fet

Recently, Sun Yuchen discussed his personal communication and life strategy role models in a conversation with Jin Ming, stating that he does not simply imitate one person, but is a "hybrid mix" of Elon Musk, Donald Trump, and Warren Buffett.Sun Yuchen mentioned an interesting coincidence that all three are alumni of his alma mater, the University of Pennsylvania. As he integrates the strategies of these three individuals, the outside world has begun to jokingly refer to the emergence of a "fourth giant," namely Sun Yuchen himself. In terms of specific strategy breakdown, he stated that his investment and risk control fully absorb the value investment philosophy that Buffett and Munger emphasize on fundamentals, bringing an old-school playbook into the crypto space, which instead creates a dimensional advantage.In terms of business growth and communication, Sun Yuchen pointed out that Musk compensates for Buffett's shortcomings in technology narratives and expectation-driven approaches, forming a dual drive of fundamentals and high-tech high growth. Regarding personal branding and communication, he mainly draws inspiration from Trump, even admitting that he initially learned spoken English by following Trump's show "The Apprentice."Sun Yuchen also shared an interesting anecdote, stating that there is actually a "mutual disdain law" among these three. For example, when he once suggested to Buffett during dinner that he acquire Tesla during its low point, Buffett declined, as he believed that if a company is too disruptive and full of "surprises," it does not align with his investment style.

Anthropic recently submitted a draft AI safety bill to U.S. lawmakers, which has been questioned by U.S. AI safety advocates. The draft sets a high threshold for the government to urgently halt dangerous models, requiring that risks involve scenarios such as biological weapons, cyberattacks, or AI going out of control, and potentially causing more than 50 deaths or serious injuries, or losses exceeding $1 billion; some provisions may also limit state government regulatory authority.Nathan Calvin, the strategic head of the AI safety organization Encode, subsequently compiled Anthropic's decisions over the past year. In February of this year, the company modified its previously strict safety policy: it had previously committed to pausing training or releases if safety measures did not keep pace with model capabilities, but the new policy no longer requires the company to independently halt R&D at higher risk stages, citing that competitors might take advantage of the opportunity. During the same period, the company invested heavily in computing power, formed a chip team, and allowed Claude to take on more AI R&D tasks.On October 21, Anthropic CEO Dario Amodei posted a lengthy rebuttal on X. He acknowledged that the company had made some mistakes but insisted that competing for cutting-edge AI capabilities did not violate its safety mission, stating that the company adheres to a race-to-the-top approach, believing that only by staying at the forefront of technology can it drive the industry to raise safety standards. He listed nine safety decisions that had cost the company commercially, including restricting the Mythos-level model's open access for months and strictly limiting biology-related requests. The safety roadmap updated by the company on October 19 states that existing policies mainly target the deployment of dangerous models, and in the future, it may be necessary to directly limit the speed of R&D, with plans to propose relevant policy options by January 15, 2027, or explain why none were proposed.

NVIDIA has committed to investing $250 million over the next five years to promote research in agent safety and security in the UK. The company stated that it will provide $50 million in computing resources support for agent safety research in the UK each year.

On October 14, Jinshi Data reported that the AI company Anthropic has requested the federal appeals court in Washington to reconsider the ruling made on September 25. This ruling upheld the Trump administration's decision to classify Anthropic as a supply chain risk enterprise that poses a threat to U.S. national security.Anthropic argued that the judge misinterpreted the law, allowing the Department of Defense to take action without any prior determination procedures. The company also stated that there is clear evidence indicating that this classification is retaliation for its statements on artificial intelligence safety issues.

Federal Reserve Governor Waller stated that the safety premium of U.S. Treasury bonds no longer exists.

ByteDance Seed recently sent an internal email announcing the establishment of the Seed Model-Alignment & Preparedness department, which will be led by Yan Lin and report to Seed head Wu Yonghui. Members of the original Responsible AI team and some members of the Chat team will transition to the new department.The department aims to strengthen investments in safety and alignment. The core of alignment is to ensure that model behavior is consistent with human intentions and safety constraints, in order to enhance the safety capabilities of Agent Models and prevent risks from cutting-edge capabilities such as chemical, biological, radiological, and nuclear threats.

According to PRNewswire, the publicly traded company Universal Safety Products (UUU) announced that it will be renamed DeFi Capital Markets on October 19 and will trade under the new ticker "DCM." In the future, it will gradually cease its original product business and focus on asset tokenization, digital assets, market making, as well as quantitative trading and blockchain financial markets.

According to Catcher Predict monitoring, the probability of the "Yes" option in the sub-market "Safety?" for the event "Falcons vs. Saints" on the Polymarket has experienced significant fluctuations, rising from 7.5% an hour ago to the current 26.5% (a fluctuation of 19%). Please note the impact of related breaking news.

According to SoSoValue data, last week's trading days (Eastern Time from September 28 to October 2) saw a net outflow of $138 million from Ethereum spot ETFs.The Ethereum spot ETF with the highest net outflow last week was Fidelity ETF FETH, with a weekly net outflow of $74.0624 million; currently, FETH has a historical total net inflow of $2.35 billion. The second was Grayscale Ethereum Trust ETHE, with a weekly net outflow of $27.8910 million; currently, ETHE has a historical total net outflow of $5.44 billion.The Ethereum spot ETF with the highest net inflow last week was 21Shares ETF TETH, with a weekly net inflow of $1.742 million; currently, TETH has a historical total net inflow of $32.88 million.As of the time of writing, the total net asset value of Ethereum spot ETFs is $17.46 billion, with an ETF net asset ratio (market value compared to Ethereum's total market value) of 5.37%, and the historical cumulative net inflow has reached $13.80 billion.

