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Noah Shinn, founder of Personal AI Agent Instinct, stated that the company's user base has been growing by about 10% to 11% daily in recent weeks, leading to a corresponding demand for computing power that is close to doubling each week. He now spends about 40% of his time considering how much computing power to purchase in advance.Shinn indicated that computing power cannot be procured in sync with user growth. Purchasing only double the current computing power would be exhausted in about a week; buying five times would run out in less than three weeks; directly purchasing ten times would mean a significant upfront investment. Many resources need to be booked months in advance, and the price for temporarily supplementing computing power could be three to four times higher. He mentioned that purchasing too little makes it difficult to support growth, while purchasing too much would amplify costs when growth slows down.He estimates that even if the daily growth rate drops from 10% to 5% to 8%, after continuous growth for 3 to 4 months, it may still be necessary to prepare computing power for a scale of 100 million users, and the waiting time for new computing power to come online would be roughly the same. He believes that this is more challenging than scaling consumer internet products like Instagram and Facebook, because each new user of the personal agent continuously consumes a large amount of model inference. He stated that Instinct's current scaling speed is even faster than similar situations encountered by Claude Code and Codex in the past.

On September 24, Zach Pandl, the research director of the cryptocurrency asset management company Grayscale, published an article in the column The Stack stating that in the wave of artificial intelligence, the demand and supply paths for the computing power required to train, run, and operate models are diverging. Grayscale believes that this imbalance favors owners of already powered and operational computing capacity and brings growth-oriented investment opportunities.Zach Pandl stated that digital demand can expand instantly, but physical infrastructure such as electricity, data centers, chips, memory, and cloud services takes years to approve, access, and build. When artificial intelligence agents perform multi-step tasks, they may consume 5 to 50 times more compute tokens than typical chatbot interactions, and increased application layer activity will transmit to the underlying computing infrastructure.The article cites data from the International Energy Agency and Lawrence Berkeley National Laboratory, stating that data centers are expected to account for about half of the growth in electricity demand in the United States by 2030, and new projects may take more than five years to connect to the grid. Even if electricity is secured, permits, specialized labor, electrical equipment, cooling systems, GPUs, high-bandwidth memory, and networks are still needed. Continuous value will flow to power producers, data center operators, and artificial intelligence cloud service providers that can convert electricity into computation.

According to CoinDesk, Ethereum co-founder Vitalik Buterin has released a long-term roadmap, referred to as Streamlining Ethereum. The vision proposes that by 2030, Ethereum will surpass a mere blockchain ledger and plans to completely restructure the protocol in the next three to four years.The transformation includes expanding transaction throughput by approximately 1000 times, introducing zero-knowledge proofs to enhance privacy and combat quantum computing threats. By 2030, the network is expected to accommodate 2TB of traditional dynamic state data, along with an additional 100TB of new scalable storage, to achieve underlying state separation and optimization. The vision also aims to establish Ethereum as an infrastructure driven by cryptographic validation, with applications extending from finance to social systems. Most developers support this plan, although some voices believe the implementation timeline is somewhat delayed.

According to Gate market data, Akamai's stock rose 21% in pre-market trading after the company signed a $11.6 billion cloud computing agreement with Anthropic.

A data center developer supported by an affiliate of Blue Owl Capital announced a bond issuance to raise $1.1 billion, betting on AI computing power, to fund the Digital Drive data center project near Richmond, Virginia. This data center has an IT capacity of 76 megawatts and will be fully leased by CoreWeave under a 15-year, $2.94 billion contract, expected to be operational from 2027 to 2028.

According to Bloomberg, a data center developer supported by an affiliate of Blue Owl Capital announced a bond issuance to raise $1.1 billion to bet on AI computing power, funding the Digital Drive data center project near Richmond, Virginia.The data center has an IT capacity of 76 megawatts and will be fully leased by CoreWeave under a 15-year, $2.94 billion contract, expected to be operational from 2027 to 2028.

