Investor attention is focused on Alphabet's second-quarter earnings report, with markets looking for evidence that the company's aggressive artificial intelligence (AI) investments are translating into sustainable revenue growth.

Alphabet is scheduled to report its second-quarter results after US markets close on July 22 (around 5:00 a.m. Korea time on July 23). While the company's strong first-quarter performance reassured investors, the second quarter has raised fresh concerns over delays to the flagship Gemini 3.5 Pro AI model, the departure of key AI researchers and rising infrastructure investment costs.

Alphabet will webcast its earnings conference call at 5:30 a.m. Korea time on July 23 through its investor relations website and YouTube channel. According to Reuters, investors are primarily looking to determine whether Alphabet's record data center investments and Gemini AI strategy are generating tangible financial returns. As a result, growth in the company's cloud business has become the most closely watched metric.

Cloud revenue expected to grow 64% year over year

According to estimates from LSEG, Alphabet is expected to post revenue of $116.93 billion for the April-June period, up 21.3% from a year earlier. Google Cloud revenue is forecast to increase 64%, while advertising revenue is expected to grow 13.7%.

Other market consensus estimates are broadly similar. FactSet expects revenue of about $117 billion, with Google Cloud revenue growing 63% to 70%. Visible Alpha forecasts revenue of $117.19 billion, representing growth of more than 20%, while projecting Google Cloud revenue of approximately $22.5 billion, up more than 65%, and advertising revenue from Search and related businesses of roughly $63.29 billion.

Advertising, including Google Search and YouTube, remains Alphabet's largest source of revenue, generating more than three times the sales of its cloud business. Nevertheless, investors are paying closer attention to cloud growth because Alphabet's enormous capital expenditures (CAPEX) must ultimately be justified by accelerating revenue. The company has guided for $180 billion to $190 billion in CAPEX this year.

Even if consensus expectations are met, representing growth from both a year earlier and the previous quarter, investors are likely to demand more. Market participants expect that even a solid earnings report could disappoint if Google Cloud's growth rate fails to exceed expectations.

Cloud performance seen as a barometer of the global AI investment cycle

The results are also expected to influence sentiment across the broader AI ecosystem. If Google Cloud growth surpasses expectations, investors may interpret that as confirmation that the AI infrastructure investment cycle remains intact, supporting continued demand for graphics processing units (GPUs) and benefiting suppliers of optical modules and networking equipment.

The key question is whether Alphabet has maintained investor confidence in its AI strategy. During the earnings call, analysts are expected to ask whether management will maintain its full-year CAPEX guidance. Investors are also seeking updates on the launch schedule for Gemini 3.5 Pro, Google Cloud's order backlog and expansion of the company's in-house AI processors, or tensor processing units (TPUs).

A significant portion of Alphabet's massive CAPEX is being directed toward AI model development. On July 16, reports that Google had delayed the launch of a new version of Gemini, the centerpiece of its AI strategy, sent Alphabet shares down nearly 4%.

Investor sentiment failed to recover afterward, with the stock falling another 2.2% in the following trading session on July 17. Some analysts estimated that Alphabet's market capitalization declined by roughly $200 billion on July 16 alone.

The decline reflected not only concerns over the Gemini delay but also broader investor anxiety about intensifying AI competition and the scale of Alphabet's AI-related capital spending.

At Google I/O 2026 in May, Google introduced the lightweight Gemini 3.5 Flash model and said Gemini 3.5 Pro, its flagship model, would launch in June. The product, however, has yet to be released, with its launch repeatedly delayed.

While Google has rolled out lighter and lower-cost models, including Gemini 3.6 Flash, Gemini 3.5 Flash-Lite and Gemini 3.5 Flash Cyber, the flagship Gemini 3.5 Pro remains unavailable. Bloomberg and other media outlets have reported that the delay could extend for several months.

Gemini flagship delayed over coding performance

The primary reason for the delay is believed to be coding performance. According to Bloomberg and Reuters, internal testing showed that Gemini 3.5 Pro failed to meet Google's expectations in coding tasks, prompting the company to postpone its release. Coding capability has become a defining benchmark for frontier AI models because it is closely linked to agentic AI functionality.

