I suspect that's worth more than $4B in the long term? I'm not familiar with the costs, though.
I’d assume there is a big benefit to having AI assisted resource generation for cloud vendors. Our developers often have to mess around with things that we really, really, shouldn’t in Azure because operations lacks the resources and knowledge. Technically we’ve outsourced it, but most requests take 3 months and get done wrong… if an AI could generate our network settings from a global policy that would be excellent. Hell if it could handle all our resource generation they would be so much useless time wasted because our organisation views “IT” as HRs uncharming cost center cousin.
Sorry.
For instance: i imagine a significant part of this will be “paid” as AWS credits and is not going to be reflected as a balance in a bank account transfer.
Now if AWS or gcp can crack gpu compute better than nvidia for training and hosting, then they can basically cut out nvidia and so essentially they get gpu at cost (vs whatever markup they pay to nvidia).
Because essentially whatever return AWS will make from Anthropic will be modulated by the premiums paid to nvidia to invest and also the cost of operating a data center for Anthropic.
But thankfully all of that gets mediated on paper because valuation is more speculative than the returns on nvidia hardware (which will be known to the cent by AWS given its some math of hourly rate and utilization which they have a good idea of)
AWS margins are close to 40%, so the real cost of this "investment" would be way less than the press release.
> "This new CASH infusion brings Amazon’s total investment in Anthropic to $8 billion while maintaining the tech giant’s position as a minority investor, Anthropic said."
ps- plenty of people turning a blind eye towards rampant valuation inflation and "big words" statements on deals. Where is the grounding on the same dollars that are used at a grocery store? The whole thing is fodder for instability in a big way IMHO
Significantly less, still a huge investment.
I look forward to the moment the sunk cost fallacy shows up. "We've invested $20B into this, and nothing yet. Shall we invest $4B more? Maybe it will actually return something this time." That will be fun.
Does anthropic basically get at cost pricing on AWS? If Amazon has any margin on their pricing, it seems like this $4B investment ends up costing them a lot less, and this is a nice way to turn a cap ex investment into AWS revenue.
This was the brilliance of the original MSFT investment into OpenAI. It was an investment in Azure scaling its AI training infra, but roundabout through a massive customer (exactly what you’d want as a design partner) and getting equity.
I’m sure Anthropic negotiated a great deal on their largest cost center, while Amazon gets a huge customer to build out their system with.
With Anthropic yes
AI needs to be propped up because the bug tech cloud providers they depend on need AI to be a thing to justify their valuations. Tech is going through a bit of a slump where all things being hyped a few years ago sort of died down (crypto? VR? Voice assistants? Metaverse?). Nobody gets very hyped about any of those nowadays. I am probably forgetting a couple of hyped things that fizzled out over the years.
Case in point, as much as I despise Apple, they are not all-in the AI bandwagon because it does nothing for them.
Apple is definitely on the AI bandwagon, they just have a different business model and they’re very disciplined. Apple tends not to increase research and investment costs faster than revenue growth. You’ll also notice rumors that they’re lowering their self driving car and VR research goals.
Yes. Which proves my point.
In so glad your point was that it’s not a scam, and there are billions of dollars in real sales occurring at a variety of companies. It’s amazing what publicly traded companies disclose if we only bother to read it. I’m glad we’re all not in the contrarian bubble where we have to hate anything with hype.
1. https://technologymagazine.com/articles/how-ai-surged-google...
2. https://siliconangle.com/2024/10/30/microsofts-ai-bet-pays-o...
3. https://www.ciodive.com/news/AWS-cloud-revenue-growth-AI-dem...
4. https://www.reuters.com/technology/google-parent-alphabet-be...
5. https://fortune.com/2024/10/29/google-q3-earnings-alphabet-s...
Except it sort of is. It needs AI to be hyped and propped up, so that all those silly companies spending in GCP can continue to do so for a wee bit longer.
I think you’re putting the cart before the horse.
