Ep 70 is an interview — "View" is the show's net read on each name; the speaker who made the argument (Ives, Luria, or Eisman) is named in "What he said." Research: QT Qualtrim · SA Seeking Alpha · STK Stock Analysis.
| Ticker | Name | Research | View | What he said | At |
|---|---|---|---|---|---|
| MSFT | Microsoft | QT · SA · STK · FA | Positive | Luria's #1 winner: it "gets the raw end of both" debates (over-spending hyperscaler and doomed software), yet "Microsoft has accelerating growth right now because they're actually executing very well where AI is a tailwind not only to the Azure business but to the office business and the infrastructure software business… and yet they're both trading at these very low multiples." Five years out "I'm still going to… get on Outlook and use Teams and PowerPoint and Excel and Word… guess who is going to stand in front of the model when that happens? Microsoft." | 35:38 |
| PLTR | Palantir Technologies | QT · SA · STK · FA | Positive | Luria's top-three winner: "there's really good software companies like Microsoft, especially Palantir." Alex Karp's pitch, translated by Ives/Luria: be model agnostic — put your data inside Anthropic's or OpenAI's model and "they know how your business operates," and if something happens to that model "you're screwed"; the value sits in the data and ontology, not the LLM. | 36:06 |
| MU | Micron Technology | QT · SA · STK · FA | Positive | Luria's cleanest dislocation: "Micron sells like it's six times earnings… as if the cycle is over," while Intel at 100× and Cerebras are priced "as if this cycle is continuing through 2030." "That is inconsistent." And the ranking has flipped: "historically the CPU market's been a little better than memory… as we sit here today, I can make an argument that the memory chip market is much better than the CPU market." | 34:14 |
| NVDA | NVIDIA | QT · SA · STK · FA | Positive | Ives' #1: "there's one chip in the world fueling the AI revolution… I don't even think there's a debate. A third-rate Nvidia chip is a year and a half to two years ahead of Huawei" — "it's really like their world, everyone else paying rent." Plus the multiplier: "for every dollar spent on an Nvidia chip, we estimate there's $8 to $10 multiplier across the rest of tech." Luria adds Nvidia is now building its own free open-source model (Nemotron) because "open source models use just as much compute as closed source models." | 36:45 |
| AAPL | Apple | QT · SA · STK · FA | Positive | Ives: "Apple is the EasyPass on the consumer AI highway… 20% of the world is going to access AI through an Apple device," and it now has "a strategy that you can monetize the 2.5 billion iOS devices, 1.5 billion iPhones." The stance is deliberate: "Apple's standing back and saying, we'll let you all worry about that… whoever wins we'll use in our models. That's what new Siri is." Eisman owns it but flags it as "a little on the outside looking in" — though its balance sheet and cash flow are "better than anybody else's." | 25:01 |
| CRWD | CrowdStrike | QT · SA · STK · FA | Positive | Ives' third winner is the sector, then the name: "cyber security budgets are going to double the next two or three years… every agent — if Steve Eisman has three agents… they have to protect three agents. It's just more surface area." Best-positioned on product and CEO: "CrowdStrike and Palo Alto were the ones… they're just able to see around corners" — "something I think investors are underappreciating." | 38:38 |
| PANW | Palo Alto Networks | QT · SA · STK · FA | Positive | Ives' other cybersecurity pick, named alongside CrowdStrike as the two best-positioned "from a product perspective and CEOs, what they've done." The March fear that Anthropic would "eat cyber security" with its own product was, in his read, backwards — agents multiply the attack surface and expand budgets. | 38:38 |
| ORCL | Oracle | QT · SA · STK · FA | Positive | Luria — who called the Sep-2025 top ("I'm always worried when everybody's on a bandwagon") — now takes the other side: OpenAI raised $122B, "the largest fund raise in history," went into "code red" and narrowed to compute, so "they are going to pay their Oracle bills… their entire backlog, $630 billion worth of backlog of compute revenue is valued by the market at zero." Ives: "or you could almost say negative." | 31:14 |
