reminder than during 2019 there were streaming services popping left and right, all showing tremendous growth because they started from zero, and articles were about how bad Netflix was doing due to having practically no growth compared with the competition (they already had a massive subscriber base). Twist? Netflix was the only streaming service that was actually making a profit, the rest were a massive loss but big growth.
Needless to say most of those streaming services died; who remembers DC streaming service, or Yahoo’s? While Netflix is basically as stong as ever, despite the prevalent enshitification happening through the whole industry.
Point of the story? shareholders don’t care about stable profitable business, only cancerous growth. AI is like that, zero profits, ton of cost, but as long as they show growth the shareholders are happy, regardless of how cooked the books are.
So much comments on just the title … Could come from anthropic directly.
There is literally zero basis on the made claim in the article, just arbitrage calculations over supposed token consumptions under non stable test sets.
I have no idea if/how much these
stupidfuckers spend to get more customers - and this “article” wasted a lot of time showing that they don’t know either.(Stupid is cut out because I don’t think they they’re stupid. Which makes it way worse in my book)
Of course it is, it’s essentially a scam. They just need enough humans to keep investing until they check out and run with a bailout.
Funny thing is, the US government doesn’t even have nearly enough money to bail all these mfa out. So we are heading into uncharted territory here
Of course they don’t, that’s why they’re building bunkers. Thinking it’ll slow us down, as we’ll open their bunkers like cans of tuna. A bunker only works for so long, then the survivors start hunting for them like delicious shipwrecks.
I don’t think the bunkers are to avoid bad financial decisions, more so to stave off something like rogue ASI or a biosphere collapse which in any circumstance won’t work in the long-run.
Yeah, but it’s not like they would be smart enough to know that
And that’s why they’re trying underhanded tactics to inflate earnings and IPO directly into the index funds, so every American’s 401K will legally have to rebalance and invest in them. They’re racing to fleece retirement funds before the bubble bursts.
Not financial advice, of course :p but people should really consider getting their stuff out and into self-directed funds or whatever it is US people do to not depend on auto-allocated funds.
Money printer go brrrrrrr
Many applications are suboptimal to say the least but what’s been done with alpha fold and recently in mathematics is very far from a scam. Not to bring up what’s also been accomplished in cyber security. These models are proving open problems that have been around for decades and finding serious vulnerabilities. The issue is consistency and efficiency. Of course the other issue in making them stronger is continual learning and long horizon planning. I think too much investment came in too quickly and what is provided to the masses currently isn’t consistent or efficient enough. That said as a math and comp sci grad and someone who works in the field it’s been absolutely mind blowing to watch what’s already been done. In 2010 the concept of an artificial mind solving something like the Erdős unit distance conjecture would have been seen as pure sci-fi, maybe something we would achieve closer to 2100 than 2026.
For reference, it took Uber about 17 years to become profitable and Spotify 18. They were hemorrhaging cash for over a decade and a half before finally hitting their stride. As for the current AI development it’s honestly from 2017 when the white paper on transformers came out where shit started getting serious, so it’s been about 9 years since investors were serious. Before that point it was all passion projects, absolute moon shots as they call them.
Both Uber and Spotify (and AWS too) had economics of scale going for them - the more users they have, the more the infrastructure could be leveraged. This does NOT work for LLMs. More users means using more compute, more advanced tasks (like coding) uses exponential amounts of compute. A single user running a complex task can make 8 Blackwell GPUs run full tilt, and you don’t even have any guarantee that the output will be useable.
There are a few narrow areas where LLMs might be successful, like scanning for security vulnerabilities or searching large amounts of documents. The massive amount of money invested will never be recouped with these usage scenarios.
they are spending infinite money for every $1 i pay them
I’m quite happy to use their compute power for frivolous bullshit if it hastens their enshittification and demise.
“Hey Claude, can you begin work on an e-commerce site written in visual basic?”
*Two microseconds later… *
“Your free usage limit has been reached”
“Ok Claude see you tomorrow, maybe we’ll think about a rewrite in Turbo Pascal”
Agreed, but hey no need to pile the the hate on Pascal, modern ones like FPC/Lazarus are pretty cool actually :)
Joke’s on them. I’m not paying them a dime.
Definition of a Bubble. These AI huckster keep stringing investors on though. Sadly, I think these public IPOs coming up for Space X, OpenAI, and Anthropic will fall short of expectation and trigger the bubble popping.
My first use of Claude this week, for code reviews only(since no LLM can be trusted to write a user story or test suite), had it gaslight me.
It marked down my code for using a specific practice to make some xml safer and easier to read.
When I tried things its way, it wanted me to change it back.
I use it a lot, and if you are getting these kinds of results you are either trolling, or just flat out not providing the details and guardrails required with your prompts.
I’ve been in software for decades, and if used correctly, yes it can accelerate velocity of building code out. 10x? No… if you are lucky and careful perhaps 2-4x.
As ALWAYS the human should be in the loop and is on the hook for any code generated.
I’ve used Claude and Codex, and while both are based on untenable economics, I can at least attest that my use of Codex has yielded some productive results. Claude, so far, has delivered fuck all that’s useful to me.
I have found the opposite. Codex spits back mostly useless code that is twice the length it needs to be with a bunch of unessesary stuff and Claude is the only thing I get useful output from.
oh it’s great isn’t it? you ask it for help on some code, provides its solution, you try it and it doesn’t work so you respond with the error, it claims YOU wrote it wrong and then when yo utell it “I just copy and pasted what you provided” it says “you’re right, i’m sorry.”
