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A bright, sunlit modern office where a lone professional sits at a sleek desk holding a tablet. On the tablet’s screen is a neatly arranged grid of privacy toggles—camera, microphone, location icons with on/off switches. In the air around them, semi-transparent lock symbols and data-flow lines drift like UI overlays. Warm, natural morning light streams through a large window, creating a hopeful, empowered atmosphere.

Inside the AI Camera Disaster That Fined Empty Seats and Trashed 5,100 Bogus Tickets - Yahoo

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Keeping its vast network of 145 million wireless users and 16 million broadband customers connected is paramount for AT&T. A brief 20-minute service disruption can lead to significant financial repercussions for the company, as it frequently offers bill credits to customers during service lapses.

While most outages are localized and generally fly under the radar, there are occasions when they impact millions of users. For instance, in February 2024, a significant wireless outage affected over 92 million voice calls and interrupted more than 25,000 911 calls, as reported by the US Federal Communications Commission. Other leading telecommunications companies like Verizon and T-Mobile have similarly faced substantial service interruptions in recent years.

In an effort to enhance outage responses and customer communication, AT&T embarked on a mission to centralize its network interruption detection system. "We need to be aware of any network issues immediately and resolve them, ideally before customers notice," stated Andy Markus, AT&T's chief data and AI officer, in an interview with Business Insider.

The initiative began in 2017 with the development of an end-to-end incident management system (EEIM), leveraging technology from various providers, including MongoDB and Snowflake. Initially focused on conventional machine learning techniques, AT&T later incorporated generative and agentic AI features, broadening the system's capabilities, according to Markus. This tool has been effectively utilized to manage service disruptions impacting both individual customers and small enterprises.

Currently, the EEIM employs a combination of AI-driven and machine-learning techniques to determine the underlying causes of disruptions, prioritize issues, suggest remote fixes, and alert customers proactively when outages occur, Markus explained.

Telecom outages can arise from numerous factors such as severe weather, technical failures, cyberattacks, and physical infrastructure damage. When customers experience connectivity issues with their mobile or landline phones, or internet services, they often inundate AT&T’s customer service departments with requests for assistance.

In early 2017, AT&T recognized the need for a more agile response to outages. The company created a cross-functional team to garner input from employees across various departments, including IT, data analytics, and AI, with the intent of aligning the new EEIM with existing network technologies while also integrating innovative AI tools into their operations.

Frontline technicians, responsible for network maintenance and repair, were also consulted, as were the network teams that monitor traffic and swiftly restore services during interruptions, Markus noted.

The EEIM enabled AT&T to reorganize an extensive dataset of 10 petabytes, equivalent to 5,000 billion pages of text. This data encompassed network logs, alarm signals, incident dispatch records, and details about outages. Markus emphasized that consolidating and restructuring this data was critical for identifying issues, predicting their occurrence, directing responses, and supporting new features.

MongoDB, established in 2007 as a document database provider, played a vital role during this phase. Its platform is designed for scalability, allowing AT&T to expand storage capacity without the need for a complete system overhaul, referred to as "sharding." This approach facilitates data distribution across multiple systems to handle extensive datasets, explained Ben Cefalo, MongoDB's chief product officer.

Additionally, the EEIM relies on Microsoft Azure for cloud computing, combined with data analytics from Databricks and incident reporting through Snowflake, Markus stated.

According to an AT&T representative, the EEIM was first implemented for broadband fiber services in early 2018 and expanded the following year to include digital subscriber lines (DSL).

In June 2018, AT&T introduced a dedicated application called Atlas, utilized by field technicians. It integrates machine learning and AI models to diagnose outages and recommend remediation plans.

AT&T had already been using these traditional AI methods prior to Markus's arrival in July 2020, but his leadership accelerated the adoption of AI across the organization. Currently, around 100,000 employees utilize AT&T's generative AI tools, processing more than 27 billion tokens daily and refining smaller language models to enhance cost management.

By early 2021, the company also launched a proactive customer notification system through the EEIM. "This really alleviates customer stress and frustration," Markus remarked.

With the introduction of generative AI features in the EEIM during the first quarter of 2022, the system began to analyze historical data to identify the root causes of outages. In early 2025, AI agents were incorporated into the platform, enabling them to gather outage information from customers, resolve issues, and provide case details to technicians when on-site intervention is needed.

Markus noted that AT&T has developed over 30 AI models to forecast potential problems like configuration errors, weather impacts, and system failures, allowing the system to act preemptively. "This capability helps us pinpoint active issues or anticipate future challenges," he explained.

Overall, the AI-enhanced EEIM has allowed AT&T to avoid 3.1 million unnecessary field dispatches and reduce customer downtime by more than 12 million hours in the past year, Markus concluded.

AT&T's AI Technology Minimizes Customer Downtime and Service Interruptions

A bright, sunlit network operations center showing a confident adult technician tapping a large touchscreen map of the U.S. with glowing green lines and nodes, signaling seamless connectivity; through a panoramic window a bustling cityscape basks in daylight; a bold AT&T logo hangs prominently above the central console.

AT&T is using AI to prevent frustrating network outages - Business Insider

- AT&T prioritizes customer connectivity, with outages potentially costing the company significantly.  
- The EEIM system uses AI to identify and resolve disruptions proactively and efficiently.  
- AI enhancements have reduced customer downtime by over 12 million hours in the past year.

For AT&T, keeping its 145 million wireless and 16 million broadband customers online is a top priority. A 20-minute outage can be costly for the telecommunications giant, which credits customers' bills when they temporarily lose service.

Typically, outages are highly localized and don't generate such national attention, but in rare instances, they can affect millions. In February 2024, a major wireless outage at AT&T blocked more than 92 million voice calls and over 25,000 attempted 911 calls, according to a report published by the US Federal Communications Commission. Other major telecom providers, including Verizon and T-Mobile, have also experienced broad service disruptions in recent years.

To respond more effectively to outages and improve communication with customers, AT&T set out to centralize its system for identifying network interruptions.

"We have to know of any network interruptions immediately and solve them, ideally before any customer feels it," Andy Markus, AT&T's chief data and AI officer, told Business Insider.

In 2017, the company began creating its end-to-end incident management system, or EEIM, built on technologies from firms including MongoDB and Snowflake. Initially grounded in traditional machine learning, AT&T added generative and agentic artificial intelligence capabilities in subsequent years, expanding the tool's functionality, Markus said. The tool has been deployed to address service disruptions for individual customers and small businesses.

Today, the EEIM leverages a mix of AI and machine-learning capabilities that can identify the root cause of a disruption, triage issues by suggesting fixes that can be handled remotely or by sending human technicians into the field, and proactively alert customers when an outage occurs, Markus said.

The tech

Telecommunications outages can stem from a wide variety of events, including inclement weather, system failures, technical glitches, cyberattacks, and infrastructure damage. As customers become aware of connection issues with their smartphones, landlines, or internet connections, they can inundate AT&T's customer service centers with requests to get their service back online.

