IMHO anyone who started using AI seriously:

1) Wouldn't want to go back

2) Wouldn't believe that it's about to replace human intellectual work

In other words AI got advanced enough to do amazing things but not 500B or T level of amazing and people with the money are not convinced that it will be anytime soon.

What is using AI seriously?

I’ve tried to use it as a web search replacement and often the information is generic or tells me what I already know or wrong.

I’ve used a code suggestions variant long before OpenAI hype started and while sometimes useful, rarely is it correct or helpful on getting over the next hurdle.

Any code from my coworkers is now just AI slop they glanced over once. Then I spend a long time reviewing and fixing their code.

I really don’t find spending time writing long form questions so a bot can pretend to be human all that time saving, especially if I have to clarify or reword it to a specific “prompt-engineer” quality sentence. I can usually find the results faster typing in a few keywords and glancing at a list of articles. My built in human speed reading can determine if what I need is probably in an article.

LLM seriousness has made my job more difficult. I would prefer if people did go back.

In my case its coding real world apps that people use and pay money for. I no longer personally type most of my code, Instead I describe stuff or write pseudo code that LLMs end up converting into the real thing.

It's very good at handling the BS part of coding but also its very good at knowing things that I don't know. I recently used it to hack a small bluetooth printer which requires its own iOS app to print, using DeepSeek and ChatGPT I was able to reverse engineer the printer communication and then create an app that will print whatever I want from my macOS laptop.

Before AI I would have to study how Bluetooth works now I don't have to. Instead, I use my general knowledge of protocols and communications and describe it to the machine and I'm asking for ideas. Then I try things and ask the stuff that I noticed but I don't understand, then I figure out how this particular device works and then describe it to the machine and ask it to generate me code that will do the thing that I discovered. LLMs are amazing at filling the gaps in a patchy knowledge, like my knowledge of Bluetooth. Because I don't know much about Bluetooth, I ended up creating a CRUD for Bluetooth because that's what I needed when trying to communicate and control my bluetooth devices(it's also what I'm used to from Web tech). I'm bit embarrassed about it but I think I will release it commercially anyway.

If I have a good LLM under my hand, I don't have to know specialised knowledge on frameworks or tools. General understanding of how things works and building up from there is all I need.

It's like a CNC machine but for coding.

I see, for single operator, no customers products it works nicely. You may find you use it less and less and will actually require that Bluetooth knowledge eventually as you grow a product.

LLMs so far seem to be good at developing prototype apps. But most of my projects already have codegen and scaffolding tools so I guess I don’t get that use out of them.

I predict that once you release your embarrassing app, you will find all the corner cases and domain secrets come rearing out with little ability of the LLM to help you (especially with Bluetooth).

The Bluetooth app thing is just an example of LLMs helping me build something I don't have beyond-basics knowledge of.

For other stuff, I still find it very useful because why would I bother to code something non-novel when I can just tell the LLM what I need?

For example, if I need a code that finds the device that given characteristics belongs(bluetooth stuff, again) to I can just tell the LLM to write it for me. It doesn't take a genius to write such a code, its elemental stuff and I would rather not spend my working memory on remembering the structures and names of variables. I copy+paste the current class that handles the bluetooth comms, tell it that I need a function for sending data to the printer and it gives me back the result. There's no art in writing such a code, its standard code for an API and I would prefer not to bother with it.

You seem to be overestimating the quality of a many production software products.

Don’t worry I am not. I understand that most prod deployed softwares are prototypes before LLMs came around.

“Before AI I would have to study how Bluetooth works now I don't have to.”

And

“It's very good at handling the BS part of coding…”

This is the part that I think is difficult in a team situation.

Learning and understanding is the important part, and certainly isn’t BS.

I understand that it really can make it seem like velocity has increased when you really are shipping things that more or less “work”, but it’s really a good practice to understand your code.

I’ve had to spend a significant amount of time fixing work that was admittedly generated using AI by other engineers, and I really fear engineers are beginning to trade deep understanding for the high of getting something that “works” with little effort.

It might “work” but you might be ignoring the work everyone around you is doing to clean up your brittle code that doesn’t scale and wasn’t thought through at inception.

It is effective because you can spend your mental energy on the things that matter, things that make difference.

Code quality actually doesn't matter when you remove the human from the loop as long as it works correctly because it becomes something made by a machine to be interpreted by a machine.

Code isn’t a binary scale of works or doesn’t - there is inefficient code and insecure code and everything else in between that still technically “works” - but a lack of understanding will eventually cause these “working” solutions to catch up to you.

You can always revisit that part of code if it doesn’t perform. For vast majority of code running on consumer devices there’s no difference between smart implementation and mediocre implementation. LLMs are great at being mediocre by default.

As for security, that mostly stems from the architecture. LLMs mediocracy also helps with following industry conventions and best practices.

In my case I never get the code being written at once, instead I make LLMs write pieces that I put together myself. Never got used to copilot or Cursor, I feel in control only with the chat interface.

