The U.S. midterm elections are just 20 days away, and Wall Street is starting to focus on a familiar question: If control of Congress changes, which sectors could face the biggest policy shifts? On November 3, Americans will elect all 435 members of the House of Representatives and roughly one-third of the Senate. Republicans currently control the White House and both chambers of Congress, but the elections could change that balance. For investors, the key is not simply whether Republicans or Democrats gain seats. It is how different congressional outcomes could affect financial regulation, crypto policy, energy investment, healthcare spending and AI infrastructure. Three scenarios are worth watching. Scenario 1: Republicans Retain Control of Both Chambers If Republicans maintain control o

Asian semiconductor stocks suffered a sharp selloff today. SK hynix dropped roughly 10%, Samsung Electronics fell 7.4%, South Korea’s KOSPI declined more than 5%, and Japan’s Kioxia also saw double-digit losses. What makes this interesting is that AI memory demand has not suddenly disappeared. Instead, investors are reacting to a potential shift in the global memory supply landscape, driven partly by a technological breakthrough from China. On October 12, Chinese memory manufacturer CXMT (ChangXin Memory Technologies) disclosed progress in its next-generation 4F² DRAM cell architecture, with plans to introduce server memory products using the technology by the end of this year. Compared with the industry’s mainstream 6F² structure, the new architecture could potentially increase memory den

U.S. stock indexes keep pushing toward new highs, but the number of stocks actually making money for investors may be much smaller than you think. According to Reuters, citing LSEG data on October 12, the S&P 500 has gained roughly 14% year to date, yet more than 200 of its constituents are still trading lower, with about a quarter down 11% or more. Even more striking, the median return among S&P 500 stocks is only around 4%. In other words, the index looks like it is enjoying a strong bull market, but investors who missed a handful of major winners may have significantly underperformed. The more interesting question is not whether the market has risen too much, but why so much of the rally is concentrated in AI and semiconductor stocks. SanDisk and Micron have each surged more tha

A new OpenAI investor disclosure is forcing the market to take a closer look at one of the most important numbers in the AI boom. According to the latest investor materials, OpenAI’s annualized revenue was approaching $50 billion at the end of September. That is still an extraordinary growth rate, but it is roughly $20 billion lower than the nearly $70 billion figure that had been circulating in the market just days earlier. The important point is that OpenAI did not suddenly lose $20 billion of revenue. The gap appears to come largely from different accounting and comparison methodologies. Some earlier estimates adjusted OpenAI’s revenue to make it more comparable with Anthropic, including revenue generated through cloud partners or distribution arrangements. OpenAI’s own reported run-rat

Samsung just delivered a set of numbers that look almost unreal. For Q3 2026, the company expects revenue of about KRW 195 trillion and operating profit of roughly KRW 107.4 trillion, or around $80.2 billion, up about 783% year over year. The main driver is still AI: HBM, traditional DRAM and NAND are all benefiting from tight supply, giving memory makers exceptional pricing power. What is more interesting is the market reaction. Despite the record-level profit, Samsung shares barely moved and remain well below their June high. That suggests investors are no longer asking, “How much can Samsung earn this quarter?” The question has shifted to: How long can these margins last? That is now the key debate across the memory sector. Demand is still strong. AI servers continue to absorb HBM, serv

Earlier this year, one of Wall Street’s favorite bearish narratives was the “SaaSpocalypse.” The logic was simple: if AI agents can write code, build apps and automate workflows, why would companies keep paying large recurring fees to Salesforce, ServiceNow and other software vendors? That fear hit the sector hard. The S&P 500 Software & Services Index fell more than 26% from late January to its April low. Now the story is starting to reverse. The software index has climbed to a new 2026 high, and earnings expectations are moving higher as well. LSEG data shows expected 2026 earnings growth for the software sector has risen from about 13.8% at the end of March to 20.6%. The key shift is that AI is starting to look less like an immediate replacement for SaaS — and more like a new mo

