Silicon Valley
Joined April 2008
techstartups retweeted
Oracle's $300 Billion bet on OpenAI is starting to crack. About $18B of loans tied to a data centre leased to Oracle in New Mexico just slid into stressed territory. The banks can't find buyers for the debt, & phase one is already 7+ months behind schedule. OpenAI has deferred its IPO & projects a $280B cash burn by 2030. Last week Larry Ellison abruptly cancelled a $7.5B share plan, no explanation given. Draw your own conclusions....
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Google needs to buy Instinct, they need that product DNA, Notebook isn't going to cut it. Instinct, Muse, Grok Bot, etc., are social ai. I've seen this play out before... I was at Google+ for 3-months running mobile, I tried buy instagram but I was two steps down from Larry, so I didn't have the pull (Kevin had told me Zuck was sniffing around), my SVP said it was 'too expensive.'.. killed me. I bounced to Google Ventures.
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Update on my bet that OpenAI has a 50/50 chance of running out of money by next summer. I know I may be wrong here— this is what 50/50 means. But here’s how I see it. The positive: OpenAI has overtaken Anthropic in terms of weekly revenue, according to OpenRouter data from July. Reflects OpenAI’s scientific excellence, which has always stood in contrast to its shaky business foundations. Impressive that @sama and @gdb have lost loads of senior scientists and yet they still replenish the brain trust. Not so positive: Anthropic appears to enjoy far more customer loyalty. Twelve month retention rates are almost double OAI’s. (H/T @FT). But, tbf, this may just reflect a period when Anthropic’s models improved faster, and maybe the tables have turned. The chief OAI worry is the same as it always was: cash burn. Latest numbers indicate that OAI expects to lose $280 billion by 2030, even though revenue is forecast to 10x between now and then. That implies a massive fund-raising challenge. OAI is running an experiment not only in an unprecedented technology; it is also testing the depth of global capital markets. OAI has used the recent hacking scares to postpone its IPO, which might have been hard to pull off anyway. So it has to raise money privately. Already, the last fund-raising round included a bunch of financial engineering—conditional future cash promises, promises of compute in lieu of cash. OAI has also tried to finance data centers by leaning on Nvidia in its guise as the central bank of AI. See the $105 billion NVidia guarantee on the Ohio data center. How many more nimble financial tricks can OAI pull? Especially in a climate where the political backlash against AI is growing and open weight competition is intense? Here’s what to watch for. OAI financial partner, SoftBank, is plotting the near-term IPO of its data-center builder SB Energy. If that plan runs into trouble, there will be knock-on effects for SoftBank and hence a hit to SoftBank’s ability to pour fresh money into OAI.
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No wonder OpenAI delayed their IPO plans. Secondary shares are being offered at an implied $475 bn valuation, down roughly 44% from the $852 bn primary round earlier this year, that makes that $1 tn IPO look near impossible right now. Hearing Anthropic is being offered around a $700 bn valuation on the secondary market, yet they’re still trying to pitch their IPO between $1 tn and $2 tn. Good luck with that. We all know how flaky secondary markets can be, so make of it what you will.
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😳 Brad Gerstner on the ex-Anthropic researcher who told America AI could end humanity in three years: "To go on national television in a highly coordinated way and sit in the living room on NBC, on ABC, on CBS, on Fox, and have a single researcher who spent six weeks at Anthropic tell the moms and dads of America that this can kill all of humanity in the next three years without any countervailing conversation is deeply irresponsible. Jensen Huang yesterday called it deeply untrue. Andrew Feldman, the founder of Cerebras, who spent decades studying this, called it utter horseshit. Elon himself said it was a coordinated psyop." - @altcap with Scott Wapner @TheJudgeCNBC on CNBC Friday, Sept 11th, 2026
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Greg Brockman says Astra's breakthrough is computer use, a near-universal connector that reaches even the software with no API "You have some pieces of software, humans can use it just fine, the AI has no access to it." "So what do you do? You have to write a very specific connector that hooks up to the APIs, and not everything's exposed, so you can't do everything that you could." "Then you think about that there are so many pieces of software that don't have APIs, and those are totally out of bounds. So we have this limited world where the AI is so restricted from helping you." "I think that we now have the technology that's almost this universal connector."
