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.
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.
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
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.
> 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.
> 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.
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.
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.
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.
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.
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.
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.
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'
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.
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").
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.
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, …”
> re-factoring a big repo of decades old fortran+C cod
Having been in academia in the past and now in software I can say with a lot of certainty that this will take a lot more upfront work than otherwise.
Academic code does not have a lot of structure. And usually lacks a lot in terms of tests. While AI is best when it can mimic patterns as well as there are tests to target.
So you will probably need to budget a few weeks to establish good patters, docs as well as testing patterns before you can seriously make it really do what you want it to do.
exactly yeah it was a code base written by atmospheric physicists I assume and I had an idea that maybe copilot could get it working to interface with some more modern software and it just didn't really have what it takes.
Even with 3 weeks I'm just not the Fortran/C programmer to get that job done so I moved on to other things.
Consider using agent mode for some things, you are definitely missing out.
The analogy I've had for myself is that it feels like using a bulldozer to dig rather than a shovel. If you use it to dig archaeological artifacts, it can make things worse than you started. A lot of the work however, is just moving dirt around, so you are wasting time by using a shovel.
Exact same experience. My background is embedded and VLSI so I hedge my bets by saying that LLM are ok for Python scripting, but not there yet for synthesizable Verilog. It is really hard to see if the "how are you using LLMs?" question is for "we are AI Native™" or a form of cheating (like in university).
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."
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.
> 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.
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
> 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.
That's just one way to use LLMs though. Recently on a flight I could not figure out how to connect my wife's earphones (i.e. put them in pairing mode) to my macbook since I was used to the old Airpods Pro case. So I asked Gemma4 26B A4B (offline, LM Studio) and was told to use the 'two tap on front of case' gesture, which worked. This situation would have been significantly more frustrating without (local) LLMs. I'm essentially carrying around a basic "how to" on everything, inaccurate though it may be, it's better than nothing.
Well Apple just released a bunch of Agent Skills. I tried it on my macOS apps and I noticed some improvements codewise and updated some deprecations I didn’t know existed in Swift.
Definitely frontend (it's what I do, every day, and I enjoy it), but I have a great deal of experience (over 25 years), writing some pretty robust backend stuff. I just don't enjoy it as much.
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.
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.
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.
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?
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"
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.
I've noticed several companies replacing deterministic systems in their support flows with a LLM version that is slower and worse. Many interfaces simply aren't better with AI added
The real best case scenario is using LLMs to help build deterministic systems. Instead of asking an LLM to do some task that you know will be repeated, instead ask the LLM to build a program (Python script or whatever) to do the task.
Making systems fully deterministic ignores the entire purpose of having agents involved.
IMHO the best of both worlds option is agents working with deterministic CLIs. Where the agent does the reasoning (and text generation) but uses CLIs to carry out all of the actions (issuing refunds, unblocking accounts, or whatever).
It's possible to get very reliable and consistent work out of agents when they're using well written prompts with well designed CLIs.
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.
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."
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.
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.
When I hear about engineers who are bored with coding, I have to imagine it's because the task of "programming logical work flows" has become rote to them.
Instead of refining their approach, or challenging their current knowledge base for discovery of inefficiencies or baseless assumptions, they'd rather hit an "easy" button.
I understand the desire to NOT do work. I understand the desire to spend quality time and free time with family. And I understand the idea that familiarity breeds contempt.
What I don't understand is the willingness to replace a deterministic language/framework/approach with a probabilistic slop machine.
As a contractor who built a lot of predictive systems and workflows in last three years I can tell you that quite often there is a specific request to put AI into it even when it is not needed and would objectively make the system worse, slower and more expensive.
I 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.
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).
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.
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.)
"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?
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.
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.
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
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.
> 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.
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.
I don’t see the contradiction? If the inventory of clicks is declining and the number of businesses bidding on clicks is more or less constant, why wouldn’t that increase price?
When was the last time you used Google? The first entry (and a few after that) is always spam.
Anyone who does a search and accepts the first answer just doesn't care much or is incompetent. Anyone with any critical thinking whatsoever does way more than that if they want a correct answer.
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 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.
"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
70% of people report reducing meat consumption, but research has shown that these intentions have very little correlation with people's actual behavior.
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?
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.
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.
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).
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.
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.
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.
> More than 30 percent of the US working-age population is using AI [meaning about 70% isn’t], an increase of 3 percentage points from the end of 2025
This makes me less bearish on the AI investments that are being made, if 70% of the working age population isn't using AI then there still is a lot of growth.
The future is here, it's just not evenly distributed (yet)
One 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.
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.
I only use AI for software development. For writing, I don't use it at all except to translate source materials. So yes, AI is only for software development in my case.
The real question is whether I have any value outside of software development. Sometimes I get the feeling that AI is replacing the value I have in society.
