Fireside Alpha

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Fireside Alpha

Fireside Alpha

@firesidealpha

Summary and synthesis of the best business, technology, and consumer conversations

United States 가입일 Haziran 2026
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Fireside Alpha
Fireside Alpha@firesidealpha·
Jeff Bezos: The Moon is a gift "It's so near Earth. We can get there in 3.5 days. We can return in 3.5 days. You can go anytime you want. You don't have to wait for the planetary alignment to be just right. You can only go to Mars every two years or so... And then the Moon's gravity well is so much lower than the Earth's that when you get materials from the Moon, you can lift them off the Moon with 28x less energy per kilogram than is needed to lift something off the Earth... And that's a really valuable thing if you are producing liquid oxygen for example on the Moon, lifting that into space is very easy compared to lifting liquid oxygen off of Earth... So as we go about exploring the solar system which we will do and as we build colonies on Mars and so on, the Moon is an important first step."
Fireside Alpha@firesidealpha

Great fireside. Jeff Bezos (@JeffBezos) and Dave Limp (@davill) w/ host Mike Massimino (@Astro_Mike). Discussion centered around cost of launch as the key variable for things like orbital data centers to off-planet heavy industry, and more. Relevant for $SPCX. Notes below: 1/ The launchpad explosion was a gut punch that barely moved the timeline, and the recovery itself is the culture argument. Bezos says the long-lead items that usually set a schedule, the propellant tank farm holding liquid hydrogen and liquid oxygen, survived, and a booster sitting in the nearby integration facility took shrapnel but lived. Blue Origin pulled in 400 pieces of heavy equipment from a construction crew down the road working around the clock, cleared the debris, and started rebuilding the day before this talk. He still commits to flying New Glenn again before the end of the year, which makes the failure a stress test of the team rather than a reset of the program. 2/ The entire thesis is that space needs the same cheap shared infrastructure the internet had, so small players can do big things. Bezos frames Blue Origin's job as building the road to space, the heavy infrastructure other companies ride, the way global networks let two kids in a dorm room build a giant company over the last two and a half decades. The cost of admission to space is still very high, and lowering it is the precondition for any dynamic space economy. From an investment standpoint, the read is that he is positioning Blue Origin as a picks-and-shovels layer, not a single end product. 3/ Demand for launch is the proof the space economy is real, and the constraint is supply, not demand. Bezos calls demand for launch insatiable right now, with a tremendous backlog already on the books and every launch company in the same position. He names the drivers as LEO communications, national security missions, and, ahead, orbital compute and lunar resources. His one-line summary, that they are supply constrained and not demand constrained, is the cleanest signal in the whole conversation for where the bottleneck and the pricing power sit. 4/ Launch cost runs on a virtuous circle, and reusability plus rate manufacturing is how you spin it. Bezos lays out the loop directly: expensive launch forces satellites to carry long lifetimes and high cost, cheaper launch lets satellites get cheaper, cheaper satellites drive more launch demand, more demand means more practice, and practice drives cost down again. Reusable boosters are the entry point because a booster that lands can be reused many times, and rate manufacturing keeps the flywheel fed. He is explicit that you cannot count on the exact timing, only the direction. 5/ The hard part is not building one rocket, it is building the machine that builds the machines. Limp, who says he came from consumer electronics and has done aerospace for two and a half years, argues that any single engine or rocket is tractable, and that the factories pushing them out at rate are what take time and thought. Blue Origin is standing up an engine factory in Huntsville and a rocket factory in Orlando, with heavy vertical integration down to raw materials. The stated goal is a hundred flights a year, which means a hundred second stages and hundreds of engines, so the factory is the product. Bezos adds that a BE-4 engine now comes off the line every four days. 6/ Rocket engines live right at the limit of physics, and the BE-7 just set a multi-decade endurance record. Bezos puts main combustion chamber temperatures at 5,000 to 6,000 degrees Fahrenheit, past the melting point of any material, which forces lightweight high-performance turbo pumps and regeneratively cooled channels just to keep the hardware intact. The BE-7, the roughly 10,000-pound-thrust lunar lander engine, ran continuously for 41 minutes, beating a 36-minute space shuttle main engine test from about 30 years ago. He frames reliability on that engine as paramount because it is what lands humans on the moon. 7/ Choosing liquid hydrogen for the lander is a bet on refueling from lunar ice, not just on performance. Bezos explains that liquid hydrogen outperforms hydrocarbon fuels but is so low in density it would make a booster stage gigantic, so it earns its place on upper stages, the same split the Apollo Saturn V used. The forward-looking reason is that water ice in permanently shadowed craters near the lunar poles can be electrolyzed into liquid oxygen and liquid hydrogen. By matching the lander's fuel to what the moon can produce, he sets up in-situ refueling on the surface rather than hauling every kilogram from Earth. 8/ Moon first is a sequencing discipline, and the moon's case is proximity plus a shallow gravity well. Bezos calls the moon a gift, reachable in three and a half days each way, available anytime rather than on the two-year planetary window Mars demands. The economic hook is that the moon's gravity well is so much lower that lifting materials off it takes 28 times less energy per kilogram than lifting them off Earth, which makes lunar-produced liquid oxygen cheap to move into space. His framing that skipping steps does not actually make you faster is the sequencing argument behind going to the moon before Mars. 9/ Blue Origin's near-term lunar cadence is concrete, and it ramps fast. Limp lays out a Mark 1 lander early next year carrying three metric tons to the surface as a Pathfinder, the largest object ever landed on the moon, then a mid-year Artemis 3 rendezvous with the human-rated Mark 2 lander meeting near 450 kilometers in low Earth orbit, then a second Mark 1 landing NASA's VIPER rover late in the year. He says Artemis 3 was announced the prior week and that an astronaut named Luca from Italy will be on the flight. He stresses these are coming off an assembly line now, and the team is named the lunar permanence group, which signals the intent to stay rather than visit. 10/ As a species we are barely warming up, which is how Bezos frames every timeline. Bezos pushes back on the idea that the world has caught up to Blue Origin, saying that in space terms humanity has not even begun and this is the earliest of early days. He separates the Apollo landings, which he calls a real accomplishment, from the permanence Blue Origin is now after. The investor consequence is patience, because he treats current capability as a starting line, which is consistent with the multi-decade horizon Limp describes him holding. 11/ Apollo was pulled forward in time at an unsustainable price, and Bezos argues now is the affordable moment. He notes the United States spent almost 3 percent of GDP to reach the moon during the race with the Soviets, a level no one would sustain today. His point is that the first lunar program was a geopolitical sprint done before the economics were ready, while the current push can be built on cost-effective, repeatable hardware. His aside that 3 percent of GDP would buy fusion is the same idea sharpened: cost discipline, not raw spending, is the constraint he respects. 12/ A lot of compute is heading to orbit, and Bezos thinks the chips eventually get made there too. He expects compute and solar cells built in space using asteroids, near-Earth objects, and the moon, with answers beamed back to Earth, and says ultimately even the chips the compute runs on get manufactured off-planet. He pairs this with Project Sunrise, an orbital-compute constellation in sun-synchronous orbit. The framing turns space from a launch market into a manufacturing and compute market, which is a far larger pool than payload delivery. 13/ Orbital data centers are a cost question, not a physics question, and Bezos says the lines will cross. He dismisses heat rejection as an easily solvable physics problem and reframes the whole debate as the cost of producing solar cells, satellites, and launch. Project Sunrise sits in sun-synchronous orbit where satellites are in sunlight more than 99 percent of the time, so solar collection runs nearly continuously instead of the half-darkness of a typical 90-minute low Earth orbit. He concedes he does not know exactly when terrestrial and orbital compute costs cross, only that you have to build now to be ready when they do. 14/ Blue Origin is building two constellations aimed at the high end, and the scale is what keeps launch constrained. The high-bandwidth LEO network, TeraWave, is described as fast earth-to-space optical links optimized for enterprises, hyperscalers, and governments, distinct from the consumer focus of Starlink and Amazon's LEO. Its first phase alone is 5,000-plus satellites in LEO plus another hundred in medium Earth orbit, with shells that want to be 5,000 to 20,000 each. Each constellation can absorb hundreds of launches, which is exactly why launch capacity stays the binding constraint. 15/ Prometheus is Bezos's bet that AGI for engineering needs different training data than a language model. He describes Prometheus as a set of tools to let engineers invent and build faster, and says it cannot be done with traditional large language models. His analogy is that an LLM has read the entire corpus of human knowledge and is unbelievably good at symbol manipulation, but reading a thousand books on gymnastics would still leave you a terrible gymnast. Designing real physical objects needs a different kind of training data, so Prometheus is a model built specifically to do engineering. 16/ The payoff Bezos wants from Prometheus is compressing the dream-to-build cycle from a decade to a year. He says a modern jet engine with 10 percent more thrust is a ten-year program even for someone who has built fifty of them, counting design, testing, and standing up the factory. The Prometheus goal is to take that to five years, then three, then two, then one. He ties it back to a civilizational claim, that all wealth comes from invention, from the plow to the steam engine, so accelerating the invention loop compounds into real productivity and prosperity. 17/ Bezos argues AI creates a labor shortage rather than redundancy, because invention is bottlenecked by capability, not ideas. He says he totally disagrees that AI makes humans redundant, and that it instead lets people identify and tackle an endless backlog of problems. His claim is that we are limited today not by imagination but by what we can actually build, and that most good ideas die in someone's head because executing them is too hard. He points to vibe coding as the early version, where he can now write an iOS app in an afternoon, and wants the same speed for physical objects coming off a 3D printer. 18/ Decisiveness is a sorting problem between one-way and two-way doors, and treating every call as irreversible is what slows big companies. Bezos says Blue Origin, now 14,000 to 15,000 people, has to fight the one-size-fits-all thinking that makes every decision feel the same size, using the image of a one-size robe that never fits. Consequential and nearly irreversible decisions should be made slowly and carefully, while reversible ones, even important ones, should be pushed to single individuals with good judgment. He says traditional aerospace gets slow precisely because life-safety-critical missions tempt teams to treat every decision as life-safety-critical. 19/ Resourcefulness still carries full weight, and Bezos frames it as an attitude that any problem is solvable. Asked whether his grandfather's lesson holds in an age of AI, he tells the story of summers on a South Texas ranch from age four to sixteen, repairing bulldozers and even making veterinary needles from heated wire. The lesson he draws is an approach to life, that any problem is solvable is a good starting point, even when a solution takes a long time. His warning is the inverse, that starting from the belief a problem is unsolvable becomes a self-fulfilling prophecy. 20/ Limp's read on Bezos is the best line on management style in the conversation. He calls Bezos the most tactically impatient and the most strategically patient person he has ever met, an oxymoron that somehow works, and notes that after two and a half years he thinks Bezos now knows more about rockets and rocket engines than about e-commerce. Limp also admits he arrived a skeptic on the claim that Blue Origin could become a bigger business than Amazon and has since become a believer, which is the candid version of the bet the whole conversation is selling.