| GOOGL | Alphabet (Google) | QT · SA · STK · FA | Positive | Luria: "Google is an AI winner" — a year ago at $180 it was the AI loser; by end-2025 it was the only winner ("a flippity flip"); now there's a pullback because "Google's model is no longer state-of-the-art… a distant second consumer chat… distant third on enterprise AI. So maybe they're not the winner, they're a winner." The kicker: search advertising growth accelerated — "instead of it dying, it accelerated… because they're using AI to sell us more ads for more money." Ives: "the best positioned hyperscaler relative to the crowd," and the $85B raise "was the right move." | 20:59 |
| NOW | ServiceNow | QT · SA · STK · FA | Positive | Explicitly kept out of the loser bucket. Ives: "look at Bill McDermott and ServiceNow — I wouldn't put them" with the losers. Luria puts it on the winning side of software consolidation: the CIO cutting from 100 packages to 30 keeps the big platforms — "Microsoft and ServiceNow and Adobe and even Salesforce can do this for me and I don't need these smaller companies." | 39:57 |
| AMZN | Amazon | QT · SA · STK · FA | Neutral | Luria names it as one of "the three big hyperscalers" (with Microsoft and Google) that buy the chips and provide compute — a layer of the value chain that "adds just as much value, if not more, if the model is open source." Eisman's closing: the moat, if there is one, comes from Microsoft/Google/Meta/Amazon building out the data centers. Referenced as a layer, not rated. | 12:01 |
| META | Meta Platforms | QT · SA · STK · FA | Neutral | Cited on both sides. Eisman's capital-intensity charge: "Microsoft never raised any capital. Meta never raised any capital" — and now the business is capital-intensive, "which all other things being equal is a negative." Ives: investors underestimate that Meta can "use part of what they spend in capex to ultimately… monetize it." Luria on Llama: American labs avoid open source because "you can charge a lot more for a closed-source model" — "Meta tried an open-source model… it didn't really go that well." | 23:21 |
| AMD | Advanced Micro Devices | QT · SA · STK · FA | Neutral | The middle term in Luria's multiple comparison: "Micron six times earnings. AMD 50 times earnings. Intel 100 times earnings" — and "Intel and AMD make CPUs… Micron makes memory," in a world where he argues memory is now the better market. Priced closer to the cycle-continues camp than the cycle-is-over camp. | 34:34 |
| TSM | Taiwan Semiconductor (TSMC) | QT · SA · STK · FA | Neutral | Luria's first link in the value chain: "the companies that make the stuff that makes chips, primarily ASML and TSMC." Ives adds the field note from a Taiwan fab where "they're working 18 hours a day," and demand:supply for chips running "15 to 1" across his recent Asia trip. | 11:39 |
| ASML | ASML Holding | QT · SA · STK · FA | Neutral | Named with TSMC as the equipment layer that "makes the stuff that makes chips" — one of the three pre-model layers Luria argues capture value regardless of whether the winning model is open or closed source. | 11:39 |
| NFLX | Netflix | QT · SA · STK · FA | Neutral | Ives' analogy for a moat that only looks like one in hindsight: "Netflix was first. They built it. They spent a ton of money. At first investors didn't recognize and now where do you go? Netflix basically owns content." His claim is that hyperscalers are "step by step building their moat in front of us" the same way. | 10:39 |
| Moonshot | Moonshot AI — Kimi K3 (private, China) | — | Neutral | Eisman's third counterargument, framed as the LLM providers' nightmare: "the price that they charge for tokens is like a fifth of what the other LLMs are charging… I've got a business with no moats, everybody's spending a ton of money, all these Chinese companies are coming in at a much lower price. That spells to me price war." Put in the seat: "if I was the head of Anthropic or OpenAI… I'd be petrified because I'm charging five to seven times more than this model." | 8:37 |
| xAI | xAI (private) | — | Neutral | Counted in Luria's AI-revenue tally only: OpenAI + Anthropic run-rate is "clearly above $75 billion," and "by the time you include Gemini's revenue and maybe a little bit Meta and xAI we're above a hundred billion dollars of revenue from what was zero a couple of years ago." | 12:29 |
| INTC | Intel | QT · SA · STK · FA | Negative | The wrong half of Luria's dislocation: "for Intel to be worth what it is… this cycle has to go through 2030 because their current valuations are not otherwise justified." At 100× earnings it is "trading as if the cycle is continuing for five more years" — against a CPU market he argues is now worse than memory. (Note the contrast with the Jul-24 wrap, where Eisman called Intel's print a blowout.) | 34:59 |
