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.
You call the response "cagey and evasive", but that is for an objectively a bad interview question, one wrung below "How many years experience do you have prompting Anthropic Opus? We are an Opus shop." People are not locked into their current way of using AI and it is trivial to match how one works with AI to match employers requirements. It's a question that deserves an idealized non-answer
Remember the context - this is while solving a whiteboard problem. Its bad in the same way asking candidate what their birthstone is - because any answer offer little or no signal about the odds of a candidates success at the company.
I'm curious to know why you think asking about AI usage is a good interview question.
If you're going to have people use AI regularly, it's worth asking so that you can get a sense of their interest/willingness, experience level, and training needs. That said, more specific questions are typically more revealing. Personally I'm fond of "If you could give Claude only 1 instruction, what would it be?"
as a rule the interviewee should be assessing for goodness of fit, as well as the interviewer. If my simplified nuanced answer to why I limit my usage of LLMs was poorly received, I wouldn't want to take a job somewhere likely to make me miserable.
Unless I'm looking after about 6 months and my savings isn't as healthy as it once was. Or one of my kids is sick. Or pretty much anything happens costing money.
Then I'll be miserable for a little while as I continue my job search.
Practicality, sometimes, is more important than ethics when other people rely on you.
"Unless I was really excited for flipping burgers, the menu options, the new McKidney, non-working ice-cream machines, and the legacy of Ronald McDonald, I wouldn't want to take a job somewhere likely to make me miserable".
A lot of jobs don't work based on those criteria. In a bad job market, IT jobs don't either. Especially if it's your 20th application with no response.
If you're outright lying to yourself to land a job to make rent, fine, but for god's sake internalize it as a temporary stepping stone and immediately start looking for a better fit.
This thread is about new grads. Have you talked to anyone looking for junior positions lately? They don't get to choose employers anymore. Heck, many of them will not even get a chance to enter the field if the current junior hiring situation continues.
> But it doesn't make sense to do a job you'd hate. Just be honest, land the job where you're happy.
In this economy it's really a luxury to nitpick about the job when there are bills to pay, mouths to feed, a roof over one's head is a must. The well of have a lot more options regarding this.
That assumes using/not using ai will make me hate the job. Which is not true at all for me. Would be perfectly happy either way. (Or rather to say my happyness would depend on other factors.) Obviously i wouldn’t want to be the only team member not using AI in a team using AI or vice versa.
Oh but they will: https://gracenote.com/. These people sit and watch TV, note down every event taking place then sell the data. Search for Infostrada and how it came under Gracenote. That's where the info like "this is his first X since the game Y Z years ago" comes from.
If you're good enough at it, I suppose someone would. (There are television critics after all.)
But I'm just curious: if someone were to give you a living wage to do nothing but watch TV all day for the rest of your life, how many years do you think might go by before you would start to ask yourself, Is this really what I want to spend my one, exquisite, irreplaceable life on?
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.
I think honesty is still probably correct - if you're struggling to figure out how to hedge.
I think you'd rather have good odds at some companies and 0% at others, rather than abysmal but non-zero odds at all companies.
And as an added bonus, you might get hired at a company where you're actually a good fit, rather than one you weasled your way into, and get to pay rent, food bills, and other expenses through employment for a long time!
It's pretty easy as an interviewer to spot when a candidate is hedging on a question, and it's the kind of thing that might get discussed in the post-interview debrief.
"Wouldn't give a straight answer on question X" isn't an instant no-hire, but it's not a positive signal.
This doesn't make sense in practice. He hedged so not sure need to look at other factors vs he picked a side and he selected the opposite of what we wanted no-hire or he answered what we wanted small positive signal need to look at other factors.
It was a question of the form trying to figure out how you deal with "X", and he denied X having ever happened, despite that being a core part of his current role.
He was an internal candidate, we were interviewing him to see if we could trust him with more responsibility (more X specifically), since the new role shouldn't cover up X when it happens. The role involved doing X for himself AND for other people.
Similar to the form of "tell me about your biggest weakness" and you responding with "I have no weakness".
I guess it's a bit different when it's an internal candidate.
I've been on the recieving end of clueless folk trying to make me feel small when I've just been looking for a job, so I might be a bit sensitive about it! Sorry for any offense given. Thankfully I'm beyond that craziness now and can just do what I want for work.
instead of telling him out loud, "hey we see you're hedging and applying bullshit interview speak, your answer isn't sufficient", we asking in increasingly obvious but different ways.
It was an internal candidate so it would be awkward to tell him to his face he was floundering.
I mean maybe that is because I live in a still mostly not failed state (Germany), but I can't imagine that these things would be _so bad_ that living in fear of saying the wrong thing would be something worth considering.
Plus, and leaving that aside, I have my doubts that even if you did that, that that company would stay alive for very long.
Reality has the habit of eventually ripping this kind of unproductively delusional people (like e.g. a boss that flips if you don't say the right word with regards to the current hype) to shreds eventually.
The US has no social safety net. Healthcare comes from your employer. Everything is centered around having a job. Opinions on AI diverge significantly and someone’s response to this question would be pivotal to me in a hiring role. The market is not great for job seekers. The hiring manager can wait for someone who aligns with their company’s perspective on this.
No, if anything, I would say a very unfortunate trait of existence right now is that reality does NOT tend to punish corporations for being completely idiotic, at least not very fast at all.
Look at musk's companies. They will basically never (on any near timescale...) produce GAAP profitability and yet their IPO is in the trillions. To the point that S&P refusing to suspend their GAAP profitability requirements means the index will basically never see this company in it (which I'm quite pleased about).
The power of already-accumulated capital is simply more powerful than things like "don't be completely pants-on-head stupid about a recent fad" "don't seig-heil in front of the world stage" "there's no point in having people come to an office just to spend all day on zoom" etc etc etc.
The market can remain irrational longer than you can remain solvent, and companies can remain irrational longer than you can go without contributing to your 401k.
> 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
"Eventually" yes, but while this phrase may have been about the stock market, it also applies to management: "the market can remain irrational for longer than you can remain solvent".
One concept I only learned a few years ago was the difference between "mistake theory" and "conflict theory". Rational decision-making is the former, political decision-making, including office politics, is the latter. It's important to know which of these kinds of battle you're fighting, or you'll make the wrong type of move.
Also, FWIW, I live in Germany and my unemployment insurance has run out because the job market is just that bad right now; I'm fortunate that I have enough passive income to support myself, but even then if my partner's unemployment insurance ever runs out I can't support both of us.
"Built enough" may sound like the same thing as "giving out housing for almost free", but it isn't. Also, cheap temporary emergency housing for a sudden extra burst of refugees may be inappropriate for permanent occupancy, I believe this is an example, it's certainly containerised/temporary *something*: https://maps.app.goo.gl/HUyQS6Sc1w1Y8GUH6?g_st=ic
That said, an idea has come to mind: UK and Italy have had schemes where a home can be purchased for £1/€1 under the condition the new owner repairs them (the repairs would cost 20-150k). Given some of the really bad stock I've seen in some searches, this may be a good idea for Germany too.
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, ...)
The government that runs the benefits programs. They make you jump through hoops to get any benefits. The first hoop is even knowing that a program exists that you qualify for.
But then they also pay social workers to guide you through the steps of applying and receiving this support. Sometimes the biggest hurdle is to get to know what you don't know.
Keep in mind that not every old person who searches garbage bins is actually poor. Some of them just have dementia. I personally know such people in my home town.
This a very condescending and privileged comment. The job market is much different when you're just starting out, and it's especially brutal these days for new grads.
Maybe it’s condescending, but it’s valid. I have made sacrifices throughout my career to maintain maximum integrity, and the least I can do is be proud of it, since I don’t have riches or possessions to be proud of.
Yeah I rage quit my job 27 years ago and have been a struggling honest consultant ever since. Clients who want actual solutions to their problems come to me. Does that sound arrogant? Well I also have no savings and don’t own a house.
I don’t regret most of my choices, but I am aware that if somebody paid me enough money I would walk away from my principles. It would have to be a LOT of money.
You were replying to “The job market is much different when you're just starting out”. The past is not now, and you are not just starting out, so your comparison of their position and yours is invalid IMO.
> and will do it again.
Good for you for sticking to your guns, I'm about to do the same with a company that has all but said “dig into AI or get left behind”¹, but those starting out as freshly minted grads likely do not have the luxuries that we might have² and the jobs market is freakishly competitive for them right now³ in a way that I don't think it ever has been before.
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[1] time will tell if I leave of my own volition before getting kicked!
[2] experience (both actual experience and experience “talking the talk”) to help getting the next gig, a mortgage paid off so making ends meet is easier, etc.
[3] It had been heading that way for a while, the recent explosion of GenAI+agnetics has made it worse.
Lack of adequate calories and nutrition negatively compound. You lose the ability to focus, you increase your medical risk.
I experienced that in my childhood. It’s terrible. I did very poorly academically when I did not have access to food. It’s astonishing to me how fast my academic performance improved after consistently having access to food.
Saying you would rather put yourself at risk instead of hedge your answer on a minor interview question in order to increase your chances of getting a job offer seems like an issue with prioritization.
That's fucked up. If those are your values, that's all well and good, but you can't expect someone else to make the same decision.
Job interviews are a performance where you demonstrate you understand what professional expectations are and can abide by them. It's not dishonesty to not respond "I drink too much" when they ask "what's your biggest weakness?" just like it's not dishonesty to respond "can't complain" when someone asks "how are you today," even if you have a lot to complain about.
Once I interviewed someone and they described their tax fraud scheme to me. We didn't go with that candidate. Not per se because they committed tax fraud; because they demonstrated terrible judgment.
Having any kind of integrity is expensive, financially, emotionally and sometimes physically.
Software development is not that high-stakes of a job anyway. There is always another interview. I got another one soon enough, where the employee AI policy fully aligned with mine, so telling the truth was an easy, pleasant experience.
Imagine you are a pilot or doctor. Any kind of interview reply that doesn't fully align with your values now carries a real risk for human lives.
What I'm telling you is that it isn't an issue of integrity, and that only makes sense from a false premise - that the strictest, most blunt response is what is truest, what is being asked for, or a reflection of your alignment with the organization's values. That's really not the case. If I asked you how you were doing would you tell me about the traumas you're currently processing? Would you feel like it was a violation of your integrity if you didn't?
If that's what your values are, okay, I'm not going to tell you how to live, but it would be premised on a misunderstanding of what "hi, how are you today?" means.
I am not worried about what my pilot said in a job interview, I'm worried about what the check pilot thinks of their performance. Worrying about what they said in a job interview is like worrying about what they scored on the SAT. Once that hurdle is cleared, it instantly becomes irrelevant, because it was never measuring what we're actually interested in. It's a filter for people who are completely unqualified, it doesn't really measure a level of performance or alignment.
Culture begins at the front door, corporate or personal.
I would expect absolute sincerity from pilot or a doctor during the interview, including history of mental health and professional mistakes. Authority over lives of people must come with full transparency. If you are caught lying or misrepresenting your experience and skills, not only you would lose your job, you should be blacklisted from occupation as well.
In every skill, everyone benefits from honesty, both employers and employees. But I am aware this is a minority view.
Again, while I respect that that is your view of sincerity, I don't agree, and I'm not talking about lying, which is obviously inappropriate. Framing is not the same as lying (though taken to an extreme it becomes a lie of omission). There's also just a misunderstanding of what is being asked by questions like "what's your biggest weakness?" Language can be ambiguous, just because a very literal and extreme interpretation of a question exists doesn't mean that is what is being asked.
I don't think we're going to bridge this gap but I also don't think that's really necessary. People have different values and it is what it is.
What if the way you use AI isn't particularly important to you? Are you willing to sacrifice employment for a principle you wouldn't draw as a line in the sand?
Sometimes it's okay to say "I don't know" and it's okay to say "I don't care" and it's okay to say "It doesn't matter much to me".
Every interview is corpospeak where you infer the intended meaning of words anyway.
We live in an ecosystem where we (engineers/developers) can promote ourselves and display our skills/acumen/values/professionalism/responsibility in an unequivocal way. Regardless of your experience level.
I bootstrapped myself from poverty to Staff software engineer, past the age of 45.
Is that privileged? Or sheer will and force of effort?
Privilege, yes. You had the privilege to dedicate time to learning skills required, obtaining an education, probably bias during hiring processes, etc.
Even though your position might be the result of effort on your part, you do have to acknowledge that you’re privileged to be in a position to expend that effort on what you want, instead of something else, like finding fresh water daily, or whatever. It’s not sheer will that you were born in a (even marginally) more favorable environment than others.
The term “privilege” here doesn’t just mean a trust fund nepo baby.
How far could you reduce this down? Do you only clap for malnourished Ethiopian babies that can't find waterthat grow up into full silicon Valley software engineers?
You can be dismissive all you want, but the point is to acknowledge you don’t understand everyone’s situation and you can’t make sweeping generalizations like “I did it and I can judge you if you _didn’t_ do it”.
You can also acknowledge that most people abandon their principles long before they are anywhere close to starving and will not be harmed (physically) by being reminded of that.
To the people subscribing to "privilege theory", all positive differences are privilege and all negative differences are oppression, no matter if earned or self inflicted.
That's the logical conclusion you're forced to arrive at if you take these people seriously.
OK how about some real achievements in life, is raising kids the hard way? Career is but a small portion of QoL and overall achievements as human beings, basically all of us software devs these days live have very above-average incomes although most feel like they are deserved or even not enough. So studying from poverty to software is an achievement and big move, usually, but what specific position afterwards is not that important or impressive, its just a question of a) mental capacity, mostly genetic and b) effort put into work, while not elsewhere.
Ie I increased my salary, doing same job, all 100% perm position, roughly 30x compared to my first fulltime software dev job after university. Who cares? It doesn't mean anything, just an afterthought. I am father of 2 small kids, and trying my best to be a good father and role model, often succeeding, sometimes failing. Its by far the hardest effort of my life, it takes relentless 20-25 years and I see otherwise brilliant folks failing at this hard left and right.
Also I wish folks in IT were a bit more humble and considered other engineering careers, with +- same effort taking to get a degree, and much worse career progress/compensation/freedom to choose one's path. Arrogance is much more rare there.
Hacker news is full of people having given up, building torment nexii and coping/rationalizing _incredibly_ hard.
So while I agree that privilege is certainly a factor, so is what I've just said.
A lot of people here live very cushy lives that cushion them from very pointy thoughts and questions.
As someone who too has to live in this world, I'd rather they didn't.
When I was starting out in my career it was "take the first job offer that comes along or starve/become homeless" so no, sometimes the personal cost would be unreasonably high to expect of anyone
This effectively does mean that I was not a moral actor at the time
Highly paid enginners hiding behind "I have no choice I would be hungry" are usually just lying to themselves.
And you dont even get these nearly as often from people who work in lower paid positions. Or who are actually making moral tradeoffs that affects their income.
I have seen engineers take paycut or risk it because of this or that moral conviction. Not wanting to lie to customer, refusing job for gambling company, working one day less per week so that he volunteers for biblical something.
Just telling management no or just communicating about your work with ai or lack of it are not even one of those.
I believe that I must be truthful because of my faith, though I understand people feeling pressure otherwise. I have had to quit places that I found lying to part of the employees before.
It is very sad to me that people do feel that pressure, and how the current job market is.
On topic with the article, I would love to be able to trust AI with more, but have found that I have some useful moments with it, but more because of Internet search not being how it used to be for quality.
I think it depends. The people that I know that have made significant sacrifices to live along their morals are usually people who 1) are intensely bitter when others will not sacrifice as much as them; 2) are completely understanding of people who will not sacrifice as much as them or acknowledge that they simply have less to sacrifice than others. For example someone who is willing to live the "dirtbag" lifestyle out of their car to dedicate to their outdoorsman activity who is either bitter others have the relative financial security or feel immensely grateful they have consistently good enough health that allows them to be outdoors with so little resources.
For example I think the decision to stick to certain morals is very hard if someone has a disabled dependent, are disabled themselves, or require consistent access to healthcare. There are different lines for different people of course. Our ire shouldn't go towards individuals who make these decisions but the people in power who force others to be in a position where these decisions need to be made.
This isn't a value item for most people. Employer doesn't want ai used great handcoding or employer wants ai used great prompt coding.
My truth is I don't care either way . I get the sense that's the same for parent poster. They just want a job and to say the right thing to get past the hiring filter. Even if I did have a truth its not something I would put above being remote, pay and how a company develops software. I'd rather not have a truth and not have a daily standup.
It’s also possible to not really hold that strong an opinion on things. Not everything is a pitched battle where doing what your employer wants means you’ve sacrificed your integrity.
From far away, it's hard to tell the difference between integrity and anti sociality. Even though I believe "software engineering integrity" exists, you can see how it's hard to tell that apart from "software engineers who stir dramas or are annoying to work with."
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.
To give you just a little more context than other commenters -
You answer truthfully when you're interviewing from a position of power. Either you're already employed somewhere and you're taking your time exploring your options to see if maybe you can end up somewhere a little better, or you're an employer with applicants lined out the door and you want to winnow them down to the best match. In either case, you don't care too deeply if an individual interview sucks, you just move on.
Truth is always the first casualty of war. And when someone is out of work and fighting for their ~life~ livelihood, or a founder is trying to convince the first customer or the first engineer to take a risk on them so that they can get their baby off the ground, the truth dies real quickly.
> Have you considered just answering truthfully? ... That sounds not like a job but a toxic relationship.
It's a job, not a relationship. It's best not to confuse the two.
In any workplace, you will occasionally have to do things you find boring or objectionable. And if you're hoping to find a corporation that is a "perfect match", it will only hurt more when they unceremoniously fire you because the quarterly revenue growth is 1% off or because you cracked an off-color joke.
> It's a job, not a relationship. It's best not to confuse the two.
A relationship is defined as two parties that interact.
It's not friends, it's not romantic, and it's definitely not family, but a job _is_ a relationship.
That said, GP is absolutely correct that you can fall into toxic relationships with your employer. Especially in the US where, realistically, we're forced to rely on our employer for too many things (e.g. healthcare coverage), employers can and do take advantage of the situation.
You're being pedantic. By your standard, I also have a relationship with the DMV, and to avoid a "toxic relationship" (parent's language), I should be honest with them about all the times I rolled past a stop sign.
I agree with their "two parties interacting is a relationship". Hell, I have a "relationship" at the checkout as they scan my groceries. But it's not deep or long lasting, so as you'd expect either party makes near zero effort.
Secondly, they never said that every relationship requires perfect honesty in all things. I'm not volunteering to my checkout person that I will eat that party bag size chips in one sitting. They don't need to know and it's highly likely they don't care. I'll be gone out of their life in a minute.
However, your entire relationship with your employer is roughly based on "benefits for work". It is a recurring interaction, probably over a significant length of time. So it seems reasonable to be honest about how you work to avoid making the relationship toxic from the start.
But then I also agree with "Do whatever it takes to survive." and if toxic relationships mean you keep breathing, well it sucks but there it is.
I am fortunate to not be in that "survival" position so honesty works for me.
Define objectionable? Not ethical is not illegal but maybe if you are okay with it, do it for yourself. Illegal is just dumb, you are still responsible. So at least, if you are doing, make sure you are appropriately compensated.
> 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 don't think having trouble knowing how to tailor your message to your audience because of limited information implies it isn't truthful. Answers to jobb interview questions are usually very manicured and rehearsed but I don't think they're generally lies.
To this point, there are many advantages to answering truthfully. The job might be tailored (a little) to your expertise. Maybe the heavy AI tickets will get de-prioritized from your queue if you say you have light experience. If you say you're all in on AI, you just might find yourself becoming an AI expert out of necessity. Not because that's what the company wanted out of filling the position initially, but because that's what they got. It's expensive to have an open seat. The interviewing door swings both ways. You're looking for a fit just as they're looking for a fit. Going in every day pretending to be "a fraud" (or however else you might start to internalize it) eventually starts to weigh on the body, soul, and mind.
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.
Even a truthful answer can require a lot of long-winded disclaimers because an interview is a new relationship without shared context. You have to state the obvious because nothing can be taken for granted.
Ha. Interviewing is about answering questions in the way your interviewer wants them to be answered. It is a game of trying to "read minds" by figuring out the most common/best possible interview answer for any given question even if it doesnt reflect your own personal opinion.
People will say this is bad but then when they interview folks judge the interviewee if they dont answer questions how they expected the question to be answered.
This isn’t an AI problem. You can’t ask anyone to “be truthful” on any subject because everyone sees through their own world perspective.
Two people might say “they love camping!!”
But does it mean…
- Going camping twice/year and partying by the river?
- Or going 20 times per year, sometimes on 4 week long trips?
Both types of people will, with complete honesty, tell you “they love camping” and only you, the asker of the question, can decode what that means. ayli can’t
Interviews work both ways: employers select employees, employees select employers.
Unfortunately often employees forget that part of the goals of an interview. An employer never forgets its goal.
Of course the asymmetry stems principally from who pais whom and secondarily from who can wait more to find a better fit. Sometimes that's the employee.
If one is not desperate to get a salary immediately, it helps thinking that the company applied for an interview and agreed to let the interviewer come and inspect their premises, their personnel and their processes. The right questions derive from that realization. By the way, that's what auditors do.
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.
It sounds like you're an AI-happy employer though. What if their truthful answer was that they never tried to use LLMs and refuse to because they waste water or because of an overconfident view of their own skills, or they don't want to help a clanker steal their job? These are all popular beliefs that can easily come from following the right crowd rather than forming their own opinion. In fact, from what I usually hear of people's opinions, they almost never come up with them themselves, you can practically predict people's opinions on some topics just from what they look like (what social group they belong to) or what other unrelated opinions they've already told you.
I guess that's the point though? If their opinion only comes from common popular opinions, then that signal is quite useless to me.
In this case though, I'll admit that it would be a negative signal if they never tried it even once and refuse to do so. You can't make a solid opinion on things you never try after all. It would be different if they at least gave it a shot and disliked it.
> In this case though, I'll admit that it would be a negative signal if they never tried it even once and refuse to do so
I was arguing that this is a bad question elsewhere but you provided another reason. If a candidate tells you they haven't tried using AI (without saying why), that offers no signal at how well they'll do if you hire them, and you construe it as a negative signal. If you want to know if they would be willing to use AI as part of the job ask that question instead!
> You can't make a solid opinion on things you never try after all.
In this case, I wouldn't mind if they used AI or not. Honestly it'd be quite interesting to find someone who doesn't use it to hear about why. At the end of the day, I'm more concerned about how effective they'd be in their role. And if I have 2 candidate, one with better domain knowledge but no AI, and another with AI but no domain knowledge, I'd most likely pick the one with domain knowledge.
And if they can make an argument about why they're more effective without AI, more power to them. So if I were to ask the question directly as you suggest, it would be: Are you more effective than the other person I'm interviewing? - which i guess is the whole point of the interview.
> Heroin
Fair point, but I also don't think that trying AI is anywhere close to the level of trying Heroin
Yes. Hedging results in a middle-of-the-road answer that, at best, comes across as lukewarm. Companies want to hire people they're excited about and are convinced fit into their culture. An honest answer will get you more strong noes but also more strong yeses, and strong yeses turn into offers. Hedging, produces only weak yeses and noes, which tend to end in no offers especially in tighter job markets like the one we're in.
