I’m buying semis. I don’t see AI, construed broadly, as ever shrinking from its current position.
I’m loading up on vacuum tubes.
So you’re saying we wont have any crowdsourced blockchain Web 2.0 AIs?
Quantum! don’t forget quantum, you filthy peasant.
Please, stay with the time. We’re at Web 6.0 already.
The drones doing airstrikes might!
“Built to do my art and writing so I can do my laundry and dishes” – Embodied agents is where the real value is. The chatbots are just fancy tech demos that folks started selling because people were buying.
Eh, my best coworker is an LLM. Full of shit, like the rest of them, but always available and willing to help out.
Too bad it actively makes all of your work lower quality via the “helping”.
Just like every other coworker, it’s important to know what tasks they do well and where they typically need help
Though the image generators are actually good. The visual arts will never be the same after this
Sometimes I really regret having signed onto an instance that disables downvotes.
Compare it to the microwave. Is it good at something, yes. But if you shoot your fucking turkey in it at Thanksgiving and expect good results, you’re ignorant of how it works. Most people are expecting language models to do shit that aren’t meant to. Most of it isn’t new technology but old tech that people slapped a label on as well. I wasn’t playing Soul Caliber on the Dreamcast against AI openents… Yet now they are called AI opponents with no requirements to be different. GoldenEye on N64 was man VS AI. Madden 1995… AI. “Where did this AI boom come from!”
Marketing and mislabeling. Online classes, call it AI. Photo editors, call it AI.
There is this seeming need to discredit AI from some people that goes overboard. Some friends and family who have never really used LLMs outside of Google search feel compelled to tell me how bad it is.
But generative AIs are really good at tasks I wouldn’t have imagined a computer doing just a few year ago. Even if they plateaued in place where they are right now it would lead to major shakeups in humanity’s current workflow. It’s not just hype.
The part that is over hyped is companies trying to jump the gun and wholesale replace workers with unproven AI substitutes. And of course the companies who try to shove AI where it doesn’t really fit, like AI enabled fridges and toasters.
Goldman Sachs, quote from the article:
“AI technology is exceptionally expensive, and to justify those costs, the technology must be able to solve complex problems, which it isn’t designed to do.”
Generative AI can indeed do impressive things from a technical standpoint, but not enough revenue has been generated so far to offset the enormous costs. Like for other technologies, It might just take time (remember how many billions Amazon burned before turning into a cash-generating machine? And Uber has also just started turning some profit) + a great deal of enshittification once more people and companies are dependent. Or it might just be a bubble.
As humans we’re not great at predicting these things including of course me. My personal prediction? A few companies will make money, especially the ones that start selling AI as a service at increasingly high costs, many others will fail and both AI enthusiasts and detractors will claim they were right all along.
Like what outcome?
I have seen gains on cell detection, but it’s “just” a bit better.
This is easy to say about the output of AIs… if you don’t check their work.
Alas, checking for accuracy these days seems to be considered old fogey stuff.
The part that is over hyped is companies trying to jump the gun and wholesale replace workers with unproven AI substitutes. And of course the companies who try to shove AI where it doesn’t really fit, like AI enabled fridges and toasters.
This is literally the hype. This is the hype that is dying and needs to die. Because generative AI is a tool with fairly specific uses. But it is being marketed by literally everyone who has it as General AI that can “DO ALL THE THINGS!” which it’s not and never will be.
The obsession with replacing workers with AI isn’t going to die. It’s too late. The large financial company that I work for has been obsessively tracking hours saved in developer time with GitHub Copilot. I’m an older developer and I was warned this week that my job will be eliminated soon.
The large financial company that I work for
So the company that is obsessed with money that you work for has discovered a way to (they think) make more money by getting rid of you and you’re surprised by this?
At least you’ve been forewarned. Take the opportunity to abandon ship. Don’t be the last one standing when the music stops.
I never said that I was surprised. I just wanted to point out that many companies like my own are already making significant changes to how they hire and fire. They need to justify their large investment in AI even though we know the tech isn’t there yet.
Even if they plateaued in place where they are right now it would lead to major shakeups in humanity’s current workflow
Like which one? Because it’s now 2 years we have chatGPT and already quite a lot of (good?) models. Which shakeup do you think is happening or going to happen?
I don’t know anything about the online news business but it certainly appears to have changed. Most of it is dreck, either way, and those organizations are not a positive contributor to society, but they are there, it is a business, and it has changed society
I don’t see the change. Sure, there are spam websites with AI content that were not there before, but is this news business at all? All major publishers and newspapers don’t (seem to) use AI as far as I can tell.
Also I would argue this is no much of a change except maybe in simplicity to generate fluff. All of this existed already for 20 years now, and it’s a byproduct of the online advertisement business (that for sure was a major change in society!). AI pieces are just yet another way to generate content in the hope of getting views.
Computer programming has radically changed. Huge help having llm auto complete and chat built in. IDEs like Cursor and Windsurf.
I’ve been a developer for 35 years. This is shaking it up as much as the internet did.
I quit my previous job in part because I couldn’t deal with the influx of terrible, unreliable, dangerous, bloated, nonsensical, not even working code that was suddenly pushed into one of the projects I was working on. That project is now completely dead, they froze it on some arbitrary version.
