资本效率 · 717

Arbitrum promotes ZK settlement, and L1 withdrawals are expected to be shortened from days to hours

Comparing news, the Arbitrum development team said that zero-knowledge (ZK) proofs are being introduced into the Arbitrum platform to achieve a multi-proof settlement model. Through ZK certification, the settlement time for the Arbitrum block to Ethereum L1 is expected to be shortened from a few days to a few hours. While improving the capital efficiency of users, cross-chain bridges, and protocols, security is maintained through multiple proof mechanisms. The relevant capabilities target Arbitrum One and dedicated chains based on the Arbitrum platform, and Offchain will submit an upgrade proposal to the DAO in the future. Current developments include: it is possible to run the same state transition function as optimistically in SP1 zKVM to generate ZK proofs for real mainnet blocks; Stylus' WASM contract can be proven along with Solidity contracts; the team launched an independent Rust validator to make ZK certification a first-class model in parallel with standard verification; the BoLD settlement protocol already supports acceptance of ZK proofs and endorsements by the Quick Confirmation Committee. The original controversial game is still a fallback path, forming ZK proofs, proof committees and fraud Multi-proof architecture to prove collaboration. The team says it's continuing pressure drop to prove the cost and gradually incorporate the relevant code into Nitro's main production path. The next focus includes further optimizing certification and moving to Ret-based execution, completing L1 message inbox attestation, and connecting ZK settlement capabilities to node configurations so that each chain can be enabled as needed.

1d ago

From crypto mining farms to AI clouds: Why does a16z say the “new cloud” burns money as it grows?

Source: a16z New Media Author: Moses Sternstein, a16z Original title: Charts of the Week: Head In The Neoclouds Editor's Note: In the context of generative AI driving a new round of computing power investment, market discussions on AI infrastructure are shifting from “whether there are enough GPUs” to “who can provide computing power in a sustainable way”. When model training, inference requirements, and data center expansion became consensus, a lower-level question began to emerge: Can the rapid increase in computing power demand actually translate into stable profits and cash flow? In “Charts of the Week” published by a16z New Media, author Moses Sternstein moved in from new cloud companies such as CoreWeave, Nebius, and Applied Digital to discuss the growth, valuation, and profit conflicts of the AI computing power market, and further extended to horizontal SaaS, model routing, and cutting-edge lab talent competition. In this article, instead of simply judging whether AI demand is strong, the author breaks down current AI transactions into a set of lower level structural issues: how existing infrastructure is being repriced, why revenue growth is not simultaneously improving market expectations, and why the AI industry's competitive focus is shifting from simple expansion to efficiency and return. The first is the rediscovery of the value of infrastructure. In the past, land along railway lines, gas pipelines, and cable television networks all served specific industries and were later transformed into telecommunications and internet infrastructure. Today, a similar revaluation of assets happened again. Originally serving cryptocurrency mining, some new cloud companies already have operating experience with electricity, computer rooms, cooling systems, and high-density computing; after the outbreak of AI demand, these capabilities were quickly transformed into scarce computing power supplies. The point is that AI infrastructure competition doesn't start entirely from scratch; early advantages often come from a recombination of old assets, energy resources, and engineering capabilities. Second, high revenue growth and profit uncertainty coexist. The early revenue growth rate of new cloud companies such as CoreWeave once surpassed the initial stages of cloud giants such as AWS, but the capital market did not receive the same level of recognition. The reason is that the new cloud is not a typical asset-light software business. GPU procurement, power access, data center construction, chip depreciation, and debt interest will rise simultaneously with scale, or even faster than revenue. This means that revenue expansion can only prove that AI computing power is in high demand, but it cannot automatically prove that the business model has a sufficiently high return on capital. What the market is really waiting for is whether these companies can turn orders and revenue into sustainable free cash flow. Third, the value of software is being re-differentiated according to the impact of AI. In the past, the market feared that generative AI would generally weaken SaaS companies' moats, but Atlassian's performance suggests AI could also be a tool to increase customer spend and product stickiness. At the same time, cybersecurity and observability software continues to receive valuation premiums as AI expands potential risks and increases companies' reliance on proven solutions. This means that the so-called “end of SaaS” will not happen evenly. Whether AI is an alternative product, lower prices, or expand demand, is becoming the new standard for software valuation differentiation. Fourth, AI applications are shifting from “stacking tokens” to optimizing tokens. In the past, companies often preferred to directly call the most capable models or give engineering teams a budget to test on their own; now, companies such as Databricks have begun to use intelligent routing to match models with different prices and performance according to the difficulty of the task to reduce costs while maintaining results. A decrease in the unit price of tokens does not necessarily mean a contraction in total AI spending: as unit costs decrease and application scenarios increase, total token consumption and overall market size may continue to rise. Efficiency and demand are not mutually exclusive, but may form a mutually reinforcing cycle. If I were to reduce this article to one judgment, it would be: AI infrastructure has proven itself to generate rapid growth, but the next phase of success or failure will depend on whether the company can transform growth into greater capital efficiency. In this sense, the topic discussed in this article is not only whether CoreWeave can become the next generation of cloud giants, but whether the entire AI industry can move from expanding computing power to sustainable commercial returns...

