Is the 'Niu Lai' model Ox Alpha actually GLM-5.3 Flash? Over 90% of prediction markets identify it as Zhipu.

Bitcoin’s momentum has faded after recently climbing to near $80,000, as traders wait for U.S. inflation data and additional policy signals from the Federal Reserve. To date, the leading cryptocurrency has risen around 22% since August 20, a rally backed by robust capital inflows into Bitcoin ETFs. However, the sharp price increase has spurred profit-taking and technical overbought conditions, which could limit further short-term gains. From a technical perspective, the $80,000 level serves as a critical resistance zone for Bitcoin. If the cryptocurrency can hold a breakout above $83,000, its upside potential may expand further, with target ranges pointing to $95,000 to $100,000.

Nvidia will release its second-quarter financial results after US stock market close this Wednesday, with market focus centered on five key areas: AI infrastructure financing, progress on Vera Rubin servers, open model development, sales in China, and gross margin pressure. First, Nvidia recently announced a large-scale AI infrastructure financing initiative and is supporting a major data center project in southern Ohio. Investors are closely watching the company’s level of involvement in these deals and how these financing arrangements will ultimately generate returns. On the product front, Nvidia has stated that its next-generation Vera Rubin servers are expected to start shipping in the third or fourth quarter of this year. The prior Blackwell line was delayed for months due to design flaws, so whether Vera Rubin can proceed as scheduled will be a key watch point. Meanwhile, Nvidia is reported to have acquired the technology and engineering talent of open-weight AI model startup Poolside via a $6 billion licensing deal, to accelerate the development of its own Nemotron open model. Additionally, market participants will monitor Nvidia’s sales performance in China. On the gross margin front, the company currently maintains a high level of around 75%, but as memory prices rise and competition intensifies from custom chips and new processors, Nvidia’s ability to pass costs to customers may become more challenging.

Per HyperTracker data, in Hyperliquid Builder’s 30-day revenue ranking, MetaMask leads with ~$1.3248 million, followed by Phantom at ~$1.2568 million, and Trust Wallet third with ~$900,200. The full breakdown: MetaMask: $1.3248M, 30-day trading volume $1.43B; Phantom: $1.2568M, $2.29B; Trust Wallet: $900.2k, $1.5B; Invo: $658.4k, $1.87B; fomo: $430.1k, $971M; 0xc7...b71a: $291.6k, $294M; 0x7c...e781: $242.3k, $636M; Trasia: $229k, $457M; Rabby: $227.7k, $1.13B; Blockchain: $181.2k, $160M. The Hyperliquid Builder Code mechanism allows wallets, trading frontends, tools, or bots to add origin tags to transaction orders via the on-chain Builder field, earning a share of fees from the transaction flows they route. Data shows top wallets and frontends are emerging as key revenue capture entry points for the Hyperliquid ecosystem, with MetaMask and Phantom both generating over $1.2 million in Builder revenue over the past 30 days.

Australia’s second-largest pension fund, Australian Retirement Trust (ART), is betting against the trend on the yen. Managing around A$370 billion (roughly $265 billion) in assets, ART has steadily increased its yen holdings over the past six months, lifting its yen over-allocation to a multi-year high. The fund added to its yen positions when USD/JPY neared 160, with some of the capital coming from reducing its U.S. dollar exposure. Jimmy Louca, senior portfolio manager at ART, stated that the market may have overestimated the pressure energy prices are exerting on the yen while underestimating the likelihood of a Bank of Japan (BOJ) interest rate hike. Currently, interest rate swaps show an approximately 80% probability of a BOJ rate hike in September, and an October rate increase is almost fully priced in by the market. A Reuters poll found 57% of economists expect the BOJ to raise rates to 1.25% in September. Meanwhile, ART is currently underweight U.S. Treasuries by about 0.5 percentage points, citing reasons including U.S. inflation remaining above target, economic resilience, and the AI investment boom competing with the government for capital. Louca projects the 30-year U.S. Treasury yield could rise further toward 5.5%.

Beating AI Express: Surge in AI computing power demand continues to drive up storage chip prices, with upstream cost pressure starting to pass through to consumer electronics and hardware end products. Recently, Amazon has raised prices for multiple hardware products, including its best-selling entry-level Echo smart speaker, which jumped from $49.99 to $79.99 (a roughly 60% increase), and the 16GB base model e-reader, which rose from $109.99 to $149.99 (up around 36%). Before Amazon, tech firms including Apple, Microsoft, and Dell have already been responding to rising storage costs by raising product prices and cutting standard-equipment RAM in their hardware. Market research firm IDC previously warned that the global memory supply crunch could last until 2027, with the industry projecting storage chip prices may peak in 2028 before gradually declining.

Beating AI Express: SemiAnalysis founder Dylan Patel predicted in a recent podcast that by 2028, OpenAI and Anthropic could capture 70% to 80% of the world’s new AI computing power, with their combined capacity potentially exceeding 100GW. Patel noted the two companies currently account for roughly 30% of global annual new computing power, a share rising rapidly. He pointed out that leading AI labs’ business models are evolving, with AI computing power output efficiency improving significantly; Anthropic now generates around $50 million in revenue per megawatt of computing power, a figure that could climb to $100 million. This allows OpenAI and Anthropic to purchase or lease computing power at high prices of $25 million to $50 million per megawatt. More notably, Patel forecasts global AI-related capital expenditure will reach approximately $11 trillion between 2024 and 2029, with over $5 trillion needing to be financed via debt. Given AI infrastructure’s potential returns are far higher than those of traditional industries, tech giants may accept higher financing costs, which could push up overall credit rates and squeeze traditional asset valuations and highly indebted economies. Additionally, Patel believes future new computing power may not be primarily used for external model inference services, but will instead flow more toward internal R&D and model self-improvement at AI labs.