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$3.5T Market cap · 2026-11-06
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@BullishWiener I'm happy $GOOGL is being more aggressive with supply procurement like $NVDA was. They've been way too complacent and let others bottleneck their own supply chain. CSPs and hyperscalers will be in a rude awakening if they're hoping for Chinese memory to save them.
$GOOGL is now discussing deals to extend their memory LTAs through 2035 from KeyBanc channel checks. Samsung already has 70% of their capacity allocated through 2031, and this move would suggest a more durable cycle for $MU, $SKHY, and the others. They also expect HBM prices up 80% to 100% in the Q1 2027. Having all your capacity potentially allocated out into 2035 via LTAs would ease a lot of memory durability fears.
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.
$AVGO's Hock Tan is right that capital is now the constraint on AI, and that is healthy. The $60bn banks are placing for Anthropic's lease of $GOOGL's TPUs shows how it gets eased: by moving risk, not reducing it. We read his two remarks as cause and effect: tokens are not yet earning a return, so capital is expensive. That brake stops a buildout outrunning its returns. The $42bn senior slice leans on Broadcom's A-minus credit through a partial guarantee of the chips' residual value. The rest is expected to wait for Anthropic's IPO. The brake works where Broadcom's credit stops. On the guaranteed slice the risk has moved, not shrunk: out of a loan spread lenders would price, into a chip deal the seller priced, onto a balance sheet already long the same cycle, where it is too small to move the shares. Google sits on the same outcome three times: Anthropic shareholder, TPU lessor, Broadcom's customer for those chips. The IPO junior lenders are waiting for also puts a public price on Google's stake, and Broadcom's guarantee is safest if it goes well. Spreading risk only reduces it when the holders are diversified against it. None here are. Each piece is ordinary. The total is capital arriving ahead of proven returns, and two of the market's largest companies carrying more of one lab's downside than their labels suggest.
$META $GOOGL pretty funny to see $2T company talk trash and then delete their post after proven wrong - very bubbly behavior Imagine Lisa Su talking shit on X to Jensen about GPUs And @alexandr_wang account is 100% run by Meta's marketing team
We think Google's ($GOOGL) TPU volumes alone could add roughly $11 billion to $14 billion a year to Broadcom ($AVGO) revenue on our illustrative estimates, and Anthropic and OpenAI are a larger opportunity on top. April supply-chain estimates put Google's TPU demand at 4.2 million to 4.5 million units in 2026, rising above 6 million units in 2027. We allow for MediaTek's allocation to rise from around 600,000 units in 2026 to 1 million units in 2027 and assume Broadcom supplies the remainder. At the 6 million-unit level in 2027, that leaves Broadcom with roughly 1.1 million to 1.4 million additional chips compared with 2026. At an illustrative $10,000 of revenue per chip, that would add $11 billion to $14 billion in annual revenue from higher volumes alone. The actual increase will depend on which designs ship and their selling prices. Our working estimates put Anthropic's demand for Broadcom-supplied TPUs at around 1 gigawatt in 2026, rising to 5 gigawatts in 2027. OpenAI's separate custom-chip program has demand estimated at 1.3 gigawatts in 2027 and 5 gigawatts in 2028. Anthropic's TPU purchases overlap with the Google TPU opportunity, so we count those sales once. Together, the Anthropic and OpenAI estimates point to around 10 gigawatts of demand by 2028. Assuming Broadcom earns $10 billion to $20 billion for each gigawatt supplied, that represents a $100 billion to $200 billion revenue opportunity across those deployments.
OpenAI and Anthropic will rent most of the AI capacity they plan, but mainly from the big clouds rather than the listed neoclouds Convequity's AI Bubble Barometer (ABBX) tracks the data-centre capacity each company plans and how much of it it expects to rent from partners rather than own. 1. OpenAI plans 21.2 GW, of which 18.0 GW is to be supplied by partners. Anthropic plans 13.8 GW, all of it from partners. Together the two labs plan to rent 31.8 GW. 2. That is 2.8 times the 11.2 GW that the five hyperscalers, $MSFT, $AMZN, $GOOGL, $META and $ORCL, plan to rent from others. 3. The labs' named suppliers are mostly AWS, Oracle, Google, Azure and SB Energy. CoreWeave, a listed neocloud, and Nscale, a private one, appear on the lists, but the bulk of the 31.8 GW is not with listed neoclouds. 4. The seven listed neoclouds, $CRWV, $NBIS, $IREN, $APLD, $CORZ, $CIFR and $BTDR, plan to build 33.62 GW of their own. Much of that is gross site power rather than contracted IT capacity. 5. The labs are also behind most of the recent growth in AI capacity plans. Since 14 Jul, the total capacity that the 15 ABBX companies say they plan to have has risen by 22.19 GW, from 131.23 GW to 153.42 GW on 7 Oct. OpenAI and Anthropic raising their own plans accounts for 13.7 GW of that rise (62%). We think the labs' rental demand mainly supports $AMZN, $ORCL, $GOOGL and $MSFT. The neoclouds will need to fill their pipelines largely with other customers, including the hyperscalers' own 11.2 GW of planned rentals, so we would not read the labs' plans as demand for the neoclouds.