A new pattern is emerging across the AI infrastructure boom: the companies selling compute are starting to help finance the companies buying it. According to Anthropic’s IPO filing, Broadcom has agreed to provide up to $42 billion in financing to support Anthropic’s AI infrastructure buildout. The financing could cover roughly one-third of Anthropic’s five-year $125.2 billion TPU lease commitment and may include convertible instruments that could eventually become equity. The headline number is huge, but the structure matters even more. Broadcom is already deeply involved in Google’s TPU ecosystem and is expected to help Anthropic access roughly 3.5GW of next-generation TPU capacity starting in 2027. Now Broadcom is also helping Anthropic finance that infrastructure. So the relationship is

HDD stocks took a sharp hit in the latest session. $Seagate Technology(STX)$ and $Western Digital(WDC)$ both fell about 10.2%, even as the broader tech market held up much better. This was not a broad AI hardware selloff. The pressure was concentrated in HDD names, and the trigger came from Toshiba. Toshiba reportedly plans to invest around ¥60 billion to expand its Philippine operations and roughly double HDD capacity for AI data centers by fiscal 2027 compared with 2025 levels. The company is also targeting a much larger share of the global HDD market over time. That immediately raised a key concern for investors: if the industry’s No. 3 player starts adding meaningful capacity, how long can Seagate and W

Memory stocks have been one of the hottest parts of the AI trade, but Michael Burry is leaning the other way. Burry has continued to add to his bearish exposure on $Micron Technology(MU), even as DRAM pricing remains firm and AI-related demand stays strong. What makes the trade interesting is the timing: he is not shorting memory because the current fundamentals look weak. He appears to be betting that today’s strength eventually creates tomorrow’s oversupply. That is the core debate in memory right now. The bullish case is straightforward. AI servers need more HBM, more server DRAM and more enterprise SSD capacity. Hyperscalers are still expanding infrastructure, memory content per server keeps rising, and supply remains tight in several key categories. In that environment, strong pricing

Apple’s latest Mac update is about more than faster hardware. The new Mac mini and Mac Studio are being positioned as machines that can run AI agents, large language models and enterprise workflows locally. The high-end Mac Studio can support up to 512GB of unified memory, while multiple Macs can be linked together for distributed inference. Apple has even demonstrated four Mac Studios running a trillion-parameter model using a standard wall outlet. The more interesting part is how Apple is selling the economics. Cloud AI usually charges by usage. The more tokens a company consumes, the more it pays. Apple’s pitch is different: buy the hardware once, then keep running workloads locally without paying for every model call. That matters when AI usage becomes frequent. If an enterprise agent

AI may be moving from “answering questions” to actually doing work — and that could make CPUs important again. The latest AI trade is no longer just about GPUs. Meta’s new AI agent, Muse, has pushed investors to rethink what the next phase of AI infrastructure may actually require. Why?Because an AI agent does much more than generate an answer. It may need to:open a browser,search the web,fill out forms,call APIs,run tools,manage files,and keep working in the background for minutes or even hours. That changes the compute equation. For a chatbot, the workflow is relatively simple: Prompt → GPU inference → Response For an AI agent, it looks more like: Think → Browse → Execute → Check → Think Again → Continue GPUs still handle the heavy model inference. But many of the surrounding workloads —

3.2T and 6.4T solutions are showing up at ECOC 2026. The next AI bottleneck may be shifting from raw compute to connectivity. As AI clusters scale from thousands to hundreds of thousands of GPUs, one problem is becoming increasingly important: How do you move massive amounts of data between those GPUs fast enough — and without burning too much power? That is exactly why optical interconnects are becoming a bigger part of the AI infrastructure story. At ECOC 2026, Coherent showcased a 3.2T OSFP optical module as well as a 6.4T NPO optical engine designed for next-generation AI scale-up and scale-out networks. The direction is becoming clear: 800G → 1.6T → 3.2T And the upgrade is not just about higher headline speeds. The bigger shift is that optics are moving closer to the chip. As GPU dens