The White House Artificial Intelligence Agreement shows that Nvidia, OpenAI, Anthropic, xAI, and Google will regularly discuss artificial intelligence safety standards.

On September 28, 2026, OpenAI published a safety-related article, stating that before continuing any cutting-edge reinforcement learning training, structured safety documentation should be required. Ideally, such documentation should reach the evidence-based structured risk argument level used in safety-critical industries like aviation and nuclear power. OpenAI views this as a direction for effort while acknowledging the complexity arising from the emergence of AI capabilities, making it difficult to achieve the same level of rigor.The article focuses on cutting-edge reinforcement learning training and does not cover the broader alignment attributes required for internal and external deployments. The recommendations in the article reflect current practices, which are expected to continue evolving and are being implemented internally at OpenAI. Technical safeguards should cover model alignment, isolation, and monitoring, including avoiding speculative positive reinforcement rewards, offline alignment assessments and stress testing, preventing automated scorers from seeing thought chains, as well as multi-layer infrastructure security, sandbox red teaming, limiting high-bandwidth cross-sample communication, and immutable preservation of agent records.Operational guidelines include preemptive dissent across teams, approvals that can be vetoed by senior leadership, accountability of training leads for safety arguments and incident responses, as well as fail-safe pauses, internal oversight, audit access, and escalation by severity. In response to serious misalignment events, OpenAI proposes controlled access to original records, root cause analysis, operational and cultural reviews, and treating incident-derived assessments as regression tests; results of investigations should be made public, along with reviews and operational changes, and affected third parties should be notified as soon as possible.

According to The Wall Street Journal, OpenAI has canceled plans to release a new artificial intelligence model due to safety concerns.

Market news reports that Anthropic is collaborating with NVIDIA (NVDA.O) to conduct research on AI safety.

According to Catcher Predict monitoring, in the Polymarket event "Vikings vs. Buccaneers," the sub-market "Safety?" saw the probability of the "Yes" option fluctuate dramatically, soaring from 26.5% an hour ago to the current 50% (a fluctuation of 23.5%). Please note the impact of related breaking news.

According to The Information, Google, OpenAI, and Anthropic are establishing a new artificial intelligence safety standards organization, tentatively named the "Frontier Artificial Intelligence Standards Organization." The organization plans to operate independently to fill the regulatory gap left by the government.

Arthur Hayes published a new long article titled "Safety First," with the core argument that the claims of "safety first" by Anthropic, OpenAI, and SpaceX, which lead to a slowdown in AGI development, are not out of concern for human welfare but rather due to economic realities. The market does not want AI; it wants AI at "Chinese prices," meaning it needs intelligence that is 100 times cheaper than what is currently available. Hayes points out that "safety first" essentially destroys the demand for computing power. If the spending on training new models decreases and laboratories shift towards efficiency optimization, customers will spend less on computing power. The three major AI laboratories do not generate any profits, and their demand for computing power supports over $10 trillion in investment-grade debt and hundreds of billions in low-quality debt, which rely on profitable tech companies like Nvidia, Broadcom, Google, and Microsoft for off-balance-sheet endorsements. The real backstop is the holders of insurance policies in the United States.Hayes cites an analysis by Nick Nameth that reveals a "self-insurance scam": private equity giants (such as Apollo, KKR, Brookfield, etc.) acquire insurance companies, stuffing AI data center debt and SaaS private credit impacted by AI into insurance assets, and then provide false endorsements with minimal capital through affiliated self-insurance reinsurance companies. Nameth estimates that the total amount of these false reinsurance assets reaches $1.54 trillion. Once the AI data center debt is downgraded by rating agencies due to insufficient demand for computing power, insurance companies will be forced to add capital, while the affiliated reinsurance companies will be unable to pay, leading to insolvency for the insurance companies. In most states in the U.S., the insurance protection limit is only $250,000 to $300,000, and existing insurance companies only pay into the protection fund afterward, which encourages all parties involved to maximize risk-taking. When AIG was bailed out in 2008, TARP funds ultimately flowed to Goldman Sachs and led to record bonuses, while the general public only received foreclosure notices; Hayes believes this scenario will repeat itself.For cryptocurrency investors, the conclusion is a win-win situation. If the U.S. government chooses to become the "last buyer of computing power," it will print money in the name of national security to fund unproductive economic goods, driving up financial speculation and Bitcoin prices; if the government chooses to bail out insolvent insurance companies, it will also need to print money to cover bad AI debts, increasing the money supply and pushing up Bitcoin. Hayes specifically points out that the Federal Reserve voted unanimously last week to raise interest rates by 25 basis points, and RMP bond purchases have stopped since August 14, but commercial banks have taken over to create over $100 billion in currency, and the interest rate hike allows banks to earn an additional $7.5 billion in excess reserve interest each year. This money will be used to expand loans and market speculation, and the net effect remains stimulative. The fluctuations in the cryptocurrency market, which saw a slight increase at the end of August, are about to end, the supply of dollars will continue to grow, and Bitcoin and some selected altcoins will rise. Hayes also described this situation as "incredibly wonderful," stating that the government will not allow the free market to stop building AI data centers, there will be an oversupply of spot computing power, the usage of AI agents will increase, and the surge in money printing will drive investors to chase cryptocurrency assets.

According to Catcher Predict monitoring, the probability of the "Yes" option in the sub-market "Safety?" for the "Giants vs. Rams" event on the Polymarket has experienced significant fluctuations, soaring from 9% an hour ago to the current 50% (a fluctuation of 41%). Please note the impact of related breaking news.