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Fireside Alpha
Fireside Alpha@firesidealpha·
Vista has a "factory" they've been sending their companies through (54 so far, 20 remaining). He sees 4 archetypes of agents within software: 1) Fixer that never sleeps 2) Orchestration agent who manages multiple agents 3) Workhorse agent who works on complex projects 4) Sidekick, working alongside human
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Fireside Alpha
Fireside Alpha@firesidealpha·
Agentic companies to see more economic rent capture via labor/services capture -- he called out an insurance business using agents to solve for better outcomes, which is where pricing models are headed vs seats. But for now, market still re-rating now as not enough proof points yet.
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Fireside Alpha
Fireside Alpha@firesidealpha·
SaaS considerations- Robert Smith (Vista Equity) sees enterprise software ending up in 3 states: 1) Agentic state, agentic workflows 2) Enabling delivery of software at lower cost (dev, CS, GTM) 3) No right to exist if no sovereignty of workflows and datasets.
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Fireside Alpha
Fireside Alpha@firesidealpha·
Great fireside: Bill Ackman (@BillAckman, Pershing Square) on Markets, AI, and Concentrated Investing (notes below, link in reply) Ackman spoke about the closed-end structure, why concentration earns its differentiated record, and on AI including his $MSFT investment. He owns the hyperscalers he can value at high returns on capital, worries about the model companies rather than the infrastructure builders, and is rebuilding Howard Hughes into an insurance compounder modeled on Berkshire. More from him: 1/ Permanent capital is the edge because everyone else is a forced seller. Ackman says markets have turned increasingly short-term, with capital concentrated in multi-manager pod shops paid on near-term performance under tight risk limits, and in index funds that pull an increasing share of companies out of the active flow. When a few marginal buyers and sellers set the price, very high-quality businesses can trade at deeply discounted prices, and short-term capital gets forced out at exactly the wrong moment. Nearly 100% of Pershing Square's capital now sits in permanent structures so the firm can buy when others have to sell. 2/ A good business is an infinite-life entity, so owning it for a month or two means missing the point. Ackman frames a company as an infinite-life entity whose value is the present value of the cash it generates. Holding it for a month or two forfeits the significant opportunities that come from owning it through the cycle. The permanent-capital base is what lets him act on that time horizon rather than talk about it. 3/ He keeps a library of high-quality companies, and right now they are cheap. When a host suggested he keeps ideas in a stable ready to bring onto the field, Ackman corrected the metaphor to a library, a set of very high-quality companies he would love to own at the right price. He says the firm is going through a period where exactly those businesses are available at very attractive prices. The discipline is patience until the forced selling shows up. 4/ Most active managers cannot beat the market, and his 22-year record is built on accepting that he will look different. Ackman grants that the substantial majority of active managers cannot beat the index, while a minority can, and the opportunity set for that minority grows as short-term capital crowds the field. Over 22 years, he says, the concentrated strategy has compounded at roughly 600 to 700 basis points per annum above the index. Year to date the divergence runs the other way, the S&P up about 6.5 percent on the morning he checked while Pershing Square sits in negative territory, which he treats as the expected cost of a highly concentrated book deploying into a cheap market. 5/ The SpaceX listing is a live example of the forced-seller mechanic he profits from. Ackman walks through an endowment that holds SpaceX in its private book, watches the position move to public at a much higher value on the IPO, and is then forced to sell to bring its public allocation back into line. He credits S&P for declining to fast-track SpaceX and other mega-IPOs while their free float stays very low, and contrasts that with NASDAQ caving to the marketing opportunity to win the listing from the New York Stock Exchange. The low free float, he and the hosts agree, will likely drive dramatic price moves. 6/ Benchmark the trust to the index, but over years, not months. A host floated the idea that an index is the wrong yardstick for a concentrated vehicle, and Ackman pushed back directly. He says an index is the right comparison, only the time horizon should be several years rather than several months. Judged over months, a concentrated book will diverge hard in both directions, which tells you nothing about the strategy. 7/ Concentration into 8 to 12 names is obvious arithmetic, and the crowd avoids it because differentiation gets people fired. Ackman says peers regularly tell him they would run money exactly his way for themselves but their clients will not let them. His logic is plain, own more of idea one, two, or three rather than idea twenty. Most managers diversify for a smoother outcome and hug the index, because adverse-to-others performance is the way to get fired, and a closed-end structure removes the overnight redemption that punishes that divergence. 8/ Twenty years of track record turned activism from public letters into private conversations. Ackman recalls the early days when nobody knew Pershing Square and a CEO would not return his calls even after a 10% stake. After two decades, companies know who he is and welcome the firm, so the work that once needed an open letter or a ballroom now happens behind closed doors. He adds that he can build a portfolio today of already-well-run, high-quality businesses with what he estimates is a forward expected return in the mid-to-high 20s percent. 9/ Corporate America now has to navigate a more interventionist president. Ackman points to the Intel case, where the Biden administration made a grant to support a struggling chipmaker and Trump responded by wanting government ownership in exchange for the taxpayer investment, the deal that converted unpaid CHIPS Act grants into a government equity stake. The lesson he draws is that you do not want to get on the wrong side of the president, and corporate America is paying attention to politics from that angle. He frames it as a feature of the current capital-markets environment rather than a passing episode. 10/ He credits the Iran strike as a long-term win paid for with short-term pain at the pump. Ackman says Trump knew the strike would push world oil and therefore gasoline prices higher, a cost the consumer feels every few days when the price at the pump swings 20 to 30 percent. He rates a nuclear Iran the larger risk to the United States, so he reads the move as a generationally positive decision taken at the cost of short-term political risk. He expects the war to resolve before the midterms, with energy prices and shipping rates reverting quickly once it does. 11/ On tariffs he backed the goal and faulted the execution, and says the 90-day pause was his idea. Ackman agrees the United States let trade partners take advantage over time and that tariffs are a reasonable tool to fix it. The overnight imposition of massive tariffs struck him as very high risk, and he says he initiated the 90-day pause idea that the president then took up. He notes the Supreme Court has constrained the president's ability to use tariffs without Congress, which limits the tool going forward. 12/ AI does not have an image problem, in his read, it has the fastest adoption in history. A host put it to Ackman that AI is unpopular with US consumers, and he disagreed flatly. Consumers reacted incredibly favorably and actually use the product, with the original OpenAI rollout the fastest adoption of any product in history by user count. He concedes a real fear of job loss, stronger than for any technology in his lifetime, but says he is increasingly confident AI will be a large net boom for job growth as people adapt. 13/ The hyperscaler capex is not a balance-sheet gamble, it is the price of staying in the superintelligence race. Ackman distinguishes Microsoft, Google, and Meta sharply from the startups, saying they are not risking their balance sheets and are spending into enormous demand. The first company to superintelligence holds an enormous competitive advantage, so none of them can afford to lose the race. He separates this growth investment from value-destroying maintenance capex, comparing it to a retailer forced to upgrade every store, because the hyperscaler spend buys compute the market will pay for at returns vastly above cost of capital, the Buffett definition of a great business. 14/ He worries far more about the LLM business model than about the companies building the infrastructure. Ackman does not question the enormous demand for compute or the power of the tool, and expects plenty of money to be lost because not every AI-adjacent private company becomes the next Anthropic. The frontier keeps moving, with OpenAI first, Anthropic seeming to pull ahead, and Gemini making it a race, while the vast majority of users will not need the best model for what they actually do. His worry sits with the model layer, not the picks-and-shovels. 15/ The Webvan parallel is the warning, all-you-can-eat pricing only lasts while you can raise capital. Ackman compares early OpenAI to Webvan, the dot-com grocery business whose massive consumer subsidy worked only as long as it could raise money. Early AI adoption rode the same all-you-can-eat pricing, and the move to per-token pricing for enterprise customers is exposing the bill, with departments burning through their budgets fast. He expects far more price sensitivity from corporate customers as a result. 16/ Language models are commoditizing, and for most jobs the free model is good enough. Ackman says there has been effectively no differentiation in everyday use, where people just click the most powerful model out of habit. Open-source and free models are good enough for many use cases, and you do not need Anthropic's top Claude Mythos model to find a restaurant on vacation. A host adds that Silicon Valley startups are increasingly using China's cheaper models, sharpening the pricing pressure on returns. 17/ Alphabet's 80 billion dollar raise looked unnecessary to him, until he read it as opportunism. Ackman says he was surprised Alphabet issued 80 billion dollars of stock it did not need. His read is that Alphabet looked at its valuation, saw a wall of new money coming to market as AI rivals were forced to raise, and issued into the froth to harden its competitive position. Pershing Square exited its own Alphabet stake this quarter, he says, at 31 to 32x earnings after making roughly three-and-a-half to four times its money over three to four years, and rotated the capital into a large Microsoft position. 18/ Google's real weapon against OpenAI and Anthropic is the token price. Ackman argues Alphabet could cut token pricing significantly given its balance-sheet advantage, which would make life genuinely hard for the model companies that have been burning huge amounts of cash. He reads OpenAI as more challenged than Anthropic, with Anthropic pushing into enterprise and seeming to overtake OpenAI, and Gemini already defending Google's position well. The model labs are reliant on capital markets to fund the burn, which is why they are now going public. 19/ Microsoft becomes the gateway to AI in the enterprise, and the seat price is the moat. Ackman frames a typical Microsoft 365 enterprise seat at about 20 dollars for a bundle that would cost 50 dollars or more bought piecemeal from Zoom, Gmail, and the rest, across what he puts at something approaching half a billion users. With Copilot, Microsoft becomes the platform through which people access AI at work, the way Uber became the default for rides, pointing the CIO to the lowest-cost model that does the job. Software is a company-by-company call, where high-cost niche products priced at 10 to 30 thousand a year per user are highly replaceable while scaled low-cost platforms are the winners. 20/ Howard Hughes is being rebuilt into an insurance compounder on the Buffett model. Ackman explains the legacy, Pershing Square bought 25% of General Growth after the stock fell 99.5% in the financial crisis, led the restructuring, and made its most successful equity investment ever, with Howard Hughes spun out to hold the unwanted assets. Pershing Square now controls about 47% of Howard Hughes and is converting it from a pure-play real-estate owner into a diversified holding company led by the specialty insurer Vantage, a roughly $2.1 billion acquisition that closed the week before the recording. The plan copies Buffett, invest the insurance float in short-term treasuries at effectively no risk and the surplus in common stocks the Pershing Square way, the engine that compounded Berkshire's per-share value at about 20% over 60 years. 21/ The reason other insurers do not run this playbook is that the investing talent never shows up. Ackman says the best investors do not take jobs at insurance companies, because the pay and prestige steer them to hedge funds and long-only managers instead. Buffett's edge was being a great investor who owned an insurer and could manage the float at no cost, and Pershing Square is doing the same for Vantage. Owning so much of Howard Hughes makes managing that portfolio for free directly in the firm's own interest, which he calls the competitive advantage.
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Fireside Alpha@firesidealpha·
Intel just rehired the man who once bought the business it was quitting. In 2000, Seok-Hee Lee left Korea for $INTC and won its highest technical award three times, a rare run for an engineer. He went home, rose to run SK hynix, and in 2020 spent about nine billion dollars buying Intel's NAND memory business, the unit Intel had chosen to exit. He rebranded it Solidigm and chaired it. Today, twenty-six years after he first joined, Intel brought him back to lead advanced packaging at Intel Foundry under Lip-Bu Tan. He bought what Intel was getting out of. Now he runs what Intel is betting its future on.
Jukan@jukan05

