Xiaobaok

@dee_bosa you can say the same thing to MSFT

IBM risks getting squeezed from both sides by AI. 1) IT budgets moving to AI infrastructure 2) AI could now cut software & consulting spend Anthropic research showed where this was headed over a year ago. 37% of Claude use was computer/math work (coding, debugging and databases closely tied to IBM), nearly 4x any other category.

DRAM expansion pace is limited by physics

@amir Google’s own Search and Claude can solve that residual value issue, but not for a neocloud.

@xiaob123 Google said zero TPUs are idle fwiw. You’re right about durability of Nvidia and residual value but they have been a pain to set up in new generations.

Wtf? what Google was saying is illogical

@amir NVDA system once you stand them up has much durable residual value several years after, which is the anchor of these neoclouds’ valuation. Imagine these guys are stuck with a bunch of old TPUs not many people want to use after 2 years

@xiaob123 How so?

@_clarktang NVDA winning formula is better residual value augmented by a competitive hosting layer

I think there is general confusion around how AI works, AI tokenomics, and ultimately *what is actually priced in* for the AI trade - and that some of the existing arguments are at odds with one another Firstly to clear this up - what Brad and Gavin are saying are completely in agreement, what Gavin is laying out here is the *mega bull case* as he so states in the first sentence of his tweet lol The base case we are all living with is that the labs are going to continue to generate a significant amount of revenue this year and next year. OpenAI was already the fastest growing company of all time (and still is)... but Anthropic has just grown *SO* fast that OpenAI's growth look slow by comparison The basic chain for all of this together is as follows: Power (generation, interconnect, regulation) -> DC Shell (construction, equipment, regulation) -> Semiconductors (compute, memory, interconnect, adv packaging, wafer capacity) -> Hardware (networking, storage) -> Software (data, infra, inference) -> Models (open, closed, agentic loops, harness) How each of these interact with one another affects the ultimate cost - which is model cost Consider the following: Nvidia manufactures the bleeding edge chip for training and inference. It is very good at both training, and inference. Nvidia is the largest customer of TSMC, the memory players, substrates, lasers, transceivers etc - anything you can name on. And now to soon include power into this equation. The unit of compute is fungible because the software runs ubiquitously across all clouds, multiple industries, across all models. It is bankable by increasingly more financial institutions - infrastructure PE funds, even some IG debt now - because it is ubiquitous and observable what the market is. For this Nvidia charges the highest compute margins - ~80% on hardware. Consider the labs: Anthropic and OpenAI are inferencing across a fleet of *largely Nvidia / Google TPUs w/ some incremental gains of Trainium*. There are new entrants to the field - Cerebras, AMD, and potentially some 2027 tapeouts of new ASICs - OAI Jalapeno, new start ups etc. Anthropic and OpenAI make the best models, with a dominant share of wallet $ (Assume ~$100B ARR) at an estimated gross margin of ~70%. (economic estimates vary from 40-90% depending on what you are including). But almost certainly contribution margins on model inferencing is pushing the number higher than 70%. After establishing that though, I think it's incredibly important to state that while these things seems at odds with one another, this balance is not necessarily zero sum. The thought experiment Yes it is true that if Nvidia margins were 0, OpenAI and Anthropic could offer their intelligence at cheaper rates. How much cheaper? My estimate is NVDA DC = ~12.5B / yr Amazon Basics ASIC DC = ~$6B / yr (About 1/2 the cost - so if NVDA hardware is 2x the performance, then the cost advantage goes away - and actually that ASIC is worse off bc has much worse recontracting value so arguably depreciation curve should be shorter) So really, the labs cutting NVDA out could only offer the tokens at ~50% to 60% cheaper at their own economics. Is that signficant? Certainly. Is it an OOM difference? Not necessarily - so that's why they have prudent attempts to diversify away from NVDA (it's just good business), but they continue to rely (and actually if considering Ant's share gains, are increasing their spend on NVDA - while having competing programs). In the case of Open Source vs Closed - Nvidia obviously wants the proliferation of this because by definition all OS models will run best on Nvidia hardware out of the gate. Yes NVDA hardware will be good, but they will have this lead because of everything NVDA has been doing for the last 4 years in developing their platform ecosystem from the infrastructure (partnerships, funding, neoclouds) to the software (vLLM / other inferencing sw, inference clouds, Nemotron, NIMs, Nemoclaw etc), to install base (sovereign clouds, global partnerships, neoclouds, hyperscalers, etc) - to proliferate NVDA around the world. Anywhere there is inference that exists outside of a walled garden (the proprietary labs) - Nvidia will exist. The only ones who could potentially cut NVDA out are the labs. And the value that is captured from the labs are estimated to be in the hundreds to trillions of $ - which are obviously of much value to the world if it were offered much more cheaply. Which brings us to the debate at hand -- which one is right? The truth is no one knows. You can ask the labs, you can ask Jensen - anyone who tells you definitively is just lying to you. But you can build a plausible path to the future state using a few reasoning blocks. Here's a reasoning thread (feel free to generate your own thinking): - Bull case: Spend on the world's intelligence is about $30T / yr - What would you spend to augment that, maybe worth 30-50% of that? $10-15 T as a market? - Bear case: about 30M software developers in the world each earning $100K a year = $3T spend in salary. GitHub commits up 3x = $9T of productivity on $100B of ARR? *Even if you assume 90% of this is slop and useless, you would get $900B of ROI on $100B of spend* I have more reasoning chains, but I thought this one by Jensen was compelling - but this is where we can't give too much away :) But in spirit of crowdsourcing - some other interesting ideas I have that I am still thinking about (and encourage you all to consider as well): - Optimizations always happen - the question is just to what extent and for what reason - Agentic revenues was really what unlocked step function revenue growth - if open source is really just 6mo behind, then we should see really good agentic capabilities out of open models now too - Harness and model now tightly have to be integrated - Open Source never really makes sense as a sustainable business model - businesses investing at this scale always has to find a way to monetize that - "there is no free lunch" - not just a one model fits all... the only player that has an incentive to train on the frontier and keep completely free IS Nvidia - Rev / GW of AI labs are already nearing the highest metrics ever - now to be fair Meta and GOOG never really thought of Rev / GW as metric to lead their buildouts - was always a cost to doing biz - but it's not like we are being "stupidly inefficient" with power spend now - true mkt creation - wafer constrained, power constrained world. what's the optimal move?

