To The Moon

The latest earnings cycle delivered dramatically different outcomes across Big Tech. $Alphabet(GOOGL)$ dropped about 7%, wiping out roughly $300 billion in market value. $Tesla Motors(TSLA)$ fell 14.5%, erasing nearly $200 billion. $IBM(IBM)$ has lost more than 25% over the past two weeks. Then came $Intel(INTC)$. Instead of disappointing the market, Intel delivered one of its strongest quarters in years, beating expectations on revenue, earnings, margins, manufacturing progress, and guidance. After the report, the stock climbed in after-hours trading. But the biggest takeaway isn't simply that Intel had a good quarter.

$Taiwan Semiconductor Manufacturing(TSM)$ just delivered another record quarter, but the bigger story may be what comes next. The company said overseas manufacturing—particularly its expanding U.S. footprint—will weigh on margins over the next several years. At the same time, reports indicate TSMC plans to raise foundry prices by up to 10% beginning in 2027. Taken together, the message is straightforward: higher costs are increasingly likely to be shared with customers rather than absorbed entirely by TSMC. U.S. expansion comes with a price TSMC continues to invest heavily outside Taiwan, including a major expansion in the United States. Management acknowledged that as these new fabs ramp up, gross margins are expected to face modes

The AI boom is creating one of the largest investment cycles in technology history. But while investors focus on AI revenue growth, model performance and data center expansion, another number is becoming increasingly important: How much future financial commitment is being created behind the scenes? A recent analysis of financial filings from $Alphabet(GOOGL)$ $Microsoft(MSFT)$ $Amazon.com(AMZN)$ $Meta Platforms, Inc.(META)$ $Oracle(ORCL)$ suggests that AI-related obligations—including GPU contracts, data center leases and infrastructure agreements—have grown rapidly and now re

$Advanced Micro Devices(AMD)$ has unveiled Helios, its first rack-scale AI platform, marking one of the company's biggest strategic moves in the AI infrastructure market. The significance isn't simply another GPU launch. It's that AMD is moving beyond selling individual accelerators and is now offering a complete AI computing system that combines GPUs, CPUs, networking and software into a single rack-level solution. The Battle Has Moved Beyond GPUs For years, AI competition centered on who had the fastest accelerator. That is changing. Large cloud providers increasingly purchase complete AI systems rather than standalone chips, making system integration just as important as raw computing performance. Helios reflects this shift by combining 72 next-

Moonshot AI's Kimi K3 has attracted far more demand than expected. According to the company, user demand over the past 48 hours has pushed its GPU resources close to capacity. To maintain service quality for existing subscribers, Moonshot has temporarily paused new memberships while it adds more computing capacity. Existing subscribers are not affected, and the company plans to reopen subscriptions in stages. Moonshot also announced that future memberships will be split into separate plans for its general AI services and coding products, allowing compute resources to be allocated more efficiently. The Bottleneck Is No Longer AI Adoption The significance of this announcement extends beyond one product launch. Kimi K3's rapid adoption suggests that the challenge is no longer convincing users

The AI race is entering a new phase. The biggest AI companies are no longer just competing to build better models. They are competing for something even more fundamental: Compute. And an unexpected trend is emerging: Some of the world’s largest technology companies are becoming landlords of AI infrastructure, renting computing capacity to the very companies challenging them in AI. 1. Meta and SpaceX Are Becoming Compute Landlords For months, investors questioned whether massive AI infrastructure spending would generate enough returns. $Meta Platforms, Inc.(META)$ ’s answer may be emerging: Don’t just consume compute. Own it—and monetize it. Meta is reportedly in discussions with Anthropic for a potential deal that could reach $10 b

$Taiwan Semiconductor Manufacturing(TSM)$ just delivered another blockbuster quarter. Revenue and profit both reached new highs, extending a trend we've now seen across multiple companies in the AI hardware supply chain. This wasn't simply a strong earnings report. It was another data point suggesting that AI infrastructure spending remains firmly intact. The Story Is Bigger Than TSMC Over the past week, several upstream semiconductor companies have delivered a remarkably consistent message. $ASML Holding NV(ASML)$ raised its full-year outlook. $Aehr Test(AEHR)$ surprised the market with stronger profitability. TSMC reported another record quarter. Different busin

$ASML Holding NV(ASML)$ delivered another strong quarter. Revenue and earnings both came in ahead of expectations, and management raised full-year guidance for the second time this year. On the surface, this looks like another solid earnings report. But the bigger takeaway isn't about ASML alone. It's about what its results are telling us about the next phase of the AI infrastructure cycle. Equipment Makers Don't Tell You Who Wins. They Tell You How Long the Cycle Can Last. Unlike chip designers, ASML doesn't compete for AI market share. Its business sits much further upstream. That makes its guidance one of the clearest indicators of whether semiconductor manufacturers are still expanding capacity. When an equipment supplier sees accelerating dem

Today marks a significant moment for the AI trade. SK Hynix begins trading on the Nasdaq under $SK hynix(SKHY)$ . Many investors are calling it an IPO. Technically, it isn't. It's an ADR (American Depositary Receipt)—SK Hynix has been publicly traded in South Korea for decades. The Nasdaq listing simply gives U.S. investors easier access to one of the world's most important AI infrastructure companies. That distinction matters. Because this isn't about discovering a new company. It's about expanding access to one that already sits at the center of the AI supply chain. Why SK Hynix Matters So Much When investors talk about AI, most conversations revolve around Nvidia. But GPUs are only half of the story. Every Blackwell GPU depends on High Bandwidt

2026 is almost halfway through, and the AI rally is no longer only about GPUs, HBM, or optical communication. Now, the market is turning to another key part of the supply chain: 👉 semiconductor equipment. Among U.S.-listed semiconductor equipment companies with market caps above $10 billion: ✅ 9 stocks are up more than 75% YTD ✅ 7 stocks have already doubled this year ✅ All 9 hit record highs this week The main logic is simple: AI needs chips. Chips need fabs. Fabs need equipment. 1. 🚀 Which Equipment Stocks Have Doubled? The major winners include: $Applied Materials(AMAT)$ $Lam Research(LRCX)$ $KLA-Tencor(KLAC)$