While this industry surely is frustrating and full of pitiful fraudsters, I don't think that what you're saying is fair or leading us anywhere.
Most of our stuff in this world actually does work, and the reason why it does is that skilled (teams of) people that care have built it.
Meaning that these people can be found in many _many_ places.
The skilled interviewer is rare. But if truly skilled they understand why people hedge and would not consider that dishonesty but a skillset the company might need. A semi-skilled interview might pick up on that and assume the worst.
Very few jobs are looking for opinioned most are looking at people who might fit in unless you are hiring to distory from without.
We all filter and “nudge” the truth during interviews. We all cater our responses to the person in front of us. Let’s not pretend otherwise. Your interviewers sure aren’t.
The two job interviews I had recently - the interviewers were clearly AI maximalists. My answer each time had been to the effect of "yeah I use it but I make sure to check it over thoroughly since it can make mistakes" and I'm guessing that wasn't the answer they were looking for.
It's a gamble of the dice as to whether the engineering manager is equally realistic about LLMs or has unrealistic expectations about what LLMs can or can't do.
Because almost every HR department now has a directive to only let people through the screening process who say they are using "fully agentic workflows" even though that's moronic.
> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products.
I'm an old hat on both sides of this type of discussion from a post-grad view.
Recommendation: use it to own the conversation and to signal mutual fit. Yes, your idea of AI lover versus hesitant matters. I recommend reframing the question to pivot to your fit to the org (and org fit to you) question. Show/concisely explain how you consider whether LLMs are fit to a task and how to tell it improves outcomes.
An outcome focus and willingness to show thought process around a common use case will be a substantially strong response.
Just in general these questions are probes on curiosity and ability to show depth too. I’m astounded by suggestions of stating flat out refusal to even try out LLMs or suggestions to over praise the merits as if the interviewers want to hear binary answers. A well thought out pros and cons story wins over binary yes/no answers at pro and anti ai companies alike.
> A well thought out pros and cons story wins over binary yes/no answers at pro and anti ai companies alike.
The issue with this is, you need to know how to really program to be able to articulate the pros and cons, which a new grad would mostly likely not have.
For example, if you want to include how AI can onboard quickly, you really need to understand the pain points like, I tried asking people but really, everybody is busy. Or I've found coding agents help me speed up making code changes, but it some situations, they can help accelerate making mistakes.
I think the issue that a lot new grad are faced with is, you don't know, what you don't know.
still 10x better than the 'finish this leetcode tweak algorithm in 20 minutes and tell me your thought process along the way, and yes you will never need that skill in the real job but we need find out who had time to cram for the algorithm books in the last few months'
I've unfortunately been doing alot of interviews. Leet code is common. Or "practical" which seems to more or less mean implement some kind of kv store class with some trick constraint.
I think what would be great is to have eg a concise example where it works well for you and a concise example where it doesn’t. This shows you have explored it and thought about it enough to explain interesting observations. It’s good to then be ready to go deeper if of interest.
I think you will find that both AI-pilled as well as AI-sceptics will actually agree on that approach, because it rejects the question of "how much" and rather reflects it towards the "when to use AI".
Depending on who it is, you can then talk more about the beginning, end or center piece of it.
Replying as a person who acted a hiring manager for decades: their loss.
Hiring for technical roles, I love long-winded answers as long as they are coherent. I don't want slogans, I want to understand how you reason through problems and that I can trust your judgment. Everything else is secondary.
Note that this doesn't mean "rambling". Get to the point. But if you want to show me nuance / reasoning, I want to hear it. Also a good way to spot bullshitters.
> 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.
I've not observed that anywhere. It takes a couple of hours to figure out how to use agents. It might be a slight negative if I suspect you're not demonstrating enough curiosity about what's one of the most significant developments in tech in a long time, but the assumption is easily overcome if you geek out about something else.
True, but please don't give job seekers false hope with this statement. I commonly see 60 - 180 applicants for one open position. Good luck finding a hiring manager who wants to take a bet instead of going with proven experience.
I think upskilling is the right move in this environment and it is dead simple: Invest a couple of days to show initiative, learn agents yourself and be able to speak from true experience.
I love that we’re already talking about “proven experience” for a technology that’s essentially 15 months old, arguably only broke into the mainstream 3-6 months ago, has an unclear RoI for many companies, and seems to be changing quickly in both cost and “best practices.”
You’re more or less admitting that you’re playing trendy tech lottery. Which is fine, but maybe not generalizable to the whole industry.
15 months, 15 months ago, is not the same 15 months now. You'd be ignorant to think this a trend that will just fade. If we look at that has happened the last 15 months, it'll keep getting bigger and better. Hopefully not more expensive though.
I don't know about that, and I am 100% biased so take what I say with a grain of salt. My position is very much this: you may not trust coding agents to make code changes, but if you're not willing
to treat them as a research aid or have them work for you, you're pretty much saying they can't help you work more efficiently.
It's a fork of BurntSushi/ripgrep. What I hope to show with it is that you don't have to use coding agents to code. They can be used to surface knowledge that's buried in documents, issue comments,
PR discussions, and other places.
Believing coding agents are trendy would be like saying search was trendy in 1998. They're not going to change the world the way Anthropic wants us to believe, but they will shape how humans develop software. And I think for the better, since AI is capable of processing information at scale to help you move forward.
Regardless of how you think about LLMs (I do find them useful), there’s something really odd to think that you can select for “proven experience” in a young technology where current experience appears to have little to do with experience 15 months ago, and where the biggest boosters fully claim it will have nothing to do with experience in 15 months.
What you’re selecting for is enthusiasm, knowing the current shibboleths of the in-group, and possibly for who knows how to use them to make a good demo.
And, fair enough, if that's what you want. But it's not "proven experience" in my mind.
I said "with" and not "at". I was tasked with doing interviews and reshaping the interview process as multiple companies within the last 18 months through fractional roles, consulting and co-founding.
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.
This is also describing an interview scenario where the interviewee is trying to throw everything at the wall hoping to stick. Sure there is discretion in how much to elaborate, but it is a performative act, where someone is trying to demonstrate that they have deep knowledge about a topic and can appreciate nuance that not everything is black and white.
Not OP also but it typically signals that you're not confident with your answers. If I am actually curious about it, I'd ask a followup question for them to expand.
Often the hiring manager will have the person to be hired somewhere in his report chain. So if a person can't effectively communicate and can't properly respond to a "I only have 2 minutes, shoot", then I am getting a future liability into the company that will slow down all future communications.
I much rather prefer someone who needs 3 seconds to triage a question and tell me: "This is X, I know this, here is the solution" or "This is Y, I don't know it, but I will get back to you within 24h".
I do absolutely not want a "Well let's think jointly about this for a couple of minutes". There is no jointly with your boss. Let's do a some math of a 1:12 manager to direct report ratio. That means for every hour you have, your boss only has 5 minutes. And if you talk to your boss' boss, they have 25 seconds for every of your hours.
I absolutely do want to work with people who want to think jointly about interesting questions for a couple of minutes. Give me your long-winded (thoughtful!) answers. Let me see how you think. Let me see how well I (and others) can think through things with you. That's what the point of an interview is, IMO. And I've been gainfully employed in tech for 15 years now with that attitude, often in environments with other like-minded folks, often involved in the hiring decisions that have led me to work with those other like-minded folks.
So in the same interest of helping post-grad job seekers, do what you've gotta do to get yourself paid, but maybe don't presume that vibe_that_works speaks for every hiring manager.
That does sound like a bad org tho, sorry to say that.
Not to disagree of course that time is limited, but in my experience, optimizing it this harshly leads to poor results, because eventually, you just get leapfrogged by reality.
Hyper-optimized systems are brittle and can't really adapt to the market changing.
But yeah, I guess they still need developers. Just doesn't sound like a fun job :D
Just trying to fix the misunderstanding: I am not saying that you will have a literal 25 seconds meeting with your boss's boss. I am just making a math argument taking typical orgchart ratios.
So let me take this a step further. You want to meet your boss' boss for 10 minutes to present them something. 10 minutes of his time are an equivalent of more than 20 hours of your time. So if your initial idea was to "take maybe 1-2h" to prepare for this -> You are underprepared by at least one order of magnitude.
This is a very strange mindset. Even if you want to treat everything as sort of billable hours this doesn't really make sense because the average boss's boss's isn't paid anywhere near 144x. If a SWE spends 100 hours to save their boss's boss one hour, they're wasting a ton of money.
I mean I am no expert, but to me it sounds like the org you're describing seems to lean away from the "engineering" side of things and into the "org for the sake of org".
Which might not be ideal, because "orging for the sake of org" to my understanding consumes significant resources not going into building products/marketshare/shareholder value.
But then again, I'm no hiring manager in such a structure, so this is probably just an uninformed take.
> I do absolutely not want a "Well let's think jointly about this for a couple of minutes".
But why?
Most of my most fulfilling experiences in tech have come out sitting down and hashing out a problem with someone else (including with managers/leaders).
It sounds like a miserable org if I am not expected/allowed to have an actual back and forth conversation with my boss. If I'm employed to be on a team working on an aligned common goal, why would I not use that collective skill and experience to my fullest advantage?
You're describing a coding sweatshop. What is the point of any discussion at all then? If the "boss" can't carve out enough time, that's their own problem. Letting that stress propagate to the team is plain bad leadership.
I know you might think some of these candidates don't have other much better choices to find work, but they absolutely do.
The former. I would not expect any candidate - especially a new grad - to have actually used GasTown style setups unless they were coming from another company with money to burn on those sorts of multiple-agent scenarios. I barely run more than one or two Claude sessions at once because it slams into the usage limit so fast.
I could steer the conversation to explore if the candidate had insight into why GasTown or other "swarm" models exist though. Do they know what a system prompt is? How context is managed/affects output? How harnesses can delegate to simpler models, and when to use different classes of model? I wouldn't expect anything particularly deep here, but bonus points for a solid understanding/experience of the tooling and how to get the most out of token budgets is valuable.
To the parent point: even if you refuse to use LLMs for whatever reason, I would expect any coding applicant to have at least tried them. I'd assume the person is living under a rock if they said no.
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.
4 years into job hunting. Answering truthfully does not work. Nobody likes the truth, and every bit of advise i get from anyone is to lie (though, some of them use euphemisms to avoid saying "lie").
This might not describe you, but I've met quite a few people who made similar claims. The problem usually wasn't that their counterparty didn't want to hear the truth. More commonly, the problem was that these persons assumed (and were convinced) that they knew the truth. Truth is rarely absolute and someone claiming to know it is a red flag in my book. Double so if multiple persons indicated their disagreement. Again, not knowing the exact question and answer it is impossible to say if you are an exception, but even if you are, you need to improve communication skills - what good is knowing "the truth" if others reject it?
That said, best of luck on the job hunt! Sometimes it just takes some time for the right opportunity to come along.
Truth is easy when the question is about your own involvement in something. You just don't lie and don't intentionally deceive. This isn't about technical arguments, or moral quandary, or ambiguous situations, it's about marketing yourself in a world where you are expected to lie about your accomplishments to look better. Citing metrics you know are not accurate, but make you look good. Overselling your own involvement in a project. Up-selling the impact of a project by not disclosing the parts you know would undermine your perceived competence.
I would hope this is true both in the context of LLMs and more broadly, but I think this is especially not the case for LLMs. It's hard to take the idea that companies are trying to hire people with reservations about LLMs seriously when many companies have LLM use mandates. It is counterproductive in the eyes of the employer to hire employees that will be combative on LLM from day one.
> re-factoring a big repo of decades old fortran+C cod
Having been in academia in the past and now in software I can say with a lot of certainty that this will take a lot more upfront work than otherwise.
Academic code does not have a lot of structure. And usually lacks a lot in terms of tests. While AI is best when it can mimic patterns as well as there are tests to target.
So you will probably need to budget a few weeks to establish good patters, docs as well as testing patterns before you can seriously make it really do what you want it to do.
exactly yeah it was a code base written by atmospheric physicists I assume and I had an idea that maybe copilot could get it working to interface with some more modern software and it just didn't really have what it takes.
Even with 3 weeks I'm just not the Fortran/C programmer to get that job done so I moved on to other things.
I am also on the job market, but as a Senior. Pro-tip: ask them this question before they ask you. “One quick question I have about the company culture, …”
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.
Cards on the table, after posting I had doubts about this and had to look it up! Executive summary: "digger" = hole, "bulldozer" = flattish, "steamroller" (or just "roller") = flat as a pancake.
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'd take a candidate who fights for a view I disagree with over one who hedges to please everyone (Strong opinions weakly held, but willing to change their mind if/when the data tells a different story)
You should just be honest. If you're not a good fit for the company then you should honestly be eager to discover this.
> I've been responding with a sort of long winded answer
"I don't. I personally don't find value in them for the type of work I do. I am also uncomfortable with using their outputs under the current copyright regime. I also question how competitive any organization can possibly be if LLMs become the main driver of their work products."
> I've had more bad results than good the few times I've tried them
"I prefer to write correct code rather than debug bad code generated from a limited context window."
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 know of a few shops (names you've heard of!) who are trying to transition to AI-only coding (i.e where engineers must vibe code...) - despite already having a few production bugs with their current approach.
I've personally started to dislike agentic coding, not because of the LLM side of things, but rather, because the non-LLM workflow itself is stupid.
The UI/UX is built around one-shotting the code as a single fully baked unit. There should be intense competition around having the best code review tools to adjust the fine details and yet everything is devolving into a text prompt, even CAD tools where being off by a millimeter or less can make a part completely useless even if it is 99% of the way there.
All of these tools are assuming that the LLM already achieved superhuman AGI capable of reading your mind while making zero mistakes.
> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer
That this doesn't have a clear and obvious answer one can expect shows how the issue is politics, not strategy.
When you apply as a mechanic, there is no such weird political debates about certain power tools where people have passionate opinions on which tool to use.
The reason you've had more bad results than good is because you haven't fully learned how to use LLMs yet. They are not as simple as they first appear. I think a lot of people think using a coding agent is just a case of firing it up and telling it what to do and expecting to get it right first time. When it doesn't they just think it's no good and like you abandon the effort.
The reason a technical interviewer will be asking this question is because they want to see how you adapt to using new technologies, LLMs being one of the most disruptive technology that has hit the tech industry since at least the internet. You will likely be expected to use LLMs and they will want to know that you are someone who truly understands the capabilities of them - upsides and downsides, where to use them, what guardrails you need to put in place.
I'd encourage you to revisit the re-factoring task you worked on. Work out why it didn't work, work out what didn't work about it and if you have the chance try again, but use different techniques, there's a lot of conversations going on about what people find working and not working - try to join that conversation. Try to document what you learn. Then in the interview discuss these rather than just saying you gave up. The interviewer isn't going to check up on how successful your project was, they just want to know how you think and how you approach problems.
> AI has gotten so good that despite any misgivings, “everyone is using A.I.”
In my experience, it's a mixed bag. I wrote this comment[0], yesterday. It reflects my current work, and how I am integrating an LLM.
I have used it for two parts of my project:
1) The backend (PHP), and
2) The frontend (Swift)
It has been a huge help, in both, but #2 is a cautionary tale. It really needs adult supervision, in developing native UIKit Swift apps. I'm realizing how truly bad the code it wrote was. I mean, terrible.
That's jarring, because it did a great job with #1. It made sound, reasonable design decisions, and provided code that is better than what I would write.
With #2, it behaved exactly like an inexperienced engineer, panicking, when confronted with real-world problems. My rewrite is going to feature a much simpler, sound approach.
All that said, it has been a net positive, and has increased my productivity by a large margin.
I guess the lesson I needed to get from this, is that it is good at helping me to find problems, but maybe not so good at fixing them.
I'd like to add that there is almost no way of "running away" from it.
If I search for anything on the internet I am almost guaranteed to be handed pages and pages of AI generated content.
In lieu of that I found that directly prompting for an answer tends to yield better results nowadays. Not because it's good per-se, but because having control over the prompt beats having little to no control over it though search by proxy.
It saddens me to see that high quality content is drowned in this sea of garbage to the point of being almost impossible to find.
I think this is where the circle closes with the "dead internet theory"... you go to Reddit, and see bots commenting on posts created by bots.
Then you go on to search for something, and find only results that are clearly AI generated pages and come to the conclusion that directly prompting some LLM is better than reading an AI slop page that's output by the same AI for slightly less specific prompt.
My concern is that this will only get worse over time - which is great for companies selling AI tokens and bad for society and whoever wants to interact with other humans over the internet.
This would be expected. The corner cases people faced with PHP throughout the decades have been well documented on the internet for eons.
Swift, not so much. It's relatively new. Looking at AI's abilities like an engineer's career span scaled about 10-20x of time makes it make a bit more sense.
It's going to be worse at newer/niche things, intuitively - which is only going to get worse as it "learns" from garbage outputted by other LLMs moving forward.
No doubt in my mind, a future Apple model will be the best to use for this purpose. They likely have more swift to train on than anyone else, and would benefit directly from more quality apps, rather than the slop flowing into the App Store (>1k app submissions per hour; they claim)
That's just one way to use LLMs though. Recently on a flight I could not figure out how to connect my wife's earphones (i.e. put them in pairing mode) to my macbook since I was used to the old Airpods Pro case. So I asked Gemma4 26B A4B (offline, LM Studio) and was told to use the 'two tap on front of case' gesture, which worked. This situation would have been significantly more frustrating without (local) LLMs. I'm essentially carrying around a basic "how to" on everything, inaccurate though it may be, it's better than nothing.
There's probably a lot more PHP code to regurgitate, while I'm guessing a lot of Swift apps are proprietary. That's the problem.. they aren't coding from principles, they're pattern matching their training data
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 wonder if it's expertise gives you ability to see flaws and push the LLM past its acceptable point.
I haven't really used LLMs much for coding (sabbatical before LLMs got good at coding, now looking for work) but I found with chats that they are great at exploring well trodden territory but as soon as you go a little bit off the beaten path they flail horribly
They both do acceptably (but PHP better), as long as I don't push hard. The Swift that I get is ... meh, usually.
However, my PHP server, by design, is extremely conservative. It's meant to run on cheap shared hosting. I don't push the edges. The LLM seems to do a great job of respecting that, while still giving me good, modern, code.
The swift, on the other hand, has highly optimized UI (which also means that I'm not using SwiftUI). It shits the bed, when I push it.
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.
My experience was different. I found it extremely good at fronting technology like react while I had to hand hold it for the backend tasks. Even with fable it was the same.
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.
As a Claude Code user, you almost certainly will. Even using the same OpenAI model with an agent like Codex will likely perform better. An agent can test and iterate on its own solution until it meets the defined success metrics. If you’re working within an existing code base, having preexisting code to demonstrate similar implementations also significantly improves the quality of results, and that’s something an agent can dig into as part of its solution process. I haven’t used ChatGPT in a while, so maybe it’s more sophisticated than it used to be, but I think you’ll see much better results with the latest agent tools.
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.
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.
I've noticed several companies replacing deterministic systems in their support flows with a LLM version that is slower and worse. Many interfaces simply aren't better with AI added
The real best case scenario is using LLMs to help build deterministic systems. Instead of asking an LLM to do some task that you know will be repeated, instead ask the LLM to build a program (Python script or whatever) to do the task.
If it's a one-off script/program that doesn't require additional "domain knowledge", sure. But what if you need to give as context your whole backend repository because you need to take into account a few business rules? Why give anthropic/openai access to my "secret sauce" (e.g., company private repos)?
In that case, it's way better to simply write the code yourself.
From all possible concerns, "giving access to anthropic/openai" to your "secret sauce" is the least important one for 99% of the companies out there.
No, is not way better to simply write the code yourself. Most of the code is written faster and better with Claude Code or equivalent. Very niche code is better written by hand. Even then, you're probably better off nudging something like Claude Code in the direction you need it to go. There's nothing interesting about writing it yourself unless you're still learning to code (in which case is a learning exercise for you, not only about the outcome).
Your sauce is not worth stealing to OpenAI. They have bigger fish to fry
In fact - had there ever been a single case in the history of the internet where a service provider of any kind - hosting, storage, email, search, anything - has ever stolen intellectual property or trade secrets from a user and benefitted from it? Has that literally ever happened?
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.
I think it just depends on the workflow. A lot of times (probably the majority of the time) a well crafted deterministic process is what people want.
Sometimes you can have a multistep deterministic workflow with a decision that needs made somewhere in the middle, which is where an LLM that can call tools is useful.
I think there are much less repeated tasks where you would want a fully agentic process (but there are many ad hoc tasks where that is exactly what you want).
>Making systems fully deterministic ignores the entire purpose of having agents involved.
That sounds backwards to me. I hope that most places don't see "having agents involved" as the ultimate purpose, but will use agents where it makes sense, i.e. when deterministic systems fall short.
At some level everything an agent does is through a "programmatic interface" (tool calls).
Some people might use skill-based scripts, MCPs, or some kind of raw access to a database. My point is that well designed CLIs are the optimal programmatic interface, for many reasons.
At Big Tech Company I Work At the LLM is quite happy to make raw API calls. If it thinks the data is big, then it'll write a Python tool to do it.
The reason crafted backing CLIs are useful is you can guide the LLM towards stuff that is immediately useful rather than hoping the nondetermism can separate the wheat from the chaff.
Take CI: is it interesting to know which tests passed? Maybe, but probably not. What is really interesting is what failed. Instead of having the LLM go out and talk directly to the CI system, write an intermediate CLI that filters out less actionable stuff by default, and have a flag that'll deliver the full dump if necessary.
It's a skill to do this stuff, and it's a lot of hard won experience than something I think is easily teachable. You kind of have to feel out your model and how it "thinks" about solving problems.
And then a new model version comes out and you have to learn it all again!
I've already commented on other posts that having LLMs build deterministic and testable tools is the real unlock.
Even for things like customer service, a LLM that analyzes customer support transcripts and then updates your call tree to better route people is a huge win.
The best case scenario of LLM is transforming input into output where both are languages and accuracy doesn't matter, e.g. "rewrite this poem in pirate speech."
Because typing “code” takes time and significant amounts of it.
We are slowly waking up to the fact, which was always true, that “coding” is just a fanciful preparatory task in order to appease the spirits properly so that we may invoke the spirit of what we are actually after: a live, running process that does useful things. Code is completely useless when separated from that fact.
Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding. Knowing when it does and when it does not have this property is a skill of its own.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
I believe this is the general belief about basically every human skill, that if you stop doing the technical fundamentals you get worse at understanding the activity. The question is whether coding is like sailing a square-rigged wooden ship, which became completely useless knowledge after the invention of the steam engine, or if it's like playing an instrument, which while technically unnecessary after the advent of MIDI and other tools, absolutely hurts your ability to arrange, compose and perform if the skill is neglected.
For my money: I think the AI scenario is more like the latter, but "humans are worse at coding" isn't the consequence I see coming. I worry that in ten years we will be awash in software that's impossible to understand. I don't think that's happened in any human industry ever. Someone has always understood how the machines are built, even if they're very remote from the users of the machine.