When junior dev makes a mistake, you can explain it to them and they will not make it again. When they use llm to make a mistake, there is nothing to explain to anyone.
I compare this shake more to an earthquake than to anything positive you can associate with shaking.More business for me. As a DevOps guy, my job is to create automation to flag “ terrible, unreliable, dangerous, bloated, nonsensical, not even working code”
And so, the problem wasn’t the ai/llm, it was the person who said “looks good” without even looking at the generated code, and then the person who read that pull request and said, again without reading the code, “lgtm”.
If you have good policies then it doesn’t matter how many bad practice’s are used, it still won’t be merged.
The only overhead is that you have to read all the requests but if it’s an internal project then telling everyone to read and understand their code shouldn’t be the issue.
The problem here is that a lot of the time looking for hidden problem is harder than writing good code from scratch. And you will always be at a danger that llm snuck some sneaky undefined behaviour past you. There is a whole plethora of standards, conventions, and good practices that help humans to avoid it, which llm can ignore at any random point.
So you’re either not spending enough time on review or missing whole lot of bullshit. In my experience, in my field, right now, this review time is more time consuming and more painful than avoiding it in the first place.
Don’t underestimate how degrading and energy sucking it is for a professional to spend most of the working time sitting through autogenerated garbage, and how inefficient it is.
This is a problem with your team/project. It’s not a problem with the technology.
A technology that makes people put bad code is a problematic technology. If your team/project managed to overcome it’s problems so far doesn’t mean it is good or overall helpful. Peoole not seeing the problem is actually the worst part.
Sir, I use it to assist me in programming. I don’t use it to write entire files or functions. It’s a pattern recognizer.
Your team had people who didn’t review code. That’s a problem.
I hardly see it changed to be honest. I work in the field too and I can imagine LLMs being good at producing decent boilerplate straight out of documentation, but nothing more complex than that.
I often use LLMs to work on my personal projects and - for example - often Claude or ChatGPT 4o spit out programs that don’t compile, use inexistent functions, are bloated etc. Possibly for languages with more training (like Python) they do better, but I can’t see it as a “radical change” and more like a well configured snippet plugin and auto complete feature.
LLMs can’t count, can’t analyze novel problems (by definition) and provide innovative solutions…why would they radically change programming?
I hardly see it changed to be honest. I work in the field too and I can imagine LLMs being good at producing decent boilerplate straight out of documentation, but nothing more complex than that.
I think one of the top lists on advent of code this year is a cheater that fully automated the solutions using LLMs. Not sure which LLM though, I use LLMs quite a bit and ChatGPT 4o frequently tells me nonsense like “perhaps subtracting by zero is affecting your results” (issues I thought were already gone in GPT 4, but I guess not, Sonnet 3.5 does a bit better in this regard).
Maybe some postmortem analysis will be interesting. The AoC is also a context in which the domain is self-contained and there is probably a ton of training material on similar problems and tasks. I can imagine LLM might do decently there.
Also there is no big consequence if they don’t and it’s probably possible to bruteforce (which is how many programming tasks have been solved).
I think you’re spot on with LLMs being mostly trained on these kinds of tasks. Can’t say I’m an expert in how to build a training set, but I imagine it’s quite easy to do with these kinds of problems because it’s easy to classify a solution as correct or incorrect. This is in contrast to larger problems which are less guided by algorithmic efficiency and more by sound design/architecture.
Still, I think it’s quite impressive. You don’t have to go very far back in time to have top of the line LLMs unable to solve these kinds of problems.
Also there is no big consequence if they don’t and it’s probably possible to bruteforce (which is how many programming tasks have been solved).
Usually with AoC part 1 is brute-forceable, but part 2 is not. Very often part 1 is to find the 100th number, and part 2 is to find the 1 000 000 000 000th number or something. Last year, out of curiosity, I had a brute-force solution for one problem that successfully completed on ~90% of the input. Solution was multi-threaded and running on a 16 core CPU for about 20 days before I gave up. But the LLMs this year (not sure if this was a problem last year) are in the top list of fastest users to solve the problems.
Just to precise, when I said bruteforce I didn’t imagine a bruteforce of the calculation, but a brute force of the code. LLMs don’t really calculate either way, but what I mean is more: generate code -> try to run and see if tests work -> if it doesn’t ask again/refine/etc. So essentially you are just asking code until what it spits out is correct (verifiable with tests you are given).
But yeah, few years ago this was not possible and I guess it was not due to the training data. Now the problem is that there is not much data left for training, and someone (Bloomberg?) reported that training chatGPT 5 will cost billions of dollars, and it looks like we might be near the peak of what this technology could offer (without any major problem being solved by it to offset the economical and environmental cost).
Just from today https://www.techspot.com/news/106068-openai-struggles-chatgpt-5-delays-rising-costs.html
ChatGPT 4o isn’t even the most advanced model, yet I have seen it do things you say it can’t. Maybe work on your prompting.
That is my experience, it’s generally quite decent for small and simple stuff (as I said, distillation of documentation). I use it for rust, where I am sure the training material was much smaller than other languages. It’s not a matter a prompting though, it’s not my prompt that makes it hallucinate functions that don’t exist in libraries or make it write code that doesn’t compile, it’s a feature of the technology itself.
