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Nvidia CORP

NVDA

251 stories

$4.6T Market cap · 2026-11-06

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@ParadisLabs 1 click

Paradis on X

Memory fundamentals are still very strong (stronger than Q1). $MU CEO: - "We expect memory and storage supply demand conditions to be much tighter in fiscal 2027 and 2028 than they were in 2026." - "We do not have line of sight to when supply and demand will return to balance." Back in July, $NVDA supply commitments rocketed from $119B -> $279B which was "primarily related to the procurement of memory", with ~$175B of commitments for 2028-29... $MU also said that >75% of FY27 output is already committed + "a majority of discussions with our customers today are already around 2028." So as a reminder....ultra long-term demand visibility in the context of the memory market. Samsung’s Q3 prelim last week was 107.4T Won O.P. on 195T Won revenue: - So that's ~55% margins company-wide. - Micron did 87% GM in their most recent quarter - $SNDK 84.6% GM. All accelerating/stabilizing margins as of their most recent fiscal Q's. Mass production from new capacity (from Micron, Samsung and SKHY) is also not expected to come online for quite some time. In other words, the shortage will intensify next year at least. Then long-term contracts solidify long-term earnings further via price floors + committed volumes: - $MU with 26 SCAs worth >35% of revenue through to 2030 with some now running into 2031. - $MU CFO: "even at floor prices, we expect margins meaningfully above any prior cycle peak margins." - $SKHY has ~10 LTAs of ~5 yrs w/ pricing structures "designed to respond to price fluctuations." - Sammy (as of recent reporting) holds ~70% of memory capacity under LTAs until 2031 with $MSFT, $NVDA and $GOOGL, with ~25% of contract value prepaid. - $SNDK also has their "New Business Model" agreements with 8 customers that lock in ~66% of fy28. Then on buybacks, $MU will step up returns from December - "over time, we expect to return 100% of our excess cash." Just crazy how much cash these companies are bathing in rn. Obviously there are risks, mainly with positioning and sentiment towards the AI trade currently - thanks to macro/ARR fears/pacing talks/other stuff I'm probably forgetting rn. China is ofc another risk with CXMT but I don't see how they'll be a massive problem for HBM / US hyperscaler RDIMM where they'll continue to face US restrictions + massive qualification walls imo. But ultimately, memory fundamentals are in-tact and very strong. Stronger than Q1.
@aleabitoreddit 1 click

Serenity on X

Physical AI + Humanoids are the next progression of AI. $NVDA CES 2025 projections and Elon's prediction on $TSLA + 1 Billion humanoids in the next decade, seems to be speeding up. New statement from Unitree CEO after testing GPT-6 on their humanoid program was that: - In August, before GPT-6's release, Wang said the industry could take two to three years at its fastest pace, or as long as a decade, to reach its "ChatGPT moment" - Now, OpenAI's GPT-6 model prompted expectations that a breakthrough could happen "within 1 or 2 years" - He defined that moment as robots being able to complete around 80 per cent of tasks in 80 per cent of unfamiliar environments using voice or text commands. - "It's possible that, say, a GPT-7 model released next year could directly crack the problems of embodied intelligence" - "The whole embodied intelligence industry has been shaken up after the release of GPT-6" Just looking at some benchmarks on robotic hand manipulation, "On the bowl task Astra placed the block in 19 of 20 trials, against Fable 5.1's 8 of 20". But for the puzzle task "Astra completed the insertion 2 times in 20 against Fable 5.1's 2 in 20"".. so we're getting there. But going from 2-3 years (potentially to a decade), and compressing that down to 1 or 2 years (to achieve 80%+ of tasks in unfamiliar environemnts) is a significant change in outlook.
@aleabitoreddit 2 clicks

