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172 stories

$3.5T Market cap · 2026-11-06

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@BlueDuckCap

BDC on X

Evan, it's disingenuous to offer Waymo as evidence of an instance where they should NOT have raised external capital. Fact is, $GOOGL Search is one of the most profitable and dominant businesses of all time supporting billions of cumulative earnings, FCF, and trillions in market cap. The margin for error at $SNAP is much thinner; the cumulative score card at SNAP measured by stock price, is not even in the same universe as $GOOGL. Sure they could have funded Waymo internally. Maybe they should have. But SNAP is not GOOGL. Not even in the same conversation.

NEW POSITION: Google LEAPS

Alphabet bought long-dated GOOGL calls after a 12.6% earnings drop, betting AI monetization and distribution overcome capex pressure and…

GOOGL $3.5T
Hated Moats 1 click
@aleabitoreddit

Serenity on X

I'm going to give a contrarian take on Neoclouds like $NBIS and say the theme looks compelling after the valuation crash. Since there's a reason why Elon's $SPCX is heavily focused on this market full of "guppies" that would die: He sees how attractive the economics are after selling compute to $GOOGL and Anthropic. And this is reflected by his actions not words, from SpaceX's recent $40B financing for GPUs procurement. $AMZN, $MSFT, and even $META also see how profitable the economics are and are accelerating their AI DC buildouts. I'd argue if a Neocloud has: - Close relationships with $NVDA + others for allocations - Existing contracted capacity + permitting - Well funded counterparties, such as Hyperscalers. - Attractive economics, such as financing their procurement correctly (not the way $CRWV has with high debt interest unless they refinance). Then I think they'll do just fine... However, the bigger worry is more toward newer players starting financing or the permitting process, since barrier to entry has now become extremely high. For financing, read through on $IREN earnings has shown public Neoclouds can adapt extremely well to tight markets: As seen with their 90% prepayments for GPU capex on a 5Y deal. Their older H100 GPUs recontracted at double their previous pricing and 12% higher contracted pricing also point against depreciation fears. And in the case DC buildouts slow from permitting delays... it heavily favors some over others. Rosenblatt's recent note for example stated they see little risk with $NBIS primary buildout that was in Missouri, Pennsylvania, and South Carolina. And there's probably less risk with developers in places like Australia. Markets have also show this infinite compute arbitrage can't go on forever, hence the reluctance to further finance capex, via things like ATMs. But ironically, tighter financing and hypothetical restrictive permitting would increase the competitive advantage of established neoclouds. And create a larger moat against newer DC developers.
@StableBread

Fajasy on X

The companies spending the most on AI are paying for more of it with debt. To understand whether debt-funded spending like this is indicative of a bubble, we can look at William Quinn and John Turner’s bubble triangle, which says a bubble needs three sides: marketability (how easily an asset trades), money and credit, and speculation. The AI boom arguably has all three sides: → Marketability: Index funds held 52% of U.S. long-term fund assets at the end of 2025, up from 19% in 2010, and they buy each stock in proportion to its size, so the biggest AI stocks get the most of every new dollar. → Money and credit: $MSFT, $GOOGL, $AMZN and $META are on track to spend more than $730B on capex in 2026, up from $410B in 2025, and their Q3 reports didn’t change that. $AMZN still expects ~$220B, and $META narrowed its range to $135-145B. Investors are borrowing more too, with margin debt at $1.48T in September, just below June’s record $1.50T. → Speculation: AI startups raised 82.7% of the money U.S. venture capital firms invested in 2026 through September, a record share, up from 65.4% in 2025. So more of the private money is betting on a single theme. $META’s also raising money by selling stock. Its October 27 pricing term sheet shows it sold 40M shares at $625 through forward sale agreements, plus $12.5B of mandatory convertible preferred stock, which together bring in $37.4B of net proceeds once the forward sales settle by November 2027.
@aleabitoreddit