| CRM | Salesforce | QT · SA · STK · FA | Negative | Luria's named loser: "Salesforce is in the category of software that they're trying to cut… because Salesforce has not been adding value to them in years and it keeps charging them more and more for that less value every year. That's a bad business that's been declining, regardless of AI." The mechanism is the AI budget squeeze — "there's a crowding out of products that didn't make their customers happy." Eisman singled this out in his closing as the most interesting call of the episode. | 35:38 |
| ADBE | Adobe | QT · SA · STK · FA | Negative | Ives' loser: "names like Adobe, where you had such a moat, you have such an install base — and they essentially miscalculated what AI is going to do to the business model." His template is the company that "sits on a treadmill at 2.5 speed… no different than 1995, a typewriter company" that put out a press release saying "this internet thing… we're sticking to our guns" and "a year later they were bankrupt and gone." | 38:58 |
| INTU | Intuit | QT · SA · STK · FA | Negative | Ives: "you could say the same thing for names like Intuit" — same miscalculation as Adobe. The threat as he frames it: the models "could actually do your taxes," so the question is "what does it ultimately take out of its market share." Same 1995-typewriter framing. | 39:31 |
| IBM | IBM | QT · SA · STK · FA | Negative | Eisman drops it in as the live evidence for Luria's budget-squeeze mechanism — "hence the problems IBM had last week when they pre-announced, which was stunning." Luria: "Exactly." Companies "have to spend so much on AI right now that they're looking at their budget and saying where can I cut." | 35:23 |
| Cerebras | Cerebras Systems | — | Negative | Named with Intel as the other name whose valuation only works "if this cycle is continuing through 2030" — Luria: "for Cerebras to be worth what it is, for most of the semicap and optical companies to be worth what they're trading at today, this cycle has to go through 2030 because their current valuations are not otherwise justified." | 33:53 |
| Medallia | Medallia (private, PE-owned software) | — | Negative | The SaaSpocalypse's first body, per Luria: PE-owned software companies "that's gutted them, that are not renewing their products… because the private equity assumed that the stream goes on forever. Those companies are going to be gone." And: "it's already happened. It happened in Medallia last week… this is why there's distress around private equity." | 40:46 |
| Anthropic | Anthropic (private) | — | Negative | The moat question lands here. Eisman's wrap: "unclear still to me how much of a moat Anthropic or OpenAI have." Luria's two charges: the model-dependency risk — "the government told Anthropic to rein in Fable and they didn't… if you were a business that built your business directly on top of a Fable model, you're out of business"; and the policy play — "why are Sam and Dario scaring us? Because they're pulling the ladder. What they want is friendly regulation. They want to shut out open source, shut out Chinese companies… so those two are the only winners." Credit where due: combined OpenAI+Anthropic run-rate is above $75B, "real economic activity." | 48:58 |
| OpenAI | OpenAI (private) | — | Negative | Two-sided. The bear case: closed-source pricing "five to seven times" Kimi K3, and on Sep-11-2025 it "did not have money… very little revenue" behind $1.4T of commitments. The repair: "$122 billion, the largest fund raise in history… they went into code red… they narrowed their focus to only the things that really matter, which is really compute" — so Oracle gets paid. Still, Eisman doubts the moat, and Luria puts it with Anthropic in the "pulling the ladder" regulatory-capture charge. | 30:43 |
| Huawei | Huawei (private, China) | — | Negative | Ives' measure of the US lead: "a third-rate Nvidia chip is a year and a half to two years ahead of Huawei in China… any big Chinese tech company would want an Nvidia chip over Huawei." He extends it to policy — "for the first time in 30 years, the US is ahead of China," and the only way that reverses is domestic data-center moratoriums. | 36:45 |
"View" is the episode's net read (Positive / Neutral / Negative), attributed by speaker in the cell — not a price rating. Through-line: the AI debate has split into layers, and the money is in noticing where the market prices two layers inconsistently (MU vs INTC), where a backlog is marked at zero (ORCL), and which software vendors a budget-squeezed CIO consolidates into (MSFT, NOW, PLTR) versus out of (CRM, ADBE, INTU, and the whole PE-owned private tier). Research: QT Qualtrim · SA Seeking Alpha · STK Stock Analysis.
