to result in particularly understandable code. It is a lot like LLM generated prose: it often looks entirely reasonable at a surface level, but has a of weirdness/incorrectness hidden beneath the surface. But that surface level makes it very hard to avoid glossing over the details when reviewing the code. For this reason, I personally find it's much more effort to carefully review code than it is to write it.
Humans make mistakes all the time, but their code tends to naturally be structured for human understanding (to some degree based on skill/experience) because they themselves needed to understand it to write it.
I think LLMs are very useful tools, but after quite a lot of experience using them, I think it's generally better to use them as a sounding board, or to help you get unstuck or remove points of friction. Using them to write all of your code (at least for me) seems like a net negative.
I also think it's extremely easy to overestimate how much time they save. It feels like they're a productivity boost because it takes less intense focus to implement something. But I've experienced several instances where actually writing the code myself would have been both quicker and have resulted in better code.
All that being said, it can also be really hard to not write all of your code with agents once you get used to it. There's also a kind of slot-machine-like effect where you write a prompt, excited for the result, and when it doesn't quite come out right, you think "ah just one more prompt and it'll be good." It's hard to see when you're actually doing it though.
It's also weird to me how much people think typing is what the LLM is replacing. Typing was never the hard part. It's the translation of the high-level idea into an unambiguous process that's hard. That's also the valuable part, that requires thinking through the edge cases and consequences of decisions, and that just gets glossed over when using an LLM unless you rigorously review what the LLM has done.
At the end of the day there's a real tradeoff to be made, and it's worth being conscious of what's being given up.
> a live, running process that does useful things
That is one of the things code does. It also communicates the developer's thoughts about how that process should work to others. If the latter is neglected, the code becomes very difficult to collaborate on. Very few lines of code that are written are "write once". Mostly they're changed, repeatedly, over time by many people. The live, running process is a very temporary entity by comparison. Yes, it needs to exist and do useful work. No, it is absolutely not the only thing that matters.
If you already know what the inputs/outputs are, why should you spend days or weeks of your life typing it out rather than giving it in a well-specified and tested form to an LLM to get it done a hundred times faster?
Because a vanishingly small share of professional software engineering work involves pure functions whose implemention doesn't matter.
Democratizing access to code generation so that people can craft little personal utility scripts and quickly draft mocks/prototypes is great, but a lot of people on sites like this work on much more sophisticated projects and understand that developing a thorough enough specification for AI to be able to run with isn't meaningfully different in effort than what we've already been doing for the last 50 years. And in fact, for those fluent with their traditional tools and workflows, trying to craft those specifications as an English prose conversation for AI is often much more work that takes longer and is less reliable.
This is a truth that many are having a hard time accepting. Getting shoved into the light so fast is blinding.
Because the LLM version will have countless number of bugs and security holes, which means you will spend weeks or months of your life fixing them.
Because it’s rarely so black and white. Knowing the inputs and outputs is merely the first steps, you need to think about the transitions too as they have their own costs.
Those costs don’t disappear and it’s truly naive to think they don’t matter. Take security issues, they may arise because what you thinks was the input is merely a subset of the true input range. And the extra possibilities lead to unforeseen behavior.
A lot of programming is about ensuring that the input and the output are the sets defined in the specs. And the rest is that the transition/relation is the right tradeoffs of performance, correctness, and costs.
I am seeing similar things in just regular tooling and development. Things that can be solved deterministically or what would have been a simple CLI 5 years ago are now an LLM integration.
Instead of using the LLM to create deterministic tools, we are using LLMs to replace them. It's completely backwards and I don't know why people (especially high ranking people in my company at least) seem to think that this is the way forward. No, I don't want a whole CI pipeline that is just LLM prompts. Yes it's very easy, but it's expensive, slow and prone to failure in ways you can't even predict.
Same things like using LLMs for the code review process. What would have been a simple linting rule is now a pass with an LLM rather than using the LLM to create the linting rule, which it is absolutely excellent at creating.
>I am seeing similar things in just regular tooling and development.
Yes, and we're also seeing lots of companies claiming they're using "AI" and it's just deterministic under the hood.
My management is pushing for us to come up with ideas on where we can use LLMs in our product. The whole team has been very resistant for this exact reason. Anything we can think of will only make things worse, and we’ve already been told anything above a 1-2% failure rate is unacceptable. If anything we need more structure and standards to hit that, not less.
We just got dropped into hackatons for having ideas a few weeks ago, AI at all costs, similar feeling.
I believe that llm’s can be used to re-imagine experiences but it’s definitely not the way people think. The constraint is imagination and thinking about complex trade offs more than anything else. Which is the essence of innovation.
The agent paradigm will eventually give way to experiences that are a hybrid of deterministic and non deterministic and you won’t even know the llm was involved or visible.
Luckily for programmatic or logic following, smaller models tend to do better, it can be surprising at first to see the more expensive models do worse at a task but it’s true.
