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Amazon spends another $2.7B on Anthropic
- chowchowchow 3y agoIn other words Amazon gives Anthropic 2.7B of AWS credits!
- turnsout 3y agoMaybe, but there are very real costs associated with running those servers, so it’s not totally imaginary money.
- chowchowchow 3y agoOf course. I didn't say AWS credits were cost-free to Amazon.
- nicce 3y agoI wonder what are the real costs when compared to stated "gift card". Probably the biggest secret AWS has. Since there is air in the numbers, I wonder how much it increases artificial value of the relevant parties. Is Amazon getting more leverage over the company with less real money?
- CuriouslyC 3y agoYes, but Amazon also gets a partner that helps guide their buildout of machine learning features in AWS, and endless dogfooding.
- turnsout 3y agoOh absolutely—there was a meeting of the minds, and both sides are getting value.
- deleted 3y ago[deleted]
- bagels 3y agoIn exchange for equity?
- mark_l_watson 3y agoI am glad to see Anthropic get more financial support, or as another person here said, AWS credits. Hopefully not so far topic as to be uninteresting: I am very happy with the state of LLM tech. There are open weight models that are very capable on modest home computers (like mistral-7B-instruct-ver-2), and beefed up home computers (I can barely run mixtral-8-7B, but it runs well enough). And there are many open weight specialty models that I find useful. For commercial offerings, OpenAI, Anthropic, Mistral, etc. have affordable APIs, and there are a bunch of companies like Scaleway for running open weight models too large or inconvenient to run at home. At the application layer, I like the new emphasis on agents, open source apps that use local models, and highly refined products like Perplexity, OpenAI app/web, Google’s workplace integrations, and all the stuff that Microsoft is doing. There are problems like the environmental costs of training and running models, but things will get more efficient and people will realize the utility of smaller special purpose models.
- brcmthrowaway 3y agoWhats the best model that can work on a Mac Studio?
- akmittal 3y agoLlama or mistral both are good. Running using ollama
- LeoPanthera 3y agoRAM is the limiting factor, but Mixtral 8x7 is probably the state of the art for self-hosted LLMs right now. If you are low on RAM you can run a quantized version, at the expense of reduced quality.
- resource_waste 3y agoIMO Berkley Sterling which is a fine-tuned llama. I'm not really sure what you are expecting to do with CPU. You might be able to get some <400 token responses and have fun, but you aren't going to be doing 2000 token encyclopedia style responses unless you are going to wait ~20 minutes for a response. Alternatively, you can get like a $800 gaming laptop that has 8gb vram or use something like vastai where you can get 12gb vram for $0.10 an hour.
- VirusNewbie 3y agoIt's interesting that Anthropic also has gotten quite a large investment from Google, and uses Google for its training. I wonder if this is a good move from the standpoint of playing the two major cloud providers off each other. Here it says they're going to use Amazon's chips for training and inference, but...Amazon doesn't have its own chips yet??? So I wonder how these deals are structured? Does amazon have to supply a custom AI chip that beats some benchmark? Would be pretty great if that part of the deal to use their chips involved beating TPUs, and if somehow Google's TPUs continue improving, they don't have to switch. All in all, I'm very impressed with Anthropic as a company. I think they're the new OpenAI.
- Bellyache5 3y agoNo idea if it's any good or not, but Amazon has their own "Inferentia" chips. https://aws.amazon.com/machine-learning/inferentia/ https://aws.amazon.com/machine-learning/inferentia/
- deleted 3y ago[deleted]
- jedberg 3y ago> Here it says they're going to use Amazon's chips for training and inference, but...Amazon doesn't have its own chips yet??? Amazon has had its own chips for years. They bought a company called Annapurna Labs in 2015, who made the chips[0]. https://aws.amazon.com/machine-learning/inferentia/ https://aws.amazon.com/machine-learning/inferentia/ https://aws.amazon.com/machine-learning/trainium/ https://aws.amazon.com/machine-learning/trainium/ [0] https://en.wikipedia.org/wiki/Annapurna_Labs https://en.wikipedia.org/wiki/Annapurna_Labs
- deleted 3y ago[deleted]
- VirusNewbie 3y agoAh, thanks for the correction, I wasn't aware. I wonder how it compares to TPU and I really would love to know if the decision to switch is made by the engineers or the finance folks simply due to the credits.
- KuriousCat 3y agoSometimes I wonder how companies like Amazon arrive at those numbers? Why 2.7 billion? Why not 1 billion or 3 billion? I am very curious in this case why that number is the sweet spot that ensures optimal returns.
- eyjafjallajokul 3y agoFrom the article, > The tech and cloud giant said Wednesday it would spend another $2.75 billion backing Anthropic, adding to its initial $1.25 billion check.
- saganus 3y agoThat doesn't explain anything though.
- scottyah 3y agoIt does give it the round number that OP was asking about.
- deleted 3y ago[deleted]
- saganus 3y agoRight, but still doesn't explain why 4 billions instead of 1 or 10. I think the OP was expecting something like "we value the company at 8 billions because reasons, so we want to fund for half of it" or something like that.
- deleted 3y ago[deleted]
- lnxg33k1 3y agoMaybe they are not calculating how much they want to spend but how much percentage they want at stake in a certain entity and then negotiate how much its worth?
- rubiquity 3y agoInvestments as gift cards that can only be used at the investor is essentially quid-pro-quo, anti-competitive, and should be illegal. That goes for AWS, Microsoft, and anyone else doing it.