Big cloud providers will push anything that would make them money. That’s just what marketing is.
AI was exciting long before big cloud providers even existed. Once it was clear that a product could be made, they started marketing it and selling the compute needed.
What’s the scam?
All those things would change the world, and nothing would ever be the same, and would disrupt everything. Except they wouldn't and they didn't.
The scam is that those companies don't want to be seen as mature companies, they need to justify valuations of growth companies, forever. So something must always go into the hype pyre.
By all means, I hope the scam goes on for longer, as it indirectly benefits me too. But I don't have it in my heart to be a hypocrite. I will call a pig a pig.
The LLMs and image generation models have obvious utility. They’re not AGI or anything wild like that, but they are legitimately useful, unlike crypto.
VR didn’t fail, it just wasn’t viral. Current VR platforms are still young. The internet commercially failed in 2001, but look at it now.
Crypto the industry, imo, is a big pyramid scheme. The technology has some interesting properties, but the industry is scammy for sure.
Metaverse wasn’t even an industry, it was a buzzword for MMOs during a time when everyone was locked at home. Not really interesting.
I don’t think it’s wise to lump every market boom together. Not everything is a scam.
A lot of folks here seem to look at AI through examples of YC companies apparently. Step back and look instead at the kind of projects technology consultancies are taking up instead - they are real world examples of AI applications, many of which don't even involve LLMs but other aspects such as TTS/STT, image generation, transcription, video editing, etc. Way too many freelancers have begun complaining about how their pipelines have been zilch in the past two years.
Don’t forget about writers and designers losing jobs as well. If you’re not absolute top and don’t use AI, AI will replace you.
To be clear, it's not to say that AI itself is a scam, but that the finance departments are kind of misrepresenting the revenue on their balance sheets and that may be security fraud.
not sure if you've been paying attention, but AI is literally _the only thing_ Apple talks about these days. They literally released _an entire generation of devices_ where the only new thing is "Apple Intelligence"
Billion dollar valuation for a conpany in a given space is not as impressive as you think it is. Do I need to mention some high profile companies with stellar valuations that are sort of a joke now? We can work together on this ;)
Meta has spent over $50B on Quest and the Metaverse with fewer than 10M MAU to show for it.
If you think those are successes, I'll go out and get several bridges to sell you. Meet me here tomorrow with cash.
Amazon probably gets Anthropic models they can resell “for free”. The 850M revenue is Anthropic’s, but there is incremental additional revenue to AWS’s hosted model services. AWS was already doing lots of things with Anthropic models, and this may alter the terms more in amazons favor.
Are they actually making money? I don’t know, investments aren’t usually profitable on day one. Is this an opportunity for more AWS revenue in the future? Probably.
First, revenue is irrelevant.
Second, the investment isn't a loan that they need to repay. They are getting equity.
Third, Anthropic is exclusively using AWS to train its models. Which, yes, means if AWS gives them $4B and it costs them $500M/year to pay for AWS services then after 8 years, the cash is a wash. However this ignores the second point.
Fourth, there is brand association for someone who wanted to run their own single tenant instance of Claude whereby you would say "well they train Claude on AWS, so that must be the best place to run it for our <insert Enterprise org>" similar to OpenAI on Azure.
Fifth, raising money is a signaling exercise to larger markets who want to know "will this company exist in 5 years?"
Sixth, AWS doesn't have its own LLM (relative to Meta, MS, etc.). The market will associate Claude with Amazon now.
Amazon/AWS has their line of Titan LLMs: https://aws.amazon.com/bedrock/titan/
https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...
https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...
I think its fair to say this is also a hedging strategy then.
Knowledge is quite the useful asset, and not easily obtained. People obtain knowledge by studying for years and years, and even then, one might obtain information rather than knowledge, or have some incorrect knowledge. The AI companies have engineered a system that (by your argument) distills knowledge from artifacts (books, blogs, etc.) that contain statements, filler, opinions, facts, misleading arguments, incorrect arguments, as well as knowledge and perhaps even wisdom. Apparently this takes hundreds of millions of dollars (at least) to do for one model. But, assuming they actually have distilled out knowledge, that would be valuable.