Greg Brockman says 300 million people come to ChatGPT with health questions every week, and reveals OpenAI is building a bottoms-up clinicians product, top-down enterprise product for hospitals, and a three-sided marketplace in health "It's actually very surprising to me how little airtime what we're doing in health gets relative to how many people it's actually helping." "We have like 300 million people each week coming to ChatGPT for health queries. 300 million people, that's a huge number." "Then we're also building a bottoms-up clinicians product, and we're building a top-down enterprise product for hospitals." "So we have this three-sided marketplace in health, and what we're going to be able to do there is things like, you want to find people for clinical trial enrollment, that's a hard problem, but we actually may have the ability to help find people that would otherwise not be found." "That both helps the patient and helps these drugs be able to move faster."
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This is serious competition for NVDA. The startup says it took just 44 days after getting its test chips back from Taiwan Semiconductor Manufacturing to have them up and running inference workloads—the computing processes that allow AI models to respond to user queries—a process that usually takes six months or more. When Etched needed help designing servers and racks to integrate its chips into one big system, the company in 2024 recruited Brian Loiler, a top systems engineer who spent nearly 23 years at Nvidia. Loiler has since recruited to Etched about a dozen engineers from Nvidia who, in some cases, turned down attractive counteroffers, he said. wsj.com/tech/ai/a-21-billion…
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Excellent, candid back & forth on complex subjects that benefit from a good faith Saturday afternoon exchange! Thx @_sholtodouglas & @GavinSBaker for kicking off & super helpful to have @DarioAmodei weigh in. 🙏
1/2 Thanks Gavin for an especially thoughtful exchange. I don't usually spend much time on social media but I wanted to engage here because it really brings out the heart of an important conversation. First, on regulation, I think that “either concentrate it in the hands of a chosen few companies and politicians via regulation or distribute it widely” is a false choice.  I know that there’s a sort of Silicon Valley shorthand where regulation = regulatory capture = concentration of power, but I’ve always found this to be an overly simplified picture of the world.  Many people outside this bubble think of regulation as something that constrains corporate power and benefits ordinary people.  I don’t necessarily agree with that perspective either, rather I think it’s complicated and really depends on what the “regulation” consists of.  But in particular I think that those in the “regulation = regulatory capture = concentration of power” frame often underrate the decentralizing power of objective and fair institutional processes.  A crude analogy is that the formal court system can sometimes feel stuffy and elitist, but it does a much better job of defending the rights of vulnerable individuals than the alternative, mob justice.  At their best, institutions can vest power in ideas rather than people, and thereby decentralize that power. This is why Anthropic has always made its policy proposals very carefully.  We try very hard to make proposals that disadvantage (slow down) frontier AI companies while *advantaging* smaller competitors.  California’s SB53 (which we supported), and even the much-maligned SB 1047 (which we were ambivalent on), completely exempt any company below a certain amount of revenue or model training costs from being covered at all (it was $500M for SB 53, lower for 1047 but we objected to that).  More recently the testing process we’ve advocated for at CAISI and the White House involves more rigorous tests for frontier models than off-frontier models — something that differentially advantages challengers.  Similarly, the “Pacing the Frontier” letter envisions (or at least Anthropic’s preferred implementation of it envisions) modulating the pace of the very best models while not constraining those who are catching up.  This hurts the business interests of the frontier labs and helps challengers, including open-weights! Overall my view is that AI is *structurally* a technology that tends to concentrate power, for reasons that have nothing to do with regulation (more to do with the extreme implications of the scaling laws).  Open-weights do help some with this but are nowhere near a sufficient solution because they simply shift the concentration somewhat to those with the most compute and chips (which are roughly the frontier labs plus maybe hardware providers).  By contrast I think the right “rules of the road” can simultaneously (a) address AI’s cyber/bio/alignment risks, (b) institutionally constrain the power of the frontier AI companies, and (c) leave room for open-weights models while also addressing the specific risks that they bring. BTW I do not think that the events of the last few months have “failed to result in [my] preferred regulatory path”.  The approach that the Trump administration is reported to be taking — pre-deployment testing for frontier models, and also testing of open-weights models when they get closer to the frontier — is one that I am very supportive of, though of course I have to see the details to be sure.  I am also supportive of Demis Hassabis’ ideas around a FINRA-like entity.  This contrasts with six months ago when most of the industry was still pushing for preemption of all state regulation and no apparent federal approach either.
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I heard second hand (so ymmv) that SALP’s Anthropic position was marketed but went unsold. Is this true? Why, if accurate, is no one reporting why nobody bought the private Anthropic portion SALP tried to sell? What does smart, public money think of valuation? Is the below comment common knowledge? Could it explain part of the reason why folks passed?