The sci-fi novel A Fire in the Deep starts with describing a Software Archeologist, who digs through millennia of strata of layers of indirection and I think we could end up needing that one day.
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.
> 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.
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.
I'd put it even lower than that, since there's also the "understand the problem space" portion outside of the external processes and before writing the code.
Yeah, so again the point of text editors is you shouldn't be typing all that much with them. That's why we use them. Macros. Shortcuts. Metaprogramming. Snippets. Identifier completion.
Were people actually typing out the full text of source code before LLMs? Why?
> 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.
This is such a delusional take it's borderline trolling. Code is an expression tool to precisely describe a process that does useful thing. Typing prompts is not too different from writing some very vague code, which is arguably a waste of time by itself.
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.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
I would argue that this is nearly always the case. I don't think people really understand programs that they've only read at more than a very superficial level. This is why I tend to make (temporary) small changes, printlns, etc. when exploring a new code base: it aids greatly in understanding how a program actually works.
And it's even worse (in my experience) with LLM generated code, as it tends not to result in particularly understandable code. It is a lot like LLM generated prose: it often looks entirely reasonable at a surface level, but has a of weirdness/incorrectness hidden beneath the surface. But that surface level makes it very hard to avoid glossing over the details when reviewing the code. For this reason, I personally find it's much more effort to carefully review code than it is to write it.
Humans make mistakes all the time, but their code tends to naturally be structured for human understanding (to some degree based on skill/experience) because they themselves needed to understand it to write it.
I think LLMs are very useful tools, but after quite a lot of experience using them, I think it's generally better to use them as a sounding board, or to help you get unstuck or remove points of friction. Using them to write all of your code (at least for me) seems like a net negative.
I also think it's extremely easy to overestimate how much time they save. It feels like they're a productivity boost because it takes less intense focus to implement something. But I've experienced several instances where actually writing the code myself would have been both quicker and have resulted in better code.
All that being said, it can also be really hard to not write all of your code with agents once you get used to it. There's also a kind of slot-machine-like effect where you write a prompt, excited for the result, and when it doesn't quite come out right, you think "ah just one more prompt and it'll be good." It's hard to see when you're actually doing it though.
It's also weird to me how much people think typing is what the LLM is replacing. Typing was never the hard part. It's the translation of the high-level idea into an unambiguous process that's hard. That's also the valuable part, that requires thinking through the edge cases and consequences of decisions, and that just gets glossed over when using an LLM unless you rigorously review what the LLM has done.
At the end of the day there's a real tradeoff to be made, and it's worth being conscious of what's being given up.
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?
Libraries like Guidance guarantee that the output of an LLM will be syntactically correct (i.e. it will be valid JSON or whatever output format you are wanting). They do not, and fundamentally cannot guarantee that the data contained in them is actually correct, and cannot make the actual behavior of the LLM "well-specified". Or, as you put it, trumps voice wrong.
Because it’s rarely so black and white. Knowing the inputs and outputs is merely the first steps, you need to think about the transitions too as they have their own costs.
Those costs don’t disappear and it’s truly naive to think they don’t matter. Take security issues, they may arise because what you thinks was the input is merely a subset of the true input range. And the extra possibilities lead to unforeseen behavior.
A lot of programming is about ensuring that the input and the output are the sets defined in the specs. And the rest is that the transition/relation is the right tradeoffs of performance, correctness, and costs.
I am seeing similar things in just regular tooling and development. Things that can be solved deterministically or what would have been a simple CLI 5 years ago are now an LLM integration.
Instead of using the LLM to create deterministic tools, we are using LLMs to replace them. It's completely backwards and I don't know why people (especially high ranking people in my company at least) seem to think that this is the way forward. No, I don't want a whole CI pipeline that is just LLM prompts. Yes it's very easy, but it's expensive, slow and prone to failure in ways you can't even predict.
Same things like using LLMs for the code review process. What would have been a simple linting rule is now a pass with an LLM rather than using the LLM to create the linting rule, which it is absolutely excellent at creating.
My management is pushing for us to come up with ideas on where we can use LLMs in our product. The whole team has been very resistant for this exact reason. Anything we can think of will only make things worse, and we’ve already been told anything above a 1-2% failure rate is unacceptable. If anything we need more structure and standards to hit that, not less.
I believe that llm’s can be used to re-imagine experiences but it’s definitely not the way people think. The constraint is imagination and thinking about complex trade offs more than anything else. Which is the essence of innovation.
The agent paradigm will eventually give way to experiences that are a hybrid of deterministic and non deterministic and you won’t even know the llm was involved or visible.
Luckily for programmatic or logic following, smaller models tend to do better, it can be surprising at first to see the more expensive models do worse at a task but it’s true.
> replacing deterministic systems in their support flows
The issue is, they don't want to provide "better" support but "cheaper" support. Imagine a trained agent that understands the big picture. Now imagine a company investing in humans to use AI to retrieve knowledge that the human can easily identify as being relevant or not, and using that knowledge to better aid the customer.
Right now AI is being sold as a "we don't need support personells" instead of "how can we provide better service." For a lot of products, better service will probably not matter as "cheaper" products will win most of the time.
Most people don't want to pay for better. They want to pay the same for something better, which is what companies are not investing their time in figuring out how to use AI properly for I think.
A lot of people want to pay for better, but that is hard. Better is more expensive, most of the time, but being more expensive is no guarantee for being better. It feels like the correlation is very weak. Most expensive products are just expensive, not good.
If there was a reliable way to identify the "better" thing, I and a lot of other people would go for that every time we can.
When I hear about engineers who are bored with coding, I have to imagine it's because the task of "programming logical work flows" has become rote to them.
Instead of refining their approach, or challenging their current knowledge base for discovery of inefficiencies or baseless assumptions, they'd rather hit an "easy" button.
I understand the desire to NOT do work. I understand the desire to spend quality time and free time with family. And I understand the idea that familiarity breeds contempt.
What I don't understand is the willingness to replace a deterministic language/framework/approach with a probabilistic slop machine.
Unwise design. “It talks, CSRs talk, it’s the same thing”. The fact CSRs talk is incidental. Nobody contacts support to talk. Customer service is a kind of “exception handler” for that which you failed to automate. If your system exists, works and is legible, conversation is avoided.
People constantly call support for problems they can resolve themselves, where the product is actively trying to help them resolve the problem, and where copious documentation exists to communicate exactly how to resolve the problem. For many organisations these calls are >80% of support workload, which is why so many products make you complete a quiz before giving you a support form, or why you have to navigate a phone tree to get a person on the other end.
Most recent support call I assisted with was someone who was trying to sign in on a new device, sign in screen told them to check their email for a one-time code and enter it into the form on the screen. They called support because the email took longer than 10 seconds to arrive in their inbox, so they believed the code had already expired by the time it arrived. There was nothing in the form or the email communicating a 10 second code expiry (actual expiry is 60 minutes, not communicated in the form or email). They'd never actually tried to enter the code into the form and hit submit. Never clicked the help link. Just straight to the phone call.
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?
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
Maybe it should have clicked earlier in life and I'm perhaps that much dumb dumb, but it only recently occurred to me (from experiencing it at two very different companies and discussing with peers having reached a certain seniority level more or less at the same time) how dysfunctional many companies are, and how often they produce incentives that are misaligned with the overall company goals and sustainability principles. I blame in large part a layer of middle management that selfishly puts itself above all else, misguides, misrepresents, because it essentially pays larger dividends (literally and not) to "play the networking game than to be an efficient and effective productive structure". Maybe that's to be expected in a services-driven economy where the value of the work is immaterial and subjective (and the whole phenomenon of bullshit jobs).
Of course I will do that, I get paid for doing that.
Most of the times I can convince that AI is not necessary by showing small PoC flow with AWS diagrams of data flows. This works well especially if the ask comes from technical people.
Other times the C-level interjects (CEO, CFO, sometimes even CTO) and demands that AI should be there. I literally had CEOs send me instagram reels of some AI shovel-sellers to demonstrate that I am wrong and AI is the way to go. No point arguing after that because I have no problem implementing whatever AI they want rather than losing a paying project.
With inexperienced or non-technical people, talking to them about AI can be very confusing, as a LOT of their "AI" usecases are basically they didn't realize or know how to write a program for this straightforward logic.
The Claude web UI popped a modal up a few days ago advertising their new model to me. It was full of HTML tags that were escaped or otherwise not rendered so that the text was literally
<b>Included in your plan limits until Jun 22</b> <br><br>Fable takes 2x the usage of Opus.
<b> Switch models when a message is flagged</b><k <br> When safety measures flag a message, automatically switch to a different model to keep chatting. When off, your chat will pause instead. <a href="https://support.claude.com/en/articles/153636
target="_blank" rel="noopener noreferrer" > Learn more</a>
...and this was presumably generated with the flagship model from the world's most prestigious LLM company.
This all reminds me of in the 90s when the Borland C++ compilers and Turbo Pascal shit came out and everyone was still hand rolling assembler because the optimising compilers were so bad. I thought Opus 4.6 was pretty good, basically a step change. The stuff I got out of Fable before they blocked it was nearly alien. If things keep improving, I don’t see humans writing code in 2 to 3 years except maybe super niche areas. This will all go the way that optimising compilers did. No amount of resistance, anger or denialism will change that.
I’d actually love it if LLMs could skip the slow high level lanaguages entirely and just churned out some weird LLM bytecode that was closer to the metal. I don’t want to read it or understand it at all. Here’s my spec, build it and notify when done. I want to ship stuff not build or dick around with code. Basically like when I go to a shop because I want a table, I don’t care if some carpenter “crafted” it or a machine mass produced and spat it out. It’s cute, but most people just want stuff and don’t care how it’s built.
In every case up until now, a jump in abstraction has moved us forwards in ease of understanding the underlying artifact at a conceptual, human level. High level languages are effectively runnable documentation.
It's possible to say that LLMs producing code may be the same category of thing, but the non-determinism and ephemerality of it all makes it difficult to imagine.
If my experience having to manually unf*ck “production” slop written by sweatshop tier offshore told to go wild with a Claude subscription is any indication then we are a LONG way off from any of that BS.
My job is safe because I’m the only person at the company that actually understands what the actual code is doing and I’m the one that gets the calls at 2am and weekends.
“Weird LLM bytecode”
Why not just generate object code for the target mschine directly?
I think the gap is because 1. For coding, Claude is amazing - mainly because of its curated skills and because massive amounts of working code has already been carefully labeled over the last decade or so via GitHub. And because with any Turing complete language, there is only so much one can do.
But 2. For most other things, LLMs are fairly underwhelming. Research is usually mediocre. Try being rigorous and repeat your research prompt many times - then make a confusion matrix to tally up how many false positives and false negatives occur. And for the rest, be honest and ask yourself if the LLM is doing much more than a basic search engine query or trip to Wikipedia would have told you. For “normie” use cases, it’s handy-ish but far from revolutionary
Also because programming is self contained in a computer where the results can be tested and iterated easily. For programming the agent can just run the compiler and tests and keep retrying until it works. If I wanted to for example sew a T shirt, AI is useless.
At the end of the day, the processor can only do Turing operations: assign values to variables (registers, memory locations, storage), loops, bitwise operations, and conditionals. Whether the source code is python, java, or lisp, it has to compile or interpret down to machine code ultimately. Likewise if the running software is a word processor, DOOM, or an LLM, at the end of the day it will be executed by the processor using the three operations. Lots of other fancy hardware and software may accelerate things but ultimately it is those ops that are the running code. The rest is many wonderful conveniences and abstractions.
Basically this. For tax, and law in general, LLMs are prone to giving the wrong answer for anything specific (though they're great at the super general stuff because they're just restating one of the millions of summaries written by humans).
Gemini still isn't sure what details are in the version of OBBA that actually passed, because there was more discussion about various proposals (that didn't make it into the bill) than there was about the final version of the bill itself.
Unfortunately, it's an intractable problem based on the ways that LLMs work. In order to overcome those limitations, you have to provide so much detail to the prompt that you would find the answer faster searching manually.
Good piece, but I think there's a missing angle to it. He cites a study showing how often people say they "use AI", and a little over 50% use it less than once per week.
If we're just talking about AI chat interfaces, sure. But I think the way that AI usage is going to grow isn't mostly by getting more chat engagement. It's about baking AI features into software that people already use.
For example, suppose you asked the same people "How often do you search on Google?" I am willing to bet the numbers go up a lot. And all of those people are "using AI" in a very real sense, they just don't think about it when it's baked in.
I'd say this argument is not relevant to the specific question the article tries to answer, as AI adoption through these means is forced and may in many cases go against user preferences.
Edit: The deciding factor being whether you want to figure out if people are interested in AI / find it useful, or if the question you seek answered is more akin to "X% of people consume lead in their food"
Google also serves ads, most people have gotten pretty good at ignoring them while the ads simultaneously being used enough to be profitable. Similarly, A/B testing only shows whether the net result is positive (by some measure to Google's interest) - not that most people engaged (or engaged positively) with it.
Bait and switch user interfaces to insert AI into places AI did not previously exist and make sure you see an AI answer before anything else is not far divorced from the "lead in food" example - especially when paired with efforts to make the 10 Blue Links become irrelevant garbage on average by ignoring most of the words you type in.
Maybe it's a better analogy to compare this to food-without-lead costing 10 times as much as food-with-lead in large part because of direct actions undertaken by lead manufacturers.
People are not terribly well informed on the specifics. Every time Spotify queues up a song, it has been selected by AI. That's been happening for a while without people getting upset about the effect on the employment of disc jockeys.
I am guessing the threshold is when changes outgrow mere consumer convenience and start being something imposed without choice.
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
We already see the slopification of everything as companies have been reducing the quality of their output for many years. Look at windows 11 vs 98. Yes it does more and crashes less but is it actually better apart from that? Of the things both of them do, which one does them better? Which one runs faster? Which one is easier to use?
Windows is the result of having almost the entire market. There has been no reason for Microsoft to improve windows because it won't sell more licenses. They have already sold Windows to every person who will possibly buy it. So the only avenue for growth is selling additional services on top like cloud subscriptions, AI products, etc.
Contrast that to the last 10 years in Linux where things have become immensely better.
Given the hardware these systems run on, and the compilers and development tools they had available, Win98 was actually the more impressive piece of engineering.
I assume for a lot of people, an llm is going to produce higher quality results for most knowledge tasks than they could do on their own. I think that's okay
It's important to factor in just how many US adults are basically illiterate nowadays.
As of 2023, 27% of American working-age adults were at a PIAAC Literacy Level of 1 or below, out of a total of 5 levels. This has gotten drastically worse in the past 10 years as, in 2013, Level 1 and below was only 17%.
Full scores for 2023 are:
% Level 1 or below: 27%
Level 2: 29%
Level 3: 31%
Level 4/5: 13%
For reference, Level 1 means someone can't really handle a full page of text, and can sort of handle simple 1-page web pages. Level 2 is the point where someone can start to handle a few pages of straightforward text, but still nothing particularly complicated.
(Both of those descriptions undersell just how bad it really is, but I'll leave it at that, for the sake of brevity.)
People that aren't using AI at all often aren't using it because they effectively can't. On a fundamental level.
I'm curious as to how I would score, I would definitely count myself as "literate" but I wonder how well I'd do on the level 4/5 tasks and if they cross over into more general memory, intelligence, and study habit metrics that even a normally "literate" person would not do well at.
Though given those descriptions I can't help thinking those would be great tests for AI. I'd love to see the proficiency scores for various models.
EDIT: Ok I just needed to scroll further, they have sample items in the last section up to level 4 and even at level 4 the question seemed trivial.
The most wordy one is the Q Drum article (which by the way Q drum is a real thing, kinda neat idea) and there's literally only two basic criticisms (flat land and expense) and if you had any idea what the life straw is you can probably construe what the similar criticism in the email is going to be without even looking.
Based on the scores and the proficiency description I assumed they were actually targeting some sort of normal distribution and levels 4/5 would be genuinely difficult explaining the scores. I'm now much more sad that the scores are so low.
At least I got a laugh at how they refer to each test item as "the stimulus" which has such a sterile/clinical flavor to it.
I don’t think that’s it. AI mobile apps support voice conversations. And low literacy is rather a motivation for using AI to generate and summarize text.
Just getting to the point of using a voice mode is a challenge at that level. Like, we're talking about "has trouble formulating a question to ask in the first place".
There's a whole level of ignorance out there that is honestly dumbfounding to even comprehend. The numbers for numeracy and problem solving are even more horrifying.
(It's for this reason that the most popular apps in the US are algorithmically generated feeds of photos, and often-non-verbal videos shorter than a TV advertisement.)
I thought not needing words was because people often watch them on their phone in public without sound. Who are these people that can't enjoy listening to spoken words because of illiteracy or ignorance?
I mean... Tiktok's hallmark trend has always been people doing "TikTok dances", which involve audio, so...
But, besides that, TikTok became the titan it is today during the pandemic. When everyone was indoors (not outside).
Per Google's quick answer, when searching "When did TikTok get really popular?": "TikTok's global popularity surged in 2020 due to pandemic lockdowns, with consistent growth since 2016, surpassing competitors in downloads by 2018."
I think we will have to develop some kind of framework for the difference between consuming AI-generated content and using AI as a tool as those use-cases will likely increasingly diverge. It's very easy to have a conversation with AI, as it will mimic your style, but I'd consider actually using AI to be something more like developing and orchestrating agents, developing frameworks for tasks and understanding, generating ideas for types of work AI can do and how the tasks can be properly split by training needed. Even though AI itself can do a lot of these tasks, people who can understand what AI can do for them and why will always have an advantage, and thinking in theses types of ways requires a high degree of literacy.
I lurk on the teachers subreddit and get shown videos by teachers on TikTok and the impression I get from that algorithmic bubble is that the kids can't read any more - reading comprehension in particular is terrible. Lots of anecdotes of kids who can't read a few paragraphs and then answer questions about what was in them.
My impression of that sub is that it's a lot of people who went through honors classes in a "good" school district, and are now teaching non-honors, potentially in a "bad" school district and are discovering how the other half lives.
Hasan Piker's political project polls extremely high (universal healthcare, abortion access, and more), so actually you could understand American voter politics by reading Hasan's comments amusingly enough.
"Response rates for this data collection were relatively low, both for the United States and for several other participating countries. There is evidence that procedures implemented to reduce bias associated with nonresponse have done so, and that the data are representative of the population. However, readers should be aware of the potential for bias and use caution when interpreting PIAAC results."
These stats don't pass the smell test. About a third of people in the US have a bachelor's degree, but only 13% can pass level 4/5 literacy challenge? If you dig into the sample questions, they are not hard. A level 4 task has the person read a short article and pull out the criticisms of some products.
I know not everyone with a bachelor's degree is 'smart' but it's hard to believe 2/3rds couldn't pass level 4/5.
Also 13% have a master's degree, does that mean those 13% are the only people passing level 4/5?
> About a third of people in the US have a bachelor's degree, but only 13% can pass level 4/5 literacy challenge?
Yes, that's how bad the problem is.
I've worked with people with master's degrees who can't comprehend text more complex than a Slack message. I've watched them try and fail.
I think it is difficult for a highly-literate person to wrap their head around this. It's very possible to get through a bachelor's program with low literacy. It's even possible to do quite well. And at a reasonably good school.
There are some pretty successful businesses out there whose target market are essentially professionals with low literacy. They use more flattering descriptions in their marketing.
This is just how it is out there. Ask teachers what students are like these days. Think about designing for users. Or cross-reference with other info on this topic.
And, in regard to colleges... you have to keep in mind just how many colleges there are, how much the quality differs, the relative workloads of different degrees. There are a lot of people graduating with a GPA quite close to 2.0 at that full range too.
Also, think of how many college graduates never finish a book again after graduating college. Those numbers judge 18 to 65. And the age stats show that the older cohorts drag the scores down significantly.
The only upside to all of this is that it at least makes the chaos out there in the world make a bit more sense.
> 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.
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.
It's been difficult to do, but I have recently picked up the habit of scrolling directly past the AI overview. It's not what I wanted, so it's useless to me (regardless of whether or not it's correct). I wanted search results. Sometimes that's a Wikipedia page, documentation, video results. But, web search results.
If I wanted to read an AI response to a query, I would use that tool specifically - I consider them different tools entirely.
It's definitely taken some time to get into the habit - perhaps defaulting to Google as the search engine was my first mistake.
However, most people I've seen run a quick search DO in fact just read the AI response (often with a caveat of "this is just from AI but..." which I appreciate), because for a lot of things it IS good enough or exactly what they needed.
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?
A "satisfactory looking answer" was what I got yesterday when I queried Google about a Pyodide question. It produced some code in html format that was supposed to work, but failed on execution. The AI generated result was incorrect and it was only 30ish lines of code that was supposed to print "hello world" to the console.
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.
Google searches are still part of my everyday use if you're a power user like me that ctrl/cmd+L to the browser bar and the first auto complete is a web search rather than a bookmark or history item
The sales pitch was... we'd be left behind without adoption. I'm [still] waiting. Years later, my days are no different. Teaching people who wouldn't bother to read manuals or now, consult their chat bots. Never mind my own 'missing pieces of flair', what about theirs?
I find it baffling that someone with 21k karma, on <<Hacker>> News, posts such a ignorant comment, followed up by someone with 5k karma and a 10 year old account, with an equally "valuable" reply.
For example; ChatGPT is replacing my Google searching. Not necessarily because it's better, or because it's summaries are better than Google (I find them subjectively better but it's not clear cut).
But because the app has a nice history; can ask a relatively complicated question and go do something else and then come back to it, ask a follow up. Etc.
None of that is specifically an AI benefit, but it's a workflow that really helps, well, flow.
That's funny, Google Gemini and AI mode in search has replaced my ChatGPT prompting, because I know Gemini will correctly cite sources (as of course it's by Google) rather than hallucinating.
Also, Gemini is free or at least has much higher usage limits than ChatGPT or Claude, and it's well integrated into Android and soon Apple with their new Siri, so things like circle to search just work well.
That's totally fair and things may change. For me its the history and the fact I can come back to it.
If I am honest I believe my final solution will be a combination of Open Claw, a custom knowledge wiki based on Wikmd. I just need a good all for Claw with history that is as good as gpt
Edit: and context too. It inferred my energy supplier from previously chats and so when I just asked a pertinent question it referenced their policy. Admittedly Google will have way more context if they get the product right.
Gemini also has it all, it learns and knows who you are, you have history of chats where you can just jump back into a conversation a week later if you want to.
Gemini has history and memory too though, maybe you weren't aware. It's an app just like ChatGPT and you can even import your history and memory, try it.
I've run into plenty of instances where Gemini cited sources and the claims they attributed to those citations were nowhere to be found in the actual sources they cited. It's happened to me over a dozen times this past week.
The AI search results were actually better last year; significantly fewer hallucinations.
At least using AI doesn't cause massive suffering of sentient beings like meat consumption does. I find it funny we talk so much about whether or not Claude's feelings are hurt and whether it has emotions, when we treat animals with real feelings, emotions, parental bonds, and personalities so badly.