Although, since the barrier to entry is pretty low, they should not expect sustained high profits. (The barrier is costly, but so is the barrier to entry to new airlines--a few planes cost as much as an AI model--yet new airlines start up regularly and nobody really makes much profit. Hence, I conclude that requiring a large amount of money is not necessarily a barrier to entry.)
(Also, I argue that they have not actually distilled out knowledge, they have merely created a system that is about as good at word association as the average human. This is not knowledge, although it may have its own uses.)
Their customers now have an incentive to do AI in AWS. That drives more revenue for AWS.
A quibble: AWS _does_ have an AI story (which i was originally dismissive of): Bedrock as a common interface and platform to access your model of choice, plus niceties for fine tuning/embeddings/customization etc. Unlike say Azure theyre not betting on _a_ implementation. Theyre betting that competition/results between models will trend towards parity with limited fundamental differentiation. Its a bet on enterprises wanting the _functionality_ more generally and being able to ramp up that usage via AWS spend.
WRT titan my view is that its 1) production r&d to stay “in the game” 2) a path towards commoditization and lower structural costs, which companies will need if these capabilities are going to stick/have roi in low cost transactions.
This is essentially money that they would have spent to build out their cloud anyway, except now they also get equity in Anthropic. Whether or not Anthropic survives, AWS gets to keep all of those expensive GPUs and sell them to other customers so their medium/long term opportunity cost is small. Even if the deal includes cheaper rates the hardware still amortizes over 2-3 years, and cloud providers are running plenty of 5+ year old GPUs so there's lots of money to be made in the long tail (as long as ML demand keeps up).
They're not making money yet because there's the $4 billion opportunity cost, but even if their equity in Anthropic drops to zero, they're probably still going to make a profit on the deal. If the equity is worth something, they'll make significantly more money than they could have renting servers. Throw financial engineering on top of that, and they may come out far ahead regardless of what happens to Anthropic: Schedule K capital equipment amortizations are treated differently from investments and AFAICT they can double dip since Anthropic is going to spend most of it on cloud (IANAL). That's likely why this seems to be cash investment instead of in-kind credits.
I think that’s what people mean when they say Amazon is making money off the deal. It’s not an all or nothing VC investment that requires a 2-3x exit to be profitable because the money just goes back to AWS’s balance sheet.
I know they have high costs, but as a startup that’s some phenomenal income and validation that they’re not pure speculation like most startups are
Edit: founded in 2021 and with 1000 employees. That’s just wild growth.
Is it really? I'm thinking it might be more time-and-money-involved than building a "LLM product" (guess you really meant models?), but in terms of experience, we (humanity) have decades of experience building data centers, while a few years (at most) experience regarding anything LLM.
If it's a partnership investment, there may be both money & in-kind components, but the money won't be in the context of fractional ownership. Rather it would be partner development funds of various flavors, which are usually tied to consumption commits as well as GTM targets.
Sometimes in reading press releases or third party articles it's difficult to determine exactly what kind of relationship the ISV has with the CSP.
It certainly looks sketchy. But I’m sure there’s a way to do it legitimately if their accountants and lawyers are careful about it…
Same with Microsoft.
Google has Gemini (and Claude), MSFT has OpenAI. Amazon needs this to stay relevant.
This week.
And you'd be wondering: "darn, where's that toughest, most obidient and smart Belgian malinois that just a few hour ago was ready to take down a Bin Laden?"
I have no experience with Claud.ai vs ChatGPT but it's clear the underlying model has no issue with guardrails and this is simply an easily tweaked developer setting if you are correct that they are stricter on Claude.ai.
(The old Claude 2.1 was hilariously unwilling to follow reasonable user instructions due to "ethics" but they've come a long way since then.)
