Milk Road AI (@MilkRoadAI)

Micron will be a $3,000 stock within a few years and Jensen Huang just spent a week in Korea telling the world exactly why (Save this). Jensen announced four new products at the Korea event and every single one of them has memory at the center of its architecture. Vera Rubin, the next generation AI supercomputer, needs massive quantities of HBM. The new Vera CPU needs large amounts of LPDDR5. RTX Spark, the first major PC reinvention in 40 years according to Jensen, needs a lot of LPDDR5. And Nvidia's new robotics and autonomous driving platforms are being built in deep partnership with the Korean memory and electronics ecosystem. Every single growth vector for Nvidia in 2026 and 2027 runs directly through memory and Micron is the only US based company that manufactures all of it. Here is what the numbers look like right now. Fiscal Q2 2026 revenue came in at $23.86 billion, up 196% year over year, with 75% gross margins and $6.9 billion in free cash flow, a quarterly record. Management guided Q3 revenue to $33.5 billion at roughly 81% gross margins, with EPS of $19.15. These are not the numbers of a cyclical memory company but rather the numbers of a company that has been structurally repriced by the largest demand supercycle in the history of the semiconductor industry. The reason the bull case reaches $3,000 comes down to three things that have never been true at the same time in Micron's history. First, the entire 2026 HBM supply is already sold out under multi-year contracts. CEO Sanjay Mehrotra told analysts that Micron can currently only fulfill 50% to two thirds of key customers' HBM demand at any price. Second, Micron has begun volume shipment of HBM4 12-Hi specifically for Nvidia's Vera Rubin platform, the exact product Jensen was talking about in Korea and has signed its first five year strategic customer agreement, converting what was historically a quarterly negotiation business into something closer to a long-term recurring revenue model. Third, Wolfe Research's bull case model points to $160 billion in calendar year 2027 revenue and $80 in EPS. At even a 20x earnings multiple, modest for a company with this growth profile, that is a $1,600 stock. UBS has already tripled its price target to $1,625. The path to $3,000 requires HBM4 to ramp smoothly, supply constraints to persist into 2027 as Mehrotra says they will, and hyperscaler AI capex to continue growing at its current trajectory, all three of which Jensen Huang just confirmed in Seoul. The HBM total addressable market alone is projected to reach $100 billion by 2028, a forecast Micron itself already pulled forward two years ahead of schedule because demand arrived faster than anyone modeled. Micron trades at roughly 9x forward earnings today. That is cheaper than a grocery chain, for a company growing revenue at 196% year over year, with its entire production sold out, supplying the infrastructure for the most important technology buildout in history. Come join Milk Road Pro for our full breakdown of the Micron bull case how we think about the HBM4 transition timeline, what multi-year customer contracts mean for Micron's valuation multiple expansion, and our entire AI thesis. Link below!

Masayoshi Son has been right twice in a way that changed the world and he is making the same call again (Save this). Alibaba, $20 million in 2000 turned into $130 billion. ARM, bought for $32 billion in 2016 when the market thought it was a smartphone chip business, now the architecture underneath every major AI chip being built today. Now he is saying AI is 50 times bigger than the dot-com era and he is not concerned about corrections. He says if there is one, that is the best buying opportunity of the decade. When asked where the next trillion-dollar company comes from, he says it's in physical AI and in robotics. Masa has spent three years assembling every piece of the stack required to own this category. SoftBank holds 90% of ARM, the architecture inside every major AI chip deployed globally today, including Nvidia's Vera CPU, Amazon Graviton, Google Axion, and Microsoft Cobalt. Every robot running edge inference will almost certainly run on ARM. SoftBank completed a $40 billion investment into OpenAI in late 2025, making it the largest external backer of the company building the cognitive layer that physical robots will run on. In October 2025, SoftBank acquired ABB Robotics for $5.4 billion, one of the most mature industrial robot manufacturers in the world, deployed across thousands of factories globally. SoftBank then created Roze AI, consolidating its robotics investments with a target $100 billion IPO already in process with Goldman Sachs, JPMorgan, and Morgan Stanley as underwriters. The market is beginning to confirm the thesis. The humanoid robot market was roughly $3 billion in 2025 and Barclays projects it reaches $200 billion by 2035 at a 48% compound annual growth rate. SoftBank is the most complete expression of the physical AI thesis available in public markets today, ARM for the chip royalties, OpenAI for the cognitive layer, ABB for manufacturing, Roze AI for the robotics platform, and Stargate for the compute infrastructure underneath all of it. Son has not just identified the next wave and has built the stack to own it before the market agrees with him. Come join Milk Road Pro and get our full physical AI breakdown which names we're watching across the robotics stack and our full AI thesis. Link below

