苹果 · 2288

[Comparative Daily News Picks] Anthropic plans to include anti-AI sentiment as the main risk factor in the prospectus; Strategy's stock price hit a two-month high, and STRC returned above $96; Bernstein: Even if the “Clarity Act” is not passed, the SEC and CFTC will speed up rule-making; Dalio: The US debt crisis may break out within three years, and it is recommended to increase gold holdings

Daily AI · Crypto · Macro · Market Highlights, Bitpush helps you set priorities ↓ AI · News [Anthropic plans to include anti-AI sentiment as the main risk factor in the prospectus]. According to CNBC, Anthropic is expected to list the public's negative sentiment about artificial intelligence and data centers as a risk factor in the IPO prospectus to be released in the next few weeks. According to people familiar with the matter, Anthropic recently held a pre-listing “market trial” meeting with bankers and investors. Investors focused on competitive pressure, the impact of open source models on profit margins, and the risks that may be brought about by a slowdown in data center construction. Anthropic is currently valued at close to $1 trillion in the private equity market and is preparing to hit a major IPO. However, as Americans' concerns about AI replacing employment and data center expansion heat up, the related backlash sentiment is becoming a new challenge facing the company's listing. The company has previously achieved an annualized revenue operating rate of more than 65 billion US dollars. [Apple cuts Siri and Vision Pro team positions, and resources shift to AI and new devices] Compared to news, Apple (AAPL.O) is laying off employees from various teams responsible for Siri's digital assistants and Vision Pro headsets. The total impact of this layoff is more than 200 people. Of these, about 100 jobs in the Vision Pro department have been abolished, and about 100 other positions in the Siri and software teams have been cut. The move is part of the company's efforts to focus resources on new devices and artificial intelligence. People familiar with the matter said that in this adjustment, Apple has basically shut down a team dedicated to the Vision Pro game business, while also reducing the size of the department responsible for producing immersive video content for the device. Apple admitted in a statement that the company is making adjustments to some teams “to drive business development and provide the best experience for users.” [Castle Securities: Over 80% of the overall risk in the Situational Awareness Fund portfolio has been divested] According to the Financial Times, Castle Securities founder Ken Griffin responded to the company's acquisition of Situational Awareness assets under Leopold (Leopold) in a letter to clients on Friday. According to a letter obtained by CNBC, Griffin told clients that Castle Securities had divested more than 80% of the overall risk in the original purchased portfolio by conducting more than 100 major transactions (with a market value of more than $4 billion). In his letter, Griffin wrote, “A transaction of this scale would not have been possible without the full cooperation of the transaction teams and lead brokerage teams of the banks serving the two companies. I am very grateful for their dedicated efforts to complete the portfolio transfer quickly.” Griffin also confirmed that the company's flagship multi-strategy fund, the Wellington Fund, had a return of 5.94% in July, which is the fund's best monthly performance since 2022. [AI cloud company Nscale seeks to raise 3 billion US dollars in US IPOs] In comparison, AI cloud company Nscale is reportedly seeking to raise 3 billion US dollars in a US IPO. In the crypto market [Strategy stock price hit a two-month high, STRC returned above $96], the Bitcoin treasury company Strategy (MSTR) stock price rose to a two-month high today as the Bitcoin price briefly broke through $79,400. It broke through $120 during the intraday period, then partially regained its gains. Meanwhile, the price of STRC, Strategy's preferred stock product, also surpassed $96 for the first time since June. Previously, STRC's price once fell below $70 due to concerns about its ability to pay dividends and the ability of the stock price to maintain the $100 target for a long time. [Bernstein: Even if the Clarity Act is not passed, the SEC and CFTC will speed up rulemaking] Comparing news, the Bernstein analyst team led by Gautam Chhugani released a report stating that regardless of the procedural voting results of the “Clarity Act” on September 15, the certainty of US crypto regulation is expected to increase. They expect the SEC and CFTC to accelerate rulemaking in areas such as native crypto asset issuance, tokenized stocks, perpetual futures, computing power derivatives, and predictive markets. This regulatory clarity of expectations has become one of the broader supporting factors in the crypto market. 【A...