GPTs are statistical text generators after all, they don’t “understand” the problem.
It’s also pretty young, human toddlers hallucinate and make things up. Adults too. Even experts are known to fall prey to bias and misconception.
I don’t think we know nearly enough about the actual architecture of human intelligence to start asserting an understanding of “understanding”. I think it’s a bit foolish to claim with certainty that LLMs in a MoE framework with self-review fundamentally can’t get there. Unless you can show me, materially, how human “understanding” functions, we’re just speculating on an immature technology.
As much as I agree with you, humans can learn a bunch of stuff without first learning the content of the whole internet and without the computing power of a datacenter or consuming the energy of Belgium. Humans learn to count at an early age too, for example.
I would say that the burden of proof is therefore reversed. Unless you demonstrate that this technology doesn’t have the natural and inherent limits that statistical text generators (or pixel) have, we can assume that our mind works differently.
Also you say immature technology but this technology is not fundamentally (I.e. in terms of principle) different from what Weizenabum’s ELIZA in the '60s. We might have refined model and thrown a ton of data and computing power at it, but we are still talking of programs that use similar principles.
So yeah, we don’t understand human intelligence but we can appreciate certain features that absolutely lack on GPTs, like a concept of truth that for humans is natural.
You’re missing it. Use Cursor or Windsurf. The autocomplete will help in so many tedious situations. It’s game changing.
Exactly this. Things have already changed and are changing as more and more people learn how and where to use these technologies. I have seen even teachers use this stuff who have limited grasp of technology in general.
My kid’s teachers had what I thought was a fantastic approach - have the kids write an outline. Use an LLM to generate an essay from that outline, then critique the essay
Computers have always been good at pattern recognition. This isn’t new. LLM are not a type of actual AI. They are programs capable of recognizing patterns and Loosely reproducing them in semi randomized ways. The reason these so-called generative AI Solutions have trouble generating the right number of fingers. Is not only because they have no idea how many fingers a person is supposed to have. They have no idea what a finger is.
The same goes for code completion. They will just generate something that fills the pattern they’re told to look for. It doesn’t matter if it’s right or wrong. Because they have no concept of what is right or wrong Beyond fitting the pattern. Not to mention that we’ve had code completion software for over a decade at this point. Llms do it less efficiently and less reliably. The only upside of them is that sometimes they can recognize and suggest a pattern that those programming the other coding helpers might have missed. Outside of that. Such as generating act like whole blocks of code or even entire programs. You can’t even get an llm to reliably spit out a hello world program.
Large context window LLMs are able to do quite a bit more than filling the gaps and completion. They can edit multiple files.
Yet, they’re unreliable, as they hallucinate all the time. Debugging LLM-generated code is a new skill, and it’s up to you to decide to learn it or not. I see quite an even split among devs. I think it’s worth it, though once it took me two hours to find a very obscure bug in LLM-generated code.
If you consider debugging broken LLM-generated code to be a skill… sure, go for it. But, since generated code is able to use tons of unknown side effects and other seemingly (for humans) random stuff to achieve its goal, I’d rather take the other approach, where it takes a human half an hour to write the code that some LLM could generate in seconds, and not have to learn how to parse random mumbo jumbo from a machine, while getting a working result.
Writing code is far from being the longest part of the job; and you gingerly decided that making the tedious part even more tedious is a great idea to shorten the already short part of it…
What is your favorite flavor of kool aid?
Grape, my nigga.
See now, I would prefer AI in my toaster. It should be able to learn to adjust the cook time to what I want no matter what type of bread I put in it. Though is that realky AI? It could be. Same with my fridge. Learn what gets used and what doesn’t. Then give my wife the numbers on that damn clear box of salad she buys at costco everytime, which take up a ton of space and always goes bad before she eats even 5% of it. These would be practical benefits to the crap that is day to day life. And far more impactful then search results I can’t trust.
I agree with your wife: there’s always an aspirational salad in the fridge. For most foods, I’m pretty good at not buying stuff we won’t eat, but we always should eat more veggies. I don’t know how to persuade us to eat more veggies, but step 1 is availability. Like that Reddit meme
- Availability
- ???
- Profit by improved health
It’s been years… maybe we don’t need the costco size for the love of pete.
So true.
There’s a good point here that like about 80% of what we’re calling AI right now… isn’t even AI or even LLM. It’s just… algorithm, code, plain old math. I’m pretty sure someone is going to refer to a calculator as AI soon. “Wow, it knows math! Just like a person! Amazing technology!”
(That’s putting aside the very question of whether LLMs should even qualify as AIs at all.)
In my professional experience, AI seems to be just a faster way to generate an algorithm that is really hard to debug. Though I am dev-ops/sre so I am not as deep in it as the devs.
As a devops person, I’m constantly jumping back and forth to whatever programming language and tools each team uses. Sometimes it takes a bit to find the context, and I’m hoping ai can help. Unfortunately, allowing the ai to see code is currently off limits by corporate policy, so it only helps in those situations where I need to generate boilerplate
In my jobs there have slways been certain stule requirements to the code. AI doesn’t take those into account. So I would have to rework the code anyway. And of course there are the local libraries it know nothing about.