5d ago22#a16z

CoreWeave and Nebius financial reports reveal AI cloud computing power trends: supply shortages continue, CSP is moving towards an “AI infrastructure operating system”

Comparing news, analyst Qinbafrank said in an article on the X platform that CoreWeave (CRWV) and Nebius's latest financial reports show that the AI cloud computing power (CSP) industry is entering a stage of rapid expansion, and the focus of industry competition is shifting from simply providing GPU leasing to building an AI infrastructure platform covering computing power, software, data, and operational capabilities. Currently, demand for AI computing power still clearly exceeds short-term deliverable supply. At the same time, AI computing power pricing power is being strengthened, but price increases are mainly focused on high-value resources. CoreWeave said that the price of various GPU computing SKUs generally increased by about 25% in July; Nebius revealed that the price of previous-generation GPUs increased by more than 30% compared to the first quarter, the average annualized revenue of new contracts in the second quarter exceeded 20 million US dollars per MW, some projects reached 20 million to 25 million US dollars, and short-term emergency capacity prices even reached 40 million to 50 million US dollars per MW. However, the price increase mainly occurred in short-term capacity, next-generation GPUs, large-scale clusters, and production-level AI inference scenarios. The raw computing power of traditional low priority and long-term price locking did not rise at the same time. Judging from the profit model, the project-level return on AI computing power is becoming more clear, but the company's overall return on capital (ROIC) still needs time to be verified. Nebius revealed a more clear project payback cycle for the first time, and CoreWeave reduced the pressure on GPU investment through long-term contracts and asset-level financing. However, the two companies are currently still in a phase of high capital investment, and depreciation and financing costs continue to reduce profit margins. However, more and more individual projects can close the economic model, which means that the AI infrastructure business model is gradually maturing. Additionally, both CoreWeave and Nebius are upgrading to an “AI infrastructure operating system.” In the future, CSP competition will not only rent GPU hours, but will cover complete service systems such as AI training, inference, storage, networking, model deployment, monitoring, security governance, and agent operation environments. In terms of capital models, the two companies are also taking different paths: Nebius favors an asset-light model, investing in the construction of AI data centers through capital partners to provide AI infrastructure operations and software capabilities; CoreWeave promotes a hybrid cloud model through the Omni strategy, deploying a complete AI cloud platform to the customer's own data center and GPU resources, and places more emphasis on enterprise-level and sovereign AI delivery. Overall, the AI cloud computing power industry is evolving from a “GPU renter” to an “AI infrastructure platform.” Short-term supply constraints will still support computing power prices, while long-term competitive focus will shift to capital efficiency, software capabilities, and whether it can become an infrastructure operating system in the AI era.