19h agoBitpushNews#Compare Daily Picks

Apple cuts Siri and Vision Pro team positions and shifts resources to AI and new devices

According to the news, Apple (AAPL.O) is laying off employees from various teams responsible for Siri's digital assistants and Vision Pro headsets. The total impact of this layoff is more than 200 people. Of these, about 100 jobs in the Vision Pro department have been abolished, and about 100 other jobs in the Siri and software teams have been cut. The move is part of the company's efforts to focus resources on new devices and artificial intelligence. People familiar with the matter said that in this adjustment, Apple has basically shut down a team dedicated to the Vision Pro game business, while also reducing the size of the department responsible for producing immersive video content for the device. Apple admitted in a statement that the company is making adjustments to some teams “to drive business development and provide the best experience for users.”

23h agoWendy#starters

US Stock Value Investing Is Heading Into Another Trap

Source: Shenchao TechFlow Original title: (Opinion: Value investing in US stocks is not equal to fundamental investment) When “fundamentals are dead” becomes a consensus, investors who blindly organize giants will eventually experience astonishing capital destruction. Guide: When the market shouted “fundamentals are dead” and the capital frenzy formed a group of tech giants, the author used an astronomy discovery to unravel the logical loopholes behind this narrative. Starting from the composition of valuation multiples, this article reminds investors to distinguish between the true quality of an enterprise and the premium that the market is willing to pay. It is particularly cautionary about long-term allocation in the crypto and technology sector. I promise this introduction won't be as long as the last one on the weather. But please give me 90 seconds. More than 100 years ago, a woman named Henrietta Levitt was doing the tedious job of measuring the brightness of thousands of stars on photographic negatives (the way they were imaged before film appeared). She noticed one characteristic of a class of pulsating stars: the slower they pulsate, the brighter they themselves are. ¹ This might just seem a little interesting today, like “OK, that's pretty cool.” But at the time, astronomers couldn't tell the difference between a dark star very close to Earth and a very bright star far away. For them, the two left the same stain on the photographic film. Visual brightness is a messy mix of these two variables: how bright the thing itself is, and how far away it is from us. Henrietta's work decouples these two things: if you can observe the rate of pulsation, you can know its true luminosity; if you know its true luminosity, you can reverse the distance based on how dark it looks. Astronomers call it “standard candlelight.” A few years later, a man named Edwin Hubble discovered one of these pulsating stars, applied Levitt's math, and discovered what he had always thought was a cloud of gas within our galaxy; in fact, it was an entire independent galaxy, one million light years away. So in simple terms, the observable universe has grown about a trillion times larger, just because one person has figured out how to tell the difference between what things look like and what they actually look like. That in itself is obviously pretty cool. But another interesting thing is that around the same time period, two other astronomers each independently drew a scatterplot. One axis was actual luminosity, and the other axis was temperature. They discovered that stars are not randomly distributed in this space, but rather clustered into different families. The meaning behind this is: stars with the exact same visual brightness may and do belong to a completely different family, have a completely different past, and most importantly, have a completely different future... So what is written in the star? Over the past few years, there has been much discussion about markets, narratives, capital, company building, and financial nihilism. This feeling seems to have reached a feverish climax as the tech and financial world begins to face a very different future than a few decades ago. What is particularly clear is that separating progress from asset prices has become more noisy and in many ways more repulsive. But as an investor who makes a living by buying assets that (hopefully) outperform, a simple framework is: forward returns are roughly equal to growth in fundamentals multiplied by changes in valuation multiples (and multiplied by the dividends you've collected along the way). In this case, the valuation multiplier can very cleanly correspond to the smudges on the photographic film. It's an observable data point, but it entangles two things that the market can't directly see: how good the company actually is, and how far (or how long) its future cash flow is now. I think most of the money that can be made comes from investors who are most capable of unraveling these two variables earlier than others (or “perception of differences”), and we will continue to see astonishing capital ruin for investors who treat their stains as stars. Value investing is not equal to fundamental investing. I think there is a misunderstood view: fundamental investing has historically dominated the creation of excess returns. Most of these legends come from the Graham, Buffett, and Tiger Foundation lineage, as well as numerous narratives built around this group of people. It is believed that by some point in the 2000s, this approach was no longer effective, and anyone who invested in this way was overwhelmed by momentum, trends, and “direct buying tech giants.” The conclusion was (and still is?) It's “fundamentals are dead.” ² The modern version of “fundamentals don't matter” itself isn't stupid. It's rooted in a lot of ideas that many of us on the Compound team have written before. The biggest companies get the most mechanical purchases, and the software industry has a winner-take-all economic law. AI means that giants can transform scale into moats faster than challengers, and there are also reasons why the market's microstructure embeds momentum more deeply into our market infrastructure. These are all real...