Serenity on X

Sure... Random train of thoughts on markets now, optics like $LITE / memory + $IREN / Anthropic. On Memory: - Demand visibility on $MU / Samsung / SK Hynix extend into 2030-2031. Samsung has LTAs covering 70% of their capacity into 2031 (likely with SK Hynix type price floors)... and their operating profit is already absurd. There's a consensus that shortage in 2027 will be much more severe than 2026... Even $SNDK expends gross margins to exceed 80% entering 2030. You're going to hear Acer and other CEOs that want memory prices to go down cite bear cases like CXMT/YMTC, and Chinese players. But just looking at supply procurement that $NVDA is doing + hyperscalers, demand visibility is absurd for the next 5 years. And by that time, cumulative FCF coming in from these take/pay + pricing floor contracts might exceed some of their entire marketcaps so it feels like there's some baseline. Near-term is always volatile (IV dropped a ton across $EWY to $DRAM to $MU), so people with call options likely got nuked, along with underlying prices. But if Samsung is 3x 2027E, has 5 year LTAs with 70% of capacity committed from hyperscalers, and price floors, it's kinda hard to see these names suffer a sustained crash... For optics: - $LITE is literally sold out into 2029, starting discussions into 2030. With demand visibility lasting into 2031… with hyperscalers largely funding the capacity buildout. Their 2028 Greensboro fab expansion was ~$5B in UHP laser capacity revenue alone, yet it’s basically allocated toward $NVDA (50%+ with options for more) and co from channel checks. Existing supply chain bottlenecks are extremely severe as optical content/XPU skyrockets entering 2027-2028. So everything from DSPs/TIAs, photodiodes, lasers, InP/SOI substrates, wafers (epi, SOI, etc) are expected to go brrr. And what does that imply to anyone else with enormous amounts of capacity coming online like $AAOI + $SIVE + $MTSI capacity expansion in 2027? Or other players like Sumitomo / Furukawa? But again it's not quite the same situation as memory since the BOM % is extremely low in a cluster... If micron/samsung raised another 2000% hyperscalers would go bankrupt. But doing that for InP substrates seems possible. Or a 100% increase on CW laser content. I actually thought $LITE was overvalued at $100B+ MC, relative to capacity expansion on your smaller optical players to their respective MCs. So I chose to position accordingly (But lumentum does deserve premiums for demand visibility). For the companies leading the charge like $NVDA / $AVGO / $MRVL: - Nvidia has 70% forward revenue growth FY2028 which is supply constrained growth. - Broadcom 100% Y/Y for 2027/2028 which is also supply constrained growth. - Marvell FY2031 upgraded guidance to $70B-$90B from $46B analyst projections... They all seem to be very happy.... and read through on demand for next 2-3 years is enormous (also echoing what $INTC CEO said today on the panel with $AVGO CEO). So you have overwhelming growth on memory + photonics + from the leaders... But the nuance is not everything in the AI supply chain get the same economic benefit. HBM/Laser/CPU/MLCC and other players in the bottlenecks look to be printing money. And seem to be the largest beneficiaries of the current bottlenecks. Test players are also getting price hikes + capacity allocated by players like $NVDA, but economic impact seems a tad lower than other players since forward P/E for many I modeled were still in the 45x+ range even if you hiked prices like 10-30%. As for capex intensive players... there's some like $IREN ($25-30B for 2027) that despite a deal with Anthropic ended up dropping. I don't think markets seem to appreciate players spending a lot of money + ongoing financing (think they're like halfway through their $6B ATM). Just some hopium for $IREN investors, $NBIS also signed a deal with $META at the start of the year (but markets didn't care at