Google is therefore believed to be delaying the launch until the model reaches its internal performance targets.

Adding to investor concerns has been the recent departure of several key AI researchers.

While movement of AI talent between major technology companies and startups is common, one of the most significant departures was Noam Shazeer, one of the co-leaders of the Gemini project. Shazeer joined OpenAI, prompting Reuters to describe the move as the loss of a key leader behind Gemini.

Another high-profile departure was John Jumper, chief scientist at Google DeepMind, who joined Anthropic. Jumper shared the 2024 Nobel Prize in Chemistry with Google DeepMind CEO Demis Hassabis for developing the AI protein-structure prediction system AlphaFold.

In addition, two core Gemini researchers, Jonas Adler and Alexander Pritzel, joined Anthropic in June. Combined with the delay of Gemini 3.5 Pro, these departures have intensified investor scrutiny of Google's competitiveness in AI models at a time when rivals continue introducing increasingly capable systems.

Following Anthropic's Claude Fable 5, competitors including OpenAI with its GPT-5.6 family, xAI with Grok 4.5 and Meta with Muse Spark 1.1 have released US models with significantly enhanced coding and agentic capabilities, while China's Moonshot AI unveiled Kimi K3 on July 16, sending shockwaves through the US AI industry. Gemini 3.5 Pro will need to demonstrate performance at least on par with those offerings.

AI search impact on advertising also under scrutiny

Another major topic expected to dominate the earnings call is the outlook for Alphabet's advertising business.

Advertising remains the company's primary revenue source. Revenue from Search, YouTube and related businesses is expected to approach $70 billion this quarter, although growth is projected at only 13% to 14%. Investors are increasingly focused on whether AI-powered search features, including AI Overviews and AI search advertising, are cannibalizing Google's traditional advertising business or creating new monetization opportunities.

One potential catalyst for improving investor sentiment is Google's next-generation AI chip strategy. According to The Information, Alphabet is developing a new AI server chip under the codename Frozen v2.

The chip is reportedly not a direct successor to Google's existing TPUs but a specialized processor designed to complement them by improving inference performance for Gemini models.

According to The Information, Frozen v2 is intended to implement certain Gemini functions directly in silicon, allowing AI models to run more efficiently on hardware. Reuters reported that Google declined to comment on the report.

Wall Street analysts continue to view Alphabet's greatest competitive advantage as its full-stack AI ecosystem and consumer base. The company uniquely combines Search, YouTube and Android with a vast global consumer base, proprietary AI models, Google Cloud and custom AI chips under one roof. Analysts argue that no other AI or cloud provider—including Amazon Web Services (AWS), Microsoft, Meta, OpenAI or Anthropic—currently possesses such a comprehensive and integrated AI ecosystem.

Alphabet Inc. reported second-quarter revenue of $119.8 billion, slightly ahead of market expectations and up 24% from a year earlier. Revenue from its closely watched cloud business reached $24.8 billion, posting 82% growth and significantly exceeding analysts' consensus estimates. Despite the strong results, Alphabet shares fell about 4% during trading after the company announced plans to further increase capital expenditures (CAPEX).

​During its second-quarter earnings conference call, which began at 5:30 a.m. on July 23, Alphabet raised its full-year CAPEX outlook to $195 billion-$205 billion, up by $15 billion from its previous guidance of $180 billion-$190 billion.

​Alphabet shares initially fluctuated following the earnings release and traded little changed before declining nearly 5% in after-hours trading after the increased investment plan was announced. Reuters reported that investor concerns were driven not only by the higher CAPEX outlook but also by continued delays to the company's flagship artificial intelligence (AI) models.

​Chief Financial Officer Anat Ashkenazi defended the increase in capital spending, saying demand continues to exceed the company's investment pace. She said aggressive investment in AI data centers enabled Alphabet to secure computing capacity faster than expected, helping Google Cloud achieve its strongest quarterly growth on record.

​Alphabet's second-quarter revenue of $119.8 billion exceeded the LSEG consensus estimate of $116.9 billion and the FactSet and Visible Alpha forecasts of $117.0 billion-$117.1 billion by about 2.3%. Total advertising revenue came in at $81.6 billion, slightly above the LSEG estimate of $81.1 billion.