👀

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Fireside Alpha@firesidealpha·
$MU CEO (Sanjay Mehrotra) on why memory is a key enabler: "Intelligence is all about data, and data is all about memory. And when you look at token economics, that relies on memory. And as token usage grows, with greater context windows, greater key value caching and larger and larger models, it just requires not just compute but requires an ability to remember, ability to have more memory and higher performance memory as well."
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Fireside Alpha@firesidealpha·
$60B for Cursor ($SPCX), and top VCs call it inexpensive. Great inside-baseball @twistartups roundtable on that, why tokens are the new hard drives, Altman's free-token trap, OpenAI's leaked numbers and more with @Jason @alex @TurnerNovak @bling0. Their insights: 1/ SpaceX buying Cursor reads as cheap to the panel, not expensive, and the multiple is why. Calacanis frames a reported roughly $60 billion price against a $4 billion revenue run rate as a 15x multiple, and calls that incredible rather than rich. The deal was reportedly assembled before SpaceX went public and closed immediately after, which he treats as the trigger that let it happen. Set against private AI multiples, a mid-teens revenue multiple for the company that owns the coding surface is the bargain read the whole segment builds from. 2/ The recurring Silicon Valley move is a platform watching an app's usage, then building the app and taking the layer. Calacanis describes Anthropic seeing that a large share of its usage came through Cursor, telling its partner it would keep that internally, then shipping Claude Code. He stacks it against Microsoft hosting Lotus 1-2-3 and Mitch Kapor at its events before launching Excel, and calls it consistent across the history of the industry. Platforms steal the application layer when they see enough there, in his phrasing, which is the structural reason an independent app sitting on someone else's model is exposed. 3/ The acquisition logic is that Cursor lost its compute and its model, and SpaceX hands both back. In Calacanis's telling Cursor woke up with no compute, no foundation model, and a platform partner that had just shipped a competing product, so it called a code red to build its own model and had nowhere to train it. SpaceX brings Colossus and what he calls essentially unlimited compute, with more once compute moves to space. The framing he reaches for is peanut butter and chocolate, two halves that solve each other's problem. 4/ A reported $40 billion funding round became a roughly 50% acquisition premium. Calacanis says, sourced to street talk, that Cursor was raising around $40 billion when the deal came together, that the company was at a $2 billion run rate at the time, and that Elon paid about a 50 percent premium over the round. Those figures do not square cleanly with the $4 billion run rate and roughly $60 billion price cited at the top, which is the kind of gap a contested private deal produces. Treat the premium as the panel's reasoning rather than a confirmed term. 5/ Ben Ling argues the deal beats Cursor's likely outcome as an independent company. Asked whether Cursor would be worth less on its own, Ben Ling says that in terms of terminal outcome and reach, almost certainly. The comparison he offers is Instagram and YouTube inside their acquirers, both of which he saw from the inside, and which he says became far larger than anyone expected once they had a parent. The claim is that distribution and balance sheet inside a giant beat the standalone path, not that the standalone path fails. 6/ Negative gross margins on a breakout product are treated as a YouTube-style feature, not a flaw. Ben sets Cursor's reported negative 23% gross margins against YouTube being deeply gross-margin negative when Google bought it for $1.6 billion, with most views unmonetizable and a Viacom lawsuit hanging over it. YouTube is now a juggernaut reaching roughly three billion people a month in his framing, an arc he watched from inside the company. Early unit economics on a category-defining asset, in that argument, say little about where it lands. 7/ The deal solves Cursor's revenue-passthrough problem and SpaceX's missing-customer problem at once. Turner Novak frames Cursor's core issue as paying most of its revenue out to a model provider, which stops once it is inside SpaceX with its own compute. He notes SpaceX is building data centers in space without obvious customers, and Cursor becomes the channel that delivers that compute whether end customers exist yet or not. Both sides get the piece they were missing, distribution for the compute, compute economics for the app. 8/ Calacanis's sharpest warning is to founders taking free tokens for equity, and it names names. He recounts Sam Altman reportedly offering Y Combinator companies on the order of a million dollars in tokens each for a slice of equity, and tells founders flatly not to take it. The reasoning is the same shiv pattern, that OpenAI can study the token usage of every company that signs up and rebuild the most successful ones as free product. He calls it a final warning and says there is no free beer and no free pizza, only a price, while stressing the pattern is structural and not personal to Altman. 9/ The bigger bet under the whole show is that frontier tokens commoditize toward the price of bandwidth. Calacanis puts at least a trillion dollars, possibly two to three, into frontier model buildout, then says he does not think the labs make tokens profitable enough to justify it. His comparison is hard drives and bandwidth, inputs that got cheaper until they stopped being where the money was. The counterweight, is the J-curve, that Uber and Tesla and YouTube all looked like permanent money losers before they flipped, so heavy early loss is not proof the model fails. 10/ The practical version of the commoditization bet is open-source models behind a router, and Calacanis says he already cannot tell the difference. He describes routing his own coding and agent jobs through tools that let him pick Kimi or DeepSeek per task, and finding the output close enough that the frontier premium is hard to justify on routine work. The architecture he sketches is a switch plus what he calls a maestro layer that remembers which model did which job best, naming Perplexity's Model Council, which fires several models at once and reports where they agree. Ben reframes the whole thing as insourcing versus outsourcing, that an open-source model a hundred times cheaper and roughly as good wins the bulk of the budget. 11/ Calacanis expects local workstations to pull serious AI work off the cloud and out of the data center. He points to a new AMD desktop machine, which the broadcast pegs both at 128GB for $1,500 and, in a producer's correction, at 120GB for $4,000 in different breaths, and argues the near future is employees on $10,000 workstations with a terabyte of RAM, processing locally and networked into a company supercomputer. The pitch is that nobody hands their data to anyone and the data center need shrinks, with ExoLabs-style daisy-chaining of Macs as the early hacker version. He dates the developer adoption to roughly 2027 and the mass push from Dell, AMD, and Apple to 2028. 12/ OpenAI's leaked numbers read to the panel as a gross-margin story, with a caveat about who paid for the compute. One panelist calls the margin improvement in the leaked 2024 and 2025 figures the notable line, more than the revenue growth everyone already expected. Turner cautions that a large amount of the cost was effectively free Microsoft Azure credits tied to its investment, so the headline burn may overstate real cash burned, and adds the numbers are at least six months stale. The shared read is that scaling a sales force, not optimizing margin, is the current job, because the industry is still in its growth innings. 13/ Turner Novak argues B2B AI is far more profitable than consumer, which cuts against OpenAI's base. The claim is that a consumer using a chat assistant as a better search engine spends little, while a business signing a contract to ingest documents and drive decisions is easy to serve and pays a great deal. He points to enterprise AI portfolio companies making real money, his own among them, without much optimization yet. The implied tension is that OpenAI's largely consumer footprint is the harder place to extract margin. 14/ Where value finally accrues, chip versus model versus app, the panel calls a genuine open question. Posed the question directly, the Nvidia card, the frontier model, or the application layer, Ben says his gut is the app layer while conceding it is only a gut. Calacanis counters that the hardware players tend to capture a lot, and Turner adds that most technologies commoditize until distribution and a sales force decide the winner. Nobody on the panel claims to know, which is itself the read, that the value-capture layer of this buildout is still unsettled. 15/ Seed-to-Series-A graduation has fallen by roughly half, and the panel splits cyclical from structural. Calacanis calls the decline from about 50% to about 25%, sourced to a cited Lightspeed analysis, worse than he expected. One read is purely cyclical, that 2021's flood of new funds and dollars inflated pull-through and the retraction simply normalized it. The harder read, which Ben Ling adds, is structural, that if you are not an AI company right now you largely cannot raise a Series A, so a whole cohort of non-AI startups stalls regardless of the cycle. 16/ M&A reopening is the panel's bull case for venture, and it ties trapped SaaS value to policy. Calacanis frames the prior FTC posture under its former chair Lina Khan as a chilling effect that blocked deals like Figma-Adobe and broke the recycling of capital back to founders. He wants antitrust to wave through anything under a $250 billion buyer and grow the Mag-7 into a Mag-70. The concrete stakes are what the panel calls a SaaS-apolis, hundreds of billions in venture value trapped in companies too slow to IPO and previously unable to sell. 17/ On Snap's new AR glasses the panel lands on bold but a generation early. The hardware is described as roughly $2,200 and technically impressive, and the panel agrees the look is the gate for mass adoption, citing Meta's Ray-Ban partnership as the version people actually wear. Calacanis calls Snap's chief a product genius who has mismanaged the public company on stock-based comp, and pegs the glasses as one generation from working. The forward read is that AR wins where VR does not, and that Apple ships a comparable product within a couple of generations. Lastly, Calacanis's standing advice to founders is to keep your secrets off the platforms you sit on, because token usage is a roadmap. He pairs the warning with Apple as the rare platform that deliberately under-builds its own apps, his examples being a basic Notes app and the missing native sleep score, to avoid competing with its developers and protect the 30%. The same restraint, in his telling, is exactly what Microsoft and Facebook do not show, which is why the safe assumption is that a strong-enough app on someone else's stack eventually gets cloned.
This Week in Startups@twistartups

🚨 TWiST is LIVE with a VC Roundtable at 12PM CT! Today, we're joined by @TurnerNovak, solo GP of @BananaCap_. What started as a meme account from Ann Arbor turned into one of the best seed track records in the game: Bun got bought by Anthropic, Bee by Amazon, BeReal by Voodoo, and Chainguard turned into a multi-billion-dollar machine. While everyone else is writing the seed-stage's obituary, Turner keeps cashing in exit checks. We'll see you in one hour!