Markus said that in the first quarter of 2017, AT&T sought to develop a system that would be more responsive to outages. When the initiative started, AT&T gathered a cross-functional team to encourage broad input from employees across IT, data, and AI. The goal was to develop an EEIM around AT&T's existing network technology while developing new AI-enabled tools to integrate into workflows.

Feedback was also sought from the frontline field technicians responsible for maintaining and repairing the company's network, and from the network team that monitors traffic and quickly restores services when interruptions occur, Markus said.

Markus said AT&T also used its EEIM to reorganize 10 petabytes of data — the equivalent of 5,000 billion pages of printed text. This included pulling in network logs, network alarms, incident dispatch tickets for tracking network disruptions, and outage details. He added that the data needed to be consolidated and reorganized to allow AT&T to identify issues and predict when they may occur, direct team members to take action, and support new features and functionality.

MongoDB, a document database vendor, played a central role at this step. Founded in 2007, MongoDB's platform is designed to be elastic, meaning that as data and traffic needs grow, AT&T only needs to add more capacity, not re-architect the entire platform as new data sources are added, said Ben Cefalo, MongoDB's chief product officer. Cefalo calls this method "sharding" and said it allows data to be distributed across multiple machines and handle large data sets.

AT&T's EEIM also relies on the cloud computing platform Microsoft Azure, data analytics via Databricks, and incident reporting via Snowflake, Markus said.

An AT&T spokesperson said the EEIM was launched for broadband fiber in the first quarter of 2018. In the first quarter of the following year, it expanded to cover digital subscriber lines, or DSLs.

In June 2018, AT&T launched a separate application called Atlas, which is used by field technicians and relies on a blend of machine learning and AI models to understand the root cause of an outage and recommend a plan to fix it.

AT&T had embraced these traditional forms of AI before Markus joined the company in July 2020, but his arrival spurred broader companywide AI adoption. Markus said 100,000 employees have access to AT&T's generative AI tools and that the company consumes over 27 billion tokens per day, while also fine-tuning small language models to help manage costs.

By the first quarter of 2021, the company had also launched its proactive customer notification system, Markus said, which is enabled through its EEIM. "That solves a lot of customer tension and frustration," he said.

The outcome

Generative AI features were added to the EEIM in the first quarter of 2022, enabling the system to identify the root causes of outages based on historical data from similar situations in AT&T's past, Markus said.

During the first quarter of 2025, AI agents were added to the platform, Markus said. They can interact with customers to gather information about outages, take action to resolve them, and provide case details to technicians if they need to go into the field to solve problems.

Markus said AT&T has also built more than 30 AI models to help predict when configuration issues, weather, system failures, and other problems may occur, enabling the system to be more proactive.

"It allows us to really drill down into the signals and trends that help us identify either an active issue or an issue that may be coming," Markus said.

In total, the AI-enabled EEIM has helped AT&T prevent 3.1 million unnecessary field dispatches and reduced customer downtime by more than 12 million hours over the last year, Markus said.

AT&T's AI System Reduces Customer Downtime and Outages

At Fireworks' headquarters in San Mateo, California, the founders are making waves in the tech industry. Pictured in the front row are Chenyu Zhao, CEO Lin Qiao, and Benny Chen, while the back row features James Reed, Pawel Garbacki, Dmytro Ivchenko, and Dmytro (Dima) Dzhulgakov.

Finance leaders are increasingly uneasy about the rising expenses associated with the latest artificial intelligence models, prompting many to encourage their teams to explore open-source options. This trend is proving beneficial for Fireworks, a cloud startup vying with tech giants like Amazon and Google to provide infrastructure that developers can integrate into their applications. Recently, the Nvidia-backed startup announced that it has surpassed $1 billion in annualized revenue, a remarkable fivefold increase compared to the previous year. Additionally, Fireworks has successfully secured $1.5 billion in a funding round, pushing its valuation to $17.5 billion.

"We're witnessing a super-linear surge in demand," Lin Qiao shared during a CNBC interview at Fireworks' headquarters. "This is a unique moment to capitalize on this market opportunity."

While Fireworks may not rival the likes of Anthropic and OpenAI—whose valuations have soared beyond $800 billion each this year, nor does it come close to the trillion-dollar tech giants—it is clear that the company's impressive revenue achievement indicates a level of dissatisfaction with offerings from leading AI labs.

Moreover, this success highlights a growing narrative that Amazon, Microsoft, and Google do not have a total grip on the cloud computing landscape.

The market is responding favorably to other user-friendly cloud infrastructure providers as well, with DigitalOcean shares soaring 149% this year amidst rapid growth. Similarly, CoreWeave, which leases Nvidia graphics processing units (GPUs), raised $1.5 billion through its IPO last year and has reached a valuation of $42 billion.

Fireworks operates within the inference cloud market by managing computing infrastructure for AI models, standing alongside emerging players like Baseten and Together AI. The company is also entering the training GPU market, competing with established entities such as CoreWeave, Lambda, and Nebius.

Nvidia-supported Fireworks achieves a $17.5 billion valuation as firms seek more affordable AI solutions.

A bustling financial district at warm golden hour light, with a sleek skyscraper front and center bearing a giant Fireworks logo above bold “$17.5 B” figures on its digital marquee. Below, an Nvidia logo banner hangs over the entrance. Overhead, stylized fireworks bloom in the sky as suited professionals pause on the plaza to watch—natural sunlight casting long, dramatic shadows across the scene.

Nvidia-backed Fireworks hits $17.5 billion valuation as companies pursue cheaper AI models - CNBC

- Fireworks has surpassed $1 billion in annualized revenue, growing fivefold from last year.  
- The company raised $1.5 billion with a valuation of $17.5 billion amid rising demand.  
- Finance executives are shifting to open-source AI models, boosting competition in cloud services.

Fireworks founders pose for a photo at the startup's headquarters in San Mateo, California. From left in front row: Chenyu Zhao, CEO Lin Qiao and Benny Chen. Back row: James Reed, Pawel Garbacki, Dmytro Ivchenko and Dmytro (Dima) Dzhulgakov.

The cost of the latest artificial intelligence models is increasingly breeding anxiety among finance executives, who have started directing employees to consider open-source alternatives.

That's boosting cloud startup Fireworks, which competes with Amazon and Google to host models that developers can weave into applications. The Nvidia -backed company said Thursday that it has exceeded $1 billion in annualized revenue, five times what it had last year, and it has now raised a $1.5 billion round at a $17.5 billion valuation.

"We're seeing super-linear demand," Lin Qiao, Fireworks' co-founder and CEO, told CNBC in an interview at the company's headquarters in San Mateo, California. "This is a once-in-a-lifetime opportunity to have this kind of market."

Fireworks is much smaller than Anthropic and OpenAI , which investors have valued above $800 billion each this year, nor is it close to the top names in technology, whose market capitalizations are counted in the trillions. But the startup's revenue milestone suggests that companies aren't completely satisfied with the models coming out of the top labs.

