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Anthropic expands partnership with Google and Broadcom for next-gen compute
- mikert89 6mo agoThere's no limit to the algorithms. People dont understand yet. They can learn the whole universe with a big enough compute cluster. We built a generalizable learning machine
- totaa 6mo agothe question is will we experience resource constraints before we get there? what if the step up to post-scarcity is gated by a compute level just out of our reach?
- teaearlgraycold 6mo agoNot sure if this is satire. Edit: What we have built is a natural language interface to existing, textually recorded, information. Transformers cannot learn the whole universe because the universe has not yet been recorded into text.
- supliminal 6mo agoIt’s more than likely not.
- erelong 6mo agoPoe's (c)law?
- bryogenic 6mo agoPoe’s (C)law: The more absurd AI-generated content becomes, the more likely people are to believe it is real.
- 0x3f 6mo agoBased on a glance at their other comments: not satire.
- alfalfasprout 6mo ago100% agreed. Sadly, lots of people out there with the "trust me bro, just need more compute". Hopefully we don't consume all the planet's resources trying.
- xvector 6mo agoI reevaluated my priors long ago when I saw that scaling laws show no sign of stopping, no sign of plateau. Strangely some people on HN seem to desperately cling to the notion that it's all going to come to a halt. This is unscientific. What evidence do you have - any evidence - that the scaling laws are due to come to an end?
- deleted 6mo ago[deleted]
- rishabhaiover 6mo agoI suspect it's not that people do not see the progress, they fail to fully trust laws not truly backed by physics like the transistor laws. We empirically see that scaling works and continue to work.
- esafak 6mo agohttps://en.wikipedia.org/wiki/Neural_scaling_law https://en.wikipedia.org/wiki/Neural_scaling_law
- 0x3f 6mo agoAll the curves have been levelling off as expected. Not really sure what you're talking about.
- solenoid0937 6mo agoThey have not, every successful pre-train as of late has had performance increases greater than what the scaling laws predict.
- lukeschlather 6mo agoTransformers operate on images and a variety of sensor data. They can also operate completely on non-textual inputs and outputs. I don't know what the ceiling on their capabilities is, but the complaint that they only operate on text seems just obviously wrong. There are numerous examples but one is meteorological forecasting which takes in a variety of time series sensor inputs and outputs e.g. time-series temperature maps. https://www.nature.com/articles/s41598-025-07897-4 https://www.nature.com/articles/s41598-025-07897-4
- firecall 6mo agoAFAIK the data does not need to be text.
- teaearlgraycold 6mo agoWell diffusers are trained unsupervised on raw pictures. I don't know how they train multi-modal LLMs on images, but yes obviously they are consuming other media than just text. I don't think, but would be happy to be corrected, that models glean much of their "knowledge" from non-textual training data.
- mikert89 6mo agoyou couldnt be more wrong
- teaearlgraycold 6mo agoPlease tell me more. When I ask an LLM a question, and get a text response, can that response incorporate non-textual information from visual training data?
- IsTom 6mo agoThere are limits to algorithms. AI won't solve the halting problem nor will it solve EXPTIME problems in polynomial time.
- Eufrat 6mo agoCan someone explain why everything is being marketed in terms of power consumption?
- teaearlgraycold 6mo agoBecause that’s the limiting factor
- Animats 6mo agoSomehow we must be doing this wrong. "Do you realize that the human brain has been liken to an electronic brain? Someone said and I don't know whether he is right or not, but he said, if the human brain were put together on the basis of an IBM electronic brain, it would take 7 buildings the size of the Empire State Building to house it, it would take all the water of the Niagara River to cool it, and all of the power generated by the Niagara River to operate it." (Sermon by Paris Reidhead, circa 1950s.[1]) We're there on size and power. Is there some more efficient way to do this? [1] https://www.sermonindex.net/speakers/paris-reidhead/the-tragedy-of-third-generation-religion/ https://www.sermonindex.net/speakers/paris-reidhead/the-trag...
- whimsicalism 6mo agopretty sure evolution spent more time and energy getting there then we ultimately will
- huflungdung 6mo ago[dead]
- brianjlogan 6mo agoI'd imagine one day there will be a limiting factor of cash to burn as well.
- Animats 6mo agoWe're getting close. The first big AI bankruptcy can't be far off.
- skybrian 6mo agoI guess gigawatts is how we roughly measure computing capacity at the datacenter scale? Also saw something similar here: > Costs and pricing are expressed per “token”, but the published data immediately seems to admit that this is a bad choice of unit because it costs a lot more to output a token than input one. It seems to me that the actual marginal quantity being produced and consumed is “processing power”, which is apparently measured in gigawatt hours these days. In any case, I think more than anything this vindicates my original decision not to get too precise. [...] https://backofmind.substack.com/p/new-new-rules-for-the-new-new-economy https://backofmind.substack.com/p/new-new-rules-for-the-new-... Is it priced that way, though? I assume next-gen TPU's will be more efficient?
- brokencode 6mo agoGigawatts seems like more a statement of the power supply and dissipation of the actual facility. I’m assuming you can cram more chips in there if you have more efficient chips to make use of spare capacity? Trying to measure the actual compute is a moving target since you’d be upgrading things over time, whereas the power aspects are probably more fixed by fire code, building size, and utilities.
- delichon 6mo agoMeasuring data centers in watts is like measuring cars in horsepower. Power isn't a direct measure of performance, but of the primary constraint on performance. When in doubt choose the thermodynamic perspective.
- pepperoni_pizza 6mo agoGigawatts are units of power, gigawatthours are units of energy. The equivalent of cars would be pricing by how much gas you burned, not horsepower.