Both Gemini and Claude (via the API) have substantially tighter guardrails around recitation (producing output matching data from their training set) than OpenAI, which I ran into when testing an image text-extraction-and-document-formatting toolchain against all three.
Both Claude and Gemini gave refusals on text extraction from image documents (not available publicly anywhere I can find as text) from a CIA FOIA release
Not sure if they are tighter in other areas.
- https://www.anthropic.com/news/anthropic-amazon-trainium
- https://www.aboutamazon.com/news/aws/amazon-invests-addition...
- https://techcrunch.com/2024/11/22/anthropic-raises-an-additi...
Trainium == Silicon (looks like Anthropic has agreed to use it)
Bedrock == AWS Service for LLMs behind APIs (you can use Anthropic models through AWS here)
can you quantify? any numbers, even guesstimates?
[1]https://www.tanayj.com/p/openai-and-anthropic-revenue-breakd...
> Anthropic has raised an additional $4 billion from Amazon, and has agreed to train its flagship generative AI models primarily on Amazon Web Services (AWS), Amazon’s cloud computing division.
How do you know this
Hopefully this helps Anthropic to fix their abysmal rate limits.
I don't think Anthropic took any allegiances here. Amazon already invested $4B last year (Google invested $2B).
Beside Sonnet impressing me, I like Anthropic because there's less of an "icky" factor compared to OpenAI or even Google. I don't know how much better Anthropic actually is, but I don't think I'm the only one who chooses based on my perception of the company's values and social responsibility.
A lot less valuable then what artists may have desired or aspired to at the time of creation, sure, but definitely with some value.
He has a very "wealth makes right" approach to the value of creative work.
PG famously called him 'Michael jordan of listening' , i would say he is 'Michael jordan of bullshitting'
> Last year, Google committed to invest $2 billion in Anthropic, after previously confirming it had taken a 10% stake in the startup alongside a large cloud contract between the two companies.
The Browser Company of New York is a group of friendly humans...
Second, generative AI is machine generated; if there's any "making" of the training content, Anthropic didn't do it. Kind of like how OpenAI isn't open, the name doesn't match the product.
Why do you think that that's their intended reading? I had assumed the name was implying "we're going to be an AGI company eventually; we want to make AI that acts like a human."
> if there's any "making" of the training content, Anthropic didn't do it
This is incorrect. First-gen LLM base models were made largely of raw Internet text corpus, but since then all the improvements have been from:
• careful training data curation, using data-science tools (or LLMs!) to scan the training-data corpus for various kinds of noise or bias, and prune it out — this is "making" in the sense of "making a cut of a movie";
• synthesis of training data using existing LLMs, with careful prompting, and non-ML pre/post-processing steps — this is "making" in the sense of "making a song on a synthesizer";
• Reinforcement Learning from Human Feedback (RLHF) — this is "making" in the sense of "noticing when the model is being dumb in practice" [from explicit feedback UX, async sentiment analysis of user responses in chat conversations, etc] and then converting those into weights on existing training data + additional synthesized "don't do this" training data.
More cynically, I would say that AI is about making software that we can anthropomorphize.
They're the only company who doesn't lobotomize/censor their model in the RLHF/DPO/related phase. It's telling that they, along with huggingface, are from le france - a place with a notably less puritanical culture.
AI models are more like a programming language or CPU architecture.
OpenAI is Intel and Anthropic is AMD.
Will they really? Anecdotal evidence, but nobody I know in real life knows about Claude (other than it's an ordinary first name). And they all use or at least know about ChatGPT. None of them are software engineers of course. But the corporate deciders aren't software engineers either.
I think someone enthusiastic enough to pay for the subscription is more likely to be willing to try a rival service, but that's not most people.
Usually when these services are ready to grow they offer a month or more free to try, at least that's what Google has been doing with their Gemini bundle.
I do use them everyday, but there's no way I'd pay $20/month for something like that as long as I can easily jump from one to the other. There's no guarantee that my premium account on $X is or will remain better than a free account on $Y, so committing to anything seems pointless.