The achievement also presents new evidence that Amazon, Microsoft and Google are not totally dominating in cloud computing.

Shares of easy-to-use cloud infrastructure vendor DigitalOcean are up 149% so far this year as growth has accelerated. CoreWeave , which rents out Nvidia graphics processing units, or GPUs, raised $1.5 billion in an initial public offering last year and is now worth $42 billion.

By managing computing infrastructure for models, Fireworks does business in the inference cloud market, alongside startups such as Baseten and Together AI. It's also started providing GPUs for training AI models, like neoclouds CoreWeave, Lambda and Nebius .

Nvidia-backed Fireworks hits $17.5 billion valuation as companies pursue cheaper AI models

Jensen Huang, the CEO of Nvidia Corp., addressed the media following the company's "Japan AI Ecosystem" event in Tokyo on Thursday, July 16, 2026.

On Wednesday, Nvidia announced the launch of a new AI model aimed at robots and vision AI agents, enhancing its presence in Japan’s physical AI sector.

The new model, dubbed Cosmos 3 Edge, functions as a world model designed to enable systems to perceive and interact with physical environments in real time. Cosmos 3 Edge is built to learn from a broader range of inputs, offering advantages over traditional large language models (LLMs). This rollout follows the earlier introduction of Cosmos 3 in May.

This regional expansion is a focal point of CEO Jensen Huang's two-day visit to Japan, where Nvidia is solidifying its physical AI initiatives by creating a coalition with prominent local companies such as Fujitsu, Hitachi, and Kawasaki Heavy Industries.

"The next frontier of AI lies within the physical realm, presenting a unique opportunity for Japan," Jensen Huang stated during his announcement on Wednesday. "Japan has been at the forefront of modern manufacturing. Now, it has the chance to redefine it for a future dominated by intelligent industries."

The tech powerhouse’s collaborations with Japanese firms come shortly after Microsoft’s significant $10 billion investment in the country, aimed at enhancing AI infrastructure and cybersecurity measures. SoftBank, a major Japanese investment firm, has also heavily invested in the AI surge and is seeking to partner with both Microsoft and Sakura Internet to foster AI development in Japan.

The AI market in Japan is projected to grow to $27.9 billion by 2029, presenting lucrative investment opportunities for U.S. companies, as reported by the International Trade Administration. This growth is largely fueled by Tokyo's proactive efforts to encourage AI integration across various industries and the willingness of local companies to establish international collaborations.

Ajay Rajadhyaksha, Barclays' global chairman of research, commented last month to CNBC that Japan has a competitive edge in Asia, bolstered by diverse AI applications and a promising structural growth outlook.

Nvidia reveals new AI model and enhances Japan's physical AI infrastructure.

Warm morning sunlight bathes a sleek Tokyo AI research center, Nvidia’s logo bold above its entrance and a Japanese flag waving nearby. A silver AI delivery robot glides along stone tiles as two engineers inspect 3D circuit schematics on a tablet, Mt. Fuji rising in the distance.

Nvidia unveils new AI model and expands Japan’s physical AI ecosystem - CNBC

- Nvidia launched Cosmos 3 Edge, an AI model for real-time physical environment navigation.  
- CEO Jensen Huang emphasized Japan's potential to lead in intelligent industry reinvention.  
- Japan's AI market is projected to reach $27.9 billion by 2029, attracting U.S. investment.

Jensen Huang, chief executive officer of Nvidia Corp., speaks to members of the media following the company's "Japan AI Ecosystem" reception in Tokyo, Japan, on Thursday, July 16, 2026.

Nvidia unveiled a new AI model for robots and vision AI agents on Wednesday, deepening its push into the physical AI market in Japan.

The company's new model, Cosmos 3 Edge, is a so-called world model, designed to help systems perceive and navigate physical environments in real time. Cosmos 3 edge is a World models are systems that can learn from a wider range of inputs compared to large language models (LLMs). The rollout follows the launch of Cosmos 3 in May.

The regional expansion takes center stage during CEO Jensen Huang's two-day visit to Japan, where the Silicon Valley chip giant is expanding its physical AI footprint by forming a coalition that local industrial giants, including Fujitsu, Hitachi, and Kawasaki Heavy Industries, intend to join, according to Nvidia.

"The next frontier of AI is in the physical world, and this is a once-in-a-generation opportunity for Japan," Nvidia CEO Jensen Huang said in a Wednesday statement. "Japan invented modern manufacturing. Now, it has the opportunity to reinvent it for the age of intelligent industries."

The tech giant's partnership with Japanese firms comes just months after Microsoft 's $10 billion investment in the country, which aims to build out AI infrastructure and beef up cybersecurity. Japanese investment giant SoftBank has bet heavily on the boom in AI. It's looking to partner with Microsoft and Sakura Internet to develop AI in Japan.

Japan's AI market is expected to reach $27.9 billion by 2029, opening doors for U.S. firms to invest, according to the International Trade Administration. This growth is driven by Tokyo's active push to promote AI adoption across industries, coupled with the eagerness of local firms to forge international partnerships.

Ajay Rajadhyaksha, global chairman of research at Barclays, told CNBC last month that the country holds an advantage in Asia, driven by its diverse AI and clean structural growth stories.

Nvidia unveils new AI model and expands Japan’s physical AI ecosystem

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I run a workplace management platform. Here's how AI is reshaping both the culture and physical form of offices. - Yahoo

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NVIDIA has announced a collaboration with Noetra Corp. to establish the NVIDIA Vera Rubin AI factory, which will be equipped with 13,750 Vera CPUs and 27,500 Rubin GPUs. This significant infrastructure will provide a robust 140 megawatts of data center capacity, leveraging the NVIDIA DSX platform.

This initiative, backed by Japan's Ministry of Economy, Trade and Industry (METI), is poised to supply the essential computing capabilities for Japan's FRONTia Project. This project aims to enhance the nation's industrial ecosystem across various sectors, including manufacturing, logistics, and healthcare.

The NVIDIA Vera Rubin AI factory will focus on developing open multimodal foundation models, which are crucial for creating AI agents, digital twins, robotics, and other physical AI applications. Notably, this facility represents the world’s first national AI infrastructure specifically designed for physical AI, reinforcing Japan's AI landscape across multiple industries.

The factory will be constructed using NVIDIA Vera Rubin NVL72 racks, integrated with the NVIDIA DSX™ platform and interconnected through NVIDIA Spectrum-X™ Ethernet networking. This setup will allow for the creation of advanced multimodal models that support diverse AI deployments.

Central to the FRONTia Project, which is titled "Development of Multimodal Foundation Models with a View to AI Robotics and Physical AI," this effort unites Japan's manufacturing strengths, practical industrial data, and global technology partnerships to innovate reliable multimodal foundation models for physical AI.