I have no doubt that as AI gets more expensive, my employer would lay off more developers to pay for more AI tokens, until there are very few developers left. And the hilariously sad part is, the current developers keep training the AI to do their job. Eventually I expect they will lay off almost all the developers. It really feels like we're going to be stabbing each other in the back just to be the last one to get let go.
I don't think AI has any real value for software development, personally. The quality just isn't there, unless you invest so much effort that you may as well have written it yourself. But the market can stay irrational longer than you can stay solvent, and even though I think the industry will get over the idiocy of having LLMs write software, there's no telling how long that will take. So it's a scary time to work in tech even if I think the trend will ultimately reverse.
> 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.
As of ~8 months ago the quality is most definitely there, for almost every form of programming I've experienced.
If you're working in some vanishingly rare domain then maybe it's not yet, but most coding challenges are very much in the wheelhouse of the current frontier models.
I envy you. For me, AI is faster than the code I write myself in many, many cases. It might replace the average developer, but a talented developer like you probably won't be replaced
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.
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.
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.
everyone might not be using ai.
but i see myself reaching for it for every small thing these days.
it's like every curiousity or lifestyle choice or optimization is something ai can help research.
i am not saying it's really powerful or great.
but the lure is undeniable. because of how low friction it has become.
Software engineers are definitely in a bit of a bubble here. Are we just early adopters who see the value sooner, or does it uniquely benefit software engineering, or do we just like cool automation and we're deluding ourselves that this adds value beyond the cost?
Yes I believe software benefits uniquely, just like building tooling and automating software have long been easier in software than other domains. Humans defined all the rules of the world you live in, humans wrote strict rules in methodically parsable formats.
The moment you have to interact with the physical world or humans (psychological, imaginative, aesthetic, etc), there are often undiscovered or changing rules—or no rules at all. Or systems are subject to perturbations beyond a defined scope.
The other thing I believe is software developers are experts at doing the things that allow them to make doing those very things easier and more automated. And they do this in public, perfectly documented online.
Both because of the things I described above and because software developers have created the largest machine-accessible training set for plying their trade of any trade, ML—that is ultimately interpolating massive datasets to do things—is unsurprisingly uniquely successful for software tasks.
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.
They are great on exploring, understanding and finding bugs in existing codebase.
They are great for simple or one time scripts/programs.
They are terrible, really terrible coders. The overengineering is so deep in their training that no matter what is your prompt, your skills or agents.md/claude.md, if you don't babysit them continuously, at some point they will just fuck up your codebase.
Everyone is using AI, issue is not just everyone recognizes what AI actually is, how broadly it's used.
Looking things up and asking questions was always something for a minority of the population so the language model usage being relatively low isn't a surprise.
Problem arises if the non-AI segment is leveraged to create regulations that impact the AI using segment negatively.
Not everyone but most. And I've been having this discussion with people around me a lot lately and everyone that has the ability to think more than half a step ahead sees it(and frankly we are fed up). I previously discussed how a friend admitted that he's never seen the code that powers his project at an S&P 500 company. Yesterday I was talking to another friend and former coworker who complained that when cloudflare went out a month or so ago, his entire team just slammed their laptops and went home cause they couldn't work(no sloppus/sloppenai). Another friend of mine: her dad is in hospital with a terminal disease and her mom (in her late 50's or early 60's, idk) uses chatgpt as a personal therapist. Gatorade-fed crops here we come, Leeerooooy Jeeeenkins!
I'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.
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.
The numbers given in the article are actually consistent with what is usually meant by “everyone” in such statements. Sure, it’s not literally everyone. But it’s a very significant percentage, especially given how quick the adoption has been.
I think what people mean by everyone varies a lot, which is why I wanted to draw attention to more specific numbers. For example, in the Datos data cited[1], on desktop 86% were using traditional search engines >10 visits/month vs. only 21% for AI chat tools. That is indeed a very significant percentage, but more than 4x less than search and (at least I) wouldn't say that ~1/5 is "everyone."
It's funny lately I've been seeing the cursor advertisements all with some premise of regular young person wants to develop an app and the ads really do focus on the simplest of premises: the only ones I've seen in these skits are essentially variants on the "todo app" web app tutorial
the tech is pretty good at helping identify simple bugs when they happen and to write short sections of code given very explicit instructions but yeah I have yet to see good examples of short one sentence ideas turned into a working product that looks better than anything that could be a UDemy tutorial app.
I honestly just use it as a search engine to get around SEO garbage and ads.
My wife uses it for a (non-computer related) business though and it's great for all sorts of normally tedious marketing/social media type jobs though. Stuff that doesn't really require accuracy just needs text on pictures that looks good quickly.
I think everyone just has FOMO and doesn't want to lose to competitors. Eventually it'll die down.
