They need trillions of dollars in returns. VC's won't finance tech startups for decades.
I use Cursor sometimes, and VSCode + Continue with llama.cpp, and it's great. That's not worth billions. It's definitely not worth trillions.
Now someone will respond about how it's just a stepping stone, and how the billions are justified by _something completely imaginary, and not invented yet, and maybe not ever_ e.g. agents.
That seems like a suspect claim. If you're saying that you, personally, cannot create billions of dollars in value with Cursor & friends that is certainly true - but you are in no position to make a judgement call about where the cap on value creation is for the LLM market is worth based on your personal use cases. LLMs don't just do code completion. We really can't estimate how much potential value is being created without doing some serious data diving and studying of cases.
A better argument would be that the DeepSeek experience suggests these companies have no moat and therefore no way to earn a return on capital. But LLMs are probably going to generate at least trillions of dollars in value because they're on par or ahead of Wikipedia and Google for answering many queries then they also have hundreds of ancillary uses like answering medical questions at weird hours or creative/professional writing.
That is a problem for the VC’s that bet wrong, not for the world at large.
The models exist now and they’ll keep being used, regardless of whether a bunch of rich guys lost a bunch of money.
No, it's not. The first half of the article talks about how useless the actual product is, how the only reason we hear about it is because the media loves to talk about it.
So now my company makes more money, and the work gets done faster, but I can't say I feel appreciative. I'm sure it's great for founders though, for whom doing the work is merely an obstacle to having the finished product. For me, the work is the end goal, because I'm not hired to own the result.
I do the fun bit: having creative ideas and trying them out.
It would be so nice to have a productivity Linux OS that just works on all my devices without tinkering. I want to stop supporting the closed source monopolies, but the alternatives aren't up to par yet. I am extremely hopeful that they will be once mega corps inevitably decay and people tire of the boom-bust cycle.
As technologists, we all want beautifully designed tools, and I'm increasingly seeing that these are only created by passionate and talented people who truly care about tech, unlike megacorps that only care about enriching their board and elite shareholders.
The author spends a good amount of bytes telling us that they don't want to hear this argument even though they expect it.
> not much worse than a junior dev and 100x faster.
Is there a greater hell than this!?
Especially given that you can ask an LLM to optimise code and on multiple runs it can not tell if it's is improving or degenerating.
Yes — junior management using LLMs and 100x more cocksure.
While the timeline is unclear; it seems likely that LLMs will obsolete precisely the skills that developers use to earn their income. I imagine a lot of them feel rather threatened by the rapid rate of progress.
Pointing out that it is already operating at junior dev quality and rapidly improving is unlikely to quiet the discontent.
If you think LLMs operate at "junior dev" capacity you either don't work with junior devs and is just bullshitting your way around here, or you just pick pretty awful junior devs.
LLMs are alright. An okay productivity tool, although its inconsistencies many times nullify productivity gains - By design they often spit out wrong results that look and sound very plausible. A productivity blackhole. Its mistakes are sometimes hard to spot, but pervasive.
Beyond that, if your think that all a dev does is spit out code, and since LLMs can spit out code it can replace devs in some imaginary timeline, you are sorely mistaken. The least part of my work is actually spitting out code, although it is the part I enjoy the most.
I honestly feel way nore threatened by economic downturns and the looming threat of recession. The only way LLMs threaten me is by being a wasteful technology that may precipitate a downturn in tech companies, causing more layoffs, etc nd so forth.
I'm in a middle. I enjoy Zed and its predictions, I utilize R1 to help me to reason. I do _not_ ever want to stop programming. And I see so often whenever somebody less experienced than me shows me look how Cursor did this with three prompts, can we merge? And the solution is just wrong and doesn't solve the hard issues.
For me the biggest issues are the people who want to see the craft of programming gone. But I do enjoy the tooling.
I’m not particularly worried. I think it’s obvious that software engineering is definitely an “intelligence complete” problem. Any system that can do software engineering can solve any problem that requires intelligence. So, either my job is safe or I get to live through the fall of almost all white collar disciplines. There’s not a huge middle ground.
Although perhaps this is just the programmer stereotype of thinking that if someone can code, they can do anything.
These types of articles are just catching the next meme wave, which will be hating on and making fun of "AI" of all sorts.
What it goes into is how over hyped and over valued these companies are. They've blown through $5bn of compute each in a year and their revenue is abysmal. Microsoft won't report on ai separately, probably because it's abysmal.
I'm positive on LLMs for coding. But I think I have to agree with their assessment. Coding seems like the best area for these tools and what we see now is great. It's probably even worth $10b to the IT industry maybe eventually. But they're not paying for it yet, clearly. And I also think it's just not going to have huge significance outside our industry. The people I rub shoulders with outside of work have not mentioned or asked about it once, which is not necessarily meaningful but it does reveal the limits of hype too.
But like you said, in a few more years we'll see! It does feel like there's some missing pieces yet to be figured out to truly "reason" and generalize.
This makes me think of a quirk I discovered recently which is that ChatGPT simply won't generate a picture of a 'full glass of wine'. It generates pictures with all sorts of crazy waves/splashes in the glass but the glass is always half full no matter how you prompt it.
I'm not enough of an expert to make any deductions from this, but I think it hints at what the limitations of the currently models are.
For medium complexity things, I can get them done quickly without manual coding if I have a clear understanding in mind of what the implementation should look like. I supply the requirements, design and strategy and it's fairly easy to "keep things on the rails". The "write a PRD first" hack (https://www.aiagentshub.net/blog/how-to-10x-your-development...) works pretty well. Agent with YOLO mode and terminal access rips, particularly if you have good tests.
For tasks where I know the spec of the feature but don't clearly understand how I would design / implement the feature myself, it's hit-and-miss. Mostly miss for me.
I also haven't had much success with niche libraries, have to stick to the most popular library/tool/framework choices or it will quickly get stuck.
There's a Quentin Tarantino quote where he says there are 2 kinds of film critics. There are those who love movies and there are those that love the movies they love.
