Alibaba has officially launched Qwen 3.8-Max, its latest artificial intelligence model. First introduced as a preview version on July 20, Qwen 3.8-Max features 2.4 trillion parameters and supports a context window of up to 1 million tokens. Alibaba describes it as the most powerful flagship model in the Qwen family.

Alibaba said Qwen 3.8-Max ranked fifth on the Text Arena benchmark and second on the Vision Arena benchmark, demonstrating advanced capabilities in coding, real-world task execution, research and long-horizon problem solving. The company said the model was designed as a multimodal foundation model with native visual intelligence capabilities.

Qwen 3.8-Max is available through Alibaba Cloud's Model Studio as an application programming interface (API) service. The model weights are scheduled for release next week, and the model is also accessible through Alibaba's all-in-one workplace AI agent platform, QwenWork.

Built on Qwen 3.5 architecture, Qwen 3.8-Max combines Sparse Mixture-of-Experts (Sparse MoE) architecture with a hybrid attention mechanism. Alibaba said this architecture enables large-scale models to generate inference results efficiently.

Under this architecture, only 95 billion of the model's 2.4 trillion parameters are activated during inference. This allows the model to deliver performance on complex tasks while reducing computational costs.

Alibaba also highlighted Qwen 3.8-Max's autonomous coding and long-horizon task execution capabilities, claiming the model can work independently for extended periods without human intervention. In internal testing, the company said the model autonomously completed a real-world software engineering project over a 16-day period.

According to Alibaba, when tasked with building a self-evolving agent framework from scratch, Qwen 3.8-Max established an engineering loop that integrated user feedback, community best practices and self-generated testing data. The model then repeatedly generated code, conducted testing, created previews and analyzed logs before completing a self-evolving agent framework called "oh-my-cli."

The framework has been released as open-source software on GitHub. Qwen 3.8-Max also ranked fourth on the Frontend Code Arena benchmark.

Alibaba further said the model demonstrated problem-solving capabilities in highly specialized fields, including designing novel methodologies that surpassed results reported in existing research papers.

As a multimodal foundation model, Qwen 3.8-Max can transform static input data into dynamic and interactive knowledge structures, according to Alibaba. The company said the model can process hundreds of pages of documents, entire television series or 100-hour livestreams and convert them into searchable and interactive knowledge bases.

Alibaba said Qwen 3.8-Max can be used to edit raw personal footage into polished vlogs or generate immersive educational animations from text prompts alone. According to the company, the model can also reconstruct front-end web projects from a single user interface screenshot, transform 2D floor plans into detailed 3D interior visualizations, and create interactive games through natural-language instructions.

Woorinet, a provider of wired and wireless telecommunications equipment, has joined the South Korean government's Artificial Intelligence Data Center (AIDC) Alliance and is pursuing a national research and development (R&D) project to secure core technologies for Data Center Interconnect (DCI) networks linking AI data centers.

Launched under the leadership of the Ministry of Science and ICT (MSIT), the AIDC Alliance is a cooperative framework designed to strengthen South Korea's global competitiveness in AI data center technologies through collaboration among industry, academia, research institutes and government organizations across the entire value chain, including AI semiconductors, data centers, networking, power, cooling and software. The alliance is organized into demand, supply and infrastructure divisions. Woorinet has joined the supply division and will participate in the commercialization of next-generation DCI technologies.

As AI models continue to grow in scale, distributed training and distributed inference environments that simultaneously utilize graphics processing unit (GPU) resources across multiple data centers are becoming more common. This trend is increasing the need for backend network infrastructure capable of connecting AI computing resources.

Woorinet said it was selected in May as the lead organization for the Institute of Information & Communications Technology Planning & Evaluation (IITP)'s national R&D project titled "Development of AI Data Center Transmission Technology for Horizontal Expansion of AI Resources Across Data Centers." Working with the Electronics and Telecommunications Research Institute (ETRI), Sangmyung University and Pukyong National University, the company is developing ultra-high-speed transmission technologies between AI data centers and pursuing next-generation DCI technologies that would allow distributed AI resources to be utilized as a single AI infrastructure.

For AI data centers, DCI technology requires ultra-high bandwidth, lossless and ultra-low-latency communications, and advanced security capabilities. Woorinet said technologies based on the Ultra Ethernet Consortium (UEC) standard that can connect data centers located tens to hundreds of kilometers apart without data loss will become a key factor in determining AI infrastructure competitiveness.

To address these networking requirements, Woorinet said it is developing next-generation DCI technologies based on 16-terabit-per-second Packet Optical Transport Network (POTN) systems and wavelength-division multiplexing (WDM) optical transmission technologies capable of reliably transporting massive volumes of data.

The company also emphasized that it is strengthening its technological capabilities for building ultra-high-speed, ultra-low-latency transmission environments between AI data centers through proprietary optical transmission and network control technologies, including POTN, Packet Transport Network (PTN), Reconfigurable Optical Add-Drop Multiplexer (ROADM) and Transport Software-Defined Networking (T-SDN).

SpaceX reported stronger-than-expected revenue growth in its first quarterly earnings release since going public in June, but investor attention quickly shifted to the company's massive artificial intelligence (AI) infrastructure spending and continued losses in its AI and space businesses. The stock fell about 7% in after-hours trading following the earnings announcement.

For the second quarter of 2026, SpaceX reported revenue of $7.814 billion, up 92% from a year earlier, and a net loss of $541 million. While the company remained unprofitable, losses narrowed from the prior-year period. Earnings per share (EPS) came in at negative $0.09, beating the consensus estimate of negative $0.26.