all, since Nebius dropped to ~$70-80 at the lowest point?). But did 6 months later, as the time to revenue inflection gets closer and you're past the ATM/capex window, markets would likely care more. I don't think it's irrational but at the same time, the fundamentals have improved from Mirantis acquisition to others. I also think it was known for awhile it was either OpenAI/Anthropic from CEO comments earlier too? But either way to be a more risk-off environment where funds seem to be rotating to larger names as a flight to safety. $CRWD / $PLTR as just two example, but my take is that the valuations of these rotations are reaching clown levels. And that certain AI supply chain beneficiaries in comparison are undervalued now. I was thinking markets would just rotate to names that are extremely profitable like Samsung or names that have <2X P/E, so it was surprising to see $KOSPI and some legacy memory players get sold off today. Since all of this is colliding with uncertainty about sht macro environments and renewed sustainability concern of AI capex/commitments from frontier labs like OpenAI/Anthropic from due to that FT piece recently. I've always been sounding the alarm about the contractual backlog OpenAI has with $CBRS, $MSFT, $ORCL and others earlier in the year. But OpenAI did just raise $122B or so , so I don't quite think they're at risk of going down anytime soon. I also don't think overall capex can just exponentially go up, but even if it stalls / hovers around a certain point or even drops... Capex toward certain areas like optics will still explode as hyperscalers fight over allocations when CPO scale up comes about, but might drop in others where more capacity comes online like test or DRAM. So it's a lot more nuanced than capex down = everything down or vice versa. I also slightly agree with community agreements that open weight models wouldn't lessen demand on AI infrastructure (otherwise $NVDA wouldn't be funneling funds into Reflection), but all the contractual obligations that Anthropic has might get messy if their models/company get cannibalized. Either way, I'd expect some of these frontier lab valuations to likely compress but I do think Jensen knows what he's doing though to avoid contagion. But basically: - Extremely difficult market now, can't just throw money at any AI supply chain company like $GLW and expect it to go up. There's cause for concern of rate hikes mixed with financing per $AVGO CEO but not to the point you just blindly sell off everything. Also I think markets are stupid and ties baskets together so that leads to mispricing + individual buying opportunities with certain chokepoints too. If capacity + demand is visible and forward p/e compresses like $AAOI to single digits off hundreds or thousands of percent growth from hyperscalers off $471m transceiver revenues and hundreds of thousands of monthly ELSFP capacity. Or $SIVE coming online with 100M+ laser capacity + multiple foundries with ASP hikes on lasers. I wouldn't lump them into other AI supply chain names in IP like $RMBS or others. My take is that AI supply chain beneficiaries with closer inflection points, demand visibility, and forward P/E compressing due to economic leverage would likely outperform medium term despite current volatility. TLDR: - Memory strongest risk/adjusted return with LTAs + pricing floors extending to 2030's + buybacks. - My opinion is that optical supercycle for 2027-2028 with CPO/NPO is going to look absurd relative to capacity coming online of smaller suppliers. - Overwhelming fundamentals/growth colliding with sht macro + media scares - Markets are stupid and groups things in baskets and sells them all off together or buys overvalued names, creating short/long opportunities I'd give a prayer to people on short term options given underlying drop + IV decline, but there could be a reversal... who knows. Most valuable thing right now is time.
@convequity