- lotsofpulp 3y agoWho is being harmed? Were the owners of Anthropic unable to refuse the deal?
- chowchowchow 3y agoIt does create a misrepresentation of the valuation as AWS is able to represent the investment as the retail price ($2.7B) while actually they're only parting with the COGS for the AWS services (<$2.7B).
- dmix 3y ago> It does create a misrepresentation of the valuation as AWS is able to represent the investment as the retail price ($2.7B) Represent it where? In 3rd party journalists who cover it? Or on public market financial statements?
- nicce 3y agoAssuming that Amazon got some shares based on the stated value, and if the value is misleading, they got more shares than they should. It includes the profit-value, defined by Amazon itself, that Amazon is getting from running their own servers. Not the real, actual costs.
- chowchowchow 3y agoTaxes seem like one possibility for this to be taken advantage of. Invest $X, get $Y kicked back, but calculate losses or gains against the higher $X basis.
- kolbe 3y ago
- nextworddev 3y agoThis is a typical Amazon deal. Similar to how they “invested” into Plug Power, etc.
- ChrisArchitect 3y agoOfficial Amazon release: https://www.aboutamazon.com/news/company-news/amazon-anthropic-ai-investment https://www.aboutamazon.com/news/company-news/amazon-anthrop...
- encoderer 3y agoimagine a founder doing a secondary and getting aws credits.
- wantsanagent 3y agoProfessionally I really appreciate this partnership. Its much easier for me to build on top of fully hosted AWS services than it is for me to build on 3rd party non-aws services. Over the last year quality of available models have gone up while prices have come down significantly. Getting an email saying "Hey you can tell your boss that you're going to save hundreds of thousands on AWS costs" is great and it's been happening with surprising regularity from Bedrock.
- HarHarVeryFunny 3y agoOn the related subject of the vast amounts of money these LLM companies are both receiving as investments, and spending on training, there was an interesting concept mentioned by Dwarkesh in a teaser for an upcoming interview... The current trajectory is that the size of these models, and number of FLOPs needed to train them, is growing much faster than the cost of compute is coming down. GPT-4 apparently cost around $100M to train, and it seems people are expecting that may quickly rise to $1B, and maybe then $10B for upcoming generations. I assume these numbers are based on projected 10x per generation increase in tokens processed during training. So, with these kind of numbers, apparently there's a thought floating around that if they don't achieve AGI in next few model generations then that may stall AGI progress until different more efficient methods are developed. Private companies may be willing to spend $1B, then $10B to train more powerful models, but are likely to balk at $100B or above unless there's an obvious payback on the horizon. Any sunken training costs may essentially be cumulative from one generation to the next, unless each generation can pay for itself. If we assume a new model every year, then can a $10B model pay for itself in a year before being replaced the next year by an even more expensive one?
- selfselfgot 3y agoIs AGI anything more than a marketing term. It’s very “tech” to hold this out as a promise to investors. Doubt it will happen goalposts will be moved.
- TaylorAlexander 3y agoI mean maybe it’s more than a marketing term, but like Uber’s self driving car plan it can absolutely be a carrot for investors that is never achieved.
- benreesman 3y agoOpinions differ, and I can offer only hearsay on the cost of training GPT-4 (which I’ve heard at something like 340MM). The next generation is looking to cost more like a few trillion, which I know sounds extravagant. So don’t take it from me, take it from Trevor Blackwell on HN: https://news.ycombinator.com/item?id=39666368 https://news.ycombinator.com/item?id=39666368 Yes, these things cost too much to make economic sense absent some non-disclosed advantage to a small group of people deploying them with zero oversight.
- lijok 3y agoThis is a dangerous bubble in the making due to the terms in these deals. Early 2000s all over again. This should be illegal.
- nickpsecurity 3y agoI think one of the biggest mistakes is that they just mostly put money into compute. They’d be better off investing in high-quality, human-generated data that covers every metric they’re interested in. That’s both knowledge and domain-specific application. Id go further for multi-modal to license examples of everything a human sees, hears, and does in their life up to college age. Keep mixing that with new architectures to match human performance. Then, lots of spin-offs of textual and multi-modal models with domain-specific, fine tuning like CompSci people are doing. If they did that, we’d see even larger leaps in their performance. They could also build custom models on this stuff while charging customers for the data mixes they use. Those sales would incentivize more high-quality data to be created.
- erikson 3y agoAin’t gonna fix sagemaker either
- breck 3y agoI'm not surprised. Nowadays I keep 2 LLM tabs open: ChatGPT 4 and Claude 3 Opus. Claude 3 Opus is noticeably smarter than ChatGPT 4. Like, it's not a tossup, it's a clear winner. I expect the lead to change again soon, but Anthropic is doing a great job.
- anonylizard 3y agoOpus is big, because it gave people the first taste of what post-GPT-4 capabilities look like. For one, its a major improvement in coding, and a gigantic leap in creative writing abilities, going from high schooler writing to professional prose. Like all the chatbot addicts have once again swapped their addiction to Opus now. Clearly LLMs are not even close to peaking yet. Its also sad how awful Gemini is. Gemini 1.0 Ultra is clearly not noticeably better than GPT-4 turbo despite a year's head start. Google is therefore not even going to release Gemini 1.0 Ultra API, instead going back to the oven to train 1.5 Ultra.
- tiffanyh 3y agoIs Amazon actually cutting a check for $2.7B … or are they simply giving Anthropic AWS credits that AWS will then get to recognize as revenue?