Basically, folks nowadays think that this article[1] was aspirational rather than a cautionary tale.
[1] https://thedailywtf.com/articles/Classic-WTF-No-Quack
> replacing deterministic systems in their support flows
The issue is, they don't want to provide "better" support but "cheaper" support. Imagine a trained agent that understands the big picture. Now imagine a company investing in humans to use AI to retrieve knowledge that the human can easily identify as being relevant or not, and using that knowledge to better aid the customer.
Right now AI is being sold as a "we don't need support personells" instead of "how can we provide better service." For a lot of products, better service will probably not matter as "cheaper" products will win most of the time.
Most people don't want to pay for better. They want to pay the same for something better, which is what companies are not investing their time in figuring out how to use AI properly for I think.
> Most people don't want to pay for better.
A lot of people want to pay for better, but that is hard. Better is more expensive, most of the time, but being more expensive is no guarantee for being better. It feels like the correlation is very weak. Most expensive products are just expensive, not good.
If there was a reliable way to identify the "better" thing, I and a lot of other people would go for that every time we can.
That's the completely opposite of what people should do. The laborious task of programing logical work flows is the only reason AI is useful for me.
When I hear about engineers who are bored with coding, I have to imagine it's because the task of "programming logical work flows" has become rote to them.
Instead of refining their approach, or challenging their current knowledge base for discovery of inefficiencies or baseless assumptions, they'd rather hit an "easy" button.
I understand the desire to NOT do work. I understand the desire to spend quality time and free time with family. And I understand the idea that familiarity breeds contempt.
What I don't understand is the willingness to replace a deterministic language/framework/approach with a probabilistic slop machine.
Yeah but did number go up? Can CEO check a box to show investors?
Now that’s real value.
As a contractor who built a lot of predictive systems and workflows in last three years I can tell you that quite often there is a specific request to put AI into it even when it is not needed and would objectively make the system worse, slower and more expensive.
The AI psychosis is a real thing.
Haha, i have a colleague, he is the "AI-is-for-everything-let-me-check-Claude-first":
Regardless which task is handed to him, he "discusses" it first with Claude and very often comes back with like "The AI said... X"
I have one too. He'll say "Claude says this:" and pastes a screenshot of some Claude Code output. Most of the time it's wrong, or makes assumptions that won't hold true. Or it comes up with some overcomplicated solution and I'm like "This is like a 10 line change, right here".
These people just destroy their ability to read and understand the systems they're working with. I actually see it as them making themselves redundant. Because if you can't understand anything without Claude, and Claude doesn't even give the right answers, then what are you worth?
I talk to Claude because I'm very talkative but I have nobody to talk to.
Where I work, management hasn't considered integrating AI at all, yet some clients are very vocal about it being the future and worry we are going to be left behind. Most people just don't care, and I worry the squeaky wheel will eventually get the grease.
> worry we are going to be left behind.
I bet lemmings are grateful they were left behind.
It beggars belief that people think that they should rush in some uncertain direction, like some drawbridge is going to be lifted the moment people work out what the right direction is. It's utter stupidity.
Every single person who bootstrapped becoming powerful did it by rushing into things, but it's a high variance strategy because you could also end up destitute
I keep seeing requests to replace what would be a perfect UNIX shell script with agents, like what is the benefit other than being able to say we're doing AI?
Maybe it should have clicked earlier in life and I'm perhaps that much dumb dumb, but it only recently occurred to me (from experiencing it at two very different companies and discussing with peers having reached a certain seniority level more or less at the same time) how dysfunctional many companies are, and how often they produce incentives that are misaligned with the overall company goals and sustainability principles. I blame in large part a layer of middle management that selfishly puts itself above all else, misguides, misrepresents, because it essentially pays larger dividends (literally and not) to "play the networking game than to be an efficient and effective productive structure". Maybe that's to be expected in a services-driven economy where the value of the work is immaterial and subjective (and the whole phenomenon of bullshit jobs).
So then, do you put AI into it anyway because they asked for it, or do you tell them that you won’t do that?
> you tell them that you won’t do that?
Of course I will do that, I get paid for doing that.
Most of the times I can convince that AI is not necessary by showing small PoC flow with AWS diagrams of data flows. This works well especially if the ask comes from technical people.
Other times the C-level interjects (CEO, CFO, sometimes even CTO) and demands that AI should be there. I literally had CEOs send me instagram reels of some AI shovel-sellers to demonstrate that I am wrong and AI is the way to go. No point arguing after that because I have no problem implementing whatever AI they want rather than losing a paying project.
With inexperienced or non-technical people, talking to them about AI can be very confusing, as a LOT of their "AI" usecases are basically they didn't realize or know how to write a program for this straightforward logic.
models will get smarter, this wont be an issue
The Claude web UI popped a modal up a few days ago advertising their new model to me. It was full of HTML tags that were escaped or otherwise not rendered so that the text was literally
...and this was presumably generated with the flagship model from the world's most prestigious LLM company.Intelligence, which I assume to be a synonym for "smart" requires the capacity to acquire and apply knowledge from experience.