- delichon 6mo ago1 horsepower = 745.7 watts
- gausswho 6mo ago[dead]
- cebert 6mo agoI’m surprised Anthropic wanted to partner with Broadcom when they have such a negative reputation with antics such as their VMWare acquisition.
- Eufrat 6mo agoI think it’s also important to add the context that Broadcom’s CEO, Hock Tan, went on CNBC in October and had a vacuous conversation with Jim Cramer about their OpenAI “deal” at the time [0]. Nothing of substance was said, it was just endless loops about the opportunity of AI. It is now 6 months later and there has been nary a peep from Broadcom about any updates. I think Anthropic is a more grounded company than OpenAI because Sam Altman is insane, but it is still playing the same game. [0] https://www.youtube.com/watch?v=pU2HhJ3jCts https://www.youtube.com/watch?v=pU2HhJ3jCts
- jeffbee 6mo agoBroadcom makes the TPU. If you want TPUs, you are working with Broadcom whether you want to or not.
- thundergolfer 6mo agoBroadcom builds the TPU chip. Google designs it. You can’t avoid partnering with Broadcom if you want TPUs in significant volume .
- alephnerd 6mo agoBroadcom designs it as well [0], though GCP also works on design as well. [0] - https://www.broadcom.com/products/custom-silicon https://www.broadcom.com/products/custom-silicon
- Jyaif 6mo agoTSMC builds the TPU chip. Broadcom does the rest of the electronics (motherboard, networking, etc...)
- nsteel 6mo ago
- ketzo 6mo ago$19B -> $30B annualized revenue in a month? Feels like the lede is buried here!
- ai-x 6mo ago[flagged]
- mrcwinn 6mo ago[flagged]
- baron816 6mo agoI think you can argue that AI is going to explode and take over the economy, and it’s still a bubble. I think one possible route is that cloud capacity just becomes totally commoditized and none of the hyperscalers will be able to extract the kinds of profit margins that would allow them to make a good return on their investment (model makers will fall victim to this too). Ultimately, what may happen is that market competition for everything explodes since AI and robots can do all the work, prices for everything (goods, services, assets) collapses, and no one is really any richer than anyone else.
- zozbot234 6mo agoEven if the AI frontier becomes "totally commoditized" it will still be reliant on a scarce factor, namely leading-edge chips. Chipmakers will ultimately capture that value, because competing it away would require expanding the industry and that's a very slow process involving billion-dollar expenses planned far in advance (multiple years, and that lead time can only expand further as the required scale gets even larger).
- kdkl 6mo agoExcept you're neglecting the fact that LLMs can become more efficient. The magical thing about software is that efficiency gains can come pretty quickly relative to other industries.
- edinetdb 6mo ago[flagged]
- mahadillah-ai 6mo agoInteresting to see Anthropic investing in compute infrastructure. The bottleneck I keep hitting is not raw compute but where that compute lives — EU customers increasingly need guarantees their data stays in-region. More sovereign compute options in Europe would unlock a lot of enterprise AI adoption.
- semiinfinitely 6mo ago[flagged]
- 243341286 6mo ago[dead]
- holografix 6mo agoI don’t understand Claude Code’s moat here. What can it do that opencode can’t or couldn’t fairly easily implement?
- aurareturn 6mo agoThe moat is in: 1. Opus and Sonnet. 2. Compute capacity. Anthropic has much more of it than your average coding startup. 3. The developing ecosystem around Claude Code.
- bdangubic 6mo agonone of the three are even remote moat
- aurareturn 6mo agoYou're right and the your reasoning is great. Anthropic should fold and give up their $30 billion ARR just announced in the OP. Shut it all down, no moat here. /s
- MrOrelliOReilly 6mo agoHow so? Opus and Sonnet are frontier models which cannot easily be replicated. Compute has real physical constraints which require appropriate procurement at this scale. At least those two points seem like pretty strong moats against the majority of companies.
- zozbot234 6mo agoYou don't need to "replicate" Opus and Sonnet, you just need to match their overall performance at lower cost. That's been absolutely doable so far, with a steadily decreasing lag time.
- bdangubic 6mo agoabsolutely! I am sooooo confused why people think either claude or openai have any sort of moat outside of mom&pop only heard about those on the tv
- nopurpose 6mo agoHow is compute shortage to satisfy demand manifested? Obviously they never close sign-ups, so only option is to extended queues? But if demand grows like crazy, then queues should get longer, yet my pro claude plan seems snappy with only occasional retries due to 429.
- blueblisters 6mo agoThey have several levers for demand destruction. From Anthropic's POV, I suspect this is least to worst bad - reducing the surface area of "acceptable use" (e.g., blocking third-party tools OpenClaw) - tighter usage limits and more subscription tiers - increasing existing subscription prices - moving to usage based model completely - taking away compute from training next gen models (future demand destruction)
- car 6mo agoTPU architecture explained https://news.ycombinator.com/item?id=47637597 https://news.ycombinator.com/item?id=47637597
- chimpanzee2 6mo agoOn a tangential note: It seems the whole theater with the DoD is over for now, am I seeing this right?
- enesz 6mo ago[flagged]
- NeoBild 6mo agoInteresting timing given the quantum computing timeline pressure from this week's cryptography discussions. $30B run-rate and gigawatts of TPU capacity — and meanwhile the most interesting AI work I've seen lately runs on a phone in Termux with no cloud dependency at all. Both things are true simultaneously.
- xnx 6mo agoThis is not a good sign for Nvidia. They might have to step up their TPU game and price competitiveness.
- Mecha_SalesCast 6mo agois anthropic interested in TPU?