1d ago深潮TechFlow#US stocks

Apple shares hit a three-week high, rising 2.1%

Comparative news, according to Gate market data, the Apple (AAPL.O) stock price hit a three-week high and recently rose 2.1%.

3d ago

Cantor Fitzgerald plans to open Kalshi prediction market to its institutional clients

Comparative news, according to the Wall Street Journal, Cantor Fitzgerald plans to open Kalshi's prediction market to about 3,000 institutional clients, including family offices and hedge funds, which can trade events such as weather, commodities, and corporate performance contracts. Susquehanna International Group will provide quotes and liquidity for related transactions. Cantor will act as a broker to trade high-volume event contracts for clients, and may distribute relevant positions to other investors through private negotiations. Cantor Co-CEO Pascal Bandelier said that hedge funds have expressed interest in trading contracts linked to iPhone sales rather than indirectly betting on changes in sales through Apple's stock price; the family office is concerned about using contracts related to weather, crop production, and oil prices to hedge risks. Joe Grubb, head of business development at Susquehanna Predictions, said that AI supply chain risk and computing power prices may also become application scenarios in the prediction market. Institutional clients can also propose new market topics according to their own needs. Relevant companies have discussed with investors the types of contracts they wish to launch. Kalshi has been stepping up its institutional client expansion efforts in recent months. This year, it has completed its first major transaction and reached a partnership with Interactive Brokers. Max Crowley, vice president of business development at Kalshi, said the agency's need to hedge against specific event risks already exists.

3d ago

Samsung's advanced foundry increased prices by up to 15%, and AI demand boosts production capacity

Comparative news, according to a report by Reuters quoting people familiar with the matter, Samsung Electronics has raised the price of new orders for some advanced foundry services by up to 15%, and production capacity is tight due to a surge in demand for AI chips. The company has long lagged behind TSMC in the field of foundry. Demand from Chinese customers is particularly strong, but Samsung needs to prioritize service to US customers and reserve part of its production capacity to support its own chip production, and is unable to accept all orders. Affected by US restrictions on exports of advanced chip equipment to China, Chinese companies are more dependent on overseas foundries, and some Chinese customers have accepted the biggest price increase. According to people familiar with the matter, Samsung raised the price of the 4 nm (SF4) process in July. Chinese and US customers increased 10% to 15%, Taiwanese customers increased the price of 5% to 10%; the price of 5 nm (SF5) wafers increased by 10% to 15%, and the older 8-nm process increased by nearly 10%. The SF4 production line at the Pyeongtaek plant has been operating at full capacity since the end of last year, not only producing logic chips for customers such as Qualcomm, but also providing basic raw films for Samsung's own HBM. The analysis points out that after TSMC's advanced production capacity was fully booked, customers switched to Samsung and Intel, giving Samsung room to raise prices. Samsung's foundry business has continued to lose money since 2022, but the company expects to reverse losses relatively quickly, driven by increased utilization rates, improved yield, and stronger prices. In the second half of the year, industrial revenue is expected to increase by double digits over the same period last year, advanced processes will account for more than half, and AI and high-performance computing applications will account for more than 30%. Tesla, Apple, Broadcom, Nvidia, etc. have reached relevant cooperation with Samsung, and Google is also discussing the use of the SF4 process.