These models do not have any experience. They're not sentient. And are in no way capable of being "smart", let alone becoming "smarter".
They say this every time. Just wait for the NEXT model bro THEN everything will be be fixed.
Ok wait maybe not the next one but surely the one after!
Hasn’t happened yet and there is no evidence it will.
This all reminds me of in the 90s when the Borland C++ compilers and Turbo Pascal shit came out and everyone was still hand rolling assembler because the optimising compilers were so bad. I thought Opus 4.6 was pretty good, basically a step change. The stuff I got out of Fable before they blocked it was nearly alien. If things keep improving, I don’t see humans writing code in 2 to 3 years except maybe super niche areas. This will all go the way that optimising compilers did. No amount of resistance, anger or denialism will change that.
I’d actually love it if LLMs could skip the slow high level lanaguages entirely and just churned out some weird LLM bytecode that was closer to the metal. I don’t want to read it or understand it at all. Here’s my spec, build it and notify when done. I want to ship stuff not build or dick around with code. Basically like when I go to a shop because I want a table, I don’t care if some carpenter “crafted” it or a machine mass produced and spat it out. It’s cute, but most people just want stuff and don’t care how it’s built.
Come talk to me when it isn't an issue.
> AI has gotten so good that despite any misgivings, “everyone is using A.I.”
In my experience, it's a mixed bag. I wrote this comment[0], yesterday. It reflects my current work, and how I am integrating an LLM.
I have used it for two parts of my project:
1) The backend (PHP), and
2) The frontend (Swift)
It has been a huge help, in both, but #2 is a cautionary tale. It really needs adult supervision, in developing native UIKit Swift apps. I'm realizing how truly bad the code it wrote was. I mean, terrible.
That's jarring, because it did a great job with #1. It made sound, reasonable design decisions, and provided code that is better than what I would write.
With #2, it behaved exactly like an inexperienced engineer, panicking, when confronted with real-world problems. My rewrite is going to feature a much simpler, sound approach.
All that said, it has been a net positive, and has increased my productivity by a large margin.
I guess the lesson I needed to get from this, is that it is good at helping me to find problems, but maybe not so good at fixing them.
[0] https://news.ycombinator.com/item?id=48515217
I'd like to add that there is almost no way of "running away" from it. If I search for anything on the internet I am almost guaranteed to be handed pages and pages of AI generated content. In lieu of that I found that directly prompting for an answer tends to yield better results nowadays. Not because it's good per-se, but because having control over the prompt beats having little to no control over it though search by proxy.
It saddens me to see that high quality content is drowned in this sea of garbage to the point of being almost impossible to find.
I think this is where the circle closes with the "dead internet theory"... you go to Reddit, and see bots commenting on posts created by bots.
Then you go on to search for something, and find only results that are clearly AI generated pages and come to the conclusion that directly prompting some LLM is better than reading an AI slop page that's output by the same AI for slightly less specific prompt.
My concern is that this will only get worse over time - which is great for companies selling AI tokens and bad for society and whoever wants to interact with other humans over the internet.
This would be expected. The corner cases people faced with PHP throughout the decades have been well documented on the internet for eons.
Swift, not so much. It's relatively new. Looking at AI's abilities like an engineer's career span scaled about 10-20x of time makes it make a bit more sense.
It's going to be worse at newer/niche things, intuitively - which is only going to get worse as it "learns" from garbage outputted by other LLMs moving forward.
Also, I suspect most "production" Swift –the type of stuff written by seasoned experts– (I just had to add em-dashes ;) is behind closed-source walls.
No doubt in my mind, a future Apple model will be the best to use for this purpose. They likely have more swift to train on than anyone else, and would benefit directly from more quality apps, rather than the slop flowing into the App Store (>1k app submissions per hour; they claim)
> which is only going to get worse as it "learns" from garbage outputted by other LLMs moving forward
You seem to assume that autoregressive pretraining (and unfiltered behavior cloning, maybe) are the only ways to improve LLM performance.
That's just one way to use LLMs though. Recently on a flight I could not figure out how to connect my wife's earphones (i.e. put them in pairing mode) to my macbook since I was used to the old Airpods Pro case. So I asked Gemma4 26B A4B (offline, LM Studio) and was told to use the 'two tap on front of case' gesture, which worked. This situation would have been significantly more frustrating without (local) LLMs. I'm essentially carrying around a basic "how to" on everything, inaccurate though it may be, it's better than nothing.
Absolutely. I use it often, for stuff I used to "just Google." Other than a predilection for giving me CLI walkthroughs, it is usually fine.
My experience was different. I found it extremely good at fronting technology like react while I had to hand hold it for the backend tasks. Even with fable it was the same.
