Rocky_BTC

Outdoor camping is really addictive, and it's time to spoil the family again this weekend! 🥸 The 10-year US Treasury yield has broken the warning line, reaching 4.6%. It's time to get on board with risk asset corrections and spend extra time with family and enjoy nature this weekend! 🧐

BlackRock is ready to invest $5-10 billion in #SpaceX! Feels like after the IPO it will stabilize at 2 trillion, and in the next 5 years push towards 10 trillion, definitely! 🥸 I'm planning to buy a little on various Pre-IPO platforms, which one is the cheapest right now?! 🧐

Today I saw a shocking piece of news that left me completely stunned. 🧐 Schroders, the British asset management giant with a 220-year history, has directly announced its exit from the Chinese public fund market! You have to understand, this is a long-established European financial aristocrat, playing with money since the Napoleonic era, managing assets worth trillions. So what happened? They just couldn’t survive in the Chinese asset management market, with a net outflow of £2.2 billion in the first quarter, and in the end, they had to quietly shut down. I was puzzled: this year, both the A-share and US stock markets have been so strong, retail investors can make money by buying almost anything, so how could a globally renowned fund company end up with losses? That takes some serious skill! Later, I figured out there’s a more terrifying truth behind this. The financial market nowadays is no longer about competing with fund manager experience or the depth of research teams. What are we competing on? We’re competing on #AI computing power! We’re competing on the iteration speed of quantitative robots! Just look at the latest ChatGPT update for personal finance, which is only available to ChatGPT Pro users in the US! I asked around through friends, and now over 85% of domestic private and public funds have already adopted fully automated AI quantitative systems. And this stuff isn’t static; it can learn in real-time and optimize multi-factor strategies on the fly. Every surge or crash you see on the market might be the result of dozens of AI Agents battling it out in milliseconds. How can the human brain keep up with that speed? An old European institution like Schroders, whose AI capabilities lag far behind China and the US, still trying to survive in the Chinese market with traditional methods? Getting crushed by domestic AI quantitative robots was inevitable! On a bigger scale, in the future of personal investment and wealth management, the real competition won’t be about your financial IQ, but how powerful the AI you use is. Can your Agent arbitrage global markets 24/7? Can it capture price differences in milliseconds? Can it evolve itself? If your Agent can’t beat others, you’ll just be harvested in the capital markets—that’s an iron rule. I’m increasingly convinced that the crypto market is heading down the same path. High-frequency quant and AI-driven market makers have long modeled every move of retail investors. You think you’re trading actively, but every step you take is predicted by someone else’s algorithm. Times have changed, brothers. It used to be humans playing the market; now it’s AI playing AI, and humans are just the capital providers. 😅

Today, the first phase of #OKX's dual-currency earning strategy expired, and I started switching to a new product—"structured products." Currently, the annualized yield of 10.2% is indeed very attractive. Comparing it with the dual-currency earning strategy expiring on July 31, which only has 7.9%, and that's VIP3 data! (See👇Figure 2) This account is specifically used for cash flow to pay the mortgage. At present, through various #OKX financial products, I can achieve about $3,000 per month, basically covering the monthly mortgage. The key point is that it accrues interest weekly, which is very comfortable! I plan to test it for a while. I have a somewhat unconventional plan: if this interest rate remains relatively stable, I intend to turn the house into a business loan. The domestic interest rate is 3.2%, with interest-only payments for 5 years before principal repayment. The house is currently appraised at about 10 million RMB, borrowing 70%, which is 7 million RMB. Taking advantage of the high RMB exchange rate, I will convert it to 1.03 million USDT, locking in about a 7% annualized yield, earning $70,000 a year—steady happiness! 🧐 Is this plan feasible or not? Friends in the comments, please share what risk points ⚠️ I should be aware of?!