Convequity on X

Why I think Nvidia buying Reflection AI helps Palantir more than it hurts $NVDA wants its models inside enterprises, not just hyperscalers, neo-clouds and AI labs. Buying Reflection AI would speed that up. The FT reported on 10 Oct that Nvidia is in early talks to buy Reflection, raise its stake, supply it more chips, or hire its team and license its technology. Bloomberg's source says no terms have been reached. Nvidia has already invested $800m, so a buyout or a bigger stake means pricing a company it already partly owns. The enterprise push Nvidia agreed in September to buy Hugging Face for $12.9bn. Jensen Huang says more than 200,000 companies use it to deploy AI. Reflection is validated on Dell's AI Factory with Nvidia, and $PLTR's sovereign AI stack runs on Nvidia's AI factory design. Reflection is built for enterprise code. Its Asimov agent reads a company's code, Slack messages, emails and documents. Its first model, Beam (501bn parameters, 23bn active), is trained for coding. Reflection says the weights ship this month under Apache 2.0, for on-prem or air-gapped use. The bear case: Nemotron is moving too slowly at Palantir One reading is that Nvidia wants Reflection because its Nemotron models are moving too slowly with Palantir. I think that is unlikely, given how Palantir has executed. At AIPCon on 10 Sep, Nvidia's supply-chain team said Nemotron 3.5 Lightning, post-trained in Palantir, beat Nemotron 3 Ultra by a large gap on Nvidia's own benchmark. It runs on-prem and retrains on one GPU in a couple of hours. Cisco will offer Nemotron post-trained with Palantir in its AI Factory with Nvidia. L3Harris said open models fine-tuned on its own data beat the frontier models it had used within 48 hours, at 95% lower cost. Palantir has kept shipping open-model deployments. In June it launched an engine to run Nemotron in sovereign environments, and customers own the retrained models. On 1 Oct Armada became its first certified modular data-centre partner. On 12 Oct $ONDS formed Regna AI on AIP and open-weight models. The likelier case: Nvidia is backing more than one route Palantir fits open models to a company's data, permissions and workflows. Reflection is built to read the company's code. Buying a team that has already built that is quicker than building it in-house. The limit is vendor API rules Reflection reaches a company's systems through the vendors' APIs, and vendors decide what passes through them. $SAP's April API policy bars AI agents that plan or execute API calls, and bulk data extraction, outside SAP-endorsed pathways. Since May 2025, Slack's API terms bar third-party apps from using Slack data to train a large language model or bulk-exporting messages. Those rules apply to Palantir too. In May, SAP made Palantir AIP for data migration an SAP Endorsed App. An independent Reflection would likely need similar approval from each vendor whose systems it reads. Who pays the compute rent Reflection trained Beam on Nvidia GB300s rented from $SPCX at Colossus 2 in Memphis, under a June deal worth up to $6.3bn: about $150m a month through 2029. $NBIS agreed in July to sell it $1bn more capacity. If Nvidia buys Reflection, it takes on those contracts and pays SpaceX rent on Nvidia chips that SpaceX bought. In an acqui-hire, the contracts stay with what remains of Reflection unless the deal says otherwise. The FT says an acqui-hire would likely avoid a long regulatory review. Nebius holders should watch which company ends up owing that $1bn contract. What it means for Palantir The risk is an independent Reflection selling its own forward-deployed team and turnkey stack, which is how Palantir sells. If Nvidia owns Reflection, I expect Beam to reach AIP customers the same way Nemotron does, with each customer fine-tuning it on its own data inside Palantir.
@convequity 1 click

Convequity on X

3D chips could create more room for specialist EDA startups. We are not convinced that more difficult chip design automatically translates into a large revenue windfall for $SNPS, $CDNS and Siemens. Fine-pitch stacking changes decisions that used to be made within one die. Put a circuit upstairs and its wires may get shorter, but its heat affects the circuit below. Placement, signal timing and power delivery have to be optimized together across the stack. That creates a reason for $AVGO and $NVDA to build custom tools around their architectures. Broadcom already describes a proprietary design flow built on industry-standard tools. Nvidia publishes its own research on assigning connections across tiers in 3D chips. Our inference is that the chip designers will keep some of the most differentiating optimization work in-house. A startup that solves a costly gap in cross-die planning or optimization could also win business without replacing an entire EDA suite. The incumbents still supply implementation, verification and manufacturing sign-off tools, and they are developing 3D capabilities themselves. Internal tooling is evidence that customers need more than the standard flow; it does not establish that incumbent spending falls. We see a more interesting startup opportunity where a tool can shorten repeated design iterations or improve the resulting chip enough to earn a separate budget. That is the part of the 3D transition we would investigate before assuming the largest EDA vendors capture most of the upside.zz
@SouthernValue95 10 clicks