Not understanding how Bluetooth works while building a Bluetooth thing seems like… a problem, though. Like, there are going to be bugs, and you’re going to have to deal with them, and that is where the “just ask the magic robot” approach tends to break down.

Funny enough, you already don't have access to low level radio so building a "Bluetooth thing" is just about dealing with some libraries and API.

Bugs happen but its not that different from any other type of bugs. Also, you end up learning about Bluetooth as bugs and other unexpected behavior happen. The great thing about LLMs is that they are interactive, so for example when collecting Bluetooth packets for analysis I ended up learning that the communication with Bluetooth is a bit like talking through a middleman and some packet types are only about giving instructions to the Bluetooth chip and others are actually about communicating with a connected device.

Using LLM for coding something you don't understand is much different than Googling something, then copy+paste a snippet from Stackoverflow because you can ask for instant explanation and modifications for testing edge cases and other ideas.

Oh come on, I'm not an "AI believer", but it regularly does things for me like write complex SQL queries that I can then verify are correct. Just something like that will often save me 20-40 minutes over doing it manually. There is _something_ there, even if it's not going to replace the workforce anytime soon.

You have an entirely valid worry and I get a bit scared at my use of AI because of this. I fear that dev jobs might go away or become third world only jobs like electronics manufacturing but in the mean time its scary how much it atrophies your mind. At the same time, it has opened up a universe of answers to questions I wouldn't normally ask because the bar was too high. Everyone seems to have their own unique stories.

For example just today, I dumped a crash log from the Mac version of Microsoft Remote Desktop into it. This damn app locks up 10 times a day for me causing a "Force Quit" event and subsequent crash dump to be generated. Normally what can I do with that crash dump other than send it off to Apple/Microsoft? It identified where it thought the crash was coming from: excessive right clicking causing some sort of foundational error in their logic. Avoiding right clicking has solved the issue for me. Now that I write this out, I could have spent hours upon hours finding a needle in a haystack and that would probably made me a better developer but the bar is too high, there is too much other work I have to get done than to chase this. Instead I would have just lived with it. Now I have some closure at least.

Again it seems like everyone has got their own unique stories. Is AI taking everything over? Not yet. Can I go back to pre-AI? No, its like going back to Windows 95.

The only part I would quibble with is the fear that superficial AI generated code becomes widespread. It's not that I think this won't happen, and I wouldn't want it on my team, but I think it could actually increase demand for competent software engineers.

I got into coding about a decade ago when cheap outsourcing had been all the rage for a number of years. A lot of my early experience was taking over very poorly written apps that had started off with fast development and then quickly slowed down as all of the sloppy shortcuts built up and eventually ground development to a halt. There's a decent chance LLMs lead to another boom in that kind of work.

For mass production/scalability, I absolutely agree with you.

For products that won't be scaled, I imagine it becomes just another abstraction layer, with the cost of human input outweighing the cost of the additional infrastructure / beefing up hardware to support the inefficiencies created.

Either you’re using it wrong, or you are using the wrong tools.

For search, try kagi fastgpt (no subscription required):

https://kagi.com/fastgpt

For code completion, I’ve found it’s not good at jumping hard hurdles, but it is a bit better than find replace (e.g. it finds things that are syntactically different, but semantically related), and can notice stuff like “you forgot to fix the Nth implementation of the interface you just extended”.

It’s also good at “I need to do something simple in a language I do not know”.

I’ve definitely encountered ai slop from coworkers. I’m sure they also produce stack overflow copy paste garbage too. Dealing with their newly-found increased “productivity” is an open problem.

Insisting on strict static typing helps. The LLMs can’t help with that, and it forces a higher bar before compilation succeeds.

I shut off Kagi search LLM stuff because I don’t need an LLM typing stuff out while I’m literally looking at a list of results.

> What is using AI seriously?

It's buying into the hype. AI is crypto 2.0, the true believers live in their own bubble and their own fantasy world.

> 1) Wouldn't want to go back

I went back. It sucks pretty bad, actually.

I went back. It’s fine at solving small problems and jump-starting an investigation, but only a small step better than a search engine. It’s no good for deep work. Any time I’ve used it to research something I know well, it’s got important details wrong, but in a confident way that someone without my knowledge would accept.

RLHF trains it to fool humans into thinking it’s authoritative, not to actually be correct.

I use it to get the keywords and ideas, then use the normal search engine to get the facts. Still, even in this limited capacity I find the LLMs very useful.

That’s similar to how I use it. Useful, but not game changing. Definitely not at the level of the hype.

This is exactly the experience I've had. Recently started learning OpenTofu(/Terraform) and the company now had Gemini as part of the Workspace subscription. It was great to get some basic going, but very quickly starts suggesting wrong or old or bad practices. Still using it as a starting point and to help known what to start searching for, but like you said, it's only slightly better than a regular search engine.