Google Cloud significantly outperformed analysts' expectations, with revenue rising 82% year over year to $24.77 billion, representing a 10.3% upside surprise. The advertising business remained solid and largely met consensus expectations.

​However, Alphabet's Free Cash Flow (FCF) turned negative for the first time. FCF is calculated by subtracting CAPEX from Operating Cash Flow (OCF), which represents cash generated from the company's core business operations.

​Alphabet consumed $5.9 billion in cash during the quarter. Reuters cited Thomas Monteiro, Senior Analyst at Investing.com, as saying that increasing capital expenditures after posting the company's first negative FCF was inappropriate.

​During the conference call, Alphabet also highlighted the commercialization of its Tensor Processing Unit (TPU). According to Ashkenazi, the company generated its first TPU sales revenue during the quarter, although meaningful revenue from commercial contracts is expected to begin next year.

​Business Insider reported on Alphabet's second-quarter earnings. Alphabet Chief Executive Officer Sundar Pichai said the Gemini app now has 950 million monthly active users, approaching the scale of OpenAI's ChatGPT.

Reuters reported that while Google Cloud has positioned Alphabet as one of the primary beneficiaries of the AI boom, investors remain concerned about delays in the rollout of Gemini's latest AI models and increasing competition from rival systems. Analysts also directed numerous questions during the conference call regarding Alphabet's AI strategy.

​Pichai acknowledged, as previously reported by international media, that the company still needs to improve coding performance.

​"We continue to lead in many frontier areas," Pichai said. "There are also areas where we believe further improvement is needed, particularly coding and agentic coding."

​He added that Alphabet continues testing Gemini 3.5 Pro while training Gemini 4, saying, "We are dedicating significant computing resources and effort to Gemini to maintain our competitiveness."

​Pichai also said the Gemini app has reached 950 million monthly active users, while daily active users have tripled over the past year.

​Meanwhile, Microsoft Corp. and Amazon.com Inc. are scheduled to report quarterly earnings next week.

CocktailIO, an artificial intelligence (AI) and cloud computing company, has been selected as a consulting provider for the advanced consulting track of the South Korean government's 2026 Cloud Service Adoption and Expansion Program for Small and Medium-sized Enterprises, led by the Ministry of Science and ICT and the National IT Industry Promotion Agency (NIPA).

The company said it has already completed its first consulting match following its designation as a program provider, demonstrating the competitiveness of its cloud consulting capabilities at an early stage.

Building on its experience in infrastructure optimization, CocktailIO plans to offer end-to-end cloud services spanning consulting, solution deployment and operational management. The company aims to help participating businesses implement cloud migration strategies tailored to their individual operating environments.

Under the program, CocktailIO will provide advanced consulting by conducting detailed analyses of each company's business environment and developing optimized cloud adoption strategies. Participating SMEs will be able to reduce consulting costs through government support while using the recommendations to accelerate AI transformation (AX) initiatives and improve business operations.

The company said it plans to leverage the project to expand customized technical support and advanced consulting services for enterprises preparing to adopt AI-driven digital transformation, further strengthening its cloud and AI business.

Lenovo Korea has completed the deployment of a Lenovo ThinkAgile HX hyperconverged infrastructure (HCI) solution powered by Nutanix at Jangan University, helping the institution modernize its IT infrastructure while improving service continuity.

Jangan University needed a highly reliable and easy-to-manage platform capable of delivering uninterrupted digital services to students and faculty despite having limited IT personnel. To replace its aging infrastructure, the university deployed a Nutanix HCI solution built on Lenovo ThinkAgile HX650 V3 nodes.

According to Lenovo Korea, the infrastructure modernization has significantly improved system uptime while reducing unexpected service interruptions, minimizing disruptions to the university's academic administration systems. The deployment has also reduced platform management and administrative workloads by approximately 30%, allowing IT staff to focus on more strategic initiatives.

The Lenovo ThinkAgile HX650 V3 integrates computing, storage and virtualization into a single platform and is powered by 5th Gen Intel Xeon Scalable processors. Lenovo said the system's highly scalable architecture enables organizations to expand infrastructure without disrupting existing operations, making it well suited for future growth.