Developers and enterprises in Japan will have widespread access to the pretrained weights from Noetra’s multimodal foundation models, in addition to a suite of software tools, including NVIDIA Nemotron™, NVIDIA Cosmos™, NVIDIA Isaac™ GR00T open models, and NVIDIA NeMo™ libraries. This comprehensive resource is designed to expedite the creation of intelligent AI applications and enhance innovations in physical AI.

Jensen Huang, founder and CEO of NVIDIA, expressed pride in collaborating with Japan, stating, "Japan invented modern manufacturing. Now, it is building the AI factories that will power the next industrial revolution." Meanwhile, Ryosei Akazawa, Japan's Minister of Economy, Trade and Industry, highlighted the importance of collaboration between Japan and major global innovators like NVIDIA to harness Japan's unique competencies, signaling a robust future for the nation's physical AI ecosystem.

Hironobu Tamba, CEO of Noetra, emphasized the complexity and scale of the challenges ahead, stating that no single company can address them alone. By working collectively with global partners, Noetra aims to promote Japanese-developed multimodal models and drive the widespread application of physical AI across various industries.

Equipped with the NVIDIA Vera Rubin DSX architecture, this AI factory will not only deliver substantial data center capacity but also optimize AI performance through the advanced networking solutions and silicon design provided by NVIDIA. The DSX platform acts as a reference design that helps infrastructure developers streamline their production timelines, improve token throughput per megawatt, and enhance operational reliability and efficiency.

Japan’s AI Robotics Strategy, released in March, set an ambitious target of capturing over 30% of the global AI robotics market by 2040, which equates to an opportunity worth approximately $133 billion. To support this vision, METI continues to promote a multimodal foundation model program dedicated to robotics and physical AI as part of Japan's larger industrial AI framework.

As the capacity of the AI factory grows, it will facilitate the training of AI models with trillions of parameters, granting organizations throughout Japan access to one of the most sophisticated AI environments available, thereby laying the groundwork for a new wave of intelligent manufacturing and robotics advancements.

Japan's government, industry leaders, and NVIDIA unveil the world's first national AI infrastructure.

A sunrise scene outside Japan’s National Diet Building: government ministers and industrial executives in business attire stand poised with oversized golden scissors at a red ribbon stretched across the entrance. Above them, a large Nvidia logo banner hangs, and a single Japanese flag flutters on a flagpole. Warm natural light illuminates the classic stone façade and the group, conveying optimism and progress.

Japan Government, Industrial Leaders and NVIDIA Launch the World’s First National AI Infrastructure - NVIDIA Newsroom

- NVIDIA partners with Noetra to establish the Vera Rubin AI factory in Japan.  
- The AI factory will support Japan's FRONTia Project for advancing physical AI technologies.  
- It aims to enhance Japan’s AI robotics market share, targeting over 30% by 2040.  

News Summary:

NVIDIA to partner with Noetra Corp. to build the NVIDIA Vera Rubin AI factory with 13,750 Vera CPUs and 27,500 Rubin GPUs to deliver 140 megawatts of data center capacity based on the NVIDIA DSX platform.

The initiative, supported by Japan’s Ministry of Economy, Trade and Industry (METI), will provide the computing foundation for Japan’s FRONTia Project to strengthen the country’s ecosystem across manufacturing, logistics, healthcare and more.

AI factory to create open multimodal foundation models to develop AI agents, digital twins, robotics and physical AI applications.



NVIDIA today announced it is working with Noetra Corp. to launch an NVIDIA Vera Rubin AI factory with 13,750 NVIDIA Vera CPUs and 27,500 NVIDIA Rubin GPUs for national physical AI . Supported by Japan’s AI and industry leaders, the initiative marks the world’s first national AI infrastructure for physical AI, strengthening the country’s AI ecosystem across manufacturing, logistics, healthcare, telecommunications and more.

The new AI factory, established by Noetra, will be architected with NVIDIA Vera Rubin NVL72 racks using the NVIDIA DSX™ platform , connected and scaled with NVIDIA Spectrum-X™ Ethernet networking. It will enable the development of open multimodal foundation models that power AI agents, digital twins, robotics and other physical AI applications.

The NVIDIA Vera Rubin AI factory will provide the computing foundation for Japan’s FRONTia Project, which refers to the project titled, “Development of Multimodal Foundation Models with a View to AI Robotics and Physical AI,” launched by METI. The project brings together the country’s manufacturing expertise, real-world industrial data and global technology leaders to develop highly reliable multimodal foundation models for physical AI.

The pretrained weights of Noetra’s multimodal foundation models will be made broadly available to domestic model developers and enterprises alongside software such as NVIDIA Nemotron™, NVIDIA Cosmos™, NVIDIA Isaac™ GR00T open models, NVIDIA NeMo™ libraries and more. This will accelerate the development of agentic AI and physical AI applications.

“Japan invented modern manufacturing. Now, it is building the AI factories that will power the next industrial revolution,” said Jensen Huang, founder and CEO of NVIDIA. “NVIDIA is honored to partner with Japan and its industrial leaders to build the AI infrastructure that will power the country’s industries, its economy and a new generation of innovation.”

“Japan has launched the FRONTia Project, which will serve as the core of the country’s physical AI ecosystem,” said Ryosei Akazawa, Japan’s Minister of Economy, Trade and Industry. “By fostering collaboration between Japan and leading global innovators — including NVIDIA — and leveraging Japan’s strengths, such as its onsite expertise and manufacturing technology infrastructure, we will build highly reliable multimodal foundation models and contribute to solving global social challenges.”

“Bringing physical AI into the real world requires enormous computing, data and foundational technologies — challenges no single company can solve alone,” said Hironobu Tamba, CEO of Noetra. “Together with partners across Japan and around the world, Noetra will advance Japan-developed multimodal foundation models and accelerate the deployment of physical AI across Japanese industries by broadly sharing the results of our research.”

Built on the NVIDIA Vera Rubin DSX AI factory architecture, the AI factory will deliver 140 megawatts of data center capacity combined with the NVIDIA Spectrum-X Ethernet networking platform, NVIDIA BlueField® DPUs, and tightly codesigned silicon, systems and software to provide breakthrough AI performance, lower token costs and massive scale for frontier AI training.

NVIDIA DSX provides a reference design and platform for AI factories, helping infrastructure builders accelerate time to production, increase token throughput per megawatt and operate with greater reliability and efficiency.

Advancing Japan’s Physical AI Ambitions

Japan’s AI Robotics Strategy, released in March, sets a goal for the country to capture more than 30% of the global AI robotics market by 2040, representing an estimated $133 billion opportunity. To help achieve the goal, METI is advancing a multimodal foundation model program for robotics and physical AI as part of Japan’s broader industrial AI policy.

As the AI factory expands, it will support training trillion-parameter-scale AI models, giving organizations across Japan access to one of the world’s most advanced AI environments and laying the foundation for the next era of intelligent manufacturing and robotics.