Starlink remained the company's largest growth engine. Revenue from the Connectivity segment, which includes Starlink, reached $4.291 billion, exceeding analyst expectations of $3.83 billion. Subscriber numbers doubled year over year to 12 million, while segment revenue increased 66%.

The AI segment, which includes xAI, Grok and social platform X, generated approximately $2.6 billion in revenue, representing year-over-year growth of 247%. The company attributed the expansion to the integration of xAI and growing demand for AI computing services. However, the AI division posted an operating loss of $1.26 billion, although losses narrowed from the previous quarter.

SpaceX's third business segment, Space—which includes launch services and the Starship program—generated revenue of $962 million, up 29% from a year earlier, while recording an operating loss of $542 million. Although the AI segment delivered the fastest growth rate, Starlink's Connectivity business was the company's only profitable division.

While revenue and earnings exceeded expectations, investors focused on the scale of SpaceX's capital expenditures (CAPEX). Second-quarter capital spending surged to approximately $18.4 billion, compared with about $2.8 billion a year earlier. Most of the spending was directed toward AI data centers and computing infrastructure. SpaceX argued that the investments are necessary to support long-term growth, but investors expressed concern about the company's cash consumption.

During the earnings call, Chief Executive Officer (CEO) Elon Musk said SpaceX could generate $1 trillion in annual revenue by 2030 and suggested there was a "non-zero chance" of reaching that milestone as early as 2029. Musk also predicted that Starlink could eventually become one of the world's largest internet service providers.

"At some point, Starlink may carry a majority of internet traffic" in countries where the service is authorized, he said.

The earnings call drew significant attention as the company's first public earnings presentation following its IPO. However, investor and media reaction was notably cautious. TechCrunch noted that Musk frequently made more aggressive claims than other executives during the call, while senior management often provided more measured interpretations.

One example involved Starlink's future market position. After Musk said Starlink could handle a majority of global internet traffic within a decade, President and Chief Operating Officer (COO) Gwynne Shotwell later described the outlook more conservatively, saying Starlink could account for a "significant portion" of global internet traffic in the coming years.

A similar contrast emerged regarding revenue expectations. Chief Financial Officer (CFO) Brett Johnson said annual recurring revenue (ARR) could potentially reach $100 billion by year-end. About 20 minutes later, Musk described the figure as "not a question mark" and suggested it could be even higher.

Johnson attributed the ARR outlook to faster returns on new computing infrastructure investments, $6.7 billion in additional cloud-service contracts scheduled to begin in the October quarter, and revenue contributions from Cursor, which SpaceX recently acquired.

Investors remain skeptical about Musk's long-term targets. The $1 trillion revenue goal was first introduced during the company's IPO roadshow in June, when management discussed 2031 as a possible target year. During the latest earnings call, Musk accelerated that timeline by one year to 2030.

Based on SpaceX's reported 2025 revenue of $18.67 billion, reaching $1 trillion by 2030 would require approximately 54-fold growth over five years, implying a compound annual growth rate (CAGR) of roughly 120%.

SpaceX's current trajectory is stronger than that baseline suggests. With quarterly revenue already exceeding $7.8 billion, annualized revenue for 2026 could approach $32 billion or more. Even so, Wall Street projections remain far below Musk's target.

During the IPO roadshow, Goldman Sachs estimated SpaceX's 2030 revenue at roughly $470 billion, while Morgan Stanley projected approximately $330 billion. Both firms served as lead underwriters for the IPO, and even their forecasts were widely viewed as aggressive. As a result, some investors question whether the $1 trillion target represents a genuine internal objective or a narrative designed to support market sentiment.

Despite the stronger-than-expected quarter, the stock reaction underscored investor skepticism. SpaceX shares had risen 9.43% during regular trading on Aug. 4 to close at $125.33 ahead of the earnings release, but subsequently fell about 7.5% to 8% in after-hours trading.

Musk's trillion-dollar revenue vision remains far more ambitious than prevailing market expectations, but investors seeking clearer paths to profitability appeared unconvinced by promises tied to distant milestones and long-term projects.

Nature, Published online: 05 August 2026; doi:10.1038/s41586-026-10865-1

Despite most Australians supporting clean technology, the campaign against wind and solar infrastructure has proven to be effective, striking a chord with many of those living in remote and rural areas. So how do we bring everyone along on the transition to a renewable energy-focused future? And who’s putting up roadblocks?

The Guardian’s environment and climate correspondent Graham Readfearn joins Reged Ahmad to unpack Australia’s relationship with renewables

Read more:

‘Extremely effective’ organised campaigns against wind and solar undermining support for renewable energy

Karl’s ‘great net zero debate’ was anything but – so beware Coalition claims of renewables taking over ‘two Tasmanias’

Nature, Published online: 04 August 2026; doi:10.1038/s41586-026-10958-x

Nature, Published online: 04 August 2026; doi:10.1038/d41586-026-02424-5

POSCO DX has announced its transformation into an AI-Native Company, built on two strategic pillars: physical artificial intelligence (AI) for industrial operations and agentic AI for enterprise workflows.

The company said it is focusing on expanding physical AI to build intelligent factories where people, AI and industrial equipment or robots work together seamlessly. The strategy centers on applying physical AI technologies to automate and optimize the operation of large-scale industrial equipment through robotization.

POSCO DX said the technology has already been deployed in selected operations. It has applied physical AI to steel mill equipment weighing tens of tons, including overhead cranes that transport steel products, reclaimers that move raw materials in storage yards and ship unloaders, improving worker safety and productivity.

The company is also converting the tacit knowledge of highly experienced operators into core datasets for physical AI, enabling AI to collaborate with human operators in control rooms while managing robots and industrial equipment.