A lot of developers really seem to love the technology they love.
These people are where most of the negativity is coming from. And my guess is that the people who are encouraged by LLMs and not negative (mostly) aren't taking time out of their days to write long blog posts or argue about it online.
It's not the technology, it's the stupid overhype. It really feels like all the HODL bitcoin cultists have finally gotten over their lost apes and found a new technogod.
So many people in these threads are convinced it's about to gain sentience. That's not going to happen. You get the people outing themselves by saying "it does my job better than me!"
If you say something honest and direct like "their output is mediocre and unreliable" or "the RNG text generator is not capable of thinking, you're just Clever Hans-ing yourself" or "if it does your job better than you, that says more about you than about it", you get people clutching their pearls like a Stanford parent whose kid got a D.
arXiv has turned into a paper mill of AI startups uploading marketing hype cosplaying as "research".
I wrote a custom MCP to grab tickets from my Kanban board, Roo will pull down the tickets and start implementing them. I then have another agent that QAs, and either kicks the ticket back, or moves the ticket to human review.
I’m doing this on a real world micro SaaS. It has about 50 paying customers, and I’m the sole developer. I did a complete rewrite and the AI was able to complete about 90% of the project. I estimate I can get about a week’s worth of coding done in a day with this setup. I haven’t even scratched the surface of optimizing this workflow.
I’m also just one guy working nights and weekends, I’m sure there are many startups solving this same problem. It’s amazing to be shipping features this quick, but as a developer I’m terrified of what this is going to do to our careers.
So it's from middle management levels riding the hype train, and possibly trying to save money and getting bonuses for it at the expense of other people.
Just like when offshoring was in its same point on Gartner's hype curve.
"Everyone has a model, but no one has a business".
Disclaimer: I’m the developer behind CodeBeaver
I happen to value human creativity.
Personally, I find that waiting for the code to generate, then reviewing the code carefully, then deciding if I need to rewrite it to be more painful, more error prone, and much slower than writing the code correctly.
Especially since this AI junior never learn from it’s mistakes.
I think it speaks to different approaches to how individuals write code.
> How great is it gonna be in a year or two?
I would bet that it’s about the same (not great code, generally), but the tools fail to generate responses less often and likely would have more context.
Hopefully they become fast enough to run offline or at least feel more instantaneous.
I'm able to do a so much more using LLMs (Mistral-Large, Qwen2.5 and R1 locally, Claude via API) than without them.
I have to get the IDE setup properly now.
I had a complex finance situation that I was struggling with, both from a mathematical/taxation perspective and a personal psychological finance hangup. I spent a few good hours talking to it through everything and had a mental breakthrough. To get the same kind of insight, I would have to pay a financial advisor AND a psychologist for several hours.
That all of this was free while someone calls it a "con" seems completely wrong
(I got my CFA cousin to look over the numbers and he agreed with R1's advice, fwiw)
Who? How? This is not what I've seen where I work. There's a bunch of hubbub and generalized excitement, and lots of talk about what could be done, or what might be done, but not very much actual doing. I must just be a clueless "mid".
If ChatGPT disappeared tomorrow (or derivatives like Copilot, etc.), I would be mildly inconvenienced. Then I'd go back to reading docs, writing code slightly slower and carry on. In fact, I did this already, several times (Copilot with GPT-3.5, Cursor, Copilot with GPT-4, Zed with Claude, etc.)
It takes some time for technology to mature, usually at least a decade or two. Even once the iPhone was released it took a few years until it became indispensable.
"It may or may not produce something useful, in a few decades" cannot justify the present level of spending; that's just not going to fly. Without concrete results _soon_, the whole thing is in very big trouble.
Everyone thinks their new thing is the T-1.
Here: https://www.wheresyoured.at/longcon/#:~:text=Also%2C%20uhm%2...
Maybe the original Yahoo! style curated list of categorized links would actually be more useful for me at this point than Google with all the SEO spam.
That kind of high-quality directory combined with ChatGPT would probably replace Google for me.
I kind of agree with this, but Google is still, IMO, the best way to search these sites even when you know they exist because most of them have terrible local search.
Google searches with the site: tag are one of the few ways in which I find google search to be somewhat useful. Its pretty terrible these days at more generalized search due to their capitulation to SEO.
The internet is much more faster, but you can do stuff locally if someone took care to download documentations (which I did because of the above reasons).
If smartphones had disappeared in 2008, most users would be mildly inconvenienced. They'd go back to using a flip phone and a TomTom, or printing out map directions, and sending emails on their laptop with WiFi. No employer expected them to have a chat app on their phone* or use PagerDuty, no businesses required them to download an app to purchase services. People called taxis on the phone.
Perhaps in 5-10 years people will stop putting any effort into documentation or organizing information, (some companies are already ahead of the curve on this one) and our jobs will become that much harder without an LLM to sift through all the information.
* I'm ignoring the BlackBerry world here which was always pretty niche.
The LLM needs the documentation more than I do
> If the cloud disappeared, I wouldn't be able to build apps anymore.
Seems rather hyperbolic. Colos and shared hosting were a thing long before the cloud craze, and still continue to be a thing. I figure if nothing else a lot of people would go back to the still relatively low touch environment of uploading PHP or CGI scripts, which honestly seems like a pleasant change these days.
DuckDuckGo/Kagi are where good search is at.
Except this is exactly what you would do if Google disappeared and did before Google existed. You're applying different standards.
The last production code I wrote was over 20 years ago. I don't know React and TypeScript. I recently created a SaaS MVP using Windsurf/Sonnet/React/Refine.dev/Supabase in 8 days. We already have live humans excitedly using the product.
The SaaS product is a recreation of an app that I tried as a startup a few years ago, which never got to even this level of traction and failed. We failed for many classic reasons, but one of the main reasons was that we had no truly technical co-founder, and could only afford an off-shore dev. Iteration took around 24 hours. Using Windsurf, a product shmoe like myself can iterate in 2 minutes.
Of course we will have to get a real React dev on board if we start to get real traction, but the LLM tools allowed us to explore an opportunity that would not have existed without them.
