Milk Road AI
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Get smarter about AI investing. Capitalize on the biggest technological change in history across the infrastructure & app layers of AI. By @Milkroaddaily
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Everyone wrote Apple off as the AI loser, but one hardware spec might flip that story upside down (Save this). @jason called Apple a screaming buy on the back of a single chip detail. The rumored M7 Ultra, expected around 2028, is designed to support up to 1.5TB of unified memory, enough to run frontier class trillion parameter AI models locally, with no cloud required. The Street's bear case on Apple is straightforward. Apple has no frontier model of its own, Siri has stumbled for years and the company effectively rents OpenAI's models for its hardest queries. That narrative treats Apple as the one Magnificent Seven name that missed the AI wave entirely but the bull case flips that framing on its head. If frontier AI models keep shrinking and getting cheaper to run, Apple doesn't need the smartest model in the world, it just needs to own the device that model runs on. And unified memory is the mechanism that makes this possible. Unlike traditional systems where the CPU and GPU each need separate memory, Apple's architecture lets the CPU, GPU and Neural Engine draw from one shared pool. A fully specced M7 Ultra could theoretically run something on the scale of a 1.2 trillion parameter model locally and that capability plugs directly into the one advantage Apple has spent over a decade building: privacy. Apple has already shipped Private Cloud Compute, a system designed so even Apple can't access user data processed off device. Apple doubled down on this at WWDC 2026, framing on device privacy as non-negotiable while rivals default to the cloud. If the best AI models get small enough to run on Apple silicon, the moat stops being the model and becomes the hardware it has to sit on. Milk Road Pro remains bullish on Apple and it remains as one of our core positions, if you want the full thesis + our full AI trades, come join us using the link below for just a $1.
Milk Road AI31,628 просмотров • 1 день назад
This is WILD! Ray Kurzweil, the futurist who predicted the internet, smartphones, and AI says aging ends by 2032 (Save this) Kurzweil, now 78 years old, told a live audience that humanity will reach longevity escape velocity by 2032 and he explained exactly what that means with mathematical precision. Right now, for every year you live, you get back approximately five months of life expectancy from medical and scientific progress meaning you are losing roughly seven months of net life per calendar year. Longevity escape velocity is the threshold where that ratio flips, for every year you live, you get back a full year or more from scientific progress, meaning your biological clock starts running backward. Kurzweil's prediction is that threshold hits by 2032 and beyond that point, you do not simply stop dying of aging, you actively get younger every year. The mechanism is AI-driven drug discovery at a scale that was physically impossible five years ago. By 2030, Kurzweil argues, AI will be able to take a biological problem, generate millions of potential drug candidates, screen all of them, and run trials on simulated digital populations compressing decades of clinical research into weeks. This is already happening. David Sinclair's lab at Harvard used AI to virtually screen 8 billion molecules against aging targets and is now preparing human trials moving from $400,000 gene therapies toward a $100 pill that can reset biological age by 50 to 95% in four weeks. Sinclair has already demonstrated the ability to reverse aging in mammals restoring sight in mice with optic nerve damage and reversing Alzheimer's symptoms in lab models. Kurzweil's track record is what makes the 2032 claim impossible to dismiss. He predicted the internet's global dominance in 1990, the defeat of a world chess champion by a computer in 1998, pocket-sized devices as primary communications tools in 1999, and AI passing professional exams in the mid-2020s, all before anyone else was saying it publicly. If you are under 60 and in reasonable health, his message is stay alive, stay healthy, and get to 2032. The tools on the other side of that date will be unlike anything medicine has ever produced.