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Fireside Alpha@firesidealpha·
To understand the $AAPL price increases and the Memory move, read/save this. Sanjay Mehrotra (CEO, Micron $MU, and co-founder $SNDK) spoke with Jodi Shelton recently covering: memory as the key enabler in AI, supply constraints >2026, and how memory is getting more advanced: 1/ After 45 years he calls this the most exciting moment the industry has ever had. Mehrotra says memory has crossed from being a component inside a phone or a PC to actively enabling intelligence in AI, and that the best is yet to come. He frames Micron as the only US company in semiconductor memory manufacturing, a national treasure in his words, sitting at the center of the AI revolution. The tone is structural rather than victory-lap, because the rest of the conversation is about why the demand is durable and the supply is not easy. 2/ Intelligence runs on data, data runs on memory, so token economics is a memory story. His cleanest line is that intelligence in AI is all about data and data is all about memory, which makes memory a direct input to intelligence rather than a passive store. As token usage grows with larger context windows, more key-value caching, and bigger models, the system needs not just compute but the ability to remember, at higher performance and lower power. He puts all of this in very early innings, with AI demand having long legs from here. 3/ He treats AI as a change in the nature of the industry, not just another up-cycle. Asked directly whether this is one more memory cycle at a bigger scale or something that changes the business, Mehrotra says there is no AI without semiconductors and memory is the backbone. His reasoning is that every direction the workload moves, larger models, training into inference, data center out to the edge, agentic orchestration, raises memory intensity rather than lowering it. The claim is that the demand driver is secular, which is the whole argument over whether Micron's run is a peak or a re-rating. 4/ Supply is extremely short for a physical reason that will not clear quickly. Mehrotra explains that high-bandwidth memory and high-performance parts like LPDDR and leading-edge DRAM all consume far more wafers as their die sizes grow, so meeting demand takes tremendous greenfield capacity. Building those fabs, equipping them, qualifying the lines, and ramping the supply each take a long time. The fundamentals, he says, have changed on both the demand side and the supply side at once. 5/ He will not predict when supply catches demand, and sees it tight well beyond 2026. The most datable claim in the interview is that he cannot project the crossover and expects the industry to stay short well past 2026, because a greenfield fab runs three to four years from groundbreaking to first wafers before any ramp. He adds a second squeeze, that each new technology generation now delivers less bit-growth productivity than the last, so scaling buys less relief than it used to. The two together are why he treats the shortage as durable. 6/ Micron saw the HBM wave in 2021, when it was a rounding error. Mehrotra says the company began arguing that greenfields had to be built back in 2021, when high-bandwidth memory was less than a single-digit percent of one percent of the memory industry. By the December 2023 earnings call Micron was flagging that leading-edge supply would tighten through 2024 and 2025, even as a brutal 2023 pushed the broader industry to slow its capacity investment. He is candid that nobody, Micron included, projected AI would advance this fast. 7/ The US buildout is real money: about $200 billion and 90,000 jobs. Mehrotra ties Micron's roughly $200 billion US investment to new and expanded fabs in Manassas, Boise, and Syracuse, and to about 90,000 jobs across the ecosystem. He frames Micron as the only memory maker in the Western Hemisphere and points to customer capex approaching a trillion dollars in the coming quarters as both the opportunity and the obligation he has to supply. The political backdrop is a White House delegation to China he joined, where his takeaway was simply that talking is good. 8/ His capital discipline is to pour the shell and defer the equipment. Mehrotra describes building the greenfield fab shells now and then equipping them on demand, staging the expensive tools against confirmed demand projections rather than committing all the capital up front. He says investments are made with data and discipline, not blindly, and that Micron does not carry self-doubt about the opportunity even as it manages the risk. The half-built fab is the optionality that lets a feast-and-famine business expand without betting itself. 9/ Memory is hard technology that is getting harder, and the field has shrunk to a near-oligopoly. Mehrotra pushes back on the idea that memory is a commodity, citing the physics, chemistry, and materials science behind producing trillions of reliable bits, and says it is getting harder as performance, not just density, becomes the job. "People often misunderstand how hard it is to make memory. It's hard stuff. And now it's getting even harder." The industry that once held dozens of players is down to three DRAM makers, only one of them in the US. He backs the innovation claim with more than 61,000 patents and an inventor who has passed Einstein's count. 10/ The discipline traces to a childhood of operating under constraints. Mehrotra describes humble, middle-class beginnings in India, a home with no television or phone and a refrigerator that arrived only when he was about a teenager, where education was the clear priority. He argues that learning to operate under constraints in childhood becomes ingrained and turns out to be exactly the skill a capital-intensive business demands. He draws a straight line from scarcity at home to capital discipline at Micron. 11/ The lesson he repeats most is tenacity, and it comes from his father at a visa window. Denied a US student visa three times, Mehrotra watched his father refuse the answer, wait for the consul to return from lunch, and make a 20-minute unbroken case until the official stamped the passport. He calls it a performance of a lifetime and the moment he learned that the pursuit of success starts with tenacity, while also crediting plain luck that the consul walked back in at all. His father modeled the same conviction in refusing corruption and in sending a daughter to engineering school in 1960s India. 12/ Three mentors turned an engineer into an operator. Mehrotra credits a Berkeley professor who steered him to Intel, his first boss George Perlegos, who taught him that engineering is not just design but test, manufacturability, and quality, and SanDisk lead founder Eli Harari, who moved him from engineer to business leader. He treats those relationships as the actual curriculum of his career. The pattern is that each mentor widened the lens, from the chip to the line to the business. 13/ He is explicit that his wife's sacrifice made the career possible. Mehrotra says his wife gave up her own career as an accountant and controller in tech to raise their daughters, and that where he is today would not have been possible otherwise. He describes a 42-year arranged marriage as a study in commitment and adjustment, and credits her with two lessons that stuck, to think for a few seconds before blurting out a response, and after his first nervous CNBC hit, that he has a nice smile and should use it more often. Both daughters became engineers. 14/ He thinks the immigrant-engineer background is a genuine business edge. Asked about the number of Indian-born leaders running the industry, Mehrotra points to thriving in intense competition from school onward, operating under resource constraints, growing up amid diversity, and the adaptability of moving to a new country with no housing and no network. He frames the hunger of a foreign student establishing himself as an asset that carries into running a company. The argument is that constraint and risk, early and repeatedly, train the exact muscles leadership uses. 15/ His leadership style is detail-deep when needed, quiet by default, and built on the team. Mehrotra accepts the micromanager label, arguing that when technology, products, customers, and competition all move fast, a leader has to be able to drop into the details and still hold the big picture. He describes himself as quiet and deliberately a listener who processes before deciding, and says his style only works because of a strong team. The Micron culture he describes is one where all ideas are heard and the best ideas win, anchored on tenacity and on people. 16/ On women in the industry, he frames it as merit first and a talent pool too big to ignore. Mehrotra says the case for women in semiconductors was never about compromising merit, which an industry like this cannot afford, but about not ignoring fifty percent of the talent pool. He points to Micron's women-in-innovation program and to a chip-to-communities effort that sends team members into middle and high schools to spark interest in STEM. The framing connects directly to a talent shortage he says the $200 billion buildout will only sharpen. Lastly, the career almost went a very different way. Mehrotra says that in 1984 he was about to co-found the startup that became Atmel, until Floyd Kvamme took him to lunch, talked him out of it, and arranged a year of applications-engineering work in London so his wife could join him from India. That year, watching customers struggle with chips he had designed, taught him a customer focus that is now one of Micron's stated core values. The road not taken is where he says he learned the thing he runs the company on.
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Alex@Alex_Intel_·
@Chaimeisenberg Ya they need alot more shells Lip-Bu has a 10 year plan
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Fireside Alpha@firesidealpha·
Massive win for the Bottleneck Bros
Fireside Alpha@firesidealpha