Disclaimer: I happened to use Windsurf, but there are other options like Cursor, which you might have better luck with. I am not on Mac OS, so Cursor was not an option for me.
Now you're being fed incorrect answer by the search engine built in llm, it's impossible to find legit reviews: they're either sponsored reviews or written by bots, image search is next to useless, it's impossible to find a recipe, you can't tell if they're legit or if they're written by an LLM and will completely fall apart because as it turns out the best cookie recipe isn't the average of all known cookie recipes
I willingly pay $80/month for a smart phone and its ecosystem. Google search (for general queries) is worth a lot less to me, since it's fairly easily replicated by federating a search across a dozen sites where most answers arise now. So I might pay $5/mo for that, or maybe $20/mo for code queries (to be paid by my employer of course). Internet access for desktop/laptop computing or for media streaming might be worth $40/mo.
So what are the end uses of AI that I'm willing to pay generaously for? If GenAI is supposed to revolutionize the infosphere, the question has to be, what service will it provide that can justify its cost -- the $trillion investment in infrastructure that is underway?
I have absolutely no idea what AI's killer app could be. IMO, not even a dedicated secretary/tutor/companion is worth more than $50/mo to the average bloke. And that drip of revenue per user isn't nearly enough to justify the development costs that AI is incurring now.
That's why it's not a completely product hype driven bubble since there is a useful product there
LLMs very obviously aren't a replacement for live human written information which is findable with a search engine.
AI is primarily used to write text and code. Actually integrating it into workflows across markets will take time.
A great article that is neither overly pessimistic or optimistic is Benedict Evan's The AI Summer [1]. He argues that there's a lot of excitement with big corporates but their actual adoption is low so far.
"an LLM by itself is not a product - it’s a technology that can enable a tool or a feature, and it needs to be unbundled or rebundled into new framings, UX and tools to be become useful. That takes even more time"
[1] https://www.ben-evans.com/benedictevans/2024/7/9/the-ai-summ...
"Con", in this context, is short for confidence. A con-man is a confidence man.
"a swindler who exploits the confidence of his victim"
It's a con.
Likewise here AI is real and important but a lot of the companies are cons.
I think the key here is actual usage and adoption. If early adopters (marketers and coders) keep using the new tools for real work over the long term then it's a positive signal.
The term "AI Winter" dates back from the 80s, and that should tell us something.
At every cycle, we have insane hype (remember when "expert systems" would replace doctors?), a lot of investment. Hype fails to catch up to reality, investors get spooked. Nobody talks about that flavor of "AI" for a few years, even though we usually get new and useful tools.
>Expert systems were formally introduced around 1965
and hyped in the 1970s, launched in the 1980s and found to be a bit rubbish https://en.wikipedia.org/wiki/Expert_system
> a cynical bubble inflated by OpenAI CEO Sam Altman built to sell into an economy run by people that have no concept of labor other than their desperation to exploit or replace it.
He brings up the concept of labor and applies a moral judgement about "replacing" and "exploiting" labor.
And then he throws the kitchen sink at the technology. People use it sure, but it's because journalists write about it. How it's expensive to train. Throw a bunch of explicits and call it a hot take.
It's the equivalent of a vegan trying to convince you that eating meat is morally wrong, and will give you cancer, and make you fat, and give you ED, and ...
This doesn't work primarily due to the fact that most people reading this got real value from an LLM. And I'm sure the author did as well. Claiming otherwise is dishonest. So what is his problem?
Speaking for myself, I've never gotten any real value from an LLM and their disappearance would not affect me in the slightest.
It sounds more like you were upset about his assertion because YOU derive value from an LLM, and are projecting that as some sort of dishonesty on the author's part.
Also, it was his intention to throw "the kitchen sink at the technology" as a means of showing its lack of value. In the same way a vegan would do exactly as you mention to show all the arguments AGAINST eating meat. It is meant to strengthen the intended argument through overwhelming evidence.
My guess: He's just posting a hot-take to farm for engagement.
I'm not saying he's wrong about everything. I'm just pointing out that he has a small incentive to be engaging.
This is missing the most interesting changes in generative AI space over the last 18 months:
- Multi-modal: LLMs can consume images, audio and (to an extent) video now. This is a huge improvement on the text-only models of 2023 - it opens up so many new applications for this tech. I use both image and audio models (ChatGPT Advanced Voice) on a daily basis.
- Context lengths. GPT-4 could handle 8,000 tokens. Today's leading models are almost all 100,000+ and the largest handle 1 or 2 million tokens. Again, this makes them far more useful.
- Cost. The good models today are 100x cheaper than the GPT-3 era models and massively more capable.
If nothing else, my workflows as a software developer have changed significantly in these past two years with just what's available today, and there is so much work going into making that workflow far more productive.
And I’d argue it took decades to actually achieve some of the things we were promised in the early days of the internet. Some have still not come to fruition (the tech behind end to end encrypted emails was developed decades ago, yet email as most people use it is still ridiculously primitive and janky)
this is exactly the problem
The more productivity AI brings to workers, the fewer employees employers need to hire, the less salary employers need to pay, and the less money workers have for consumption.
capitalist mode of production
Not very well in my experience. Last time I checked ChatGPT/DALL-E couldn't understand the its own output to know that what it had drawn was incorrect. Nor could it correct mistakes that were pointed out to it.
For example, I ask it to draw an image of a bike with rim brakes it could not, nor could it "see" that what was wrong with the brakes that it had drawn. For all intents and purposes it was just remixing the images it had been trained on without much understanding.
Evaluating vision LLMs on their ability to improve their own generation of images doesn't make sense to me. That's why I enjoy torturing new models with my pelican on a bicycle SVG benchmark!
If the cost-to-serve is subsidized by VC money, they aren't getting cheaper, they're just leading you on.
The subsidies are going to the training costs. I don't know if any model is running at a profit once training/research costs are included.
If we're stretching, we can talk about opportunity cost. But the people spending and creating the "bubble" don't have better opportunities. They're not nations that see a ROI on things like transportation infrastructure or literacy.
