Uniswap founder responds to pools.trade test token: has waived creator fees and implemented automatic buyback and burn.

According to on-chain analytics firm Lookonchain, a wallet address created a new wallet, deposited $153,000 into Hyperliquid, and opened a 40x-leveraged long position on 97.92 BTC (valued at approximately $6.22 million), with a liquidation price of $62,825.14.

According to monitoring by Onchain Lens, a whale has deposited 60,000 HYPE tokens into Hyperliquid, selling more than 30,000 of them for $1.77 million. Two active time-weighted average price (TWAP) sell orders for HYPE remain, including a $2.1 million order with approximately 15 hours left to execute.

Derek Chiang, founder of ZeroDev and a former Offchain Labs team member, has announced he is joining Ethlabs, stating he will focus on advancing Ethereum technologies including real-time proofs, light clients, and fast finality into real-world products and commercial scenarios. Chiang noted that adoption of many Ethereum core technologies lags significantly behind their potential, citing that some cross-chain bridges still rely on multi-signature verification for Layer 1 deposits. He hopes Ethlabs will act as a "GTM (Go-To-Market) force" for Ethereum technologies, convincing developers to adopt them through technical and product strengths rather than just concepts.

GF Securities' overseas investment research arm has released a review of CoreWeave's Q2 2026 financial report, retaining a Buy rating and a $172 price target. Analysts are bullish on CoreWeave due to factors including strong demand for AI infrastructure, a 25% across-the-board price hike, a massive $129 billion order backlog providing high revenue visibility, a new financing structure supporting shorter contracts, and operating costs that are 47% lower than traditional cloud giants. The firm projects CoreWeave's 2026-2028 revenues to reach $12.7 billion, $27.3 billion, and $41.8 billion respectively, with EBITDA of $7.5 billion, $15.6 billion, and $21.2 billion over the same period.

Fu Peng, chief economist of New Fire Group, noted that Tencent’s latest free cash flow has turned negative, a trend similar to major tech firms including Alibaba, Silicon Valley’s Meta, and Google. Large-scale AI capital expenditure has disrupted the previous model of robust free cash flow paired with stable share buybacks and dividend payouts. Fu Peng added that the market’s tolerance for AI’s “investment-return mismatch” has dropped significantly since Q2 this year. Application-side revenue remains in the validation phase relative to cumulative investments of hundreds of billions of yuan, and investors need to see quantifiable long-term returns. Current investor sentiment has shifted from earlier hype to caution; the next several quarters will be a critical window. This shift means investors must see truly optimistic, quantifiable, and certain revenue growth to resolve their doubts. Thus, during this “ROI proof or disproof” waiting period, barring major external systemic shocks, the market will remain stuck in a dilemma: “can’t not invest, but also can’t invest freely”. The market will show support from fundamentals (preventing aggressive bearish bets) and scrutiny on ROI (discouraging blind bullish positions). Capital markets are waiting for a “Killer App”, while the industry is actually waiting for “breakthroughs in unit computing power costs” and “inflection points in workflow restructuring”.

Goldman Sachs notes two overinterpretations emerging in the market regarding the macro impact of AI capital expenditure: on one hand, investors are underestimating AI investment’s boost to the tech, energy and data center sectors; on the other, they are overstating the AI boom’s impact on the overall U.S. economy and investment in other industries. Goldman Sachs economists Jessica Rindels and David Mericle project in a new report that AI-related investment could reach roughly $600 billion this year, equivalent to about 2% of U.S. GDP, 10% of corporate fixed investment, and 15% of equipment investment. This size is enough to explain why AI infrastructure remains one of the most important trading themes in U.S. stocks, and why Nvidia, cloud providers, data centers, power equipment and the semiconductor chain continue to attract capital. However, Goldman Sachs also warns that AI investment’s direct contribution to GDP is not as large as headline figures suggest. The reasons: a large amount of AI equipment relies on imports and may not be fully counted in U.S. domestic output; at the same time, AI construction does crowd out some resources, with the crowding-out effect concentrated in three areas: cloud providers shifting internal budgets from traditional cloud services to AI, data center construction crowding out other commercial building resources, and AI-related debt financing driving up borrowing costs for other companies. In other words, AI investment is changing capital flows, but it is not strong enough to single-handedly rewrite the U.S. economic cycle. Goldman Sachs estimates that after accounting for direct and indirect impacts, AI’s net boost to U.S. GDP growth in 2026 may only be around 0.1 percentage points. This means AI remains a core driver of corporate earnings and structural stock market trends, but it should not be simply extrapolated as a reason for a broad acceleration of the U.S. economy.