Fight technology with technology. I’m sure you can specify a style for it to generate, but we already run everything through a prettifier configured for what we look for …. Unless you mean a higher order like naming or architecture
See now, I would prefer AI in my toaster.
A big issue that a lot of these tech companies seem to have is that they don’t understand what people want; they come up with an idea and then shove it into everything. There are services that I have actively stopped using because they started cramming AI into things; for example I stopped dual-booting with Windows and became Linux-only.
AI is legitimately interesting technology which definitely has specialized use-cases, e.g. sorting large amounts of data, or optimizing strategies within highly restrained circumstances (like chess or go). However, 99% of what people are pushing with AI these days as a member of the general public just seems like garbage; bad art and bad translations and incorrect answers to questions.
I do not understand all the hype around AI. I can understand the danger; people who don’t see that it’s bad are using it in place of people who know how to do things. But in my teaching for example I’ve never had any issues with students cheating using ChatGPT; I semi-regularly run the problems I assign through ChatGPT and it gets enough of them wrong that I can’t imagine any student would be inclined to use ChatGPT to cheat multiple times after their grade the first time comes in. (In this sense, it’s actually impressive technology - we’ve had computers that can do advanced math highly accurately for a while, but we’ve finally developed one that’s worse at math than the average undergrad in a gen-ed class!)
The answer is that it’s all about “growth”. The fetishization of shareholders has reached its logical conclusion, and now the only value companies have is in growth. Not profit, not stability, not a reliable customer base or a product people will want. The only thing that matters is if you can make your share price increase faster than the interest on a bond (which is pretty high right now).
To make share price go up like that, you have to do one of two things; show that you’re bringing in new customers, or show that you can make your existing customers pay more.
For the big tech companies, there are no new customers left. The whole planet is online. Everyone who wants to use their services is using their services. So they have to find new things to sell instead.
And that’s what “AI” looked like it was going to be. LLMs burst onto the scene promising to replace entire industries, entire workforces. Huge new opportunities for growth. Lacking anything else, big tech went in HARD on this, throwing untold billions at partnerships, acquisitions, and infrastructure.
And now they have to show investors that it was worth it. Which means they have to produce metrics that show people are paying for, or might pay for, AI flavoured products. That’s why they’re shoving it into everything they can. If they put AI in notepad then they can claim that every time you open notepad you’re “engaging” with one of their AI products. If they put Recall on your PC, every Windows user becomes an AI user. Google can now claim that every search is an AI interaction because of the bad summary that no one reads. The point is to show “engagement”, “interest”, which they can then use to promise that down the line huge piles of money will fall out of this pinata.
The hype is all artificial. They need to hype these products so that people will pay attention to them, because they need to keep pretending that their massive investments got them in on the ground floor of a trillion dollar industry, and weren’t just them setting huge piles of money on fire.
The answer is that it’s all about “growth”. The fetishization of shareholders has reached its logical conclusion, and now the only value companies have is in growth. Not profit, not stability, not a reliable customer base or a product people will want. The only thing that matters is if you can make your share price increase faster than the interest on a bond (which is pretty high right now).
As you can see, this can’t go on indefinitely. And also such unpleasantries are well known after every huge technological revolution. Every time eventually resolved, and not in favor of those on the quick buck train.
It’s still not a dead end. The cycle of birth, growth, old age, death, rebirth from the ashes and so on still works. It’s only the competitive, evolutionary, “fast” model has been killed - temporarily.
These corporations will still die unless they make themselves effectively part of the state.
BTW, that’s what happened in Germany described by Marx, so despite my distaste for marxism, some of its core ideas may be locally applicable with the process we observe.
It’s like a worldwide gold rush IMHO, but not even really worldwide. There are plenty of solutions to be developed and sold in developing countries in place of what fits Americans and Europeans and Chinese and so on, but doesn’t fit the rest. Markets are not exhausted for everyone. Just for these corporations because they are unable to evolve.
Lacking anything else, big tech went in HARD on this, throwing untold billions at partnerships, acquisitions, and infrastructure.
If only Sun survived till now, I feel they would have good days. What made them fail then would make them more profitable now. They were planning too far ahead probably, and were too careless with actually keeping the company afloat.
My point is that Sun could, unlike these corporations, function as some kind of “the phone company”, or “the construction company”, etc. Basically what Microsoft pretended to be in the 00s. They were bad with choosing the right kind of hype, but good with having a comprehensive vision of computing. Except that vision and its relation to finances had schizoaffective traits.
Same with DEC.
The point is to show “engagement”, “interest”, which they can then use to promise that down the line huge piles of money will fall out of this pinata.
Well. It’s not unprecedented for business opportunities to dry out. It’s actually normal. What’s more important, the investors supporting that are the dumber kind, and the investors investing in more real things are the smarter kind. So when these crash (for a few years hunger will probably become a real issue not just in developing countries when that happens), those preserving power will tend to be rather insightful people.
If only Sun survived till now, I feel they would have good days
The problem is a lot of what Sun brought to the industry is now in the Linux arena. If Sun survived, would Linux have happened? With such a huge development infrastructure around Linux, would Sun really add value?