ARGOS Identity, an AI-powered identity platform company, has been selected for the 2026 Future Industry-Based Enterprise Activation Support Program (Open Innovation) jointly operated by Gangnam-gu and The Invention Lab.

The program is an open innovation initiative designed to foster collaboration between large and mid-sized enterprises and startups. It supports proof-of-concept (PoC) projects and commercialization through business diagnostics, key performance indicator (KPI) development, one-on-one matching with corporate partners, investor presentations and demo days. A total of 108 companies applied, with eight ultimately selected.

Through the program, ARGOS Identity plans to expand the commercial adoption of Omni, its AI agent-based operations automation platform, by validating its use across multiple industries, including telecommunications and retail.

In the telecommunications sector, the company aims to automate document review during customer subscription and service activation. In retail, it plans to standardize unstructured data collected from physical stores and online channels before integrating the information into enterprise systems. In financial services, ARGOS Identity will explore automating Know Your Business (KYB) and Anti-Money Laundering (AML) review processes for global corporate customers.

According to the company, Omni automates repetitive document review and data verification tasks using AI agents. The platform analyzes external documents—including business registration certificates, contracts, application forms and supporting documents—verifies them against enterprise-defined policies, and transfers validated information into internal systems. Cases requiring additional review are routed to human operators through a human-in-the-loop (HITL) workflow.

ARGOS Identity said it has strengthened its AI-powered document analysis and identity verification technologies by supplying electronic Know Your Customer (eKYC) solutions to customers across financial services, fintech, commerce and ticketing industries. Through the new program, the company plans to expand beyond identity verification into AI agent-driven enterprise document automation.

Worldwide end-user spending on artificial intelligence (AI) models and platforms is expected to reach $64 billion in 2026, according to Gartner. The forecast represents a 63.4% increase from $39 billion in 2025. Spending on generative AI models is projected to rise 117%, while expenditure on AI platforms is expected to grow 36.9%.

As companies subject their AI budgets to stricter scrutiny, they are placing greater emphasis on utilization efficiency, cost controls and measurable outcomes, Gartner said. AI spending is consequently shifting toward vendors that can demonstrate clear value across cost, latency, performance and reliability.

Vendors that integrate evaluation, cost transparency and usage-tracking capabilities into customer workflows are therefore gaining an advantage by making AI adoption easier to manage and optimize, Gartner said. However, as spending shifts toward usage-based models, vendors are under growing pressure to demonstrate actual adoption, sustained use and reliable profitability.

Driven by these trends, the market for domain-specific language models (DSLMs) and specialized generative AI models is forecast to grow 210% in 2026.

AI infrastructure software company Xiilab has joined the Korea Data Center Energy Efficiency Association (KDCEA) as a member, stepping up its push into the artificial intelligence (AI) data center market.

Xiilab said it holds Nvidia Elite Partner certification, the highest partner tier in Nvidia's software ecosystem, and has secured proprietary technology that maximizes the utilization of idle graphics processing unit (GPU) resources through its in-house solution, AstraGo.

The company said it plans to use its KDCEA membership to actively participate in technology and policy discussions with key member companies and industry experts while identifying practical collaboration opportunities related to data centers. CEO Chae Jung-hwan will oversee the company's activities within the association.

"As the industry enters the Nvidia Vera Rubin generation, system engineering technologies within AI data centers are becoming increasingly important," Chae said. "The larger the AI data center environment, the more critical the efficient operation of GPU resources becomes in determining overall performance."

KDCEA is a non-profit organization operating under South Korea's Ministry of Trade, Industry and Resources. More than 100 companies, including data center operators, design firms, investors and solution providers, participate in the association.

Remote connectivity and digital workplace solutions provider TeamViewer has added an artificial intelligence (AI)-powered feature called Tia Scripting to its TeamViewer DEX (Digital Employee Experience) platform, enabling IT teams to generate device automation scripts using natural-language prompts without requiring specialist coding expertise.

TeamViewer said Tia Scripting allows IT teams to describe a problem they want to solve or an intended outcome in plain language, after which the system generates a device automation script that can be reviewed and deployed without advanced programming knowledge.