So unless the discussion is taken more broadly and higher taxes are on the table, there really isn't a cost or subsidy imo.
This. IIUC to serve an LLM is to perform an O(n^2) computation on the model weights for every single character of user input. These models are 40+GB so that means I need to provision about 40GB RAM per concurrent user and perform hundreds of TB worth of computations per query.
How much would I have to charge for this? Are there any products where the users would actually get enough value out of it to pay what it costs?
Compare to the cost of a user session in a normal database backed web app. Even if that session fans out thousands of backend RPCs across a hundred services, each of those calls executes in milliseconds and requires only a fraction of the LLM's RAM. So I can support thousands of concurrent users per node instead of one.
Anecdote:
I often front-load a bunch of package.jsons from a monorepo when making tooling / CI focused changes. Even 10 or 20k tokens in, Claude says things like "we should look at the contents of somepackage/package.json to check the specifics of the `dev` script."
But its already in the context window! Given the reminder (not reloading it, just saying "its in there"), Claude makes the inference it needs for the immediate problem.
This seems to approximate a 'working memory' for the assistant or models themselves. Curious whether the model is imposing this on the assistant as part of its schema for simulating a thoughtful (but fallible) agent, or if the model itself has the limitation.
I agree, though personally I'm liking the "big thing" as well. R1 is able to one-shot a lot of work for me, churning away in the background while I do other things.
> Multi-modal
IMO this is still early days and less reliable. What are some of your daily use cases?
> Context lengths
This is the biggest thing IMO (Models remaining coherent at > 32k contexts)
And whatever improvements have caused models like Qwen2.5 to be able to write valid code reliably vs the GPT-4 and earlier days.
There are a whole lot of useful smaller niche projects HF like extracting vocals/drums/piano from music, etc
For images I use it for things like helping draft initial alt text for images, extracting tables from screenshots, translating photos of signs in languages I don't speak - and then really fun stuff like "invent a recipe to recreate this plate of food" or "my CSS renders like this, what should I change?" or "How do you think I turn on this oven?" (in an Airbnb).
I've recently started using the share-screen feature provided for Gemini by https://aistudio.google.com/live when I'm reading academic papers and I want help understanding the math. I can say "What does this symbol with the squiggle above it?" out loud and Gemini will explain it for me - works really well.
There hasn't been much R&D progress, though. Sure, as another commenter pointed out, context lengths have gotten longer and chat models can interpret images now, but the industry figureheads have been pushing agents, and we're not much closer to those than we were two years ago when GPT-4 came out. Current models simply are not consistent enough to do the kind of agentic stuff that AI valuations are predicated upon, nor is there any sign that a significantly smarter GPT-5 is just around the corner. Multi-modal chat is cute, but OpenAI is burning money. They're all burning money, and they don't have a product. They imply and imply that there's something big on the horizon, but it's been years, and there just isn't a killer app yet. Their platform isn't good enough, and it's not improving in the ways it would need to in order for Godot to arrive and for agents to be feasible.
That said, I'm learning a new sdk and I've moved 500-1k searches a month from kagi and google to llms.
because profit of this can not cover the investment in this industry
adoption of iphone/smartphone/internet brought new products, including those for reproduction and those for consumption
but generative AI is totally different with iPhone, consumers maybe willing to buy a new ai-powered iphone __just like how they bought new iPhones for every 2years before__
> The current landscape imho should be viewed as an R&D race amongst private actors
in fact, it's a CapEx race, you don't need to R&D anything (ofc you must pretent you do)
that's why it's a con
> The AI Bubble will burst “any day now”
"The canary in the coal mine to look at is when Satya Nadella or Sundar or Zuckerberg say, ‘You know that $80bn of capex I said I was going to do? I think I’m going to cut that by two-thirds.’ That’s what you need to look for."
that's the day
While he was chairman of the central bank through January 2006, Greenspan always denied there was a
bubble in the nationwide U.S. real estate market,
saying only that a certain number of metropolitan real estate markets could
see declines in home values because of a localized run-up in prices. That view of any real estate bubble as
a merely a local phenomenon is a condition he
termed as "froth" in congressional testimony in 2005, as well as in subsequent comments.He comes across as just a ludicrously unpleasant, spite-filled person.
> I'm fucking tired of having to write this sentence.
> I am so very bored of having this conversation
> I don't care about this number!
> Shut the fuck up!
> This isn't the early days of shit.
> Didn't we just talk about this? Fine, fine.
> $3.25 billion a quarter is absolutely pathetic.
> This isn’t real business! Sorry!
> He said in one of his stupid and boring blogs that
> This man is full of shit! Hey, tech media people reading this — your readers hate this shit! Stop printing it! Stop it!
> It's here where I'm going to choose to scream.
> Dario Amodei — much like Sam Altman — is a liar, a crook, a carnival barker and a charlatan, and the things he promises are equal parts ridiculous and offensive.
> Why are we humoring these oafs?
> Despite Newton's fawning praise
> Nobody talks like this! This isn’t how human beings sound! I don’t like reading it!
> Ewww.
> I'm sorry, I know I sound like a hater, and perhaps I am, but this shit doesn't impress me even a little.
> I know, I know, I'm a hater, I'm a pessimist, a cynic, but I need you to fucking listen to me: everything I am describing is unfathomably dangerous
> expensive, stupid, irksome, quasi-useless new product
> I know this has been a rant-filled newsletter, but I'm so tired of being told to be excited about this warmed-up dogshit.
> I refuse to sit here and pretend that any of this matters.
> I'm tired of the delusion. I'm tired of being forced to take these men seriously.
When I read this kind of thing, it’s very apparent that this is being driven entirely by spite not insight. He’s just so angry about everything. There are 57 exclamation marks in this article!
GenAI is - imo - an assistant. Copilot does effectively templating.
I can have ChatGPT read an email and check it for tone.
Claude can comment on camera kit.
Claude does a very nice image recognition for obscure things.