I'd love to see credible numbers on that. I find it hard to believe that stupid corporate mandates are responsible for more than a small fraction of usage, but without data I have just my own instincts to go on there.
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.
> 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.
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
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.
> 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.
> 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.
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.
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.
> 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.
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?"
Said by no-one who has a decent paying job and has bills to pay
> Have you considered just answering truthfully?
It's 2026, you gotta sell your soul just to get a phone screening
Not everyone has that luxury when there are bills to pay and mouths to feed.
Would being truthful improve my chances of being hired?
> 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.
> 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.
> 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.
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.
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?
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.
your post on Who's Hiring provides some needed context...
want a Flutter developer who is unusually strong at directing AI-driven software delivery. This is not a traditional "write the code yourself" role.
https://news.ycombinator.com/item?id=47223956
> Any long-winded answer to a question is immediate out and has been for years.
Why?
If the winding path is actually interesting and gives you insights into how the person works, why would that be a bad thing?
> 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?
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").
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.
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, …”
> 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.
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.
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.
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.
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.
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).
some employers may like this wishy washy answers.
personally i find this offensive and would disqualify the candidate.
They might not have an answer in mind. They might just be exploring how you adapt to new tools and methods.
> 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."
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.
One trick is to ask them that question first to gauge their perspective on it first
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.
I personally think "I pretty much use it as a faster and more flexible StackOverflow" is probably the most neutral position you can have on it
That's probably not going to be enough for AI maxxers, but it probably won't be too much of a turn off for anyone but the most extreme AI minners, and everyone in between will probably be fine with it.
Frankly I plan to steer well clear of any "the majority of our code is AI generated" shops for the foreseeable future. Seems like disasters waiting to happen and I'd rather let other people step on those rakes
The disaster isn’t even waiting to happen. It’s actively happening.
Look at the uptime and incident rate of all the big tech companies that have gone all in on AI generated code
"for entertainment value, when i'd like to see how an enthusiastic 5-year-old would react to the task."
Your 5 year old is going to a heck of a kindergarten.
> 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.
> which is only going to get worse as it "learns" from garbage outputted by other LLMs moving forward
You seem to assume that autoregressive pretraining (and unfiltered behavior cloning, maybe) are the only ways to improve LLM performance.
That's just one way to use LLMs though. Recently on a flight I could not figure out how to connect my wife's earphones (i.e. put them in pairing mode) to my macbook since I was used to the old Airpods Pro case. So I asked Gemma4 26B A4B (offline, LM Studio) and was told to use the 'two tap on front of case' gesture, which worked. This situation would have been significantly more frustrating without (local) LLMs. I'm essentially carrying around a basic "how to" on everything, inaccurate though it may be, it's better than nothing.
Absolutely. I use it often, for stuff I used to "just Google." Other than a predilection for giving me CLI walkthroughs, it is usually fine.
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.
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.
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.
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.
Isn’t it because Swift (and SwiftUI, if you used that) changes the recommended approach to solving X every 18 months?
Which LLM though? Models can still be significantly different in their capabilities.
That's likely. I generally use ChatGPT (latest), but as a chat interface (not an agent). I suspect that I might get better stuff from Claude (maybe).
Might be because there are less Swift projects to train with.
But I've seen Claude write crazy code in Python and JavaScript, too
My theory is that most of the Swift code in the public domain, is basically demo code. Short, idealized, code samples to demonstrate issues and solutions; much like you would see in StackOverflow.
PHP has huge, entire frameworks and systems, refined over years.
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’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!
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?
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"
So far, I'm not using "AI" for anything
Still haven't tried it
Based.
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.
I've noticed several companies replacing deterministic systems in their support flows with a LLM version that is slower and worse. Many interfaces simply aren't better with AI added
The real best case scenario is using LLMs to help build deterministic systems. Instead of asking an LLM to do some task that you know will be repeated, instead ask the LLM to build a program (Python script or whatever) to do the task.
Making systems fully deterministic ignores the entire purpose of having agents involved.
IMHO the best of both worlds option is agents working with deterministic CLIs. Where the agent does the reasoning (and text generation) but uses CLIs to carry out all of the actions (issuing refunds, unblocking accounts, or whatever).
It's possible to get very reliable and consistent work out of agents when they're using well written prompts with well designed CLIs.
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.
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?
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.
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.
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.
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 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.
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?
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.
Come talk to me when it isn't an issue.
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.)
"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_...
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.
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.
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).
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.
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.
> 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.
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.
What Im question is how is Google increasing Price-per-Click each year if people are clicking less and less on the links below the AI search result
I don’t see the contradiction? If the inventory of clicks is declining and the number of businesses bidding on clicks is more or less constant, why wouldn’t that increase price?