TeamViewer DEX is an IT operations platform that monitors the health of employee PCs, laptops and applications in real time and automatically resolves issues before they affect users. By detecting anomalies through continuous monitoring, the platform can initiate automated remediation across thousands or even tens of thousands of managed devices.

The company said conventional IT automation tools are largely built around predefined templates and standard operating procedures, limiting their ability to address configuration issues, compliance requirements and recurring operational problems. It said Tia Scripting is designed to overcome those limitations by enabling customized automation tailored to individual IT environments.

According to TeamViewer, Tia Scripting supports organization-specific automation by reflecting each company's unique IT infrastructure and operating conditions. After an IT team enters a problem in natural language, the feature generates a script optimized for that scenario. The script can then be reviewed before being securely deployed to designated devices or device groups.

The company said the feature can be configured to verify whether security certificates have been applied, check the operational status of critical applications, prevent unauthorized software modifications and automatically restore security software if it is removed. It can also quickly generate customized automation scripts to address recurring connectivity issues caused by network settings, as well as validate organization-specific device management policies and regulatory compliance requirements.

TeamViewer also said IT teams can reduce the time spent resolving recurring issues by capturing proven remediation methods within the platform and reusing them instead of addressing device problems individually. As enterprise device environments continue to expand and become more complex, the script reuse capability enables organizations to repeatedly leverage validated automation assets, improving overall IT operational efficiency.

AI and data infrastructure solutions provider HS Hyosung Information Systems said on July 20, citing a report from industry analyst firm GigaOm, that Hitachi Vantara has been recognized as both a Leader and Fast Mover for the sixth consecutive year in the firm's evaluation of unstructured data management platforms.

HS Hyosung Information Systems is a joint venture established by Hyosung Group and Hitachi Vantara, with each company holding a 50% stake. The company is responsible for distributing and providing technical support for Hitachi Vantara's solutions in South Korea.

According to the 2026 GigaOm Radar for Unstructured Data Management report, Hitachi Vantara's VSP One platform was recognized for its integrated data management capabilities, policy automation, operational visibility and overall efficiency.

The report highlighted several key strengths, including enhanced governance for unstructured data through policy automation, data classification, Immutable Object Lock for data immutability, and data retention and tiering policies. It also cited enterprise workflow integration that automates data movement, indexing and transformation across multiple data sources, improving operational efficiency. In addition, the report recognized the platform's unified data fabric architecture, which enables organizations to manage structured and unstructured data distributed across on-premises and external environments under consistent policies.

HS Hyosung Information Systems said the recognition demonstrates Hitachi Vantara's enterprise capabilities in unstructured data management, including governance, automation and data control across hybrid IT environments.

According to the company, VSP One is a unified data management platform for block, file and object storage across hybrid cloud environments. By extending the management capabilities of VSP 360 to VSP One Object, the platform enables centralized monitoring and policy administration. It also optimizes metadata processing to improve performance for small object workloads by up to four times.

Google's Gemini 3.5 Pro has reportedly been delayed due to performance issues, while Chinese AI companies have unveiled a new generation of frontier-class models that are rapidly narrowing the gap with leading US systems.

Moonshot AI recently introduced Kimi K3, a 2.8-trillion-parameter model, while Alibaba unveiled a preview of Qwen 3.8 Max, its flagship 2.4-trillion-parameter multimodal model.

Kimi K3, in particular, has attracted attention from international media after posting benchmark results approaching those of Anthropic's Fable 5 and OpenAI's GPT-5.6. On several benchmarks, it outperformed GPT-5.6 Sol, OpenAI's highest-end reasoning model. Reuters quoted analysts as saying that the long-held view that Chinese AI models lag US frontier models by roughly six months is becoming increasingly difficult to defend.

Moonshot AI plans to release Kimi K3's model weights on July 27. In Artificial Analysis (AA)'s Intelligence Index, an independent benchmark of leading AI models, Kimi K3 ranked third with a score of 57, behind only Anthropic's Fable 5 and GPT-5.6, surpassing Gemini 3.0 Pro, Claude Opus 4.8, GPT-5.5 (xhigh), Claude Sonnet 5 and GLM-5.2.