Japan Government, Industrial Leaders and NVIDIA Launch the World’s First National AI Infrastructure

In a recent discussion, Paul Triolo from DGA Albright Stonebridge Group shed light on China’s significant steps to enhance its capital markets, reflecting a sophisticated approach to its artificial intelligence (AI) industrial strategy. Triolo emphasized the implications of the public offerings of Chinese firms involved in AI hardware and advanced language models. He also explored how U.S. export restrictions and the inclusion of certain Chinese companies on various entity lists might impact the growth and development of the AI sector in China. This evolving landscape raises important questions about the future dynamics of global competition in AI technology.

Paul Triolo: China's opening of capital markets reflects the advancement of its AI industrial policy.

A warm-lit scene of a golden microchip sprouting green shoots that morph seamlessly into upward-trending stock market graphs; a single Chinese flag waves gently in the background, symbolizing the maturity of China’s AI industrial policy and its unleashed capital markets.

China's 'unleashing' of capital markets shows a maturity of its AI industrial policy: Paul Triolo - CNBC

- China demonstrates maturity in AI industrial policy through capital market developments.  
- Public listings for AI hardware and large language models are gaining momentum.  
- U.S. export controls may significantly impact China's AI industry and companies.  

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China's 'unleashing' of capital markets shows a maturity of its AI industrial policy: Paul Triolo

Paul Triolo of DGA Albright Stonebridge Group discusses the public listing of Chinese AI-related hardware and large language models, as well as how the U.S.' export controls and placing of Chinese companies on various entity lists could affect the sector.

China's 'unleashing' of capital markets shows a maturity of its AI industrial policy: Paul Triolo

Shares of prominent Chinese technology firms Alibaba and Baidu experienced a significant uptick on Thursday following their collaboration with Apple to implement their artificial intelligence tools. Alibaba's shares, listed in Hong Kong, surged by 5% after the company announced that its Qwen AI model would be incorporated into Apple’s services within China. In the U.S., Alibaba's shares also closed slightly higher after a spokesperson confirmed to CNBC that "Qwen will be integrated into Apple Intelligence experiences across iOS, iPadOS, macOS, and visionOS for users in China."

Baidu's shares in Hong Kong climbed by 4% as the company revealed its ongoing partnership with Apple to develop AI features for iPhones available in China. This collaboration comes on the heels of reports from late June indicating that Baidu's AI chip division, Kunlunxin, is eyeing an initial public offering that could value the unit at around $50 billion. In a recent announcement, the Cyberspace Administration of China designated Apple Intelligence, along with several other AI services from companies like Huawei Technologies, as approved service providers for smartphone-based applications.

The partnership between Apple and Alibaba is expected to enhance user experience by allowing seamless access to Qwen's capabilities, such as text and image comprehension and generation, without requiring users to switch between different applications, according to an Alibaba representative. Apple has yet to provide a response to requests for additional comments from CNBC.

Alibaba and Baidu stocks soar in Hong Kong following Apple AI collaboration

A sunlit Hong Kong trading floor with warm morning light streaming through floor-to-ceiling windows. In the center, a rising green arrow on an electronic ticker carries oversized 3D logos of Alibaba, Baidu, and Apple climbing skyward. The Hong Kong flag flutters subtly in the background as suited traders exchange confident glances at soaring share prices on nearby screens.

Alibaba and Baidu shares jump in Hong Kong on Apple AI partnership - CNBC

- Alibaba and Baidu's shares rose following their partnership with Apple for AI tools.  
- Alibaba confirmed its Qwen AI model will integrate with Apple services in China.  
- Baidu is collaborating with Apple on AI features for iPhones in China.

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Chinese tech company Baidu, best known for its search engine, also operates cloud, mapping and other internet-based services. Bloomberg | Bloomberg | Getty Images

Shares of Chinese tech giants Alibaba and Baidu rose Thursday on their partnership with Apple for deploying their AI tools. Hong-Kong listed shares of Alibaba rose 5% after the company confirmed that its Qwen AI model would be integrated into Apple services in China. U.S.-listed shares of Alibaba had closed slightly higher overnight after an Alibaba spokesperson told CNBC that "Qwen will be integrated into Apple Intelligence experiences within iOS, iPadOS, macOS, and vision OS for users in China."

Stock Chart Icon Stock chart icon Alibaba HK shares

Baidu 's Hong Kong-listed shares gained 4% as the company confirmed that it was working with Apple on Apple Intelligence features for iPhones in China. This comes amid reports in late June that its artificial intelligence chip unit Kunlunxin is targeting an initial public offering in the city, which could value its affiliate at $50 billion. The Cyberspace Administration of China in a notice on Wednesday included Apple Intelligence, along with six other smartphone-based AI services including Huawei Technologies, in a list of approved service providers. The Apple-Qwen combination will allow users to access the model's capabilities, "like text and image understanding and generation, without needing to jump between tools," the Alibaba spokesperson added. Apple did not immediately respond to CNBC's request for comments.

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Alibaba and Baidu shares jump in Hong Kong on Apple AI partnership

Mira Murati, previously the Chief Technology Officer at OpenAI and now the CEO of Thinking Machines, recently spoke at the Wall Street Journal's WSJ Tech Live Conference held in Laguna Beach, California, on October 17, 2023. This marks a notable moment in her career following the founding of Thinking Machines Lab in February 2025, alongside several senior researchers from OpenAI. The company made headlines by raising an unprecedented two billion dollars in its seed round, achieving a remarkable twelve billion dollar valuation without releasing any products. Major investors include tech giants like Nvidia, AMD, Cisco, Andreessen Horowitz, and Jane Street, setting high expectations for the emerging firm.

This week, Thinking Machines unveiled its inaugural model, Inkling. It employs a mixture-of-experts transformer architecture featuring an impressive total of 975 billion parameters, with 41 billion being active per token. Inkling was trained on an extensive dataset of 45 trillion tokens, which encompasses text, images, audio, and video. It is designed to accommodate a context window of one million tokens. Despite these formidable numbers, the model's performance has not set it apart from its competitors. Thinking Machines readily acknowledges this, stating that Inkling is not currently the strongest model available, whether open or closed. Benchmark tests such as Humanity's Last Exam, Terminal Bench, and SWE-Bench Verified reveal that it lags behind leading Chinese open models, such as Zhipu's GLM 5.2 and Moonshot's Kimi K2.6.

What stands out is the fact that this twelve billion dollar American lab, staffed by the creators of ChatGPT, has introduced a model that does not outperform its free Chinese counterparts. A deeper investigation into this phenomenon reveals some intriguing insights.

For the past eighteen months, U.S. authorities have accused Chinese labs of basing their models on pilfered American technology. Following DeepSeek's impressive debut in early 2025, the rapid response from OpenAI and government officials was that DeepSeek had utilized distillation techniques on OpenAI models. Distillation is the process of training one's model using the outputs of a more advanced one. Subsequent Congressional inquiries have asserted that Chinese AI companies were engaging in coordinated distillation efforts aimed at U.S. frontier models, leading to accusations of theft.