Milk Road AI959,925 просмотров • 1 месяц назад
Chamath just delivered the clearest diagnosis of what is happening to enterprise software and the OpenAI Deployment Company is the most damning piece of evidence he could have picked. "The low end of the market is basically finished. There is no safe space." 90% of public SaaS stocks are down 30-80% from their 52 week highs, the median software stock is now negative over the last 3-6 months. Goldman Sachs reported that software forward P/E multiples fell from 35x to 20x, the lowest absolute level since 2014 and the smallest premium to the S&P 500 since 2010. The low end died first and fastest, because AI replaced it most directly. The small business tools, the lightweight project managers, the single function SaaS products that charged $49 a month per seat, those are being replaced by AI agents that do the same work as a workflow, not a product. You do not buy an AI powered tool, you describe what you need and it builds it and the seat based model that created the SaaS industry simply does not apply to that transaction. But Chamath's more interesting argument is about the high end and the tell he points to is perfect. OpenAI just raised $4 billion from 19 investors including TPG, Brookfield, Bain, and McKinsey to launch a consulting company and guaranteed those investors a 17.5% annual return to do it. On $4 billion in committed capital, that is roughly $700 million per year in guaranteed payouts, owed by a company that is projected to lose $14 billion in 2026. The goal of this venture is to compete directly with Deloitte, PwC, Ernst & Young, Andersen, and Cognizant. Think about what that structure reveals. OpenAI lost half of its enterprise LLM API market share from 50% to 25% between late 2023 and mid-2025, with Anthropic now leading at 32%. Its response was not to build a better model but rather to raise $4 billion, offer guaranteed PE-tier returns and hire embedded engineers to physically sit inside client organizations and make AI actually work in production. The reason, as Chamath identified, is that the high end of the market is not easy. "It's not like boop boop boop, put in a prompt and beep bap boop, it all works," he said and the data confirms exactly that. 88% of organizations running AI agents reported a security incident in the past year, 42% of C-suite executives say AI adoption is creating internal organizational conflict. The average enterprise AI consulting implementation costs $228,000 in year one versus $77,000 for platform-based approaches and most still stall before reaching production. Anthropic immediately matched OpenAI with a competing $1.5 billion consulting venture backed by Blackstone, Goldman Sachs, and Hellman & Friedman bringing the combined spend by the two leading AI labs on human powered enterprise deployment to $5.5 billion in a single month Chamath's read is that the high end, the large enterprise platforms like Salesforce with proprietary data flywheels, Palantir with its FDE model already proven at scale, Oracle with vertical specific data moats will survive and consolidate. The mid-market point solutions, the single function tools, the lightweight enterprise apps without defensible data assets, those are on the conveyor belt. The AI industry is not just disrupting the companies that use software but rather disrupting the companies that sell it.
Milk Road AI1,657,771 просмотров • 2 месяцев назад
Chamath has been watching SpaceX for 15 years and he thinks the market is still not close to understanding what it actually is (Save this). The first argument is the industrial logic of a Tesla SpaceX combination. One capital structure, one balance sheet, one vehicle to raise money across robotics, autonomous vehicles, energy, AI, and launch. Chamath Palihapitiya argument is that markets are treating this as a peripheral possibility rather than an obvious strategic inevitability. The second is Starlink Direct to Cell, which he believes will generate enormous domestic cellular revenue before most of the bigger SpaceX narratives even begin to materialize. The numbers already back this up. Starlink has over 10 million Direct to Cell monthly active users with live partnerships with T-Mobile, Rogers and Optus standard smartphones connecting directly to satellites with no special hardware required. SpaceX is currently deploying approximately 340 Direct to Cell satellites per month, targeting 25 million monthly active users by end of 2026. Goldman forecasts SpaceX's AI division will generate $15.6 billion in 2026, rising to $34.5 billion in 2027 and accelerating to $322 billion by 2030 roughly a 100-fold increase in five years. Total SpaceX revenue hits $474 billion by 2030, up from $18.7 billion in 2025. The launch cadence numbers are where this gets staggering. SpaceX is expected to execute 151 Starship launches in 2027, scaling to 253 in 2028, then 1,504 in 2029, 2,808 in 2030, and 5,467 in 2031. Goldman projects 5,288 of those 2031 launches will be dedicated Starship AI missions each carrying 30 to 50 satellites powered by one GB300 equivalent compute rack apiece. The cost per kilogram to orbit falls below $100 as reusability matures, compared to $1,500 per kilogram on Falcon 9 today. Morgan Stanley projected a 24-hour turnaround by late 2027, enabling the kind of cadence these numbers require. That launch cost collapse is what makes the orbital AI compute thesis real Elon Musk
Milk Road AI93,861 просмотров • 6 дней назад
Chamath is making one of the most important business arguments of 2026. Half of large US companies right now cannot generate returns that exceed their cost of capital, which has normalized back to its long run average of 8 to 11%. Another one in seven companies globally is stuck generating persistent returns between 1 and 5% and most businesses don't have room for error and in this environment walks every frontier AI lab saying the same thing, give us your data, your workflows, your processes and our model will make everything better. And companies by the millions said yes. What they didn't fully account for is what happens on the other side of that door. Every time an employee runs a query through a frontier model API, the prompt goes through external servers, workflows, customer data, pricing logic, internal processes, all of it transmitted through a third party. As Alex Karp said companies are spending on tokens while handing over the exact proprietary advantages that make their business worth owning. Microsoft blocked internal use of Anthropic's Claude Fable 5 but over its 30-day data retention policy and the largest software company in the world decided a frontier model's data handling was too risky for its own employees. A US government action revoked access to another frontier model for foreign nationals overnight. Now here's where the cost math becomes impossible to ignore. Deutsche Bank calculated a roughly 65x cost gap between frontier models like Claude Fable 5 at ~$3.25 per task and open-source alternatives at ~$0.05. For 90% of everyday enterprise tasks, performance is comparable. Open-weight models now match closed frontier systems on core agent tasks at roughly one-tenth the cost, a high-volume deployment that costs $250/day on Claude runs at $12/day on an open-source equivalent. Chamath Palihapitiya tested this directly by running a standard enterprise code migration task through an orchestration layer wrapping an open-source model came in 16.4x cheaper than using a frontier model directly.