The model demonstrated particularly strong performance in agentic AI and coding. It ranked fourth in AA's Coding Agent Index, trailing only GPT-5.6 Sol, GPT-5.6 Terra and Fable 5.

Kimi K3's overall ranking exceeded its coding-agent ranking largely because of its relatively weak performance on the SWE-Atlas-QnA benchmark.

AA's Coding Agent Index is calculated as the average of three benchmarks: Terminal-Bench v2, DeepSWE and SWE-Atlas-QnA. Kimi K3 scored 84% and 64% on the first two benchmarks, placing it among the top performers, but achieved only 23% on SWE-Atlas-QnA, significantly reducing its overall average.

Even so, Kimi K3 outperformed Fable 5 on Terminal-Bench v2 and exceeded SpaceXAI's Grok 4.5 and Claude Opus 4.8 on DeepSWE. It also ranked first on Frontend Code Arena, outperforming GPT-5.6.

The model also posted leading results on several agent benchmarks. It narrowly surpassed GPT-5.6 Sol on τ³-Banking, which evaluates tool use in multi-turn customer service scenarios modeled after retail banking environments.

Kimi K3 also ranked first on AutomationBench-AA, which evaluates SaaS workflow automation, and SpreadsheetBench 2, which measures spreadsheet task performance. On BrowseComp, a benchmark for web search and information retrieval, it achieved a state-of-the-art score of 91.2, exceeding GPT-5.6 Sol's previous record of 90.4.

The model ranked second on AA-Briefcase, CharXiv (RQ) with Tool, and Zerobench with Tool, and third on GDPval-AA v2, which measures workplace task performance across 44 occupations.

Its performance was comparatively weaker on several of the most demanding benchmarks. Kimi K3 ranked sixth on Humanity's Last Exam (HLE), behind Gemini Pro 3.1 Preview, and also placed sixth on CritPt, a benchmark evaluating reasoning over unpublished physics research. The top four positions on CritPt were occupied by GPT models, followed by Fable 5.

High performance comes with higher costs

Artificial Analysis noted that Kimi K3 remains relatively expensive to use because it is currently accessible only through Moonshot AI's API ahead of the release of its open weights. In addition, the model does not currently allow users to disable reasoning, meaning reasoning tokens are always generated and billed.

According to AA, Kimi K3 also tends to produce unusually verbose responses. Across the entire Intelligence Index evaluation, the model generated approximately 130 million output tokens, more than double the benchmark average of 63 million. The higher token usage substantially increases inference costs.

Once Kimi K3 becomes available as an open-weight model and third-party hosting providers begin offering it, market competition could reduce pricing. However, its enormous 2.8-trillion-parameter scale is likely to keep costs well above those of DeepSeek. Although Kimi K3 uses a Mixture-of-Experts (MoE) architecture that activates only 16 of 896 experts during inference, limiting computational requirements relative to its total parameter count, the GPU memory and compute resources required to serve the model remain substantial.

Alibaba introduces Qwen 3.8 Max

Alibaba unveiled a preview of Qwen 3.8 Max at the World Artificial Intelligence Conference (WAIC) in Shanghai on July 19.

The new model contains 2.4 trillion parameters, representing a significant increase from its predecessor. It is a large multimodal model capable of processing text, images, video and documents within a single system. Alibaba said it also plans to release the model weights as open source in the near future.

The company described Qwen 3.8 Max as its flagship model for coding and AI agents while claiming that its general capabilities rank just behind Fable 5. Alibaba highlighted significant improvements in agentic coding, software development, front-end development and function calling, as well as stronger tool use, complex workflow execution and multi-step task automation.

Alongside the preview release, Alibaba integrated Qwen 3.8 Max into its Token Plan, Qoder and QoderWork platforms.

Because the model was announced only recently, independent benchmarking organizations such as Artificial Analysis have not yet published evaluation results. However, early community testing on platforms including Aider Leaderboard, LiveBench and WebDev Arena suggests that the model is competitive with GPT-5.6 and Kimi K3 in front-end development, agentic coding and web user interface generation.

Weights & Biases (W&B) had previously ranked the earlier Qwen 3.7 Max among the top models for spreadsheet reasoning and workflow automation, raising expectations for the new release.