At a press briefing on July 29, Cho Seok-joo, executive vice president and head of the AX Research Institute at POSCO DX, outlined the company's progress in developing physical AI technologies.

POSCO DX is also expanding the deployment of industrial robots in hazardous and labor-intensive environments. The company said it provides end-to-end robotic automation services covering consulting, design, implementation and operations. Together with POSCO, it has developed a platform for managing heterogeneous robot fleets to optimize robot operations.

The company is also designing optimized robotic systems through simulation technologies and developing Vision-Language-Action (VLA) models for humanoid and other mobile robots to improve locomotion and manipulation capabilities.

As examples of practical deployment, POSCO DX highlighted robots that remove impurities from molten zinc baths in galvanizing processes, robotic band cutters used in cold rolling operations and automated guided vehicles (AGVs) capable of transporting steel products weighing up to 65 metric tons. The company is also deploying robotic systems for raw material sampling and logistics automation at plants producing environmentally friendly materials and automotive components.

For office and management functions, POSCO DX is promoting agentic AI through its proprietary Agentee platform. According to the company, Agentee manages the entire lifecycle of AI agents—from recruitment and evaluation to development, promotion and retirement—in a manner similar to managing human employees. The platform enables domain-specific "AI employees" to automate office and business operations.

POSCO DX said its AX consulting services first analyze customer workflows to determine how responsibilities should be divided between employees and AI agents. Based on those workflows, Agentee either creates customized AI agents or reuses existing ones. The company said the platform standardizes every stage from design to deployment, enabling rapid development of enterprise AI agents.

POSCO DX demonstrated several internal use cases, including finance, human resources, labor management and project management. In one example, an AI agent automates monthly pre-closing financial risk inspections by scanning all accounting accounts, identifying abnormal entries and missing journal records, and generating risk reports. The process, which previously required about three hours, now takes approximately one minute. The company estimates that expanding AI agents across all financial closing activities could improve productivity by at least 30%.

POSCO DX currently operates approximately 200 AI agents across its own organization and other POSCO Group companies. It plans to establish standardized AI agent models for the group before commercializing the validated platform for external customers.

The company said it is the only domestic provider with full-stack capabilities spanning operational technology (OT) on factory floors and enterprise information technology (IT) systems.

"Leveraging our expertise across both industrial operations and office environments, we aim to become the company best positioned to deliver physical AI and agentic AI services by integrating advanced AX technologies," POSCO DX said.

At the same briefing, Kim Byung-seok, head of the AI Workforce Task Force at POSCO DX, presented the company's agentic AI strategy.

Separately, POSCO Group has adopted a Mission-oriented AI Transformation (AX) strategy centered on three pillars—process, work and value—to accelerate AI adoption across the group.

At the POSCO CEO Investor Day held on July 2, the group unveiled its Triple-Core growth strategy spanning industrial resources (steel), strategic resources (including lithium, cathode and anode materials, and rare earth elements) and energy resources (LNG and renewable energy). The group also announced plans to expand into physical AI for process industries by leveraging decades of experience in factory automation, intelligent manufacturing and large-scale industrial data.

The POSCO CEO Investor Day is the group's annual investor relations event, where the chairman and senior executives present long-term growth strategies, future business plans and financial targets to shareholders, institutional investors and securities analysts.

Autonomous trucking startup Mars Auto has signed a memorandum of understanding with the Korea Post Logistics Support Foundation and logistics company Logisquare to accelerate the adoption of autonomous driving technologies for commercial freight transportation. The partnership will expand paid autonomous freight services within Korea Post's logistics network while using real-world driving data to enhance the company's end-to-end (E2E) autonomous driving AI model.

The three-way agreement is part of the South Korean government's national AI initiative, "Software-Defined Vehicle (SDV) Transformation and AI-Based End-to-End Autonomous Driving Model Advancement." In November last year, Mars Auto was selected by the Ministry of Trade, Industry and Resources and the Korea Evaluation Institute of Industrial Technology (KEIT) to lead a project developing E2E AI-based autonomous freight transportation technology for heavy-duty trucks. The project has a total budget of 18.2 billion won and is being carried out by a consortium of 13 organizations, including the Korea Post Logistics Support Foundation and Logisquare.

Under the partnership, Mars Auto will install its Copilot autonomous driving system on postal freight vehicles operated by the Korea Post Logistics Support Foundation, while Logisquare will oversee operations for autonomous commercial freight routes. By collecting diverse real-world driving data from heavy-duty truck drivers, the partners aim to improve E2E AI performance while increasing operational efficiency and safety through fuel savings of more than 10% and reduced driver fatigue during long-distance and nighttime operations.

Postal freight vehicles equipped with Mars Auto's Copilot system will operate on major highways and local roads connecting 22 regional mail processing centers and approximately 3,700 post offices across South Korea. Because the system is not limited to a specific vehicle manufacturer or truck model, it can collect driving data from a broad range of commercial vehicles. The data will be transmitted in real time over SK Telecom's wireless network to the National AI Project training infrastructure located in Yeongdeungpo, Seoul. Copilot has already been installed on 20 postal freight vehicles, with additional deployments planned.

Mars Auto's autonomous driving technology relies on AI and camera-based sensors rather than LiDAR or high-definition (HD) maps. The company believes the quantity and quality of real-world driving data are critical to improving AI performance and said the partnership will support its goal of commercializing Level 4 fully autonomous driving.

Separately, on July 20, Mars Auto became the first company in South Korea to receive government approval to launch an autonomous trailer business under the Ministry of Land, Infrastructure and Transport's mobility regulatory sandbox program.

AI infrastructure software company Xiilab announced on July 29 that it has joined the AI Data Center (AIDC) Alliance, a public-private consultative body led by South Korea's Ministry of Science and ICT.