#OpenAI hasn't gone public yet, but its computing power subsidiary is about to IPO, which is quite interesting.🧐 Previously, Jensen Huang mentioned that inference demand will grow by a billion times in the future. Next Thursday, May 14, the dark horse AI chip of the inference era, Cerebras ($CBRS), will go public with a price range of $115-$125, aiming to raise up to $3.5 billion, with a valuation of $26.6 billion. It will also be the first Pre-IPO project of @MSX_CN, which is very much worth looking forward to! Today, let's break down this Cerebras company, analyze its valuation, and share some of my personal judgments and opinions. To understand this OpenAI-related inference chip dark horse, you need to know Sam Altman's capital layout. We all know how powerful NVIDIA is in the AI chip field: large model companies burn money, cloud providers buy cards, startups line up for GPUs, and most profits flow to NVIDIA, the shovel seller. This is the current industry status. But in this monopoly situation, all major large model companies hope to have a loyal Plan B solution. For example, Google Gemini partnered with Broadcom to use TPU solutions, and OpenAI has always wanted to support its own loyal forces. So on May 6, OpenAI brought together NVIDIA, AMD, Intel, Broadcom, Microsoft—chip companies that should be competitors—to create the MRC network protocol. On the surface, it looks like technical cooperation, but actually, OpenAI wants to redistribute the pie. Looking deeper, I believe #OpenAI wants to break NVIDIA's full-stack monopoly. Previously, training, inference, networking, and cloud were all monopolized by NVIDIA. Now? OpenAI is starting refined operations: training for training, inference for inference, using different chips for different scenarios, and different suppliers for different stages. #Cerebras was pushed to the table at this time, responsible for the inference part. This coincides with the recent hype around inference CPU concepts, such as #AMD, #INTC, etc., hitting the hot spot. 🔥What makes Cerebras so powerful? Cerebras's core trump card is the WSE-3 chip, which directly makes an entire 12-inch wafer into a giant chip, with an area of 46,225 square millimeters, about one-third the size of an A4 sheet. Let's compare data with NVIDIA's H100: • Its area is 57 times that of H100 • Core count is 52 times • On-chip memory is 880 times • Memory bandwidth is 7000 times These numbers 📊 seem exaggerated, but the key is not size, but speed. In inference scenarios, especially long text output, real-time interaction, code generation, AI Agents—tasks requiring low latency—Cerebras's CS-3 system inference speed is 21 times faster than NVIDIA's DGX B200, with cost and power consumption reduced to one-third. This efficiency and power consumption mean that with the WSE-3 chip, OpenAI can serve more customers per unit time, which is like pure money saved. 📊Financial data is also impressive From the market trend perspective, the AI industry is shifting from training-dominated to inference-dominated, an indisputable fact. The global AI inference market size will reach $106.2 billion by 2025 and is expected to grow to $255 billion by 2030. Cerebras's technological advantage is right on this trend. Additionally, this IPO round values the company at $26.6 billion, with an issue price of $115-$125 per share. I think this is relatively cheap, although it doubled from the previous round's valuation of about $12 billion in the F round. In just two years, it doubled, but the financials are impressive. Cerebras's revenue in 2025 is projected at $510 million, a 76% increase from $290 million in 2024. Even more impressive is the net profit of $87.9 million, turning from a $485 million loss in 2024 to profitability. At a $26.6 billion valuation, the PS ratio is 52x. Compared to the hot semiconductor interconnect chip company Astera Labs (#ALAB) that went public in 2024 with a PS of 81x on the first day, and given the current hype in the inference track, I personally believe Cerebras can easily reach 80-100x PS, corresponding to a closing price of $192-$239, expecting over 50% upside! (But also need to consider the Nasdaq index on that day for a comprehensive judgment.) Not only the positives, Cerebras also has obvious issues: customer concentration is too high. MBZUAI from UAE contributes 62% of revenue, G42 contributes 24%, with the top two customers accounting for 86%. This means Cerebras must listen to big customers, limiting autonomy. Fortunately, with OpenAI's involvement, this revenue structure will improve, and OpenAI will become the largest customer. 