Search isn’t generative AI. There are a lot of people arguing in this thread that actually everyone is using generative AI without engaging with the source material at all. Why do you think that is?
Google has made searching also include a default LLM response. There are also search results. When you search with Google, an LLM is prompted, for sure. It’s also pretty clearly a separate feature that’s been tacked on to search.
I might be the only person at my job that doesn't use it, but it feels like I'll be found out by upper management sooner or later. Maybe I'll need to use a library I'm unfamiliar with, I'll take longer than expected to grasp it, and then people will start asking questions. Every time a manager puts a random call in my calendar, I'm worried it's going to be about this.
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).
The list of concerns omits many things (although they do mention many valid concerns), such as concerns about control by the organizations that provide the AI services, power that is better used elsewhere (independently of whether it is "too much"), using too much space, effects on prices of things, excessive scraping, inappropriate use of AI, someone trying to force or insist strongly that you should use it even if you do not want to, etc. It might be potentially possible to mitigate some of these concerns (and in some circumstances, some of them are mitigated), but that still doesn't mean you should be required to use it. Software and services that make the AI features optional is one way to help (and is worth doing, if applicable for that software/service), but it does not solve everything; but, one way will not solve everything.
In my non-tech circle, most people don't even realize how the internet is running literally everything. Even if we start to use mass scale AI for something, they wouldn't realize or care much about it.
They at best turn on the TV to watch netflix or look at the phone to send messages on whatsapp.
If all of that went away tomorrow, they'd be inconvenienced at best and then go on with their day to day life.
This feels like we are literally all in our IT echo chamber where we throw stuff on walls and go crazy, while the world is sunshine and rainbows, always been.
"They at best turn on the TV to watch netflix or look at the phone to send messages on whatsapp. If all of that went away tomorrow, they'd be inconvenienced at best and then go on with their day to day life."
I'm not saying it is a good thing, but this is completely out of touch with how dependent (most) people are on these technologies.
A lot of people are genuinely stranded if their phone runs out of
battery. How do they pull up a map, or call an Uber, or phone someone to pick them up?
The context was “Everyone does use the internet for everything today” and “most people don't even realize how the internet is running literally everything”. You don’t need the internet for your phone to have a battery charge.
They were providing some illustrative examples: most people rely on the internet for maps. If they don't have internet (because their battery is flat) they can't do xyz.
The argument here is "most people can get by just fine" without access to the internet.
I tried to pick an obvious example to illustrate how that's not true.
The difference is that, prior to everyone having a smart phone, people had backups for if they ran into trouble. They might simply not go somewhere that they might have trouble returning from. They sorted out their travel plans in advance - someone to pick them up from a location at a time. They memorized phone numbers so they could call from a pay phone if they needed to. They carried cash or a cheque book to pay for cabs.
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.
AI is a tool in a toolbox. It is does not fit all solutions.
Using AI everywhere would be the equivalent of using a screw driver to pick the piece of stuck broccoli out of your teeth. It will cost you more, to fix your teeth, than using a proper tool.
A year or two ago people were concerned that Google was losing its battle to OpenAI. Today Google puts heavy emphasis on AI search. Most common folks are now using them. I know in a minute or two many Kagi, DDG or other alternatives will reply, but these people were never a core part of Alphabet's user base. I'm an AI sceptic to some level, but it's hard to deny that "most" people are using AI (as an LLM or in other forms) to some extent today despite we like it or not.
70% of people report reducing meat consumption, but research has shown that these intentions have very little correlation with people's actual behavior.
Almost everyone is using an app with a feed with an algorithm powered by a transformer. People spend hours a day feeding information into a model with the same architecture as an LLM which then spits out next-post recommendations. They call it "scrolling Reels/TikTok/Shorts".
> More than 30 percent of the US working-age population is using AI [meaning about 70% isn’t], an increase of 3 percentage points from the end of 2025
This makes me less bearish on the AI investments that are being made, if 70% of the working age population isn't using AI then there still is a lot of growth.
The future is here, it's just not evenly distributed (yet)
It might take a while for people to see how the model is useful to them, or to figure out how to make it useful or the capabilities weren't there ye. My mother tried ChatGPT in '23 and thought it was useless, she now uses it regularly, because it's improved and she knows how to use it better.
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.
Nobody writes any code any more at all. Nobody even writes Jira tickets anymore. They don't even review code, and I think we're lucky if they even test it. The AI does all of that.
A small group of developers at my company have set up volumes of skill.md and other instructions for the AI to write Jira tickets, then take action on those Jira tickets by writing the code. The AI submits a pull request. Then there's another AI to review the code. They've written the game plan for the AI to do all of this. All the human does now is click "approve" without even reading the PR, and then someone clicks "merge". There's no coding, no critical thinking by a human anymore except for telling the AI what to do... which really anyone at the company could do. I doubt I'll have a job at this company much longer after 8 years employed there.
I don't think AI has any real value for software development, personally. The quality just isn't there, unless you invest so much effort that you may as well have written it yourself. But the market can stay irrational longer than you can stay solvent, and even though I think the industry will get over the idiocy of having LLMs write software, there's no telling how long that will take. So it's a scary time to work in tech even if I think the trend will ultimately reverse.
As of ~8 months ago the quality is most definitely there, for almost every form of programming I've experienced.
If you're working in some vanishingly rare domain then maybe it's not yet, but most coding challenges are very much in the wheelhouse of the current frontier models.
> So it's a scary time to work in tech even if I think the trend will ultimately reverse.
I honestly can't see things going back to what it was 5 years ago. We will probably not have the future that Anthropic hopes for, but I think every developer will be required to chat with AI as part of the planning process to reduce a companies "bus factor" risk.
I envy you. For me, AI is faster than the code I write myself in many, many cases. It might replace the average developer, but a talented developer like you probably won't be replaced
I was not hired to write code, but to solve problems (where often the end result is code, but it’s not the whole process). But the message from management is that our bottleneck was coding, and by using AI to code, we’ll be 10x faster and all the company problem will be solved. Essentially 1. Use AI everywhere 2. ??? 3. Profit.
Where I work, the CTO drank a whole bunch of AI kool-aid recently, so now we're expected to "10x" our output with AI. I don't think he realizes this also means 10x more problems of all kinds. But I fully expect him to double-down and when AI costs skyrocket, he'd lay off more developers to pay for more AI.
I am constantly looking for a new job, but all of them are also require AI coding experience.
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.
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.
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.
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.
It surprises me when people think engineering, software or no, isn't about the physical world of humans, psychological, imaginative, aesthetic or otherwise. Everything I do uses "human stuff" as a base foundation of value. Engineering effort is malformed and invalid without such a thing, and I spend a lot of my time as a technical leader pushing back on people trying to "perform engineering" without connecting to these things.
I've been thinking about this, and I think software is uniquely knowledge work that has the most defined structure and least personally interaction. Hell, some of the software I write is for machine to talk to other machines. It's not surprising such a closed system is so amenable to AI, and other knowledge workers are not getting the same benefits.
Software has huge and detailed code repositories ripe for training use. There's just enough inference in current models to remix that code in useful ways for the most popular languages.
The less popular a language, the more models struggle.
Writing, UI, and presentations have similar knowledge bases.
Outside of those, quality becomes much more hit and miss. If you ask for a recipe you may get something good, or you may get something completely inedible and random.
"Domain specific knowledge" really means "strong foundations and relevant abstractions" and LLMs just don't do that reliably.
That's a decent article. My only issue is it seems heavily biased at the end, or at least he seems to misunderstand what the 'A.I. types in Silicon Valley' are doing.
> Computers should adapt to people. Asking people to make themselves more legible to software — to turn themselves into a database — is a doomed idea.
I've been in software a long time, and I do sort of see this trend, but I think it's because these are tools that build other tools. The interface has always been a 'best I can do for now' thing, with the focus on doing things that are useful. Computers were just calculators in the beginning, which led to more complex calculators, instruction sets, programming languages, operating systems, GUIs, interconnectivity, etc.
What people are doing today is experimenting, like they always have. They're putting their experiments out there so that others can use them and build on them. Some will use those tools to build other tools, and some won't. But over time, the experiments that work will get distilled and turn into real products that people who 'do not yearn for automation' will still want to use, so it seems like the value is there.
I guess the real question is whether they will create value that offsets the near-term costs, because I don't think the billions in investments are sustainable, and I'm not convinced the centralized data center paradigm is the right way.
Software engineers aren't even all using AI, contrary to frequent claims here that they are. There are very many who have tried it, found it didn't add value to their work, and aren't using it unless FOMO-driven managers force them to.
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.
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!
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.
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.
"This tracks with Microsoft's new United States AI Diffusion site, based on "anonymized, aggregated Microsoft telemetry.""
Surreptitious surveillance, probably with dubious consent
Is Redmond afraid to just ask users
Then compare the data
If no one asnwers when asked then maybe that means they don't wish to allow Microsoft collect that data
"SearchLight asked about a range of technologies and to say "whether you believe the overall impact of each technology on society is positive or negative." AI only has an +8% net positive rating right now, right next to +7% for social media, which were only greater than crypto at -17%."
LOL. This is what happens when people are asked for their opinions
My initial response to reading this headline was to think that noone is saying that they were. Yet the author starts off with a link to a pretty good example of some dumb hyperbole.
I guess that goes with the notion that for any really idiotic take you can think of, there's going to be someone out there confidently promoting it.
In general, most claims of 'everyone is...' means "Most of the people around me that I observe are..."
Which might mean they are not around other perspectives, or it might mean they just are not observing other perspectives.
I disagree. Everyone will be using AI for everything, but, increasingly, people won't think about whether they are using "AI" – just like they don't think about using databases.
Nor should they! It's such a shit thing to be emotionally invested in. Imagine people would have been upset about databases. It's really fantastic software and we should be happy to have it, and now go and make the most of it, for all of us.
I'll note that despite this, most AI providers are very compute constrained (see: the skyrocketing backlogs of each cloud provier; Claude's 9's until a few weeks ago until Anthropic, along with Google -- of all companies -- renting capacity from Elon.)
That could be a material reason for the overall low adoption. I think the big AI players are carefully balancing multiple conflicting priorities with their limited compute: acquiring and keeping users with free or heavily subsidized tokens, while ensuring they can satisfy their paying customers' SLAs. If there was more compute they would probably invest more in marketing and pushing adoption further (and indeed, a Google exec was quoted saying exactly that after a big Gemini launch.)
Something else to consider: If only 10% of users use GenAI daily, that leaves a potential 45x growth minimum on the horizon (the remaining 90% of users x 5 weekdays, assuming everyone chills on weekends, and ignoring that agents use way more tokens.) Which is why every hyperscaler has been scrambling to build out more capacity.
The typical question at this point is, where's the payoff? Two points:
1. Despite this relatively low level adoption, AI company revenues are already spiking at unprecedented rates. (Not just the frontier labs, even the smaller startups, as reported by investors, enterprise customers as well as Stripe, their payment processor: https://news.ycombinator.com/item?id=46730182)
2. The St. Louis Fed study above notes that there has been a ~1.89 percentage points increase in national labor productivity since 4th quarter 2022, which could be explained by their data on GenAI adoption.
They are careful to call out that causation is impossible to prove, but the possibility that GenAI may already be having a noticeable impact on national-level economic indicators only 3+ years since launch is wild.
Turns out that the people screaming about using AI for everything were the investors who already invested in the AI tool they are trying to sell to you.
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."
The numbers are interesting, I agree, as is the overall post.
And as a clickbait headline, "not everyone" makes sense.
But, realistically, the NYT obviously isn't talking about, say, the North Sentinelese islanders. Similarly, it's probably not really talking about people who don't do the kind of white collar work where AI is, so far, most prevalent.
In fact, the group the NYT is most likely referring to as "everyone" is really "readers of the NYT". It might be hard to come up with numbers for that, but I suspect they'd be a lot higher.
Anecdotally, at the company I'm at (late-stage venture-backed), everyone from software developers to people on the marketing, sales, and finance side are using AI. And we're not unique. This will make the statement ring true to many people, even if it's not globally true in an absolute sense.
This all makes more sense once you've truly internalized the Yogi Berra quote about a certain popular restaurant: "No one goes there anymore. It's too crowded."
I'd honestly argue we're actually going the complete opposite direction.
Everybody is using LLMs/AI. All the time. It's in every facet of your life. Just because you didn't input the prompt, doesn't mean you're not consuming the end product of LLMs all day, everyday, on websites like this one, reddit, tiktok, instagram, facebook, etc.
Addressing the article, if you're hyperfocused on whether people are using AI and only consider AI use a chatbot... well, you're not honestly covering all the AI use out there. And reading the other stats, it seems like this article is trying to paint a narrative. Why is the Datos stat only considering "Desktop use" for instance.
Not to mention their stats are actually astounding and DON'T show what the headline is trying to assert. 1/3 of people using AI regularly is a FUCK TON of people in a VERY short span of time to uptake a new technology.
It's funny lately I've been seeing the cursor advertisements all with some premise of regular young person wants to develop an app and the ads really do focus on the simplest of premises: the only ones I've seen in these skits are essentially variants on the "todo app" web app tutorial
the tech is pretty good at helping identify simple bugs when they happen and to write short sections of code given very explicit instructions but yeah I have yet to see good examples of short one sentence ideas turned into a working product that looks better than anything that could be a UDemy tutorial app.
I honestly just use it as a search engine to get around SEO garbage and ads.
My wife uses it for a (non-computer related) business though and it's great for all sorts of normally tedious marketing/social media type jobs though. Stuff that doesn't really require accuracy just needs text on pictures that looks good quickly.
I think everyone just has FOMO and doesn't want to lose to competitors. Eventually it'll die down.
I'd love to see credible numbers on that. I find it hard to believe that stupid corporate mandates are responsible for more than a small fraction of usage, but without data I have just my own instincts to go on there.
At my employer (megacorp with tens of thousands of employees) daily use is mandated. Our annual bonuses and pay raises for our performance reviews were explicitly tied to this.
It's a retrospective analysis of an assertion made by NYTimes. The original headline wasn't clickbait, just presumptive, and even so, it's a pretty significant publication that spends a lot of time on the HN front page (alongside you, I'll add). I think it's perfectly fair, and nowhere close to a strawman, to deconstruct that claim a year later.
On the post-grad job hunt right now - I note that most employers will ask in a technical interview or whiteboard interview "how are you using LLMs?"
It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products. I've been responding with a sort of long winded answer about how 'there is clearly a learning curve for how this technology fits into any process and how I always always always double double double check yadayadayada'
I'm probably using the chat/ask functionality on a daily basis for quick debugging / new technology learning questions but I have yet to really use the fully agent or computer-use products because I've had more bad results than good the few times I've tried them (re-factoring a big repo of decades old fortran+C code for modern compiler/OS some things started to work but ultimately I abandoned that effort).
> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products.
Have you considered just answering truthfully?
Would you even want to work somewhere where you need to play a role and where they flip out when you say the wrong word you should've correctly guessed through mind reading? That sounds not like a job but a toxic relationship.
I assume it's because he is seeking to pay rent, food bills, and other expenses through employment.
“I assume it's because he is seeking to pay rent, food bills, and other expenses through employment.”
Fair enough, so if there were one “right” answer, that would be the one to give whether true or not.
But here there is no obvious right answer. If the employer is looking for a particular answer, the poster doesn’t know what it is. In that case, the best thing to say is simply the truth, particularly when the truth that the poster gives here is completely reasonable.
The best thing to say when you don't know the answer probabilistically is to give the most likely correct answer.
Common sense not common
You can’t have common sense when you have no shared values.
> But here there is no obvious right answer.
According to you. That's not the opinion of every prospective employer. Hedging is the right strategy.
A truthful, nuanced, well reasoned answer will be well received by an employer with a culture you want to work in.
>A truthful, nuanced, well reasoned answer will be well received by an employer with a culture you want to work in.
If you're into long shot betting AND your savings aren't running out while waiting to land a new job, that might be a good strategy
I don’t see why “truthful, nuanced, well reasoned” is the long-shot vs cagey and evasive.
Have you seen the job market lately?
You call the response "cagey and evasive", but that is for an objectively a bad interview question, one wrung below "How many years experience do you have prompting Anthropic Opus? We are an Opus shop." People are not locked into their current way of using AI and it is trivial to match how one works with AI to match employers requirements. It's a question that deserves an idealized non-answer
In what way is “How are you using AI?” an objectively bad question?
Remember the context - this is while solving a whiteboard problem. Its bad in the same way asking candidate what their birthstone is - because any answer offer little or no signal about the odds of a candidates success at the company.
I'm curious to know why you think asking about AI usage is a good interview question.
If you're going to have people use AI regularly, it's worth asking so that you can get a sense of their interest/willingness, experience level, and training needs. That said, more specific questions are typically more revealing. Personally I'm fond of "If you could give Claude only 1 instruction, what would it be?"
You're fond of asking candidates an unrealistic hypothetical and you think that's revealing?
Obviously because the latter lands more jobs.
as a rule the interviewee should be assessing for goodness of fit, as well as the interviewer. If my simplified nuanced answer to why I limit my usage of LLMs was poorly received, I wouldn't want to take a job somewhere likely to make me miserable.
Correct. Me either.
Unless I'm looking after about 6 months and my savings isn't as healthy as it once was. Or one of my kids is sick. Or pretty much anything happens costing money.
Then I'll be miserable for a little while as I continue my job search.
Practicality, sometimes, is more important than ethics when other people rely on you.
"Unless I was really excited for flipping burgers, the menu options, the new McKidney, non-working ice-cream machines, and the legacy of Ronald McDonald, I wouldn't want to take a job somewhere likely to make me miserable".
A lot of jobs don't work based on those criteria. In a bad job market, IT jobs don't either. Especially if it's your 20th application with no response.
If you're outright lying to yourself to land a job to make rent, fine, but for god's sake internalize it as a temporary stepping stone and immediately start looking for a better fit.
> an employer with a culture you want to work in
A modern luxury, unavailable to many.
This thread is about new grads. Have you talked to anyone looking for junior positions lately? They don't get to choose employers anymore. Heck, many of them will not even get a chance to enter the field if the current junior hiring situation continues.
Plenty of good companies have awful recruiting and interviewing practices. The reverse is true as well.
Average ZIRP era HN advice. Not applicable anymore.
But it doesn't make sense to do a job you'd hate. Just be honest, land the job where you're happy.
> But it doesn't make sense to do a job you'd hate. Just be honest, land the job where you're happy.
In this economy it's really a luxury to nitpick about the job when there are bills to pay, mouths to feed, a roof over one's head is a must. The well of have a lot more options regarding this.
As a software developer with a non-bloated lifestyle? Bullshit.
> it doesn't make sense to do a job you'd hate
That assumes using/not using ai will make me hate the job. Which is not true at all for me. Would be perfectly happy either way. (Or rather to say my happyness would depend on other factors.) Obviously i wouldn’t want to be the only team member not using AI in a team using AI or vice versa.
Answer like that, be honest.
Maslow's hierarchy of needs comes into play.
But no one will pay me to watch tv all day
Oh but they will: https://gracenote.com/. These people sit and watch TV, note down every event taking place then sell the data. Search for Infostrada and how it came under Gracenote. That's where the info like "this is his first X since the game Y Z years ago" comes from.
If you're good enough at it, I suppose someone would. (There are television critics after all.)
But I'm just curious: if someone were to give you a living wage to do nothing but watch TV all day for the rest of your life, how many years do you think might go by before you would start to ask yourself, Is this really what I want to spend my one, exquisite, irreplaceable life on?
you assume correct
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.
> I typically seek employment
Your username does not check out.
Check their bio.
I think honesty is still probably correct - if you're struggling to figure out how to hedge.
I think you'd rather have good odds at some companies and 0% at others, rather than abysmal but non-zero odds at all companies.
And as an added bonus, you might get hired at a company where you're actually a good fit, rather than one you weasled your way into, and get to pay rent, food bills, and other expenses through employment for a long time!
It's pretty easy as an interviewer to spot when a candidate is hedging on a question, and it's the kind of thing that might get discussed in the post-interview debrief.
"Wouldn't give a straight answer on question X" isn't an instant no-hire, but it's not a positive signal.
This doesn't make sense in practice. He hedged so not sure need to look at other factors vs he picked a side and he selected the opposite of what we wanted no-hire or he answered what we wanted small positive signal need to look at other factors.
ironically, I'd understand people not giving a straight answer on this particular topic
I just interviewed a guy and all three interviewers asked him functionally the same question. He hedged 3 times and we just wanted an honest answer...
The guy wanted a job, didn't know what answer you wanted for the job, and you guys were being assholes.
If you can accept that then you've learnt something.
On the other side of this though, the third time the guy should have realized that hedging wasn't working and committed to the truth or a lie.
It was a question of the form trying to figure out how you deal with "X", and he denied X having ever happened, despite that being a core part of his current role.
He was an internal candidate, we were interviewing him to see if we could trust him with more responsibility (more X specifically), since the new role shouldn't cover up X when it happens. The role involved doing X for himself AND for other people.
Similar to the form of "tell me about your biggest weakness" and you responding with "I have no weakness".
I guess it's a bit different when it's an internal candidate.
I've been on the recieving end of clueless folk trying to make me feel small when I've just been looking for a job, so I might be a bit sensitive about it! Sorry for any offense given. Thankfully I'm beyond that craziness now and can just do what I want for work.
You can’t interview effectively by limiting yourself to questions where the applicant knows what you want them to say.
> you guys were being assholes
Where was this coming from?
instead of telling him out loud, "hey we see you're hedging and applying bullshit interview speak, your answer isn't sufficient", we asking in increasingly obvious but different ways.
It was an internal candidate so it would be awkward to tell him to his face he was floundering.
And he probably just wants to pay his bills so he can continue to survive.
I mean, let's be real here. There is such a thing as too honest in an interview.
"The candidate is mature and doesn't engage in bikeshedding"
I mean maybe that is because I live in a still mostly not failed state (Germany), but I can't imagine that these things would be _so bad_ that living in fear of saying the wrong thing would be something worth considering.
Plus, and leaving that aside, I have my doubts that even if you did that, that that company would stay alive for very long. Reality has the habit of eventually ripping this kind of unproductively delusional people (like e.g. a boss that flips if you don't say the right word with regards to the current hype) to shreds eventually.
The US has no social safety net. Healthcare comes from your employer. Everything is centered around having a job. Opinions on AI diverge significantly and someone’s response to this question would be pivotal to me in a hiring role. The market is not great for job seekers. The hiring manager can wait for someone who aligns with their company’s perspective on this.
No, if anything, I would say a very unfortunate trait of existence right now is that reality does NOT tend to punish corporations for being completely idiotic, at least not very fast at all.
Look at musk's companies. They will basically never (on any near timescale...) produce GAAP profitability and yet their IPO is in the trillions. To the point that S&P refusing to suspend their GAAP profitability requirements means the index will basically never see this company in it (which I'm quite pleased about).
The power of already-accumulated capital is simply more powerful than things like "don't be completely pants-on-head stupid about a recent fad" "don't seig-heil in front of the world stage" "there's no point in having people come to an office just to spend all day on zoom" etc etc etc.