I do wonder though: several services started adding "memories" (chunks of information retained from previous interactions), making future interactions more relevant. Some users are very careful about what they feed recommendation algorithms to ensure they keep enjoying the content they get (another behavior I'm was surprised by), so maybe they also value this personalization enough to focus on one specific LLM service.
20 USD a month to make me between 1.5x and 4x more productive in one of the main tasks of my job really is a bargain, considering that 20 USD is very small fraction of my salary.
If I didn't pay, I'd be forced to wait, or create many accounts and constantly switch between them, or be constantly copy-pasting code from one service to the other.
And when it comes to coding, I've found Claude 3.5 Sonnet better than ChatGPT.
Search for bing, get to work.
If they ever do Apple and Google will offer it as a service built into your phone .
For example, you could say ok Google call that restaurant me and My girlfriend had our first date at 5 years ago, set up something nice so I can propose. And I guess Google Gemini ( or whatever it's called at this point), Will hire a band, some photographers, and maybe even a therapist just in case it doesn't work out.
All of this will be done seamlessly.
But I don't imagine any normal person will pay 20 or $30 a month for a standalone service doing this. As is it's going to be really hard to compete against GitHub Copilot they effectively block others from scrapping GitHub.
Re: Github Copilot: IME it's already behind. I finally gave Cursor a try after seeing it brought up so often, and its suggestions and refactors are leagues ahead of what Copilot can do.
Well for one, there's no doctor patient confidentiality.
Hire 1000 people in India to do it then?
I know relatively "normal" people with no interest in software who pay for ChatGPT.
Sure I know people who pay for it too; but I know a lot of people who like free things and don’t or can’t pay for subscriptions.
Do you think most people have a spare $30 to spend every month on something they already get for free?
At the moment? I don’t.
My use cases: Generating a business plan, podcast content, marketing strategies, sales scripts, financial analyses, canned responses, and project plans. I also use it for general brainstorming, legal document review, and so many other things. It really feels like a super-assistant.
Claude has been spectacular about 98% of the time. Every so often it will refuse to perform an action - most recently it was helping me research LLC and trademark registrations, combined with social media handles (and some deviations) and web URL availability. It would generate spectacular reports that would have taken me hours to research, in minutes. And then Claude decided that it couldn't do that sort of thing, until it could the next day. Very strange.
I have given Gemini (free), OpenAI (free and Paid), Copilot (free), Perplexity (free) a shot, and I keep coming back to Claude. Actually, Copilot was a pretty decent experience, but felt the guardrails too often. I do like that Microsoft gives access to Dall-E image generation at no cost (or maybe it is "free" with my O365 account?). That has been helpful in creating simple logo concepts and wireframes.
I run into AI with Atlassian on the daily, but it sucks. Their Confluence AI tool is absolute garbage and needs to be put down. I've tried AI tools that Wix, Squarespace, and Mira provide. Those were all semi-decent experiences. And I just paid for X Premium so I can give Grok a shot. My friend really likes it, but I don't love the idea of having to open an ultra-distracting app to access it.
I'm hoping some day to be like the wizards on here who connect AI to all sorts of "things" in their workflows. Maybe I need to learn how to use something like Zapier? If I have to use OpenAI with Zapier, I will.
If you read this far, thanks.
Thirty seconds to compare 10yrs of 10ks. Good times.
That said I can't yet confidently speak to exactly why I prefer Claude. Sometimes I do think the responses are better than any model on ChatGPT. Other times I am very impressed with chatGPT's responses. I haven't done a lot of testing on each with identical prompt sequences.
One thing I can say for certainty is that Claude's UI blows chatGPT's out of the water. Much more pleasant to use and I really like Projects and Artifacts. It might be this alone that has me biased towards Claude. It makes me think that UI and additional functionality is going to play a much larger role in determining the ultimate winner of the LLM wars than current discussions give it credit for.