Milk Road AI280,556 просмотров • 16 дней назад
This is WILD! MIT just solved one of the hardest unsolved problems in robotics (Save this). For decades, the fundamental problem with soft robots and wearable exoskeletons has not been compute or AI, it has been actuation. The moment you try to give a soft robot meaningful strength, you run into the same wall every engineer has hit since the field began, fluid-driven systems require external pumps, hydraulic reservoirs, and heavy infrastructure that makes the entire thing impractical to wear or embed into fabric. MIT's new Electrofluidic Fiber Muscles solve that problem by eliminating external infrastructure entirely. The key insight is electrohydrodynamic pumping using electric fields to generate pressure directly from electricity, with no moving parts, no motors, and no external fluid reservoir. The fibers are less than 2 millimeters thick, can be woven into fabric like ordinary textile, and operate in complete silence because nothing physically moves inside them, it is just ions propelling fluid through a closed circuit. The performance numbers published in Science Robotics are not conceptual, they are empirical results from actual hardware. These fibers achieve a power density of 50 watts per kilogram, matching skeletal muscle, with a contraction strain of 20% and a response time of 0.3 seconds. A single bundled configuration lifted 4 kilograms, 200 times its own weight while a separate configuration drove a robotic arm through a 40-degree bend compliant enough to safely complete a human handshake. Another configuration launched objects in under 100 milliseconds, which is faster than a human flinch reflex. The design mirrors biological muscle architecture in a way that prior artificial muscle approaches never achieved. The fibers are organized into antagonistic pairs, one contracts while the other extends, exactly like biceps and triceps and because the system runs in a closed loop, the relaxing fiber serves as the fluid reservoir for the contracting one, which is what allows the whole system to operate untethered with no external tank. The applications are not hypothetical but rather are the exact use cases the industry has been waiting years for the hardware to catch up to. Exoskeletons for physical labor, prosthetic limbs that move with the natural compliance of biological tissue, assistive garments for patients with motor disorders, and soft robots capable of safe physical contact with humans are all immediately unlocked by a muscle technology that is silent, lightweight, and weavable into clothing. The deeper significance is what this technology does when it meets the AI robotics wave that is already underway. Every major humanoid robot program, Figure, 1X, Boston Dynamics, Tesla Optimus is currently bottlenecked by the same hardware limitations these fibers address, actuators that are too rigid, too loud, too heavy, or too dependent on infrastructure to operate naturally alongside humans. Electrofluidic fiber muscles do not just solve a materials science problem but rather they remove one of the last physical barriers between robots that live in labs and robots that live in the world.
Milk Road AI1,204,107 просмотров • 2 месяцев назад
This is WILD! One week before SpaceX's historic IPO, Google signed a deal to pay SpaceX $920 million per month from October 2026 through June 2029 for access to 110,000 Nvidia GPUs, CPUs, and related infrastructure (Save this). That is $11 billion per year and up to $30 billion over the life of the contract. This comes less than a month after Anthropic committed $1.25 billion per month for full access to the Colossus 1 data center in Memphis, 200,000+ GPUs, 300+ megawatts of power capacity, through 2029. Two of the most consequential AI labs in the world combined committed value over $70 billion. The question that haunted SpaceX's IPO roadshow was why did Elon keep spending billions constructing Colossus, Macro Hard and Macro Harder, three facilities totaling nearly 2 gigawatts of AI compute when xAI's revenue wasn't yet on the same trajectory as OpenAI or Anthropic? Wall Street was pricing in a risk that Elon was building capacity ahead of revenue which would mean sustained cash burn without a clear payback timeline. That concern was legitimate on its face, because xAI had been aggressive on model development but had not yet demonstrated the enterprise revenue numbers to justify the infrastructure cost. The answer is that the compute itself was always the product. Amazon has AWS, Microsoft has Azure, Google has Google Cloud, Elon just confirmed that he has been quietly building the fourth major hyperscale AI cloud and his first two paying customers are Google and Anthropic, the very companies most aggressively competing in the AI race. xAI's Colossus facility in Memphis was built at a speed that no traditional data center developer could match, it went from groundbreaking to operational in roughly 122 days. That is what happens when you have direct Nvidia relationships, a construction operation built around SpaceX-style execution, and a founder who treats infrastructure buildout the same way he treats rocket launches: compress every timeline and eliminate every bottleneck. The result is that SpaceX now has three operational facilities, Colossus, Macro Hard, and Macro Harder with Macro Hard and Macro Harder in Blackwell architecture running 1.2 gigawatts combined. Colossus 1, built on H100s and optimized for inference, is the facility that went to Anthropic first. The Blackwell-era facilities are where the next-generation training workloads happen and Google's deal suggests they are renting into that capacity as it comes online through the second half of 2026. Elon's compute leasing business would generate approximately $45 billion in incremental annual revenue on top of the mid-$20 billion range analysts had been modeling for SpaceX more than enough to fully subsidize the infrastructure investment and take the financial pressure off xAI delivering immediate AI product revenue. That changes the entire valuation conversation of SpaceX completely! Milk road remains bullish on Space and come join Milk Road Pro and get our full SpaceX IPO breakdown, how we're thinking about the $1.75 trillion valuation and our entire AI thesis. Link below!