Even if we accept that all people are satisfied with the AI search overviews, that would still only be everyone for one thing.
When was the last time you used Google? The first entry (and a few after that) is always spam.
Anyone who does a search and accepts the first answer just doesn't care much or is incompetent. Anyone with any critical thinking whatsoever does way more than that if they want a correct answer.
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 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.
"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
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.
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.
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.
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.
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?
"But they will", "They do, but they don't know", "They do, indirectly"
I don't get these comments.
Just AI fandom
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.
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.
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.
> 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.
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.
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.
I only use AI for software development. For writing, I don't use it at all except to translate source materials. So yes, AI is only for software development in my case. The real question is whether I have any value outside of software development. Sometimes I get the feeling that AI is replacing the value I have in society.
I have no doubt that as AI gets more expensive, my employer would lay off more developers to pay for more AI tokens, until there are very few developers left. And the hilariously sad part is, the current developers keep training the AI to do their job. Eventually I expect they will lay off almost all the developers. It really feels like we're going to be stabbing each other in the back just to be the last one to get let go.
What are they doing to train AI to do their job?
I don't think AI has any real value for software development, personally. The quality just isn't there, unless you invest so much effort that you may as well have written it yourself. But the market can stay irrational longer than you can stay solvent, and even though I think the industry will get over the idiocy of having LLMs write software, there's no telling how long that will take. So it's a scary time to work in tech even if I think the trend will ultimately reverse.
> 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.
As of ~8 months ago the quality is most definitely there, for almost every form of programming I've experienced.
If you're working in some vanishingly rare domain then maybe it's not yet, but most coding challenges are very much in the wheelhouse of the current frontier models.
I envy you. For me, AI is faster than the code I write myself in many, many cases. It might replace the average developer, but a talented developer like you probably won't be replaced
Where I work, the CTO drank a whole bunch of AI kool-aid recently, so now we're expected to "10x" our output with AI. I don't think he realizes this also means 10x more problems of all kinds. But I fully expect him to double-down and when AI costs skyrocket, he'd lay off more developers to pay for more AI.
I am constantly looking for a new job, but all of them are also require AI coding experience.
True, but you're somehow involved in it even though you don't use AI.
Holy crap. Solar energy is only considered a net positive on society by 65% of people??
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.
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.
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.
Not to take away from the post, but "everyone is not" should probably be "not everyone is".
everyone might not be using ai. but i see myself reaching for it for every small thing these days. it's like every curiousity or lifestyle choice or optimization is something ai can help research.
i am not saying it's really powerful or great. but the lure is undeniable. because of how low friction it has become.
Reminds me of this article: https://www.theverge.com/podcast/917029/software-brain-ai-ba...
Software engineers are definitely in a bit of a bubble here. Are we just early adopters who see the value sooner, or does it uniquely benefit software engineering, or do we just like cool automation and we're deluding ourselves that this adds value beyond the cost?
Yes I believe software benefits uniquely, just like building tooling and automating software have long been easier in software than other domains. Humans defined all the rules of the world you live in, humans wrote strict rules in methodically parsable formats.
The moment you have to interact with the physical world or humans (psychological, imaginative, aesthetic, etc), there are often undiscovered or changing rules—or no rules at all. Or systems are subject to perturbations beyond a defined scope.
The other thing I believe is software developers are experts at doing the things that allow them to make doing those very things easier and more automated. And they do this in public, perfectly documented online.
Both because of the things I described above and because software developers have created the largest machine-accessible training set for plying their trade of any trade, ML—that is ultimately interpolating massive datasets to do things—is unsurprisingly uniquely successful for software tasks.
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.
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.
Anyone doing a basic google search right now, is "technically" using Ai.
No, everyone is not using AI for everything - yet.
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.
Not everyone but most. And I've been having this discussion with people around me a lot lately and everyone that has the ability to think more than half a step ahead sees it(and frankly we are fed up). I previously discussed how a friend admitted that he's never seen the code that powers his project at an S&P 500 company. Yesterday I was talking to another friend and former coworker who complained that when cloudflare went out a month or so ago, his entire team just slammed their laptops and went home cause they couldn't work(no sloppus/sloppenai). Another friend of mine: her dad is in hospital with a terminal disease and her mom (in her late 50's or early 60's, idk) uses chatgpt as a personal therapist. Gatorade-fed crops here we come, Leeerooooy Jeeeenkins!
Embarrassing NPC behavior to throw in the towel on working because you lost your crutch.
I 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.
As a part of the "don't use won't use" crowd it has been exhausting explaining that we exist lol
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.
I am using AI to take on a fun large scale analysis of churches in USA.
I also just bought a completely mechanical film camera to learn a new old skill with no tech to fall back on.
I 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.
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-...
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.
or for anything
> 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.
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.