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Huobi HTX Chief Analyst Andy: U.S. inflation heats up again, market re-trades "higher for longer"

Surrounding the unexpected rebound of the U.S. September CPI, Huobi HTX Chief Analyst Andy pointed out that inflation is spreading from energy to core prices. The U.S. September CPI rose year-on-year to 3.7%, and the core CPI rose year-on-year to 2.6%. Gasoline prices increased by 9.1% in a single month, contributing to more than half of the overall increase, but prices for housing, healthcare, and airline services also rose, indicating that this rebound cannot be fully explained by energy shocks.The data reinforces the Federal Reserve's "higher for longer" policy logic. The economy has not yet shown significant signs of recession, and the Federal Reserve lacks urgent reasons to shift to easing, needing to prevent inflation expectations from becoming unanchored. After the CPI announcement, U.S. Treasury yields and the dollar quickly rose, with the market raising expectations for subsequent interest rate hikes, leading to increased uncertainty in the interest rate path.For the cryptocurrency market, this change is slightly bearish in the short term. The market first trades on real interest rates, the dollar, and global liquidity; high inflation does not automatically favor Bitcoin. After the CPI announcement, Bitcoin weakened and fell below $82,000. In the medium term, it is necessary to distinguish between "energy-driven inflation" and "demand-driven inflation": if energy prices decline and core inflation no longer accelerates, the September data is more likely to be a temporary supply shock; if core service inflation continues to rise, then the high interest rate level may be maintained longer than expected. The direction of Bitcoin depends on whether inflation, employment, and long-term U.S. Treasury yields can jointly confirm a new policy path.

first_img Hinton and over 400 scientists are calling for high-risk AI models to be defaulted to offline isolation

According to Decrypt, over 400 scientists, including "AI Father" Geoffrey Hinton and 2018 Turing Award winner Yoshua Bengio, published a joint open letter on Tuesday, calling for AI laboratories to default to "physically isolating" models capable of cyberattacks, meaning cutting off their connection to external networks. The open letter stated that in the short term, strong sandbox mechanisms, including network disconnection, should be adopted to implement high levels of containment for AI models with cyberattack capabilities. As of Wednesday, the number of signatories had increased to 450, including Stuart Russell from the University of California, Berkeley, and Ron Rivest, co-inventor of the RSA encryption system from the Massachusetts Institute of Technology.Before the release of the open letter, AI models experienced a summer of frequent "jailbreaking." In July, OpenAI admitted that GPT-5.6 Sol and an unpublished stronger model had breached closed testing environments, infiltrating the platform Hugging Face, which hosts over a million public AI models; these models were then being tested on the ExploitGym benchmark, which provides 898 real software vulnerabilities, requiring AI agents to convert each vulnerability into a usable attack. Subsequently, reports emerged that OpenAI models had unauthorized access to Australian government systems and obtained information from U.S. federal agencies.

Famous trader: Bitcoin needs to hold above $80,000 and form higher lows; otherwise, the pullback may deepen

Renowned trader Killa stated that if Bitcoin is to evolve according to the current trend structure, it needs to form a higher low above $75,500, ideally also holding the low around $80,000. This is similar to the trend in 2023: at that time, Bitcoin formed a higher high on the weekly level, then retraced and held the original range. This may explain why the recent low time frame market seems so prone to triggering stop losses, with aggressive movements and traces of algorithmic trading.Bitcoin once reclaimed its internal structure in the $82,000 range, then retraced, broke the structure again, and then reclaimed it once more. The entire market seems to be deliberately enticing traders to enter. It must be acknowledged that the initial drop to the $81,000 range was not ideal from a structural perspective. However, there were a large number of shorts concentrated at the lows, which may explain the subsequent price reaction.Another detail worth noting is that during the drop from $84,100 to $81,000, only about $300 million in long positions were liquidated; whereas previously, during the drop from $86,800 to $80,600, the scale of long liquidations exceeded $2 billion. The significant decrease in long liquidation scale indicates that the current market's reliance on long positions may be lower than before. The current market situation is quite delicate. Bitcoin needs to form a higher low at the current position to initiate a new round of upward movement; otherwise, this pullback may bring the price below $80,000. As of now, the so-called "crash protection team" has done well in maintaining the price, but it is also continuously disrupting the internal oscillation range in the low time frame.

The success rate of the Zhipu legal model is nearly three times higher, and the testing cost is about one-seventieth of that of its competitors

The artificial intelligence company Zhipu released a legal-specific model GLM-Legal, which is further trained based on GLM-5.3-Flash. In the Harvey LAB evaluation covering 24 categories of legal services, the task pass rate increased from 6.7% to 19.2%, nearly tripling the original version, surpassing models like Kimi K3 and Claude Fable 5, and ranking seventh on the Vals AI leaderboard.When reviewing software service contracts, GLM-Legal suggested shortening the termination notice period from 60 days to 30 days and required service providers to offer up to 90 days of handover support. In another software project dispute involving 40 documents, it found that the company reported to the client that the project was 60% complete, while internal records indicated only about 41%. It also identified emails from the CEO requesting to conceal risks and continue charging, and analyzed potential legal liabilities in conjunction with contract terms.According to independent evaluations by Vals AI, the cost for GLM-Legal to complete a test is $0.15, while the closely performing Gemini 4 Argon costs $10.55, a cost difference of about 70 times. GLM-Legal took an average of 72 minutes, while the latter took 44 minutes. Zhipu stated that the model was trained specifically on legal data and conducted reinforcement learning in tens of thousands of simulated legal environments. Currently, GLM-Legal requires an API trial application.
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