I was a huge fan of Sun also, they revolutionized the industry far above their footprint. However their approach seemed more research or academic at times, and didn’t really work with their business model. Red Hat figured out a balance where they could develop opensource while making enough to support their business. The Linux world figured out a different balance where the industry is above and beyond individual companies and doesn’t require profit
The problem is a lot of what Sun brought to the industry is now in the Linux arena. If Sun survived, would Linux have happened? With such a huge development infrastructure around Linux, would Sun really add value?
Linux is not better than Solaris. It was, however, circumstantially more affordable, more attractive, and more exciting than Solaris at the same time. They’ve made a lot of strategic mistakes, but those were in the context of having some vision.
I mean this to say that the “huge development infrastructure around Linux” is bigger, but much less efficient than that of any of BSDs, and than that of Solaris in the past. Linux people back then would take pride in ability to assemble bigger resources, albeit with smaller efficiency, and call that “the cathedral vs the bazaar”, where Linux is the bazaar. Well, by now one can see that the bazaar approach make development costs bigger long-term.
IMHO if Sun didn’t make those mistakes, Solaris would be the most prestigious Unix and Unix-like system, but those systems would be targeted by developers similarly. So Linux would be alive, but not much more or less popular than FreeBSD. I don’t think they’d need Solaris to defeat all other Unix systems. After all, in early 00s FreeBSD had SVR4 binary compatibility code, similarly to its Linux compatibility code, which is still there and widely used. Probably commercial software distributed in binaries would be compiled for that, but would run on all of them. Or maybe not.
It’s hard to say.
But this
The Linux world figured out a different balance where the industry is above and beyond individual companies and doesn’t require profit
is wrong, everything about Linux that keeps going now is very commercial. Maybe 10 years ago one could say it’s not all about profit.
I know I’m an enthusiast, but can I just say I’m excited about NotebookLLM? I think it will be great for documenting application development. Having a shared notebook that knows the environment and configuration and architecture and standards for an application and can answer specific questions about it could be really useful.
“AI Notepad” is really underselling it. I’m trying to load up massive Markdown documents to feed into NotebookLLM to try it out. I don’t know if it’ll work as well as I’m hoping because it takes time to put together enough information to be worthwhile in a format the AI can easily digest. But I’m hopeful.
That’s not to take away from your point: the average person probably has little use for this, and wouldn’t want to put in the effort to make it worthwhile. But spending way too much time obsessing about nerd things is my calling.
From a nerdy perspective, LLMs are actually very cool. The problem is that they’re grotesquely inefficient. That means that, practically speaking, whatever cool use you come up with for them has to work in one of two ways; either a user runs it themselves, typically very slowly or on a pretty powerful computer, or it runs as a cloud service, in which case that cloud service has to figure out how to be profitable.
Right now we’re not being exposed to the true cost of these models. Everyone is in the “give it out cheap / free to get people hooked” stage. Once the bill comes due, very few of these projects will be cool enough to justify their costs.
Like, would you pay $50/month for NotebookLM? However good it is, I’m guessing it’s probably not that good. Maybe it is. Maybe that’s a reasonable price to you. It’s probably not a reasonable price to enough people to sustain serious development on it.
That’s the problem. LLMs are cool, but mostly in a “Hey this is kind of neat” way. They do things that are useful, but not essential, but they do so at an operating cost that only works for things that are essential. You can’t run them on fun money, but you can’t make a convincing case for selling them at serious money.
I’ll pay a bit more for the next model of my phone that promises on device ai, or actually already did. We’ll see if that turns into something useful.
So far the bits and pieces I’ve played with are not generative ai, but natural language processing and inferencing. The improved features definitely make my phone a more useful piece of hardware, but not revolutionary
Totally agree. It comes down to how often is this thing efficient for me if I pay the true cost. At work, yes it would save over $50/mo if it works well. At home it would be difficult to justify that cost, but I’d also use it less so the cost could be lower. I currently pay $50/mo between ChatGPT and NovelAI (and the latter doen’t operate at a loss) so it’s worth a bit to me just to nerd out over it. It certainly doesn’t save me money except in the sense that it’s time and money I don’t spend on some other endeavor.
My old video card is painfully slow for local LLM, but I dream of spending for a big card that runs closer to cloud speeds even if the quality is lower, for easier tasks.
Being able to summarize and answer questions about a specific corpus of text was a use case I was excited for even knowing that LLMs can’t really answer general questions or logically reason.
But if Google search summaries are any indication they can’t even do that. And I’m not just talking about the screenshots people post, this is my own experience with it.
Maybe if you could run the LLM in an entirely different way such that you could enter a question and then it tells you which part of the source text statistically correlates the most with the words you typed; instead of trying to generate new text. That way in a worse case scenario it just points you to a part of the source text that’s irrelevant instead of giving you answers that are subtly wrong or misleading.
Even then I’m not sure the huge computational requirements make it worth it over ctrl-f or a slightly more sophisticated search algorithm.
Even the success case is a failure. I’ve had several instances where Google returned a nice step by step how to answer a user’s questions, correctly, but I can’t forward the link and trust they’ll see the same thing
Multiple times now, I’ve seen people post AI summaries of articles on Lemmy which miss out really, really important points.
Well an example of something I think it could solve would be: “I’m trying to set this application up to run locally. I’m getting this error message. Here’s my configuration files. What is not set up correctly, or if that’s not clear, what steps can I take to provide more helpful information?”