Well Apple just released a bunch of Agent Skills. I tried it on my macOS apps and I noticed some improvements codewise and updated some deprecations I didn’t know existed in Swift.
Looking forward to that.
Yeah it comes with Xcode 27
Would you describe yourself as more skilled at frontend engineering or at backend engineering?
Definitely frontend (it's what I do, every day, and I enjoy it), but I have a great deal of experience (over 25 years), writing some pretty robust backend stuff. I just don't enjoy it as much.
I'm nowhere near that level of experience, although I've done both as well. I'm more backend oriented. And my experience has been the opposite. When I ask for backend code, footgun after footgun appears on my screen. With frontend code, much less of an issue, as far as I can tell. Part of me believes this is because I'm less skilled at frontend, and I don't bat an eye when the LLM plops down yet another useMemo (I've since learned that this is rarely needed). But in your case this argument can hardly be made. With 25 years I trust your ability to spot a good design on either end of the stack. So then I don't know where this discrepancy comes from. Maybe my prompting skills leave something to be desired.
I don't do "megascale" backends, though. My code is generally smaller-scale stuff that's designed to be deployed on a wide variety of cheap hosting, and is pretty conservative. It doesn't "push the limits."
I'm unlikely to run into many of the problems that (for example) the PornHub developers hit, several times an hour.
In that case, I benefit from folks like you, that allow me to have solutions that scale down to my level.
In my experience the language has become irrelevant for me, I created a system like mix of revenuecat and firebase and I’m not even sure what language which part is. It has client side libraries that are swift and kotlin, the Identity management is Swift but the iAP/Subscription tracking is go IIRC. It’s all integrated somehow and works very well.
That's the thing, the Swift works fine, but is incredibly brittle. I think it would collapse, at the first bump in the road.
That's fine, for a lot of corporate applications, but not for the stuff I write. I'm anal, I know, but that's how I roll.
Which LLM though? Models can still be significantly different in their capabilities.
That's likely. I generally use ChatGPT (latest), but as a chat interface (not an agent). I suspect that I might get better stuff from Claude (maybe).
I’m going to guess you are better at frontend than backend.
The classic AI Gell-Mann effect.
The guess is correct.
The diagnosis, however, is not.
Have a great day!
Might be because there are less Swift projects to train with.
But I've seen Claude write crazy code in Python and JavaScript, too
My theory is that most of the Swift code in the public domain, is basically demo code. Short, idealized, code samples to demonstrate issues and solutions; much like you would see in StackOverflow.
PHP has huge, entire frameworks and systems, refined over years.
There is also a lot of low quality PHP code out there, and a lot of legacy code in a language that I am told (I have not used if for years myself though) has changed a lot.
Same with C++. You don't want to write C++, the way that I used to.
That's one of the things that I appreciate about the PHP that the LLM provides. It uses modern idioms that make better use of the modern language.
I do not know about crazy, but certainly sub-optimal. For example a loop over DB query results instead of modifying the code to work with a single query.
I found that asking it to refactor for performance and safety often addresses these issues.
I think the gap is because 1. For coding, Claude is amazing - mainly because of its curated skills and because massive amounts of working code has already been carefully labeled over the last decade or so via GitHub. And because with any Turing complete language, there is only so much one can do.
But 2. For most other things, LLMs are fairly underwhelming. Research is usually mediocre. Try being rigorous and repeat your research prompt many times - then make a confusion matrix to tally up how many false positives and false negatives occur. And for the rest, be honest and ask yourself if the LLM is doing much more than a basic search engine query or trip to Wikipedia would have told you. For “normie” use cases, it’s handy-ish but far from revolutionary
> People are consuming AI like they eat meat: some are embracing it, some are limiting their use of it, and some are avoiding it altogether.
That's an interesting analogy as, despite the real ecological issues with it and principled arguments against meat eating, in general meat consumption has trended upward globally in country after country for decades.
Maybe this is because I live in Wyoming, but "AI is not ubiquitous, there are some people, like Vegans, who eschew it" is not the most compelling argument.
I fear AI is going to be used for everything not because it's the best solution, but because people are inherently lazy and just want to get their thing done, and they don't care so much about the quality.
"low effort and convenient" seems to consistently win over "best quality" and this is going to be a downgrade in everything, for everyone
I assume for a lot of people, an llm is going to produce higher quality results for most knowledge tasks than they could do on their own. I think that's okay
Anyone who does a Google search gets a satisfactory looking answer as the very first entry. I daresay most people don't go beyond that, not even the entries on the first page, let alone go to the next. I argue that this is at the level of everyone for everything.
> Anyone who does a Google search gets a satisfactory looking answer as the very first entry.