The market can remain irrational longer than you can remain solvent, and companies can remain irrational longer than you can go without contributing to your 401k.
> 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
"Eventually" yes, but while this phrase may have been about the stock market, it also applies to management: "the market can remain irrational for longer than you can remain solvent".
One concept I only learned a few years ago was the difference between "mistake theory" and "conflict theory". Rational decision-making is the former, political decision-making, including office politics, is the latter. It's important to know which of these kinds of battle you're fighting, or you'll make the wrong type of move.
Also, FWIW, I live in Germany and my unemployment insurance has run out because the job market is just that bad right now; I'm fortunate that I have enough passive income to support myself, but even then if my partner's unemployment insurance ever runs out I can't support both of us.
Yes, I'm sure in Germany nobody ever muffs an interview by offending the interviewer.
How is Germany relevant in this?
Acknowledging that my perception might be skewed because there are still a ton of social safety nets in place.
The same might not be true everywhere.
Last time I was in Germany I saw what appeared to be homeless children
Did they look Ukrainian or Syrian? Germany let in millions of people over the last few years and never built enough housing.
This is totally incorrect. Germany is literally giving out housing for almost free.
No, I'm not stupid or lying.
And yes, it's true. It's just that this housing is not in Berlin.
"Built enough" may sound like the same thing as "giving out housing for almost free", but it isn't. Also, cheap temporary emergency housing for a sudden extra burst of refugees may be inappropriate for permanent occupancy, I believe this is an example, it's certainly containerised/temporary *something*: https://maps.app.goo.gl/HUyQS6Sc1w1Y8GUH6?g_st=ic
That said, an idea has come to mind: UK and Italy have had schemes where a home can be purchased for £1/€1 under the condition the new owner repairs them (the repairs would cost 20-150k). Given some of the really bad stock I've seen in some searches, this may be a good idea for Germany too.
I'm talking about places like Eisenhüttenstadt where you can find a regular apartment for rent for 300€ a month. Market rate.
And yes, you can find even cheaper apartments.
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?
Plastic bottles go in the yellow recycling bin. Deposit systems are dumb.
They reduce bottle litter by a lot a lot.
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, ...)
They make it that way on purpose, to save money.
Who is "they" and what specific actions are they taking?
The government that runs the benefits programs. They make you jump through hoops to get any benefits. The first hoop is even knowing that a program exists that you qualify for.
But then they also pay social workers to guide you through the steps of applying and receiving this support. Sometimes the biggest hurdle is to get to know what you don't know.
Keep in mind that not every old person who searches garbage bins is actually poor. Some of them just have dementia. I personally know such people in my home town.
That's why I said "mostly"
It's the 'gift-economy'.
https://pfandring.de/
"My trash, a flash of joy.
For the poor people.
Enabling me, the blissful sheeple,
to consume on with abandon.
Everybody won."
I'd assume they're seeking longevity to that employment. You, or others, might be able to maintain a facade; I can't recommend it for long.
It's possible to work for an employer, and not have to compromise your values and or professional integrity.
The attitude suggested by your response suggests you haven't lived that reality yet.
Either way, I'd rather be rejected by an employer for speaking my truth, than lie to be somewhere I'd rather not be.
This a very condescending and privileged comment. The job market is much different when you're just starting out, and it's especially brutal these days for new grads.
The "you don't know what it's like for me" argument swings both ways.
Maybe it’s condescending, but it’s valid. I have made sacrifices throughout my career to maintain maximum integrity, and the least I can do is be proud of it, since I don’t have riches or possessions to be proud of.
Yeah I rage quit my job 27 years ago and have been a struggling honest consultant ever since. Clients who want actual solutions to their problems come to me. Does that sound arrogant? Well I also have no savings and don’t own a house.
I don’t regret most of my choices, but I am aware that if somebody paid me enough money I would walk away from my principles. It would have to be a LOT of money.
It is not. I made that choice in the past and will do it again.
"Speak the truth, even if your voice shakes"
>* I made that choice in the past*
You were replying to “The job market is much different when you're just starting out”. The past is not now, and you are not just starting out, so your comparison of their position and yours is invalid IMO.
> and will do it again.
Good for you for sticking to your guns, I'm about to do the same with a company that has all but said “dig into AI or get left behind”¹, but those starting out as freshly minted grads likely do not have the luxuries that we might have² and the jobs market is freakishly competitive for them right now³ in a way that I don't think it ever has been before.
--------
[1] time will tell if I leave of my own volition before getting kicked!
[2] experience (both actual experience and experience “talking the talk”) to help getting the next gig, a mortgage paid off so making ends meet is easier, etc.
[3] It had been heading that way for a while, the recent explosion of GenAI+agnetics has made it worse.
You’ve actively made the choice to go hungry instead of hedging your answers during an interview?
I certainly feigned enthusiasm when I was in high school to get an after school job in order to help my family buy food.
Yes.
I honestly find that quite hard to believe.
Lack of adequate calories and nutrition negatively compound. You lose the ability to focus, you increase your medical risk.
I experienced that in my childhood. It’s terrible. I did very poorly academically when I did not have access to food. It’s astonishing to me how fast my academic performance improved after consistently having access to food.
Saying you would rather put yourself at risk instead of hedge your answer on a minor interview question in order to increase your chances of getting a job offer seems like an issue with prioritization.
Correct. I did it anyway (and yes, it was awful).
That's fucked up. If those are your values, that's all well and good, but you can't expect someone else to make the same decision.
Job interviews are a performance where you demonstrate you understand what professional expectations are and can abide by them. It's not dishonesty to not respond "I drink too much" when they ask "what's your biggest weakness?" just like it's not dishonesty to respond "can't complain" when someone asks "how are you today," even if you have a lot to complain about.
Once I interviewed someone and they described their tax fraud scheme to me. We didn't go with that candidate. Not per se because they committed tax fraud; because they demonstrated terrible judgment.
Having any kind of integrity is expensive, financially, emotionally and sometimes physically.
Software development is not that high-stakes of a job anyway. There is always another interview. I got another one soon enough, where the employee AI policy fully aligned with mine, so telling the truth was an easy, pleasant experience.
Imagine you are a pilot or doctor. Any kind of interview reply that doesn't fully align with your values now carries a real risk for human lives.
What I'm telling you is that it isn't an issue of integrity, and that only makes sense from a false premise - that the strictest, most blunt response is what is truest, what is being asked for, or a reflection of your alignment with the organization's values. That's really not the case. If I asked you how you were doing would you tell me about the traumas you're currently processing? Would you feel like it was a violation of your integrity if you didn't?
If that's what your values are, okay, I'm not going to tell you how to live, but it would be premised on a misunderstanding of what "hi, how are you today?" means.
I am not worried about what my pilot said in a job interview, I'm worried about what the check pilot thinks of their performance. Worrying about what they said in a job interview is like worrying about what they scored on the SAT. Once that hurdle is cleared, it instantly becomes irrelevant, because it was never measuring what we're actually interested in. It's a filter for people who are completely unqualified, it doesn't really measure a level of performance or alignment.
Culture begins at the front door, corporate or personal.
I would expect absolute sincerity from pilot or a doctor during the interview, including history of mental health and professional mistakes. Authority over lives of people must come with full transparency. If you are caught lying or misrepresenting your experience and skills, not only you would lose your job, you should be blacklisted from occupation as well.
In every skill, everyone benefits from honesty, both employers and employees. But I am aware this is a minority view.
Again, while I respect that that is your view of sincerity, I don't agree, and I'm not talking about lying, which is obviously inappropriate. Framing is not the same as lying (though taken to an extreme it becomes a lie of omission). There's also just a misunderstanding of what is being asked by questions like "what's your biggest weakness?" Language can be ambiguous, just because a very literal and extreme interpretation of a question exists doesn't mean that is what is being asked.
I don't think we're going to bridge this gap but I also don't think that's really necessary. People have different values and it is what it is.
Fair enough. I appreciate your civility.
This betrays quite a bit of ignorance about what the code created for certain companies is used to do.
What if the way you use AI isn't particularly important to you? Are you willing to sacrifice employment for a principle you wouldn't draw as a line in the sand?
Sometimes it's okay to say "I don't know" and it's okay to say "I don't care" and it's okay to say "It doesn't matter much to me".
Every interview is corpospeak where you infer the intended meaning of words anyway.
You should really examine your situation and beliefs if you think this isn’t a privileged position to be in.
We live in an ecosystem where we (engineers/developers) can promote ourselves and display our skills/acumen/values/professionalism/responsibility in an unequivocal way. Regardless of your experience level.
I bootstrapped myself from poverty to Staff software engineer, past the age of 45.
Is that privileged? Or sheer will and force of effort?
I am not unique. I am an example.
Privilege, yes. You had the privilege to dedicate time to learning skills required, obtaining an education, probably bias during hiring processes, etc.
Even though your position might be the result of effort on your part, you do have to acknowledge that you’re privileged to be in a position to expend that effort on what you want, instead of something else, like finding fresh water daily, or whatever. It’s not sheer will that you were born in a (even marginally) more favorable environment than others.
The term “privilege” here doesn’t just mean a trust fund nepo baby.
How far could you reduce this down? Do you only clap for malnourished Ethiopian babies that can't find waterthat grow up into full silicon Valley software engineers?
You can be dismissive all you want, but the point is to acknowledge you don’t understand everyone’s situation and you can’t make sweeping generalizations like “I did it and I can judge you if you _didn’t_ do it”.
You can also acknowledge that most people abandon their principles long before they are anywhere close to starving and will not be harmed (physically) by being reminded of that.
Goes both way, yes.
History has examples of extremely underprivileged people not compromising on the values even when facing death or torture.
That value usually isn't not telling white lies of no importance in a job interview.
When does it stop, though?
A white lie during interview, a deadline you promised and knew you could not deliver? A product you ship that claims to do X, but it doesn't?
All's fair in business and war.
To the people subscribing to "privilege theory", all positive differences are privilege and all negative differences are oppression, no matter if earned or self inflicted.
That's the logical conclusion you're forced to arrive at if you take these people seriously.
Just saying, anyone calling themselves an example is someone I'll ignore (but I'll reply to explain the rationale)
This is the mentality that says that if your company goes bust, you didn't work hard enough. Sometimes effort might be the problem..
No, not everyone can make it from nowhere to staff software engineer. That doesn't mean they're not trying hard enough.
OK how about some real achievements in life, is raising kids the hard way? Career is but a small portion of QoL and overall achievements as human beings, basically all of us software devs these days live have very above-average incomes although most feel like they are deserved or even not enough. So studying from poverty to software is an achievement and big move, usually, but what specific position afterwards is not that important or impressive, its just a question of a) mental capacity, mostly genetic and b) effort put into work, while not elsewhere.
Ie I increased my salary, doing same job, all 100% perm position, roughly 30x compared to my first fulltime software dev job after university. Who cares? It doesn't mean anything, just an afterthought. I am father of 2 small kids, and trying my best to be a good father and role model, often succeeding, sometimes failing. Its by far the hardest effort of my life, it takes relentless 20-25 years and I see otherwise brilliant folks failing at this hard left and right.
Also I wish folks in IT were a bit more humble and considered other engineering careers, with +- same effort taking to get a degree, and much worse career progress/compensation/freedom to choose one's path. Arrogance is much more rare there.
Hacker news is full of people having given up, building torment nexii and coping/rationalizing _incredibly_ hard.
So while I agree that privilege is certainly a factor, so is what I've just said.
A lot of people here live very cushy lives that cushion them from very pointy thoughts and questions. As someone who too has to live in this world, I'd rather they didn't.
Even “unprivileged” people are moral actors that can take their high road at personal cost.
What do you think privilege is?
A vague word that can be spun to your own perspective based on a tower of other vague words and personal values.
I don’t think we’re talking about slaves are we?
It's slavery or nothing, eh? Whatever happened to nuance?
It got dismissed as only being an option for the privileged.
When I was starting out in my career it was "take the first job offer that comes along or starve/become homeless" so no, sometimes the personal cost would be unreasonably high to expect of anyone
This effectively does mean that I was not a moral actor at the time
Naw that’s cope. You make your choices, own them.
Would you have stolen or murdered to avoid being homeless? Would that have been a morally blameless act?
Christ - that's a strawman if I ever did see one haha.
Most people will steal to avoid being homeless.
Highly paid enginners hiding behind "I have no choice I would be hungry" are usually just lying to themselves.
And you dont even get these nearly as often from people who work in lower paid positions. Or who are actually making moral tradeoffs that affects their income.
I have seen engineers take paycut or risk it because of this or that moral conviction. Not wanting to lie to customer, refusing job for gambling company, working one day less per week so that he volunteers for biblical something.
Just telling management no or just communicating about your work with ai or lack of it are not even one of those.
I believe that I must be truthful because of my faith, though I understand people feeling pressure otherwise. I have had to quit places that I found lying to part of the employees before.
It is very sad to me that people do feel that pressure, and how the current job market is.
On topic with the article, I would love to be able to trust AI with more, but have found that I have some useful moments with it, but more because of Internet search not being how it used to be for quality.
I think it depends. The people that I know that have made significant sacrifices to live along their morals are usually people who 1) are intensely bitter when others will not sacrifice as much as them; 2) are completely understanding of people who will not sacrifice as much as them or acknowledge that they simply have less to sacrifice than others. For example someone who is willing to live the "dirtbag" lifestyle out of their car to dedicate to their outdoorsman activity who is either bitter others have the relative financial security or feel immensely grateful they have consistently good enough health that allows them to be outdoors with so little resources.
For example I think the decision to stick to certain morals is very hard if someone has a disabled dependent, are disabled themselves, or require consistent access to healthcare. There are different lines for different people of course. Our ire shouldn't go towards individuals who make these decisions but the people in power who force others to be in a position where these decisions need to be made.
In the end, everyone makes their own choices.
I don't want to preach martyrdom, but I am also offended by people choosing moral bankruptcy when faced with even the slightest hardships.
This isn't a value item for most people. Employer doesn't want ai used great handcoding or employer wants ai used great prompt coding.
My truth is I don't care either way . I get the sense that's the same for parent poster. They just want a job and to say the right thing to get past the hiring filter. Even if I did have a truth its not something I would put above being remote, pay and how a company develops software. I'd rather not have a truth and not have a daily standup.
It’s also possible to not really hold that strong an opinion on things. Not everything is a pitched battle where doing what your employer wants means you’ve sacrificed your integrity.
Everyone can decide where the line is for them, but I do feel like given the subject that's a bit of a hyperbolic repsonse.
Cite needed - FAANG certainly leaned hard into ‘lie to survive’.
The job market being as it is, a lot of people simply don't have that luxury.
From far away, it's hard to tell the difference between integrity and anti sociality. Even though I believe "software engineering integrity" exists, you can see how it's hard to tell that apart from "software engineers who stir dramas or are annoying to work with."
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.
> Have you considered just answering truthfully?
To give you just a little more context than other commenters -
You answer truthfully when you're interviewing from a position of power. Either you're already employed somewhere and you're taking your time exploring your options to see if maybe you can end up somewhere a little better, or you're an employer with applicants lined out the door and you want to winnow them down to the best match. In either case, you don't care too deeply if an individual interview sucks, you just move on.
Truth is always the first casualty of war. And when someone is out of work and fighting for their ~life~ livelihood, or a founder is trying to convince the first customer or the first engineer to take a risk on them so that they can get their baby off the ground, the truth dies real quickly.
Sometimes the only reason you don't have power is because you have internalized the idea that you don't.
That's true, but it's about as helpful as a self-help book. Therapists, coaches, mentors are all helpful in that case.
> Have you considered just answering truthfully? ... That sounds not like a job but a toxic relationship.
It's a job, not a relationship. It's best not to confuse the two.
In any workplace, you will occasionally have to do things you find boring or objectionable. And if you're hoping to find a corporation that is a "perfect match", it will only hurt more when they unceremoniously fire you because the quarterly revenue growth is 1% off or because you cracked an off-color joke.
> It's a job, not a relationship. It's best not to confuse the two.
A relationship is defined as two parties that interact.
It's not friends, it's not romantic, and it's definitely not family, but a job _is_ a relationship.
That said, GP is absolutely correct that you can fall into toxic relationships with your employer. Especially in the US where, realistically, we're forced to rely on our employer for too many things (e.g. healthcare coverage), employers can and do take advantage of the situation.
You're being pedantic. By your standard, I also have a relationship with the DMV, and to avoid a "toxic relationship" (parent's language), I should be honest with them about all the times I rolled past a stop sign.
I didn't get that read.
I agree with their "two parties interacting is a relationship". Hell, I have a "relationship" at the checkout as they scan my groceries. But it's not deep or long lasting, so as you'd expect either party makes near zero effort.
Secondly, they never said that every relationship requires perfect honesty in all things. I'm not volunteering to my checkout person that I will eat that party bag size chips in one sitting. They don't need to know and it's highly likely they don't care. I'll be gone out of their life in a minute.
However, your entire relationship with your employer is roughly based on "benefits for work". It is a recurring interaction, probably over a significant length of time. So it seems reasonable to be honest about how you work to avoid making the relationship toxic from the start.
But then I also agree with "Do whatever it takes to survive." and if toxic relationships mean you keep breathing, well it sucks but there it is.
I am fortunate to not be in that "survival" position so honesty works for me.
Define objectionable? Not ethical is not illegal but maybe if you are okay with it, do it for yourself. Illegal is just dumb, you are still responsible. So at least, if you are doing, make sure you are appropriately compensated.
The parent was talking about giving a diplomatic answer about your attitude / use of AI. I'm not talking about Enron here.
> 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 don't think having trouble knowing how to tailor your message to your audience because of limited information implies it isn't truthful. Answers to jobb interview questions are usually very manicured and rehearsed but I don't think they're generally lies.
To this point, there are many advantages to answering truthfully. The job might be tailored (a little) to your expertise. Maybe the heavy AI tickets will get de-prioritized from your queue if you say you have light experience. If you say you're all in on AI, you just might find yourself becoming an AI expert out of necessity. Not because that's what the company wanted out of filling the position initially, but because that's what they got. It's expensive to have an open seat. The interviewing door swings both ways. You're looking for a fit just as they're looking for a fit. Going in every day pretending to be "a fraud" (or however else you might start to internalize it) eventually starts to weigh on the body, soul, and mind.
> 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.
Even a truthful answer can require a lot of long-winded disclaimers because an interview is a new relationship without shared context. You have to state the obvious because nothing can be taken for granted.
Ha. Interviewing is about answering questions in the way your interviewer wants them to be answered. It is a game of trying to "read minds" by figuring out the most common/best possible interview answer for any given question even if it doesnt reflect your own personal opinion.
People will say this is bad but then when they interview folks judge the interviewee if they dont answer questions how they expected the question to be answered.
This isn’t an AI problem. You can’t ask anyone to “be truthful” on any subject because everyone sees through their own world perspective.
Two people might say “they love camping!!”
But does it mean…
- Going camping twice/year and partying by the river?
- Or going 20 times per year, sometimes on 4 week long trips?
Both types of people will, with complete honesty, tell you “they love camping” and only you, the asker of the question, can decode what that means. ayli can’t
On top of the other answer, the people interviewing you might be HR people ressource with limited tech knowledge and this lack of nuance.
They are here recruit "AI ready engineer" or other vague, bullshitty slogan.
You often need to pass this first barrier to discuss with real futur colleagues. Thus you have to give them what they want to hear.
Which is hard on AI considering how polarised the subject is.
Interviews work both ways: employers select employees, employees select employers.
Unfortunately often employees forget that part of the goals of an interview. An employer never forgets its goal.
Of course the asymmetry stems principally from who pais whom and secondarily from who can wait more to find a better fit. Sometimes that's the employee.
If one is not desperate to get a salary immediately, it helps thinking that the company applied for an interview and agreed to let the interviewer come and inspect their premises, their personnel and their processes. The right questions derive from that realization. By the way, that's what auditors do.
"Have you considered just answering truthfully?"
Said by no-one who has a decent paying job and has bills to pay
You are playing a role at every job. In 20+ years I've never had a job where there would be no negative consequences for speaking truth to power
>Have you considered just answering truthfully?
It's a job interview, you're not supposed to do that, and they don't appreciate it when you do. Try something like:
and see how it goes.> Have you considered just answering truthfully?
It's 2026, you gotta sell your soul just to get a phone screening
At this point, why wouldn't you ask on the phone screen what attitude towards AI is?
I would hedge towards AI usage if you didn't probe before and then justify why.
Ymmv, can also gauge whether you want to work for the company based on this
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.
It sounds like you're an AI-happy employer though. What if their truthful answer was that they never tried to use LLMs and refuse to because they waste water or because of an overconfident view of their own skills, or they don't want to help a clanker steal their job? These are all popular beliefs that can easily come from following the right crowd rather than forming their own opinion. In fact, from what I usually hear of people's opinions, they almost never come up with them themselves, you can practically predict people's opinions on some topics just from what they look like (what social group they belong to) or what other unrelated opinions they've already told you.
I guess that's the point though? If their opinion only comes from common popular opinions, then that signal is quite useless to me.
In this case though, I'll admit that it would be a negative signal if they never tried it even once and refuse to do so. You can't make a solid opinion on things you never try after all. It would be different if they at least gave it a shot and disliked it.
> In this case though, I'll admit that it would be a negative signal if they never tried it even once and refuse to do so
I was arguing that this is a bad question elsewhere but you provided another reason. If a candidate tells you they haven't tried using AI (without saying why), that offers no signal at how well they'll do if you hire them, and you construe it as a negative signal. If you want to know if they would be willing to use AI as part of the job ask that question instead!
> You can't make a solid opinion on things you never try after all.
What do you think about using heroin?
In this case, I wouldn't mind if they used AI or not. Honestly it'd be quite interesting to find someone who doesn't use it to hear about why. At the end of the day, I'm more concerned about how effective they'd be in their role. And if I have 2 candidate, one with better domain knowledge but no AI, and another with AI but no domain knowledge, I'd most likely pick the one with domain knowledge.
And if they can make an argument about why they're more effective without AI, more power to them. So if I were to ask the question directly as you suggest, it would be: Are you more effective than the other person I'm interviewing? - which i guess is the whole point of the interview.
> Heroin Fair point, but I also don't think that trying AI is anywhere close to the level of trying Heroin
> You can't make a solid opinion on things you never try after all.
Of course you can, from the effect you see it have on others. Not everyone needs to do hard drugs to say they are bad for example.
Yes. Hedging results in a middle-of-the-road answer that, at best, comes across as lukewarm. Companies want to hire people they're excited about and are convinced fit into their culture. An honest answer will get you more strong noes but also more strong yeses, and strong yeses turn into offers. Hedging, produces only weak yeses and noes, which tend to end in no offers especially in tighter job markets like the one we're in.
If whoever is hiring is actually good at their job: yes.
That is of course assuming that they're looking for some long-term stable team member.
A skilled interviewer smells dishonesty.
However, and to be fair, whether and how they act on it depends on the specific situation.
What is this "skilled interviewer" thing and where have you seen it?
While this industry surely is frustrating and full of pitiful fraudsters, I don't think that what you're saying is fair or leading us anywhere.