Serenity on X

Little rant, but to show you how rampant deleveraging has become: - $CIFR is worth 44% LESS than when they announced their initial $GOOGL / $AMZN colo deals. - $AAOI is now ~ the same price BEFORE they secured major hyperscaler orders, raised $1.6B for capacity expansion and gave their suprising initial -> $378M+ projection -> ~$471M revenue forecast. - $ESMT ($2B MC) is the perfect example representing the memory sector. B-BUT AI NAMES ARE NOT PROFITABLE??? Q3 Earnings: $526.2M gross profit, $330M net income. To give you a comparison of what that looks like, the net income of $CRWD ($251B MC) was literally $5.3M. $NOW ($113B MC) was $277M. $TSLA ($1.5T) was $787M. Different industries/durability, but if we care about what's actually hitting the balance sheet: ESMT is sitting on ~$937M in net assets ($740M net cash, $278M inventory, etc), so EV goes down to $1.2B ish. Annualized P/E: 1.6x... People keep saying "Peak Earnings", "Cyclical", "no moat" but who in the world would want to build an old ass DDR2 line + fab and qualify it? From ESMT commentary, we'll likely see ASP hikes Q4 to make up for potential consumer demand dropping. And further supply tightening with Samsung/Micron discontinuing DDR2/DDR3 lines. Winbond was leaving DDR2 lines to pursue higher end memory. People keep saying China, but even Gigadevice was leaving DDR3 to pursue higher end memory. "DDR3's share of our DRAM revenue will continue to decline until we eventually stop selling DDR3 products."Nanya discontinued DDR2 lines few days ago. This is while majority of ESMT revenue comes from DDR2/DDR3. In ~3 quarters of constant earnings, ESMT would have more assets than their entire MC. Who is going to stop buying $40 DLink cameras if their $3 memory cost goes to $3.5? $MU / Samsung / $SKHY / $SNDK share similar parallels where durability is contractually enforced via take/pay LTAs with price floors. But markets are pretending that it just won't happen, despite Samsung already being the most profitable company in the world. Markets should care about how much FCF is actually hitting the balance sheets, but are just indiscriminately selling off everything now. But continue rotating capital into 47x P/E companies like Walmart or 180x forward P/E names like $CRWD.
@SouthernValue95 1 click

SouthernValue on X

$GOOG superior monetization of free tier AI is a significant advantage over GPT: “US visitors from ChatGPT ads generated $0.89 in revenue per visit, versus $2.45 for Google Search ads – 64% less. Conversion rates were 0.37% versus 1.17%. Among 87 advertisers using both, 73 recorded zero ChatGPT-driven purchases. The researchers estimate a ChatGPT click is worth about $0.26 if advertisers want Google's return on ad spend. One advertiser reportedly paid $1.72 per click,”
@SouthernValue95

SouthernValue on X

The Anthropic S1/A should ease concerns over the risk of Anthropic backlog exposure for $AMZN $GOOG $AVGO: +$132B of cash and investments +$82B of undrawn loans +$50B run-rate revenue less non-compute cash costs +$100B IPO proceeds Against that, they must fund: -$70B compute due in the NTM, and -$447B over the next 4yrs. If Anthropic revenue is flat from here, they could still amply fund opex and compute commits in the next 4yrs with ~$60-70B to spare.
@convequity

Convequity on X

Spirit's data auction shows what office AI is missing. Google ($GOOGL) opened at $5m and was selected at $10m for Spirit Airlines' internal data in August. Mercor was the $7.5m backup. Then micro1 proposed $12.5m. The sale still awaits court approval, with a hearing scheduled for November 6. The material includes emails, Teams conversations and business records. Google's counsel confirmed that it wants the data for AI training. I think this competition for records of real work will keep pushing up the value of enterprise data. This fits our earlier argument about why coding agents advanced faster than office automation. Coding has public repositories and tests that provide fast feedback. In office work, the context behind a decision is often scattered across private documents and conversations. Knowing how to use a spreadsheet is only part of the job. Knowing why an exception was approved, and what happened afterward, is much harder to learn from the public internet. My read is that Google is preparing for that gap. Internal records could help models learn how work moves through an organization, provided those records can be turned into tasks with outcomes that can be checked. Also it remains an open opportunity for $MNDY, and other AI-native productivity startups to tacle. Gemini 4 Carbon already leads Artificial Analysis' AutomationBench-AA, which tests simulated workflows across business applications. Google is competitive in the kind of work this data could help models learn. Anthropic's IPO filing also puts adoption in perspective. As of September, over 50% of the Fortune 500 generated at least $100,000 each in trailing-year revenue for Anthropic. Among the publicly listed customers in that group, spending on Anthropic averaged under 0.1% of total expenses in 3Q26. We still think enterprise AI is falling short on office automation. Having access to a model tells us little about how much work a company can delegate to it. I find Google's enterprise opportunity more compelling over time than Meta's emphasis on personal AI. Google already has Workspace and Cloud as routes to customers. If better training on real work makes its models reliable enough to complete office tasks, it has a way to sell that capability. The unlock still requires models that can carry a task through the office environment with limited supervision. That remains some distance away. I think acquiring real work histories now could be a fruitful long-term bet for Google.

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