🎯Deep binding between OpenAI and Cerebras Latest data shows OpenAI and Cerebras signed a multi-year cooperation agreement worth over $20 billion. Cerebras will provide OpenAI with 750 megawatts of computing power, deployed through 2028. But this is not just a procurement contract. OpenAI's founder Altman, president Brockman, former chief scientist Ilya, and board member Adam Angolo—all core executives—have personally invested in Cerebras. OpenAI also uses loans, warrants, and other financial instruments to establish long-term interest alignment with Cerebras. Simply put, Cerebras is now OpenAI's chip division. Besides, in March, Cerebras partnered with AWS to launch the CS-3 system on Amazon cloud services, becoming the first non-GPU AI accelerator to enter the mainstream cloud provider supply chain. Also, clients include GlaxoSmithKline, the U.S. Department of Energy, and multiple national labs, validating its technical strength from multiple dimensions. 💡OpenAI's capital strategy OpenAI's real intentions are clear: • Continue using NVIDIA high-end GPUs for training • Introduce Cerebras low-latency solutions for inference • Purchase some GPUs from AMD • Open network protocols • Bet on multiple cloud services: AWS, Azure, Google Cloud • Possibly develop self-designed chips in the future This is a computing power combination strategy, matching different workloads with different systems, no longer relying solely on NVIDIA's full-stack solution. OpenAI is transforming from a model company to a computing architecture company. Previously, it passively accepted chip vendors' technical routes; now it actively designs computing power combinations that fit its needs. OpenAI wants to downgrade chip suppliers from "platform providers" to "module suppliers." Supporting Cerebras is the most important part of this strategy. Looking ahead, Cerebras's IPO day stock price has a very high chance of exploding! ⚡Impact on NVIDIA In the short term, Cerebras's IPO won't significantly impact NVIDIA, like a minor pimple on the body. NVIDIA currently holds 80-90% of the AI chip market share, with CUDA ecosystem, GPU supply chain, NVLink network—these moats are hard to shake in the short term. But in the long term, the threat exists. Previously, AI companies had no choice but to use NVIDIA GPUs. Now, at least in inference scenarios, customers have viable alternatives. This choice weakens NVIDIA's pricing power. When OpenAI can say "I use Cerebras for inference, NVIDIA for training," NVIDIA loses its "all-in-one" bargaining power. The AI inference market is growing rapidly. Forecasts show a 28.9% CAGR globally from 2026 to 2032. Inference scenarios are more suitable for specialized chips. When the inference market surpasses training, NVIDIA's relative weakness in inference will become a bigger problem. NVIDIA is shifting from "sole supplier" to "one of the core suppliers." This change is not because NVIDIA is weaker, but because the market is bigger, customers are stronger, and demands are more complex. 🧐My judgment What’s truly worth watching about Cerebras's IPO is not just another AI chip company going public, but that OpenAI is starting to price inference as a separate business. When the inference market is validated as independently priced, the AI computing power market truly begins to stratify. The demand differences between training and inference become clear, and specialized chips prove their advantages in segmented scenarios. NVIDIA's narrative of "one chip rules all" no longer fully holds. The market will move from "general GPU monopoly" to "scenario-based chip combinations." And it's not just OpenAI doing this; Anthropic is also allying with Amazon and Google. Leading AI companies are diversifying procurement to reduce dependence on NVIDIA. A single supplier's "complete solution" is no longer the optimal choice. Finally, worth mentioning is that this #MSX first Pre-IPO project is about to go public—it's Cerebras this Thursday. Stay tuned!🧐 DYOR🙏

Parents always think outdoor camping is just playing house! It's a weekend pastime where no one is seen and nothing serious is done! Today, taking advantage of Mother's Day, I personally took my parents to play house! After the experience, they said, can we come again next week when we have time?! I think outdoor camping 🏕️ might be the best way for modern people to recharge their bodies. Mountains, rivers, lakes, seas, forests, and grasslands are the best resting places for the soul. Once you try it, you'll get addicted! 😂 Happy Mother's Day, remember to give your mom a call ☎️!