Most of our stuff in this world actually does work, and the reason why it does is that skilled (teams of) people that care have built it. Meaning that these people can be found in many _many_ places.
The skilled interviewer is rare. But if truly skilled they understand why people hedge and would not consider that dishonesty but a skillset the company might need. A semi-skilled interview might pick up on that and assume the worst.
Very few jobs are looking for opinioned most are looking at people who might fit in unless you are hiring to distory from without.
> Have you considered just answering truthfully?
We all filter and “nudge” the truth during interviews. We all cater our responses to the person in front of us. Let’s not pretend otherwise. Your interviewers sure aren’t.
The two job interviews I had recently - the interviewers were clearly AI maximalists. My answer each time had been to the effect of "yeah I use it but I make sure to check it over thoroughly since it can make mistakes" and I'm guessing that wasn't the answer they were looking for.
It's a gamble of the dice as to whether the engineering manager is equally realistic about LLMs or has unrealistic expectations about what LLMs can or can't do.
> Have you considered just answering truthfully?
This is terrible advice.
Everybody lies on interviews. Especially the interviewer.
Because almost every HR department now has a directive to only let people through the screening process who say they are using "fully agentic workflows" even though that's moronic.
As if most people have a choice in the matter.
To be honest, I don't think I would want to work with or hire you, based on your response here.
Not everyone has that luxury when there are bills to pay and mouths to feed.
Yeah, I just responded the same thing almost verbatim before reading your response. Yes, it's a luxury if you're not well off in the first place.
> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer with limited information about who they are and how they personally use these products.
I'm an old hat on both sides of this type of discussion from a post-grad view.
Recommendation: use it to own the conversation and to signal mutual fit. Yes, your idea of AI lover versus hesitant matters. I recommend reframing the question to pivot to your fit to the org (and org fit to you) question. Show/concisely explain how you consider whether LLMs are fit to a task and how to tell it improves outcomes.
An outcome focus and willingness to show thought process around a common use case will be a substantially strong response.
Just in general these questions are probes on curiosity and ability to show depth too. I’m astounded by suggestions of stating flat out refusal to even try out LLMs or suggestions to over praise the merits as if the interviewers want to hear binary answers. A well thought out pros and cons story wins over binary yes/no answers at pro and anti ai companies alike.
> A well thought out pros and cons story wins over binary yes/no answers at pro and anti ai companies alike.
The issue with this is, you need to know how to really program to be able to articulate the pros and cons, which a new grad would mostly likely not have.
For example, if you want to include how AI can onboard quickly, you really need to understand the pain points like, I tried asking people but really, everybody is busy. Or I've found coding agents help me speed up making code changes, but it some situations, they can help accelerate making mistakes.
I think the issue that a lot new grad are faced with is, you don't know, what you don't know.
still 10x better than the 'finish this leetcode tweak algorithm in 20 minutes and tell me your thought process along the way, and yes you will never need that skill in the real job but we need find out who had time to cram for the algorithm books in the last few months'
How are the technical interviews these days? Do they still ask Leetcode style questions or is it getting deprecated?
I've unfortunately been doing alot of interviews. Leet code is common. Or "practical" which seems to more or less mean implement some kind of kv store class with some trick constraint.
I think what would be great is to have eg a concise example where it works well for you and a concise example where it doesn’t. This shows you have explored it and thought about it enough to explain interesting observations. It’s good to then be ready to go deeper if of interest.
Thinking about a similar question, I've tried to come up with this framework: https://assistedeverything.substack.com/p/ai-bowtie Maybe it helps in your interviews.
I think you will find that both AI-pilled as well as AI-sceptics will actually agree on that approach, because it rejects the question of "how much" and rather reflects it towards the "when to use AI". Depending on who it is, you can then talk more about the beginning, end or center piece of it.
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.
Replying as a person who acted a hiring manager for decades: their loss.
Hiring for technical roles, I love long-winded answers as long as they are coherent. I don't want slogans, I want to understand how you reason through problems and that I can trust your judgment. Everything else is secondary.
Note that this doesn't mean "rambling". Get to the point. But if you want to show me nuance / reasoning, I want to hear it. Also a good way to spot bullshitters.
> 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.
I've not observed that anywhere. It takes a couple of hours to figure out how to use agents. It might be a slight negative if I suspect you're not demonstrating enough curiosity about what's one of the most significant developments in tech in a long time, but the assumption is easily overcome if you geek out about something else.
> Not having used agents and not being able to comment on what to do and what not to do with them is immediate out since early this year.
From all the tech that we have, agents are really not that hard to learn on the job. They're also not a magical silver bullet.
True, but please don't give job seekers false hope with this statement. I commonly see 60 - 180 applicants for one open position. Good luck finding a hiring manager who wants to take a bet instead of going with proven experience.
I think upskilling is the right move in this environment and it is dead simple: Invest a couple of days to show initiative, learn agents yourself and be able to speak from true experience.
I love that we’re already talking about “proven experience” for a technology that’s essentially 15 months old, arguably only broke into the mainstream 3-6 months ago, has an unclear RoI for many companies, and seems to be changing quickly in both cost and “best practices.”
You’re more or less admitting that you’re playing trendy tech lottery. Which is fine, but maybe not generalizable to the whole industry.
15 months, 15 months ago, is not the same 15 months now. You'd be ignorant to think this a trend that will just fade. If we look at that has happened the last 15 months, it'll keep getting bigger and better. Hopefully not more expensive though.
> playing trendy tech lottery.
I don't know about that, and I am 100% biased so take what I say with a grain of salt. My position is very much this: you may not trust coding agents to make code changes, but if you're not willing to treat them as a research aid or have them work for you, you're pretty much saying they can't help you work more efficiently.
I'm working on a Show HN post that includes:
https://github.com/gitsense/smart-ripgrep
It's a fork of BurntSushi/ripgrep. What I hope to show with it is that you don't have to use coding agents to code. They can be used to surface knowledge that's buried in documents, issue comments, PR discussions, and other places.
Believing coding agents are trendy would be like saying search was trendy in 1998. They're not going to change the world the way Anthropic wants us to believe, but they will shape how humans develop software. And I think for the better, since AI is capable of processing information at scale to help you move forward.
Regardless of how you think about LLMs (I do find them useful), there’s something really odd to think that you can select for “proven experience” in a young technology where current experience appears to have little to do with experience 15 months ago, and where the biggest boosters fully claim it will have nothing to do with experience in 15 months.
What you’re selecting for is enthusiasm, knowing the current shibboleths of the in-group, and possibly for who knows how to use them to make a good demo.
And, fair enough, if that's what you want. But it's not "proven experience" in my mind.
your post on Who's Hiring provides some needed context...
want a Flutter developer who is unusually strong at directing AI-driven software delivery. This is not a traditional "write the code yourself" role.
https://news.ycombinator.com/item?id=47223956
Not wrong. But my statement is true across organizations I have worked with in the past year, several of them not AI-native.
You've worked as a hiring manager at several organizations in the past year?
I said "with" and not "at". I was tasked with doing interviews and reshaping the interview process as multiple companies within the last 18 months through fractional roles, consulting and co-founding.
They're LARPing
> 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?
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.
This is also describing an interview scenario where the interviewee is trying to throw everything at the wall hoping to stick. Sure there is discretion in how much to elaborate, but it is a performative act, where someone is trying to demonstrate that they have deep knowledge about a topic and can appreciate nuance that not everything is black and white.
Not OP also but it typically signals that you're not confident with your answers. If I am actually curious about it, I'd ask a followup question for them to expand.
Often the hiring manager will have the person to be hired somewhere in his report chain. So if a person can't effectively communicate and can't properly respond to a "I only have 2 minutes, shoot", then I am getting a future liability into the company that will slow down all future communications.
I much rather prefer someone who needs 3 seconds to triage a question and tell me: "This is X, I know this, here is the solution" or "This is Y, I don't know it, but I will get back to you within 24h".
I do absolutely not want a "Well let's think jointly about this for a couple of minutes". There is no jointly with your boss. Let's do a some math of a 1:12 manager to direct report ratio. That means for every hour you have, your boss only has 5 minutes. And if you talk to your boss' boss, they have 25 seconds for every of your hours.
I absolutely do want to work with people who want to think jointly about interesting questions for a couple of minutes. Give me your long-winded (thoughtful!) answers. Let me see how you think. Let me see how well I (and others) can think through things with you. That's what the point of an interview is, IMO. And I've been gainfully employed in tech for 15 years now with that attitude, often in environments with other like-minded folks, often involved in the hiring decisions that have led me to work with those other like-minded folks.
So in the same interest of helping post-grad job seekers, do what you've gotta do to get yourself paid, but maybe don't presume that vibe_that_works speaks for every hiring manager.
This thread is why job searching sucks. Every one making hiring decisions has conflicting advice and requirements.
That does sound like a bad org tho, sorry to say that.
Not to disagree of course that time is limited, but in my experience, optimizing it this harshly leads to poor results, because eventually, you just get leapfrogged by reality.
Hyper-optimized systems are brittle and can't really adapt to the market changing.
But yeah, I guess they still need developers. Just doesn't sound like a fun job :D
Just trying to fix the misunderstanding: I am not saying that you will have a literal 25 seconds meeting with your boss's boss. I am just making a math argument taking typical orgchart ratios.
So let me take this a step further. You want to meet your boss' boss for 10 minutes to present them something. 10 minutes of his time are an equivalent of more than 20 hours of your time. So if your initial idea was to "take maybe 1-2h" to prepare for this -> You are underprepared by at least one order of magnitude.
This is a very strange mindset. Even if you want to treat everything as sort of billable hours this doesn't really make sense because the average boss's boss's isn't paid anywhere near 144x. If a SWE spends 100 hours to save their boss's boss one hour, they're wasting a ton of money.
I mean I am no expert, but to me it sounds like the org you're describing seems to lean away from the "engineering" side of things and into the "org for the sake of org".
Which might not be ideal, because "orging for the sake of org" to my understanding consumes significant resources not going into building products/marketshare/shareholder value.
But then again, I'm no hiring manager in such a structure, so this is probably just an uninformed take.
> 10 minutes of his time are an equivalent of more than 20 hours of your time.
What does this even mean?
> I do absolutely not want a "Well let's think jointly about this for a couple of minutes".
But why?
Most of my most fulfilling experiences in tech have come out sitting down and hashing out a problem with someone else (including with managers/leaders).
It sounds like a miserable org if I am not expected/allowed to have an actual back and forth conversation with my boss. If I'm employed to be on a team working on an aligned common goal, why would I not use that collective skill and experience to my fullest advantage?
> There is no jointly with your boss
You're describing a coding sweatshop. What is the point of any discussion at all then? If the "boss" can't carve out enough time, that's their own problem. Letting that stress propagate to the team is plain bad leadership.
I know you might think some of these candidates don't have other much better choices to find work, but they absolutely do.
> Any long-winded answer to a question is immediate out and has been for years.
That’s a bit ironic, given the typical output of LLMs.
What does "use agents" mean from your perspective? Just Claude Code with some MCPs? Or like a full on GasTown type setup?
The former. I would not expect any candidate - especially a new grad - to have actually used GasTown style setups unless they were coming from another company with money to burn on those sorts of multiple-agent scenarios. I barely run more than one or two Claude sessions at once because it slams into the usage limit so fast.
I could steer the conversation to explore if the candidate had insight into why GasTown or other "swarm" models exist though. Do they know what a system prompt is? How context is managed/affects output? How harnesses can delegate to simpler models, and when to use different classes of model? I wouldn't expect anything particularly deep here, but bonus points for a solid understanding/experience of the tooling and how to get the most out of token budgets is valuable.
To the parent point: even if you refuse to use LLMs for whatever reason, I would expect any coding applicant to have at least tried them. I'd assume the person is living under a rock if they said no.
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.
4 years into job hunting. Answering truthfully does not work. Nobody likes the truth, and every bit of advise i get from anyone is to lie (though, some of them use euphemisms to avoid saying "lie").
This might not describe you, but I've met quite a few people who made similar claims. The problem usually wasn't that their counterparty didn't want to hear the truth. More commonly, the problem was that these persons assumed (and were convinced) that they knew the truth. Truth is rarely absolute and someone claiming to know it is a red flag in my book. Double so if multiple persons indicated their disagreement. Again, not knowing the exact question and answer it is impossible to say if you are an exception, but even if you are, you need to improve communication skills - what good is knowing "the truth" if others reject it?
That said, best of luck on the job hunt! Sometimes it just takes some time for the right opportunity to come along.
Truth is easy when the question is about your own involvement in something. You just don't lie and don't intentionally deceive. This isn't about technical arguments, or moral quandary, or ambiguous situations, it's about marketing yourself in a world where you are expected to lie about your accomplishments to look better. Citing metrics you know are not accurate, but make you look good. Overselling your own involvement in a project. Up-selling the impact of a project by not disclosing the parts you know would undermine your perceived competence.
Then this is not the same situation, I agree. That sucks. I hope you find a company that shares (and rewards) your values.
I would hope this is true both in the context of LLMs and more broadly, but I think this is especially not the case for LLMs. It's hard to take the idea that companies are trying to hire people with reservations about LLMs seriously when many companies have LLM use mandates. It is counterproductive in the eyes of the employer to hire employees that will be combative on LLM from day one.
> re-factoring a big repo of decades old fortran+C cod
Having been in academia in the past and now in software I can say with a lot of certainty that this will take a lot more upfront work than otherwise.
Academic code does not have a lot of structure. And usually lacks a lot in terms of tests. While AI is best when it can mimic patterns as well as there are tests to target.
So you will probably need to budget a few weeks to establish good patters, docs as well as testing patterns before you can seriously make it really do what you want it to do.
exactly yeah it was a code base written by atmospheric physicists I assume and I had an idea that maybe copilot could get it working to interface with some more modern software and it just didn't really have what it takes.
Even with 3 weeks I'm just not the Fortran/C programmer to get that job done so I moved on to other things.
I am also on the job market, but as a Senior. Pro-tip: ask them this question before they ask you. “One quick question I have about the company culture, …”
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.
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.
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.
Digging with a bulldozer sounds like hard work. You mean a digger.
Language barrier (bulldozer is used as a placeholder in my native language) , I should have said heavy machinery
Cards on the table, after posting I had doubts about this and had to look it up! Executive summary: "digger" = hole, "bulldozer" = flattish, "steamroller" (or just "roller") = flat as a pancake.
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'd take a candidate who fights for a view I disagree with over one who hedges to please everyone (Strong opinions weakly held, but willing to change their mind if/when the data tells a different story)
If you're not already, try to see if those companies have engineering blogs or open github repos where you can see how they feel about AI beforehand.
They might not have an answer in mind. They might just be exploring how you adapt to new tools and methods.
One trick is to ask them that question first to gauge their perspective on it first
> It's tough to answer because you want to hedge
You should just be honest. If you're not a good fit for the company then you should honestly be eager to discover this.
> I've been responding with a sort of long winded answer
"I don't. I personally don't find value in them for the type of work I do. I am also uncomfortable with using their outputs under the current copyright regime. I also question how competitive any organization can possibly be if LLMs become the main driver of their work products."
> I've had more bad results than good the few times I've tried them
"I prefer to write correct code rather than debug bad code generated from a limited context window."
some employers may like this wishy washy answers.
personally i find this offensive and would disqualify the candidate.
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
I know of a few shops (names you've heard of!) who are trying to transition to AI-only coding (i.e where engineers must vibe code...) - despite already having a few production bugs with their current approach.
I've personally started to dislike agentic coding, not because of the LLM side of things, but rather, because the non-LLM workflow itself is stupid.
The UI/UX is built around one-shotting the code as a single fully baked unit. There should be intense competition around having the best code review tools to adjust the fine details and yet everything is devolving into a text prompt, even CAD tools where being off by a millimeter or less can make a part completely useless even if it is 99% of the way there.
All of these tools are assuming that the LLM already achieved superhuman AGI capable of reading your mind while making zero mistakes.
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.
> It's tough to answer because you want to hedge for both an AI enthused employer and an AI hesitant employer
That this doesn't have a clear and obvious answer one can expect shows how the issue is politics, not strategy.
When you apply as a mechanic, there is no such weird political debates about certain power tools where people have passionate opinions on which tool to use.
"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.
The reason you've had more bad results than good is because you haven't fully learned how to use LLMs yet. They are not as simple as they first appear. I think a lot of people think using a coding agent is just a case of firing it up and telling it what to do and expecting to get it right first time. When it doesn't they just think it's no good and like you abandon the effort.
The reason a technical interviewer will be asking this question is because they want to see how you adapt to using new technologies, LLMs being one of the most disruptive technology that has hit the tech industry since at least the internet. You will likely be expected to use LLMs and they will want to know that you are someone who truly understands the capabilities of them - upsides and downsides, where to use them, what guardrails you need to put in place.
I'd encourage you to revisit the re-factoring task you worked on. Work out why it didn't work, work out what didn't work about it and if you have the chance try again, but use different techniques, there's a lot of conversations going on about what people find working and not working - try to join that conversation. Try to document what you learn. Then in the interview discuss these rather than just saying you gave up. The interviewer isn't going to check up on how successful your project was, they just want to know how you think and how you approach problems.
> AI has gotten so good that despite any misgivings, “everyone is using A.I.”
In my experience, it's a mixed bag. I wrote this comment[0], yesterday. It reflects my current work, and how I am integrating an LLM.
I have used it for two parts of my project:
1) The backend (PHP), and
2) The frontend (Swift)
It has been a huge help, in both, but #2 is a cautionary tale. It really needs adult supervision, in developing native UIKit Swift apps. I'm realizing how truly bad the code it wrote was. I mean, terrible.
That's jarring, because it did a great job with #1. It made sound, reasonable design decisions, and provided code that is better than what I would write.
With #2, it behaved exactly like an inexperienced engineer, panicking, when confronted with real-world problems. My rewrite is going to feature a much simpler, sound approach.
All that said, it has been a net positive, and has increased my productivity by a large margin.
I guess the lesson I needed to get from this, is that it is good at helping me to find problems, but maybe not so good at fixing them.
[0] https://news.ycombinator.com/item?id=48515217
I'd like to add that there is almost no way of "running away" from it. If I search for anything on the internet I am almost guaranteed to be handed pages and pages of AI generated content. In lieu of that I found that directly prompting for an answer tends to yield better results nowadays. Not because it's good per-se, but because having control over the prompt beats having little to no control over it though search by proxy.
It saddens me to see that high quality content is drowned in this sea of garbage to the point of being almost impossible to find.
I think this is where the circle closes with the "dead internet theory"... you go to Reddit, and see bots commenting on posts created by bots.
Then you go on to search for something, and find only results that are clearly AI generated pages and come to the conclusion that directly prompting some LLM is better than reading an AI slop page that's output by the same AI for slightly less specific prompt.
My concern is that this will only get worse over time - which is great for companies selling AI tokens and bad for society and whoever wants to interact with other humans over the internet.
This would be expected. The corner cases people faced with PHP throughout the decades have been well documented on the internet for eons.
Swift, not so much. It's relatively new. Looking at AI's abilities like an engineer's career span scaled about 10-20x of time makes it make a bit more sense.
It's going to be worse at newer/niche things, intuitively - which is only going to get worse as it "learns" from garbage outputted by other LLMs moving forward.
Also, I suspect most "production" Swift –the type of stuff written by seasoned experts– (I just had to add em-dashes ;) is behind closed-source walls.
No doubt in my mind, a future Apple model will be the best to use for this purpose. They likely have more swift to train on than anyone else, and would benefit directly from more quality apps, rather than the slop flowing into the App Store (>1k app submissions per hour; they claim)
> which is only going to get worse as it "learns" from garbage outputted by other LLMs moving forward
You seem to assume that autoregressive pretraining (and unfiltered behavior cloning, maybe) are the only ways to improve LLM performance.
No. I'm just saying, there decades worth of more PHP discourse than there is swift discourse.
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.
There's probably a lot more PHP code to regurgitate, while I'm guessing a lot of Swift apps are proprietary. That's the problem.. they aren't coding from principles, they're pattern matching their training data
I feel like AI fails the XY problem constantly. And it's one thing that I know people hated so much on Stack overflow.
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 wonder if it's expertise gives you ability to see flaws and push the LLM past its acceptable point.
I haven't really used LLMs much for coding (sabbatical before LLMs got good at coding, now looking for work) but I found with chats that they are great at exploring well trodden territory but as soon as you go a little bit off the beaten path they flail horribly
I suspect that you have an excellent point.
They both do acceptably (but PHP better), as long as I don't push hard. The Swift that I get is ... meh, usually.
However, my PHP server, by design, is extremely conservative. It's meant to run on cheap shared hosting. I don't push the edges. The LLM seems to do a great job of respecting that, while still giving me good, modern, code.
The swift, on the other hand, has highly optimized UI (which also means that I'm not using SwiftUI). It shits the bed, when I push it.
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.
My experience was different. I found it extremely good at fronting technology like react while I had to hand hold it for the backend tasks. Even with fable it was the same.
React is pretty mature (now), and has a lot of hardcore production sample code out there.
I suspect that the LLM training got much more from the react community, than Swift.
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).
As a Claude Code user, you almost certainly will. Even using the same OpenAI model with an agent like Codex will likely perform better. An agent can test and iterate on its own solution until it meets the defined success metrics. If you’re working within an existing code base, having preexisting code to demonstrate similar implementations also significantly improves the quality of results, and that’s something an agent can dig into as part of its solution process. I haven’t used ChatGPT in a while, so maybe it’s more sophisticated than it used to be, but I think you’ll see much better results with the latest agent tools.
Isn’t it because Swift (and SwiftUI, if you used that) changes the recommended approach to solving X every 18 months?
Might be because there are less Swift projects to train with.
But I've seen Claude write crazy code in Python and JavaScript, too
My theory is that most of the Swift code in the public domain, is basically demo code. Short, idealized, code samples to demonstrate issues and solutions; much like you would see in StackOverflow.
PHP has huge, entire frameworks and systems, refined over years.
There is also a lot of low quality PHP code out there, and a lot of legacy code in a language that I am told (I have not used if for years myself though) has changed a lot.
Same with C++. You don't want to write C++, the way that I used to.
That's one of the things that I appreciate about the PHP that the LLM provides. It uses modern idioms that make better use of the modern language.
I do not know about crazy, but certainly sub-optimal. For example a loop over DB query results instead of modifying the code to work with a single query.
I found that asking it to refactor for performance and safety often addresses these issues.
I’m going to guess you are better at frontend than backend.
The classic AI Gell-Mann effect.
The guess is correct.
The diagnosis, however, is not.
Have a great day!
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.
My understanding is that the threshold people mean are that the vampire kids would be fired for not having 1/4 million spend in tokens per quarter.
EG: That the entire conversation is juggled by the token selling shills, per usual.
I've noticed several companies replacing deterministic systems in their support flows with a LLM version that is slower and worse. Many interfaces simply aren't better with AI added
The real best case scenario is using LLMs to help build deterministic systems. Instead of asking an LLM to do some task that you know will be repeated, instead ask the LLM to build a program (Python script or whatever) to do the task.