This quoted post is unavailable.
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Why does China keep releasing open-weight models? Well, releasing frontier open-weight models commoditizes the closed-weight models of OpenAI and Anthropic, which challenges their businesses, which ultimately causes the splintering of their most valuable asset, their teams. People will leave and start new companies leveraging open-weight models if the primacy of OpenAI or Anthropic is uncertain. If that splintering is achieved, then Chinese frontier labs will stay ahead -- and eventually, once they're ahead far enough, switch over to closed models. At that point, they win. If they have the best models, then the rest of the world will use them -- whether open or not. At that point, the rest of the world is dependent on their models. And when people ask Kimi, Kimi shapes their thoughts. And once Kimi is strategically important enough, of course the CCP will have a hand in steering it. If AI leadership becomes a test of state capacity, rather than a test of decentralized, free-market innovation, then China is playing on a home field advantage. Releasing best-in-class open-weight models is a reasonable part of that strategy. So to answer the question, it's as simple as: China wants to win, and is more concerned by beating the US than by any AI safety debate. (As an aside, it's worth noting -- and it did not occur to me previously -- that perhaps already the schism of OpenAI into Thinky, Anthropic, etc. may have meaningfully reduced the US advantage in the AI race.)
registering confusion: i don't really understand why Xi is still allowing Kimi to release such powerful open models. this is something i've publicly said i expect to stop soon. it doesn't make sense to me that the CCP would want open frontier capability easily available to other countries. it could still be that Xi is asleep at the wheel, or that K3 is just a cycle of capability behind where they start to take serious notice. but if things don't change soon then i'm just wrong / missing something.
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homeboy you're the one sellling public market investors on short-term space datacenters
Elon Musk
@elonmuskJul 11
He takes scamming to a whole new level
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LeBron James turns 42 in December, just committed to a 24th NBA season, and is training with an exercise most players half his age would fall off. Kneeling on an inflated rubber ball, pressing dumbbells overhead. The difficulty is the point: it hammers the one system aging destroys first. Here's the physics. A stability ball gives you two shifting contact points on a surface that rolls in every direction. Adding dumbbells overhead raises your center of mass by more than a foot, turning your body into an inverted pendulum. A 2-degree lean that means nothing on flat ground becomes a torque your muscles have to catch immediately or you're on the floor. Catching it happens below consciousness. Stretch receptors in your muscles detect the drift and fire corrections through spinal reflex loops in under 100 milliseconds. Conscious reaction takes about a quarter second. One set on that ball is thousands of micro-corrections, all running through the reflex circuitry, none through the thinking brain. That circuitry is exactly what age attacks. Fast-twitch muscle fibers atrophy decades before slow-twitch ones. Proprioception, your body's sense of where it is in space, degrades alongside them. It's why elderly people fall, and why aging athletes lose the first step while their bench press barely moves. The strength stays. The correction speed goes. Kareem retired at 42 after 20 seasons. Vince Carter stretched to 43 in a bench role. LeBron just averaged 20.9 points, 7.2 assists and 6.1 rebounds in season 23, then raised it to 23.2 in the playoffs. Aging takes your balance years before it takes your strength. The longest career in NBA history belongs to a guy hammering the thing that quits first.