Qwen 3.8 Max also employs an MoE architecture, although Alibaba has not disclosed the number of active parameters. That figure will play a key role in determining inference costs. Qwen 3.7 Max was priced at $2.50 per million input tokens and $7.50 per million output tokens, but the substantially larger 3.8 Max model could carry higher pricing.

Google faces mounting pressure

The release of increasingly competitive Chinese models has coincided with reports that Gemini 3.5 Pro has been delayed because it failed to meet Google's internal coding performance targets.

The model had originally been scheduled for release in June. According to Bloomberg and other media outlets, Gemini 3.5 Pro fell short of internal benchmarks in code generation and debugging, potentially delaying its launch by several months. Google reportedly attempted to improve performance by fine-tuning the model with additional coding datasets in June 2026, but the results were insufficient, forcing engineers to restart training.

Pressure on Google has intensified following OpenAI's release of GPT-5.6, which significantly improved coding capabilities and cost efficiency, as well as new flagship models from Meta and SpaceXAI — Muse Spark 1.1 and Grok 4.5, respectively — emphasizing coding and agentic AI.

OpenAI, Anthropic and Google have generally been regarded as the three leading frontier AI developers. Analysts say that if Google's next-generation model fails to outperform competing systems from Meta and SpaceXAI, the company could lose its position among the industry's leading frontier model providers.

With Chinese competitors rapidly improving, Google appears to have opted to prioritize model quality over speed of release. Following Bloomberg's report, Google's share price fell nearly 4%. A Google spokesperson said the company continues to release models rapidly while balancing performance and cost efficiency and is testing Gemini 3.5 Pro and an upgraded Flash model with partners.

Chinese AI models gain ground in the US

Meanwhile, data from AI model aggregation platform OpenRouter indicate that Chinese AI models accounted for a record 58% of AI tokens used by US companies during the first week of July, surpassing US-developed models for the first time.

Bloomberg's analysis of OpenRouter's public traffic data found that Chinese models accounted for 58% of total usage, reaching as high as 63% during some weeks. Chinese models processed roughly 18 trillion tokens during the week, compared with approximately 5.5 trillion tokens for US models.

DeepSeek remained the most widely used individual model. Bloomberg and other international media noted that Chinese models accounted for less than 10% of usage at the beginning of 2025, highlighting the rapid shift over roughly a year.

OpenRouter provides unified access to more than 400 AI models from companies including OpenAI, Anthropic, Google, Meta, DeepSeek and Alibaba through a single API. Because its publicly available traffic data reflect actual model usage by AI startups and developers, OpenRouter has become a closely watched indicator of real-world AI market share.

International media attributed the shift largely to aggressive pricing. Developers building coding automation and agentic workflows are increasingly adopting Chinese open-weight models, which can cost 10 to 50 times less than leading US frontier models.

Digital workplace platform provider Omnissa has introduced Workspace ONE, a unified endpoint management (UEM) platform that now supports Windows Server management. The addition enables enterprises to manage Windows servers alongside desktops, laptops, mobile devices and rugged computing devices through a single platform.

Omnissa said Workspace ONE is built on a cloud-native architecture that enables organizations to enforce server security policies, automate operations, perform software updates and manage server configurations. The company said the platform can reduce reliance on complex and costly legacy server management tools while allowing servers and endpoint devices to be administered under consistent policies and automated workflows.

Omnissa also emphasized that Workspace ONE offers a lower-cost approach to managing Windows Server environments than Microsoft System Center Configuration Manager (SCCM). According to the company, the platform strengthens security and regulatory compliance while improving the efficiency of server lifecycle management.

Workspace ONE is positioned by Omnissa as an intelligent hub for enterprise endpoint management.

According to the company, the platform supports continuous enforcement of server security policies through over-the-air (OTA) configuration management. Compared with traditional management methods that rely on network domains, Workspace ONE provides greater operational flexibility. Its cloud-native management capabilities also enable real-time monitoring of server security posture and can automatically execute corrective actions when necessary.

The platform further incorporates artificial intelligence (AI) and machine learning (ML) analytics to proactively identify and address server performance degradation and security risks. In addition to remote control capabilities, administrators can remotely view installed server roles, hardware and software inventory, and access logs, providing greater visibility into server operations.