> Wouldn't believe that it's about to replace human intellectual work

Yea idk about that one chief. I have been working in ML (specifically scaling of large model training) at FAANG for the past 8 years, and have been using AI for my work since basically the first time this became even slightly usable, and I don’t share your optimism (or pessimism depending on how you see it). Yes it’s still pretty bad, but you have to look at rate of improvement, not just a static picture of where we are today.

I might still be wrong though and you may be right, but claiming that anyone using AI believes like you do is flat out false. A lot of my colleagues also working in ML researcher think like me btw.

It's a figurative speech, obviously its a spectrum where some believe that AGI is around the corner or that all this is nothing more than some overblown statistics exercise and LLMs have nothing to do with actual intelligence.

In my opinion, this generation of AI is amazing but isn't it.

I have started using AI for all of my side projects, and am now building stuff almost everyday. I did this as a way to ease some of my anxiety related to AI progress and how fast it is moving. It has actually had the opposite effect; it's more amazing than I thought. I think the difficulty in reasoning about 2) is that given what interesting and difficult problems it can already solve, it's hard to reason about where it will be in 3-5 years.

But, I am also having more fun building things than perhaps the earliest days of my first code written, which is just over 7 years now. Insofar as 1) goes, yes, I never want to go back. I can learn faster and more deeply than I ever could. It's really exciting!

Lining up for whatever the next thing is. "Look, we know we said AR/VR was the next big thing in the late tens and LLMs were the next big thing in the early 20s, but quantum is the next big thing now. For real, this time!"

(Not entirely sure what the next fad will be, but some sort of quantum computing thing doesn't feel unlikely. Lot of noise in that direction lately.)

Curiously, all of these three (VR/AI/QC) are limited by hardware. But AI is the only one that has seen meaningful progress by just throwing more contemporary hardware at it. Sure, future hardware might bring advancements to all of them. But if you're making an investment plan for the next quarter, the choice is pretty obvious. This is why AI rules the venture capitalist sector instead of fusion or other long term stuff.

Of the three, QC is different in that it's not a solution looking for a problem. If we ever scale QC to the point where it can do meaningful work (the "if" is doing a lot of work there - per your point about hardware), then I don't see it fumbling like the other two have. We have immediate and pressing needs that we know how to solve with QC. The other two are very much research coming up with cool toys, and product fucking around so that they can find out what to use them for.

When considering scientists perhaps, but when you look at the general population the other two have way bigger ramifications on daily life than QC.

Quantum AI will be the next buzzword, once AGI loses steam.

Nadella is looking for the world to grow at 10% due to AI enhancement, like it did during the industrial revolution.

That seems like a low bar because it already is- it's just not equally distributed yet.

My own productivity has grown far more than 10% thanks to AI, and I don't just mean in terms of dev. It reads my bloodwork results, speeds up my ability to repair a leak in my toilet tank, writes a concise "no I won't lend you money; I barely know you" message... you name it.

Normally all of those things would take much longer and I'd get worse results on my own.

If that's what I can do at the personal level, then surely 10% is an easily-achievable improvement at the enterprise level.

All I hear is anecdotal statements from people claiming LLMs have made them some percent more productive. Yet few actually say how or demonstrate it.

For the last year, I've tried all sorts of models both as hosted services and running locally with llama.cpp or ollama. I've used both the continue.dev vscode extension and cursor more recently.

The results have been frustrating at best. The user interface of the tools is just awful. The output of any models from Deepseek to quen to Claude to whatever other model is mediocre to useless. I literally highlight some code that includes comments about what I need and I even include long explicit descriptions etc in the prompts and it's just unrelated garbage out every time.

The most useful thing has just been ChatGPT when there's something I need to learn about. Rubber ducking basically. It's alright at very simple coding questions or asking about obscure database questions I might have, but beyond that it's useless. Gotta keep the context window short, or it starts going off the rails every single time.

I think its about scope and expectations. I have had some form of AI code completer in my neovim config for 3 years. It works flawlessly and saves me tons of keystrokes. Sure sometimes it suggests the incorrect completion but I just ignore it and keep coding as if it didn't exist. I am talking about line by line, not entire code blocks, but even that it does well at times.

From what I have seen the people that have the most success have AI building something from scratch using well known tooling (read: old tooling).

The problem is that doesn't immediately help most people. We are all stuck in crap jobs with massive, crusty code bases. Its hard for AI because its hard for everyone.

If LLM chatbots are making you vastly more productive in a field, you are in the bottom 20% of that field.

They're still useful tools for exploring new disciplines, but if you're say a programmer and you think ChatGPT or DeepSeek is good at programming, that's a good sign you need to start improving.

If you have to tell others that then perhaps some introspection for yourself might be helpful. Comes across more as denial than constructive commentary.

I do believe the benefit decreases the more senior or familiar the work is but there is still a noticeable benefit and I think it largely depends on the velocity and life cycle of the product. I think you get less benefit the slower the velocity or the more mature of a product. To deny it like in your post is simply being an intellectual minimalist.