What I have become persuaded of is that the /completions API is simply not much more than +10% or a low key helper.
I do not need a dumber-than-intern agent going ape on my codebase at speed, which is, approximately, what the codegen tools seem to do.
I saw a self driving car startup using a GPT neural network to recognize images during driving. I would assess that class of use as plausibly very promising.
I would also hazard that Shirkys BS jobs thesis is being proved true, because if a hallucinating ai can do it...
Anyway.
I don't think the fundamentals justify the spend. I think there's too much vitriol, but there's also too much hype & by a country mile too.
Maybe Im crazy but this alone is a trillion dollar market cap industry imo. msft is worth 3 trillion off the back of similar products. If LLMs are seen as indispensable by every office worker in the country, as I think they are, and every employee has a subscription for $20 a month we're looking at many billions in revenue.
> I do not need a dumber-than-intern agent
two tells that you have not updated since 2023
Open source projects would have a lot more compute to work with.
I would strongly argue that coding assistants are AI’s first killer app. Copilot, Cursor, Windsurf etc.
By your logic I could claim a quantum computer with qubits on the scale of the mass of the sun is a killer app for doing RSA encryption breaking. And I would be making an equally useless statement.
These IMO are relatively useful things. But probably (in their current state) will not justify the valuation of the companies involved and the massive investment occurring right now.
I don't know how the future will unfold. I do think it is reasonable to be somewhat bearish on what has been promised vs. what has been released.
To use these tools properly, you need to know how to build the same thing precisely.
I have heard "top" engineers at various places say it makes them 2x faster, or whatever, but I would like to see this assessed by timed testing, as is sometimes done for evaluating software engineering.
Copilot may let me type less, but I have not seen the wall clock effects, which is a very hard thing to measure (time perception is very unreliable).
The product is ChatGPT, actually.
If LLMs are a bubble, then you should expect most of OpenAI's revenue to come from its API (which is used by startups which have raised money to do "magic AI stuff", and the bubble would pop when investors would stop giving the money). But according to https://futuresearch.ai/openai-revenue-report, revenue from the API accounts only for 15%, the other 85% being the different subscriptions offers, including 55% of ChatGTP Plus subscriptions -- that is, _direct consumers_.
This doesn't prove that it isn't a bubble (the consumers could realize it's useless and then leave some time later), but it makes it less likely IMO.
> This report draws on a variety of sources, including those not easily found by search engines, e.g.:
> * Personal anecdotes of pricing info from sales calls
> * A blog that used DNS records to infer which Fortune 500 companies pay for ChatGPT Enterprise
> * Transcripts of OpenAI exec interviews
> Just as important are the data points FutureSearch rejected when they didn’t corroborate more trustworthy sources. The core assumption made here is the $3.4 billion in ARR that Bloomberg and TheInformation reported Sam Altman said in a staff meeting.
With LLMs it feels we are getting near to 90-10. Finding the bug in those good-looking pieces of generated code is pretty hard. (After all, you did not pay a lot of attention to the generated code, it looked pretty solid) Some will argue that the LLM should spot the bug, Indeed, it should ask clarifications about the requirements. One day… but you need an expert to understand and answer the questions for that last 10%.
How I write unit tests: Open the chat menu, paste in function signature, describe the tests I want. Out pop the tests. Run, fix code as needed. Add more tests, etc. Super easy.
It's just this bubble is so public, rapidly moving, and capital intensive.
It's a bubble in the respect that the hype around integrating into existing companies/software is likely often falling flat.
It may not be a bubble in that all of the best/useful/valuable use cases of AI are in new software, which have yet to prove themselves in the enterprise. This makes sense because you can't just bolt it onto existing software/organizations and expect it to work because they're built around the way things used to be, similar how when factories first tried to integrate electricity.
For example, I'm sure Palantir is doing some good stuff, but I just have doubts about how useful AI can be in the context of existing companies. And their valuation seems insane, which screams bubble, especially since they're an older companies and less 'AI-native' than the newer ones, like the clunky ways Salesforce and Microsoft implement AI.
But do I expect startups to continue to emerge that approach problems in AI-native ways that help companies reorganize? Yes, it's just a question about how long it takes these companies to work their way into the enterprise and earn enough credibility to drive organizational change and restructuring.
The 'bubble' question is really about whether this latent/potential productivity will be enough to inflate the bubble before it bursts.
My money is on yes, but rather than picking a winner at the app layer and trying to win the lottery I'm heavily invested in the boring stuff like chips (NVDA) and those building data centers with low P/Es (back when I bought them), thus a lot of room to grow even conservatively.
This is the real problem. Companies have ALREADY starting laying off significant percentages of their workforce because they're buying into the AI "digital worker" hype without any idea of how exactly AI is going to do the jobs of 80% of THEIR employees across all departments in the next year or so.
I shocked he really believes it in his closing thought.
Maybe his rant would be bit more digestible if it contained sections with: "here's what I tried and it did not work". But that would make it not a rant but actual research with value.
Which then invites the question: why is this person's opinion on the subject relevant? Do they have some credentials that make it more valuable than a random comment with a similar rant (of which there are plenty) on Reddit or HN?
I think ChatGPT and similar generative AI did fundamentally redefine what software could be. Everyone rushed to implement generative AI into every software. Even MS Paint has AI now. Before this, the idea that you would have it was unthinkable.
If it doesn't make any money, that's a separate issue.
>If generative AI disappeared tomorrow — assuming you are not somebody who actively builds using it — would your life materially change?
To put it in another way, you could live without the ability to drag and drop, but that doesn't mean it hasn't redefined user interfaces.
I find it interesting that he almost equates OpenAI === LLMs and misses the fact that the hype is not purely industry driven. For instance, the number of machine learning papers in the last year has quite literally doubled.
This is also typical of an Americentric view on innovation that we don't report on the quiet revolution happening in education in underdeveloped countries that are a direct result of the accessibility of this unprofitable technology.
I don't think we need killer application right now
We also forget the internet bubble happend first
I think the author is looking at LLMs through the lens of Sam Altman's hype narrative and I wonder why we care so much that
It is not clear to me why the author feels the need to have the conversation.