Vocês falam que é impossível o LeBron jogar tudo isso, com 42 anos… Malandro, difícil MESMO é fazer esse exercício!!! O CARA TA APOIADO COM OS JOELHOS NUMA BOLA DE PILATES E FAZENDO DESENVOLVIMENTO COM HALTER 🏋🏿‍♂️👑
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Rob Wachen was 16 when doctors told him that the bump on his back was stage 4 bone cancer, and that his odds of surviving were under 30%. Surgery meant he would probably live but might never walk again; radiation meant he would keep walking but might not survive. His parents told him the decision was his alone, and he chose surgery. The necrosis analysis came back below the threshold, which meant he needed radiation anyway. One of the only machines in the world that could deliver it was in Boston, so his parents moved there with him while he went through months of treatment in a wheelchair. He spent two years in chemotherapy, surgery, and learning to walk again. Before the diagnosis, he had been running a six-figure business, competing in Taekwondo, and playing semi-professional saxophone. He has said that surviving something like that requires hoping for something, and that he always knew if he got through it, he wanted to do something that mattered. Gavin Uberti's story is stranger in a different way. At 12, he was leading teams in Minecraft to build analog chips, sending precise architecture specs for hundreds of thousands of blocks to group chats. At 17, he got his first job writing high-performance code, and because a 17-year-old cannot sign a legally binding contract, the company skipped the NDA, sat him down, and said, "Gavin, don't share this information." That company sold to Apple for $200 million. The next one he worked at sold to Nvidia. In high school robotics, Gavin and his friend Sanford left their 20-person school team to compete as a two-person unit, on the theory that everyone else split their attention between documentation, outreach, and the robot, and they would only build the robot. At one point they held the world record for the highest score in the competition. The two met at Harvard, where Rob ran Prod, a nonprofit accelerator whose alumni companies, Cursor and Mercor among them, are now worth more than $100 billion combined. When GPT-4 launched with image uploads, Rob dug up a photo of his back from before his diagnosis and asked the model what the bump could be. It answered immediately that it could be a tumor and that he should get an MRI. His doctors had taken six months to reach the same conclusion. While he sat there absorbing that, a notification appeared on his screen telling him he was out of image credits for the day. The models were about to change everything, and the infrastructure to serve them did not exist. Every GPU and TPU running these models had been designed before ChatGPT. Gavin, who had spent a year optimizing AI models to run on GPUs, believed a system built specifically for inference could be radically better. In 2023, they dropped out to build it. They were 21. GPUs can't use all their advertised computing power because they overheat and slow themselves down. Etched developed what they call low voltage inference, running chips at under half the voltage of any other AI chip, borrowing an insight from Bitcoin miners that everyone in the industry insisted couldn't apply to AI. And where Nvidia's chips take 4,000 nanoseconds to send data to each other, Etched built a custom interconnect that cuts that by more than 5x, letting an entire cluster share its memory as a single pool. Mark Ross was the former CTO of Cypress Semiconductor, which sold for $9 billion. When they met him, they were a couple of guys in a dorm room telling a prestigious chip veteran they could be much faster than Nvidia, and he told them what everyone else had: "no, you can't, it will not work." But if they wanted to convince him, they should write a white paper, build a functional simulation, and show him. After a lot of very long nights, they came back with the simulation, and he looked at it and said, "huh, this works." Mark is Etched's full-time CTO. At the end of 2023, they had $15 million in the bank and needed at least $100 million more to finish the chip and build the rack. They wrote a 30-page memo and asked for it, double the largest semiconductor Series A ever raised, and every major investor in the Valley passed immediately. The market was uncertain, they were told, and two 22-year-olds could not build this type of company. Gavin started looking into how hard it would be to go back to Harvard. They called everyone they knew and survived on small checks, a million here, two million there, enough not to run out of money that month, then a five million check, then ten. At a board meeting, they looked at a spreadsheet of soft commits totaling $103 million. When a critical vendor in Bangalore fell a year behind, they shipped a dozen engineers to India for six months, and Gavin lived there for four and a half, running 24-hour development cycles with 12-hour handoffs between continents. When the chips came back and a clock-domain bug threatened to corrupt every result, people quit and called the problem unsolvable. The fix required aligning two clock signals to within 50 trillionths of a second, on every chip, two billion times a second. Gavin solved it in two weeks. They taped out a working chip on their first attempt, the first hardware company founded after ChatGPT to do it, and when silicon came back they had it running inference in a rack in 40 days. A well-known AI chip company took 10 months to do the same. Today Etched has more than 400 people, drawn from Nvidia, Google's TPU team, and Broadcom, and half of them live within five minutes of the office. The company has over $1 billion in customer orders and just raised $800 million. As Rob says, "We're in a new era of intelligence, where the cost of producing intelligence is so dramatically much cheaper than the value of the intelligence, that we are in a many-year, probably many-decade, supply shortage of these tokens." Whoever produces the most tokens, he believes, will be the most valuable company in the world.