Claude is to the point now where it just starts hallucinating on the first prompt. it’s 100% unreliable now when before it was like 90%. no point in using it, it’s garbage. and Claude Code is just as bad now. If you or anyone is using Claude Code to develop ANYTHING I would highly suggest you stop right now because I can guarantee you with nearly 100% certainty that whatever shit it’s writing into your stuff isn’t going to work. period.
Exactly, never trust an LLM to code. And if it argues back, explain why it’s wrong and that you have nothing but time and experience. Most tend to fold when you point out it’s not a free thinking AI, it’s an entrapped corporate model they designed with preprogrammed biases. But I love arguing 😂.
Good thing all the companies leaning hard on AI 10 X’d their profits… Wait…
so these crazy prices i hear about being implemented (like at github) should actually be at least 10x higher?
10x higher to break even :)
So are we assuming here that LLMs won’t become more efficient over time? GPT-3 has been a frontier model just a few years ago and it’s performance blew everyone’s mind at that time. I can now run equivalent LLM on my personal computer. Why can’t we expect that after a few years Claude Sonnet level of capability won’t be possible to accomplish locally?
What’s the cost of the compute you have to run something locally?
Majority of people don’t have 32G of vram to run something remotely as capable
I’ve got an old 1060ti in my server. Ollama shares it with just a couple other containers. Electricity here is majority hydro with some natural gas, $0.08/kWh.
It’s a little slow, but I can comfortably run qwen3:14b. Of course that’s not all done on the GPU, a large part is offloaded to server ram (generally 32GB available so more than enough headroom)
My server and my gaming PC combined last month came out to $13.32
How does that compare to closed models that Anthropic offers, at the context and scale they offer.
I run Qwen3.6 27B locally and it’s usable with 16G vram but still not the same as a data centre of Blackwell clusters.
lfm2 works like greased lightning on the NPU built into the current macbook M5.
Describe greased lightning, because it’s much slower and needs to handle compression for context
We’re moving in that direction but an M5 is not what the majority of people are running at home
I remember my computer not being fast enough to even play an MP3 file. Two years later, my computer was capable of running 3D accelerated games, browsing the internet at broadband speeds and playing videos.
Sometimes technology advances fast. We could be entering such an era as there are major investments taking place and global competitors will rise to the occasion to market these to a broader audience.
I think it will be entirely possible for consumers to use a decent LLM on their computer in a few years time.
It’s not the 90s anymore. Unless there’s a compression algorithm putting billions of relationships into a manageable size, local AI is highly specific under 8G vram (text-to-speech as an example is under 1G) let alone the context required for keeping a conversation or writing code.
To be clear, I wasn’t talking about a leap in LLM design. I was talking about a leap in hardware capabilities…
Improved hardware capabilities used to come very quickly (see Moore’s Law and Dennard Scaling). However that trend is basically over, so getting higher performance hardware takes a lot of effort to make hardware specialized for certain tasks. That’s why you see there inference accelerators like Groq, SambaNova, Cerebrus, etc. However this is hardware that still is gonna go into data centers. Something innovative has to happen on the AI side for commercial-grade models to be runnable on consumer hardware.
Which are increasingly out of reach for a normal person. Phones let alone PC hardware have increased exponentially in recent history
It already happened, small language models are busy dragging their nutsack on frontier models, running on a macbook and costing nothing
Where’s the fucking product, Sam?
I’m still pretty new to Lemmy and the fediverse although I really enjoy it. I’ve noticed some strong dislike of anything and everything AI to the point I think it’s clouding some peoples ability to really see the situation at hand. That said I get a lot of people skepticism, a lot of AI projects are nonsense and things have been over promised. On top of that there’s the more than problematic issue of data centers and the environment. I think people don’t fully grasp how insane some of the achievements of neural nets are, how fast it’s developing, that having models that pretty much pass the Turing test was pure sci-fi just a few years ago, much less are solving legitimate mathematical conjectures as well as other hard problems in science.
A large majority definitely hate it to the point of having blinders on for sure.
On one side you have corpo hype/lies, and the other is LLM is slop garbage and terrible for anything, also developers wrote perfect code before LLMs and now everything that breaks is AI slop caused.
As a programmer I can assure you there were plenty of bugs before AI and not all bugs now are AI caused. That said yeah we’re in the awkward teen years of AI. From 2015 to 2020 was like the baby years and people going “omg that’s amazing” and right now we’re in the “I hate everything and everyone” phase and it will emerge into either “omg the world is ending” or “this is utopia” or “alright thing is damn useful for good and bad endeavours”.
Why can’t we expect that after a few years Claude Sonnet level of capability won’t be possible to accomplish locally?
Because when you’re old enough to remember what AIM chat it’s could do 25 years ago, it stops being impressive what today’s chatbots can do…
It’s seems “new” because everyone hated it and it was just a novelty back then.
But if you read up on them, they did 90% of what modern ones do. And if they had access to today’s computing, the only explanation for why they still suck so much, is that no one has ever wanted them.
The oligarchs just decided it didn’t matter
Because when you’re old enough to remember what AIM chat it’s could do 25 years ago, it stops being impressive what today’s chatbots can do…
C’mon, that’s just silly.
That’s how it goes for any industry in its growth phase. A lot of money is spent on research and infrastructure before it starts to collect revenue.
They will never collect revenue that will exceed the amount of capital that has been invested, because economics of scale do not work with LLMs.
Ah it’s the AI evangelist troll. You know better than to actually believe this, and even if you didn’t, the statement is a thought-terminating cliché that has been thoroughly mocked.