Human consciousness gives us the ability imagine future states in the universe and make them come true.
The results will speak for itself.
"I need you to fucking listen to me: everything I am describing is unfathomably dangerous, even if you put aside the environmental and financial costs."
Personally I think he's lost it a bit. I mean say he's right in that LLMs plateau and investors lose some money. Life will go on.
> It sure is! But it doesn't really prove anything other than that people are using the single-most-talked about product in the world. By comparison, billions of people use Facebook and Google. I don't care about this number!
> User numbers alone tell you nothing about the sustainability or profitability of a business, or how those people use the product. It doesn’t delineate between daily users, and those who occasionally (and shallowly) flirt with an app or a website. It doesn’t say how essential a product is for that person.
Both of these "arguments" could be applied to any of the big tech giants of the last 25 years - Google, Amazon, Facebook, Uber, whatever (and there'd be other incumbents used by billions of people before them). I don't believe these arguments discount ChatGPT from having the potential to continue growing like a Facebook. And who cares how many journalists Altman knows, you don't get a product written about that much unless it's truly a groundbreaking product.
> And even then, we still don't have a killer app! There is no product that everybody loves, and there is no iPhone moment!
There sure is, it's called programming. He called out quality earlier on, but the quantity and speed and direction the AI can take (as well as its rate of improvement) is breathtaking. My own output has 10x'd easily since GPT-4 came out (although some of that means I'm needing far less hours in certain places). And guess what? The code quality is generally fine.
> Where are the products? No, really, where are they? What's the product you use every day, or week, that uses generative AI, that truly changes your life? If generative AI disappeared tomorrow — assuming you are not somebody who actively builds using it — would your life materially change?
Ok, the product is called ChatGPT, or Claude, or DeepSeek or whatever, and if it disappeared overnight, my programming productivity would drop dramatically. I would not seek to take on as ambitious projects in as short of a time frame as I am doing now.
I don't know, as a user and developer both of AI/LLMs, this article isn't hitting the mark for me. There are legitimate criticisms of the field, but I'm not seeing them thus far.
Edit - I'll say I agree with the Deep Research criticisms. These products are very underwhelming. They're literally to help people do a research report which needs to be done, but won't be used or read critically by anyone report.
All of the valuable uses are personal. It makes me personally feel more productive at work. It helps me personally understand some topic better. It gives me an idea in a personal project.
That's all really cool, but that is not what the valuation is about. The valuation is about a false science fiction and hype bubble about agentic this or that or AGI or whatever, and this is driving very questionable decisions for wasting possibly trillions of dollars and tons of energy.
The plus side is that there is some really cool personally useful tech here, and we will probably end up with very good open source implementations and cheaper used GPUs once the bubble bursts.
I don't get these statements. This line of thinking is so egregious, FTX made an ad about it. This is survivorship bias. If 'wrong' predictions about some can be discrediting, then why not right predictions to establish credibility? The same people were right about the Segway, crypto, metaverse, web3, that dog walking startup that Softbank burned money on, and countless other harebrained endeavors.
Even in your example, Uber is a financial crime. It is still using accounting tricks to show profit.
I haven't shelled out the $200/month for OpenAI's Deep Research offering, but similar products from Google and Perplexity are extremely useful (at least for my use case). I would never present the results unchecked / unedited, but the Deep Research products will dig much deeper for information than Perplexity could be persuaded to previously. The results can then be fed into another part of the process.
It’s basically functioning as a team of entry-level junior engineers at this point.
Previously I was having to spend a fair amount of time writing tickets and providing context, but lately I’ve fed all my meeting transcripts and such into an LLM and it interactively creates Jira tickets for me. Each one takes me maybe 30s to read before I confirm them and the assistant creates the actual tickets.
What’s the total business opportunity of making all knowledge workers 10% more productive (to pick a more modest goal than outright replacement)?
Do we have any empirical evidence of this? It seems like it'd be an easy experiment to run - task a number of teams with building a particular product, some with Copilot and some without, and see what happens.
I've tried copilot myself, and at times it makes me feel more productive, but I can't tell if it's truly helping me overall.
Regardless of the hostile tone of the article, this stuck out to me as an incredibly poignant description of the current tech/finance elites' mindset.
As most of us who have tried LLMs can attest, they are indeed stochastic parrots with no capacity for knowledge or understanding. This is best exemplified by their non-deterministic outputs, wherein they give different answers to the same question if asked enough times. This is not how a human brain works. Perhaps it is a small building block, but the systemic architecture required to reach brain level is currently not in sight based on what I'm seeing.
I think you may find some humans do that too.
That's also the only use of LLMs we've found.
1. Reformatting notes or bits of information into something more formal (something I consider actually counterproductive in a way, since formal is often more verbose, but that's expected in certain contexts...)
2. Sifting through the crap of the internet to answer obscure questions. The Google replacement that has been needed.
I think this type of job suits LLMs perfectly... At the end of the day it's just a statistical NLP tool.
I wouldn't trust the analysis on anything important, but that gives you the source links so you can still verify yourself.
They just released a benchmark today to try to attempt it (OpenAI).
The future, probably within 10 years, is most tasks being handled by small on-portable-device models (7B parameters or so; see Apple's Intelligence thing), a middle ground of workhorse models (pushing closer to 30s and 70s) running on more capable ML-focused chips in laptops and workstations, and home and office servers for the biggest professional users running on dedicated servers.
And then there's the apps. Whoever makes the "Stripe for generative AI" with multiple models with different levels of data provenance, security, SLA, etc for different use cases tied together with support for custom fine tuning stands a good chance of sweeping the market post-collapse.
My understanding of the zeitgest on HN about Apple Intelligence was definitely not leaning towards "they nailed it". Not even in the ballpark of "promising", I'd say.