According to Cointelegraph, the European Banking Authority (EBA), the European Insurance and Occupational Pensions Authority (EIOPA), and the European Securities and Markets Authority (ESMA) stated in a joint risk update that advancements in quantum computing could undermine the cryptographic systems that secure blockchain transactions, communications, and databases.Google Quantum AI researchers estimated in March that the number of physical quantum bits required to break the cryptographic techniques used by many cryptocurrencies is about 20 times less than previously estimated. There currently exists no computer capable of executing such an attack.Bitcoin developer Jameson Lopp and others proposed in February to gradually phase out the current signature scheme and to restrict the usage of untransferred funds five years after the proposal is activated; this proposal has not yet been adopted. The Ethereum Foundation plans to enable Ethereum's execution, consensus, and data layers to be resistant to quantum attacks by December 2029.

During an exclusive interview with China Central Television's finance program, Tesla CEO Elon Musk stated that China's AI large models are overall outstanding. He mentioned that, under limited computing power, the output per unit of computing power of Chinese models is already close to the world's top level.Musk assessed that China will solve the computing power bottleneck faster than most people expect. He roughly estimated that in the next 2 to 3 years, China could bridge the computing power gap relying on lithography technology and chip manufacturing capabilities.

According to Grayscale's official announcement, it has renamed its Grayscale Bitcoin Miners ETF to Grayscale AI Compute ETF, with the New York Stock Exchange trading code changed from MNRS to GCPU, and the tracking index changed from the Indxx Bitcoin Miners Index to the Indxx High-Performance Computing Index.The fund will invest in companies that build and operate AI infrastructure, with about half of the holdings in native AI computing companies, and the remainder in digital infrastructure operators that have publicly committed to transitioning to AI businesses, including companies that have transitioned from Bitcoin mining.

Tesla CEO Elon Musk stated that China's AI large models perform excellently overall, with unit computing power output performance nearly at the top globally. He believes that China will solve the computing power issue faster than expected, anticipating that within two to three years, it will close the computing power gap relying on lithography technology and chip manufacturing.

Vitalik Buterin, co-founder of Ethereum, delivered a keynote speech at the 12th Global Blockchain Summit, stating that the blockchain was initially a primary tool. Although it has characteristics such as openness, neutrality, and security, it is not scalable and is completely transparent, lacking privacy protection mechanisms.Subsequently, privacy transactions and scalable verification brought by zero-knowledge proofs have changed this situation and have been transformed from planning into actual EIPs, included in the Ethereum roadmap. Technologies such as fully homomorphic encryption are also beginning to be applied, and indistinguishable obfuscation may also be applied on-chain in the future. Beyond privacy, the parallel construction and proof of blocks can make extreme scalability possible, and industry thinking has shifted from digital assets to digital assets plus information, from purely on-chain to user-end, transaction memory pools, a complete pipeline on-chain, and from who can send what to who can see what.Currently, blockchain has gradually evolved from a simple ledger to a cryptography-based secure computing network. With the development of AI, providing cryptographic rules to AI for automated formal verification can significantly enhance verification efficiency, allowing the chain to carry more possibilities.

BlackRock released a white paper titled "Machine Native Economy: How Digital Assets Connect Intelligence, Business, and Computing." The white paper points out that the extraordinary growth of AI is a defining technological theme, and digital assets represent a parallel financial infrastructure, where both intersect in the interaction between AI systems and economic networks. AI is machine-native intelligence, and digital assets are machine-native currency. Agent-based AI uses blockchain as programmable infrastructure, connecting intelligence with economic activities and extending AI to real-world actions such as purchasing and trading.The white paper lists three overlapping areas: LLM and blockchain share similar tokenization, with the former encoding language into numerical values and the latter tokenizing economic value for machine-verifiable transfer and settlement; agent-based commerce requires machine-native payment rails, with stablecoins, native crypto assets, and on-chain assets serving as tools, while traditional rails have limitations in low-value programmable transactions around the clock; computing becomes the digital asset market, with hyperscale cloud revenue expected to exceed $1 trillion annually by 2030, and standardized computing power claims can serve as financing and programmable settlement use cases.

The launch of NVIDIA GPU rental price futures has encountered obstacles. The Chicago Mercantile Exchange originally planned to introduce computing power futures contracts in early October but is expected to be unable to obtain regulatory approval on time.The U.S. Commodity Futures Trading Commission (CFTC) is extending its review to study how the contracts operate and to solicit industry opinions.

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.