9d ago

Bitget launches $300 million Archimedes program to provide dedicated funding to quantification and asset management agencies

In comparison, Bitget announced the launch of “Project Archimedes (Project Archimedes)” to set up a special fund with a total size of 300 million US dollars to provide capital support to quantitative trading companies, asset management institutions, and market makers. The plan includes two sub-projects: the US$100 million “Financial Support Plan”, which focuses on supporting emerging and growing quantitative institutions using market-neutral strategies; the US$200 million “Interest-free Loan Program” targets institutions with mature strategies and a certain transaction scale. They can obtain interest-free funds if they meet the corresponding transaction volume or position criteria to reduce financing costs and expand the scale of the strategy. Bitget CEO Gracy Chen said that as competition for institutional transactions intensifies, capital, execution efficiency and risk control are becoming important factors in whether strategies can be scaled up. The Archimedes Program hopes to help mature teams scale up their strategies through capital support, and is expected to support more than 50 projects over the next six months. At the same time, relying on the Bitget Unified Account (UTA), institutions can use rToken spot positions as derivatives margin, and can simultaneously maintain tokenized stock exposure and contract strategies without cross-account transfers, further improving capital efficiency. Archimedes plans to adopt a long-term cooperation framework, implement rolling admission and phased deployment. In the future, progress in the number of participating institutions, scale of funding deployment, and strategic distribution will be disclosed regularly. This article is sponsored by GENG, Build Your Fortune on GENG (https://geng.one)

10d agoburnking

Bitunix Analyst: Non-agricultural disruptions are compounded by Japanese and US intervention, global assets are once again facing high capital cost constraints

Comparing news, non-farm payrolls in the US unexpectedly fell by 23,000 in July, the first negative increase since February this year. Although the unemployment rate fell to 4.1%, the total non-farm payrolls data for May and June were drastically revised, indicating that the resilience of the US job market is weakening. This makes the Fed's policy trade-off between inflation and employment more complicated. In particular, differences among officials over interest rate hikes have widened recently, and the risk premium of monetary policy will still be reflected in US bond yields and dollar asset valuations. Meanwhile, the summary of opinions from the Bank of Japan's July meeting sent a stronger signal of interest rate hikes. Some members believe that a more flexible or even more active approach to policy normalization should be adopted. The weak yen prompted Japan and the US to rarely intervene in the foreign exchange market, showing that the exchange rate issue is no longer just Japan's own monetary policy issue, but is gradually affecting US debt holdings, US dollar liquidity, and the global arbitrage trading structure. If expectations for subsequent interest rate hikes in Japan heat up further, the cost of Japanese yen arbitrage capital rises, it may also increase fluctuations in highly valued and highly leveraged assets. US debt is in another critical position. Besent recently supported the Japanese yen's intervention, discussed FIMA liquidity instruments, and adjusted long-term bond issuance statements, all essentially pointing to reducing the pressure on the long-term US bond market. However, in an environment where fiscal deficits, inflation, and energy costs are still high, the support that the Treasury can provide is limited. What really determines the long-term yield is still the path of inflation, the Federal Reserve's policy, and the market's pricing of US fiscal sustainability. The industrial side, on the other hand, presents a completely different picture. Demand for SpaceX, AI servers, HBM, and NAND is still booming, and corporate capital expenditure continues to expand, but SanDisk and Western Digital stock prices plummeted after earnings reports, reflecting that the question is no longer just whether performance has increased, but whether the company can continue to exceed already extremely high market expectations. The AI industry's core contradictions continue to focus on capital efficiency and affordability. Therefore, what the market really needs to observe this week is not a single data, but whether cooling employment can offset the pressure of inflation and fiscal factors on long-term interest rates, and whether AI's high capital expenditure can continue to be converted into sufficient cash flow to support high valuations. The US July CPI announced on Wednesday will be an important verification. If inflation is still sticky, weak agriculture may not be enough to provide room for a continued downward trend in interest rates; conversely, if inflation cools down at the same time as employment, the pressure of high interest rates on global risk assets will have a chance to be substantially relieved. Overall, global assets are still in an environment where high financial demand, high capital expenditure, and high capital costs coexist, and volatility and asset differentiation are expected to remain high.