ChatGPT is always okay at that as long as you have everything set up according to the most common scenarios, but it tells you a lot of things that don’t apply or are wrong in the specific case. I would like to get answers that are informed by our specific setup instructions, security policies, design standards, etc. I don’t want to have to repeat “this is a Java spring boot application running on GCP integrating with redis on docker… blah blah blah”.
I can’t say whether it’s worth it yet, but I’m hopeful. I might do the same with ChatGPT and custom GPTs, but since I use my personal account for that, it’s on very shaky ground to upload company files to something like that, and I couldn’t share with the team anyway. It’s great to ask questions that don’t require specific knowledge, but I think I’d be violating company policy to upload anything.
We are encouraged to use NotebookLLM, however.
oh wow who would have guessed that business consultancy companies are generally built on bullshitting about things which they dont really have a grasp off
I saved a lot of time due to ChatGPT. Need to sign up some of my pupils for a competition by uploading their data in a csv-File to some plattform? Just copy and paste their data into chsatgpt and prompt it to create the file. The boss (headmaster) wants some reasoning why I need some paid time for certain projects? Let ChatGPT do the reasoning. Need some exercises for one of my classes that doesn’t really come to grips with while-loops? let ChatGPT create those exercises (some smartasses will of course have ChatGPT then solve those exercises). The list goes on…
You are an asshole if you’re uploading student data to a mining operation.
Well, I hope the data protection official of my school won’t find out. Oh wait, shit. He did find out. It’s me and idgaf.
Where do you work?
Just copy and paste [student personal data] into [3rd parties database]
Yeah, that’s a problem, especially in Europe. Im unsure about US, but it’s definitely a breach of GDPR.
ChatGPT is basically like a really good intern, and I use it heavily that way. I run literally every email through it and say “respond to so and so, say xyz” and then maybe a little refining, copy paste, done.
The other day, my boss sent me an excel file with a shitload of data in it that he wanted me to analyze some such way. I just copy pasted it into gpt and asked it, and it spit out the correct response. Then my boss asked me to do something else that required a bit of excel finagling that I didn’t really know how to do, so i asked gpt, and it told me the formula, which worked immediately first try.
So basically it helps me accomplish tasks in seconds that previously would’ve taken hours. If anything, I think markets are currently undervalued, because remarkably, fucking NONE of my colleagues or friends are using it at all yet. Once there’s widespread adoption, which will pretty much have to happen if anyone wants to stay competitive once it gains more traction, look out…
That also is my experience.
Yeah, and Wikipedia is one of the most useful sites on the net, but it didn’t exactly result in the entire web becoming crowdsourced.
The poem about AI that often gets posted says “What are you trying to avoid? The living [of a life]?”
And yeah, that’s what it’s for, dodging shit you don’t want to do. I gotta produce some useless bullshit that no one’s going to read or care about: AI.
I don’t even mind AI art for things like LinkedIn posts, blogs like “What is warehouse management?” or “Top 10 finance trends in 2025” - SEO spam that no human will read. No one wants to write it, read it, or care about it- its just a x kb file to tell Google to look here.
Those pupils will really thank you when they grow up and there isn’t enough fresh water because all the data centres are using it up far faster than it can be replenished.
https://utulsa.edu/news/data-centers-draining-resources-in-water-stressed-communities/
The thing about tech bubbles is everyone rushes in full bore, on the hope that they can be the ones whose moonshot goes the distance. However even in the case where the technology achieves all its promise, most of those early attempts will not. Soon enough, we’ll be down to the top few, and only their datacenters will need to exist. Many of these failures will go away
Many of these failures will go away
In a rational, non-capitalist world, yes. In our world, all of those data centres will last until they can’t find a way to squeeze some sort of profit out of them.
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You can read it here:
Thanks – it has been clear enough that an another AI winter is coming. Likely latest when the Global Financial Crisis 2 is here.
But the line must go up!
The article does mention that when the AI bubble is going down, the big players will use the defunct AI infrastructure and add it to their cloud business to get more of the market that way and, in the end, make the line go up.
That’s not what the article says.
They’re arguing that AI hype is being used as a way of driving customers towards cloud infrastructure over on-prem. Once a company makes that choice, it’s very hard to get them to go back.
They’re not saying that AI infrastructure specifically can be repurposed, just that in general these companies will get some extra cloud business out of the situation.
AI infrastructure is highly specialized, and much like ASICs for the blockchain nonsense, will be somewhere between “very hard” and “impossible” to repurpose.
Assuming a large decline in demand for AI compute, what would be the use cases for renting out older AI compute hardware on the cloud? Where would the demand come from? Prices would also go down with a decrease in demand.
Relaunching Stadia?
Haha. I believe the AMD Instinct / Nvidia Datacentre GPUs aren’t that great for gaming.
To have a bubble you need companies with no clear path to monetization, being over-valued to an extreme degree. This leaves me wondering : what company specifically ? Are they talking about nVidia ? OpenAI ? MidJourney ? Or the slew of LLM-powered SaaS products that have started appearing ? How exactly are we defining “over-valuation” here ? Are we talking about the tech industry as a whole ?