$INTC Lip-Bu Tan's endgame for the stock in his tenure: 10x in 10 years. He's at 6x so far. On No Priors, Intel's CEO on saving the company: the US government stake, $NVDA's $5B bet, CPU's comeback, Terafab with Musk, and chipmaking in the US. More notes below (save this): 1/ He took the hardest job in semis at 66 to save a company he calls iconic, not for himself. Tan says people told him he should retire rather than take on the hottest job in the industry, and his reasoning was that Intel matters too much to the semiconductor ecosystem and to the United States to let go. He frames the decision as doing one more, and he is explicit that he does not need the job and took it purely to save Intel. The personal-detachment framing is the same one he uses later to defuse the Trump confrontation, which tells you how he operates under pressure. 2/ The single most surprising moment of his first year was President Trump asking him to resign over a conflict of interest. Tan says nothing in his prior jobs or training prepared him for getting that call early one morning, with no exceptions offered. His move was to take the personal issue off the table first by reminding himself he did not need the job, then work out how to be helpful to Intel. He got a meeting within days, explained his story of being born in Malaysia, raised in Singapore, MIT-educated, and living only in the US, and Trump listened and gave him the chance. 3/ The whole turnaround runs on one operating cadence: crawl, then walk, then run. Tan says this was his culture at Cadence and now at Intel, and it means starting humble, listening to the customer, then deciding to walk, then finally deciding to run. He pairs it with two structural moves from day one: driving accountability and faster decisions into a company used to layers of bureaucracy, and having all of engineering report directly to him because he is an engineer by training and wants to know what went wrong. The method is the throughgoing logic of the interview, the lens he applies to product, hiring, and capital alike. 4/ He repaired a balance sheet he calls horrible with three named backers, and the government stake is the deliberate part. Tan says he is delighted the US government became a big shareholder, and the argument he made to Trump is that TSMC started with the Taiwan government as a shareholder, as did peers in Japan and Singapore, so this is the infrastructure support a government provides. The second backer is his old-time friend Jensen Huang, whose 5 billion dollar investment he says is now worth 25 billion or more, and the third is Masayoshi Son of SoftBank, on whose board Tan once sat. The mix of sovereign, strategic, and friendly capital is exactly the capital-stack argument he returns to when advising others on capital-intensive bets. 5/ CPU is back in demand, and the training-to-inference ratio is moving Intel's way. Tan says that with agentic AI and inference, CPUs have become highly in demand, and the ratio he watches has shifted from one-to-eight CPU-to-GPU in training toward one-to-four, maybe lower. He says AI model developers tell him that for reinforcement learning and for the speed of orchestrating many agents, the CPU is actually better. The reversal matters because Intel's data-center server business is built on the CPU, and a demand tailwind there is the nearest-term piece of the thesis. 6/ Foundry is a trust business, and the contrarian decision to double down was about the country, not the math. Tan says he faced loud voices in the market telling him foundry was too expensive and would not work, and he decided to bite the bullet because it is critical for the United States and the industry over the longer term. He frames foundry as a service and trust business: customers will only hand over wafer orders if the yield, defect density, and cycle time are right, because a yield miss means a revenue miss that leaves the customer toast. He says he has respect for TSMC and they are a great partner, but both need more capacity to serve customers, which is the cooperative framing underneath the competitive bet. 7/ Terafab is Elon Musk's own fab, and Intel is the collaborator making it faster. Tan says Musk decided to build his own fab because his robots and cars need a lot of silicon, and Intel is delighted to enable him to get to production quicker using some of Intel's technology and process. He calls Musk unconventional in a way he finds refreshing, someone who questions every traditional step, and says he works with a very good team there weekly. On Musk's line about wanting to smoke inside the clean room, Tan says he does not go that far, though maybe in some part of the clean room you can, and he keeps an open mind about it. 8/ Making the most advanced chips in America is becoming the bottleneck, and that is the reason to do it. Tan says everyone lived through the supply-chain crisis and the lesson is that you cannot depend on one or two players in one geography, so more people will realize making chips in the United States is critical. He points to Intel's 14A process at roughly 1.4 nanometer with planning already underway for 1 nanometer and 0.7 nanometer, geometries he likens to something thinner than a human hair where any mistake at any step goes down the drain. The precision required is precisely why manufacturing becomes the scarce thing, which inverts the cost objection into a moat argument. 9/ The real bottlenecks on AI demand are power, helium, and memory, not just chips. Tan lists power constraint as the one everyone knows, with some countries simply lacking the power. He flags helium as an underappreciated and significant input for semiconductor manufacturing, and memory as the current acute shortage that everyone is scrambling for, where adding fab capacity takes a couple of years and the cost gets passed through to customers as prices rise. The list reframes the AI buildout as a multi-input supply problem, with several non-chip constraints that move independently. 10/ When you hit the physics wall you change the materials, and he has invested across the new ones. Tan says scaling will get more expensive and harder but he can see a path from 18A to 14A to 10 and 7, and beyond that you go back to the materials and the chemical table. He names gallium nitride, silicon carbide, and indium phosphide as new materials he has backed, glass and diamond as packaging insulators he has invested in through a glass-substrate venture called 3DGS and a diamond foundry, and Intel's next-generation advanced packaging as its answer to TSMC's CoWoS. His engineer's framing is that you always hit a wall, then find a way to jump over it or work around it. 11/ Semiconductors are hot again, and the capital that once fled the room now crowds in. Tan says that 18 years ago he would pitch semiconductors to a tier-one VC partner meeting and half the room would make an excuse to leave, then the other half would ask if he had any software or services. Now, by his count, Jensen Huang's Nvidia is a 5.3 trillion dollar market cap, Broadcom and TSMC are around 2 trillion each, Lisa Su's AMD is almost 800 billion, and Intel is close to 600 billion. The reversal is the backdrop for the whole rerating: capital that used to demand he own 20 percent of a startup now writes 1 billion dollar venture checks into the same category that used to clear the room. 12/ His investing has always started from one question: where is the bottleneck and is the customer crying for it. Tan says that across 159 IPOs and 126 M&A exits, with 38 percent of his semiconductor investing in the US, his first screen is always where the bottleneck is and what you are trying to solve. He cites backing optical interconnect at Celestial AI because interconnect speed became the constraint in the cluster, and power management where conversion from 48 volts to 1 volt loses a lot of power, noting ADI's acquisition of Empower in that space. The discipline is bottleneck-first, demand-pull, real-customer investing, the same lens he now runs Intel through. 13/ The most important thing in a capital-intensive bet is a partner who stays through the bad times. Tan says many people are enjoyable to work with in the good times but walk away when the company is in trouble, and he wants partners who have worked through multiple hard-then-successful companies, an almost-bankrupt club that eventually takes off. He extends the rule to the kind of investor: individuals who are knowledgeable in the space rather than just a brand-name firm, and growth-stage and hedge-fund friends who understand the public market and can tell an entrepreneur where not to go. His read is that capital is not fungible, and the right co-investor is a strategic asset, not just money. 14/ The winning company in 10 years is a laser-focused niche player that can scale into a full stack. Tan says, framing it as his personal view, that the winner articulates and laser-focuses on one niche area, finds the right partner, and can scale, and he ties it back to a full-stack solution. He admires how Jensen Huang built a platform company around CUDA, and how Anthropic and OpenAI changed the game in a more elegant way, and he hopes Intel can play that role because it has the XPU, advanced packaging, and foundry to build purpose-built silicon for different workloads. The endgame he describes is putting Intel's pieces together into purpose-built silicon, which is the strategic destination of the crawl-walk-run path. 15/ What investors most misunderstand about Intel, in his telling, is the size of the product opening and the patience the foundry demands. Asked directly, Tan says people are only starting to recognize the turnaround four months into his crawl phase. The piece they miss is on the product side: Intel spent decades selling PCs and servers built for humans, and the new dimension is millions of agents that need compute and a software stack, plus physical AI, a market he calls huge and a game that is not over. On foundry he is blunt that Intel is very distant from TSMC and has to humbly rebuild the IP, the yield, the defect density, and the cycle time, work he does not expect to surface until around 2030 to 2032. His ask is that investors read the six-times shareholder return in 14 months as a beginning, not a result. 16/ He is chasing a 10x from a much bigger base, and has already returned six times in 14 months. Tan says that as a venture investor at heart he always looks for 10x, and notes that at Cadence he delivered close to 76 times from his 2.42-dollar interim-CEO starting point, and about 85 times by the time he retired as executive chairman. At Intel the base is bigger, so he sets the goal at 10x over five to ten years, and says the company has already made six times the return to shareholders in 14 months with a lot of room to go. The candor about a lower multiple off a larger base is the calibrated version of the same venture instinct he built his career on. Lastly, the buildout is not the bet, the application is. Tan says he does not see anything slowing the massive AI buildup down because the workload keeps increasing, and the only real brake is the supply constraint. But he insists on looking past the infrastructure to the application, arguing that not everyone who builds will win, that some will win big and others go sideways or get acquired, just as the internet produced an Amazon and a Netflix alongside many that disappeared. The discipline he hands the listener is to find the application that is humongous and sustainable, and if you believe in it, double and triple down.