1. The Generative AI is very very useful
2. The Generative AI is a epic bubble and it will kill us all (financially) one day
It's entirely appropriate to describe it as a 'con', as long as you look deep enough into OpenAI, SoftBank, MSFT, NVDA, SMCI, etc.
it's a con, but it's useful, and it will kill us all
It's been made fairly clear that the insiders are setting the stage for governments to back them (bail them out).
Performance improves every year, and costs become 3x-10x lower every year for the same level of performance. The difficulty of the code we want it to write does not increase 3x-10x per year. So there is no cost problem in just a couple years.
The level of denial and anger about LLMs on HN is astounding to me. Is this just defensiveness from software engineers worried about losing their jobs? An inability to extrapolate cost or performance trends just a couple years forward? Personal criticism against Altman and Musk? What am I missing?
It is ridiculous to say that generative AI is a con at this point when it is by far the best way to search the internet (in spite of hallucinations).
and will trigger the collapse of the wider asset price bubble, with consequent economic turmoil - an unfortunately necessary reset, in my opinion.
I suppose the good news is that likely the current US administration will wear this one, though not completely being their fault (much like Covid), and a political reset will also ensue.
To succeed here, AI doesn't have to be cheap or good, it only has to be cheaper than human staff.
What is this guy even talking about now? Zitron has gone so far off the rails.
I use them both. A lot. I'd hate to me without them.
[1] https://blog.curtii.com/blog/posts/the-laypersons-guide-to-a...
> OpenAI burned more than $5 billion last year.
Well, this is semi-true. When speaking about LLM technology, must be honest, and make difference of base (or foundation) model training, vs fine-tune it for purpose.
Sure, if you just use base model, you also could gain some profit, but real value of LLM achievable if you got already done base model and fine-tune it on your target task.
What this mean - base LLM are just learn language structure from really huge dataset (for example, entire Wikipedia), and this is really expensive, but when you fine-tune LLM from for example, your corporation product documentation, it will become AI-consultant about your corporation. Or you could fine-tune LLM from children story book, and it could indefinitely generate texts similar to that story. BTW, rumors said, some orgs fine-tuned GPT-3 on their company codebase and have very interesting results on code generation (much better than with base model).
Fact, base model training really cost millions (Llama-2 official cost $5 millions, and I believe it much more than claims of Chinese about deepseek R1 cost also $5 millions).
But fine-tune GPT-4o now cost about 20 bucks for 1 million tokens, and inference is $3.75 per million input tokens and $15 per million output tokens. For GPT-4o mini, training cost is $3 per million tokens, and inference is $0.30 per million input tokens and $1.20 per million output tokens (from official announce on OpenAI developer community).
If you consider fine-tuning of GPT-3 class model (or for example, similar open source model), official prices are just few bucks for million tokens (run it on your own infrastructure will be slightly more expensive), which I think very tolerable and already affordable for small companies.
And I admit, just few Billions of market is not scale of big thing, but I think, it is just because conservative corporate tops, and because security problems of current implementations, and will change nearest years.
While I don't really understand what fuels this person's Substack Forensic Journalist energy, I can only say that I am thrilled to pay $20 to OpenAI because it delivers outrageous value to me as a solo, self-taught "engineer"* designing reasonably complex physical devices intended for sale. Air quotes because here in Ontario, if you don't got the ring, you don't got no business using the title.
So my first hand gut reaction is that people who cannot fathom meaningful use of modern LLMs are by definition people who are not trying to solve complex problems in domains they aren't yet super confident in. No judgement, and this is intentionally reductive; an LLM skeptic is lots of other things, too. Just saying that if you want to build hard things, reasoning models are dramatic force multipliers.
OTOH I would never trust anything built by someone not super confident while heavily relying on a LLM.
general API plumbing ? call this api, combine the json results & spit it out. Then slap a React / Next.js frontend ?
lately, I have been doing your classical business apps - due to the domain rules - ai is pretty useless - but I have found perplexity and deep mind to be smarter stack overflows. that's it.
For anything even remotely complex or involved, it's just not that useful. Interestingly, It seems to shine the most when doing boilerplate stuff in widely used languages such as python or javascript, but it's extremely bad with terraform.
Bailey (the clickbait): “Generative AI is a con!!”
Motte (narrow defendable argument): OpenAI and Anthropic have not shown that building a proprietary model and selling inference is a sustainable business.
- maybe ALL humans would fail the test in some way, eg. let's say everybody gets at least 10 of those wrong, and the average person gets 100 of those wrong.
- still, as long as most people correctly get each word right, your LLM would get every single response correct (because for each item in the test, 900+ people out of a thousand gave the same correct answer in the training set).
In that sense, it's totally possible for a system trained on a vast vat of average-human input to generate super-human outputs.
But yeah, I don't think LLMs (the current core architecture) can provide super intelligence. I think it needs a bit more than next token prediction architecturally speaking.
We’re still in the “what the heck is the point solution here” phase, with a lot of anticipation for platform and system-level shifts. There are some point solutions—like coding assistants—that make existing workflows more efficient and higher quality, but they haven’t translated easily to other domains. Platform solutions require completely rethinking workflows holistically, and system solutions demand restructuring everything that depends on those workflows. That’s going to be slow and messy. Including financially messy.
The book likens this to the introduction of electricity. Initially, electrification meant new individual machines in factories organized around steam power. Steam power was hard to turn on and off and not at all portable. Actually getting the full benefit of electricity meant redesigning factories around electricity use as-needed (not just when the steam engine was running) and spatially organize around task efficiency (not proximity to the steam energy production). All that was not a quick shift.
I very much sympathize with the author's frustration over hype that fails to understand the underlying technology and puts unwarranted faith in a small collection of corporate leaders. But I do think that this technology does have a high degree systems change potential and possibly the momentum to see it through this time. Not that we know how that will play out of which actors or forces will bring it to fruition. It really doesn't feel the same as the other tech crazes of the last two decades.
Yes. My coding sessions would surely be different. I now have a very fast junior developer who has excellent knowledge of various libraries, though I have to check their code. Write me a function that accepts X and outputs Y. It works great!
Yes, the business model of OpenAI et al is probably unsustainable. I couldn't care less.