A jargon-free summary of the view on each name — what it is and why it was framed that way, with the speaker named. (Plain-language companion to the table above; renders on each ticker's consolidated page.)
Micron makes memory chips — the DRAM and high-bandwidth memory that sits next to an AI accelerator and feeds it data. Gil Luria's argument isn't about the company at all; it's about a contradiction in how the market is pricing the chip sector.
Two groups of chip stocks imply two opposite futures. Intel (about 100× earnings) and Cerebras are only worth their prices if the AI spending cycle keeps running through 2030. Micron at roughly 6× earnings is priced as if the cycle is already finished — as if next year's profits fall off. Both cannot be true, and that gap is what Luria calls a "dislocation." On top of that the usual hierarchy has flipped: CPUs (Intel, AMD) were historically the better business than memory, but "as we sit here today, I can make an argument that the memory chip market is much better than the CPU market." Cheap price on the better business, expensive price on the worse one.
Intel is the other end of the same trade. At roughly 100× earnings, Luria says the valuation "is not otherwise justified" unless the AI build-out keeps running at full speed for another five years — a very specific bet most owners probably haven't consciously made.
The business behind it is the CPU market, which he argues is now the weaker of the two chip markets. So you are paying the highest multiple for the part of the cycle he trusts least. Note the deliberate tension with the previous week's wrap, where Eisman called Intel's actual quarter a blowout: the print was great, but a great print at 100× is not the same thing as a good investment.
Nvidia designs the chips almost every AI data centre is built around. Dan Ives' claim is that there simply isn't a contest: "a third-rate Nvidia chip is a year and a half to two years ahead of Huawei," so even Chinese buyers want Nvidia — "it's really like their world, everyone else paying rent."
His second point is about how far the money travels. For every dollar spent on an Nvidia chip, he estimates "$8 to $10 multiplier across the rest of tech" — CPUs, memory, networking and telecom gear, data-centre cooling and power. That is why he treats a strong Nvidia order book as an indicator for the whole complex rather than one company's results.
Luria adds a wrinkle that cuts the same way: Nvidia is now funding a free open-source model (Nemotron), because open models consume just as many chips as closed ones and Nvidia would rather the world's free model be American than Chinese. Giving software away to protect hardware demand.
Salesforce sells the software companies use to track customers and sales. Luria's case against it doesn't need AI to work: "Salesforce has not been adding value to them in years and it keeps charging them more and more for that less value every year. That's a bad business that's been declining, regardless of AI."
What AI adds is a forcing event. Company technology budgets are not growing fast enough to cover the new AI spending, so chief information officers have to fund it by cancelling something. The first things cut are the products people don't feel they're getting value from — and Salesforce, on this reading, is on that list. Eisman singled this out in his closing as the sharpest call of the episode.
Palantir builds the layer that sits between a company's own data and whatever AI model it uses — organising the data, defining what the terms mean, and running the workflows on top. Luria names it with Microsoft as one of the "really good software companies."
The pitch, as the guests translate CEO Alex Karp, is model agnosticism: don't wire your business directly into one AI provider. Two risks if you do. First, competitive — put your data inside a model company's system and "they know how your business operates," and they may later compete with you. Second, existential — "if you build your business on top of a model… and something happens to that model, you're screwed." Their live example: when regulators forced a change to one Anthropic model, any business built directly on it was suddenly out of business. Palantir sells the insulation against that.
Microsoft is punished twice over by the current AI mood — once for spending heavily on data centres that may not earn a return, and once as a software company AI is supposed to make obsolete. Luria's view is that both fears are misapplied to this particular company, which is why it is his number-one pick.
On the software half: agents will be using Excel, Word, Outlook and Teams, but the human is still there too, and "guess who is going to stand in front of the model when that happens? Microsoft." On the data-centre half: growth is accelerating right now — Azure, Office and the infrastructure software business are all being helped by AI, not hurt. "And yet they're both trading at these very low multiples." Good business, bad narrative, low price.
Oracle rents out computing capacity to AI companies, above all OpenAI. Its share price has been a round trip — about 200, then 330 on a single enormous backlog announcement in September 2025, then down to about 140 when it emerged that most of that backlog was one deal with a customer that had almost no money.