The AIDC Alliance was established by the government together with industry, academia and research organizations to foster AI data centers as a national strategic industry and a new export growth engine. The alliance discusses policies and institutional improvements related to AIDC development, including power infrastructure, site development and financing.

The South Korean government plans to build a total of 18.4GW of AI data center capacity by 2035 while expanding cumulative investment to more than 1 quadrillion won. It also aims to develop a domestic full-stack AI ecosystem integrating locally developed neural processing units (NPUs), cloud platforms, power and cooling systems, and operational software.

Xiilab said it plans to deploy its AstraGo platform during the operational phase of AI data centers to improve GPU utilization through Kubernetes-based GPU scheduling, automatic GPU partitioning and multi-node distributed training.

The company said it has established expertise in AI infrastructure services, including AI cluster design, reference architecture (RA)-compliant deployment, performance validation and operational software. Xiilab added that it recently achieved Nvidia Elite Partner status in the software category.

Kolon Benit is expanding its enterprise software business with a portfolio of in-house solutions covering business planning, consolidated accounting, financial disclosure and manufacturing digital transformation (DX). The company aims to broaden its customer base by targeting growing demand for regulatory compliance and workflow automation in finance and manufacturing.

Kolon Benit's proprietary software portfolio includes r-PLANNA for business planning and profit forecasting, BENIT SIGMA for consolidated accounting, DSDFlow for electronic financial disclosures and r-CoCoAna, a manufacturing DX platform. The company plans to progressively integrate artificial intelligence (AI) capabilities into these products to further automate business planning, profit forecasting, financial consolidation, regulatory reporting and manufacturing operations.

The r-PLANNA solution helps companies analyze projected costs and profitability during the business planning process. Built on SAP S/4HANA, it performs repeated scenario-based forecasting and analysis by incorporating variables such as exchange rates, selling prices, raw material costs and production volumes. Kolon Benit said the solution uses a proprietary calculation engine based on the same mechanism as SAP systems, enabling highly reliable forecasts. In customer validation projects, forecast production costs matched actual costs with approximately 99% accuracy.

BENIT SIGMA and DSDFlow support financial reporting workflows from group consolidation through regulatory disclosure. BENIT SIGMA collects and consolidates financial data from multiple subsidiaries to produce consolidated financial statements while automating complex accounting processes, including intercompany transactions and ownership relationships. The solution supports both SAP and non-SAP enterprise resource planning (ERP) environments and has accumulated more than 100 customers since its launch in 2009.

To prepare customers for the implementation of IFRS 18 in 2027, Kolon Benit recently introduced the next-generation BENIT SIGMA Pro. The new solution provides dual reporting capabilities that allow users to compare financial statements prepared under existing accounting standards with those based on IFRS 18. It also includes annotation functions for generating Management-defined Performance Measures (MPMs) specified by corporate management. The company plans to further automate repetitive consolidation, financial reporting and data validation tasks by incorporating AI technologies.

DSDFlow converts financial data generated during the consolidation process into formats required for submission to South Korea's Financial Supervisory Service's Data Analysis, Retrieval and Transfer (DART) system while supporting preparation and validation of disclosure documents.

In manufacturing, Kolon Benit is strengthening its business with the r-CoCoAna platform. The system collects and integrates real-time data from production equipment and manufacturing processes, providing a unified platform for managing production, quality, inventory, equipment and energy operations using data-driven analytics.

The platform combines a Connected Data Platform (CDP), which links data from both legacy and modern manufacturing equipment, with a Manufacturing Operations Management (MOM) system that integrates production planning, process execution, quality management and equipment management. It also utilizes technologies such as vision AI to automatically detect product defects and analyze equipment and process data.

Kolon Benit said it has built manufacturing DX expertise through r-CoCoAna since establishing a dedicated manufacturing business organization in 2014. Over the past three years, the company has carried out projects for manufacturing affiliates within the Kolon Group, including Kolon Industries, Kolon Life Science and Kolon Pharmaceutical, covering data collection and analysis, equipment monitoring, process optimization and quality stabilization.

The company said those projects enabled it to build end-to-end execution capabilities spanning factory assessments, process improvement planning, system implementation and operational stabilization. Going forward, Kolon Benit plans to expand its integrated manufacturing DX package—which combines consulting, data collection and analytics, system implementation and AI-based operations—to external customers to help reduce costs, improve productivity and enhance product quality.

Microsoft shares rose more than 8% in after-hours trading following the release of its fiscal 2026 fourth-quarter (April–June) results. The gains were driven by stronger-than-expected growth in the company’s Azure cloud business, lower-than-anticipated capital expenditure (Capex) plans, and continued confidence in its cash-generation outlook. Microsoft also issued fiscal 2027 first-quarter guidance that exceeded market expectations for both revenue and cloud growth.

Quarterly revenue reached $90.01 billion, surpassing the consensus estimate of $87.62 billion. Revenue from Azure and other cloud services increased 43%, exceeding analyst expectations. StreetAccount had projected growth of 40.2%, while Visible Alpha forecast 39.98%.

Azure is reported within Microsoft’s Intelligent Cloud segment, which includes Azure and other cloud services, server products and related cloud services, and enterprise services. Microsoft does not disclose standalone Azure revenue. In its earnings report, the company said Azure and other cloud services revenue grew 43% during the quarter.

Chief Executive Officer (CEO) Satya Nadella disclosed that Azure’s annual revenue exceeded $100 billion for the first time. CEO Nadella also emphasized that paid licenses for Microsoft 365 Copilot had surpassed 30 million.