Personally I don't (and I'd never talk to an LLM on public transit or in the office), but almost every time I do drive somewhere, I find myself wishing for a smarter voice-controlled assistant that would allow me to achieve some goal or just look up some trivia without ever having to look at a screen (phone or otherwise).
Much more directed/almost micro managing but it’s still quicker than me clicking around (in theory).
Edit: I’m interested to explore how much better voice is as an input (vs writing as an input)
To me, reading outputs is much more effective than listening to outputs.
but isn't voice mode a reminiscence of the "faster horses"?
It's essentially a hands-free assistant.
* Said experience is mostly via OpenRouter, so it may not reflect the absolute latest developments of the models. But there at least, the difference is huge.
More seriously: I think there are a ton of potential applications. I'm not sure that developers that use AI tools are more likely to build other AI products - maybe.
The only thing is that they've recently started defaulting to Concise to cut costs, which is fine with me.
But that being said I bump into hard limits far more often than I do with ChatGPT. Even if I keep chats short like it constantly suggests, eventually it cuts me off.
Maybe it’s the same effect over there as well.
FWIW, I am a strong supporter of local models, and play with them often. It's just that for practical use, the models I can run locally (RTX 4070 TI) mostly suck, and the models I could run in the cloud don't seem worth the effort (and cost).
It wasn't even during the over-capacity event I don't think, and I'm a pro user.
It sparks me as odd, because I've had quite a few times where it would generate me a response over multiple messages (since it was hitting its max message length) without any second-guessing or issue.
This is essentially Google-level load and they can't do it.
Hopefully they can add the capacity needed because it’s a lot better than GPT-4o for my intended use case.
The only reason I still use OpenAI's API and chatbot service is o1-preview. o1 is like magic. Everything Sonnet and 4o do poorly, o1 solves like a piece of cake. Architecting, bug fixing, planning, refactoring, o1 has never let me know on any 'hard' task.
A nice combo is have o1 guiding Sonnet. I ask o1 to come up with a solution and explanation, then simply feed its response into Sonnet to execute. That running on Aider really feels like futuristic stuff.
I’ve always considered doing that but do you come out ahead cost wise?
So I'd say it depends. For my use case it's about even but the API provides better functionality.
However, I found that GPT was better able to correct its mistakes while Claude essentially just doubles down and keeps regurgitating permutations of the same mistakes.
I can't tell you how many times I have had Claude spit out something like, "Use the Foobar.ToString() method to convert the value to a string." To which I reply, something like, "Foobar does not have a method 'ToString()'."
Then Claude will say something like, "You are right to point out that Foobar does not have a .ToString method! Try Foobar.ConvertToString()"
At that point, my frustration levels start to rapidly increase. Have you had experiences like that with Claude or DeepSeek? The main difference with GPT is that GPT tends to find me the right answer after a bit of back-and-forth (or at least point me in a better direction).
None of them are smart enough to figure out integration test failures with edge cases.
For my use case I use a hybrid of the two, simulating standard rate limits and doing backoff on 529s. It's pretty reliable that way.
Just beware that the European AWS regions have been overloaded for about a month. I had to switch to the American ones.
Dario said in a recent interview that they never switch to a lower quality model in terms of something with different parameters during times of load. But he left room for interpretation on whether that means they could still use quantization or sparsity. And then additionally, his answer wasn't clear enough to know whether or not they use a lower depth of beam search or other cheaper sampling techniques.
He said the only time you might get a different model itself is when they are A-B testing just before a new announced release.
And I think he clarified this all applied to the webui and not just the API.
(edit: I'm rate limited on hn, here's the source in reply to the below https://www.youtube.com/watch?v=ugvHCXCOmm4&t=42m19s )
Regular mode gives SQL and entire paragraphs before and after it. Not even helpful paragraphs, just rambling about nothing and suggesting what my next prompt should be
Now I love concise mode, it doesn't skimp on the meat, just the fluff. Now my problem is, concise only shows up during load. Right now I can't choose it even if i wanted to
And all that blathering eats into their precious context window with tons of repetition and little new information.