Again, show your evidence.

You make a polite but still ad hominem "attack" about me instead of addressing my points with demonstrations of evidence.

Make a video or blog article actually showing how your use of LLMs in coding is making you more productive. Show what it's doing to help you that has a multiplier effect on your productivity.

I was not replying to you so I hope your comment was not directed at me?

Oh I see, I had replied to your comment directly where I was stating that I find it surprising that folks like yourself are so quick to attack, though looking at your response here its not that surprising.

I don't think it deserves a video or blog, like I already said the multiple posts that have made HN front page have covered it well. - Autocomplete saves me keystrokes usually - Features like Cursor's composer/agent allow me to outsource junior level changes to the code base. I can copy/paste my requirements and it gives me the diffs of the changes when its done. Its often at a junior level or better and tackles multi-file changes. I usually kick this off and go make other changes to the code base.

Now like I have said before, this depends a lot on the velocity of the team and the maturity of the code base. I think more mature products you will have less benefit on feature implementation and most likely more opportunity in the test writing capabilities. Likewise, teams with a slower cadence, thinking a bluechip software company compared to a startup, are not going to get as much benefit either.

Instead of being so aggressive, simply say why it does not work for you. These tools strive in web dev which you may not be involved in!

I see a gap between "vastly more productive" and "noticeable benefit".

good shoes help me walk a bit faster, and for longer.

they don't let me walk at the pace of a SUV.

AI is like the good shoes. they help, and make many tasks a bit easier. but they can't make me into an SUV.

and if they can, then no programmers will have jobs. which is the end-state of this whole LLM thing as far as I can tell.

I have a good shoes business. Can you give me a couple of 100 billions of dollars? Good news I promise you trillions, in a year or two or 10 maybe, who knows you can exprapolate into a future science fiction reality yourself. So when are you transfering the money?

You are now moving the goal post from us discussing is this adding value to how much is it worth. There are a lot of open debate to some of the level of investment but from the hyperscaler territory, they are flush with cash and it probably hurts more under invest and be wrong than it is to over invest.

This. I shudder to think of the hubris of a programmer who doesn’t understand pointers prompting an AI model to generate low-level system code for them. Sure it might generate a program that appears to work. But is that human reading the code qualified to review it and prevent the model from generating subtle, non-obvious errors?

Linus Trovalds: bottom 20% coder.

https://www.youtube.com/watch?v=VHHT6W-N0ak&pp=ygUMTGludXMgQ...

Linus hasn't coded in decades, let the man retire in peace.

Maybe you're too good to benefit?

It wrote a daemon for me today with a basic spec in 20 minutes of chat. Would have been a day or so between me and a junior dev. Maybe 2 hours if I wrote it and reminded myself of all the little bits and bobs.

Just in the past couple months there have been a number of "I am a senior/principal engineer and this is how I use LLMs". I would agree that the tools are not optimal yet but every iteration has improved for me.

Maybe whatever language you are coding it or whatever project you are working on is not a good fit? It is an equally perplexing situation for myself when I hear anecdotes like yours which don't align with my experience. The fact that you say everything is garbage calls into question either how you are using the tool or something else.

I can reliably use cursor's composer to reference a couple files, give a bullet list of what we are trying to do and point it to one of the better models and the output is junior engineer level or better output. When I say junior, I mean a junior who has experience with the codebase.

What kinds of projects are you working on? My experience is jot very good with these tools (expanded in a sibling comment).

Generally a lot of web-dev which is where I would assume LLMs shine the best. I noted elsewhere but I think it depends a lot on the age of the product and the level of velocity. For early life products where the speed of your velocity matters, I think you can get the most benefit. The more mature the product and the slower the team implements features, the benefits are still measurable but not as high.

Ah yeah, I can totally see how it can be useful for churning put tons of code. Even without copy-paste, just generating a ton of references and rewriting/improving them. Anecdotally, I’ve tried asking deepseek to review a few files of my code — it wasn’t bad at all, though not without false positives.

> All I hear is anecdotal statements from people claiming LLMs have made them some percent more productive. Yet few actually say how or demonstrate it.

It's very difficult to measure productivity of most people, certainly most people in office jobs, so while you can have a gut feeling that you're doing better, it's no more measurable than pre-AI individual productivity measurement was

It’s not really about objective measurements, but practical applications. Like try this in the following manner and compare it to your previous workflow. Sensible advices like the ones found in The Pragmatic Programmer.

Sure, so it's always going to be annecdotal. That doesn't mean the benefits don't exist, just means they can't be objectively measured. Just like we can't objectively measure the output of a single knowlege worker, especially output on a single day

I have a similar experience. Tried to use it for real work and got frustrated by the chat’s inability to say “I don’t know”. It’s okay for code snippets demonstrating how something can be used (stack overflow essentially), also code reviews can be helpful if doing something for the first time. But they fail to answer questions I’m interested in like “what’s the purpose of X”.