The narrative that AI will wipe out enterprise SaaS overnight is one of the most misunderstood ideas circulating in markets right now, and the evidence does not support it (Save this). @DavidSacks made this case directly and the logic is worth working through carefully. Salesforce is a system of record debugged by millions of customer support tickets over twenty five years, stress tested across thousands of enterprise deployments and deeply embedded into revenue operations at the largest companies on earth. The idea that a CFO will replace that with probabilistically generated code from an AI assistant without compliance guarantees, integration depth, audit trails, and enterprise support infrastructure is not how these decisions actually get made. The market has been pricing in the existential version of this risk anyway and the results have been extreme. Over $1 trillion in SaaS market cap was erased in the first week of February 2026 alone. Global SaaS spending is still projected to grow from $318 billion in 2025 to $512 billion in 2028 which is not the trajectory of a category being killed. The operating reality is entirely disconnected from the stock price narrative. ServiceNow beat earnings nine consecutive quarters in a row and its stock crashed 11% on the same day. Salesforce raised its full year forecast to $41.5 billion on record results and the stock still fell. Sacks makes an important distinction between survivability risk and value capture risk. The survivability risk, enterprises ripping out Salesforce for AI generated software is largely overstated. The SaaS products genuinely at risk are narrow ones charging high prices for underused features with no proprietary data and low switching costs. The value capture risk is real and it is the more sophisticated threat. AI orchestration layers like Claude CoWork are being designed to sit above all of these tools pulling data from Salesforce, ServiceNow, and Snowflake simultaneously and owning the user's primary workspace in the process. If enterprise users move from living inside Salesforce to living inside an AI agent that calls into those systems on their behalf, the SaaS platforms do not disappear but rather become infrastructure. The expansion revenue, the premium pricing power and the next decade of value creation all migrate to whoever owns that orchestration layer.

Jensen Huang just handed every AI cloud investor the clearest framework for picking winners and the question is who actually understands what he said (Save this). Compute is not just infrastructure anymore but rather revenue, and performance per watt is the mechanism by which that revenue becomes profit. The argument Jensen made at Computex deserves to be unpacked fully because it completely reframes the neocloud investment thesis. Every AI factory operates inside a fixed power envelope and once your data center is built and your power contracts are signed, that ceiling does not move. One gigawatt means one gigawatt and the only variable that determines how much money you make is how many profitable tokens you can squeeze out of each watt of electricity flowing through your facility. An operator who chooses cheaper, lower efficiency chips because the upfront cost looks attractive is not saving money and they are permanently handicapping their revenue ceiling for the life of that asset. Every watt that produces fewer tokens is a watt that will never recover those lost revenues, for as long as that infrastructure runs. Jensen's second point is about asset longevity and it is equally important to understand. AI software is evolving every few months from CNNs to Transformers to Mixture of Experts to agentic systems and that pace is not slowing down. A hardware architecture that cannot adapt to new software paradigms has a short useful life, and a short useful life means a high total cost of ownership. Infrastructure built on Nvidia's CUDA ecosystem has a built in software longevity advantage because every new model, framework, and optimization is written for CUDA first. Now apply that framework directly to Nebius, which is the most important stock in the neocloud category. Nebius built its entire infrastructure around full Nvidia integration from the ground up. Nvidia and Nebius announced a formal strategic partnership in March 2026 specifically to develop the next generation of hyperscale AI cloud deployments together. Nebius is already offering Blackwell Ultra GB300 NVL72-powered instances to customers, meaning it has the highest-performance GPU currently available commercially running inside its own infrastructure. The token economics follow directly from the architecture. Contracted power has now passed 3.5 gigawatts, with more than 75% of that capacity owned outright rather than leased. The Meta deal alone is worth $27 billion over five years, and the Microsoft agreement is worth up to $19.4 billion. The 2026 plan targets 480 megawatts of live AI cloud capacity, 150,000 GPUs deployed, and $3.7 billion in annualized revenue implying next twelve month revenue growth of roughly 489%. Q1 2026 revenue was $399 million, up 684% year-over-year, and the CEO said on the earnings call that everything Nebius builds gets sold immediately. Fully booked capacity at an AI cloud running Nvidia's best hardware, inside a power-scarce environment where performance per watt is the direct driver of profitability, means Nebius's revenue ceiling moves in direct proportion to the power it can bring online. CoreWeave, a direct comparable, trades at a materially higher multiple on a smaller contracted power base. Nebius owns more of its capacity outright, has a longer-dated and larger contract backlog on a per-gigawatt basis, and is growing revenue at a faster rate. Milk road remains extremely bullish on Nebius and come join Milk Road Pro and get our full Nebius positioning breakdown and our other AI trades for just a dollar. Link down below!

Uber's CEO said it himself that they blew through their entire 2026 AI budget in a single quarter (Save this). And today they cut 23% of the people who used to do what AI now does. These two facts are not separate stories but rather the same story. Earlier this year, Dara Khosrowshahi explained exactly what was happening inside Uber's engineering org. The company introduced Anthropic's Claude Code to engineers in late 2025, adoption reached 32% by February, 84% were classified as agentic coding users by March and by April 95% of engineers were using AI tools monthly. Roughly 70% of committed code was AI generated and round 11% of real time backend updates were being deployed by autonomous agents with no human in the loop. The annual AI budget was gone in four months and Dara's response was not to slow down adoption but rather to slow down hiring. When each engineer is producing materially more output per hour, you need fewer engineers to hit the same targets. And when AI handles the work that used to require people to hire, onboard, and manage those engineers, the HR and people operations layer beneath them, that layer contracts too. Today's announcement is that 23% of Uber's People and Places division is gone. Uber's spokesperson said the cuts are unrelated to AI but that framing does not hold up to the sequence of events. IBM understood this two years ago, when 94% of typical HR questions are answered by an AI agent, the HR Business Partner role collapses for everyone except the most senior strategic functions. The budget that used to fund that team gets reallocated to engineering and sales, the two functions that remain bottlenecked by human judgment rather than human volume. This is the pattern that will repeat across every major enterprise over the next 18 months.