He further stated that Microsoft expects to continue generating strong cash flow throughout fiscal 2027 (July 2026–June 2027).

Beginning in fiscal 2027, Microsoft will extend the estimated useful life of its data centers and office buildings from 15 years to 25 years. The longer depreciation period will result in some future data center lease agreements being classified as operating leases rather than finance leases, reducing the amount recorded as Capex under accounting rules. Microsoft emphasized that excluding this accounting change, its actual data center investment plans remain unchanged.

The move has helped ease investor concerns that surging data center spending and AI services could undermine the profitability of Microsoft’s core productivity software business.

Azure’s 43% growth also countered speculation that Google Cloud had been taking market share from Amazon Web Services (AWS) and Azure after Alphabet reported an 82% surge in Google Cloud revenue last week. The latest results indicate Microsoft remains firmly among the leaders in the cloud infrastructure market.

Microsoft’s commercial remaining performance obligations (RPO) reached $678 billion at quarter-end. RPO represents contracted revenue that has not yet been recognized. The figure increased by approximately $51 billion from $627 billion in the previous quarter. The company said all of the quarter-over-quarter increase came from customers other than frontier AI model developers such as OpenAI, suggesting demand for Microsoft’s cloud and enterprise software offerings is broadening across a diverse customer base.

The number of paid Microsoft 365 Copilot licenses rose from 20 million in the previous quarter. Analysts had expected an average of 26.9 million licenses, meaning Microsoft exceeded expectations by more than 3 million customers.

Microsoft forecast fiscal 2027 first-quarter (July–September) revenue of between $89.85 billion and $90.95 billion. The midpoint of $90.4 billion is above the consensus estimate of $89.66 billion compiled by LSEG. The company also projected Azure growth of 45% on a constant-currency basis, well above Visible Alpha’s forecast of 40.92%.

Microsoft expects first-quarter fiscal 2027 Capex of $50 billion and calendar-year 2026 Capex of $175 billion. Both figures are below previous expectations of $56.02 billion and $190 billion, respectively.

Large-scale data center investments continue to weigh on free cash flow (FCF). Microsoft reported quarterly FCF of $19.6 billion, calculated by subtracting $35.8 billion in cash capital expenditures from operating cash flow of $55.4 billion. Although FCF declined 23% from a year earlier, it significantly exceeded the Visible Alpha consensus estimate of $13.44 billion.

Total Capex for the quarter reached $41 billion, up more than 70% year over year but below market expectations of $42.37 billion. The figure included $5.6 billion in finance leases related to data center sites and other facilities. Cash spending on property and equipment totaled $35.8 billion. Total Capex in the previous quarter was $31.9 billion.

Additional investment could still lie ahead. In filings with the US Securities and Exchange Commission (SEC), Microsoft disclosed $329.1 billion in future data center lease commitments that have not yet commenced. Those agreements are scheduled to begin between fiscal 2027 and fiscal 2033, with lease terms ranging from one to 20 years.

Microsoft is also working to reduce its reliance on OpenAI technology by adding Anthropic models to its services and developing proprietary AI models. At the same time, the company is leveraging long-standing enterprise customer relationships to expand sales of its $30-per-month Copilot offering. A major agreement signed with Accenture earlier this year is one example.

Reuters noted that Microsoft has been among the weakest-performing members of the “Magnificent Seven”—Microsoft, Apple, Nvidia, Alphabet, Amazon, Meta and Tesla—so far this year, but the latest earnings report suggests the company may be positioned for a rebound. Microsoft shares had declined approximately 18% year to date before the earnings release.

Because the quarter marked Microsoft’s fiscal 2026 fourth quarter, the company also reported full-year results for fiscal 2026, which covered the period from July 2025 through June 2026.

For fiscal 2026, Microsoft reported revenue of $331.8 billion, up 18% year over year, or 16% on a constant-currency basis. Operating income rose 21% to $155.2 billion, or 19% on a constant-currency basis. Net income reached $133.7 billion, increasing 31% under generally accepted accounting principles (GAAP), or 22% on a non-GAAP basis excluding gains and losses related to its OpenAI investment. On a constant-currency basis, non-GAAP net income increased 20%. Diluted earnings per share (EPS) were $17.95, up 32% year over year under GAAP.

Both fiscal 2026 annual revenue and fourth-quarter revenue increased 18% from the previous year. However, the fact that annual and quarterly growth rates were identical does not necessarily indicate that growth was evenly distributed throughout the year. Microsoft also separately disclosed non-GAAP results excluding the impact of its OpenAI investment. During fiscal 2026, accounting gains related to that investment increased GAAP net income by $4.963 billion and diluted EPS by $0.67. The company noted that this accounting benefit does not represent cash earnings received from OpenAI.

Meta Platforms reported its second-quarter (April–June) results on July 29. Revenue exceeded market consensus, but net income and earnings per share (EPS) were pressured by increased artificial intelligence (AI) investment and large one-time charges. Free cash flow (FCF) also declined sharply. Despite that, Meta again raised its AI infrastructure spending plans for the year, maintaining an aggressive investment stance.

Meta posted second-quarter revenue of $60.801 billion, up 28% from a year earlier. Operating income totaled $18.775 billion, representing a decline of 8% year over year. Operating margin fell to 31% from 43% a year earlier. Net income came to $15.85 billion, down 14% from the same period last year.

Unlike other major technology companies, Meta does not operate a large external cloud services business, with advertising accounting for nearly all of its revenue. Strong advertising performance helped revenue slightly exceed market expectations, but operating income and net income declined due to AI infrastructure costs, legal expenses, and restructuring charges.