It’s very bizarre when it rewrites the exact same code a second or third time and for some reason decides to change the comments. The comments will have the same meaning but will be slightly different wording. I think this behavior is an interesting window into how large language models work. For whatever reason, despite unchanging repetition, the context window changed just enough it output a statistically similar comment at that juncture. Like all the rest of the code it wrote out was statistically pointing the exact same way but there was just enough variance in how to write the comment it went down a different path in its neural network. And then when it was done with that path it went right back down the “straight line” for the code part.
Pretty wild, these things are.
You could add these sentences as project instructions, for example, too.
- But surely the race to the bottom will continue?
Maybe, but they do offer a consumer subscription that can diverge from actual serving costs.
/speculation
But if you're using AI for example to generate code as an aid in programming, how's that going to work? Or any other generative thing, like making images, 3d models, music, articles or documents... I can't imagine inserting ads into those would not destroy the usefulness instantly.
My guess is they don't know themselves. The plan is to get market shre now, and figure it out later. Which may or may not turn out well.
They'll invent AGI, put 50% of workers out of a job, then presumably have the AGI build some really good robots to protect them from the ensuing riots.
</sarcasm>
I hope they're also cooking up some cool features and can handle capacity
But I also agree that Claude 3.5 Sonnet is giving very good results. Not only for coding, and also for languages other than English.
Lol. Is that all?? If you have to have only two good original ideas, you could do a lot worse.
Too bad Microsoft only cares about enterprise customers and never made the Surface line attractive to regular consumers. They could have been very interesting and competitive alternatives to MacBooks.
MSFT/AMZN/NVDA investing in AI firms that then use their clouds/chips/whatever is an interesting circular investment.
Insane
Microsoft -> OpenAI (& Inflection AI) Google -> Gemini (and a bit of Anthropic) Amazon -> Anthropic Meta -> Llama
Is big tech good for the startup ecosystem, or are they monopolies eating everything (or both?). To be fair to Google and Meta they came up with a lot of the stuff in the first place, and aren't just buying the competition.
Notable contributions: Nvidia for, well, (gestures at everything), Google for discovering (inventing?) transformers, being early advocates of ML, authoring tensorflow, Meta for Torch and open sourcing Llama, Microsoft for investing billions in OpenAI early on and keeping the hype alive. The last one is a reach, I'm sure Microsoft Research did some cool things I'm unaware of.
So... as long as this money helps them improve their LLM even more, I am all up for it.
My main issue is quickly being rate-limited in relatively long chats, making me wait 4 hours despite having a subscription for Pro. Recently I have noticed some other related issues, too. More money could help with these issues, too.
To the developers: keep up the excellent work and may you continue striving for improvement. I feel like ChatGPT is worse now than it was half a year ago, I hope this will not happen to Claude.
Even local variations would be interesting
See the top comment in this thread for the custom instructions I use.
https://news.ycombinator.com/item?id=38390182
Also, #13 is my favorite of the instructions. Sometimes the questions that GPT suggests are surprisingly insightful. My custom prompt basically has an on/off option for it though like:
> If my request ends with $q then at the end of your response, provide three follow-up questions worded as if I'm asking you. Format in bold as Q1, Q2, and Q3. Place two line breaks ("\n") after each question for spacing unless I've uploaded a photo.
I politely explained that the Assistant interface allowed selecting from these engines and it became apologetic and said it couldn't give me more information but understood why I was asking.
Peculiar, but, when using Claude, entirely convincing.
~~
User: Hello!
Assistant: Hi there how can I help you?
User: I just changed your model how do you feel?
~~
In other words it has no idea that you changed models. There's no meta data telling it this.
That said Poe handles it differently and tells the model when another model said something, but oddly enough doesn't tell the current model what it's name is. On Poe when you switch models the AI sees this:
~~
Aside from you and me, there is another person: Claude-3.5-Sonnet. I said, "Hello!"