Google has search results still? I don't use Google much anymore (thanks Kagi), but this is what ends up showing for me, I don't even see any search results anymore: https://i.imgur.com/eHIA2Df.png It seems like it's 50/50 on page reload if the LLM-reply UI expands automatically or not, which covers my entire screen. I guess Google is doing some A/B testing perhaps.
What Im question is how is Google increasing Price-per-Click each year if people are clicking less and less on the links below the AI search result
I don’t see the contradiction? If the inventory of clicks is declining and the number of businesses bidding on clicks is more or less constant, why wouldn’t that increase price?
Even if we accept that all people are satisfied with the AI search overviews, that would still only be everyone for one thing.
When was the last time you used Google? The first entry (and a few after that) is always spam.
Anyone who does a search and accepts the first answer just doesn't care much or is incompetent. Anyone with any critical thinking whatsoever does way more than that if they want a correct answer.
Pretty sure he's talking about the summary / AI answer and not the first search result
> More than 30 percent of the US working-age population is using AI [meaning about 70% isn’t], an increase of 3 percentage points from the end of 2025
This makes me less bearish on the AI investments that are being made, if 70% of the working age population isn't using AI then there still is a lot of growth. The future is here, it's just not evenly distributed (yet)
One thing I'd personally like to see a little more discussion of (at least within my social circles) is.. what exactly does "using AI" mean?
How does this connect to everyone's high level ideas/thoughts about "tech", "AI" and "morals and feels" etc. These lines can start to seem a little blurry, at least for me.
For example, would we say my partner is "using AI" (for all intents and purposes), if she's frequently using Google.com throughout the day, and then ends up picking and believing the AI generated answer overview at the top of the SERPs almost every time?
Or do we feel "uses AI", is more along the lines of the vampire kids running 1000 sub-agents on a mattress floor in SF?
I kind of find the whole spectrum really interesting because even basic phone use is now stuffed with AI, whether we choose to label it or not.
So true, just built a deterministic system to identify duplicated code. It's offline and doesn't use AI on purpose, since a gate that blocks your CI has to give the exact same answer every time, and finding dupes means comparing every function against every other (that's index work). It does NOT use AI. But ironically, I used AI to build it (https://github.com/Rafaelpta/dupehound )
> But ironically, I used AI to build it
This is a pattern I encourage - the AI might not be reliable, but with coaching, it can produce reliable tools. `colordiff` was causing issues with `less` when I was looking at diffs (character encoding issues I think), and when I asked Kimi K2.6 what to do, it built me a rust command-line diff tool in one shot that I've been using ever since (it even downloaded rust, wrote the tool, and compiled it).
Have you seen jscpd? What does your tool do differently?
Some of the advantages are second order.
For example; ChatGPT is replacing my Google searching. Not necessarily because it's better, or because it's summaries are better than Google (I find them subjectively better but it's not clear cut).
But because the app has a nice history; can ask a relatively complicated question and go do something else and then come back to it, ask a follow up. Etc.
None of that is specifically an AI benefit, but it's a workflow that really helps, well, flow.
That's funny, Google Gemini and AI mode in search has replaced my ChatGPT prompting, because I know Gemini will correctly cite sources (as of course it's by Google) rather than hallucinating.
Also, Gemini is free or at least has much higher usage limits than ChatGPT or Claude, and it's well integrated into Android and soon Apple with their new Siri, so things like circle to search just work well.
That's totally fair and things may change. For me its the history and the fact I can come back to it.
If I am honest I believe my final solution will be a combination of Open Claw, a custom knowledge wiki based on Wikmd. I just need a good all for Claw with history that is as good as gpt
Edit: and context too. It inferred my energy supplier from previously chats and so when I just asked a pertinent question it referenced their policy. Admittedly Google will have way more context if they get the product right.
I understand the point being made, but it does feel a bit like writing a post in the early days of the internet saying:
"No, everyone is not using the internet for everything."
Which would have been entirely true when written, and entirely false a relatively short time later.
Everyone does use the internet for everything today, and everyone will use AI for everything soon.
In my non-tech circle, most people don't even realize how the internet is running literally everything. Even if we start to use mass scale AI for something, they wouldn't realize or care much about it. They at best turn on the TV to watch netflix or look at the phone to send messages on whatsapp. If all of that went away tomorrow, they'd be inconvenienced at best and then go on with their day to day life. This feels like we are literally all in our IT echo chamber where we throw stuff on walls and go crazy, while the world is sunshine and rainbows, always been.
"They at best turn on the TV to watch netflix or look at the phone to send messages on whatsapp. If all of that went away tomorrow, they'd be inconvenienced at best and then go on with their day to day life."
I'm not saying it is a good thing, but this is completely out of touch with how dependent (most) people are on these technologies.
I'd say its just businesses and governments that are dependant, most people can get by just fine
A lot of people are genuinely stranded if their phone runs out of battery. How do they pull up a map, or call an Uber, or phone someone to pick them up?
You'll find it hard to pin down what you mean by "everything" otherwise you wouldn't have said that. Nobody uses the internet for everything.