The company recorded $2.4 billion in legal expenses, reportedly related to reserves for lawsuits involving child protection issues associated with Facebook and Instagram. An additional $1.18 billion in restructuring costs, including severance payments, was also recognized. Both were one-time expenses.

AI infrastructure investment and overall costs and expenses increased 55% from a year earlier. Even so, Meta raised its full-year capital expenditure (Capex) forecast to between $130 billion and $145 billion. Its previous guidance had been $125 billion to $145 billion. While the upper end remained unchanged, the minimum spending level increased by $5 billion, which investors interpreted as a signal of a gradual expansion in Meta’s Capex plans.

As credit rating agencies including Moody’s and S&P Global Ratings have warned, rising AI infrastructure spending is weighing on Meta’s FCF. The company’s FCF fell to $784 million in the quarter, down about 91% from a year earlier.

FCF is calculated by subtracting Capex from operating cash flow (OCF), representing the cash a company generates from its core business operations. It reflects the amount of cash available for shareholder returns, debt repayment, acquisitions, and other discretionary uses. While net income can be affected by non-cash items such as depreciation and valuation gains or losses, FCF provides a clearer view of actual cash available to the company and is therefore a key measure of financial health and investment capacity.

Chief Executive Officer (CEO) Mark Zuckerberg again emphasized AI as Meta’s primary growth engine. He said AI continues to improve advertising efficiency while creating new consumer services and enterprise business opportunities, remarks widely interpreted as an effort to justify the company’s higher Capex spending.

During the earnings conference call, CEO Zuckerberg provided more details on how Meta intends to use its computing resources. He said a significant portion is being devoted to training frontier AI models, with the remainder allocated to improving core businesses such as advertising, developing new consumer products, supporting business agents and developer tools, and exploring opportunities to sell computing capacity directly.

Zuckerberg also said Meta has received numerous proposals from outside parties willing to pay substantial premiums above the company’s acquisition cost for computing capacity. However, he indicated that selling AI "intelligence" built on top of that infrastructure would likely generate higher margins than selling computing resources directly. Several media outlets recently reported that Meta had been engaged in preliminary discussions to lease approximately $2 billion worth of AI computing capacity to Anthropic over two years.

Aside from Capex, Meta’s second-quarter earnings were largely affected by one-time expenses, while advertising revenue growth remained highly robust.

User metrics also continued to expand, with Family Daily Active People (Family DAP) reaching 3.6 billion, Instagram daily active users reaching 2 billion, and Threads monthly active users reaching 500 million. Family DAP measures the number of unique users who engage with at least one of Meta’s major services—Facebook, Instagram, Messenger, or WhatsApp—on a given day. After removing duplicate users, the figure indicates that roughly 3.6 billion unique people used at least one of those services daily.

While CEO Zuckerberg stressed that the competitiveness of Meta’s core services is increasingly driven by AI, investors remain concerned that the scale of AI investment is growing far faster than expected. With FCF declining sharply and Capex guidance moving higher, the timeline for investment payback could be extended. Investors were also disappointed by Meta’s third-quarter revenue outlook of $61 billion to $64 billion, which the market viewed as somewhat underwhelming.

As a result, Meta shares fell nearly 10% in after-hours trading. Investors cited weaker-than-expected net income and EPS, as well as confirmation that AI infrastructure spending is placing significant pressure on the company’s cash-generation capabilities.

Meta is increasingly relying on joint ventures with infrastructure investment funds to finance its artificial intelligence (AI) infrastructure buildout, reducing the burden of rising capital expenditures (CAPEX). Following its financing structure for the planned 5-gigawatt Hyperion data center, the company is using a similar model for a new AI data center campus under development in El Paso, Texas.

On July 28, Meta announced that it had formed a $14 billion joint venture with BlackRock to develop and own a 1-gigawatt AI data center campus in El Paso, Texas. The facility is scheduled to begin coming online in 2028. Under the agreement, funds managed by BlackRock will hold an 80% stake, while Meta will retain the remaining 20%.

Meta will contribute approximately $2.3 billion in land and construction-in-progress assets, while BlackRock will provide about $4.9 billion in cash. To align ownership with the 80:20 equity split, Meta will receive a one-time $1 billion ownership-alignment distribution. The remaining $12.5 billion will be financed through project debt, with much of the funding raised through bond issuance.

Meta previously adopted a similar financing structure for its planned 5GW Hyperion AI data center in Richland Parish, Louisiana. The facility, which will more than double its existing capacity and come online in phases through 2030, is being developed with support from Blue Owl Capital. Meta established a joint venture with Blue Owl for the project, which subsequently raised approximately $27 billion in project financing through a separate special-purpose vehicle (SPV) via bond issuance. The bonds were purchased by institutional investors including PIMCO, BlackRock, and others.

Both the El Paso and Hyperion campuses will be owned by their respective joint ventures, with Meta leasing the facilities under long-term agreements rather than holding them directly on its balance sheet. Although Meta owns a 20% stake in each venture, the structure allows the company to accelerate AI infrastructure deployment while limiting direct capital investment and keeping substantial assets off its balance sheet.

The financing model could help ease investor concerns over rapidly rising AI-related capital spending. Shares of major technology companies have frequently come under pressure following announcements of increased CAPEX, reflecting uncertainty over when AI investments will generate sufficient financial returns. Similar concerns recently weighed on Alphabet's share price despite stronger-than-expected quarterly earnings.

Meta currently expects to spend $125 billion to $145 billion on AI infrastructure this year, an increase from its previous guidance of $115 billion to $135 billion announced after first-quarter results.

Unlike cloud service providers, Meta does not currently operate a large-scale commercial data center leasing business, limiting its ability to directly monetize its AI infrastructure investments. While media reports have indicated the company has discussed leasing compute capacity to firms such as Anthropic, Meta has yet to launch a broad cloud infrastructure business.

