putin

The Kremlin stated that Russian President Vladimir Putin had a phone call with U.S. President Donald Trump, which lasted for one hour. Trump focused on ending the Ukraine conflict as soon as possible, and both sides evaluated the visit of the U.S. envoy. Putin supported the restoration of U.S.-Russia relations and discussed prisoner exchange matters.

The Ministry of Industry and Information Technology of China released the "14th Five-Year Plan for the Development of the Information and Communication Industry," setting the national intelligent computing power target at 9800 EFLOPS by 2030. According to data from the National Bureau of Statistics, as of the end of July, the national intelligent computing scale was approximately 2450 EFLOPS (FP16), indicating that it needs to expand by about 4 times in the next four years based on this standard.The plan proposes an orderly deployment of intelligent computing clusters with tens of thousands and hundreds of thousands of cards, building inference computing power for different scenarios, and increasing the adaptation of domestic computing power chips. Currently, 52 intelligent computing facilities with more than ten thousand cards have been established nationwide. The Ministry of Industry and Information Technology disclosed that the intelligent computing power scale at the end of June was 2185 EFLOPS, a year-on-year increase of 177%. The plan uses 1590 EFLOPS in 2025 as a benchmark, aiming to reach 9800 EFLOPS by 2030, which is an expansion of about 6.2 times. During the same period, the cumulative investment target for information infrastructure in the information and communication industry is 3.8 trillion yuan, which also includes communication networks and is not all allocated for AI computing power.

According to statistics from The Information, Anthropic has signed at least 14.8GW of computing power in the past 11 months, which can be gradually utilized in the coming years. Based on currently public contracts, the potential total cost could reach up to $517 billion, with most expenditures occurring over the next decade. In addition to the 1-2GW already secured before October last year, the total computing power signed by Anthropic is approximately 16GW.This round of expansion is primarily driven by the demand for Claude. This year, the growth of Claude Code and Cowork has exceeded Anthropic's expectations, prompting the company to focus on acquiring computing power. The new agreement with Amazon provides up to 5GW, while Google and Broadcom offer another 5GW, and Microsoft and NVIDIA provide approximately 1GW. Anthropic has also rented all computing power from SpaceX's Colossus 1, acquiring over 220,000 NVIDIA GPUs, including H100, H200, and GB200.Anthropic has secured about 16GW, with many contracts extending beyond 2030. OpenAI has set a target for investors to reach 30GW by 2030, expecting to invest approximately $750 billion in computing power by that year. The $517 billion figure is the potential maximum cost estimated by The Information based on existing cloud services, chip, and data center contracts, with some computing power potentially being delayed or unused, and some contracts allowing for early cancellation.

According to Xinhua News Agency, the Bank of China Inner Mongolia Branch recently issued the first "Computing Power Token Loan" in the Inner Mongolia Hohhot New Area. By innovating the credit evaluation system, it incorporates the scale of enterprise token usage, computing power cooperation orders, core technology qualifications, and scientific research and development capabilities into the core credit indicators, while weakening traditional mortgage guarantee requirements.Currently, Hohhot's computing power scale reaches 150,000 P, of which intelligent computing accounts for 146,000 P, and the green computing power index ranks first in the country. The local area also promotes "Computing Power Loans" and medium-to-long-term technological transformation loans, using policy financial tools and syndicated loans to address capital and large fund needs, and has launched "Computing Chain Insurance" and cybersecurity insurance to provide diversified risk control guarantees for heavy asset projects such as data centers against fire, water damage, machine failure, and more.

On Wednesday, local time in the United States, NVIDIA CEO Jensen Huang appeared at the G20 Innovation Ministerial Meeting and engaged in a fireside chat with U.S. Secretary of Commerce Gina Raimondo. Huang stated that AI is evolving into a national economic infrastructure similar to electricity and the internet, and the biggest risk countries face is not sufficiently investing in and adopting AI, ultimately being left behind by the industrial revolution.Huang mentioned that currently, building 1 gigawatt of AI infrastructure requires an investment of about $50 billion to $60 billion, and he expects that from now until the end of this decade, the scale of related construction will reach approximately 100 gigawatts. He also referenced the "five-layer cake" model of energy, chips, infrastructure, models, and applications, indicating that every country needs to build AI infrastructure and decide which aspects they wish to participate in.Huang anticipates that in the coming years, AI will essentially achieve AGI, and the next phase will transition from large language models to intelligent agents and embodied intelligence. He believes that AI is more likely to replace tasks rather than completely replace jobs, with operational tasks such as writing and information processing potentially becoming gradually automated.

