로이킴 (Roy Kim) Remake Album '다시 불러 봄 - Bloom Again' 'Smile Boy' Live Clip Teaser 🔗 Album Release 2026.05.20 18:00 (KST) Live Clip Release 2026.05.22 18:00 (KST) #로이킴# #RoyKim# #다시불러봄# #SmileBoy#

This is the kind of market where you simply buy what the president tells you to: Chips $INTC Intel $NVDA NVIDIA $AMD AMD $TSM TSMC AI Infrastructure $DELL Dell $VRT Vertiv $NBIS Nebius $CRWV Coreweave $IREN IREN $HUT Hut 8 Rare Earths $USAR USA Rare Earth $CRML Critical Metals $TMC The Metals Company Critical Minerals $TMQ Trilogy Metals $UAMY United States Antimony $MP MP Materials $LAC Lithium Americas Quantum $IONQ IonQ $QBTS D-Wave Quantum $RGTI Rigetti Energy $BE Bloom Energy $GEV GE Vernova $FCEL Fuelcell $PLUG PlugPower $VICR Vicor Memory $SNDK Sandisk $MU Micron $STX Seagate $WDC Western Digital $P Everpure $MRAM Everspin Space $RKLB Rocket Lab $ASTS AST SpaceMobile $PL Planet Labs $FLY Firefly Aerospace $GILT Gilat Drones $AVEX Aevex $ONDS Ondas $UMAC Unusual Machines Nuclear $XE X-energy $LEU Centrus Energy $CCJ Cameco $OKLO Oklo $UUUU Energy Fuels AVs & Robotics $TSLA Tesla $AMZN Amazon Batteries $TE T1 Energy $ELVA Electrovaya $FLNC Fluence Energy Never miss the leading stocks again:

America’s AI boom has triggered a backlash against data centres and the companies behind them. The Economist’s AI writer, Alex Hern, lays out what’s driving the discontent on the latest episode of Inside Tech

Micron has seen 6 boom & bust cycles since 2000. In 2019, hyperscaler over-ordering crashed their revenue from $30B to $21B. In 2023, the pandemic unwind cut it in half again, from $30B to $15B. Has the AI super-cycle finally broken this pattern? Or is a reckoning coming? — especially since it's the same set of buyers, buying for the same reasons. I analyze Micron through three lenses: Engineering, Finance, and Strategy. The current tailwinds, and the possible headwinds. Full Article here:

The AI boom requires an enormous amount of new power plants, chips and data centers. But it also requires something that’s less visible: new financial plumbing that makes it possible to lend against this costly hardware. Read more in today's AI Infrastructure newsletter:

🔊 Jeremy Grantham’s investment firm GMO made its name betting against the mania of the late 1990s and the mid-2000s housing boom. On The Big View podcast, he tells Peter Thal Larsen how AI has forced Big Tech firms into a fight to the death

Today I instructed my legal advisers to consider the harshest legal action against The New York Times and Nicholas Kristof. They defamed the soldiers of Israel and perpetuated a blood libel about rape, trying to create a false symmetry between the genocidal terrorists of Hamas and Israel’s valiant soldiers. Under my leadership, Israel will not be silent. We will fight these lies in the court of public opinion and in the court of law. Truth will prevail.

Israel is in the usual denial, claiming a "blood libel,” but the UN Human Rights Office said, “torture and ill-treatment, including sexual and gender-based violence, are systematically perpetrated against Palestinian prisoners.”

Cathie Wood just flagged the sleeper trade inside the AI boom that most people are completely missing. Everyone has been chasing GPUs. Nvidia, the data center buildout, the chip arms race. That trade has been obvious for two years. But OpenAI's CFO Sarah Fryer said something quite different: people are going to be really shocked by how agentic AI activates CPUs. Right now, for every CPU in an AI workload, there are 4 to 5 GPUs. That's the current ratio. Wood thinks that ratio is going to 1 to 1. Think about what that means. AI inference at scale, agents running autonomously, pipelines executing tasks across systems. The compute mix shifts dramatically away from pure GPU dominance. CPUs become a first-class citizen in the AI stack. Cathie called it going "back to the future." Intel has taken off. Flex (formerly Flextronics) is booming. Stocks that were giants in the dot-com bubble are resurging because the underlying demand for their products is real again. The GPU trade made sense at the training stage. You need massive parallel compute to train frontier models. But agentic AI runs differently. Agents are constantly orchestrating, reasoning, calling APIs, executing workflows. That workload looks a lot more like traditional computing. And traditional computing runs on CPUs. If Cathie Wood is right about the ratio collapsing to 1:1, the CPU demand signal embedded in the AI buildout is orders of magnitude larger than the market is currently pricing.