As Meta continues expanding AI infrastructure, funding projects entirely from its own balance sheet would increase debt and pressure free cash flow. By using SPVs and joint ventures, the company can mitigate those risks while offering institutional investors stable, long-term returns backed by Meta's lease commitments.

BlackRock Chairman and Chief Executive Officer Larry Fink described Meta as a long-term strategic partner, saying the transaction demonstrates the combined capabilities of BlackRock, Global Infrastructure Partners (GIP) and HPS Investment Partners in delivering investment opportunities across AI infrastructure and energy.

His comments suggest BlackRock views not only AI data centers but also the power infrastructure supporting them as an attractive investment class. The firm's emphasis on creating "investment opportunities for clients" also reflects a strategy of positioning AI infrastructure as a new asset class for institutional investors through its infrastructure funds.

The Wall Street Journal said the race to build AI data centers is increasingly becoming a challenge of raising massive amounts of capital rather than simply constructing facilities. Industry publication Data Centre Magazine similarly noted that hyperscale AI infrastructure projects are evolving from engineering initiatives into financial undertakings that require sophisticated capital structures.

Meta is scheduled to report its second-quarter earnings and hold an earnings conference call on July 30. While Wall Street generally expects the company to maintain its current AI CAPEX guidance of $125 billion to $145 billion, some analysts have suggested the forecast could eventually rise as high as $215 billion.

Snowflake, the AI Data Cloud company, has introduced Cortex AI Gateway along with a suite of AI security capabilities designed to strengthen enterprise AI security. Scheduled to enter public preview, Cortex AI Gateway is designed to secure AI agents while providing centralized visibility and control over AI consumption costs.

Snowflake said the rapid growth of enterprise AI has created the need for a new security and cost management model that enables trusted interoperability across AI systems. Cortex AI Gateway was developed to provide a unified governance layer for AI interactions across enterprise environments.

According to the company, Cortex AI Gateway can securely orchestrate and manage not only Snowflake's own AI agents, including Snowflake CoWork and Snowflake CoCo, but also third-party AI agents built on external platforms such as Claude Code and Cursor.

Cortex AI Gateway is built on technology acquired through Snowflake's purchase of Natoma in May. Snowflake said it has integrated Natoma's enterprise Model Context Protocol (MCP) platform directly into its own platform to enable secure, enterprise-grade agent interoperability.

The company said Cortex AI Gateway centrally governs how AI agents access models, tools, MCP servers and enterprise systems while intelligently routing agent requests. It also provides organizations with visibility into AI access, agent activity and token consumption, helping enterprises manage and optimize AI spending.

Snowflake also announced new security-focused integrations for third-party AI agents with Aembit, 1Password, Linx Security, Okta, SailPoint and Saviynt. The integrations, which will be available through a private preview, are intended to strengthen governance and auditing of AI agents operating across multiple platforms.

The company said AI agents must be able to operate securely across diverse enterprise platforms to deliver meaningful business value. However, many vendors continue to rely on closed ecosystems, limiting the ability of third-party agents to work seamlessly across organizational boundaries.

According to Snowflake, the new third-party agent integration capabilities provide detailed visibility into which agents accessed which data, through which platform and under whose credentials. Rather than granting AI agents unrestricted user privileges, enterprises can limit permissions to only those required for specific tasks, improving security while enabling broader enterprise AI adoption.

BC Card is partnering with database platform provider MongoDB to strengthen the competitiveness of its artificial intelligence (AI) services by upgrading its data platform.

The two companies have signed a memorandum of understanding (MOU) to jointly develop an AI-ready data platform. Under the agreement, they will collaborate on building AI-dedicated infrastructure, implementing natural language search and AI-powered recommendation services, enhancing existing AI capabilities, and establishing a common data foundation for future AI services.

BC Card plans to leverage MongoDB's document-based data platform to create a data storage and management environment optimized for AI applications while improving its ability to process and utilize unstructured data. The company expects the platform to enable faster processing and integration of diverse data types, including text, images and product information.

The upgraded platform will be applied progressively to BC Card's existing AI services. The company's commercial district analytics platform, eat.pl, will enhance its natural language search capabilities, allowing users to search sales and merchant data using conversational queries and receive more detailed information.

BC Card will also enhance its AI-powered shopping curation service, AI Hot Deal, by introducing automatic summaries of promotional offers, personalized product recommendations based on user interests and keyword alert functions, further strengthening its recommendation engine.

The two companies will also establish a shared data foundation that can centrally manage information used across multiple AI services, enabling faster development of new AI applications and tighter integration between services. BC Card said the initiative will accelerate its transformation into an AI-native enterprise.

MongoDB said its platform provides the core technologies required to deploy AI agents in production environments, including a real-time database, full-text and vector search, memory capabilities, embeddings and reranker models, all within a single integrated platform.

AI data company VAIV Company announced that its AI-based knowledge management system, VAIV KMS, has received Grade 1 Good Software (GS) Certification from the Korea Testing Laboratory (KTL), enabling the company to meet key requirements for participation in South Korea's public procurement market.

GS Certification is a government-recognized software quality certification program established under Article 20 of the Software Promotion Act. Software products awarded GS Grade 1 certification are eligible for registration on the Public Procurement Service's KONEPS online procurement marketplace and can qualify for preferential purchasing by public institutions.

VAIV Company describes VAIV KMS as an AI-powered knowledge management system that consolidates operational knowledge scattered across customer service centers and business departments, allowing employees to access relevant information when needed.