If it's a one-off script/program that doesn't require additional "domain knowledge", sure. But what if you need to give as context your whole backend repository because you need to take into account a few business rules? Why give anthropic/openai access to my "secret sauce" (e.g., company private repos)?
In that case, it's way better to simply write the code yourself.
From all possible concerns, "giving access to anthropic/openai" to your "secret sauce" is the least important one for 99% of the companies out there.
No, is not way better to simply write the code yourself. Most of the code is written faster and better with Claude Code or equivalent. Very niche code is better written by hand. Even then, you're probably better off nudging something like Claude Code in the direction you need it to go. There's nothing interesting about writing it yourself unless you're still learning to code (in which case is a learning exercise for you, not only about the outcome).
I promise OpenAI is not going to steal your “secret sauce”
Based on...?
Your sauce is not worth stealing to OpenAI. They have bigger fish to fry
In fact - had there ever been a single case in the history of the internet where a service provider of any kind - hosting, storage, email, search, anything - has ever stolen intellectual property or trade secrets from a user and benefitted from it? Has that literally ever happened?
Making systems fully deterministic ignores the entire purpose of having agents involved.
IMHO the best of both worlds option is agents working with deterministic CLIs. Where the agent does the reasoning (and text generation) but uses CLIs to carry out all of the actions (issuing refunds, unblocking accounts, or whatever).
It's possible to get very reliable and consistent work out of agents when they're using well written prompts with well designed CLIs.
Isn't this how we end up with things like: https://www.reuters.com/legal/government/high-profile-meta-a...
Yes: https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/
Although you can certainly do a better-and-worse job of preventing these kinds of issues.
I think it just depends on the workflow. A lot of times (probably the majority of the time) a well crafted deterministic process is what people want.
Sometimes you can have a multistep deterministic workflow with a decision that needs made somewhere in the middle, which is where an LLM that can call tools is useful.
I think there are much less repeated tasks where you would want a fully agentic process (but there are many ad hoc tasks where that is exactly what you want).
>Making systems fully deterministic ignores the entire purpose of having agents involved.
That sounds backwards to me. I hope that most places don't see "having agents involved" as the ultimate purpose, but will use agents where it makes sense, i.e. when deterministic systems fall short.
How else would anyone do something like issue a refund if not through a programmatic interface?
Direct access to the database, and create the "refund program" on the fly. Yes, stuff of nightmares.
yes... ha ha ha... yes!
Right thats just head cannon though. Unless of course you believe the lies you read on the Internet.
At some level everything an agent does is through a "programmatic interface" (tool calls).
Some people might use skill-based scripts, MCPs, or some kind of raw access to a database. My point is that well designed CLIs are the optimal programmatic interface, for many reasons.
Sorry what other option is there? Is it going to create an API call from scratch every time after reading a page of documentation?
Wait raw access to the database? That’s one of the options for issuing a refund?
Yes, it can do.
At Big Tech Company I Work At the LLM is quite happy to make raw API calls. If it thinks the data is big, then it'll write a Python tool to do it.
The reason crafted backing CLIs are useful is you can guide the LLM towards stuff that is immediately useful rather than hoping the nondetermism can separate the wheat from the chaff.
Take CI: is it interesting to know which tests passed? Maybe, but probably not. What is really interesting is what failed. Instead of having the LLM go out and talk directly to the CI system, write an intermediate CLI that filters out less actionable stuff by default, and have a flag that'll deliver the full dump if necessary.
It's a skill to do this stuff, and it's a lot of hard won experience than something I think is easily teachable. You kind of have to feel out your model and how it "thinks" about solving problems.
And then a new model version comes out and you have to learn it all again!
100% this.
I've already commented on other posts that having LLMs build deterministic and testable tools is the real unlock.
Even for things like customer service, a LLM that analyzes customer support transcripts and then updates your call tree to better route people is a huge win.
The best case scenario of LLM is transforming input into output where both are languages and accuracy doesn't matter, e.g. "rewrite this poem in pirate speech."
But that's not worth trillions of dollars...
Or just write it yourself?
Because typing “code” takes time and significant amounts of it.
We are slowly waking up to the fact, which was always true, that “coding” is just a fanciful preparatory task in order to appease the spirits properly so that we may invoke the spirit of what we are actually after: a live, running process that does useful things. Code is completely useless when separated from that fact.
Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding. Knowing when it does and when it does not have this property is a skill of its own.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
I believe this is the general belief about basically every human skill, that if you stop doing the technical fundamentals you get worse at understanding the activity. The question is whether coding is like sailing a square-rigged wooden ship, which became completely useless knowledge after the invention of the steam engine, or if it's like playing an instrument, which while technically unnecessary after the advent of MIDI and other tools, absolutely hurts your ability to arrange, compose and perform if the skill is neglected.
For my money: I think the AI scenario is more like the latter, but "humans are worse at coding" isn't the consequence I see coming. I worry that in ten years we will be awash in software that's impossible to understand. I don't think that's happened in any human industry ever. Someone has always understood how the machines are built, even if they're very remote from the users of the machine.
The sci-fi novel A Fire in the Deep starts with describing a Software Archeologist, who digs through millennia of strata of layers of indirection and I think we could end up needing that one day.
Do they end up determining that every weird piece of code they find must have been used for religious or ritualistic purposes?
Ascension to AI plays a quasi-religious, soteriological role in the world of the book.
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.
> 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.
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.
I'd put it even lower than that, since there's also the "understand the problem space" portion outside of the external processes and before writing the code.
So there are these things called "text editors"
Hands don't type as fast as machines, no matter what editor you use.
Yeah, so again the point of text editors is you shouldn't be typing all that much with them. That's why we use them. Macros. Shortcuts. Metaprogramming. Snippets. Identifier completion.
Were people actually typing out the full text of source code before LLMs? Why?
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.
This is such a delusional take it's borderline trolling. Code is an expression tool to precisely describe a process that does useful thing. Typing prompts is not too different from writing some very vague code, which is arguably a waste of time by itself.
> 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.
> Typing it is a complete waste of time unless getting up close and personal with it will result in some kind of useful and actionable improvement in you or your understanding.
I would argue that this is nearly always the case. I don't think people really understand programs that they've only read at more than a very superficial level. This is why I tend to make (temporary) small changes, printlns, etc. when exploring a new code base: it aids greatly in understanding how a program actually works.
And it's even worse (in my experience) with LLM generated code, as it tends not to result in particularly understandable code. It is a lot like LLM generated prose: it often looks entirely reasonable at a surface level, but has a of weirdness/incorrectness hidden beneath the surface. But that surface level makes it very hard to avoid glossing over the details when reviewing the code. For this reason, I personally find it's much more effort to carefully review code than it is to write it.
Humans make mistakes all the time, but their code tends to naturally be structured for human understanding (to some degree based on skill/experience) because they themselves needed to understand it to write it.
I think LLMs are very useful tools, but after quite a lot of experience using them, I think it's generally better to use them as a sounding board, or to help you get unstuck or remove points of friction. Using them to write all of your code (at least for me) seems like a net negative.
I also think it's extremely easy to overestimate how much time they save. It feels like they're a productivity boost because it takes less intense focus to implement something. But I've experienced several instances where actually writing the code myself would have been both quicker and have resulted in better code.
All that being said, it can also be really hard to not write all of your code with agents once you get used to it. There's also a kind of slot-machine-like effect where you write a prompt, excited for the result, and when it doesn't quite come out right, you think "ah just one more prompt and it'll be good." It's hard to see when you're actually doing it though.
It's also weird to me how much people think typing is what the LLM is replacing. Typing was never the hard part. It's the translation of the high-level idea into an unambiguous process that's hard. That's also the valuable part, that requires thinking through the edge cases and consequences of decisions, and that just gets glossed over when using an LLM unless you rigorously review what the LLM has done.
At the end of the day there's a real tradeoff to be made, and it's worth being conscious of what's being given up.
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?
The behavior of an LLM is not and cannot be “well-specified”.
trumps voice wrong
https://developers.openai.com/api/docs/guides/structured-out...
https://github.com/guidance-ai/guidance
https://github.com/noamgat/lm-format-enforcer
https://github.com/mlc-ai/xgrammar
https://github.com/dottxt-ai/outlines
Libraries like Guidance guarantee that the output of an LLM will be syntactically correct (i.e. it will be valid JSON or whatever output format you are wanting). They do not, and fundamentally cannot guarantee that the data contained in them is actually correct, and cannot make the actual behavior of the LLM "well-specified". Or, as you put it, trumps voice wrong.
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.
We understand what you’re claiming, we just think that you are wrong.
>rather than giving it in a well-specified and tested form
So, code?
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.
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.
We just got dropped into hackatons for having ideas a few weeks ago, AI at all costs, similar feeling.
Luckily for programmatic or logic following, smaller models tend to do better, it can be surprising at first to see the more expensive models do worse at a task but it’s true.
Basically, folks nowadays think that this article[1] was aspirational rather than a cautionary tale.
[1] https://thedailywtf.com/articles/Classic-WTF-No-Quack
> replacing deterministic systems in their support flows
The issue is, they don't want to provide "better" support but "cheaper" support. Imagine a trained agent that understands the big picture. Now imagine a company investing in humans to use AI to retrieve knowledge that the human can easily identify as being relevant or not, and using that knowledge to better aid the customer.
Right now AI is being sold as a "we don't need support personells" instead of "how can we provide better service." For a lot of products, better service will probably not matter as "cheaper" products will win most of the time.
Most people don't want to pay for better. They want to pay the same for something better, which is what companies are not investing their time in figuring out how to use AI properly for I think.
> Most people don't want to pay for better.
A lot of people want to pay for better, but that is hard. Better is more expensive, most of the time, but being more expensive is no guarantee for being better. It feels like the correlation is very weak. Most expensive products are just expensive, not good.
If there was a reliable way to identify the "better" thing, I and a lot of other people would go for that every time we can.
That's the completely opposite of what people should do. The laborious task of programing logical work flows is the only reason AI is useful for me.
When I hear about engineers who are bored with coding, I have to imagine it's because the task of "programming logical work flows" has become rote to them.
Instead of refining their approach, or challenging their current knowledge base for discovery of inefficiencies or baseless assumptions, they'd rather hit an "easy" button.
I understand the desire to NOT do work. I understand the desire to spend quality time and free time with family. And I understand the idea that familiarity breeds contempt.
What I don't understand is the willingness to replace a deterministic language/framework/approach with a probabilistic slop machine.
Unwise design. “It talks, CSRs talk, it’s the same thing”. The fact CSRs talk is incidental. Nobody contacts support to talk. Customer service is a kind of “exception handler” for that which you failed to automate. If your system exists, works and is legible, conversation is avoided.
This is not true IME.
People constantly call support for problems they can resolve themselves, where the product is actively trying to help them resolve the problem, and where copious documentation exists to communicate exactly how to resolve the problem. For many organisations these calls are >80% of support workload, which is why so many products make you complete a quiz before giving you a support form, or why you have to navigate a phone tree to get a person on the other end.
Most recent support call I assisted with was someone who was trying to sign in on a new device, sign in screen told them to check their email for a one-time code and enter it into the form on the screen. They called support because the email took longer than 10 seconds to arrive in their inbox, so they believed the code had already expired by the time it arrived. There was nothing in the form or the email communicating a 10 second code expiry (actual expiry is 60 minutes, not communicated in the form or email). They'd never actually tried to enter the code into the form and hit submit. Never clicked the help link. Just straight to the phone call.
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?
...and now think about Claude being shutdown by the gov... :-D
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?
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
Maybe it should have clicked earlier in life and I'm perhaps that much dumb dumb, but it only recently occurred to me (from experiencing it at two very different companies and discussing with peers having reached a certain seniority level more or less at the same time) how dysfunctional many companies are, and how often they produce incentives that are misaligned with the overall company goals and sustainability principles. I blame in large part a layer of middle management that selfishly puts itself above all else, misguides, misrepresents, because it essentially pays larger dividends (literally and not) to "play the networking game than to be an efficient and effective productive structure". Maybe that's to be expected in a services-driven economy where the value of the work is immaterial and subjective (and the whole phenomenon of bullshit jobs).
So then, do you put AI into it anyway because they asked for it, or do you tell them that you won’t do that?
I'm sorry, David, I'm afraid I can'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.
Use 'AI' to create anti-AI reels showing how much they suck at all tasks. Spam CEO's underlings.
Yeah but did number go up? Can CEO check a box to show investors?
Now that’s real value.
With inexperienced or non-technical people, talking to them about AI can be very confusing, as a LOT of their "AI" usecases are basically they didn't realize or know how to write a program for this straightforward logic.
models will get smarter, this wont be an issue
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".
The Claude web UI popped a modal up a few days ago advertising their new model to me. It was full of HTML tags that were escaped or otherwise not rendered so that the text was literally
...and this was presumably generated with the flagship model from the world's most prestigious LLM company.They say this every time. Just wait for the NEXT model bro THEN everything will be be fixed.
Ok wait maybe not the next one but surely the one after!
Hasn’t happened yet and there is no evidence it will.
This all reminds me of in the 90s when the Borland C++ compilers and Turbo Pascal shit came out and everyone was still hand rolling assembler because the optimising compilers were so bad. I thought Opus 4.6 was pretty good, basically a step change. The stuff I got out of Fable before they blocked it was nearly alien. If things keep improving, I don’t see humans writing code in 2 to 3 years except maybe super niche areas. This will all go the way that optimising compilers did. No amount of resistance, anger or denialism will change that.
I’d actually love it if LLMs could skip the slow high level lanaguages entirely and just churned out some weird LLM bytecode that was closer to the metal. I don’t want to read it or understand it at all. Here’s my spec, build it and notify when done. I want to ship stuff not build or dick around with code. Basically like when I go to a shop because I want a table, I don’t care if some carpenter “crafted” it or a machine mass produced and spat it out. It’s cute, but most people just want stuff and don’t care how it’s built.
In every case up until now, a jump in abstraction has moved us forwards in ease of understanding the underlying artifact at a conceptual, human level. High level languages are effectively runnable documentation.
It's possible to say that LLMs producing code may be the same category of thing, but the non-determinism and ephemerality of it all makes it difficult to imagine.
If my experience having to manually unf*ck “production” slop written by sweatshop tier offshore told to go wild with a Claude subscription is any indication then we are a LONG way off from any of that BS.
My job is safe because I’m the only person at the company that actually understands what the actual code is doing and I’m the one that gets the calls at 2am and weekends.
“Weird LLM bytecode”
Why not just generate object code for the target mschine directly?
Come talk to me when it isn't an issue.
I think the gap is because 1. For coding, Claude is amazing - mainly because of its curated skills and because massive amounts of working code has already been carefully labeled over the last decade or so via GitHub. And because with any Turing complete language, there is only so much one can do.
But 2. For most other things, LLMs are fairly underwhelming. Research is usually mediocre. Try being rigorous and repeat your research prompt many times - then make a confusion matrix to tally up how many false positives and false negatives occur. And for the rest, be honest and ask yourself if the LLM is doing much more than a basic search engine query or trip to Wikipedia would have told you. For “normie” use cases, it’s handy-ish but far from revolutionary
Also because programming is self contained in a computer where the results can be tested and iterated easily. For programming the agent can just run the compiler and tests and keep retrying until it works. If I wanted to for example sew a T shirt, AI is useless.
I didn't understand your comment about Turing complete languages, could you explain that part please?
At the end of the day, the processor can only do Turing operations: assign values to variables (registers, memory locations, storage), loops, bitwise operations, and conditionals. Whether the source code is python, java, or lisp, it has to compile or interpret down to machine code ultimately. Likewise if the running software is a word processor, DOOM, or an LLM, at the end of the day it will be executed by the processor using the three operations. Lots of other fancy hardware and software may accelerate things but ultimately it is those ops that are the running code. The rest is many wonderful conveniences and abstractions.
Basically this. For tax, and law in general, LLMs are prone to giving the wrong answer for anything specific (though they're great at the super general stuff because they're just restating one of the millions of summaries written by humans).
Gemini still isn't sure what details are in the version of OBBA that actually passed, because there was more discussion about various proposals (that didn't make it into the bill) than there was about the final version of the bill itself.
Unfortunately, it's an intractable problem based on the ways that LLMs work. In order to overcome those limitations, you have to provide so much detail to the prompt that you would find the answer faster searching manually.
LLMs are always amazing doing stuff you're bad at. (And always underwhelming doing stuff you know.)
Good piece, but I think there's a missing angle to it. He cites a study showing how often people say they "use AI", and a little over 50% use it less than once per week.
If we're just talking about AI chat interfaces, sure. But I think the way that AI usage is going to grow isn't mostly by getting more chat engagement. It's about baking AI features into software that people already use.
For example, suppose you asked the same people "How often do you search on Google?" I am willing to bet the numbers go up a lot. And all of those people are "using AI" in a very real sense, they just don't think about it when it's baked in.
I'd say this argument is not relevant to the specific question the article tries to answer, as AI adoption through these means is forced and may in many cases go against user preferences.
Edit: The deciding factor being whether you want to figure out if people are interested in AI / find it useful, or if the question you seek answered is more akin to "X% of people consume lead in their food"
People eating lead in their food don't know it's there.
People engaging with the AI built into Google results pages can see it, and assumedly Google's A/B testing showed that they engaged with it.
Google also serves ads, most people have gotten pretty good at ignoring them while the ads simultaneously being used enough to be profitable. Similarly, A/B testing only shows whether the net result is positive (by some measure to Google's interest) - not that most people engaged (or engaged positively) with it.
Bait and switch user interfaces to insert AI into places AI did not previously exist and make sure you see an AI answer before anything else is not far divorced from the "lead in food" example - especially when paired with efforts to make the 10 Blue Links become irrelevant garbage on average by ignoring most of the words you type in.
Maybe it's a better analogy to compare this to food-without-lead costing 10 times as much as food-with-lead in large part because of direct actions undertaken by lead manufacturers.
People are not terribly well informed on the specifics. Every time Spotify queues up a song, it has been selected by AI. That's been happening for a while without people getting upset about the effect on the employment of disc jockeys.
I am guessing the threshold is when changes outgrow mere consumer convenience and start being something imposed without choice.
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
We already see the slopification of everything as companies have been reducing the quality of their output for many years. Look at windows 11 vs 98. Yes it does more and crashes less but is it actually better apart from that? Of the things both of them do, which one does them better? Which one runs faster? Which one is easier to use?
Windows is the result of having almost the entire market. There has been no reason for Microsoft to improve windows because it won't sell more licenses. They have already sold Windows to every person who will possibly buy it. So the only avenue for growth is selling additional services on top like cloud subscriptions, AI products, etc.
Contrast that to the last 10 years in Linux where things have become immensely better.
Given the hardware these systems run on, and the compilers and development tools they had available, Win98 was actually the more impressive piece of engineering.
I assume for a lot of people, an llm is going to produce higher quality results for most knowledge tasks than they could do on their own. I think that's okay
It's practically guaranteed. Most people will have no more need to learn how to perform miscellaneous knowledge tasks well if at all, and thus won't.
It's important to factor in just how many US adults are basically illiterate nowadays.
As of 2023, 27% of American working-age adults were at a PIAAC Literacy Level of 1 or below, out of a total of 5 levels. This has gotten drastically worse in the past 10 years as, in 2013, Level 1 and below was only 17%.
Full scores for 2023 are: % Level 1 or below: 27% Level 2: 29% Level 3: 31% Level 4/5: 13%
For reference, Level 1 means someone can't really handle a full page of text, and can sort of handle simple 1-page web pages. Level 2 is the point where someone can start to handle a few pages of straightforward text, but still nothing particularly complicated.
(Both of those descriptions undersell just how bad it really is, but I'll leave it at that, for the sake of brevity.)
People that aren't using AI at all often aren't using it because they effectively can't. On a fundamental level.
Source: https://nces.ed.gov/surveys/piaac/2023/national_results.asp
those levels weren't what I was expecting.
https://nces.ed.gov/surveys/piaac/measure.asp?section=1&sub_...
I'm curious as to how I would score, I would definitely count myself as "literate" but I wonder how well I'd do on the level 4/5 tasks and if they cross over into more general memory, intelligence, and study habit metrics that even a normally "literate" person would not do well at.
Though given those descriptions I can't help thinking those would be great tests for AI. I'd love to see the proficiency scores for various models.
EDIT: Ok I just needed to scroll further, they have sample items in the last section up to level 4 and even at level 4 the question seemed trivial.
The most wordy one is the Q Drum article (which by the way Q drum is a real thing, kinda neat idea) and there's literally only two basic criticisms (flat land and expense) and if you had any idea what the life straw is you can probably construe what the similar criticism in the email is going to be without even looking.
Based on the scores and the proficiency description I assumed they were actually targeting some sort of normal distribution and levels 4/5 would be genuinely difficult explaining the scores. I'm now much more sad that the scores are so low.
At least I got a laugh at how they refer to each test item as "the stimulus" which has such a sterile/clinical flavor to it.
I don’t think that’s it. AI mobile apps support voice conversations. And low literacy is rather a motivation for using AI to generate and summarize text.
Just getting to the point of using a voice mode is a challenge at that level. Like, we're talking about "has trouble formulating a question to ask in the first place".
There's a whole level of ignorance out there that is honestly dumbfounding to even comprehend. The numbers for numeracy and problem solving are even more horrifying.
(It's for this reason that the most popular apps in the US are algorithmically generated feeds of photos, and often-non-verbal videos shorter than a TV advertisement.)
I thought not needing words was because people often watch them on their phone in public without sound. Who are these people that can't enjoy listening to spoken words because of illiteracy or ignorance?
I mean... Tiktok's hallmark trend has always been people doing "TikTok dances", which involve audio, so...
But, besides that, TikTok became the titan it is today during the pandemic. When everyone was indoors (not outside).
Per Google's quick answer, when searching "When did TikTok get really popular?": "TikTok's global popularity surged in 2020 due to pandemic lockdowns, with consistent growth since 2016, surpassing competitors in downloads by 2018."
I think we will have to develop some kind of framework for the difference between consuming AI-generated content and using AI as a tool as those use-cases will likely increasingly diverge. It's very easy to have a conversation with AI, as it will mimic your style, but I'd consider actually using AI to be something more like developing and orchestrating agents, developing frameworks for tasks and understanding, generating ideas for types of work AI can do and how the tasks can be properly split by training needed. Even though AI itself can do a lot of these tasks, people who can understand what AI can do for them and why will always have an advantage, and thinking in theses types of ways requires a high degree of literacy.
I lurk on the teachers subreddit and get shown videos by teachers on TikTok and the impression I get from that algorithmic bubble is that the kids can't read any more - reading comprehension in particular is terrible. Lots of anecdotes of kids who can't read a few paragraphs and then answer questions about what was in them.
My impression of that sub is that it's a lot of people who went through honors classes in a "good" school district, and are now teaching non-honors, potentially in a "bad" school district and are discovering how the other half lives.
That subreddit is likely an echo chamber.
You would not judge what people think about politics by only reading comments on a Hasan Piker video (or only on a Nick Fuentes video).
That's why I said "the impression I get from that algorithmic bubble"
Hasan Piker's political project polls extremely high (universal healthcare, abortion access, and more), so actually you could understand American voter politics by reading Hasan's comments amusingly enough.