3d ago

Why did he dare to vote for Yu Shu 6 years ago? 丨Exclusive secrets revealed by early investors

Source | Pencil Dao Oral Statement | Edited by Yu Shu Early Investor Zhao Nan | Wang Fang Original title: Yu Shu Early Investors: 6 years ago, why did I dare to invest? 丨Exclusive Today (August 19), Yushu Technology officially landed on the Science and Technology Innovation Board and became the “first stock of humanoid robots” in A-shares. Yushu's issue price was 150.80 yuan/share, an increase of 629.44% after opening on the first day. The market value reached 444.9 billion yuan at this point. Earlier, Pencil Road had a conversation with its early investor, Zhao Nan, to try to restore it: Why did Yu Shu dare to take action when he was still very low-key? The following is Zhao Nan's oral statement. - 01 - Meet Uki, the first time I met Uki because of the four-legged robot, was in April 2020. It wasn't an accidental encounter. Beginning in 2019, I've been watching robot tracks in a systematic way. Basically, I've looked at all the directions I could see at the time: four-legged robots, underwater robots, food delivery robots, and even some consumer-grade products. After watching that round, I actually have a relatively clear judgment in my mind: the robotics industry is not a question of “whether it will happen,” but “when will it happen.” The real question is: which directions have been proven and which are just imagination. In my opinion, four-legged robots are one of the few directions that have been proven. Look at Boston Dynamics (Boston Dynamics). It has already made robot dogs, and customers are already using it. This incident shows a very important thing: robots are not sci-fi, but have entered the real world. But at the same time, it also revealed a more essential problem — the cost problem: people are too expensive, R&D is too heavy, and the supply chain is immature. At the time, Boston Power's problems were typical: the team was the top team in the world, and labor costs were extremely high; the R&D cycle was long and the investment was huge; even early products were still using diesel instead of lithium; in the end, they were sold by Google to SoftBank Vision Fund. However, this is not to say that there is no demand for four-legged robots; it is that the cost cannot support it. To this day, four-legged robots are still one of Uki's core businesses, so my judgment at the time was: Whoever can cut costs has an opportunity. Obviously, the Chinese team has an opportunity because we have a supply chain advantage. - 02 - Why invest? It was also based on this judgment that I visited Hangzhou for the first time in April 2020 and met Wang Xingxing (founder of Yushu). That meeting actually made me realize very quickly that this isn't a “just starting” team. It was 2020 at the time, and they've been doing it for three years. When Wang Xingxing was a graduate student at Shanghai University, he already brought a four-legged robot laboratory. At that time, he started working on xDog's algorithm. By the time we met him, he had a deep understanding of every detail of the four-legged robot's functions and procedures. In other words, while many teams are still “watching the track,” they are already building up technology. This is particularly critical in the robotics industry, as it is a typical “time for ability” industry. Many things can't be smashed with money; they have to be accumulated little by little. What really made me decide were a few more specific things. First, they have mastered the robot's core underlying ability. Many people think that robots are a “complete machine product,” but in reality, there are three things that really determine the height of a robot company: driver, sensor, and controller. If you don't have these three pieces in your hands, you'll always be just a system integrator. Meanwhile, in 2020, Yu Shu already has accumulated technology and patents on these three areas. This means it's not assembling products, but building underlying capabilities. This essentially determines its upper limit. Second, it's the customer. Many robotics companies “make products first, then find customers”, but Yuuki did the opposite — they already had customers in 2020, and they are not regular customers. At the time, their customers included companies such as Apple, Nvidia, Meta, and Google. These companies buy their robot dogs to use for AI training and algorithm training. Domestic customers are mainly universities. This matter is critical because it shows two things: first, the product is already available; second, the technology has been recognized by top technology companies. Wang Xingxing always pushes everyone to do one thing: make simple products that customers can accept, and sell them one wave first. Not to make money, but to train the company's entire R&D, production, and sales system by selling products. By communicating with customers, we can also find problems and iterate on products. Third, mass production capacity. I went to their company at the time and saw something very detailed — there was a warehouse next to their office building with a press machine that was used to mold parts. At the time, Wang Xingxing explained that a four-legged aircraft...

3d ago铅笔道#Uki IPO #Wang Xing Xing

Apple settles dispute with EU App Store fees

According to Twitter, Apple said it has resolved the dispute with the EU over app store fees and will cancel each installation fee for app developers in the EU.