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Fireside Alpha@firesidealpha·
Great listen: $INTC Lip-Bu Tan x T Rowe's Long View podcast. He's been active on the podcast circuit, and with today's announcement makes sense why. In terms of arc, Tan is a guy who seemingly keeps taking jobs he did not plan to take. He wrote one resume in his life, raised $3.3 million for a fund that now runs more than $4 billion, signed up for a 3-month interim CEO stint at Cadence that became 15 years, and took the Intel seat against the advice of 80% of the people he asked because one friend told him to save the company before he retired and it touched his heart. Some number before the takes: a 5-yr turnaround plan he says he finished in 14 months, a US government stake now up more than 5x, and a CPU-to-GPU ratio he watches moving from 1:8 in training toward 1:1 in inference. 1/ He grew up the youngest of five in Malaysia, the one child his strict mother let off the hook. Tan says he was born in Muar, Johor, just north of Singapore, and followed his mother to Singapore for school before coming to the US for graduate work. His siblings were pushed through piano to the level of performance and composition, and his brother played violin in an orchestra, but Tan says he was too lazy for the piano and spent his time catching spiders and playing basketball and volleyball instead. Being the youngest, he says, came with a lot of freedom, and the music he skipped he now backs from the other side, having served on the board of the San Francisco Opera. 2/ His mother sent him to Chinese school while the other four went to English school, and he still frames his English as a survived second language. Tan says she chose, for some reason he does not fully explain, to send the last child to Chinese school to learn Confucian theory, and that she would apologize to him that his English was not good because it was a second language. He says he survived it. The detail is important to the later story because the same man who frames his English as a handicap is the one who walked into a room with President Trump and talked his way from a resignation demand into a partnership. 3/ He chose physics, then switched to nuclear engineering, partly to escape being measured against his brother. Tan says he started in physics at Nanyang Technological University because he was good at mathematics and physics was good training, and he finished it in three years. But his teachers kept telling him his brother, a Rhodes scholar who went to Cambridge and Oxford and became a cardiologist working on artificial hearts, was better. So he switched fields to do something different, applied to graduate school in the US, was rejected by Berkeley and Stanford, and was accepted at MIT for nuclear engineering, which is the only reason he says he pursued it. 4/ A nuclear accident ended his PhD and a placement officer pushed him into the job market with one company name. Tan says he never finished his PhD because of the Three Mile Island accident, which gutted the prospects for nuclear power plant work. He tells it as comedy: he liked doing homework in the placement office because it had air conditioning, the bored placement director asked his department, heard "nuclear engineering," and told him to get out because nothing was being built. Tan says he wrote exactly one resume in his whole life, to a firm the director named, EDS Nuclear, got flown out for a three-day trip, and took the job. The thread he is drawing is that the defining moves of his career were not planned, they were doors that opened. 5/ He was recruited into his first startup over a no-business lunch at 21 or 22, with nothing to lose. Tan says that during a rotation program at that first company, three executive vice presidents invited him to lunch, and he assumed they wanted to talk budget until they promised no business. The lunch was a pitch to join them in founding a startup. He says he was only 21 or 22 at the time and had nothing to lose, so he went, and that became his first startup experience. He uses it to set up a pattern he repeats across the interview, that the right people pulling you in is more important than the plan you had. 6/ He started Walden by offering to work for free and raising $3.3 million, and built it brick by brick to visit his mother. Tan says that when he turned to venture capital, he knocked on every door and got no offers, so he found a small SBIC firm and told the two partners they did not have to pay him, he would pay for himself. He raised about $3.3 million to start Walden International, which he says now manages more than $4 billion, and across his watch he and his colleagues recorded 153 IPOs and 126 successful M&A exits. Asked his master plan for building out Asia, he says he had none, he just wanted to go visit his mother in Singapore. 7/ Cadence is where he learned to turn a company around, and he became its CEO entirely by accident. Tan says he hesitated to even join the Cadence board because it was the number-two player behind Synopsys, and a friend at Synopsys told him to join the number one instead. He joined number two anyway, then got a call that the CEO had been fired and he was now interim CEO, with the stock at $2.42. He signed up for three months, tried to hand the chairman the key once a search was done, and was told to keep it. He says that three-month stint became 15 years, 12 and a half as CEO and two as executive chairman. As a first-time CEO he read three "CEO for dummies" books cover to cover, opened his inbox to all employees, and says he got more than 3,000 emails a day and promised to answer every one. 8/ One friend's appeal, not the upside, is why he took the Intel job, and 80% of his advisors told him not to. Asked directly why he did it, Tan says about 80% of the CEOs and executives he consulted told him not to take the job, on two grounds: there was no guarantee of success, and he had already proven himself at Cadence, so a failure at Intel would define the rest of his career because people only remember the last one. The thing that moved him was a thoughtful note from a friend and customer in Seattle who wrote that with all his success, Intel is an iconic company critical to the industry and the country, and asked him to save it before he retired. Tan says that touched his heart. He adds that his wife, who had said no when he was a finalist five years earlier, supported him this time because she could see his heart was already in it. 9/ His first act at Intel was structural: pull all of engineering under himself and force bad news to travel fast. Tan says that from day one he had all of engineering report directly to him, the same move he made at Cadence, so he could see for himself where the product, the foundry, and the customer problems actually were. He pairs it with a rule he repeats to the team, that they must bring him the bad news first, because if a customer tells him something his own team withheld, that person is in deep trouble. He frames it as a saying: bad news needs to travel fast, good news can always be celebrated later. Customers, he says, handed him a list of "14 screwed up" mistakes Intel had made, and he wrote down every one and went back to them item by item. 10/ Asked whether Intel's problem was strategy or execution, he answers with culture: too many layers and too many meetings. The host asks directly whether he diagnosed Intel as a strategy