Local models are highly likely to dominate in the long run as "good enough" inevitably becomes trivially cheap. This is a very different pattern of incentives and adoption compared to the internet.
I think it's more similar to the advent of personal computers. They had a brief surge and then turned into something else (smartphones, cloud, etc.) for all but a few niche cases. AI is not changing the consumer landscape. It's getting absorbed into existing platforms where there's a clear use case and benefit. It's just another expected software feature. This is far from the first time people have rejected a "personal assistant" concept and they'll just keep rejecting it.
It seems fair to leave the definition of "everything" to a reasonable person's interpretation. It's obvious that the internet is beyond ubiquitous in modern life.
I agree that where models run will will change over time, probably they'll run everywhere, but it's still the same kind of AI we are talking about.
Smartphones are personal computers.
Just about every app has a "help" button, but do you really use it? What about captions on a video or any number of other accessibility features? They're in everything, but not used for everything.
It makes perfect sense that they exist and were way overdue for an update, but they're just extra blades on the multitool. Perhaps in some designs they become more integral, but that is expected and invisible.
Yes "everything", but that's not even close to sufficient to become a huge breakthrough like the internet.
"But they will", "They do, but they don't know", "They do, indirectly"
I don't get these comments.
A counterpoint to this is that we have some real different definitions of AI.
If you consider things like the machine learning filters in your smartphone camera and Google's AI Overviews for searches it's entirely plausible that the US is currently at 75%+ of AI usage.
One of the reasons is that the free options are generally fairly poor and it’s hard to get people to sign up and actually pay for something. Especially if they assume it’s going to be similar quality.
If I worked in marketing/growth for an AI company I would try to consider some ways of breaking through this gap.
Not everyone but most. And I've been having this discussion with people around me a lot lately and everyone that has the ability to think more than half a step ahead sees it(and frankly we are fed up). I previously discussed how a friend admitted that he's never seen the code that powers his project at an S&P 500 company. Yesterday I was talking to another friend and former coworker who complained that when cloudflare went out a month or so ago, his entire team just slammed their laptops and went home cause they couldn't work(no sloppus/sloppenai). Another friend of mine: her dad is in hospital with a terminal disease and her mom (in her late 50's or early 60's, idk) uses chatgpt as a personal therapist. Gatorade-fed crops here we come, Leeerooooy Jeeeenkins!
Embarrassing NPC behavior to throw in the towel on working because you lost your crutch.
I only use AI for software development. For writing, I don't use it at all except to translate source materials. So yes, AI is only for software development in my case. The real question is whether I have any value outside of software development. Sometimes I get the feeling that AI is replacing the value I have in society.
I have no doubt that as AI gets more expensive, my employer would lay off more developers to pay for more AI tokens, until there are very few developers left. And the hilariously sad part is, the current developers keep training the AI to do their job. Eventually I expect they will lay off almost all the developers. It really feels like we're going to be stabbing each other in the back just to be the last one to get let go.
What are they doing to train AI to do their job?
I don't think AI has any real value for software development, personally. The quality just isn't there, unless you invest so much effort that you may as well have written it yourself. But the market can stay irrational longer than you can stay solvent, and even though I think the industry will get over the idiocy of having LLMs write software, there's no telling how long that will take. So it's a scary time to work in tech even if I think the trend will ultimately reverse.
As of ~8 months ago the quality is most definitely there, for almost every form of programming I've experienced.
If you're working in some vanishingly rare domain then maybe it's not yet, but most coding challenges are very much in the wheelhouse of the current frontier models.
I envy you. For me, AI is faster than the code I write myself in many, many cases. It might replace the average developer, but a talented developer like you probably won't be replaced
I was not hired to write code, but to solve problems (where often the end result is code, but it’s not the whole process). But the message from management is that our bottleneck was coding, and by using AI to code, we’ll be 10x faster and all the company problem will be solved. Essentially 1. Use AI everywhere 2. ??? 3. Profit.
Where I work, the CTO drank a whole bunch of AI kool-aid recently, so now we're expected to "10x" our output with AI. I don't think he realizes this also means 10x more problems of all kinds. But I fully expect him to double-down and when AI costs skyrocket, he'd lay off more developers to pay for more AI.
I am constantly looking for a new job, but all of them are also require AI coding experience.
True, but you're somehow involved in it even though you don't use AI.
Not to take away from the post, but "everyone is not" should probably be "not everyone is".
Everyone is using AI, issue is not just everyone recognizes what AI actually is, how broadly it's used.
Looking things up and asking questions was always something for a minority of the population so the language model usage being relatively low isn't a surprise.
Problem arises if the non-AI segment is leveraged to create regulations that impact the AI using segment negatively.
everyone might not be using ai. but i see myself reaching for it for every small thing these days. it's like every curiousity or lifestyle choice or optimization is something ai can help research.
i am not saying it's really powerful or great. but the lure is undeniable. because of how low friction it has become.