SouthernValue on X

$AMZN paid <$400M for Annapurna … crazy how easy it is to spend money these days. Interesting acquisition, though. Decart has a world model and a video model, but the core piece AMZN is interested in is its “DOS” Decart Optimization Stack which helps AI models to train or inference over any chip technology, including already working with Trainium. This is a piece of technology AMZN wouldn’t want acquired by $NVDA. Makes a lot of sense within AMZN as a way to help customers use NVDA AMD CRBS TRN at optimal performance.
@ParadisLabs 1 click

Paradis on X

I think Gavin's correct. Open-weight models that compress frontier margins are positive for AI infra -- they don't shrink demand for compute. An example is after Deepseek R1, Tencent, Alibaba and Bytedance *significantly* increased H20 orders...and smaller healthcare & education firms started buying $NVDA H20's to run DeepSeek in-house. So we saw a broadening out of the customer base for the infra providers. In the West, I think $DELL is the best read on whether the buyer base is broadening beyond frontier labs. Hyperscalers mostly buy directly from Taiwanese ODMs, so Dell's AI customers are mostly neoclouds, sovereigns and enterprises. Dell's AI customer count went from >5,000 to >6,500 last quarter (30% growth in one quarter) which management say is mainly down to enterprise acceleration. Feels like that's one of the better public data points on how fast the self-hosting buyer base is growing. Which is a net-positive for AI infra. I don't understand how it can't be?
@convequity 1 click

Convequity on X

The $70bn revenue figure reported for OpenAI in late September was a gross-basis comparison number, not OpenAI's own. OpenAI's own run rate, the one it told investors, was approaching $50bn at the end of September. The number that now needs the same scrutiny is Anthropic's, before its IPO. The two labs count partner sales differently. When a customer pays $AMZN or $GOOGL for Claude, Anthropic records the full price as revenue and books the cloud's cut as a cost. OpenAI records only its own share of such sales. The $70bn was OpenAI's number grossed up to Anthropic's method so the two could be compared. Three things follow. 1. OpenAI's revenue did not fall. Its net run rate was approaching $50bn at the end of September. Yet the stocks that fell on Thursday were its suppliers: $ORCL, $CRWV and $NVDA. 2. Anthropic's $65bn run rate at the end of July is a gross figure. On OpenAI's basis it is smaller by whatever the cloud partners keep, and that share has not been disclosed. The claim that Anthropic has overtaken OpenAI rests on two numbers measured differently. 3. Which method applies is an accounting judgement about which company controls the sale to the customer, so Anthropic's prospectus for its planned November IPO may keep the gross figure, with the partners' share inside cost of revenue. That filing will show the cost line. OpenAI, still private, does not have to. In our view Thursday's selling hit the suppliers to the lab whose number did not change. The comparison investors should redo before November is Anthropic's $65bn against OpenAI's $50bn after the cloud partners' share is taken out of both.
@aleabitoreddit

Serenity on X

Markets often miss second order implications until it shows up in direct statements. - If $LITE is completely sold out... (largely due to $NVDA bottlenecking everyone else, eg. securing Greensboro capacity), where else do your other companies like $AMD / hyperscalers / etc. get their supply procurement? - $SIVE is coming online with like 1/6th of the world's current laser capacity by themselves. (100M+ cw dfb lasers vs. 608M EML/CW globally per Trendforce). Not even including their partner foundries that they're targeting 2:1 for. - $AAOI is coming online with a ton of capacity too next year enough to produce 400k ELSFP + $471m/month of transceivers. Then combine that with other supply chain statements like "Having 2-3 suppliers used to be the norm, now it is 5-7" from Lightcounting. So as long as your products actually work + can scale, it's hard to see anyone else with capacity not get qualified into your supply chains. Since optical component shortages are a massive bottleneck now.
@convequity 1 click