With this context, let's delve into Inkling. Its architecture closely resembles that of DeepSeek-V3, a fact that both technical analysts and the Thinking Machines team itself have noted shortly after its release. The company admits that Inkling's post-training involved supervised fine-tuning with synthetic data derived from open-weight models, including the Chinese Kimi K2.5 model developed by Moonshot AI.

This raises an important point: an American lab has essentially embraced a Chinese model architecture and utilized a Chinese dataset for training its flagship product. While nobody is labeling this as theft—and rightly so, as the Chinese models in question are open-source and licensed for use—there exists a noticeable disparity in the narrative. When Chinese labs draw from American models, it’s considered criminal; however, when American labs learn from Chinese models, it is approached as engineering innovation. Such selective rhetoric poses challenges for coherent technology policy-making.

This brings us to a crucial question: why launch a model like Inkling, which isn't even the best in the market? The strategy appears calculated, benefiting from a protective market environment. Inkling is licensed under the open-weight Apache 2.0, predominantly marketed as a chatbot but also as a customizable platform through Tinker, the company’s fine-tuning tool. Thinking Machines seems to be banking on enterprises prioritizing adaptability over sheer capability.

However, could American companies not adapt superior Chinese models instead? They attempted to do so but found themselves gravitating towards Chinese open models, which are both high-quality and cost-effective. These Chinese models now represent about 45 percent of the enterprise tokens processed through OpenRouter. Companies like Coinbase have significantly cut down their AI expenditures by leveraging models like GLM and Kimi, while Cursor has developed its Composer model based on Kimi.

Despite this trend, the U.S. government is actively seeking to restrict access to these models. The State Department recently issued warnings to American businesses regarding the potential risks associated with Chinese models. Additionally, investigations have been initiated into firms like Airbnb and Cursor due to their use of Chinese AI tools. Proposed procurement bans are in the works, making it increasingly risky for any company involved with government contracts—or facing the prospect of congressional scrutiny—to engage with Chinese open models. 

Inkling is positioned to fulfill the gap left by these restrictions. It serves as the compliant alternative, not the supreme one, but rather a sanctioned low-risk option.

Finally, it is essential to reflect on the background of Thinking Machines’ founder. Mira Murati’s tenure as OpenAI's CTO means she possesses unparalleled insight into the inner workings of OpenAI’s technology. Given her expertise and the significant resources available to her, her decision to adopt an architecture inspired by DeepSeek and a training strategy influenced by Kimi carries weight. This choice suggests a shifting landscape in the realm of open research, particularly with OpenAI experiencing a noticeable slowdown. The rollout of GPT-5.6 has been limited, while Kimi K2.6 has surpassed GPT-5.5 in certain benchmarks, marking a significant moment where an open-weight model has bested a leading proprietary model. The competitive landscape between American closed labs and their Chinese counterparts is tightening, with the distinctions between them narrowing on multiple fronts. When a former leader from OpenAI opts for a design derived from Chinese models instead of her previous employer’s methodologies, it compels the market to reevaluate assumptions about the strength of OpenAI’s position.

Murati is aware of OpenAI's secrets; her AI indicates a preference for China.

A moody, shadow-filled high-tech control room: at center, a lone figure in silhouette (Mira Murati) stands poised. To her left, a large monitor glows with the word “OpenAI” in stark white text overlaid on schematics and circuit diagrams. To her right, another screen displays a vivid red Chinese flag superimposed on golden neural-network pathways. Dramatic overhead spotlight casts deep shadows, dark muted palette, cinematic tension.

Murati Knows OpenAI’s Secrets. Her New AI Signals She Prefers China’s. - Forbes

- Mira Murati's Thinking Machines Lab launched its first model, Inkling, with notable specifications.  
- Inkling, while impressive, underperforms compared to free Chinese models like Kimi K2.6.  
- The model serves as a low-risk alternative amid increasing scrutiny of Chinese AI usage.

Mira Murati, former Chief Technology Officer of OpenAI, now CEO of Thinking Machines, speaks during The Wall Street Journal's WSJ Tech Live Conference in Laguna Beach, California on October 17, 2023. (Photo by Patrick T. Fallon / AFP) (Photo by PATRICK T. FALLON/AFP via Getty Images) AFP via Getty Images

Thinking Machines Lab released its first model this week. The company was founded in February 2025 by Mira Murati, the former Chief Technology Officer of OpenAI, along with a cohort of senior OpenAI researchers. It raised a record two billion dollar seed round at a twelve billion dollar valuation before shipping a single product. Nvidia, AMD, Cisco, Andreessen Horowitz and Jane Street backed it. Expectations are enormous.



The newly released model is called Inkling. It is a mixture-of-experts transformer with 975 billion total parameters and 41 billion active per token. It was pretrained on 45 trillion tokens spanning text, images, audio and video. It supports a one million token context window. These are very impressive numbers, yet the performance in comparison with other models is less impressive. To their credit, Thinking Machines concede this themselves. In the company’s own words, Inkling is not the strongest model available today, open or closed. On benchmarks like Humanity's Last Exam, Terminal Bench and SWE-Bench Verified, it trails leading Chinese open models including Zhipu's GLM 5.2 and Moonshot's Kimi K2.6.



It is quite remarkable that a twelve billion dollar American lab staffed by the people who built ChatGPT have released a model that underperforms free Chinese alternatives. It is worth diving into, and when we do, the details end up being quite revealing.



Distillation Wars

For eighteen months Washington has accused Chinese labs of building their models on stolen American intellect. When DeepSeek stunned the AI community in early 2025, the immediate response from OpenAI and from US officials was that DeepSeek had distilled OpenAI’s models. Distillation means using a stronger model’s outputs to train your own. Congressional investigators have since claimed that Chinese AI firms ran coordinated distillation campaigns against US frontier models. The word used in Washington is theft.



With that background in mind, let’s take a look at Inkling. Its mixture-of-experts design largely follows DeepSeek-V3. That is not my characterization. It is plainly visible in the published architecture and has been noted by technical analysts within hours of release. And by Thinking Machines’ own admission, Inkling's post-training was bootstrapped with supervised fine-tuning on synthetic data generated by open-weight models including Kimi K2.5, a Chinese model from Moonshot AI.



In other words, an American lab adopted a Chinese architecture and distilled a Chinese model to train its flagship release. Nobody is calling this theft. And frankly, nobody should. The Chinese models in question are open weight and permissively licensed. Learning from published work is how science advances. But the asymmetry in rhetoric is now impossible to ignore. When Chinese labs learn from American models it is larceny. When American labs learn from Chinese models it is engineering. You cannot run a serious technology policy on that kind of selective outrage.



So Why Launch A Model That Isn’t The Best?

