AI Is Running Out of Memory VIEW IN BROWSER
Hello, Reader. Micron Technology Inc. (MU) and elephants have one thing in common: memory. Elephants store it biologically. Micron produces it in the form of DRAM and NAND – the essential building blocks that allow modern computers and artificial intelligence systems to function. Micron’s memory sits at the heart of AI, data centers, and virtually all modern computing systems. As demand for its memory chips soars, over the past year, the company has been rallying, driven in large part by shortages caused by heavy use of memory in Nvidia Corp. (NVDA) chips. The company is up around 330% over the past year, and 48% so far in 2026. The rally has elevated Micron's market cap to $525.4 billion, surpassing Oracle Corp. (ORCL), which is now worth $440.6 billion. Among the 10 most valuable U.S. tech companies, Micron’s stock is the only one up year to date. That demand just showed up in a big way. Micron released its latest earnings report last night… and it crushed expectations, with revenue nearly tripling as AI-driven memory demand surged. And yet, despite one of the strongest quarters in the company’s history, the stock fell following the report. In fact, the company says it can only meet about 50% to two-thirds of customer demand. That’s because this story is no longer about a single company — it’s about a system under strain. Micron CEO Sanjay Mehrotra told CNBC in January… Memory is a key enabler of AI. It is a strategic asset today, not like just a component in the system. And so we need it. Just like your brain, you need more memory. You need faster memory. And the memory-chip shortage shows no signs of easing, with the tech industry’s top players spending record sums to stay competitive in the AI race. That means memory companies could be among the next wave of AI stock winners. At the moment, Micron is one of the main beneficiaries of AI’s second wave. But I expect that a smaller set of asset-heavy companies will be the biggest winners. Today, I’ll detail why memory is quietly becoming a critical AI chokepoint. Then, I’ll share how you can capitalize on the opportunity. | Recommended Link | | | | When the people with the best information about where the economy is going choose another type of currency over dollars, a 47-year market veteran says that’s worth a second look. Louis Navellier has documented the pattern — and identified the key steps you should take right now. See What He Found → |  | | AI Needs Memory All memory chips and data storage are critical to the AI Revolution, but the demand for DRAM is skyrocketing specifically because modern AI workloads are extremely memory intensive. And DRAM is the only type of memory that can keep up. Large language models (LLMs) and other generative AI models have billions, or even trillions, of settings that the system needs to keep in memory. DRAM stores all these settings and the temporary calculations the model makes while running. For example, training ChatGPT-sized models can require tens to hundreds of terabytes of DRAM across graphics processing units (GPUs). In a world without enough DRAM, the AI Revolution hits a hard ceiling because it runs out of space to think. No memory means no intelligence. Nvidia CEO Jensen Huang first raised the alarm bells on DRAM earlier this year, saying the “memory bottleneck is severe." There have even been media reports that representatives from AI companies have moved into long-term stay hotels in South Korea, desperately “begging” for DRAM allocation from the other two suppliers: Samsung Electronics and SK Hynix. These purchasing managers from Silicon Valley have actually been nicknamed “DRAM beggars.” And the big DRAM manufacturers in South Korea have had to police their customers' purchases to prevent hoarding. Moreover, this DRAM shortage has no end in sight. Nearly 100 gigawatts (GW) of new data centers are scheduled to come online over the next four years. So, we can estimate that means about 50 GW over the next two years. However, there’s only enough DRAM to support the build-out of about 15 GW of AI data centers over the next two years. That’s a big supply problem. In early February, market researcher TrendForce raised its chip price forecasts, projecting that conventional DRAM contract prices will surge 90–95% in the first quarter of 2026, compared to the fourth quarter of 2025. This is one of the fastest pricing spikes the memory industry has ever seen. The DRAM beggars will continue to bid the price up, making certain suppliers the potential beneficiaries of this high-stakes bottleneck. This is a pricing power story, and that means it’s important to get in on the opportunity early. Here’s how… Own the Bottlenecks Earlier this week, I held my FutureProof 2026 special event, where I laid out a simple idea: AI demand continues to explode, but it is constrained by real-world physical bottlenecks in energy, raw minerals, and memory. Micron’s latest results — and the ongoing memory shortage — couldn’t make that clearer. Here’s the key: You want to own the bottlenecks, not the hype. Micron sits at the center of this bottleneck. But that doesn’t automatically make it the best investment. Much of that story is already widely understood — and already priced into the stock. The bigger opportunity lies one layer deeper. I believe the biggest winners in the memory bottleneck will be those with heavy assets – not the memory-chip makers themselves, but the suppliers of the infrastructure required to produce the chips – and the least competition. At FutureProof 2026, I shared five tickers – free of charge – that meet these criteria. These are companies to watch in the memory space. You can watch a replay of my broadcast here and get immediate access to those names. I also detail two other major bottlenecks affecting the AI buildout: raw materials and energy. And I share five more companies for each corresponding bottleneck. To watch my free event, simply click here. Regards, | .png)
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