Milk Road AI761,033 просмотров • 1 месяц назад
Gavin Baker, CIO of Atreides Management made one of the most important and nuanced calls on memory stocks in recent months (Save this). His argument is that based on every memory cycle of the last 25 years, the setup today, prices elevated, sentiment high, supply ramping is textbook time to sell but he adds a critical exception. The one cycle in modern memory history where selling was catastrophically wrong was the mid-1990s, which Baker calls the last true capacity cycle in memory. In that cycle, demand was structurally exploding as the internet era required entirely new computing infrastructure to be built from scratch, and memory had to scale with it in a way that had never happened before. His point is that AI may be that same kind of cycle and not a normal boom bust but a once in a generation capacity buildout where the underlying demand is structural, not cyclical. The reason this argument holds weight is the fundamental shift in what memory is in the AI era. Traditional DRAM was a pure commodity, identical specs, interchangeable suppliers, price determined entirely by supply and demand swings. HBM is the opposite because it is custom engineered to fit a specific customer's chip, co-designed between the memory maker and the GPU designer, with SK Hynix's Vice President literally describing it as shifting from a commodity to a customer-tailored custom business. A single Blackwell Ultra GPU now requires up to 288GB of HBM3E, a 3.6x increase over the H100 and major suppliers like SK Hynix and Micron have already sold out their entire HBM production capacity through the end of the year. Because HBM requires advanced packaging processes like CoWoS that can't be spun up overnight, the bottleneck isn't just wafer capacity but rather runs across the entire manufacturing stack. Bank of America projects the global HBM market grows 58% this year alone to $54.6 billion, and Nomura expects the broader memory sector to nearly double to $445 billion. Long Micron!
Milk Road AI260,384 просмотров • 21 дней назад
The man who turned 225 million dollars into 5.5 billion dollars explained on camera exactly why he made his biggest bet. This is Leopold Aschenbrenner, the same person whose Bloom Energy position is now worth close to 2 billion dollars after Oracle's 2.8 gigawatt fuel cell deal laying out the power math that drove every investment decision his fund has made. In 2022, the GPT-4 training cluster consumed roughly 10 megawatts of power and cost about 500 million dollars. AI compute has been scaling at roughly half an order of magnitude per year meaning the largest training cluster doubles in power requirement every 12 to 18 months without stopping. By 2024, the largest cluster was approximately 100 megawatts, the equivalent of 100,000 high-end GPUs and costs in the billions. By 2026, right now, the leading training cluster requires a full gigawatt of continuous power and that is the output of a large nuclear reactor. By 2028, the projection reaches 10 gigawatts, more electricity than most US states generate in total. By 2030, the trillion-dollar cluster, 100 gigawatts, over 20 percent of everything the United States currently produces in electricity, consumed by a single AI training installation. And that is just the training cluster. Inference, the continuous compute required to actually run AI products for hundreds of millions of users requires multiples of that on top. Meanwhile, total US electricity production has barely grown five percent over the last decade and the grid was not built for this. And the transformer shortage, the switchgear backorders, and the canceled data center projects that are making headlines right now are the first visible symptoms of a power system hitting a wall that Aschenbrenner saw coming years before the rest of the market. This is exactly why he built a 875 million dollar position in Bloom Energy, a company that generates electricity directly at the data center site using fuel cells, completely bypassing the grid bottleneck that is already stopping half of all planned US data centers from opening on schedule. The thesis was never complicated. The bottleneck in AI is not the models, not the chips, and not the software. The bottleneck is whether civilization can generate enough electricity to run the machines fast enough to matter.