I fixed the hinge in my oven by giving perplexity.com the make and problem. I saved an hour on the phone calling people to organise a visit some time in the next week.

Maybe you should stop using the Ai slop tools that don't work?

Very likely you would have been as successful without AI to fix your oven hinge yourself, there is tons of content about that online.

No. I'd already spend 30 Min looking at how to solve it myself. The search on perplexity was a hail marry before I started calling handymen.

I would like to propose a moratorium on these sorts of “AI coding is good” or “AI coding sucks” comments without any further context.

This comment is like saying, “This diet didn’t work for me” without providing any details about your health circumstances. What’s your weight? Age? Level of activity?

In this context: What language are you working in? What frameworks are you using? What’s the nature of your project? How legacy is your codebase? How big is the codebase?

If we all outline these factors plus our experiences with these tools, then perhaps we can collectively learn about the circumstances when they work or don’t work. And then maybe we can make them better for the circumstances where they’re currently weak.

I feel like diet as an analogy doesn't work. We know that the only way to lose weight is with a caloric deficit. If you can't do this, it doesn't matter what you eat you won't lose weight. If you're failing to lose weight because of a diet you are eating too much, full stop.

Whereas measuring productivity and usefulness is way more opaque.

Many simple software systems are highly productive for their companies.

I agree with the other commenter that said if you're "vastly" more productive as a developer due to AI, you probably weren't that good to begin with. Otherwise, please provide concrete examples.

Myself, I do find it quite useful in a few respects. First and foremost, as a "better Google/StackOverflow." If something's not working, I can describe my exact scenario and usually get pointed in the right direction. Sometimes the LLM just wastes my time by very confidently telling me some function/library that solves my exact problem exists when in fact it doesn't.

Second, IntelliJ's local LLM is sort of a smarter autocomplete. It makes some outright wrong suggestions, but when there's areas where I have to do a lot of repetitive tasks that follow a simple pattern (like for instance, mapping fields from one type of object to another), it does a pretty good job of making correct suggestions. I definitely appreciate it but it's certainly not doing things like writing a significant portion of code in my style.

I've been using Amazon Q Developer as it was provided and approved by my employer. It has been pretty good with Python codebases, Kubernetes configurations, and (not surprisingly) CDK/Cloudformation templates. I can pretty much just ask it "here's my python script, make everything I need to run it as a lambda, hook that lambda up to x, it should run in a vpc defined in this template over here", and it'll get all that stuff put together and its normally pretty solid code it generates. It seems to pull in a lot of the context of the project I have open. For instance, I can say "it should get those values from the outputs in other-cf-template.yml" and it knows the naming schemes and what not across templates, even if it didn't generate those templates.

I might go back and tweak some stuff, add some extra tags and what not, but often its pretty good at doing what I ask.

Sometimes its suggestions aren't what I was really wanting to do in my codebase, a handful of times it has made up methods or parameters of even well-known libraries. But usually, its suggestions are better than a basic IntelliSense-style autocomplete at least in my experiences.

I haven't used many of the other developer assistant plugins like say GitHub Copilot. I couldn't really say which is better or worse. But I do think using Q Developer has made me faster in many tasks.

I wouldn't expect a tool that doesn't have access to the context of my editor and the files I have open to be very useful for actually coding. There's a lot of context to understand in even a basic application. If you're just asking a locally running app in ollama "give me a method to do x", don't be surprised if it doesn't know everything else happening in your app. Maybe it'll give you a halfway decent example of doing something, but devoid of how it actually plugs in to whatever you're making it might be entirely worthless.

Seriously. It’s like half of the people in this thread are living in a completely different world.

And this is coming from someone who uses LLMs daily at the subscription, API (vscode and 3 nextjs apps) and local level. I have a custom langchain stack, prompt repo, you name it. And regardless of how little or how much I use what I have, or what soup de jour prompt or process (from Keep it simple to Prompt enhancers) I can’t say it’s made a meaningful difference in my life. Even with all of the customization and planning.

It’s a great search engine though.

Would it look like such a good search engine if the actual search engines hadn't progressively broken themselves over the last 15 years?

I swear half the time when I use it to look up the nuances of system API stuff, it's replaying forum, mailing list or Stackoverflow conversations that Google ought to be able to find but somehow can't.

And Henry Ford would reply: "Who is going to buy the cars?"

We have been living in a fake economy for quite some time where money is printed and distributed to the "tech" sector. Which isn't really "tech", but mostly entertainment (YouTube, Netflix, Facebook, ...).

Growth of the economy means nothing. The money that has been printed goes to shareholders. What the common man gets is inflation and job losses.

If you want to grow the real economy, build houses and reduce the cost of living.

> If you want to grow the real economy, build houses and reduce the cost of living.

Yes, I wonder why it is so hard for Western countries to understand that there's no future in a place where housing is more expensive than your average salary. If may look cool for a few years until most people have left or are living on the streets.