Neoclouds trade at 2.6x invested capital, less than half the hyperscalers' 6.1x The seven neoclouds in Convequity's AI Bubble Barometer (ABBX), $CRWV, $NBIS, $IREN, $APLD, $CORZ, $CIFR and $BTDR, lost 7.2% of their combined enterprise value this week, from $190.2B at the 2 Oct close to $176.5B at the 9 Oct close. Over the same week the five hyperscalers, $MSFT, $AMZN, $GOOGL, $META and $ORCL, gained 2.4%, from $13,316B to $13,637B. 1. Enterprise value divided by invested capital (EV/IC) at the 9 Oct close was 2.6x for the neoclouds and 6.1x for the hyperscalers. At the 2 Oct close it was 2.8x and 6.0x. 2. ABBX estimates the weighted average cost of capital (WACC) at 14.6% for the neoclouds and 9.7% for the hyperscalers. 3. At the 9 Oct close, neocloud share prices were 20% to 45% below each stock's best Friday close since 17 Jul: CIFR −44.5%, CORZ −33.3%, BTDR −25.8%, IREN −24.6%, APLD −24.1%, CRWV −22.0% and NBIS −20.4%. Among the hyperscalers, ORCL was 10.9% below its best, AMZN and META 4.4% and GOOGL 1.3%, while MSFT closed above its best. We think most of the discount reflects the neoclouds' higher cost of capital rather than a mispricing. We would not call 2.6x cheap until they report their July to September backlog and customer prepayment figures.
Alphabet pairs 17% Search growth, 82% Cloud growth and 500,000 weekly Waymo rides with a $195–205 billion AI capex plan.
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.
Hyperscalers are the ones ultimately funding the AI buildout. As seen with $AMZN to $GOOGL earnings commentary, the ROI is seen as wildly attractive. Even $MSFT said they’d be FCF positive during the buildout period. I was more worried about counterparty risk until OpenAI went out and raised like $168B recently.
$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.
*GOOGLE CLOUD ANNOUNCES GEMINI AGENT, A UNIVERSAL AGENT FOR WORK $GOOG up, $META fading
Morgan Stanley's Wigg: " $GOOG AI infra chief Amin Vahdat says Power lead times are directly gating capacity though GOOG would still rather remain grid-connected than fully self-generate as supplying 1 GW at 99.99%+ reliability on a standalone basis can effectively require ~2 GW of generation because of redundancy requirements. GOOGL is targeting a doubling of token-serving capability every six months. GOOGL’s 7-8 year-old TPUs are still running at 100% utilization despite the company using roughly a 6-year depreciation life,"
Despite investor doubts, $IREN is worth a small position. The shares have fallen about 17% from their 22 Sep close. The equipment IREN had ordered at 30 June is covered by the cash, committed GPU financing and prepayments it reported on 27 Aug. In the June 2026 quarter customers funded most of its capex. On signed contracts the shares are priced about where $CRWV is. The main risk is that IREN runs GPU clouds worse than its peers. 1. IREN guided FY27 capex (Jul 2026–Jun 2027) at $25–30B on its 27 Aug results call. Its enterprise value is $14.6B at the 5 Oct close. IREN's FY26 results show revenue of $0.7B, of which $0.58B was bitcoin mining. IREN expects to have shut mining down by the end of December 2026. 2. At 30 Jun 2026 IREN had $13.8B of commitments for GPUs and site work, almost all payable by 30 Jun 2027, per its annual report. Orders placed after 30 June are not in that figure. On 27 Aug IREN put its existing cash, committed GPU financing and customer prepayments at about $14B. That covers the 30 June commitments. 3. The other $11–16B of the capex guide is not yet funded. Management targets about $8B more in GPU financing and prepayments for new GPU capacity, and that funding comes with new customer contracts. IREN says it is in late-stage talks for a large share of 2027 capacity. It has not announced any signed contract for that new 2027 capacity. The rest would come from data-center debt, operating cash flow and corporate funding, which includes selling shares. By 14 Aug 2026 IREN had sold about $2.5B of shares under its $6B ATM programme, leaving about $3.5B at that date, per its annual report. It has not reported any sale since. 