problem, an execution problem, or something else. Tan goes at the culture. He says he found management stacked north of 10 layers deep and pushed it down toward five so people would actually be accountable, because in a finger-pointing culture nobody owns the failure. He says more than 100 former top executives told him they spent 89% of their time in meetings, and that the same presentation had to be repeated 36 times before anyone decided anything, often by a decision-maker with no grasp of the product. His fix is what he calls the speed of light: move like a startup, kill meetings with no agenda or deliverable, and read the material himself beforehand so a meeting is five questions and done. 11/ Asked what changes when the US government owns 10% of you, he says it starts with a morning call demanding he resign. The host asks how it affects running the company to have the US government as a large shareholder. Tan answers with the origin: an early-morning phone call telling him the President wanted him to resign over a conflict of interest, which disrupted the gym-and-swim he usually starts the day with. His move was to take himself out of the picture, on the logic that he did not need the job and came to save Intel, then get a meeting with Trump. He says he explained that he was born in Malaysia, grew up in Singapore, went to MIT, and never lived in China, and his children and grandchildren all live in the US, and they listened. The outcome he describes is the government taking 10% against his two-year turnaround pledge, with Trump giving him from Monday to Friday to work out the terms with Commerce Secretary Howard Lutnick and Treasury Secretary Scott Bessent, on commercial terms with no board seat, a stake he says is now up more than 5x. 12/ Asked where Intel's edge is now, he points to a CPU demand reversal and to building purpose-built silicon across the stack. The host asks where Tan sees his competitive advantage as AMD, Nvidia, and the hyperscalers all push into CPUs. Tan says CPUs became important again with agentic AI and inference, and that the ratio he watches has moved from one CPU to eight GPUs in training toward roughly one-to-one in inference. He concedes the field is crowded, with AMD well respected and Arm-based players including Nvidia moving in. His answer to it is to quietly build inside Intel, hiring top CPU and GPU architects to make purpose-built silicon tuned to different workloads, on the argument that owning CPU, GPU, advanced packaging, and the foundry lets Intel assemble surgical, workload-specific chips no pure-play can. 13/ Asked how he gets back on the foundry treadmill Intel fell off, he sequences it: balance sheet, then talent, then capex. The host frames foundry as a treadmill where the speed jumps every couple of minutes and asks how Intel catches back up. Tan says the first move was strengthening the balance sheet, because the talent he tried to recruit early on told him plainly they would not join a near-bankrupt company. With the balance sheet fixed he could attract leading technologists, force a proud organization to start sharing its data, and send people to physically sit at the weak older-node fabs and not come back until the problem was fixed. He says he raised capex to buy equipment given the long lead times, and ties it back to geography: advanced-technology foundry has only one US player, Intel, while more than 90% of leading-edge manufacturing sits in Taiwan, and the memory crisis is the live reminder of what depending on one geography costs. 14/ The vision is not three Intel businesses, it is one system platform, run like a basketball team. Tan says Intel is described today as a PC business, a data-center business, and a foundry, but the direction is to stitch those into a full system platform, with software, that sells more than a chip. He points to his Foxconn partnership announcement and to his Computex keynote, where he says he deliberately handed parts of the speech to colleagues. He describes his own role as a conductor or a basketball coach, cheering an executive team to deliver rather than doing everything himself, and says the goal is a championship team like the New York Knicks. The point he keeps returning to is that he cannot do it alone, so he is building a deep bench, including potential successors, the same way he did at Cadence. 15/ Asked what would eventually end this capex cycle, he says the brake is applications without ROI, not chips. This is the host's what-breaks-it question, and the highest-signal answer in the interview. Tan says the AI buildout reminds him of the internet but bigger, and that the internet produced a few big winners, an Amazon and a Netflix, who won by being laser-focused on a real application that disrupted an industry. He says he just joined the MIT CEO advisory board, where two professors presented research arguing that companies today do not yet see the ROI or the performance improvement from AI, and that while he may not agree, it keeps him alert. His conclusion is to look past the infrastructure to the application: figure out what the application and the ROI are, then design the silicon down from there, which is how he says he formed Intel's strategy. 16/ He finished the board's five-year plan in 14 months, and reset the clock to ten years on purpose. Tan says the board and he built a five-year plan when he arrived, and at a recent board meeting he was told he had accomplished it in 14 months and needed to aim bigger. So he says he is now drawing up a five- and ten-year plan and telling his team to operate as if he will be there for a decade, so nobody assumes he is leaving. He pairs the longer horizon with succession, saying he will gradually bring in talent who could replace him, the way he did at Cadence. The reset is the candid version of the same instinct elsewhere in the interview, that he raises the target the moment he clears it. 17/ The lightning round is where the operator shows: meditation while swimming, durian, and Star Wars. Asked his one word for Intel today, Tan says technology, and the first technology he loved he gives as quantum. He names Netflix as the non-tech company he most admires, which lines up with the Netflix-and-Amazon framing he used for AI winners. His must-read turns out not to be reading at all but meditation, morning and evening, and he says he does the morning session while swimming. His guilty food is durian, specifically the bitter Mao San Wang or "mountain cat" variety, which he compares to a strong cheese, and his science-fiction pick is Star Wars. Lastly, he keeps making investing calls in the room, including telling Lisa Su to take the AMD job. Tan says that when Lisa Su asked him whether she should take the CEO job at AMD, he told her that her market cap could not end up lower than the company he chaired and had to be higher, and that she went on to do a fabulous job. It is a small aside, but it captures how he treats relationships as the asset, the same instinct that has him recruiting every senior Intel hire himself and crediting his old friend the Seattle customer for the decision that put him in the chair.
Alex@Alex_Intel_

Great tidbits from Lip-Bu in the pod 1) Almost became CEO in 2021, he declined 2) Lip-Bu boosting CAPEX (he said before that's tied to external customers) 3) Intel building an ARM CPU or making ARM AGI? 4) Lip-Bu hiring top CPU architect soon 5) 10 year plan, big company $INTC

