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.
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 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.
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.
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.
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, ...)
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.
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.
> 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.
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.
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.
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.
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
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.
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.
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'
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.
> 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.
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.
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.
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.
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.
> 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.
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.
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.
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 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.
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?
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).
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
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.
> 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.
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.
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.
> 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.
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.
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.
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).
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
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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 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.
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 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.
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."
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.
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.
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'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.
Cite needed - FAANG certainly leaned hard into ‘lie to survive’.
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.
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.
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.
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
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?
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"
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.
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.
> 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.
> 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.
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.
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.
Not everyone has that luxury when there are bills to pay and mouths to feed.
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.
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
> 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.
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
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.
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.
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.
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.
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?
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.
> 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.
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.
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...
How else would anyone do something like issue a refund if not through a programmatic interface?
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?
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.
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.
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.
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.
> 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.
> 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.
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.
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 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.
This is a truth that many are having a hard time accepting. Getting shoved into the light so fast is blinding.
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.
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.
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).
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
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.
models will get smarter, this wont be an issue
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.
Come talk to me when it isn't an issue.
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.
> 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.
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.
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.
I’m going to guess you are better at frontend than backend.
The classic AI Gell-Mann effect.
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).
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.
"But they will", "They do, but they don't know", "They do, indirectly"
I don't get these comments.
> 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.
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.
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.
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?
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
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.
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.
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.
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.
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.
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.
Not to take away from the post, but "everyone is not" should probably be "not everyone is".
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.
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
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.
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.
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.
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.
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 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.
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 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.
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.
or for anything
Articles that start with no are inherently biased and only gather reads from people that agree.
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.
"Think of how stupid the average person is, and realize half of them are stupider than that."
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.
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.
> 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.
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.