We often invite the comparison to the DotCom bubble but that’s apples to oranges. You had companies making social networks for dogs or similar bullshit, valued in the billions and getting a ticker at the stock market before making a single dime. Or companies with outlandish promises such as delivering to any home in the US, in <1 hour, for a low price, and building warehouses by the hundreds before having a storefront. What would be the 2024 equivalent ? If a bubble is about to deflate then there should be dozens of comparable examples.
Exactly. There’s a very clear path to monetisation for the bigger tech companies (ofc, not the random startup that screams “AI quantum computing blockchain reeeee”).
Lemmy is just incredibly biased against AI, as it could replace a shit ton of jobs and lead to a crazy amount of wealth inequality. However, people need to remember that the problem isn’t the tech- it’s the system that the tech is being innovated in.
Denying AI is just going to make this issue a lot worse. We need to work to make AI be beneficial for all of us instead of the capitalists. But somehow leftist talk surrounding AI has just been about hating on it/ denying it, instead of preparing for a world in which it would be critical infrastructure very soon.
What job could possibly replace…? If you can replace a job with LLMs it means either that the job is not needed on the first place (bullshit job) or that you can replace it with a dice (e.g., decision-making processes), since LLMs-output will depend essentially just on what is in the training material -which we don’t know (I.e., the answer is essentially random).
I don’t think it’s just Lemmy, i had similar conversations on Reddit. People don’t realize that the companies they claim are over-valued actually have very strong business fundamentals. That’s why in articles like OP’s they will never mention any names or figures. I guess it’s very convincing for outsiders but it doesn’t stand any amount of scrutiny.
If you take OpenAI for example, they went from 0 to 3.6B$ annual revenue in just two fucking years. How is that not worth a boatload of money ? Even Uber didn’t have that kind of growth and they burned a LOT more cash than OpenAI is burning right now.
As for the “AI quantum computing blockchain reeeee” projects… well they have a very hard time raising money right now and when they do, it’s at pretty modest valuations. The market is not as dumb as it is portrayed.
How is that not worth a boatload of money?
Because they spend $2.35 billion in operating costs for every $1 billion in revenue (gross, not net).
OpenAI loses money at an absolutely staggering rate, and every indication, even their own openly stated predictions, are that those costs will only increase.
And keep in mind, right now OpenAI gets a lot of their investment in the form of compute credits from Microsoft, which they get to spend at a massively discounted rate. That means that if they were actually buying their Azure time at market value they’d be closer to spending something like $5bn to make $1bn.
Again, I really need to be clear here, I’m not saying “to make 1 billion in profit.” I’m saying “revenue”. They lose money every time someone uses their services. The more users they have, the more their losses grow. Even paid users cost them more money than they pay in most cases.
This is like a store that buys products at $10 and sells them at $4. It is the most insanely unprofitable business plan imaginable.
And it’s not getting better. Conversions to paid plans are at about 3%. Their enterprise sales are abysmal. Training costs are increasing exponentially with each new generation of models. Attempts to make their models more compute efficient have so far failed utterly.
OpenAI’s path to profitability is basically “Invent true AGI.” It’s a wild fantasy with zero basis in reality that investors are shovelling money into because investors will shovel money into anything that promises infinite growth.
Because they spend $2.35 billion in operating costs for every $1 billion in revenue
This is entirely true but also completely normal for a hyperscaler in its first years. At this stage demonstrating a huge demand and your capacity to capture a lot of it is much more important than profitability. You don’t exactly bootstrap a company at this level of CapEx.
Their revenue is still growing at a staggering rate, showing no signs of slowing down, and enterprise sales are pretty respectable. I don’t know that i’d call 3/4 of a billion in enterprise sales abysmal for a startup in its 2nd year. YMMV i guess.
Sure there is some uncertainty about their model but that’s what VC backing is for, right ? They’re not building tin can factories with known and predictable business trends, and being valued at 40x your yearly revenue (not profit !) is pretty banal for a successful early stage Deep Tech. We may personally think it’s bullshit and choose not to invest in it but it’s still far from outrageous and very far from the definition of a bubble.
For the level of investment in and hype around this company? Yes, those enterprise sales are abysmal. When there are major news articles about their product every single week, they should be doing a lot better than that.
They have demonstrated zero ability at actually “hyperscale”. They have no path to getting those costs down. Their conversion rate from free to paid users is atrocious, and they’re already raising prices on their plans which is only going to worsen those conversion rates. Their costs to build future models are ballooning exponentially, and theres a decent chance that at some point Microsoft will get sick of subsidizing their compute costs.
Is it possible that they could be successful? Yes. But a lottery ticket would probably be a sounder investment.
For the record, OpenAI themselves are telling those VCs that they should think of their investment more as a “donation” with no expectation of future profit. Absolutely oozing confidence there.
Hypergrowth cult is always “interesting”. Because it happened with X, apparently it will happen with Y. Then, unavoidably, it goes through the boom and bust cycle. I am sick of it.
For the level of investment in and hype around this company? Yes, those enterprise sales are abysmal
I don’t see the connection. How are enterprise sales specifically relevant here ? Are enterprise customers known for jumping on top of early stage products where you’re from ? Cause where i’m from they’re known for being the last ones to board.
They have demonstrated zero ability at actually “hyperscale”.