4d ago

Why is capital chasing AI Native and ignoring the old Internet

Capital doesn't reward being old-fashioned, not because old-fashioned people are at fault. The old part is clearly priced. There is no bad information, so there is no excess profit. Global venture capital was $510 billion in the first half of 2026, surpassing $44 billion for the full year of 2025 in one and a half months. More than 70% have entered AI; OpenAI and Anthropic took 217 billion dollars, accounting for 43%. With that much money, you'd think everyone could share a little bit. The truth is that distribution is more extreme than total volume, and the first sieve doesn't screen the industry, it screens people. The category that has been screened out now has an unkind name: the internet is old. Let's just say one thing: the “old man” in this article has nothing to do with age. It refers to a set of methodologies that have been formed in the mobile internet cycle, have been tested over and over, and have brought huge returns to holders. The person holding it may be 45 years old or 32 years old. It was this methodology that was being repriced, not the year of birth. Confusing these two things is Lao Deng's most common mistake and one of the most comfortable mistakes — because if the problem is someone else's age discrimination, you don't need to change a single word. 01 What is AI Native The term has been misused. They can use ChatGPT not called AI native, nor AI in the company name, let alone in their twenties. There are three things that really separate people. First, the starting point is a model, not a requirement. The order in which Lao Deng makes a product is: look at what the user wants, write down the requirements, and find technology to implement it. The order of AI natives is reversed: first figure out what level the model is capable of today and what step it is likely to reach tomorrow, and then move from this capability boundary to the external product. The former uses the model as a tool, and the latter uses the model as the foundation. There was no difference between these two kinds of things made by humans in the first edition; by the third edition, there was a difference of one species. Article 2. The default unit of an organization is not a person. The division of labor in the Internet age is the division of one thing into ten people. AI Native's division of labor is to take ten things from one person and add a bunch of agents. The CEO of a domestic application company said that the team consists of less than ten people, but a large number of AI work at night, and the first thing employees do every morning is check the work the AI handed in the night before. Cursor's side is even more extreme. Public reports mention that the company doesn't have a product manager; engineers write their own code, talk to users themselves, and participate in recruiting people themselves. Article 3. Information is first-hand. AI Native's input sources are papers, model cards, GitHub issues, original discussions on X, and self-run evals. Lao Deng's input sources are industry summits, closed-door meetings, brokerage reports, interpretation of public accounts, and finding someone to drink coffee with. This one is the least obscure and most lethal; I'll talk about that separately later. I'm satisfied with all three. The 25-year-old is an AI native, and so is the 45-year-old. I'm not satisfied with the three rules; I'm still an old man at the age of 25. AI natives are a state, not an age group. The trouble is that tickets in this state are works, not resumes. 02 The two lists spread the results of this round on the table. These are two lists. The first one is an all-AI native company. Their valuations are not rising; they are exchanging orders of magnitude. List 1 · Upstream OpenAI raised $122 billion in a single round of financing in Q1 2026, followed by $852 billion, the largest private equity financing in history. Anthropic Q2 had a single round of $65 billion, after investing $965 billion, accounting for about half of the total global venture capital for the quarter; the revenue operating rate in May reached about $47 billion. DeepSeek raised about 70 billion yuan in its first round of financing in May 2026. In April of the same year, Liang Wenfeng raised his direct shareholding from 1% to 34%, and controlled a total of about 84.29% of the shares through related entities. The Dark Side of the Moon (Kimi) was estimated at $4.3 billion in December 2025; it went for three consecutive rounds from January to February 2026 to reach 18 billion; the D round in May was about $2 billion, breaking 20 billion dollars after the investment; the July round surpassed $3.5 billion, after investing 35 billion dollars; the pre-IPO target was 50 billion dollars. ARR broke 100 million in March, 200 million in May, and held steady at 300 million US dollars in June, with APIs accounting for more than 70%. Smart Spectrum · MiniMax successively landed in Hong Kong stocks in early 2026, with a market capitalization exceeding 100 billion yuan. It was one of the first major model companies listed in China. The second one...