Plenty of housing. The problem is, people want cheap housing in places where everyone wants to live. I don't think that will happen any time soon.

This is a non-sense that spreads because of North American style of housing. If you're talking about sprawling suburban houses then you're right. But big cities have provided reasonable housing for lots of workers for centuries. The only thing you need is to build more apartments in the cities that have an excess of job positions.

No, you can't just "build more apartments". For these new inhabitants you will need more grocery stores, more bus/subway stops and overall transportation, more hospitals, more firefighters, more restaurants, more gyms, more tennis courts, more of everything.

Of course. Big cities with all this infrastructure are nothing new. They existed in the past and are big in alive in Asia and other parts of the world. Only in North America we have this bizarre world where it seems like a strange thing to build cities and provide infrastructure for workers!

There is basically no large city outside of subsaharan African & maybe the subcontinent that has that development style and anything even approaching a sustainable 2.1 total fertility rate

There is no cheap housing anywhere in the entire state of California. In the worst and poorest parts of the state where are basically no jobs or anything the housing is still way more expensive than anyone can afford.

So move out of there. Plenty of cheap housing in the country.

"there's no future in a place where housing is more expensive than your average salary."

don't get me wrong, everyone want cheaper housing but not their house

I for one would love it. If I have to sell housing then I have to buy housing, it's not a benefit to me unless I reduce my quality of life.

Maybe its time we stop seeing housing as an investment and more of a place to shelter oneself from the elements. One of the core pillars of survival

A friend tried to tell me China has a real estate crisis, because the value of houses is dropping due to building to many and people are losing on their investments. I asked him if he is sure cheap and available housing is a crisis.

Everyone in the industry losing their shirts and going out of business is a crisis. It happened 15 years ago in the US and we still haven't made it back to mid 90s level of housing starts.

You should be curious why Nadella is looking for the world to grow at that rate. That’s because he wants Microsoft to grow into $500B/year in revenue by 2030, and it will be challenging without that economic growth to grow into that target. You can grow into a TAM, try to grow or broaden the TAM, or some combination of both. Without AI, it is unlikely the growth target can be met.

https://www.cnbc.com/2023/06/26/microsoft-ceo-nadella-said-r...

What is your personal productivity metric by which you have more than 10% increase? More money earned, less money spent, fewer working hours for same income, more leisure time? It needs to be something in aggregate to mean something related to what Nadella meant. There are many individual task which LLM system can help with. But there is also may ways for those gains to fail to aggregate into large overall gains. Both on personal level and on corporate, and economy wide level.

I think the 'grow at 10%' refers to the incremental part of the entire world/market.

during the industrial revolution(steam/electricity/internet), the world was growing, there're trains, cars, netflix

bussiness grown with productivity growing, even so, we lived through 2 world wars and dozens of economic crisis

but now is very different, when you repair the tank with LLM's help, when the labour value of repairers is decreased, there's no addition value are produced

there's a very simple thought experiment abt the result of productivity growing alone:

let's assume robotics become to a extremely high level, everything humen work can be reduced to 1/100 with help of robots, what will happen next?

> let's assume robotics become to a extremely high level, everything humen work can be reduced to 1/100 with help of robots, what will happen next?

We work 35 hour years instead of 35 hour weeks?

Lol, ever the optimist I see.

It's always worth reminding people that wealth accumulation in the insanely rich isn't the only option

You’re describing exactly what happened during both the Industrial Revolution and the advent of computer automation.

Prior to computerization and databases, millions of people were required for filing, typing, and physically transporting messages and information. All of those jobs, entire fields of work were deleted by computerization.

Even "computer" was originally a job title. For a person.

Going to safely assume you've never worked at an enterprise.

Because improving the productivity of every employee by 10% does not translate to the company being 10% more productive.

Processes and systems exist precisely to slow employees down so that they comply with regulations, best practices etc rather than move fast and break things.

And from experience with a few enterprise LLM projects now they are a waste of time. Because the money/effort to fix up the decades of bad source data far exceeds the ROI.

You will definitely see them used in chat bots and replacing customer service people though.

It also gets all of these things wrong, like not paying attention to models of toilets and quirks for their repair, often speaking with an authoritative voice and deceiving you on the validity of its instructions.

All of the things you site are available via search engines, or better handled with expertise so you know how much of the response is nonsense.

Every time I use AI, it's a time waste.

Every time I contact an enterprise for support, the person I'm talking to gets lots of things wrong too. It takes skepticism on my part and some back and forth to clean up the mess.

On balance AI gets more things wrong than the best humans and fewer things wrong than average humans.

AI is a lossy data compression technique at best. One can always tell when an AI cheerleader/ex blockchain bro has hitched their financial wagon to this statistic based word vomit grift.