11d ago

Bitunix Analyst: Non-agricultural disruptions are compounded by Japanese and US intervention, global assets are once again facing high capital cost constraints

Comparing news, non-farm payrolls in the US unexpectedly fell by 23,000 in July, the first negative increase since February this year. Although the unemployment rate fell to 4.1%, the total non-farm payrolls data for May and June were drastically revised, indicating that the resilience of the US job market is weakening. This makes the Fed's policy trade-off between inflation and employment more complicated. In particular, differences among officials over interest rate hikes have widened recently, and the risk premium of monetary policy will still be reflected in US bond yields and dollar asset valuations. Meanwhile, the summary of opinions from the Bank of Japan's July meeting sent a stronger signal of interest rate hikes. Some members believed that a more flexible and even more active approach to policy normalization should be adopted. The weak yen prompted Japan and the US to rarely intervene in the foreign exchange market, showing that the exchange rate issue is no longer just Japan's own monetary policy issue, but is gradually affecting US debt holdings, US dollar liquidity, and the global arbitrage trading structure. If expectations of Japan's subsequent interest rate hikes heat up further, the cost of Japanese yen arbitrage funds will rise, which may also exacerbate fluctuations in overvalued and highly leveraged assets. US debt is in another critical position. Basent's recent support for yen intervention, discussions on FIMA liquidity instruments, and adjustments to long-term treasury bond issuance statements are all essentially aimed at reducing the pressure on the long-term US bond market. However, in an environment where fiscal deficits, inflation, and energy costs are still high, the support that the Treasury can provide is limited. What really determines the long-term yield is still the path of inflation, the Federal Reserve's policy, and the market's pricing of US fiscal sustainability. The industrial side, on the other hand, presents a completely different picture. Demand for SpaceX, AI servers, HBM, and NAND is still booming, and corporate capital expenditure continues to expand, but SanDisk and Western Digital stock prices plummeted after earnings reports, reflecting that the question is no longer just whether performance has increased, but whether the company can continue to exceed already extremely high market expectations. The AI industry's core paradox continues to shift to capital efficiency and affordability. Therefore, what the market really needs to observe this week is not a single data, but whether cooling employment can offset the pressure of inflation and fiscal factors on long-term interest rates, and whether AI's high capital expenditure can continue to be converted into sufficient cash flow to support high valuations. The US July CPI announced on Wednesday will be an important verification. If inflation is still sticky, weak agriculture may not be enough to provide room for a continued downward trend in interest rates; conversely, if inflation cools down at the same time as employment, the pressure of high interest rates on global risk assets will have a chance to be substantially relieved. Overall, global assets are still in an environment where high financial demand, high capital expenditure, and high capital costs coexist, and volatility and asset differentiation are expected to remain high.

12d ago

Western Union officially launched Stablecard stablecard payment cards, covering 37 markets in the first batch

According to the news, global remittance giant Western Union (Western Union) announced the official launch of its Stablecard based on Rain's stablecoin infrastructure. Users can directly receive USDPT, a stablecoin issued by Western Union and issued by Anchorage Digital Bank on Solana, and instantly spend at Visa-enabled merchants around the world through integrated Visa cards, and can also choose to withdraw cash. Stablecard first covered 37 markets, and Western Union plans to expand to more than 60 markets by the end of the year. The company said that stablecoins will help it reduce the need for advance funding for cross-border remittance transfers, improve capital efficiency, and support the launch of more digital financial services.

18d ago

179% during the year! RWA has skyrocketed, are institutions really entering the market?