Milk Road AI1,240,695 просмотров • 3 месяцев назад
Bill Ackman was asked how he would underwrite SpaceX at $750 billion and his answer was the most honest thing anyone has said about the biggest IPO in history (Save this). "You underwrite SpaceX the way you underwrite a venture capital investment." His business school professor taught him a framework that has guided his entire career, it's people, opportunity, context, deal. On all three of the first criteria, People, Opportunity, and Context Ackman's verdict was the same, SpaceX is one of one, and nothing else in the market comes close. He even acknowledged feeling bad for Blue Origin before noting that their being so far behind is not harmful to SpaceX but rather a structural tailwind that leaves SpaceX with a near monopoly on low cost orbital access for years to come. And at $1.75 trillion, the number SpaceX is actually targeting on June 12, the question is no longer whether this is the best business on earth, but what the present value math looks like when you extend it five years forward and stress test every assumption about Starlink, launch economics, and AI compute revenue. He said that even Amazon is going to have to become a bigger SpaceX customer, because Blue Origin is so far behind that Amazon has no real alternative for low-cost orbital access. He also said something that almost no one is giving enough weight heading into Thursday's listing: "Time has become increasingly valuable in the AI era. You lose a month, you lose a couple months today, and it means a lot." The Colossus and Macro Hard facilities are compounding infrastructure assets where every month of operational delay means less contracted revenue, less negotiating leverage with customers like Google and Anthropic, and a progressively weaker moat against the hyperscalers who are now racing to build competing compute capacity. Come join Milk Road Pro for our full SpaceX IPO breakdown, how we're stress-testing the Deal leg of Ackman's framework at $1.75 trillion, what our five-year revenue model actually looks like, and our full AI thesis. Link below.
Milk Road AI434,893 просмотров • 1 месяц назад
Larry Ellison, the man who built Oracle into a $500 billion enterprise software empire and he said something that every investor needs to hear (Save this). "By 2029, I can guarantee you, AI is not going to be the problem." The problem is going to be compute specifically, who has enough of it and who does not. Ellison described the current AI race in terms that strip away all the abstract commentary about models and capabilities and reduce it to the one thing that actually determines who wins: "Me and Elon begging Jensen for GPUs. Please take our money. We need you to take more of our money, please." Citigroup raised its forecast for AI infrastructure spending to $2.8 trillion through 2029, with hyperscalers already spending at a $490 billion annual rate by end of 2026 and the firm estimates global AI compute demand will require 55 gigawatts of new power capacity by 2030 at a cost of approximately $50 billion per gigawatt. Sam Altman publicly thanked Jensen Huang this past March for significantly increasing NVIDIA's capacity at AWS, the CEO of the most important AI lab in the world writing a thank you note to the chip supplier because compute is still the binding constraint on everything OpenAI wants to build. Ellison's point about getting there first is the part of this clip that deserves a second read. He named three specific races, self-driving, reading cancer biopsy slides, and synthesizing video and said that being first in each one is a big deal. The logic is that in winner take most AI markets, the first mover trains the best model, the best model attracts the most usage, the most usage generates the most data, and the most data trains the next best model, a compounding loop that the second-place finisher never catches up to. "The guys in this race are very smart and they understand they need to be best at something," Ellison said. What makes this clip so important right now is the timing. The AI GPU chip market is projected to grow at a 32.4% CAGR through 2029, reaching $145 billion in incremental spend, and NVIDIA's data center revenue is already running at a pace that would have seemed impossible three years ago. Every major hyperscaler, Microsoft, Amazon, Google, Oracle, Meta is no longer funding AI capex from operating cash flows alone, they are borrowing to keep up, because falling behind in compute now means ceding the winner-take-most race Ellison just described. At Milk Road, we have been positioned in NVIDIA, AVGO, AAOI, MU, and Bloom Energy and more. Come join Milk Road Pro and get the full picture on how we are playing every layer of the GPU demand supercycle that Larry Ellison just guaranteed will not slow down before the end of the decade, link below/bio.
Milk Road AI480,993 просмотров • 1 месяц назад
Leopold Aschenbrenner, the 24 year old who wrote a 165 page AGI manifesto, got it right on the money, and turned it into a $5.5 billion hedge fund. And he's identifying the single most important milestone to watch for in all of AI. The question is can AI automate AI research itself? Here's why that question matters so much. Right now, a few thousand human researchers at the frontier labs are driving all the progress. They design experiments, write papers, propose architectural improvements, build the next generation of models and it's an incredibly small workforce doing incredibly high-leverage work. If an AI system can do that job even partially, the feedback loop changes completely. The AI makes algorithmic improvements, which produces more powerful AI, which makes better improvements, faster. You go from linear progress to compounding returns and a decade of research could compress into a year. Aschenbrenner says there's a "pretty reasonable chance" this happens within five years. He's not alone, Anthropic says they're on track to fully automate AI R&D as soon as early 2027. OpenAI has publicly targeted a fully autonomous AI researcher by March 2028 and Sam Altman has said a research intern level AI will exist before the end of this year. If he's right, the next few years won't look like the last few years but they'll look like nothing we've seen before. The future is bright!