The obvious question is why release Inkling at all. The answer is found in a shrewd strategy that benefits from protectionism. Inkling is open weight under Apache 2.0. It is being pushed primarily as a chatbot but as a base for customization through Tinker, the company’s fine-tuning platform. Thinking Machines is betting that enterprises care less about the smartest general model than about a model they can make their own.



But can American companies not do that with better Chinese models? They tried to. American companies flocked to Chinese open models because they are excellent and nearly free. Chinese models came to account for roughly 45 percent of enterprise tokens routed through OpenRouter. Coinbase cut its AI bill nearly in half by moving its agents to GLM and Kimi. Cursor built its Composer model on Kimi.



But Washington is now moving to shut that door. The State Department warned American companies this month about the risks of Chinese models. House committees launched probes into Airbnb and Cursor over their use of Chinese AI. Procurement bans are being drafted. For any company that touches government work, and increasingly for any company that fears congressional subpoenas, Chinese open models are becoming untouchable regardless of what the final rules say. The risk overhang is massive, and that is all that’s needed to push companies in other directions.



Inkling exists to fill that vacuum. It is the compliant base model. Not the best base model. Just the permitted, low-risk one.



What Inkling’s Architecture Tells Us About OpenAI

Let’s revisit for a moment who the founder of Thinking Machines is. Mira Murati was OpenAI’s CTO. She knows exactly what is inside OpenAI's stack. She served as interim CEO during the November 2023 board crisis. Few people on earth have better visibility into what OpenAI has built and what it can build. Given a blank slate, two billion dollars and Nvidia's newest GB300 systems, she chose an architecture that follows DeepSeek and a training bootstrap that leans on Kimi.



That is a not insignificant data point about where the technical frontier of open research actually sits. It arrives alongside a measurable deceleration at OpenAI itself. GPT-5.6’s rollout was limited. Kimi K2.6 edged past GPT-5.5 on SWE-Bench Pro, the first time an open-weight model surpassed a leading proprietary model on that benchmark. The gap between American closed labs and Chinese open labs has compressed to months on many tasks and has inverted on some. When the person who ran OpenAI’s technology organization builds from Chinese blueprints rather than from anything resembling her former employer's approach, the market should - like any good Bayesian thinker - update its beliefs about how large OpenAI's remaining moat really is.

Murati Knows OpenAI’s Secrets. Her AI Suggests She Prefers China’s.

Recent developments in the scientific community are paving the way for a groundbreaking method of energy production known as fusion, bringing it ever closer to being a reality for humanity. 

Significant strides have been made in the realm of plasma containment—an essential aspect of fusion, where the challenge lies in maintaining superheated plasma long enough for hydrogen nuclei to combine and produce significant energy outputs. The concept behind fusion involves merging two light atomic nuclei to create a heavier nucleus, which releases a substantial amount of energy in the process.

Kevin Zondervan of Promethean Action highlights the progress being made, particularly with a startup named Avalanche, which is focused on the development of small fusion reactors for various vehicles. He notes that we are witnessing remarkable achievements, such as breakeven plasma confinement times reaching as high as 22 minutes and contracts emerging for the purchase of fusion power. While most current initiatives are aimed at electrical power plants, there is a vision for extending fusion technology to vehicles, planes, ships, and even smaller, localized reactors for remote applications.

Before realizing this potential, however, significant foundational work in fusion technology must take place. Many experts believe that artificial intelligence could emerge as the initial consumer of this type of energy. 

Dennis Whyte, a key figure in fusion development, shared insights during a TED Talk event in June. With both entrepreneurial and academic experience—including involvement with MIT—Whyte underscored that the field is still in its early stages. "To turn fusion into a practical energy solution, we need to operate at scale and cost that make it viable," he explained. He described a current project in Devens, Massachusetts, which aims to demonstrate an energy gain where the output from fusion plasma exceeds the initial input, all while focusing on commercial viability.

Looking ahead, Whyte suggests that we could see a prototype of a fusion power plant by 2030, as commitments for construction and financial backing are starting to materialize. He also pointed out the potential relationship between AI and fusion, noting that AI may be ideally suited to benefit from the local, high-power density sources offered by fusion, particularly since it avoids certain supply chain vulnerabilities. Innovations in fusion may be further accelerated by incorporating new ideas, as technological advancements from various fields often enhance established scientific principles.

AI Accelerates Energy Development in Fusion Research

A spacious fusion research facility flooded with warm natural light, a ring-shaped tokamak glowing faintly with plasma in the center, engineers in lab coats studying AI-driven data on sleek monitors, curves of neural-network graphs and real-time simulations subtly overlayed on the screens, the fusion core humming softly behind a protective viewport—capturing AI’s role in accelerating clean energy breakthroughs.

Working On Fusion: AI Drives Energy Acceleration Processes - Forbes

- Scientists are advancing in fusion energy, improving plasma containment for energy generation.  
- Startup Avalanche is designing small fusion reactors for vehicles and commercial applications.  
- MIT's Dennis Whyte projects fusion power plants could be operational by 2030.

A vibrant illustration of a glowing atomic structure with electron orbits on a dark background, symbolizing concepts in quantum physics, energy, and scientific research. getty

We’re making headway, in the scientific world, on a powerful form of energy generation. It’s called fusion, and it seems, more than ever, that sort of thing is almost within humanity’s grasp.

For one thing, scientists have been making great advances in plasma containment. That’s important, because one of the abiding challenges in the fusion puzzle is keeping the superheated plasma in place long enough to allow hydrogen nuclei to fuse, and get net energy outputs.

That’s why it’s called “fusion” – the general idea is that two light atomic nuclei combine to form a heavier nucleus. That releases energy – a whole lot of it.

“The closer we get to realizing commercial fusion power for electricity generation, the more we see records and barriers broken around the world,” writes Kevin Zondervan at Promethean Action, chronicling the efforts of a startup called Avalanche in designing small fusion reactors for vehicles. “Breakeven plasma confinement times of up to 22 minutes, contracts to buy fusion power, plans for siting commercial fusion plants, etc. All of those plans are for electrical power plants. What about cars, trucks, planes, ships, and spaceships? What about your little reactor for your farm in the middle of nowhere?”

Before that happens, though, we’ll need the less sexy process of building basic fusion technology and proving the theory to a high level, and there’s a general consensus that we wouldn’t be doing all of this unless AI was “the first customer” for this type of energy generation.

Thoughts from a Leader in Fusion I was talking recently to Dennis Whyte about all of this at a TED Talk event in June. Whyte has both the corporate and the academic credentials to speak with authority: he has started multiple companies, and is also a member of MIT faculty on energy. Whyte stressed that all of this is, essentially, in its infancy. “You actually have to do it at a scale and a cost that would eventually make fusion practical,” he said. With that in mind, he described a project that is currently underway in Devens, Mass. (See more about this type of thing here .) “Its purpose is to not only demonstrate energy gain, namely that you get more energy out of fusion plasma than you started with, but it's also doing that at an obviously commercial scale,” Whyte said.