4. On signed contracts, IREN is priced close to CRWV. On our ABBX dashboard, enterprise value is 0.88x contract value for IREN, 0.74x for CRWV and 1.60x for $NBIS. The contract values are company-reported, and each company measures them differently. IREN reports about $16.6B, of which about $5.1B is cloud service contracts and about $11.4B is GPU lease contracts. CRWV reports a $104.2B revenue backlog. NBIS reports $37.5B of remaining performance obligations. On 27 Aug IREN said its 2026 capacity was largely sold out. It had $4B of contracted annualised revenue run-rate (ARR) for 2026 capacity, which it earns once that capacity is delivered and accepted. $1B of ARR was already operating. EV is about 3.7x the contracted $4B. 5. In the June 2026 quarter, IREN's customers funded most of its capex. On our ABBX dashboard, customer prepayments and deferred revenue covered 87% of IREN's cash capex in that quarter ($1.72B of $1.98B). The same measure was 21% at NBIS and 12% at CRWV. Working-capital inflows covered 90% of IREN's capex and 36% of NBIS's. CRWV's working capital was an outflow equal to 8% of its capex. More than half of IREN's customer prepayments came under the $MSFT contract. At 30 Jun 2026, $1.01B of IREN's $1.84B of deferred revenue sat in the subsidiary that holds the GPUs for the MSFT contract, per its annual report. IREN said on 27 Aug that $3.6B of GPU financing plus customer prepayments fund about 96% of the GPU capex for the MSFT contract. IREN also said its recent contracts carry prepayments of 45–55% of GPU capex. So in that quarter customer cash, not new debt or shares, paid for most of IREN's capex. At NBIS and CRWV it paid for a small share. 6. Operations are the weak point. Some people are questioning the quality of the GPU cloud service IREN delivers to end customers. We share the concern, and it is not new to us. We recorded it in our 1Q26 rebalancing meeting and again in 2Q26 and 3Q26. One of our biggest concerns on IREN is that the CEO treats software as easy, low-value work next to power, land and data centres. That includes the orchestration software that schedules customers' jobs and manages the GPUs. We expected this to slow IREN's growth, not to threaten the business, for three reasons. (A) In our view the bottleneck in AI infrastructure today is powered capacity, not the know-how to run GPU fleets. IREN said on 27 Aug that its recent 3-year contracts price above $20M of revenue per MW of computing capacity. It estimates about a 2-year payback on the GPUs and related equipment. It also said its 2026 capacity is largely sold out. (B) Hyperscalers and other bulk buyers of IREN's capacity can manage and optimise GPUs better than IREN can. What they need from IREN is powered GPU data centres delivered on time. MSFT's $9.7B contract is for GB300 GPUs in four 50MW buildings. MSFT accepted the first, Horizon 1, in August 2026. IREN targets Horizons 2–4 in the December quarter. (C) $NVDA is partnering with IREN and is trying to make running large GPU fleets more standard and simpler. In our view NVDA wants standard fleet-management software so that more companies can run its GPUs well, which makes its chips easier to buy and use. On 7 May 2026 NVDA signed a five-year, $3.4B managed GPU cloud contract with IREN that uses Mirantis orchestration software. The same day it announced a partnership to deploy up to 5GW on NVDA's DSX reference designs. DSX is NVDA's standard set of designs and software for building and running AI data centres. In its 31 May 2026 DSX release, NVDA says the DSX OS software handles tasks such as keeping GPUs healthy, scheduling jobs and running several customers on one cluster. The same release names IREN, CRWV and NBIS among the cloud partners deploying core parts of DSX. The risk in (C) is that IREN's CEO may be too optimistic about how capable NVDA will be at this and how soon. 7. IREN is buying software and hiring managers to run its cloud better. It bought Mirantis, a cloud software company with more than 1,500 enterprise customers, on 4 Aug 2026. IREN said at completion that it paid about 12.6M shares plus about $40M. In July 2026 it hired a product chief from $ORCL (its cloud unit), a data-center development chief from $GOOGL and CyrusOne, and a security chief from $NTNX. We found no hire of a head of cloud operations. We would change our view in three cases. First, if a Horizon delivery for MSFT is missed or rejected. Second, if the Q1 FY27 report arrives with no new contract for 2027 capacity announced. Third, if IREN's weak end-user experience turns out to come from its infrastructure, meaning power, cooling, networking, storage or the GPU hardware itself, and not just from its orchestration software. The third matters because bulk buyers can replace IREN's software with their own, but they depend on IREN for the site and the hardware.