4d agoWendy#AI #DeepSeek

Fireworks that came out of Meta to talk about open source and closed source. Who will win?

Author: Silicon Valley Vector Silicon Valley Coordinates Editor: Peggy, BlockBeats Original title: Silicon Valley Coordinates x Fireworks Co-Founder Benny Chen: Open Source Models, Token Growth, Inference Optimization, and Model Customization Editor's Note: In the context of open source models speeding up and approaching cutting-edge closed-source models, industry discussions are shifting from “who has the most capable model” to “who can put models into production at a lower cost”. However, when model capabilities converged and token consumption increased, a lower-level question began to emerge: are companies really willing to pay a cheaper model call, or exclusive intelligence that can perform specific tasks in a stable manner? Recently, Cao Qingyun, host of “Silicon Valley Coordinates”, had a conversation with Chen Yufei, co-founder of Fireworks AI. Located between models and enterprise applications, Fireworks mainly provides customers with open source model inference, performance optimization, and customization services. Rather than simply discussing whether open source can catch up with closed sources, Chen Yufei's observations are closer to actual workloads: where tokens flow, why companies pay, and what is still missing from the model from proof of concept to production. In this conversation, Chen Yufei disassembled “who wins between open source and closed source” into a set of lower level structural questions: can token growth be converted into revenue, can generic capabilities replace vertical accumulation, can the low price model pass corporate evaluation, and how the inference platform can gain value between cloud vendors and application companies. First, the scale of use and commercial value of the open source model are diverging. In the past, the ability to catch up and call price were the main indicators for judging the competitiveness of open source; today, the Fireworks platform processes about 40 trillion to 50 trillion tokens every day, and the actual usage of the open source model has rapidly expanded. However, free traffic, promotional subsidies, and model price differences will cause Token statistics to overestimate some demand. Customers may heavily use lower-cost models and still hand over the highest budget to the best-performing closed source model. This means that the next phase of open source is not just expanding traffic, but proving that it can meet or even surpass cutting-edge models for high-value tasks, and turn cost advantages into willingness to pay. Second, the general model and the vertical model are beginning to evolve in different directions. In the past, every time a cutting-edge model was upgraded, it was possible to directly eliminate a number of fine-tuned models; now, vertical applications such as law, medical care, and programming are accumulating more detailed evaluations, data, and workflows, and their optimization goals are gradually separated from cutting-edge laboratories. Generic models need to increase the upper limit of capabilities, while vertical models require stable delivery of results in limited scenarios. The former can solve a wider range of problems, while the latter has a better understanding of how users define “right.” This means that the barrier for vertical companies is not just having a customized model, but being able to continuously transform industry needs into an evaluation system and migrate over and over again as the basic model is updated. Third, the bottleneck in enterprise AI implementation is shifting from model supply to evaluation capabilities. In the past, enterprise proof of concept often relied on trial experience and subjective judgment; now, when AI enters production processes such as call centers, legal searches, and medical assistance, it is no longer possible to support procurement decisions simply by “looking good.” Businesses must know what tasks the model works for, when it fails, and how much the cost and quality of switching from closed source to open source changes. Assessment is therefore no longer an ancillary tool, but an infrastructure connecting procurement, training, and production deployment. Who can define tasks, establish test distributions, and continuously update standards can truly control model choices. Fourth, the value of inference platforms is shifting from “selling cheap computing power” to organizational models, hardware, and workflows. In the past, inference optimization was mainly understood to reduce the cost of a single token; now, caching, task splitting, model routing, and context management can all directly change the task completion rate. Different models don't have to compete for the same position; they can act as performers and advisors separately. Fireworks' business logic is also based on this: instead of building asset-heavy hardware, revenue is tied to actual use of customer models through training, customization, and continuous reasoning. But the main rival in this path is not a single new cloud company, but a large cloud vendor that can simultaneously control computing power, software, and customer portals. Fifth, the rise of the open source model may not reduce infrastructure requirements; on the contrary, it may reduce model layer premiums and further push value towards reasoning and computing power. Tech giants continue to increase capital spending, not just calculating short-term returns, but measuring missed AI cycles...

4d ago律动BlockBeats#AI