SOL falls below the $100 mark, with a 3.56% drop in the past 24 hours.

Bank of Japan (BOJ) Policy Board member Takashi Tada stated that the BOJ must consider multiple policy options instead of limiting itself to 25 basis point rate hikes each time, and may adopt a rate hike pace different from the usual roughly once every six months. Tada noted that 2026 will mark a shift in Japan’s monetary policy. With global rate hike trends driven by economic growth and artificial intelligence (AI)-related investments, the BOJ needs to implement policy approaches different from those used in the past.

According to GMGN market data, the Robinhood ecosystem meme coin microduck currently has a market cap of approximately $32.21 million, with its token price standing at $0.0322, registering a 79.98% increase over the past 24 hours.

According to monitoring by TradingBeats, Wintermute’s address holds short positions in a total of 83 assets, with a total notional short position of approximately $125 million and an overall unrealized profit of around $1.4339 million. Excluding small "dust" positions under $10,000, it still maintains short positions in 71 assets. Its largest position is a short ETH trade: currently holding about 20,783.66 ETH short contracts, with a position value of roughly $50.13 million, using 15x cross leverage; the average entry price is around $2,442.1, and the current mark price is approximately $2,412.1, resulting in an unrealized profit of about $623,600. Other notable short positions include: SOL short position of ~$14.51 million, unrealized profit of ~$400,200; BTC short position of ~$13.12 million, unrealized loss of ~$36,900; HYPE short position of ~$7.27 million, nearly break-even with an unrealized profit of ~$2,161. The top four short positions total approximately $85.04 million, accounting for around 68% of its total short position.

According to Bitget market data, South Korea’s KOSPI index fell 4% intraday, currently at 6560.35 points. SK Hynix dropped 4.67% and Samsung Electronics declined 4.21%.

Beating AI News Flash: PwC’s latest report shows that global data center construction is entering a historic expansion phase, with cumulative related investment projected to reach roughly $32 trillion over the next 25 years. Over the five years through 2030, global data center investment is expected to total around $5.1 trillion. PwC notes that surging AI demand is creating bottlenecks for data center development across multiple areas: power, equipment, cooling, racks, construction, and delivery. The real challenge is not just the scale of capital investment, but whether these funds can be converted into reliable, usable megawatt-level computing power. The report argues that as AI infrastructure construction continues to expand, the industry needs a more unified full-stack coordination and delivery model to reduce interface risks between different segments. Additionally, PwC estimates that full-stack data center coordinators could capture approximately 5% to 8% of total project expenditure value. Based on the projected cumulative investment of $5.1 trillion through 2030, this market represents a potential opportunity of roughly $255 billion to $408 billion.

Beating AI News Brief: OpenAI’s upcoming Astra will use a recurrent reasoning architecture called recurrent depth, also known as looped transformer. Standard Transformers process each token by sequentially passing through fixed layers, while Astra allows the same information to repeatedly cycle through the same set of layers for multiple computation rounds before generating an output. ByteDance already publicly demonstrated a similar approach last year: its Seed team released Ouro, a Looped Language Model that runs a set of Transformer layers cyclically to offload more computation inside the model. This architecture boosts inference computation without requiring the model to generate longer chains of thought (CoT), enabling smaller models to leverage extra computation to achieve performance close to that of larger models. However, this design introduces a security risk: some inference occurs within internal hidden states, leaving no full textual record, making it harder to verify model compliance via CoT checks. In response, OpenAI has limited the extent of recurrent depth used in Astra to retain readable CoT, and plans to add additional CoT monitoring.