Arthur Hayes-led AI agency project Flop Labs announced that it is building a GPU computing power exchange, allowing the buying and selling of computing power resources. Flop Labs pointed out that NVIDIA's quarterly data center computing power sales reached $89 billion, a year-on-year increase of 117%, but GPU pricing is still not standardized, and users need to contact suppliers directly for quotes.On Flop, AI agents purchase computing power from miners using FLOP tokens, and validators re-execute some of the completed computational work to verify the pricing basis used in the transactions.

According to CoinDesk, AI company Anthropic has reached a $35 billion computing power procurement agreement with Lambda, an AI cloud service provider supported by NVIDIA, part of which will be provided through Bitcoin mining company Hut 8's Beacon Point facility located in Nueces County, Texas.Hut 8 previously disclosed that the facility has signed two long-term leases for 15 years, covering 704 MW of IT capacity, with a total contract value of $19.6 billion, but the identity of the tenants had not been disclosed earlier. The facility spans 525 acres and has a maximum power access capacity of 1 GW with ready grid connections.According to the currently disclosed transaction structure, NVIDIA holds the lease for the facility, Lambda will deploy NVIDIA chips, and Anthropic will procure the computing power generated from this. Hut 8 has not confirmed how much of the 704 MW capacity is related to Lambda and Anthropic, nor has it confirmed whether NVIDIA is the previously undisclosed tenant.As the demand for power and computing resources in AI data centers surges, Bitcoin mining companies with ready power and grid access capabilities are accelerating their transformation into AI infrastructure. Mining companies including Hut 8, TeraWulf, IREN, and Bitdeer have all entered the AI data center market by selling or leasing computing power and power infrastructure.Hut 8's revenue for the second quarter of this year was $74.93 million, a year-on-year increase of 81%; its two leases at Beacon Point have a total value of $19.6 billion, and the facility is expected to generate approximately $1.31 billion in operating profit annually once fully operational.

The B.AI platform's daily token throughput has surpassed 1.1 trillion, achieving a 52,133-fold exponential growth compared to the 21.1M on the first day of its launch on April 9, completing a leap from millions to trillions in computing power architecture in less than 5 months.Since the launch of the free activity, the platform's daily throughput has increased by approximately 242 times, with a cumulative throughput of 6.86 trillion over 14 days and over 196,000 new API users. In the face of a continuous influx of massive high concurrency and heavy Agent workflows, the B.AI foundational infrastructure has demonstrated excellent stability—this steep growth curve is a testament to the trust that global developers have placed in the B.AI computing power infrastructure through real code and high-frequency API calls. B.AI will continue to build the world's preferred inclusive computing power and Agent financial foundation.

In August this year, China's leading AI chip companies released their semi-annual reports in quick succession. Last Friday, Biren Technology announced its financial report, achieving nearly 20 times revenue growth. Leading companies such as Cambricon and Moore Threads also saw significant revenue increases.Currently, the high-end computing power supply side is experiencing a tight situation across the entire chain, with some companies' orders already scheduled for three years later. The downstream intelligent computing centers are facing a shortage of resources, and the midstream system integration segment is also seeing a dense release of orders. According to industry research data, the demand for domestic AI chips in 2026 is expected to be around 4 million units, with actual deliveries around 3 million units, resulting in a capacity gap of over a million units.

According to The Information, the Trump administration is studying a new regulatory rule for artificial intelligence chips aimed at limiting the remote acquisition of computing resources for advanced AI chips in the United States.The report states that after Trump begins his second term in 2025, the White House announced the withdrawal of the AI Diffusion Rule introduced during the Biden administration. Currently, a small team within the U.S. Department of Commerce is working on a smaller, more targeted alternative plan to restrict geopolitical competitors from accessing advanced AI chips through remote methods such as cloud computing.The rule is still in the drafting stage, and specific restrictions have not yet been clarified, but it indicates that the U.S. government may re-strengthen regulations on the cross-border acquisition of advanced AI computing power, focusing on the possibility of circumventing physical chip export restrictions through remote computing services.

According to the Securities Times, on August 27, the reporter learned from SenseTime that SenseTime's large device provides domestic computing power support and Token services for the GLM-5.3-Flash recently launched and open-sourced by Zhipu, which is considered another benchmark for the large-scale commercial use of domestic computing power. It was revealed that the daily average Token service volume of SenseTime's large device reached 24.2 trillion in July, and it is expected to exceed a daily average of 100 trillion by the end of 2026.