@mzuhair123 @nvidia Rubin has no architectural flaw. Even that call was about material shortage.

I would suggest folks not calling out Jukan here for talking about this report. Rubin's uncertainty is justified, and we should likely see an official announcement from @nvidia either denying or addressing this rumor. As far as what I have investigated, NVL72 is under a limited volume shipment phase, with CoreWeave, Dell, and Microsoft becoming primary integrators. If folks claim that evolving BoMs is a reason behind the NVL72 delay, then you don't know how supply chains work. The second possibility is some architectural flaw, but we haven't seen any details surfacing with Vera Rubin. Major problems are reported to be with Rubin Ultra, which includes a revert to a dual-die design and 16-Hi HBM4 issues. As far as my research, Rubin is under HVM since CES 2026, so mass deployment should come under the Q3-Q4 phase, which is where you'll see major hyperscaler integration announcements. Since $NVDA is too big right now, markets chatters are natural, but the best way to be decisive on them is probably by tracking the supply chain.

@RYANHINGSHING 1-2 个月还好，not a big deal

$NVDA Rubin將會推遲！！ 推遲1-2個月！！😅 謝謝大家😅

New narrative is here. AI needs humans to be hands and feet.

Like LinkedIn now becoming a useless marketing channel

@TShirtnJeans2 ROI is needed for them but big portion of GCP is either from Anthropic or on NVDA chips

@xiaob123 I never considered that angle. But it’s still strange Google has to sell weapons to a company aiming those guns at their own business.

Time to make Chrome and Edge obsolete

@TShirtnJeans2 Think GOOGL literally needs scale to get a say in supply chain

@xiaob123 Surprising to me Google keeps selling Anthropic TPUs

@jukan05 @sama wow

@sama It wasn’t until I started using GPT-5.6 that I realized how dumb my Opus 4.8 actually is.

makes us happy to see people love 5.6 sol so much!

@LinQingV 年产200万，大连加了就1%，三星能加多少？现在好像还有SLC MLC 回炉的趋势

NAND这边的扩产节奏已经比大多数人预期的要快了。韩媒今天几篇报道给出了具体细节。SK海力士下半年重启大连二厂，装V8（238层）产线，月产能目标3到5万片，2027上半年完成。三星西安厂V6到V8（236层）的转换3月底已经做完，目前在量产。 资本开支端，SK海力士大连2025年投入440.6亿韩元，同比+52%。三星西安约3.04亿美元，同比+67.5%。韩系两家在中国的NAND投资同步加速，方向都是V8。 大连二厂是SK海力士花11万亿韩元收购Intel NAND业务时拿到的，闲置了两年多。现在能重启，NAND合约价暴涨是一方面，Q2环比+70%以上，涨幅头一次超过DRAM。更关键的是VEU切换到年度审批制。以前设备商接中国工厂的NAND产线订单，审批周期和结果都不确定，直接压制采购意愿。年度批次审批把可预测性提高了很多。报道提到国内合作方已经开始把闲置NAND设备运往大连，海外供应商也收到了初步采购订单。 市场一直盯着HBM和DRAM的capex周期，NAND被默认是周期落后者。现在韩系在中国同步转V8，加上YMTC的本土扩产，NAND环节对沉积、刻蚀、检测设备的需求释放窗口，会比卖方模型里假设的更集中。半导体设备市场里NAND的贡献权重，可能要重新算了。

Definitely much better than bullshit paid marketing done for Gemini 😂

@insane_analyst 😂

I have a weird habit of engaging in random jihads against particular semis CEOs. The jihad against Matt Murphy is over. Jihad against Jim Anderson begins.

@ClaudeDevs I smell fear

@satyanadella Open source models can fully satisfy your Office use case. Why bother using GPT or Claude?

Try it out: techcommunity.microsoft.com/blog/microsoft…

Super to see GPT-5.6 with Work IQ come to Copilot Chat, Cowork, M365 apps, GitHub, and Foundry today. From multi-step agentic work to analysis and content creation, it brings stronger reasoning and higher-quality outputs without sacrificing efficiency.

@elonmusk @MarceloLima @yacineMTB But that’s what they did of Cursor, no?

I was clearly wrong about Anthropic. They are obviously currently the leader in AI. No company has released a model as good as Mythos/Fable and they will undoubtedly have Mythos 2 ready soon. And I would never cut them off in a way that hurt them badly, even as a competitor. That’s not my style. Tesla open sourced its patents and we made the Supercharger network available to all competitors, even though we could have made it a walled garden. SpaceX launches competing satellite systems with no increase in price or use of unfair terms. Even my worst enemies can attack me on this platform. …

SpaceXAI now has a legitimate frontier model that competes with opus 4.8. Also, Anthropic is completely reliant on the compute rented from SpaceXAI If Elon wanted to kill anthropic, he could. Iirc the compute lease was short term, 6 months from May without renewal promise. GG