Luria was the analyst who doubted the 330 and is now the analyst arguing the 140 overshot. OpenAI has since raised $122 billion — "the largest fund raise in history" — and cut its spending back to compute only, which means Oracle's bills get paid. The asymmetry he points to: the market currently assigns Oracle's entire $630 billion of contracted future compute revenue a value of zero. You are paying for the old Oracle and getting the AI backlog for free. (Eisman's own wrap four days later adds the caveat: roughly half that backlog is a single customer.)
Google's AI reputation has swung completely twice in eighteen months — from certain loser (AI chatbots kill search, regulators break it up) to sole winner (its own chips, its own model, its own cloud) and now to something in between. Luria's landing point: "maybe they're not the winner, they're a winner."
The negatives are real: the Gemini model is "no longer state-of-the-art," it's a distant second in consumer chatbots and third in selling AI to enterprises, and a bureaucracy loses researchers to startups. The positive is the part everyone predicted would break: search advertising growth accelerated, because AI lets Google sell more ads for more money. Cloud growth accelerated too. Ives calls it the best-positioned hyperscaler and defends the $85 billion share sale as the right move — in an arms race, being unable to spend is the bigger risk.
Apple is the one big technology company not spending enormous sums on AI, which is either a strategic failure or the whole point depending on who you ask. Eisman, who owns it, admits it's "hard to figure out exactly where they are in this ecosystem" — while noting its balance sheet and cash flow are "better than anybody else's" precisely because it isn't spending.
Ives' metaphor is a toll booth: "Apple is the EasyPass on the consumer AI highway." Roughly a fifth of the world will reach AI through an Apple device, and Apple now has a way to charge for that across 2.5 billion iOS devices. The new Siri is the strategy in miniature — Apple doesn't build the model, it rents whichever one is best and brands the result as its own: "you're not going to call it an OpenAI model… you're going to call it New Siri. And so we win anyway." Let the others burn the capital and take the headline risk.
CrowdStrike sells the software that protects a company's computers and cloud systems from attack. Ives' argument is a budget one: cybersecurity spending should "double the next two or three years."
The reason is agents. Each AI agent a company deploys is another thing that can be attacked or hijacked — "if Steve Eisman has three agents… they have to protect three agents. It's just more surface area." The earlier fear that Anthropic would launch a security product and wipe the sector out got the direction backwards: more AI means more to defend, not less. He rates CrowdStrike and Palo Alto the best-positioned on product and management.
Palo Alto Networks is the other name in Ives' cybersecurity pair — network and cloud security sold to large enterprises, increasingly as one bundled platform rather than a dozen separate tools.
Same thesis as CrowdStrike: budgets doubling as AI agents multiply what has to be defended, and management that has "been able to see around corners." Note the consistency with Luria's consolidation logic elsewhere in the episode — when a CIO cuts from a hundred software vendors to thirty, platforms that can absorb several jobs at once are the survivors.
ServiceNow runs the internal workflow software of large organisations — IT tickets, HR requests, service processes. It has been one of the loudest casualties of the "SaaSpocalypse" narrative, so it matters that both guests explicitly refuse to put it in the loser bucket: "I wouldn't put them" there, says Ives.
Luria places it on the winning side of the same squeeze that is hurting Salesforce. When the CIO consolidates from a hundred software packages down to thirty, ServiceNow is one of the platforms that absorbs the work — "Microsoft and ServiceNow and Adobe and even Salesforce can do this for me and I don't need these smaller companies." The consolidation that kills the small vendors feeds the big ones.
Adobe sells the creative and document software (Photoshop, Acrobat, and the rest) that designers and marketers have used for decades — exactly the kind of work generative AI does cheaply.
Ives' criticism is not that the moat never existed but that management misjudged what AI would do to the business model: "you had such a moat, you have such an install base — and they essentially miscalculated." His comparison is a 1995 typewriter company that issued a press release insisting the internet changed nothing, and was "bankrupt and gone" a year later. The failure mode is denial, not competition.
Intuit sells TurboTax and QuickBooks — tax preparation and small-business accounting. The threat Ives describes is direct: general-purpose AI models are getting good enough that "could it actually do your taxes?" stops being a rhetorical question, and the real issue is "what does it ultimately take out of its market share."
Same diagnosis as Adobe: a strong installed base whose owner misjudged the speed of the change. Note that neither guest quantifies the damage — this is a directional call on business-model risk, not a forecast.