I'm going back and forth with the llm agents.
They are great on exploring, understanding and finding bugs in existing codebase.
They are great for simple or one time scripts/programs.
They are terrible, really terrible coders. The overengineering is so deep in their training that no matter what is your prompt, your skills or agents.md/claude.md, if you don't babysit them continuously, at some point they will just fuck up your codebase.
Reminds me of this article: https://www.theverge.com/podcast/917029/software-brain-ai-ba...
Software engineers are definitely in a bit of a bubble here. Are we just early adopters who see the value sooner, or does it uniquely benefit software engineering, or do we just like cool automation and we're deluding ourselves that this adds value beyond the cost?
Yes I believe software benefits uniquely, just like building tooling and automating software have long been easier in software than other domains. Humans defined all the rules of the world you live in, humans wrote strict rules in methodically parsable formats.
The moment you have to interact with the physical world or humans (psychological, imaginative, aesthetic, etc), there are often undiscovered or changing rules—or no rules at all. Or systems are subject to perturbations beyond a defined scope.
The other thing I believe is software developers are experts at doing the things that allow them to make doing those very things easier and more automated. And they do this in public, perfectly documented online.
Both because of the things I described above and because software developers have created the largest machine-accessible training set for plying their trade of any trade, ML—that is ultimately interpolating massive datasets to do things—is unsurprisingly uniquely successful for software tasks.
I've been thinking about this, and I think software is uniquely knowledge work that has the most defined structure and least personally interaction. Hell, some of the software I write is for machine to talk to other machines. It's not surprising such a closed system is so amenable to AI, and other knowledge workers are not getting the same benefits.
Software has huge and detailed code repositories ripe for training use. There's just enough inference in current models to remix that code in useful ways for the most popular languages.
The less popular a language, the more models struggle.
Writing, UI, and presentations have similar knowledge bases.
Outside of those, quality becomes much more hit and miss. If you ask for a recipe you may get something good, or you may get something completely inedible and random.
"Domain specific knowledge" really means "strong foundations and relevant abstractions" and LLMs just don't do that reliably.
That's a decent article. My only issue is it seems heavily biased at the end, or at least he seems to misunderstand what the 'A.I. types in Silicon Valley' are doing.
> Computers should adapt to people. Asking people to make themselves more legible to software — to turn themselves into a database — is a doomed idea.
I've been in software a long time, and I do sort of see this trend, but I think it's because these are tools that build other tools. The interface has always been a 'best I can do for now' thing, with the focus on doing things that are useful. Computers were just calculators in the beginning, which led to more complex calculators, instruction sets, programming languages, operating systems, GUIs, interconnectivity, etc.
What people are doing today is experimenting, like they always have. They're putting their experiments out there so that others can use them and build on them. Some will use those tools to build other tools, and some won't. But over time, the experiments that work will get distilled and turn into real products that people who 'do not yearn for automation' will still want to use, so it seems like the value is there.
I guess the real question is whether they will create value that offsets the near-term costs, because I don't think the billions in investments are sustainable, and I'm not convinced the centralized data center paradigm is the right way.
Software engineers aren't even all using AI, contrary to frequent claims here that they are. There are very many who have tried it, found it didn't add value to their work, and aren't using it unless FOMO-driven managers force them to.
I disagree. Everyone will be using AI for everything, but, increasingly, people won't think about whether they are using "AI" – just like they don't think about using databases.
Nor should they! It's such a shit thing to be emotionally invested in. Imagine people would have been upset about databases. It's really fantastic software and we should be happy to have it, and now go and make the most of it, for all of us.
No, everyone is not using AI for everything - yet.
I am using AI to take on a fun large scale analysis of churches in USA.
I also just bought a completely mechanical film camera to learn a new old skill with no tech to fall back on.
I'm using AI for most things. It has been an incredible improvement to both my quality of life and my wallet. Some of the most high profile items from just the past three months:
- I'm getting my roof replaced due to hail damage. Insurance originally covered only $5k due to depreciation. I fed the insurance policy to AI. I learned about the appraisal clause and invoked it. At the end, I got another $6,500 back.
- I was having issues with plumbing. Four different plumbers came, they all said the cast iron pipes under the house need to change. Quotes ranged from $35k to $55k. I had AI walk me through the process. It taught me about the yard line vs. under-slab distinction, and suggested getting just the yard line replaced first because it's much cheaper and can fix the issue. I did that and spent $6k. The issue was fixed. I "saved" $30k for now by deferring that massive month-long project. (For brevity, I'm omitting a ton of boring technical stuff I learned about plumbing that helped me make the optimal decision - none of the contractors bothered explaining any of it.)