Source: Token Dispatch Author: Vaidik Mandloi Compiled and edited by: BitPushNews Real World Asset (RWA) tokenization has soared 179% this year. Hyperliquid's trading volume on stocks and commodities now surpasses even crypto tokens. Everyone seems to have finally come to the conclusion: traditional finance (TradFi) is finally about to fully enter the chain. But when you go back to the roots and find out who is actually buying these RWAs, you'll find that this isn't a grand story of institutions entering the market at all — because most of the money actually comes from within the crypto industry. The treasury of major agreements and DAOs is frantically hoarding stocks and converting their reserves into tokenized US debt. This article will explore in depth: why this RWA spree looks more like a “dollarization” event of cryptocurrencies themselves than an institutional downgrade attack; and what it actually means when crypto protocols themselves become the biggest buyers of these tokenized US bonds. Who is actually trading RWA? Let me take you back to a few years ago: if you follow DeFi in 2020 and 2021, you'll see simply outrageous returns. Lending pools attract dollar deposits with an annualized yield of 15% to 20%, sometimes as high as 40%. Tens of billions of dollars are pouring in, yet almost no one is questioning where this money actually came from. Because these benefits come from token emissions — the agreement mints its own governance token, distributes it as a “reward” to depositors, and counts this subsidy as revenue. This is the ultimate trick to attract investors and increase TVL (total hedged value), but it only works on one condition: the token price must continue to rise. Later, when the market crashed and governance tokens plummeted by 80%-90%, the real organic yield of DeFi was actually only 2%-3%. This is even less than the yield on US short-term treasury bonds, and the risk is much higher. This revealed the harsh truth: the crypto world took years to build a financial system that simply couldn't generate competitive returns from its own economic activity. Because those benefits come from fresh capital to buy governance tokens, not from any productive use of the capital itself. Once the inflow of this new capital slows down, the entire model will return to its original form. As a result, major agreements can only protect treasury worth hundreds of millions of dollars, denominated in self-governing tokens, but are unable to earn any competitive returns within the crypto world. Then in 2023, many tokenized versions of US Treasury bonds and dollar credit products began to be launched on the chain; for the first time, the agreement was able to deposit reserves into assets that can earn real dollar returns without even leaving the on-chain ecosystem. Since then, this has become the norm. A recent study of on-chain buyers by Arrakis tracked a total of $91.3 billion in deposits across more than 10 tokenized dollar yield products. They found that of the $124 billion in high-profile buyer funds that can be clearly attributed, the full two-thirds are solely treasury funds attributable to crypto protocols and DAOs. The rest is scattered among local crypto investors, exchanges, and market makers. (Image source: Arrakis) Of the funds being tracked, the amount from institutions such as pensions, asset management companies, or banks was zero. In this $36.2 billion market, which is constantly being hyped up by the industry as “institutional entry,” traditional investors are simply hard to find. BlackRock has launched a BUIDL fund aimed at bringing institutional funding to Ethereum. It's a fully regulated, tokenized treasury bond fund with a risk-free interest rate, designed specifically for pensions, so they can buy cryptocurrency directly without explaining it to the board of directors. But as of today, 98% of the capital is in the hands of local crypto buyers. Ethena alone accounted for more than half of the fund's total value through its USDTB product. The remaining seats of the top 10 holders were also split by agreements such as Ondo and Sky's Spark Sub DAO. (Image source: Arrakis) BUIDL is no exception; looking at the entire market, almost every top five holder of a tokenized RWA product controls more than 90% of the supply. If you want to anticipate the future, the best example is MakerDAO. In 2021, the agreement held only around 17 million DAI in real-world assets. And today, this...