Every software company just got a second life and Jensen just explained why (Save this). The conventional fear was straightforward, AI agents replace human workers, human workers use software tools, therefore agents destroy SaaS. Jensen Huang stood on stage at Computex 2026 and walked through exactly why that logic is backwards. Agents don't replace software, they consume it at machine speed, around the clock, without weekends. Here's the actual architecture Jensen laid out. An agent isn't just a large language model but rather an LLM sitting inside a harness that manages memory, orchestrates tool use, routes context, and plans iterative actions. That harness has to constantly call tools, spreadsheets, databases, browsers, and code engines, with every reasoning loop triggering another tool call. A human might use Salesforce 40 hours a week, an agent running inside a company uses it 168 hours a week and never misses a context window. The GitHub data Jensen showed on stage makes it tangible, 90 million pull requests merged, 1.4 billion commits, and 20 million new repositories created every month. As of April 2026, GitHub is processing 275 million commits per week on pace for roughly 14 billion by year end, a 14x explosion in a single year and AI agents are the source. Pull requests opened by AI agents went from 4 million in September 2025 to 17 million in March 2026 more than 4x in six months. That's AI becoming the largest software user on earth. Goldman Sachs quantified the downstream effect last month, token consumption is expected to multiply 24x by 2030, reaching 120 quadrillion tokens per month globally. A traditional chatbot consumes roughly 1,000 tokens per session, an embedded copilot burns 5,000 tokens per day while a continuously running enterprise agent? Over 100,000 tokens per day. The software companies that figured this out first are already printing money, Salesforce Agentforce hit $800 million ARR growing 169% year over year, with 29,000 deals closed. ServiceNow's Now Assist crossed $600 million in ACV, just raised its full year target to $1.5 billion, and told investors that when its agents replace a 20-person support team, total ServiceNow spend by that customer grows more than 5x even after accounting for reduced seat licenses. Workday delivered 1.7 billion AI actions across its platform in fiscal 2026. The key unlock Jensen pointed to and what investors need to understand is MCP, the model context protocol is the interface layer that makes software agent-readable. Software that supports MCP can be called by any agent, from any model, through any harness. Anthropic created it, OpenAI, Microsoft, and Google all adopted it and it was donated to the Linux Foundation. It is effectively becoming the HTTP of agentic computing. Software companies with native MCP support are plugged into the agent economy. Software companies still waiting are one product cycle away from becoming invisible to the fastest-growing category of software users in history.

The most important data point in AI just dropped and it changes the entire conversation about where we are in this cycle (Save this). Jensen Huang took the stage at NVIDIA GTC Taipei 2026 and highlighted how GitHub commits, a universal measure of global software output, climbed from 300 million in 2023 to 400 million in 2024 and 500 million in 2025. In the first few months of 2026 alone, that number has nearly tripled and Jensen's conclusion was that "Agentic AI has arrived, useful AI has arrived." Then he did the math and the numbers are staggering. 30 to 40 million professional software developers represent approximately $3 trillion worth of GDP that is their combined annual salary, generating economic output across $100 trillion worth of global industry. That same $3 trillion in developer salaries is now producing nearly three times as much output. "It's effectively $9 trillion of productivity from $3 trillion of salaries. The difference is absolutely extraordinary. This is the potential. This is the promise of AI." People talk about AI killing jobs but Jensen called it complete nonsense. His logic is that if you can hire a software engineer and generate $9 trillion worth of productive work, why would you hire fewer engineers? The answer is you hire more and the data confirms it, with a new developer joining GitHub every single second as of early 2026. GitHub COO separately disclosed that 2026 commits are on pace for 13–14 billion, a 1,300% increase from 2025 with GitHub Actions compute minutes already at 2.1 billion per week, more than double the 2025 baseline. But Jensen did not stop at the productivity argument. He connected it directly to token economics, the investment thesis that matters most for everyone in this room. "Tokens are now profitable units of revenue. Because it is now profitable, AI companies want to build more tokens, generate more tokens, build more AI factories which is the reason why compute demand here in Taiwan has skyrocketed." Every agent, every automated code commit, every workflow that runs without a human prompt consumes tokens. Taiwan's own government just upgraded its GDP growth forecast to 9.64% for 2026, a 16-year high driven entirely by AI infrastructure exports. This is exactly why Milk Road has been so convicted on the AI infrastructure buildout because the productivity data is now arriving in real time at a scale that nobody modeled.