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Justin Sun's latest personal asset allocation: BTC base position unchanged, ETH reduced by 80-90% and reinvested in US stocks

Recently, TRON founder Justin Sun shared his views on personal assets outside of TRON during a 3.5-hour offline discussion with Jin Ming. The core configuration points are as follows:BTC: As a base asset, there has been no reduction in holdings over the past year. Although concerned about the impact of quantum computing and AI algorithms, believing the market has not fully priced in the related risks, he still insists on holding and has adopted a cold wallet sheltering mode for isolation.ETH: Reduced holdings by about 80%-90% in the $3600-$4000 range last year, currently retaining only about 20% of the base asset for observation, and has not repurchased. Most of the funds from the reduction have been transferred to U.S. stocks.NVIDIA: Main position in U.S. stocks, positioned as a base asset in the AI field, heavily invested and not reduced.NASDAQ 100: Heavily held. Slightly bearish in the medium term (due to capital clustering, high interest rates, etc.), but no short positions taken, and the position is retained.A-shares: No holdings. Overall bearish on traditional assets, more inclined towards AI applications and Web3 directions.In the interview, Justin Sun emphasized that ETH is an aggressive asset, BTC is a base asset, and he is optimistic about applications in AI health management, while being bearish on certain hardware sectors. The above is a summary of the interview points, not a complete balance sheet.

first_img OpenAI: Safety justification should be submitted before cutting-edge reinforcement learning training

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.
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