> I Feel Like I'm Going Insane
> Everywhere you look, the media is telling you that OpenAI and their ilk are the future, that they're building "advanced artificial intelligence" that can take "human-like actions," but when you look at any of this shit for more than two seconds it's abundantly clear that it absolutely isn't and absolutely can't.
Either I'm the dumb one or Ed Zitron is...
Oh sure, because you can just have hundreds of reporters constantly write about your product. It's so simple. Why aren't more people thinking of that ?
>The weekly users number is really weird. Did it really go from 200 million to 300 million users in the space of three months?
According to similarweb, monthly visits grew over 1B in that timeframe so yeah sure it sounds possible.
>300 million monthly active users would mean a conversion rate of less than 4%, which is pretty piss-poor
A B2C Saas whose lowest price point is $20 will be lucky to get anywhere near 4% conversation.
>And even then, we still don't have a killer app!
The 6th (and climbing) most visited site in January is not a killer app ? Okay
1. LLMs are not very useful
2. Companies like OpenAI and Anthropic are losing tons of money
3. There is a lot of hype around them
The first seems objectively untrue - lots of people find them useful especially for coding. Not to mention the fact that they get significantly better every year.
The second is completely true but it's not clear how much that matters. Our products are being subsidized by VC firms while costs are falling by 3x-10x every year. Seems great to me.
There is a lot of hype because hype helps capitalists get rich faster. Annoying, but a small price to pay for useful technology.
I keep paying $20/mo to OpenAI even though I think Altman is a frightening snake man.
The utility that ChatGPT and other LLMs provide is undeniable. Their revenue will tell us how much value people get because nobody's going to spend $20/mo (much less $200/mo) without getting something for it.
Ah, yes, the WeWork 'community EBITDA' model.
Is the AI "business" or "market" overvalued for it's current capabilities? Yeah, I do believe so. Welcome to the financial world, which is completely separated from reality. It's like that in all sectors where something new and exciting is happening, not just IT or AI. People poor money in hoping to be early enough to make a profit. Nothing more, nothing less. The rest is marketing. Some Sam Altman guy promoting the hell out of his own product? That is literally his job, regardless of wether or not he believes it all.
But articles like these are so bizarre to me. The author acts like he has millions at stake and his money manager just won't listen and pull all investments out of AI. Hurry up, the bubble is about to burst, I will lose all my money!
Except that... they don't. They are just "old man yelling at cloud". If you believe AI is the next Metaverse or WeWork, then it will just die off by itself once the bubble pops. Why are you having so many conversations about it, where you seem to be desperately trying to convince people of the bubble/con that is AI. To the point that you're so sick of it, that you write down your arguments so you can point the blinded there instead of having those tiresome arguments.
Genuinely baffled. Spend your energy on something productive rather than destructive, perhaps?
Because that's what AI wa supposed to be in the first place. But the industry performed the swindle of renaming "AI" to "AGI", so that they can pretend the thing that exists now is "AI".
Altman uses his digital baba yaga as a means to stoke the hearts of weak-handed and weak-hearted narcissists that would sooner shoot a man dead than lose a dollar, even if it means making their product that much worse.
the only correction i would offer for the entire article is that instead of saying “shoot a man dead,” it would be more accurate to say “smother a baby with a pillow.”
I have a 6 million+ word archive with ChatGPT.
It truly is like having an army of interns, each a confident undergrad in a different subject, who have paid attention to every lecture they ever went to, event the one they'd popped acid just before going in.
It's right more often than it is wrong, but some of its clangers are almost unbelievable.
Yet, having never written a line of code, to build a python application that analyzed election data and applied the results to an interactive map, that gave constituency specific data on hover.
It invariable uses the word clarify instead of correct when challenged. Yet it knows that a clarification is refining an answer within the set of the previously proffered answer, and a correction is a revision on an answer outside of the set previously provided.
It believes that this is so consistent that, on the balance of probabilities this is coded and not purely as a result of training data.
When asked to write an article on this, and include the instances from that conversation where it had incorrectly used the word clarify, it edited the quotes to remove the evidence (probably the most egregious act I've witnessed it perform).
I still use ChatGPT, even more so now since DeepSeek got slow, but I watch it like a hawk.
I still call it out every time it prevaricates or flat out lies, it still promises to do better, it still, on being challenged, acknowledges that these assurances are dangerous lies to anyone who doesn't know it's lying.
But, for me, it is still a highly useful tool.
It frequently makes assumptions that would be made by those in a field I am unfamiliar with in a way that allows me to refine arguments.
Sharing ChatGPT chats can be a very helpful means of sharing one's thought process.
I have it on strict instructions not to create unless specifically told to, to not regurgitate what it already has, to focus on critiquing instead of echoing or praising.
Yet it still reckons 70% of its output violates these instructions.
But the remaining 30% justifies the time I spend using this remarkable, next generation, automation machine.
Because that is what it is.
*To say it is intelligent is like saying a scanner has an eye for detail. Yes, a scanner identifies every pixel but and LLM is no more a brain that a scanner is an eye. (And, yes, I know, but this is a line for people who don't know the neurological processing behind sight, which to be fair, is frequently not very logical.)
So it is a threat to people who earn money on fiverr writing bits of code or designing logos - hell yes.
It is a threat to those who code complex systems or who's designs can add actual digits to market share? hell no. Or at least not for the foreseeable future.
Just as the dotcom bubble funded the internet infrastructure that we still use today (just very inefficiently), it is unlikely these trillions will be completely wasted
Redesigning our cities around cars was one of the big mistakes of the 20th century.
car is consumer goods, we buy cars for use, not for reproduction
It's the frontier. The only new really big one we know.
It already solved real issues, see alphafold and it will continue until we hit a ceiling.
The money throwing thing is a zeitgeist issue: we have this money and people don't know what to do with it.
But yes ml is now the thing.
And no it was very very far away speaking to a machine and the machine feeling smart. If you can't see this breakthrough as what it is, you will never be excited for any new invention.
Until perhaps aliens are arriving on our door steps.