How would you define hyperscale ? They have one of the biggest GPU fleets around and are likely serving trillions of tokens monthly. That falls well within the range of my personal definition.
They have no path to getting those costs down. Their conversion rate from free to paid users is atrocious, and they’re already raising prices on their plans which is only going to worsen those conversion rates
That’s just stuff you say. Atrocious (in your opinion), no path to getting those costs down (in your opinion). Alright, we get it, that’s not a company you’d invest in, but then again your investment thesis seems pretty conservative. If a company has to make billions of enterprise sales in its 2nd year, and have double digits conversion early on, then there’s not that many successful companies you would have invested in. You certainly wouldn’t have put a dime in Uber at 48B valuation 7 years ago - well those who did made a nice return on their investment.
Is it possible that they could be successful? Yes. But a lottery ticket would probably be a sounder investment.
Isn’t that the definition of VC-backed startups ? The alternative would be to build a time machine, travel back to the 18th century, and invest in the British textile industry. Sadly they don’t make this kind of predictible, risk-free and quickly profitable enterprises nowadays.
Absolutely oozing confidence there
Oh i won’t be the one to contradict you here. Sama is one sleazy motherfucker, that’s just written on his face. Sadly it doesn’t preclude him from building a historical hyperscaler with OpenAI.
I have no idea how people can consider this to be a hype bubble especially after the o3 release. It smashed the ARC AGI benchmark on the performance front. It ranks as the 175th best competitive coder in the world on Codeforces’ leaderboard.
o3 proved that it is possible to have at least an expert AGI if not a Virtuoso AGI (according to Deep mind’s definition of AGI). Sure, it’s not economical yet. But it will get there very soon (just like how the earlier GPTs were a lot dumber and took a lot more energy than the newer, smaller parameter models).
Please remember - fight to seize the means of production. Do not fight the means of production themselves.
It’s a bubble because OpenAI spend $2.35 for every $1.00 they make. Yes, you’re mathing right, that is a net loss.
It’s a bubble because all of the big players in AI development agree that future models will cost exponentially more money to train, for incremental gains. That means there is no path forward that doesn’t intensely amplify the unprofitability of an already deeply unprofitable industry.
It’s a bubble because newer models with better capabilities only cost more and more to run.
It’s a bubble because as far as anyone knows there will never be a solution to the hallucination problem.
It’s a bubble because despite investments treating it as a trillion dollar industry, no one has yet figured out a trillion dollar problem that AI can solve.
You’re trying on a new top of the line VR headset and saying “Wow, this is incredible, how can anyone say this is a bubble?” Its not about how cool the tech is in isolation, it’s about its potential to effect widespread change. Facebook went in hard on VR, imagining a future where everyone worked from home while wearing VR headsets. But what they got was an expensive toy that only had niche uses.
AI performs do well on certain coding tasks because a lot of the individual problems that make up a particular piece of software have already been solved. It’s standard practice to design programs as individual units, each of which performs the smallest task possible, and which can then be assembled to complete more complex tasks. This fits very well into the LLM model of assembling pieces into their most likely expected configurations. But it cannot create truly novel code, except by a kind of trial and error mutation process. It cannot problem solve. It cannot identify a users needs and come up with ideal solutions to them. It cannot innovate.
This means that, at best, genAI in the software world becomes a tool for producing individual code elements, guided and shepherded by experienced programmers. It does not replace the software industry, merely augments it, and it does so at a cost that many companies simply may not feel is worth paying.
And that’s its best case scenario. In every other industry AI has been a spectacular failure. But it’s being invested in as if it will be a technological reckoning for every form of intellectual labour on earth. That is the absolute definition of a bubble.
o3 made the high score on ARC through brute force, not by being good. To raise the score from 75% to 87% required 175 times more computing power, but exactly stunning returns.
Unless we invent cold fusion between the next 5 years, they will never be economical. They are the most energy inefficient thing ever invented by humanity and all prediction models state that it will cost more energy, not less, to keep making them better. They will never be energy efficient nor economical in their current state, and most companies are out of ideas on how to shake it up. Even the people who created generative models agree that they have just been brute forcing by making the models larger with more energy consumption. When you try to make them smaller or more energy efficient, they fall off the performance cliff and only produce garbage. I’m sure there are researchers doing cool stuff, but it is neither economical nor efficient.
Untrue. There are small models that produce better output than the previous “flagships” like GPT-2. Also, you can achieve much more than we currently do with far less energy by working on novel, specialised hardware (neuromorphic computing).
Where, in that position piece, do they mention o3? Who “proved” this?
Additionally, I’m pretty sure that this “ARC AGI” benchmark is not using the same definition of AGI that you linked to by DeepMind. Conflating them is misleading. There is already so much misinformation out there about “AI”, don’t add to it.
Lastly, I struggle to take at face value essays written by for-profit companies claiming they have AGI (that DeepMind paper links to OpenAI essays). They only stand to gain monetarily by claiming that their AI is an AGI (to be clear, this is an opinion; I do not have evidence to suggest that OpenAI is being disingenuous).
Your example is strange because, as far as I know, GPTs aren’t economical either.
Why is it getting an AGI stamp now? I was under the impression humanity has not delivered a sentient AI? Which is what the AGI title was supposed to be used for…has that been pulled back again?