Please elaborate, preferably without breaking HN guidelines about dismissive name-calling

The difference is that a human will tell you things like "I think", "I'm pretty sure" or "I don't know" in order to manage expectations. The LLM will very matter-of-factly tell you something that's not right at all, and if you correct them the LLM will go and very confidently rattle off another answer based on what you just said, whether your were telling it the truth or not. If a human acted that way more than a few times we'd stop asking them questions or at least have to do a lot of "trust but verify." LLMs do this over and over again and we just kind of shrug our shoulders and go "well they do pretty good overall."

I can't count the number of times I've had a support person confidently tell me something that is obviously not applicable to my problem and makes completely flawed assumptions about cs/physics/networking/logic.

I get a lot of correct answers from llms, but sometimes they make shit up. Most of the time, it's some function in a library that doesn't actually exist. Sometimes even the wrong answers are useful because they tell me where to look in the reference docs. Ask it to search the web and cite sources, makes it easier to verify the answer.

I don't appreciate what's going on with AI art and AI generated slop, but the idea that they aren't a useful tool is just wild to me.

I'm not saying it's not useful, I'm saying that we hold humans giving us answers to a much higher standard than LLMs.

Who suddenly knows how to measure developer productivity? I thought this was impossible.

Annual growth rates during the Industrial Revolution where way lower than 10%. In the 18th century it was well below 1%, during the 19th century it was on average at 1-1.5% (the highest estimates go up to 3% annual growth for certain decades close to 1900).[0][1][2]

Some regions or sectors might have experienced higher growth spurts, but the main point stands: the overall economic growth was quite low by modern standards - even though I don't think GDP numbers alone adequately describe the huge societal changes of such sustained growth compared to agrarian cultures before the Industrial Revolution.

[0] https://web.archive.org/web/20071127032512/http://minneapoli... [1] https://www.bankofengland.co.uk/explainers/how-has-growth-ch... [2] https://academic.oup.com/ereh/article/21/2/141/3044162

Unless you’re producing 10% more money with AI you’re not doing shit.

Or fix a leaking faucet.

How close do we need to be for you to help a brother out? Feeling seriously unsupp0rted right now

Time to sort NVDA?

A fellow degenerate gambler I see. The market can remain irrational longer than you can remain solvent, trade with caution. Being early is the same as being wrong.

Highly regarded people unite :D

More seriously though: unless you have privileged information or have done truly extensive research, do not short stocks. And if you do have privileged information, still don't short stocks because unless you have enough money to defend yourself against insider trading like Musk and it's ilk, it's not going to be worth it.

It's perfectly reasonable to determine that a particular high growth stock is not going to perform as well going forward, in which case I'd shift allocation to other, better candidates.

Generally, being long equities is a long term positive expected value trade. You don't have to time the market, just be persistent. On the other hand, as you correctly alluded to, shorting equities requires decently precise timing, both on entry and exit.

A common hypothesis for why Nvidia is so hot is because they have an effective monopoly on the hardware to train AI models and it requires a crap ton of hardware.

With DeepSeek it’s been demonstrated you can get pretty damn for a lot cheaper. I can only imagine that there are tons of investors thinking that it’s better to invest their dollars in undercutting the costs of new models vs investing billions in hardware.

The question is, can Nvidia maintain their grip on the market in the face of these pressures. If you think they can’t, then a short position doesn’t seem like that big of a gamble.

it’s effectively a software moat wrt. GPU programming, there’s nothing stopping AMD from catching up besides insufficiently deep pockets and engineering culture

Not sure why AMD’s software side gets so much flack these days. For everything other than AI programming, their drivers range from fine to best in class.

I have an AMD minipc running linux that I use for steam gaming, light development, etc. The kernel taint bit is off.

There is one intel device on the pci/usb buses: wifi/bt, and it’s the only thing with a flaky driver in the system. People have been complaining about my exact issue for something like 10 years, across multiple product generations.

Nobody who controls the purse strings cares about the kernel taint bit if their model doesn’t train, if they’re burning developer time debugging drivers, if they have to burn even more dev time switching off of cuda, etc.

If AMD really cared about making money, they would’ve sent MI300s to all of the top CS research institutions for free and supported rocm on every single product. Investing any less than nvidia, the trillion dollar behemoth, is just letting big green expand their moat even more.

As I said, other than AI. The management made a big bet on crypto when nvidia made a big bet on AI.

That didn’t work out all that well in the medium term (it did in the short term), though it gave them runway to take a big chunk of intel’s server market.

Whether or not that was a good move, it’s not evidence of some engineering shortcoming.

A short position is always a gamble, because you could lose more than everything.

I think its probably foolish to short nvidia until theres at least echoes of competition.

AMD wants it to be them, but the reality is that the moat is wide.

The closest for AI is Apple, but even then, I’m not certain its a serious competitor; especially not in the datacenter.

For Gaming there’s practically no worthwhile competition. Unreal Engine barely even fixes render bugs for Intel and AMD cards, and I know this for fact.

FD: I’m recently holding shares in nvidia due to the recent fluctuation, and my own belief that the moat is wider than we care to believe, as mentioned.