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Fireside Alpha
Fireside Alpha@firesidealpha·
$INTC Lip-Bu Tan's endgame for the stock in his tenure: 10x in 10 years. He's at 6x so far. On No Priors, Intel's CEO on saving the company: the US government stake, $NVDA's $5B bet, CPU's comeback, Terafab with Musk, and chipmaking in the US. More notes below (save this): 1/ He took the hardest job in semis at 66 to save a company he calls iconic, not for himself. Tan says people told him he should retire rather than take on the hottest job in the industry, and his reasoning was that Intel matters too much to the semiconductor ecosystem and to the United States to let go. He frames the decision as doing one more, and he is explicit that he does not need the job and took it purely to save Intel. The personal-detachment framing is the same one he uses later to defuse the Trump confrontation, which tells you how he operates under pressure. 2/ The single most surprising moment of his first year was President Trump asking him to resign over a conflict of interest. Tan says nothing in his prior jobs or training prepared him for getting that call early one morning, with no exceptions offered. His move was to take the personal issue off the table first by reminding himself he did not need the job, then work out how to be helpful to Intel. He got a meeting within days, explained his story of being born in Malaysia, raised in Singapore, MIT-educated, and living only in the US, and Trump listened and gave him the chance. 3/ The whole turnaround runs on one operating cadence: crawl, then walk, then run. Tan says this was his culture at Cadence and now at Intel, and it means starting humble, listening to the customer, then deciding to walk, then finally deciding to run. He pairs it with two structural moves from day one: driving accountability and faster decisions into a company used to layers of bureaucracy, and having all of engineering report directly to him because he is an engineer by training and wants to know what went wrong. The method is the throughgoing logic of the interview, the lens he applies to product, hiring, and capital alike. 4/ He repaired a balance sheet he calls horrible with three named backers, and the government stake is the deliberate part. Tan says he is delighted the US government became a big shareholder, and the argument he made to Trump is that TSMC started with the Taiwan government as a shareholder, as did peers in Japan and Singapore, so this is the infrastructure support a government provides. The second backer is his old-time friend Jensen Huang, whose 5 billion dollar investment he says is now worth 25 billion or more, and the third is Masayoshi Son of SoftBank, on whose board Tan once sat. The mix of sovereign, strategic, and friendly capital is exactly the capital-stack argument he returns to when advising others on capital-intensive bets. 5/ CPU is back in demand, and the training-to-inference ratio is moving Intel's way. Tan says that with agentic AI and inference, CPUs have become highly in demand, and the ratio he watches has shifted from one-to-eight CPU-to-GPU in training toward one-to-four, maybe lower. He says AI model developers tell him that for reinforcement learning and for the speed of orchestrating many agents, the CPU is actually better. The reversal matters because Intel's data-center server business is built on the CPU, and a demand tailwind there is the nearest-term piece of the thesis. 6/ Foundry is a trust business, and the contrarian decision to double down was about the country, not the math. Tan says he faced loud voices in the market telling him foundry was too expensive and would not work, and he decided to bite the bullet because it is critical for the United States and the industry over the longer term. He frames foundry as a service and trust business: customers will only hand over wafer orders if the yield, defect density, and cycle time are right, because a yield miss means a revenue miss that leaves the customer toast. He says he has respect for TSMC and they are a great partner, but both need more capacity to serve customers, which is the cooperative framing underneath the competitive bet. 7/ Terafab is Elon Musk's own fab, and Intel is the collaborator making it faster. Tan says Musk decided to build his own fab because his robots and cars need a lot of silicon, and Intel is delighted to enable him to get to production quicker using some of Intel's technology and process. He calls Musk unconventional in a way he finds refreshing, someone who questions every traditional step, and says he works with a very good team there weekly. On Musk's line about wanting to smoke inside the clean room, Tan says he does not go that far, though maybe in some part of the clean room you can, and he keeps an open mind about it. 8/ Making the most advanced chips in America is becoming the bottleneck, and that is the reason to do it. Tan says everyone lived through the supply-chain crisis and the lesson is that you cannot depend on one or two players in one geography, so more people will realize making chips in the United States is critical. He points to Intel's 14A process at roughly 1.4 nanometer with planning already underway for 1 nanometer and 0.7 nanometer, geometries he likens to something thinner than a human hair where any mistake at any step goes down the drain. The precision required is precisely why manufacturing becomes the scarce thing, which inverts the cost objection into a moat argument. 9/ The real bottlenecks on AI demand are power, helium, and memory, not just chips. Tan lists power constraint as the one everyone knows, with some countries simply lacking the power. He flags helium as an underappreciated and significant input for semiconductor manufacturing, and memory as the current acute shortage that everyone is scrambling for, where adding fab capacity takes a couple of years and the cost gets passed through to customers as prices rise. The list reframes the AI buildout as a multi-input supply problem, with several non-chip constraints that move independently. 10/ When you hit the physics wall you change the materials, and he has invested across the new ones. Tan says scaling will get more expensive and harder but he can see a path from 18A to 14A to 10 and 7, and beyond that you go back to the materials and the chemical table. He names gallium nitride, silicon carbide, and indium phosphide as new materials he has backed, glass and diamond as packaging insulators he has invested in through a glass-substrate venture called 3DGS and a diamond foundry, and Intel's next-generation advanced packaging as its answer to TSMC's CoWoS. His engineer's framing is that you always hit a wall, then find a way to jump over it or work around it. 11/ Semiconductors are hot again, and the capital that once fled the room now crowds in. Tan says that 18 years ago he would pitch semiconductors to a tier-one VC partner meeting and half the room would make an excuse to leave, then the other half would ask if he had any software or services. Now, by his count, Jensen Huang's Nvidia is a 5.3 trillion dollar market cap, Broadcom and TSMC are around 2 trillion each, Lisa Su's AMD is almost 800 billion, and Intel is close to 600 billion. The reversal is the backdrop for the whole rerating: capital that used to demand he own 20 percent of a startup now writes 1 billion dollar venture checks into the same category that used to clear the room. 12/ His investing has always started from one question: where is the bottleneck and is the customer crying for it. Tan says that across 159 IPOs and 126 M&A exits, with 38 percent of his semiconductor investing in the US, his first screen is always where the bottleneck is and what you are trying to solve. He cites backing optical interconnect at Celestial AI because interconnect speed became the constraint in the cluster, and power management where conversion from 48 volts to 1 volt loses a lot of power, noting ADI's acquisition of Empower in that space. The discipline is bottleneck-first, demand-pull, real-customer investing, the same lens he now runs Intel through. 13/ The most important thing in a capital-intensive bet is a partner who stays through the bad times. Tan says many people are enjoyable to work with in the good times but walk away when the company is in trouble, and he wants partners who have worked through multiple hard-then-successful companies, an almost-bankrupt club that eventually takes off. He extends the rule to the kind of investor: individuals who are knowledgeable in the space rather than just a brand-name firm, and growth-stage and hedge-fund friends who understand the public market and can tell an entrepreneur where not to go. His read is that capital is not fungible, and the right co-investor is a strategic asset, not just money. 14/ The winning company in 10 years is a laser-focused niche player that can scale into a full stack. Tan says, framing it as his personal view, that the winner articulates and laser-focuses on one niche area, finds the right partner, and can scale, and he ties it back to a full-stack solution. He admires how Jensen Huang built a platform company around CUDA, and how Anthropic and OpenAI changed the game in a more elegant way, and he hopes Intel can play that role because it has the XPU, advanced packaging, and foundry to build purpose-built silicon for different workloads. The endgame he describes is putting Intel's pieces together into purpose-built silicon, which is the strategic destination of the crawl-walk-run path. 15/ What investors most misunderstand about Intel, in his telling, is the size of the product opening and the patience the foundry demands. Asked directly, Tan says people are only starting to recognize the turnaround four months into his crawl phase. The piece they miss is on the product side: Intel spent decades selling PCs and servers built for humans, and the new dimension is millions of agents that need compute and a software stack, plus physical AI, a market he calls huge and a game that is not over. On foundry he is blunt that Intel is very distant from TSMC and has to humbly rebuild the IP, the yield, the defect density, and the cycle time, work he does not expect to surface until around 2030 to 2032. His ask is that investors read the six-times shareholder return in 14 months as a beginning, not a result. 16/ He is chasing a 10x from a much bigger base, and has already returned six times in 14 months. Tan says that as a venture investor at heart he always looks for 10x, and notes that at Cadence he delivered close to 76 times from his 2.42-dollar interim-CEO starting point, and about 85 times by the time he retired as executive chairman. At Intel the base is bigger, so he sets the goal at 10x over five to ten years, and says the company has already made six times the return to shareholders in 14 months with a lot of room to go. The candor about a lower multiple off a larger base is the calibrated version of the same venture instinct he built his career on. Lastly, the buildout is not the bet, the application is. Tan says he does not see anything slowing the massive AI buildup down because the workload keeps increasing, and the only real brake is the supply constraint. But he insists on looking past the infrastructure to the application, arguing that not everyone who builds will win, that some will win big and others go sideways or get acquired, just as the internet produced an Amazon and a Netflix alongside many that disappeared. The discipline he hands the listener is to find the application that is humongous and sustainable, and if you believe in it, double and triple down.
Serenity@aleabitoreddit

Trump announced Intel + Apple partnership, sending $INTC up 8% today. Intel execs were reportedly surprised by the $AAPL announcement by Donald Trump. TBH, the President should lead Intel's marketing team at this point. Feel's like he's hard carrying the stock.

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