Medallia is customer-experience software, taken private by a buyout firm. It matters here as the first actual failure of the pattern Luria says will produce most of the software carnage — and the reason he insists the SaaSpocalypse is mostly a private event.
The private-equity model for software is to buy the company, stop spending on the product, and collect the subscription revenue ("I buy the software company, milk it — the revenue will continue"). That works while customers renew out of habit. It stops working when budgets get squeezed by AI spending and the CIO goes looking for things to cancel — and small, unimproved products are cut first. "It's already happened. It happened in Medallia last week… this is why there's distress around private equity."
The corollary is a credit point worth keeping: the listed software companies "are in a net cash position. They don't borrow money… software debt is private equity." The loans at risk are not on the public companies' balance sheets.
Anthropic builds one of the two leading closed-source AI models — closed meaning customers can only use it through Anthropic, which controls the code and the settings. That control is the business model and, on this episode's argument, also the vulnerability.
Three charges. Pricing: Chinese open-source models cost a fraction as much, so a closed provider is charging "five to seven times more" for something claimed to be equivalent — "I'd be petrified," says Eisman. Dependency: when regulators ordered a change to one of its models, any customer that had built directly on top of it was left stranded overnight, which is now an argument for never depending on a single model. Politics: Luria accuses Sam Altman and Dario Amodei of "pulling the ladder" — pushing a scary job-loss narrative in order to win regulation that keeps out open source and Chinese rivals, "so those two are the only winners."
The counterweight, which Luria supplies himself: Anthropic and OpenAI together are already running above $75 billion of revenue from nothing two years ago. The demand is real; the durability of the margin is what's in doubt. Eisman's own conclusion is simply "unclear still to me how much of a moat Anthropic or OpenAI have."
OpenAI is the other big closed-model provider, and the episode tells its financial story as a cautionary tale that partly resolved. In September 2025 it committed to roughly $300 billion of computing from Oracle and around $1.1 trillion more elsewhere while having "no capital" and "very little revenue" — which is what eventually took Oracle's stock from 350 to 140.
Since then it has raised $122 billion, described the trillion-plus commitments as flexible rather than fixed, and gone into "code red" — cutting everything except compute. That is enough, Luria argues, for it to pay Oracle, which is his reason for liking Oracle.
What hasn't been fixed is the same moat problem as Anthropic: cheap open-source competition, enterprises switching between models to control costs, and no established side businesses to fall back on. Eisman's follow-up wrap makes the health of these two companies the single thing to watch as a trigger for a broader AI sell-off.
Cerebras builds very large specialised AI chips. It appears in the episode purely as a valuation exhibit: alongside Intel, it is one of the names whose current price only makes sense if the AI hardware cycle runs uninterrupted through 2030 — "their current valuations are not otherwise justified."
The point is not a company-specific criticism; it's that owning it and owning Micron at the same time means holding two incompatible views of the cycle at once.
Moonshot is the Chinese lab behind Kimi K3, the model Eisman uses as the third leg of his bear case. Its significance is entirely about price: tokens — the units AI providers bill by — cost roughly a fifth of what the American closed-source labs charge, for a model claimed to be just as capable.
If that claim holds, the American labs face a choice between losing customers and cutting prices, which is what "price war" means in practice. Note the episode's counter-argument: cheap open models don't hurt the chipmakers, the equipment makers or the cloud providers at all, because open models "use just as much compute as closed source models." The price war, if it comes, is confined to the model layer.
Meta appears on three sides of the argument, which is why the net read is neutral. As evidence for Eisman's capital-intensity charge, it is one of the companies that "never raised any capital" and now runs a business that consumes it. As evidence for Ives' optimism, investors underestimate that Meta can turn part of its capex into a monetisable business rather than pure cost.
And as evidence in the open-source debate, Llama is the cautionary case: American labs avoid giving models away because "you can charge a lot more for a closed-source model," and Meta's attempt "didn't really go that well." Eisman's own wrap a few days later resolves the ambiguity much less kindly.
Summary & timestamps derived from the public YouTube video (transcript in transcript.txt) for personal study. The mid-roll advertisement is omitted from the archived transcript. Not investment advice. © The Real Eisman Playbook / Steve Eisman for source material.