Using “bubble” sort? ;)

The combination of high and climbing price to earnings ratios for a smaller subset of tech firms, outsize retail investment in tech (cloaked by people buying crypto), and macro environment factors like high interest rates stimulating risky lending has me swapping this bubble toward the top of the list.

See further: https://www.morningstar.com/news/marketwatch/20250123167/six...

Bubble sort is very resource-hungry...

it's very dangerous, shorting in a market where a gamma squeeze can occur is extremely dangerous

other markets s like Taiwan are preferable

The "elephant in the room" is that AI is good enough, it's revolutionary in fact, but the issue now is the user needs more education to actually realize AI's value. No amount of uber-duper AI can help an immature user population lacking in critical thinking, which in their short shortsightedness seek self destructive pastimes.

It's not "good enough", it's mostly overhyped marketing garbage. LLM models are mostly as good as they're going to get. It's a limitation of the technology. It's impressive at what has been done, but that's it.

It doesn't take billions of dollars and all human knowledge to make a single human level intelligence. Just some hormones and timing. So LLMs are mostly a dead end. AGI is going to come from differenst machine learning paradigms.

This is all mostly hype by and for investors right now.

LLM direct response models are quite mature, yes (4o)

LLM based MoE architectures with some kind of reasoning process ( Claude 3+, o series, R1, grok 3 with thinking ), are the equivalent of v0.2 atm, and they're showing a lot of promise.

It's pretty good for a whole class of problems that humans currently do.

"You're holding it wrong" only goes so far.

I spent more time yesterday trying to get "AI" to output runnable code, and retyping, than if I had just buckled down and done it myself.

But I don't think you can blame users if they're given an immature tool, when it really is on companies to give us a product that is obvious to use correctly.

Its not an exact analogy, but I always like to think of how doors are designed - if you have to put a sign on it, its a bad design. A well designed door requires zero thought, and as such, if "AI" usage is not obvious to 99% of the population, its probably a bad design.

Think of it like you're talking to someone so smart that they answer before you're finished explaining, and get the general idea wrong, or seem really pedantic and your misplaced use of a past tense verb that should have been active tense causes then to completely reinterpret what you're talking about. Think of our current LLMs like idiot savants, and trust them as much.

I don't use AI to write code if that code is not short and self contained. It's great at explaining code, great at strategy and design about code. Not so much at actually implementing code larger than 1/4 to 1/3rd it's output context window. After all, it's not "writing code", it's statistically generating tokens that look like code it's seen before. It's unknown if the training code in which the LLM is statistically generating a reply actually ran, it could have been pseudo code explaining that computer science concept, we don't know.

People seem to want a genie that does what they are thinking, and that is never going to work (at least with this technology.) I'm really talking about effective communications, and understanding how to communicate with a literal unreal non-human construct, a personality theater enhanced literary embodiment of knowledge. It's subtle, it requires effort on the user's side, more than it would if one were talking to a human expert in the area of knowledge you operate. You have to explain the situation so the AI can understand what you need, and developers are surprising bad at that. People in general are even worse at explaining. Implied knowledge is rampant in developer conversation, and an LLM struggles with ambiguity, such as implied references. Too many same acronyms in different parts of tech and science. It does work, but one really needs to treat LLMs like idiot savants.

Oddly enough, Databricks's query generator is named Genie. It's halfway decent, too.

Did they tell the M365 sales/marketing teams about this? My users get bombarded with sales pitches, new free trials and other commms about how wonderful copilot is. It's almost a full time job to manage people's expectations around this...

Nadella is just saying we haven't yet seen a revolution yet measurable by 10% economic growth--he makes no statement about the future.

Most people have no clue how to use AI or where to use it in their lives. There was a guy at work who was submitting command-like queries (give meeting summary) and complained about how it left out XYZ. Then I told him to ask "Give me the meeting summary with X, Y, Z" or "what did so and so say about Y."

His mind was blown.

We are in the first inning. We haven't figured out how to integrate this into everything yet.

> We haven't figured out how to integrate this into everything yet.

Maybe we don't need to. The world is fine without a sci-fi chatbot.

He doesn't actually say that, the (very biased and polemical) article writer seems to have made that up. The actual quote is:

"Us self-claiming some [artificial general intelligence] milestone, that's just nonsensical benchmark hacking to me. So, the first thing that we all have to do is, when we say this is like the Industrial Revolution, let's have that Industrial Revolution type of growth. The real benchmark is: the world growing at 10 percent. Suddenly productivity goes up and the economy is growing at a faster rate. When that happens, we'll be fine as an industry."

That's a completely different statement from "AI is generating no value"!

What kind of growth? who is going to buy all that stuff, if a third of the workforce has suddenly been made redundant?

(I always thought that this 'evil microsoft' trope is a sign of stupidity, now i am having second thoughts...)

There's a wikipedia page addressing your exact concern: https://en.wikipedia.org/wiki/Lump_of_labour_fallacy