Meta and Alphabet can fund AI with core profits, proprietary data and in-house infrastructure, giving META and GOOG pricing and…
Cain Lee 2 clicksAnthropic's $518bn of planned compute obligations is about the same size as $AMZN's entire stack of debt, leases and purchase commitments. Convequity's ABBX barometer adds up borrowings, leases, purchase obligations and leases not yet started for each hyperscaler. On that all-in measure, AMZN and Anthropic are roughly level. 1. Against valuation, the number looks normal. At the $2 trillion valuation being debated, Anthropic's obligations are about a quarter of its value. That is close to $GOOGL's ratio and inside the range of the four big hyperscalers. 2. Against revenue, it is large but not extreme. Anthropic told investors its annualized run rate was above $65bn at the end of July. The obligations are about eight times that, spread over roughly a decade. A run rate is the latest month multiplied by twelve, not full-year booked revenue, and the hyperscalers fund their stacks from large, established cash flows. 3. Reuters reports that about 80% of the obligations are non-cancelable or payable regardless of usage, while many of Anthropic's largest customers are not on long-term contracts. I think the size is about right. It fits a $2 trillion company, and at the July pace Anthropic can grow into it. The risk is in the terms: most of the cost is locked in for years, while much of the revenue can leave at short notice. The number to watch now is how much of Anthropic's revenue is under long-term contract.
Convequity’s AI Bubble Barometer (ABBX) — Week of 2 October 2026 Our weekly framework tracks valuations, returns, capex, financing risk and infrastructure capacity across hyperscalers, neoclouds and model labs. Friday 2 Oct 2026 close (US ET): - Price drove the move: EV rose about $44B to about $14,114B. Invested capital (IC) was unchanged at $2,338B. - EV/IC: 6.04x, up 0.02x from 6.02x on 25 Sep. - All-in EV/IC: 6.76x. 1. Returns. Our paired, annualized forward ROIC (which is not trailing accounting ROIC) for Q2 2026 is 30.0%, against an AI WACC of 9.8%: 3.07x ROIC/WACC and a spread of +20.3 percentage points. 2. Capex. Latest-quarter cash capex is $228.7B. 3. Debt. Borrowings are $595B. All-in exposure is $3,624B, or 6.10x borrowings, adding purchase obligations, construction commitments and uncommenced leases on top of borrowings and leases. 4. Gigawatts. Current capacity is 45.7 GW, with 153.4 GW planned. $MSFT versus $GOOGL: MSFT’s all-in exposure is about 17x borrowings, and it is renting more: external capacity is about 12% now and 21% of planned capacity. GOOGL’s all-in exposure is 10.2x borrowings, while its external capacity share remains small at about 4% now. We see a price-led rise in valuation, with modeled forward returns still well above the cost of capital. Future spending commitments are much larger than borrowings alone suggest, and the reliance on rented capacity differs across companies.
The Muse point is exactly why I think perception is increasingly the whole game. If $GOOGL acquired Muse competitor Instinct tomorrow, does $META’s implied multiple compress before a single earnings estimate changes? Probably. The market is repricing perceived competitive advantage long before anyone can quantify the actual economics.
$GOOG TPU cost per million tokens vs $NVDA Blackwell. Advantage: Google Will be interesting to see the 3P TPU ecosystem evolve now that GCP is focused on it
@finphysnerd Strongest reason I see to also own some $MSFT is they have a lot of openAI exposure, whereas $GOOG $AMZN only have Claude.
@finphysnerd TPUs are great and $GOOG has a long head start. I own $AMZN also. $MSFT's businesses seem at least as threatened as $GOOG's to me.
@orrdavid Why would you own $GOOGL over $AMZN and $MSFT which are essentially the same Capex bet but with less vulnerable businesses attached to them.
@jeremiahrogers Gemini will be as good as ChatGPT and Claude for 99% of people's normal use cases soon ... if not already. If $GOOG can connect their whole locked in ecosystem, and all its personal data, to the responses, it will be better for more things than not.
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