24d agoWendy#HYPERLIQUID #RWA topics #tokenize

Bitunix Analyst: Raising interest rates or not is not the end; the market trades as a function of Walsh's policy

Comparative news is that on the eve of the Federal Reserve's interest rate decision, the market is no longer just a dualistic choice of raising interest rates or standing still, but a change in the monetary policy decision-making model. Walsh continues to downplay forward-looking guidance, causing the market to lose the basis for predicting policies in the past. Wall Street can only find answers on its own through probabilistic trading and hedging positions. This is also the reason why unclosed federal funds futures contracts recently hit new highs, and demand for interest rate hikes is rising at the same time. Even if most institutions still expect interest rates to remain unchanged this time, what will really influence market fluctuations will be how Walsh defines the risk of inflation, whether to accept short-term shocks caused by energy prices, and whether to establish a new policy framework through post-conference speeches. In other words, the focus of this FOMC is not interest rate results, but whether the market can gradually understand Walsh's future response function, because this will directly affect the repricing of global capital's risk premium on dollar assets. The enterprise level also revealed that capital allocation ideas are changing. Amazon chose to reduce internal AI models and focus resources on cutting-edge model research, reflecting the shift in AI competition from model quantity to model quality and resource concentration. This means that large technology companies are beginning to pay more attention to capital efficiency rather than expanding their R&D footprint indefinitely. Market attention is gradually being focused on return on investment and cash flow efficiency, and the valuation logic of highly valued technology stocks may change accordingly. Meanwhile, the situation in the Middle East remains highly sensitive. Although the US and Iran continue to seek diplomatic solutions through Oman and other third parties, and Trump also sent signals of cooperation after talks with Netanyahu, Iran's firing of missiles at US military bases, attacks on Saudi oil tankers by the Houthis, and disputes over the management of the Strait of Hormuz all indicate that the conflict is still highly repetitive. The market has yet to fully account for the worst-case scenario, so every military friction may push up the crude oil risk premium again, further affecting inflation expectations and the Fed's policy space. OPEC+ has released a signal to maintain stable production in 2026 after September, which also means that the supply side will not increase significantly in the short term. If supply in the Middle East is blocked again, oil prices will be more likely to be driven by events and amplified fluctuations. It is worth noting that South Korea's KOSPI index has been drastically reduced by more than 30% from its June high, indicating that the Asian market has taken the lead in adjusting overvalued technology stocks and the global liquidity environment, in stark contrast to US stocks, which remain relatively high. If the Federal Reserve sends a more hawkish signal than market expectations, US technology stocks may face valuation pressure similar to that of the Asian market; conversely, if Walsh keeps interest rates unchanged and continues to make decisions based on data, the market focus will quickly return to the verification of corporate earnings reports and AI capital expenditure. In the short term, what the global market is really waiting for is not an answer to interest rates, but whether the three main lines of policy framework, corporate profit, and geopolitics can work together to push back the current high risk premium.

24d ago

Bitget released rToken institutional cross-asset management guidelines, and unified accounts support over 370 types of collateral assets

Comparatively, Bitget published rToken's institutional cross-asset capital management guide, detailing how market makers, hedge funds, quantitative trading companies, primary brokers and asset management companies can use their cross-asset unified accounts (UTA) to improve capital efficiency between cryptocurrencies and tokenized US stocks. The guide focuses on portfolio construction and financing strategies, covering core transaction scenarios such as cross-asset collateral, dividend arbitrage, and borrowing structures that balance capital efficiency and risk isolation, providing a practical framework for institutions to optimize multi-asset allocation. Currently, Bitget's Cross-Asset Unified Account (UTA) supports over 370 collaterable assets, including 105 tokenized US stocks. Eligible crypto assets and tokenized stocks can enter the same margin system, share collateral and offset margin requirements, and help institutions reduce idle funds scattered in exchange and brokerage accounts. Bitget CEO Gracy Chen said that institutions do not lack access to the stock market; the real challenge is how to move capital efficiently between different markets. As tokenized assets gradually enter institutional portfolios, managing crypto assets and tokenized stocks under a unified framework will provide more possibilities for risk management and asset allocation.

24d ago