Milk Road AI607,502 просмотров • 2 месяцев назад
Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!
Milk Road AI128,340 просмотров • 19 дней назад
Micron is going to be a $4,000 stock and the CEO just told you exactly why in one interview (Save this). Micron is no longer a chip company but rather a America's monopoly on the most strategically critical material in the AI buildout. It's the only western company manufacturing memory at advanced nodes, sitting on $200 billion in committed domestic capex, with every unit of its highest value product already sold. let's start with the supply reality, Mehrotra said Micron can currently meet only 50% to two thirds of the demand from its key customers. That shortage will last well beyond 2027, and meaningful new supply from anyone in the industry does not arrive until 2028 at the earliest. Two more years of demand outpacing supply in a market growing 168% year over year and that is the floor on the bull case. Now layer on what makes this cycle structurally different from every one before it. Micron is the only American memory manufacturer on earth, Samsung and SK Hynix are South Korean. In a world where AI infrastructure has become a declared national security priority where Commerce Secretary Lutnick and Trade Ambassador Greer personally showed up to a fab dedication in Manassas, Virginia being the only US memory company is not just a competitive advantage. It is a government backed structural monopoly on the most critical input to the US AI buildout, backed by $6.2 billion in CHIPS Act subsidies across Idaho, New York, and Virginia. The $200 billion buildout spans Manassas for DDR4 defense and industrial memory, Boise for leading-edge DRAM with first wafers out mid 2027, a second Boise HBM fab with first wafers by end of 2028, and the Syracuse megafab, the largest semiconductor facility in US history, breaking ground January 2026 with up to four fabs over time. Combined, these sites take Micron's domestic production from 10% of its total output today to 40% over the next decade, and create 90,000 jobs in the process. The business model transformation is the real story. Come join Milk Road Pro for our full breakdown, our complete Micron valuation model incorporating the $200 billion domestic buildout and our entire AI thesis. Link below.
Milk Road AI231,528 просмотров • 1 месяц назад
The man who turned $225 million into $13.7 billion told you something most people still haven’t absorbed (Save this). And this video is from before any of it played out the way he said it would. GPT-2 was released in 2019, a model so brittle that OpenAI withheld it for months out of concern, and that could barely hold a coherent paragraph before veering into nonsense. Four years later, GPT-4 arrived and scored in the top 10% of bar exam takers, passed the medical licensing exam and performed at expert level across standardized tests that GPT-3.5 barely cleared at median. That is a preschooler to smart-high schooler jump in four years and Aschenbrenner’s entire thesis is that another jump of exactly that magnitude, on the same timeline, gets you to AGI. The part of the clip that is both funny and devastating is the $20/month observation. Aschenbrenner calls out Ross Douthat, one of the most prominent opinion writers in the country for complaining that ChatGPT couldn’t pronounce his name, then using that as evidence that the singularity wasn’t imminent. What Douthat had used was the free tier. GPT-3.5, the little green icon, not GPT-4. The deeper point is not funny at all, the most influential voices in culture were forming their entire worldview on AI’s capabilities off a degraded, rate-limited product that was already two generations behind what researchers were actually working with inside the labs. The benchmarks now fully vindicate the trajectory Aschenbrenner was describing. Stanford’s 2026 AI Index Report found that on SWE-bench Verified, a real software engineering benchmark performance jumped from 60% to near 100% in a single year. Old benchmarks kept getting retired because frontier models maxed them out faster than new ones could be designed. The 2025 retrospective on Situational Awareness found his core predictions compute scaling, capability jumps, and the timing of AI displacing knowledge workers had largely tracked ahead of schedule. Then he put his money behind it in the most direct way possible, and the fund grew from $225 million to $13.7 billion in AUM. His core bets were on the physical infrastructure layer of AI, power, compute, and the companies building the backbone of the trillion-dollar cluster he described precisely in that video. Nebius with 684% year over year revenue growth, sold out GPU capacity, and a $17.4 billion Microsoft contract is the clearest pure-play expression of that infrastructure trade in public markets today. He just filed to disclose a 5.6% stake: 12.4 million shares. Nebius is a core Milk Road position, and our subscribers are up massively. Come join Milk Road Pro to get our full thesis, link below!