Convequity on X

$NVDA vs $AVGO: The Optical Edge NVIDIA can keep growing while facing pressure from more directions. Custom chips, optical supply constraints and specialized inference systems give customers more reasons to consider alternatives. Broadcom’s optical capabilities could help it win the accompanying compute order. Customers need both accelerators and the optical components that connect them to bring capacity online. Securing those components alongside an ASIC purchase could shift the customer’s platform choice toward AVGO. Control across lasers, photonic and electronic chips, and packaging also gives Broadcom more freedom to optimize the complete design. Embedded lasers could reduce the laser power required, easing dependence on scarce, high-power external lasers, although replacement becomes harder. For AVGO, this remains a potential design route. NVIDIA can respond through partnerships, but separate suppliers still have their own margins and product priorities. Google ($GOOGL) and Amazon ($AMZN) are expanding the alternatives through TPUs and Trainium. Our ABBX model allocates 10GW of Anthropic’s 13.8GW planned capacity to those two platforms. The plans span different timelines, but show how a few large buyers could redirect substantial spending away from GPUs. Huawei adds pressure through its integrated optical approach and the prospect of Chinese models supplying cheaper tokens internationally. That could affect both hardware choices and the prices compute providers can charge. Cerebras ($CBRS) targets workloads where faster responses matter. It can compete for latency-sensitive decoding without replacing GPUs across every stage of inference. NVIDIA’s defenses remain substantial. GPU batching supports high total output, flexibility across workloads matters, and its ecosystem is deeply established. Customers can use GPUs for prefill and specialized chips for decoding. Wider enterprise model development would also favor NVIDIA because smaller buyers have less reason to fund custom-chip programmes. At comparable valuations, we prefer adding to AVGO: it can benefit from custom-silicon adoption while helping customers secure the optics needed to deploy it. For NVIDIA, the key question is how much market share and pricing power it retains as customers gain more alternatives.
@convequity 2 clicks

Convequity on X

More $NVDA chips could end up going to SpaceX and other buyers that can borrow at investment-grade rates, leaving neoclouds with fewer chips than they need. SpaceX's planned $40bn chip borrowing shows why. The deal: $SPCX is in early talks for about $10bn of bank loans and $30bn of investment-grade debt, led by Apollo, with Pimco also in talks. It would close in 2027, and the talks could still fall apart. The comparison: in December, Apollo lent about $3.5bn to fund xAI's Colossus 2 chips through a separate company set up just for that deal. The interest rate was about 10% a year. xAI is now part of SpaceX. Investment-grade debt usually costs much less. US investment-grade corporate bonds yield around 6% on average today. The rate on the new $30bn has not been reported, so we don't know the exact saving yet. Why it could be cheaper: lenders would be relying on SpaceX's launch and Starlink businesses to repay, not only on the chips. That is the real financial payoff of putting xAI inside SpaceX. Most GPU buyers can't borrow like that. $CRWV is rated junk (Moody's Ba3, Fitch BB-) and pays 9.25% on its 2030 bonds. Standalone AI labs have no public credit ratings that we could find. Not every neocloud pays up. $NBIS has a GPU loan at a floating benchmark rate plus 2.5%, against CoreWeave's plus 5.5%. Our view: the debate on X is whether this proves AI demand or shows too much leverage. We think the bigger point is cost of capital. When chips are scarce, the buyer that borrows most cheaply can pay for the most of them, and neoclouds paying junk-level rates get squeezed out.
@convequity

Convequity on X

$NVDA: custom ASICs eat into the GPU share Our scenarios include custom ASICs rising from roughly 20% of the overall AI compute mix toward 40–50%. ABBX records 21.2GW of planned capacity for OpenAI and 13.8GW for Anthropic. Within Anthropic’s plan, ABBX allocates 5GW to AWS Trainium and 5GW to Google/Broadcom TPUs, putting these custom-chip platforms at roughly 72% of its planned capacity. That is a substantial shift in hardware demand if the deployments proceed, although the plans span different timelines. With purchasing concentrated among a few large customers, their decisions to expand on custom silicon could reduce NVIDIA’s share of spending even as the overall AI infrastructure market continues to grow.

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