Three years ago, two Harvard dropouts set out to build a better AI chip than the largest companies in the world. Almost everyone I called at the time said it was impossible. Today, Etched (@Etched) comes out of stealth with $800M total raised, $1B in signed customer contracts, and a working next-gen AI chip. This was my excuse to ask the two founders, @UbertiGavin and @robertwachen, every question I have about compute and inference. We discuss: - Why they built an entire rack and not just a chip - The two technical bets behind their architecture no one else has tried - How two founders in their twenties recruited industry legends - The night they nearly ran out of money - Why whoever produces the most tokens wins If you care about the future of compute, Gavin and Rob are two people to know. I think you will find the story of what they have built hard to forget. Enjoy! TIMESTAMPS 0:00 Intro 1:00 Why Nobody Believed Etched Would Work 14:06 Why Inference Is the Bottleneck 22:27 Gavin and Rob’s Origin Stories 33:24 Taking Huge Risks to Move Faster 49:43 Kernels, Compilers, and the AI Stack 1:02:08 Raising $100M to Survive 1:16:00 The Future of Models, Agents, and Intelligence
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Marc Andreessen says Alex Karp almost never talks about Palantir in interviews. He calls it the single best marketing strategy he has ever seen and then revealed the number that proves it works better than anything else in the history of investor communications. Every founder makes the same mistake. They think inside out. My company, my product, my story, out into the world. It feels natural. It is also why most founder content is indistinguishable from every other founder's content. Karp does the opposite. He talks about the future of the US military. He talks about superintelligence. He talks about whatever is genuinely interesting to him about the world right now. And because he is the CEO of Palantir, the company just sits there attached to all of it. Then Marc dropped the number. What percentage of Palantir investors have read the S1? Practically zero. What percentage have seen Karp on YouTube? Close to 100. A Edelman B2B study found that thought leadership content drives purchasing consideration more than product marketing does — by a factor of nearly three to one among enterprise buyers. Karp did not read that report. He just built the playbook it describes. Palantir's lawyers spent thousands of hours on the S1. It explains everything the company does with full precision. Nobody read it. Karp spent those hours talking about things that interested him. Everybody watched. The most effective investor communication Palantir ever produced was never filed with the SEC. Watch the full video on @a16z YouTube channel
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This is what’s causing Anthropic to aggressively beg for govt protection (see below). Customers are finding cheaper alternatives. Keeping employees requires continuing ultra-rich secondaries ($$$) that are dependent on revenue growth. When you can’t win on the field go to DC.
UBS says 60% of companies now watching AI budgets are moving to cheaper models and open-source Chinese models The pressure is coming from extreme bills, including users spending up to $35K/month, teams exceeding quotas by 200%, and companies cutting internal AI tools from 5 to 2. Companies are not abandoning AI, they are using model routing, which sends easy tasks to cheaper models and saves premium models for hard reasoning, code, and long-context work. Chinese open-source models such as Qwen, DeepSeek, MiniMax, GLM, and Kimi now fit the enterprise cost curve because they can be run locally or used through cloud catalogs. --- news .futunn.com/en/post/75068082/ubs-group-finds-60-have-already-started-curbing-ai-spending?level=2&data_ticket=1780870170397383
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I'm a cardiologist. Something just happened today that I genuinely did not see coming — and it could change the future of preventive medicine more than anything I've written about on this platform. Midjourney — the AI company that became famous for generating images from text prompts — just announced a medical hardware division and unveiled a working prototype of a full-body scanner unlike anything that's ever existed. It's called the Midjourney Scanner. And it works like this. You step into a shallow pool of water. You stand on a platform that slowly descends — about two inches per second — through a ring containing roughly half a million tiny ultrasonic transducers, each the size of a grain of sand. Every one of them acts as both a speaker and a microphone, sending ultrasonic waves through your body from every angle and recording what comes back. 60 seconds later, you step out. The scan is done. No radiation. No magnets. No claustrophobia. No IV contrast. Just sound, water, and an almost incomprehensible amount of computing power — roughly 2 petaflops processing 17 gigabytes per second of raw acoustic data — reconstructing a 3D map of your entire internal anatomy down to half a millimeter resolution. Organs. Tissues. Blood vessels. Bones. Muscle. Fat distribution. All segmented by AI in real time. As a cardiologist who has spent months writing about how the standard screening playbook misses the majority of future heart attacks — this is the technology I've been waiting for without knowing it existed. Here's why this matters for the future of your heart. Right now, getting a detailed look inside your cardiovascular system requires either a CT scan (radiation), an MRI (magnets, claustrophobia, 45-60 minutes, $1,000+), or a coronary CT angiogram (radiation, IV contrast, limited availability). These are powerful tools. I order them regularly