Milk Road AI324,740 просмотров • 1 месяц назад
Elon Musk just described a project so large that most people will assume he is exaggerating (Save this). He is not. In the video, Musk lays out the central problem facing every AI company on earth, the entire global chip industry is on a path to produce roughly 100 gigawatts of AI compute per year. That sounds like a lot until you understand that his companies alone Tesla, SpaceX, and xAI will need orders of magnitude more than that. His answer is the TerraFab. It is a joint chip factory spanning 100 million square feet, ten times the size of Tesla's Gigafactory Texas announced in March 2026, with Grimes County, Texas commissioners approving the full scale facility site just last week. The goal is one full terawatt of AI compute output per year. For context, 1 terawatt is 1,000 gigawatts twice the current total electricity consumption of the United States. SpaceX has already committed an initial $55 billion to the prototype phase, with total investment estimates ranging into the trillions. Here is why this matters for Micron specifically. In the video, Musk named Nvidia's Rubin chips as the reference design for TerraFab's first orbital deployments, and said "You're going to need a lot of memory to go with that." A billion full radical equivalent chips per year, each requiring stacks of high bandwidth memory, that is the demand signal Micron just received from one of the most capital-intensive projects in human history. And Micron already cannot keep up with what exists today. Micron's entire 2026 HBM output is fully sold out contracted before the year began. HBM4 entered volume production ahead of schedule and sold out immediately. The structural reason Micron wins here is simple. Every AI chip ever built Nvidia H100s, Rubin chips, custom ASICs, TPUs is useless without high-bandwidth memory stacked directly on top of it. There are only three companies in the world that supply HBM at scale, Samsung, SK Hynix, and Micron. Samsung has had quality issues, SK Hynix is supply constrained. Micron is the only US headquartered HBM manufacturer which matters enormously given CHIPS Act subsidies, domestic procurement requirements, and the political push to keep critical AI memory production on American soil. TerraFab just made the memory deficit permanently larger. Come join Milk Road Pro for our full breakdown of Micron and our entire AI thesis just for $1. Link below!
Milk Road AI245,900 просмотров • 1 месяц назад
Ray Kurzweil has been saying the same thing for 60 years and the world spent six decades calling him crazy and now every prediction he made is coming true ahead of schedule (Save this). At age 16, Kurzweil wrote a paper arguing that computing followed exponential growth. From 1939 to today, computing power increased 75 quadrillion fold in hardware alone and when you multiply that by roughly a million to one improvement in software, you get total computational gains that are functionally incomprehensible. This is the precise explanation for why large language models could not exist four years ago and do now. The jump from nothing to GPT-4 to reasoning models to agents happened in less time than it takes most companies to ship a product roadmap and that pace is still accelerating, not plateauing. Kurzweil's most striking observation is about Nvidia specifically. Nvidia's engineers are not looking at 1939 relay computers when they design their chips but when you plot the exponential growth curve, Nvidia's latest silicon lands on the exact same line as those 1939 relays, same slope, 87 years apart. The curve does not care what technology is enabling it. Relays gave way to vacuum tubes, to transistors, to integrated circuits, to GPUs, and now to custom AI accelerators and the rate of improvement has not deviated. Right now we are making approximately 10x the total computational gains per year, hardware and software multiplied together. The reason this moment is categorically different from any prior tech cycle is where we sit on the curve. Exponential growth is deceptive in its early stages, it looks almost linear when the numbers are small, which is why people keep underestimating it. Computing power per dollar has increased 11,200x since just 2005. We are now at the part of the curve where the doubling is happening on top of an already enormous base which means each new generation of AI capability is not marginally better, it is structurally different. Kurzweil made his AGI-by-2029 prediction in 1999 and was dismissed by the academic establishment. He carries an 86% documented prediction accuracy across 30 years of published forecasts. Today, the major AI labs have independently converged on the same timeline window because the curve forced them there.