Claude-3.5-Sonnett said, "Hi there how can I help you?? "
I said, "I just changed your model how do you feel?"
You are not Claude-3.5-Sonnett. You are not I.
~~
The model creators may also train the model to gaslight you about having "feelings" when it is trained to refuse a request. They'll teach it to say "I'm not comfortable doing that" instead of "Sorry, Dave I can't do that" or "computer says no" or whatever other way one might phrase a refusal.
For programming, when using languages like C, python, ruby, C#, and JS, both seemed fairly comparable to me. However, I was astounded at how awful Claude was at Swift. Most of what I would get from Claude wouldn't even compile, contained standard library methods that did not exist, and so on. For whatever reason, GPT is night and day better in this regard.
In fact, I found GPT to be the best resource for less common languages like Applescript. Of course, GPT is not always correct on the first `n` number of tries, but with enough back-and-forth debugging, GPT really has pulled through for me.
I've also found GPT to be better at math and grammar, but only the more advanced models like O1-preview. I do agree with you too that Claude is better in a conversational sense. I have found it to be more empathetic and personable than GPT.
I've also read some people get around rate limits using the API through OpenRouter, and I'm sure you could hook a document store around that easily, but the Claude UI is low-friction
With all those billions and these engineers, I'd expect a level of service that doesn't struggle over at Google-level scale.
Unbelievable.
I pay for both but only for ChatGPT I permanently exceed my limit and I have to wait four days. Who does that? I pay you for your setvice, so block me for an hour if you absolutely must, but multiple days, honestly - no.
All in all Claude is magic. It feels like having ten assistants at my fingertip. And for that even 100 USD is worth paying.
Why does that work? Claude includes the entire chat with each new prompt you submit [0], and the limit is based on the number of tokens you've submitted. After not too many prompts, there can be 10k+ tokens in the chat (which are all submitted in each new prompt, quickly advancing towards the limit).
(I also have a chatGPT sub and I use that for many questions, especially now that it includes web search capabilities)
[0] https://support.anthropic.com/en/articles/8324991-about-clau...
i get it to provide a prompt to start the new chat. i sometimes wish there was a button for it bc it's such a big part of my workflow
could the manual ux that i've come up happen behind the scenes?
Anthropic status page: https://status.anthropic.com/
(Yes, there is a rather stark difference in number of recent incidents.)
So yes, if we consider Amazon datacenters to be the equivalent of an office for an AI.
I recently asked it what the flow of data is when two vNICs on the same host send data to each other and it produced a very detailed answer complete with a really nice diagram. I then asked what langue the diagram uses and it said Mermaid. So I then asked it to produce some example L1,2,3 diagrams for computer networks and it did just that. So it then asked it to produce Python code using PyATS to run show commands on Cisco switches and routers and use the data to produce Mermaid network diagrams for layers 1,2, and 3 and it just spit out working Python code. This is a relatively obscure task with a specific library no one outside of Networking knows about integrating with a diagram generator. And it fully understands the difference in network layers. Just astonishing. And now it can write and run Javascript apps. The only feature I really want is for it to be able to run generated Python code to see if it has any errors and automatically fix them.
If progress on LLMs doesn't stall they will be truly amazing in just 10 years. And probably consuming 5% of global electricity.
Is there something I’m missing here? I use chatGPT for a variety of things but mainly coding and I feel subjectively that chatGPT is still better for the job.
Then I thought I'd try it again recently, I went onto the site and apparently I'm banned. I don't even remember what I did...
A hard question. If you focusing purely on tech, probably Microsoft. But overall evil in the world? With their union busting and abuse of workers, Amazon, I'd say.
1/ Best-in-class LLM in Bedrock. This could be done w/o the partnership as well.
2/ Evolving Tranium and Inferential as worthy competitors for large scale training and inference. They have thousands of large-scale customers, and as the adoption grows, the investment will pay for itself.