and they save lives. But they're reactive. You get them when something is already suspected. They're expensive. They're uncomfortable. And for most people, they happen once — maybe twice — in a lifetime. Imagine instead: a 60-second scan with no radiation that you could repeat monthly or quarterly. Tracking cardiac structure over time. Watching body composition shift. Detecting changes in organ size, fluid distribution, or vascular architecture before symptoms ever develop. Building a longitudinal dataset of YOUR body that AI can analyze for patterns no single snapshot would reveal. That's what Midjourney is building toward. The company plans 50,000 scanners worldwide over six years, with capacity for a billion scans per month. The first location — the "Midjourney Spa" in San Francisco — opens at the end of 2027 with 10 scanners alongside saunas, cold plunges, and a gym. The scan costs a few dollars. The experience is designed to feel like wellness, not medicine. The technology is built on Butterfly Network's ultrasound-on-chip platform — 40 modules per scanner — combined with Midjourney's own AI segmentation and reconstruction stack. David Holz, the founder, claims the system aims for image quality comparable to MRI in many aspects but at nearly 100x the speed with zero radiation. Now the caveats — because I'm a physician and the caveats matter enormously. This is a Gen 1 prototype. About a dozen people have been scanned so far. Current scan time is actually closer to 20 minutes, not 60 seconds — the system is bottlenecked by bandwidth and reconstruction algorithms. The 60-second target is aspirational for future hardware generations. It is not FDA-cleared for diagnostic use. Midjourney is starting with body composition maps — a category below diagnostic imaging in the regulatory hierarchy. The path from "beautiful 3D body scans" to "clinically validated diagnostic tool that your cardiologist can act on" runs through years of clinical trials, comparative studies against MRI and CT gold standards, and FDA review. No independent clinical validation has been published. The imaging claims come from Midjourney's own demonstrations. Comparative data against established modalities does not yet exist. And the privacy implications of full-body internal scans at planetary scale — a billion scans per month — is a conversation that hasn't even started yet. So I want to be precise. This is not ready for clinical medicine today. It may not be ready for years. Many ambitious medical hardware projects have failed in the gap between prototype and product. But. The fact that a working prototype exists — producing real segmented 3D anatomy from sound waves and compute alone — means the physics works. The engineering works. The question is no longer "is this possible" but "how fast can it be validated and scaled." And if it is validated — if the resolution holds up against MRI, if the AI segmentation proves reliable, if the regulatory path clears — then what we're looking at is the most significant new imaging modality in 50 years. For my entire career, preventive cardiology has been limited by the fact that seeing inside the body is expensive, slow, uncomfortable, and infrequent. We catch disease late because we image rarely. We image rarely because imaging is hard. A 60-second, no-radiation, spa-based full-body scan that costs a few dollars would demolish every one of those barriers. I've written about AI detecting inflamed arteries. About gene editing curing cholesterol. About GLP-1 drugs rewriting metabolic medicine. About cellular reprogramming reversing aging. This is the missing piece: the ability to see inside every human body, routinely, safely, and affordably — so all of those interventions can be deployed before the disease arrives instead of after. The company that taught AI to generate images from imagination just built a machine that generates images from the human body. The future of medicine showed up today from the last place anyone expected.
Announcing a new division of Midjourney called "Midjourney Medical"
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Which was worse?
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Re-reading the 2002 SpaceX chapter in @WalterIsaacson’s Elon Musk, when the entire industry said reusable rockets, Starlink & cheap access to space were impossible. Musk’s mindset: “If you were negative or thought something couldn’t be done, you were not invited to the next meeting.” - Tom Mueller He filled the team with reality-bending willfulness and extreme risk tolerance. People who would make things happen. That same drive created a culture that achieved the “impossible”; but also one where bad news sometimes got filtered. SpaceX 2002–2026 IPO proves it: First principles + insane determination beats conventional wisdom every time. What a journey. 🚀
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Haha this is awesome. Elon agreed to the "greenshoe" IPO option but only if the bankers and team all wore actual green shoes 😂🔥 (A "greenshoe" is a standard IPO provision that allows underwriters to sell up to ~15% more shares than originally planned if demand is strong, helping stabilize the stock price in early trading.)
And thank you to the Morgan Stanley trading crew in “Mission Control” sculpting the debut of $SPCX today. Here is the moment of first trade. P.S. Elon finally agreed to the IPO greenshoe options… but only if the bankers all wore green shoes. 👟 —> Mementos for all.
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$SPCX: $18 billion revenue, -$10 billion net income, valued at $2.2 trillion $META: $215 billion revenue, $70 billion net income, valued at $1.45 trillion But yeah it all makes sense..
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