According to the company, the system enables customer service representatives to retrieve information instantly without manually searching through large volumes of documents, including product guides, terms and conditions, and operational manuals. This can help reduce response times and improve consistency in customer service delivery.

VAIV Company plans to use the GS Grade 1 certification as a springboard for expanding beyond the financial sector into public-sector knowledge management systems and citizen complaint-handling services. The company said its capabilities have already been validated in the financial industry, where K Bank is among its knowledge management system customers.

AI computer vision company A.I.Matics has signed a memorandum of understanding (MOU) with the Chungcheong Regional Headquarters of the Korea Authority of Land and Infrastructure Safety (KALIS) to supply its AI safety driving platform for the headquarters' official vehicles.

The company said it expects the partnership to help prevent accidents involving vehicles used by KALIS employees and to establish an AI-based safety management system tailored to official-vehicle environments that require privacy protection, including data masking and automatic video blurring.

A.I.Matics' solution consists of an in-vehicle on-device AI camera and a driving safety platform. The AI camera detects hazardous situations both inside and outside the vehicle in real time while driving and immediately alerts the driver.

The platform also provides scoring reports and safe-driving statistics that enable managers to analyze drivers' risky driving patterns and identify areas for improvement. According to A.I.Matics, video footage can be accessed remotely through a PC or mobile application when recordings are needed following an accident or emergency.

Lee Hoon, chief executive officer of A.I.Matics, said the need to balance safety and privacy protection in public-sector and corporate vehicles is increasingly important.

"The solution being supplied to the Chungcheong Regional Headquarters of the Korea Authority of Land and Infrastructure Safety supports data masking designed to address the specific requirements of public institutions," Lee said.

NC AI and CMES Robotics have signed a strategic memorandum of understanding (MOU) to collaborate on physical artificial intelligence (AI) research and development and share core technologies. The two companies aim to combine their AI and robotics expertise to develop next-generation physical AI-powered robotics solutions for manufacturing and logistics industries.

According to NC AI, the partnership will focus on three areas: joint research and development of physical AI technologies, sharing of key technologies and research achievements, and the discovery and advancement of next-generation robotics solutions.

CMES Robotics brings expertise in physical AI-based 3D vision technology and precision robot control. The company has accumulated extensive industrial data, including 3D vision training datasets, robot operation data from real-world manufacturing environments, and data solutions that connect physical operations with simulation environments.

Through the collaboration, NC AI plans to advance its AI technologies based on World Models, which enable AI systems to understand physical laws and environmental changes while predicting future states, and Robot Foundation Models (RFM) designed to provide robots with generalized intelligence. The company said these technologies will help robots better understand their surroundings and make autonomous decisions.

CMES Robotics will contribute its experience in deploying AI-powered intelligent robotics solutions, including Vision Foundation Models (VFM) and RFMs trained on manufacturing and logistics data, along with its expertise in precision robot control. The company plans to use these capabilities to validate and refine AI models in real industrial environments.

The two companies emphasized the importance of creating a virtuous development cycle in which industrial data is used to train AI models and build simulation environments, while the resulting AI systems are continuously deployed and improved through real-world robotic operations.

One of the primary challenges in developing physical AI systems is securing sufficient training data for robots. NC AI said the partnership with CMES Robotics is expected to provide access to valuable industrial datasets while helping address the sim-to-real challenge, in which AI models trained in simulation environments must perform reliably in physical environments.

The companies said the collaboration could accelerate the commercialization of intelligent robotics technologies that can be deployed directly in industrial settings.

Initially, the partnership will focus on manufacturing and logistics applications. The companies plan to expand physical AI technologies into additional industries over time and jointly identify new AI robotics solutions tailored to various industrial environments.

OpenAI has launched ChatGPT for Academic Researchers, a global program that provides academic researchers in science, mathematics and engineering with free access to advanced artificial intelligence (AI) models and research tools. Applications are now open for eligible researchers affiliated with selected academic institutions.

Researchers must apply individually and meet eligibility requirements set by OpenAI. Applicants are required to verify their affiliation with a participating institution and provide information about their current research activities and plans for using AI in scientific work.

OpenAI said the program was created to ensure that access to advanced AI technologies is not limited to large corporations or well-funded research laboratories. A total of 70 universities and research institutions across the Asia-Pacific region have been selected for participation, including 17 institutions in South Korea such as Seoul National University, Yonsei University, Korea University, KAIST and POSTECH.

The full list of participating institutions, including the 17 organizations in South Korea, is available through OpenAI's ChatGPT for Academic Researchers program information page.

Participants will receive one year of free access to OpenAI's most advanced tools, including ChatGPT, ChatGPT Work, Codex and the GPT-5.6 model family. OpenAI also plans to make GPT-5.6 Sol Pro, a model designed for advanced scientific and mathematical reasoning, available to participants once access begins following the application process.

According to OpenAI, participants will receive expanded Deep Research capabilities, higher usage limits and larger context windows. The company said these features are intended to support a broad range of research workflows, including scientific reasoning, data analysis and agentic task execution.

Researchers will also gain access to more than 75 life sciences research skills covering areas such as genetics, genomics, sequencing, single-cell analysis, protein modeling and drug discovery. The program includes connectors that can link to scientific literature, public genomic and clinical databases, satellite imagery, computational notebooks, data platforms and literature management tools.

Codex is designed to assist with coding, debugging, dataset analysis and the creation of reproducible research workflows. ChatGPT Work can be used for longer-term projects, including identifying research funding opportunities, drafting grant proposals, conducting literature reviews, preparing manuscript drafts and creating materials to communicate research findings.

OpenAI said the program workspace includes business-grade privacy and security protections. Data entered by researchers will not be used to train OpenAI models by default.