The Bank of Japan has raised its policy rate by 25 basis points to 1.25%, the highest level in 31 years. The move passed by a 7-2 vote and was broadly expected by markets. The bigger question now is not the 1.25% level itself, but how far the BOJ is prepared to go from here. This matters far beyond Japan. For years, the yen has been one of the world’s cheapest funding currencies. Investors could borrow at very low Japanese rates and move that capital into higher-yielding assets elsewhere — U.S. stocks, bonds, emerging-market currencies and other risk assets. That is the basic logic behind the yen carry trade. As Japanese rates rise, that trade becomes less attractive. If the yen also strengthens, investors face both higher funding costs and FX losses. That is why every BOJ tightening cycle

One of today’s more interesting semiconductor stories is not about a new GPU or a new AI model. Reuters reported that SK hynix is in exploratory talks with Intel about producing memory chips in the U.S. for the first time. One option under discussion is for SK hynix to use part of Intel’s Ohio fab capacity. Another possibility is a joint structure involving SK hynix, Intel and potentially major cloud customers. The talks are still at an early stage, and there is no final decision yet on product scope, investment size or structure. What makes this interesting is that this is not simply another “chipmaker builds in America” story. SK hynix already has a U.S. footprint, including its advanced AI-memory packaging project in Indiana. If front-end memory production also moves closer to U.S. cust

The Fed decision tonight is important, but the market may already have moved beyond the first question. A 25bp hike is now largely priced in, which means the bigger issue is no longer simply “Will the Fed hike?” but “Does this mark the start of another tightening cycle, or is it just a one-off adjustment?” If the Fed raises rates by 25bp as expected, the target range would move higher again, but the market reaction will likely depend much more on the new dot plot and the tone of the press conference than on the headline rate move itself. The reason expectations shifted so quickly is that the latest inflation data have remained uncomfortable while the labor market has not weakened enough to give the Fed much room to ignore it. CPI and PPI both showed renewed price pressure, while payroll gr

AI-linked stocks across Asia sold off sharply today. SoftBank, Kioxia, SK hynix, Samsung and TSMC all came under pressure as investors reacted to a growing debate around whether the industry should slow the pace of frontier AI development. Anthropic CEO Dario Amodei has called for more time to evaluate safety risks before pushing model capabilities much further, while other major AI leaders have also shown support for stronger safeguards. The market’s first reaction is understandable: if even the AI labs themselves are saying “slow down,” does that mean the massive spending on GPUs, HBM, networking and data centers is also about to cool? Tiger thinks the answer may be more complicated. What may slow is the pace of frontier model training, not necessarily the overall demand for AI compute.

The storage sector is getting another major capital-markets catalyst. After SK hynix expanded its U.S. market presence, Japanese memory giant Kioxia is now reportedly planning a U.S. listing through ADSs. The goal is not simply to raise capital. Kioxia already trades in Japan. A U.S. listing would broaden its investor base, improve liquidity, and make the company much easier for global AI-focused funds to compare directly with names like Micron and SanDisk. That is what makes this story interesting. AI is starting to change how the market values storage companies. NVIDIA represents compute. SK hynix and Micron are closely tied to HBM. Kioxia is more exposed to NAND and enterprise SSDs. These are different businesses, but they are all benefiting from the same underlying force: AI data cente

Meta’s latest AI product is meaningfully different from a typical chatbot. Instead of simply answering questions, it is designed to help users actually complete tasks across areas like email, calendars, shopping, payments and travel planning. Compared with another round of “bigger model, higher benchmark” announcements, the more important shift is that Meta is pushing AI from something that talks to users into something that acts for them. That matters especially for Meta because the market’s biggest question is no longer whether the company has serious AI capabilities. The real question is when its massive AI spending starts turning into revenue. Meta has continued to invest heavily in data centers, GPUs and top AI talent, but stronger models alone do not automatically create a new busine