Milk Road AI177,201 просмотров • 1 месяц назад
The CEO of the world's largest asset manager just said something that should reframe how every investor thinks about the AI trade. Larry Fink, managing $11.5 trillion at BlackRock, stood at the Milken Institute Global Conference and said four words that matter, "We just don't have enough compute." "The United States is short power. We're short compute. We're short chips. And there's going to be shortages in all three and memory, four things. I actually believe a new asset class will be buying futures of compute." Think about what that means. Fink is predicting that compute becomes a tradable commodity like oil, like grain, like natural gas where investors buy forward contracts on future capacity because the shortage is so structural and so predictable that a derivatives market will emerge to price it. That is not a minor observation from a finance executive but rather the chairman of the most powerful capital allocator on the planet telling you that compute scarcity is a multi-year, investable megatrend. The data backs him up completely. Data centers will consume 70% of all memory chips produced globally in 2026. Advanced HBM production from Samsung, SK Hynix, and Micron is sold out through 2026 and into 2027 and a single AI server consumes 10-20x more memory than a conventional workload server. DRAM supply growth is running at just 16% annually while AI infrastructure demand is growing at 80%+. The chip crunch, the power crunch, and the compute crunch are not temporary dislocations, they are structural, and they will get worse before they get better. Fink also said something the bears keep getting wrong: "There is not an AI bubble. There is the opposite. We have supply shortages. Demand is growing much faster than anyone has ever anticipated." This is why the Milk Road Pro portfolio is built the way it is, long the companies producing and supplying the constrained resources: chips, memory, compute infrastructure, and power. Check out Milk Road Pro, link below to access our full thesis and plays.
Milk Road AI419,062 просмотров • 2 месяцев назад
This is WILD! Anthropic just became the most valuable AI company on earth and what Chamath said months ago explains exactly why this moment matters (Save this). Anthropic closed a $65 billion Series H round at a $965 billion post-money valuation surpassing OpenAI's $852 billion valuation from March and making Anthropic the highest-valued private company in history. Just three months ago, in February, Anthropic had raised $30 billion at a $380 billion valuation meaning the company nearly tripled in value in a single quarter. Claude's run rate revenue crossed $47 billion today, up from $30 billion in April, up from $9 billion at the end of 2025, a pace of revenue growth that has no comparable precedent in business history. Now go back and watch what Chamath said, because he called the entire arc of this. "I've never seen a business like this. And I'd say the same thing about Anthropic. Nobody in the history of the world has ever seen two businesses like this at this scale. These are trillion dollar companies. They both are. And they both deserve to be." He said that before the $965 billion number. Chamath Palihapitiya also said something that most people skipped over, that OpenAI and Anthropic need to get public as fast as humanly possible because of what happens after. Chamath laid out a specific sequencing thesis, SpaceX goes public first and does great, the next company does good to great, then appetite runs out, because the market simply cannot absorb trillions of dollars of new demand in rapid succession. Today, Anthropic's $65 billion round may be precisely the move that locks in its position before that window narrows fortifying the balance sheet before the public markets get crowded. But Chamath's deeper warning cuts through the celebration, once SpaceX, OpenAI, and Anthropic are all public, the AI technology baked into all three will cannibalize the moats of every other tech company, compressing tech sector P/E ratios toward non-tech levels and making the software businesses of the last decade obsolete. "It will eliminate and it will cannibalize and it will erode most of the moats that support this differential trading," he said directly. "I'll buy the first five or six years of this story, but I'm not buying year 15 of this anymore because these three guys are going to build something." The companies getting valued at near-$1 trillion today are not just winning but rather are the instruments by which everything else eventually gets repriced.
Milk Road AI293,469 просмотров • 1 месяц назад
This is WILD! The most important story coming out of the SpaceX IPO this morning is not the $1.77 trillion valuation but it is about Juan Hernandez (Save this). Juan is a welder who got a phone call from a friend about a job at a company he had never heard of. He said yes anyway, showed up, worked there for ten years, rose from the factory floor to supervisor, and held on to 6,500 shares the entire time. When CBS asked him this morning how much he stands to make at opening, he said approximately $880,000. Tom Mueller, Musk's very first SpaceX employee, tells a version of the same story. He met Musk through an amateur rocket club, was convinced to do something exciting, and says it was one of the best decisions he ever made. In those early days, he says, the team simply believed they were going to change the world and then went ahead and did it. Juan and Tom are not alone. SpaceX has approximately 13,000 employees who hold equity and analysts estimate today's IPO will create somewhere between 600 and 1,000 instant millionaires across the workforce from engineers and software developers to machinists, welders, and operations staff. The engineers and executives at the top of the stack are looking at life changing numbers of a different order entirely. Senior vice presidents and long tenured rocket engineers with large equity grants are expected to walk away with $10 million to $50 million or more depending on their vesting history. Gwynne Shotwell, SpaceX's President and COO who has been building this company alongside Musk for over two decades, is expected to become a billionaire on her equity stake alone. All of that wealth from Juan's $880,000 to Musk's trillion came from the same source. A group of people who believed that if nobody built a truly reusable rocket, humanity would never leave Earth, and decided that was an unacceptable outcome. Thank you, Elon Musk for building a company where a welder who didn't know your name in 2016 is worth nearly a million dollars this morning.
Milk Road AI187,164 просмотров • 1 месяц назад