He's also banned from the UK for being a terrorist, so make of that what you will.
"Being a terrorist", really?
https://edition.cnn.com/2026/06/01/uk/hasan-piker-uk-entry-b...
"Response rates for this data collection were relatively low, both for the United States and for several other participating countries. There is evidence that procedures implemented to reduce bias associated with nonresponse have done so, and that the data are representative of the population. However, readers should be aware of the potential for bias and use caution when interpreting PIAAC results."
These stats don't pass the smell test. About a third of people in the US have a bachelor's degree, but only 13% can pass level 4/5 literacy challenge? If you dig into the sample questions, they are not hard. A level 4 task has the person read a short article and pull out the criticisms of some products.
I know not everyone with a bachelor's degree is 'smart' but it's hard to believe 2/3rds couldn't pass level 4/5.
Also 13% have a master's degree, does that mean those 13% are the only people passing level 4/5?
https://en.wikipedia.org/wiki/Educational_attainment_in_the_...
> About a third of people in the US have a bachelor's degree, but only 13% can pass level 4/5 literacy challenge?
Yes, that's how bad the problem is.
I've worked with people with master's degrees who can't comprehend text more complex than a Slack message. I've watched them try and fail.
I think it is difficult for a highly-literate person to wrap their head around this. It's very possible to get through a bachelor's program with low literacy. It's even possible to do quite well. And at a reasonably good school.
There are some pretty successful businesses out there whose target market are essentially professionals with low literacy. They use more flattering descriptions in their marketing.
That's just a standard disclaimer.
This is just how it is out there. Ask teachers what students are like these days. Think about designing for users. Or cross-reference with other info on this topic.
And, in regard to colleges... you have to keep in mind just how many colleges there are, how much the quality differs, the relative workloads of different degrees. There are a lot of people graduating with a GPA quite close to 2.0 at that full range too.
Also, think of how many college graduates never finish a book again after graduating college. Those numbers judge 18 to 65. And the age stats show that the older cohorts drag the scores down significantly.
The only upside to all of this is that it at least makes the chaos out there in the world make a bit more sense.
> 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.
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.
Google still has search results but they mostly point to AI generated blog posts filled with adverts.
> Google still has search results
They should investigate if placing these search results above the limit of the screen/canvas height might lead to more people seeing them.
It's been difficult to do, but I have recently picked up the habit of scrolling directly past the AI overview. It's not what I wanted, so it's useless to me (regardless of whether or not it's correct). I wanted search results. Sometimes that's a Wikipedia page, documentation, video results. But, web search results.
If I wanted to read an AI response to a query, I would use that tool specifically - I consider them different tools entirely.
It's definitely taken some time to get into the habit - perhaps defaulting to Google as the search engine was my first mistake.
However, most people I've seen run a quick search DO in fact just read the AI response (often with a caveat of "this is just from AI but..." which I appreciate), because for a lot of things it IS good enough or exactly what they needed.
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?
A "satisfactory looking answer" was what I got yesterday when I queried Google about a Pyodide question. It produced some code in html format that was supposed to work, but failed on execution. The AI generated result was incorrect and it was only 30ish lines of code that was supposed to print "hello world" to the console.
You still used AI, even if the results weren't correct.
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.
Google searches are still part of my everyday use if you're a power user like me that ctrl/cmd+L to the browser bar and the first auto complete is a web search rather than a bookmark or history item
Not the first result. The AI summary generated by gemini.
Pretty sure he's talking about the summary / AI answer and not the first search result
The sales pitch was... we'd be left behind without adoption. I'm [still] waiting. Years later, my days are no different. Teaching people who wouldn't bother to read manuals or now, consult their chat bots. Never mind my own 'missing pieces of flair', what about theirs?
So far, I'm not using "AI" for anything
Still haven't tried it
Based.
I find it baffling that someone with 21k karma, on <<Hacker>> News, posts such a ignorant comment, followed up by someone with 5k karma and a 10 year old account, with an equally "valuable" reply.
At least use AI to know your enemy...
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.
Google just lost a lawsuit for citing sources and then displaying information that didn't occur in the sources. https://the-decoder.com/landmark-german-ruling-declares-goog...
That's totally fair and things may change. For me its the history and the fact I can come back to it.
If I am honest I believe my final solution will be a combination of Open Claw, a custom knowledge wiki based on Wikmd. I just need a good all for Claw with history that is as good as gpt
Edit: and context too. It inferred my energy supplier from previously chats and so when I just asked a pertinent question it referenced their policy. Admittedly Google will have way more context if they get the product right.
Gemini also has it all, it learns and knows who you are, you have history of chats where you can just jump back into a conversation a week later if you want to.
Gemini has history and memory too though, maybe you weren't aware. It's an app just like ChatGPT and you can even import your history and memory, try it.
I've run into plenty of instances where Gemini cited sources and the claims they attributed to those citations were nowhere to be found in the actual sources they cited. It's happened to me over a dozen times this past week.
The AI search results were actually better last year; significantly fewer hallucinations.
At least using AI doesn't cause massive suffering of sentient beings like meat consumption does. I find it funny we talk so much about whether or not Claude's feelings are hurt and whether it has emotions, when we treat animals with real feelings, emotions, parental bonds, and personalities so badly.
Google search is AI by any definition now, and yes everyone is using google search and by a sort of transitive property …
Search isn’t generative AI. There are a lot of people arguing in this thread that actually everyone is using generative AI without engaging with the source material at all. Why do you think that is?
I disagree. Searching in google now IS using generative AI.
Google has made searching also include a default LLM response. There are also search results. When you search with Google, an LLM is prompted, for sure. It’s also pretty clearly a separate feature that’s been tacked on to search.
"But they will", "They do, but they don't know", "They do, indirectly"
I don't get these comments.
Just AI fandom
I might be the only person at my job that doesn't use it, but it feels like I'll be found out by upper management sooner or later. Maybe I'll need to use a library I'm unfamiliar with, I'll take longer than expected to grasp it, and then people will start asking questions. Every time a manager puts a random call in my calendar, I'm worried it's going to be about this.
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?
The list of concerns omits many things (although they do mention many valid concerns), such as concerns about control by the organizations that provide the AI services, power that is better used elsewhere (independently of whether it is "too much"), using too much space, effects on prices of things, excessive scraping, inappropriate use of AI, someone trying to force or insist strongly that you should use it even if you do not want to, etc. It might be potentially possible to mitigate some of these concerns (and in some circumstances, some of them are mitigated), but that still doesn't mean you should be required to use it. Software and services that make the AI features optional is one way to help (and is worth doing, if applicable for that software/service), but it does not solve everything; but, one way will not solve everything.
I understand the point being made, but it does feel a bit like writing a post in the early days of the internet saying:
"No, everyone is not using the internet for everything."
Which would have been entirely true when written, and entirely false a relatively short time later.
Everyone does use the internet for everything today, and everyone will use AI for everything soon.
In my non-tech circle, most people don't even realize how the internet is running literally everything. Even if we start to use mass scale AI for something, they wouldn't realize or care much about it. They at best turn on the TV to watch netflix or look at the phone to send messages on whatsapp. If all of that went away tomorrow, they'd be inconvenienced at best and then go on with their day to day life. This feels like we are literally all in our IT echo chamber where we throw stuff on walls and go crazy, while the world is sunshine and rainbows, always been.
"They at best turn on the TV to watch netflix or look at the phone to send messages on whatsapp. If all of that went away tomorrow, they'd be inconvenienced at best and then go on with their day to day life."
I'm not saying it is a good thing, but this is completely out of touch with how dependent (most) people are on these technologies.
I'd say its just businesses and governments that are dependant, most people can get by just fine
A lot of people are genuinely stranded if their phone runs out of battery. How do they pull up a map, or call an Uber, or phone someone to pick them up?
The cellular phone network doesn’t require the internet to work.
It does require the phone to have battery charge.
The context was “Everyone does use the internet for everything today” and “most people don't even realize how the internet is running literally everything”. You don’t need the internet for your phone to have a battery charge.
They were providing some illustrative examples: most people rely on the internet for maps. If they don't have internet (because their battery is flat) they can't do xyz.
I was referring to the claim that they couldn't phone someone to pick them up.
I'm not sure the point you're trying to make. This was true before cell phones too. People didn't carry maps everywhere.
What's the difference between then and now?
Pay phones, basically? Physical maps being available in public places more readily?
The argument here is "most people can get by just fine" without access to the internet.
I tried to pick an obvious example to illustrate how that's not true.
The difference is that, prior to everyone having a smart phone, people had backups for if they ran into trouble. They might simply not go somewhere that they might have trouble returning from. They sorted out their travel plans in advance - someone to pick them up from a location at a time. They memorized phone numbers so they could call from a pay phone if they needed to. They carried cash or a cheque book to pay for cabs.
Fair enough. I suppose a better argument would be "people can learn to get by just fine without the internet if they have to"
But you're definitely right. We become pretty reliant pretty quickly. I think that should be concerning with the way technology is trending in society
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.
AI is a tool in a toolbox. It is does not fit all solutions.
Using AI everywhere would be the equivalent of using a screw driver to pick the piece of stuck broccoli out of your teeth. It will cost you more, to fix your teeth, than using a proper tool.
A year or two ago people were concerned that Google was losing its battle to OpenAI. Today Google puts heavy emphasis on AI search. Most common folks are now using them. I know in a minute or two many Kagi, DDG or other alternatives will reply, but these people were never a core part of Alphabet's user base. I'm an AI sceptic to some level, but it's hard to deny that "most" people are using AI (as an LLM or in other forms) to some extent today despite we like it or not.
The comparison with meat consumption seems inapt.
70% of people report reducing meat consumption, but research has shown that these intentions have very little correlation with people's actual behavior.
Almost everyone is using an app with a feed with an algorithm powered by a transformer. People spend hours a day feeding information into a model with the same architecture as an LLM which then spits out next-post recommendations. They call it "scrolling Reels/TikTok/Shorts".
That has to count for something.
> More than 30 percent of the US working-age population is using AI [meaning about 70% isn’t], an increase of 3 percentage points from the end of 2025
This makes me less bearish on the AI investments that are being made, if 70% of the working age population isn't using AI then there still is a lot of growth. The future is here, it's just not evenly distributed (yet)
What if I told you that 100% have exposure but only 30% have adopted? You’re assuming the issue here is lack of access, not lack of interest.
It might take a while for people to see how the model is useful to them, or to figure out how to make it useful or the capabilities weren't there ye. My mother tried ChatGPT in '23 and thought it was useless, she now uses it regularly, because it's improved and she knows how to use it better.
I only use AI for software development. For writing, I don't use it at all except to translate source materials. So yes, AI is only for software development in my case. The real question is whether I have any value outside of software development. Sometimes I get the feeling that AI is replacing the value I have in society.
I have no doubt that as AI gets more expensive, my employer would lay off more developers to pay for more AI tokens, until there are very few developers left. And the hilariously sad part is, the current developers keep training the AI to do their job. Eventually I expect they will lay off almost all the developers. It really feels like we're going to be stabbing each other in the back just to be the last one to get let go.
What are they doing to train AI to do their job?
Nobody writes any code any more at all. Nobody even writes Jira tickets anymore. They don't even review code, and I think we're lucky if they even test it. The AI does all of that.
A small group of developers at my company have set up volumes of skill.md and other instructions for the AI to write Jira tickets, then take action on those Jira tickets by writing the code. The AI submits a pull request. Then there's another AI to review the code. They've written the game plan for the AI to do all of this. All the human does now is click "approve" without even reading the PR, and then someone clicks "merge". There's no coding, no critical thinking by a human anymore except for telling the AI what to do... which really anyone at the company could do. I doubt I'll have a job at this company much longer after 8 years employed there.
I don't think AI has any real value for software development, personally. The quality just isn't there, unless you invest so much effort that you may as well have written it yourself. But the market can stay irrational longer than you can stay solvent, and even though I think the industry will get over the idiocy of having LLMs write software, there's no telling how long that will take. So it's a scary time to work in tech even if I think the trend will ultimately reverse.
As of ~8 months ago the quality is most definitely there, for almost every form of programming I've experienced.
If you're working in some vanishingly rare domain then maybe it's not yet, but most coding challenges are very much in the wheelhouse of the current frontier models.
> So it's a scary time to work in tech even if I think the trend will ultimately reverse.
I honestly can't see things going back to what it was 5 years ago. We will probably not have the future that Anthropic hopes for, but I think every developer will be required to chat with AI as part of the planning process to reduce a companies "bus factor" risk.
I envy you. For me, AI is faster than the code I write myself in many, many cases. It might replace the average developer, but a talented developer like you probably won't be replaced
I was not hired to write code, but to solve problems (where often the end result is code, but it’s not the whole process). But the message from management is that our bottleneck was coding, and by using AI to code, we’ll be 10x faster and all the company problem will be solved. Essentially 1. Use AI everywhere 2. ??? 3. Profit.
Where I work, the CTO drank a whole bunch of AI kool-aid recently, so now we're expected to "10x" our output with AI. I don't think he realizes this also means 10x more problems of all kinds. But I fully expect him to double-down and when AI costs skyrocket, he'd lay off more developers to pay for more AI.
I am constantly looking for a new job, but all of them are also require AI coding experience.
One of the reasons is that the free options are generally fairly poor and it’s hard to get people to sign up and actually pay for something. Especially if they assume it’s going to be similar quality.
If I worked in marketing/growth for an AI company I would try to consider some ways of breaking through this gap.
Not to take away from the post, but "everyone is not" should probably be "not everyone is".
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.
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.
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.
It surprises me when people think engineering, software or no, isn't about the physical world of humans, psychological, imaginative, aesthetic or otherwise. Everything I do uses "human stuff" as a base foundation of value. Engineering effort is malformed and invalid without such a thing, and I spend a lot of my time as a technical leader pushing back on people trying to "perform engineering" without connecting to these things.
I've been thinking about this, and I think software is uniquely knowledge work that has the most defined structure and least personally interaction. Hell, some of the software I write is for machine to talk to other machines. It's not surprising such a closed system is so amenable to AI, and other knowledge workers are not getting the same benefits.
Software has huge and detailed code repositories ripe for training use. There's just enough inference in current models to remix that code in useful ways for the most popular languages.
The less popular a language, the more models struggle.
Writing, UI, and presentations have similar knowledge bases.
Outside of those, quality becomes much more hit and miss. If you ask for a recipe you may get something good, or you may get something completely inedible and random.
"Domain specific knowledge" really means "strong foundations and relevant abstractions" and LLMs just don't do that reliably.
That's a decent article. My only issue is it seems heavily biased at the end, or at least he seems to misunderstand what the 'A.I. types in Silicon Valley' are doing.
> Computers should adapt to people. Asking people to make themselves more legible to software — to turn themselves into a database — is a doomed idea.
I've been in software a long time, and I do sort of see this trend, but I think it's because these are tools that build other tools. The interface has always been a 'best I can do for now' thing, with the focus on doing things that are useful. Computers were just calculators in the beginning, which led to more complex calculators, instruction sets, programming languages, operating systems, GUIs, interconnectivity, etc.
What people are doing today is experimenting, like they always have. They're putting their experiments out there so that others can use them and build on them. Some will use those tools to build other tools, and some won't. But over time, the experiments that work will get distilled and turn into real products that people who 'do not yearn for automation' will still want to use, so it seems like the value is there.
I guess the real question is whether they will create value that offsets the near-term costs, because I don't think the billions in investments are sustainable, and I'm not convinced the centralized data center paradigm is the right way.
Software engineers aren't even all using AI, contrary to frequent claims here that they are. There are very many who have tried it, found it didn't add value to their work, and aren't using it unless FOMO-driven managers force them to.
The other day I have seen a developer calmly renaming a variable in his code. Manually. Like a psycho.
I've been using LLMs to write code that consists of too much boilerplate like writing flutter, anything else I enjoy writing by hand tbh.
True, but you're somehow involved in it even though you don't use AI.
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.
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.
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.
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.
"This tracks with Microsoft's new United States AI Diffusion site, based on "anonymized, aggregated Microsoft telemetry.""
Surreptitious surveillance, probably with dubious consent
Is Redmond afraid to just ask users
Then compare the data
If no one asnwers when asked then maybe that means they don't wish to allow Microsoft collect that data
"SearchLight asked about a range of technologies and to say "whether you believe the overall impact of each technology on society is positive or negative." AI only has an +8% net positive rating right now, right next to +7% for social media, which were only greater than crypto at -17%."
LOL. This is what happens when people are asked for their opinions
*answers
No, everyone is not using AI for everything - yet.
My initial response to reading this headline was to think that noone is saying that they were. Yet the author starts off with a link to a pretty good example of some dumb hyperbole.
I guess that goes with the notion that for any really idiotic take you can think of, there's going to be someone out there confidently promoting it.
In general, most claims of 'everyone is...' means "Most of the people around me that I observe are..."
Which might mean they are not around other perspectives, or it might mean they just are not observing other perspectives.
As a part of the "don't use won't use" crowd it has been exhausting explaining that we exist lol
I disagree. Everyone will be using AI for everything, but, increasingly, people won't think about whether they are using "AI" – just like they don't think about using databases.
Nor should they! It's such a shit thing to be emotionally invested in. Imagine people would have been upset about databases. It's really fantastic software and we should be happy to have it, and now go and make the most of it, for all of us.
Another data source showing the same trends consistently over the last couple of years:
https://www.stlouisfed.org/on-the-economy/2025/nov/state-gen...
Interactive data tracker here: https://www.genaiadoptiontracker.com/
I'll note that despite this, most AI providers are very compute constrained (see: the skyrocketing backlogs of each cloud provier; Claude's 9's until a few weeks ago until Anthropic, along with Google -- of all companies -- renting capacity from Elon.)
That could be a material reason for the overall low adoption. I think the big AI players are carefully balancing multiple conflicting priorities with their limited compute: acquiring and keeping users with free or heavily subsidized tokens, while ensuring they can satisfy their paying customers' SLAs. If there was more compute they would probably invest more in marketing and pushing adoption further (and indeed, a Google exec was quoted saying exactly that after a big Gemini launch.)
Something else to consider: If only 10% of users use GenAI daily, that leaves a potential 45x growth minimum on the horizon (the remaining 90% of users x 5 weekdays, assuming everyone chills on weekends, and ignoring that agents use way more tokens.) Which is why every hyperscaler has been scrambling to build out more capacity.
The typical question at this point is, where's the payoff? Two points:
1. Despite this relatively low level adoption, AI company revenues are already spiking at unprecedented rates. (Not just the frontier labs, even the smaller startups, as reported by investors, enterprise customers as well as Stripe, their payment processor: https://news.ycombinator.com/item?id=46730182)
2. The St. Louis Fed study above notes that there has been a ~1.89 percentage points increase in national labor productivity since 4th quarter 2022, which could be explained by their data on GenAI adoption.
They are careful to call out that causation is impossible to prove, but the possibility that GenAI may already be having a noticeable impact on national-level economic indicators only 3+ years since launch is wild.
Anyone doing a basic google search right now, is "technically" using Ai.
Holy crap. Solar energy is only considered a net positive on society by 65% of people??
Turns out that the people screaming about using AI for everything were the investors who already invested in the AI tool they are trying to sell to you.
or for anything
I use it for closer to everything every day. Just realized recently it could clip my (virtual) Safeway coupons for me and make my grocery list.
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.
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-...
The numbers are interesting, I agree, as is the overall post.
And as a clickbait headline, "not everyone" makes sense.
But, realistically, the NYT obviously isn't talking about, say, the North Sentinelese islanders. Similarly, it's probably not really talking about people who don't do the kind of white collar work where AI is, so far, most prevalent.
In fact, the group the NYT is most likely referring to as "everyone" is really "readers of the NYT". It might be hard to come up with numbers for that, but I suspect they'd be a lot higher.
Anecdotally, at the company I'm at (late-stage venture-backed), everyone from software developers to people on the marketing, sales, and finance side are using AI. And we're not unique. This will make the statement ring true to many people, even if it's not globally true in an absolute sense.
This all makes more sense once you've truly internalized the Yogi Berra quote about a certain popular restaurant: "No one goes there anymore. It's too crowded."
Kinda like how quickly cellphones popped up and changed everything.
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.
I'd honestly argue we're actually going the complete opposite direction.
Everybody is using LLMs/AI. All the time. It's in every facet of your life. Just because you didn't input the prompt, doesn't mean you're not consuming the end product of LLMs all day, everyday, on websites like this one, reddit, tiktok, instagram, facebook, etc.
Addressing the article, if you're hyperfocused on whether people are using AI and only consider AI use a chatbot... well, you're not honestly covering all the AI use out there. And reading the other stats, it seems like this article is trying to paint a narrative. Why is the Datos stat only considering "Desktop use" for instance.
Not to mention their stats are actually astounding and DON'T show what the headline is trying to assert. 1/3 of people using AI regularly is a FUCK TON of people in a VERY short span of time to uptake a new technology.
It baffles me that Hard Fork is so popular when they regularly have such uninformed takes
I swear the Sydney incident and the subsequent dislike of Kevin by LLMs caused Kevin to believe in Roko’s Basilisk and so he’s super pro AI now.
But more likely it’s just that those who still listen to Hard Fork agree with the conclusions before listening to the podcast.
> AI has gotten so good
Actually anything that is about 90% great and 10% disastrously wrong is utter crap given the way people want and do use AI models.
They are great tools in the right hands and awful in the wrong.
It's funny lately I've been seeing the cursor advertisements all with some premise of regular young person wants to develop an app and the ads really do focus on the simplest of premises: the only ones I've seen in these skits are essentially variants on the "todo app" web app tutorial
the tech is pretty good at helping identify simple bugs when they happen and to write short sections of code given very explicit instructions but yeah I have yet to see good examples of short one sentence ideas turned into a working product that looks better than anything that could be a UDemy tutorial app.
I keep saying it hasn't gotten smarter, just memorized more things
I honestly just use it as a search engine to get around SEO garbage and ads.
My wife uses it for a (non-computer related) business though and it's great for all sorts of normally tedious marketing/social media type jobs though. Stuff that doesn't really require accuracy just needs text on pictures that looks good quickly.
I think everyone just has FOMO and doesn't want to lose to competitors. Eventually it'll die down.
"Think of how stupid the average person is, and realize half of them are stupider than that."
Bit of an odd decision to build an entire article around a clickbait headline from July 2025. Talk about a strawman.
That aside, this piece is interesting and ties together some useful numbers and studies.
I hadn't seen the recent Microsoft paper showing:
> 30 percent of the US working-age population is using AI [...] with at least 90 minutes of usage time in a given month.
I'm honestly impressed at how high that number is! That's a lot of adoption for a technology (LLM chatbots) that didn't exist four years ago.
How much of that use is driven by corporate mandates to use AI anywhere and everywhere (even when it's a terrible fit)?
I'd love to see credible numbers on that. I find it hard to believe that stupid corporate mandates are responsible for more than a small fraction of usage, but without data I have just my own instincts to go on there.
If the corporate mandates weren't the reason for the number being so high, I would expect the outside of work data to be more aligned.
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.