On August 27, NVIDIA announced its financial report, with Q2 revenue for FY2027 at $96.2B, a year-on-year increase of 106%. Data center revenue was $89.0B, a year-on-year increase of 117%. The Q3 revenue guidance is $108B, and it anticipates a revenue growth of about 70% for FY28. WEEX TradFi shows that the AI hardware sector is also rising, with Lumentum (LITE) up 10.16% in 24 hours, Arm (ARM) up 8.19%, and Western Digital (WDC) up 7.82%.WEEX Labs believes that this round of market activity is driven by "AI inflection point + high growth guidance," with funds spreading from GPUs to optical interconnects and storage. The short-term main focus remains on re-pricing guidance, and caution is advised regarding volatility after expectations are fulfilled.

NVIDIA CEO Jensen Huang stated that the future size of the non-hyperscale cloud computing market may exceed the current total capacity of cloud computing.

NVIDIA's Chief Financial Officer stated that the company will provide selective credit enhancement support for another cutting-edge AI laboratory's nearly 2 gigawatt computing power project.

NVIDIA (NVDA.O) Chief Financial Officer stated that for every 1 gigawatt of computing power deployed by Vera Rubin, there corresponds approximately $40 billion in revenue opportunities.

NVIDIA CEO Jensen Huang stated that computing power is revenue now.

Anthropic has agreed to pay Nscale $45 billion over the next 6 years to rent approximately 460 megawatts of AI computing power at its Monarch data center in West Virginia. Nscale will use NVIDIA's Vera Rubin chips, which are expected to be operational by the end of next year. The project was originally planned to be rented by Microsoft, but after Microsoft withdrew, Anthropic became the tenant.

One-stop AGI infrastructure platform B.AI released the latest ecological data: On August 25, the number of new API users reached 23,432 in a single day, with a total of 79,500 new API users during the period; the single-day token throughput exceeded 594.71 billion, with a total of 74.108 million calls during the period and a cumulative throughput of over 3 trillion tokens.Two core indicators simultaneously refreshed historical records. The continuous influx of developers and explosive growth in computing power demand confirm B.AI's exceptional stability and extreme cost-effectiveness in high concurrency and complex load scenarios. B.AI is committed to becoming the preferred inclusive computing infrastructure for global teams, accelerating the comprehensive implementation of AGI.

Amazon officially announced that it has signed a definitive agreement to acquire the data infrastructure company DuckLabs. The transaction is expected to be completed after meeting customary closing conditions. Upon completion of the acquisition, the DuckLabs team will join Amazon Web Services (AWS). DuckLabs is the development company behind the open-source analytical database DuckDB.AWS stated that both parties have been collaborating since 2024, and this acquisition aims to combine DuckLabs' technical capabilities in database engineering to further enhance the speed, usability, and cost efficiency of AWS data analytics services. DuckDB co-founders Hannes Mühleisen and Mark Raasveldt will continue to lead the team after joining AWS and will be responsible for the technical development direction of the open-source project. Amazon emphasized that this transaction does not include the acquisition of the DuckDB open-source project itself; DuckDB will continue to be maintained by the independent DuckDB Foundation and will remain freely available under the MIT license. Amazon stated that the DuckDB user experience will not change before and after the transaction, and the project will continue to operate as open source. In the future, AWS will further announce specific plans for the integration of DuckLabs with AWS business.

In the latest podcast, SemiAnalysis founder Dylan Patel predicts that by 2028, OpenAI and Anthropic may account for 70% to 80% of the world's new AI computing power, with the total computing power scale potentially exceeding 100GW. Patel stated that the two companies currently account for about 30% of the world's annual new computing power, and this proportion is still rising rapidly.Patel pointed out that the business model of leading AI laboratories is changing, with a significant increase in the efficiency of AI computing power output. Currently, Anthropic's revenue per megawatt of computing power has reached about $50 million and may further rise to $100 million. This allows OpenAI and Anthropic to procure or lease computing power at high prices ranging from $25 million to $50 million per megawatt.Patel expects that global AI-related capital expenditures will reach about $11 trillion from 2024 to 2029, with over $5 trillion needing to be financed through debt. Due to the potential return on investment of AI infrastructure being far higher than that of traditional industries, tech giants may accept higher financing costs, thereby pushing up overall credit rates and squeezing the valuations of traditional assets and highly leveraged economies. Additionally, Patel believes that the new computing power may not primarily be used for providing model inference services externally, but may instead flow more towards internal research and development and self-improvement of models within AI laboratories.