- My 2010 Hyundai Santa Fe is starting to show its age. I've taken it to multiple different repair shops, then fed their diagnoses and recommendations to AI and figured out which ones are trying to fleece me and which ones are being more careful and conservative with their repair recommendations. Probably saved several thousand dollars there. Learned a lot about cars too!
- My partner and I are converting the backyard to a wildlife sanctuary. The AI helped us plan what to plant where (depending on lots of factors like sunlight location, irrigation access, etc.) and it has been going really well. Also planned out a dragonfly pond to deal with mosquitoes. AI created a project plan, including schematics, material purchase list and step-by-step instructions.
- I've been wanting to do various other home improvement projects, but only ones that make financial sense. I took photos of my house, both inside and outside, and fed them to AI, and said "give me a list of projects I can do that will have high ROI for when I decide to sell this house". It spent 15 mins doing deep research, then came back with a long, prioritized list. If I do all the projects, I'd be spending about $40k and it would improve the house valuation by about $90k.
I can go on. There's probably dozens of stuff that I've used it for over the past year that led to massive time and money savings, and I've learned a ton as well about topics I normally would not have been exposed to or bothered to research myself. And I'm not even including all the work-related usage, both for my employer and my side business. That would be its own very long list.
Great examples. I think people not using AI for issues like these lack imagination or more charitably, simply don't know that it works so well for these. Especially non-technical people can find great value out of AI, not just SWEs.
Articles that start with no are inherently biased and only gather reads from people that agree.
or for anything
The numbers given in the article are actually consistent with what is usually meant by “everyone” in such statements. Sure, it’s not literally everyone. But it’s a very significant percentage, especially given how quick the adoption has been.
I think what people mean by everyone varies a lot, which is why I wanted to draw attention to more specific numbers. For example, in the Datos data cited[1], on desktop 86% were using traditional search engines >10 visits/month vs. only 21% for AI chat tools. That is indeed a very significant percentage, but more than 4x less than search and (at least I) wouldn't say that ~1/5 is "everyone."
[1] https://sparktoro.com/blog/new-research-20-of-americans-use-...
yeah exactly my thoughts. everyone didn't mean literally everyone.
and for the ones that are using it (especially the paid subs). the lure is undeniable.
Kinda like how quickly cellphones popped up and changed everything.
> AI has gotten so good
Actually anything that is about 90% great and 10% disastrously wrong is utter crap given the way people want and do use AI models.
They are great tools in the right hands and awful in the wrong.
It's funny lately I've been seeing the cursor advertisements all with some premise of regular young person wants to develop an app and the ads really do focus on the simplest of premises: the only ones I've seen in these skits are essentially variants on the "todo app" web app tutorial
the tech is pretty good at helping identify simple bugs when they happen and to write short sections of code given very explicit instructions but yeah I have yet to see good examples of short one sentence ideas turned into a working product that looks better than anything that could be a UDemy tutorial app.
I honestly just use it as a search engine to get around SEO garbage and ads.
My wife uses it for a (non-computer related) business though and it's great for all sorts of normally tedious marketing/social media type jobs though. Stuff that doesn't really require accuracy just needs text on pictures that looks good quickly.
I think everyone just has FOMO and doesn't want to lose to competitors. Eventually it'll die down.
"Think of how stupid the average person is, and realize half of them are stupider than that."
Bit of an odd decision to build an entire article around a clickbait headline from July 2025. Talk about a strawman.
That aside, this piece is interesting and ties together some useful numbers and studies.
I hadn't seen the recent Microsoft paper showing:
> 30 percent of the US working-age population is using AI [...] with at least 90 minutes of usage time in a given month.
I'm honestly impressed at how high that number is! That's a lot of adoption for a technology (LLM chatbots) that didn't exist four years ago.
How much of that use is driven by corporate mandates to use AI anywhere and everywhere (even when it's a terrible fit)?
I'd love to see credible numbers on that. I find it hard to believe that stupid corporate mandates are responsible for more than a small fraction of usage, but without data I have just my own instincts to go on there.
At my employer (megacorp with tens of thousands of employees) daily use is mandated. Our annual bonuses and pay raises for our performance reviews were explicitly tied to this.
It's a retrospective analysis of an assertion made by NYTimes. The original headline wasn't clickbait, just presumptive, and even so, it's a pretty significant publication that spends a lot of time on the HN front page (alongside you, I'll add). I think it's perfectly fair, and nowhere close to a strawman, to deconstruct that claim a year later.
https://www.nytimes.com/2025/06/16/magazine/using-ai-hard-fo...
"Everyone Is Using A.I. for Everything. Is That Bad?" - subheading: "Either way, let’s not be in denial about it."
It's clearly intended as rhetorical hyperbole - like "everyone's on their phone at the movie theater" or "everyone's fed up with AI hype".
If you read the actual transcript it makes it very clear that it's not claiming "Everyone is using AI" almost immediately:
> ChatGPT is the sixth-biggest website on Earth. Something like 43 percent of Americans in the work force use generative A.I.