A Timeline “It should be developed according to plan,” Whyte said of related efforts, “that by 2030 we'll see a demonstration of something that starts to look like what a fusion power plant would be, and we're actually starting to see the commitments, both financially and locally, about actually constructing these so that they would start to put power on the grid.” What does AI have to do with it? Whyte explained: “AI is a particularly interesting and very recent advancement in this,” he said, “because AI will almost certainly be the first customer for fusion. (see my reference above) Because it's almost exactly tuned up for what AI needs, namely, local high power density sources that also don't have vulnerability to (certain) supply chains.” Work on fusion, Whyte suggested compounds as new ideas come along. He described that process this way: “That often happens in technology: innovations that come in from the outside actually start amplifying what is established science elsewhere.”

Working On Fusion: AI Drives Energy Acceleration Processes

In recent years, stocks related to artificial intelligence (AI) infrastructure have been at the forefront of market growth. However, they've recently faced some challenges, prompting concerns about a possible decline in data center expansion. Nonetheless, this atmosphere appears more akin to a typical market correction following a robust period of gains.

Three semiconductor stocks that stand out as excellent buying opportunities amid the current downturn are Nvidia, Advanced Micro Devices (AMD), and Broadcom. Each of these companies boasts significant growth potential, and investments in AI data centers are expected to remain sturdy for the foreseeable future.

Starting with Nvidia, the company has experienced a decline in stock price that has brought its valuation down to a forward price-to-earnings (P/E) ratio of 16 based on analysts' expectations for its fiscal year ending January 2028. This valuation positions Nvidia as one of the most attractive options in the semiconductor space. The company's CUDA software platform has created substantial competitive advantages in the AI model training market, as a large portion of foundational AI code runs on CUDA and is tailored for its graphics processing units (GPUs).

Nvidia is also adeptly navigating the shift towards more inference and agentic AI workloads. It has evolved from merely a GPU manufacturer to a comprehensive AI infrastructure provider, creating servers specifically designed for various AI applications. Following its acquisition of Groq, Nvidia has incorporated chips optimized for inference into its CUDA ecosystem, while its networking segment is rapidly growing.

With a promising trajectory ahead, Nvidia remains a strong buy, especially given its current valuation dip.

Next up is Advanced Micro Devices. The company is well-poised to capitalize on two prominent AI trends: inference and agentic AI. AMD’s chip portfolio allows it to pursue a greater share of the AI inference market, as evidenced by its significant GPU contracts with major players like OpenAI and Meta Platforms.

Inference relies heavily on swift memory access, and AMD is focusing its innovations in this area. Its chiplet architecture enables GPUs to accommodate more memory, and through its recent acquisition of the memory optimization platform MEXT, it can virtually enhance memory capacity without compromising performance, thereby minimizing costs for clients.

At the same time, AMD is set to benefit from the surge in agentic AI, which demands greater computing power from central processing units (CPUs). As a long-standing leader in data center CPUs, AMD is strategically positioned to meet the rising demand, with projections suggesting that the CPU market in data centers could double to $120 billion by 2030. The company is already developing CPUs specifically for agentic AI applications.

Given its substantial revenue growth potential, AMD is another compelling stock to consider post-sell-off.

Lastly, Broadcom has emerged as a key player in the shift to custom AI accelerators favored by hyperscalers looking to reduce costs. The company partnered with Alphabet to create Tensor Processing Units (TPUs), and with Alphabet poised to invest up to $190 billion in AI infrastructure this year, Broadcom is on track for rapid expansion. Additionally, Alphabet's agreement to provide Anthropic with $21 billion in TPUs further enhances Broadcom’s prospects.

As interest in TPUs continues to grow, other large tech companies are seeking Broadcom’s expertise in developing tailored AI chips. The firm anticipates that this market segment could surpass $100 billion by its fiscal year ending in 2027, with Citigroup estimating that Broadcom's AI revenue might reach $180 billion by fiscal 2028. The company’s data center networking division is also expanding quickly, and after sealing a $30 billion deal with Apple, its non-AI chip sector is prepared for a rebound.

Broadcom's stock currently trades at a forward P/E of 20 based on fiscal 2027 estimates. With its significant growth potential, this valuation appears quite low, making Broadcom an attractive investment opportunity.

Three AI Stocks I Favor to Purchase Amid Ongoing Chip Price Decline

A bright morning sunbeam streams through a sleek office window, illuminating a bronze bull sculpted from circuit-board traces. Three metallic logos—Nvidia, Microsoft, Google—are etched into its sides, glowing with optimism. Behind it, glass shelves stacked with semiconductor chips spill over the edge, red arrows streaking down to show the chip sell-off. The warm natural light highlights the bullish AI stocks charging ahead.

My 3 Favorite AI Stocks to Buy on the Continued Chip Sell-Off - The Motley Fool

- AI infrastructure stocks, including Nvidia, AMD, and Broadcom, face recent market pressures.  
- Nvidia's valuation has decreased, making it a strong buy with significant growth potential.  
- AMD and Broadcom are well-positioned in AI, targeting rapid growth in their markets.  

While artificial intelligence (AI) infrastructure stocks have helped lead the market higher over the past few years, more recently, these stocks have come under pressure. There is some fear of an eventual slowdown in the data center build-out, but this does look more like a typical market breather after a nice run.

Three of my favorite semiconductor stocks to buy on this sell-off are Nvidia (NVDA +0.29%), Advanced Micro Devices (AMD 3.40%), and Broadcom (AVGO +1.28%). All three still have huge growth opportunities in front of them, and spending on AI data centers should remain strong for many years.

Nvidia

The pullback in Nvidia's stock has taken its valuation down to a forward price-to-earnings ratio (P/E) of 16 times analysts' estimates for its fiscal 2028 (which ends in January 2028). That makes it one of the best bargains in the chip space. Given the moat its CUDA software platform has established, the company is set to continue dominating the market for AI model training, as most foundational AI code was written on CUDA and optimized for its graphics processing units (GPUs).

Expand NASDAQ : NVDA Nvidia Today's Change ( 0.29 %) $ 0.61 Current Price $ 212.41 Key Data Points Market Cap $5.1T Market cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded shares. Implied market cap may vary. Day's Range $ 206.05 - $ 213.81 52wk Range $ 164.07 - $ 236.54 Volume 4.5M Avg Vol 158.1M Gross Margin 74.15% Dividend Yield 0.13%

And while the use of data center processing power is shifting toward more inference and agentic AI workloads, the company is also well positioned here. Nvidia has transformed itself from a simple GPU maker into a complete AI infrastructure player, offering end-to-end servers designed for specific AI tasks. Its acquisition of Groq gave it chips designed specifically for inference, which it has incorporated into its CUDA ecosystem. Meanwhile, its networking portfolio has become the fastest-growing part of its business.

With strong growth still ahead, Nvidia remains a top stock to own, and a good buy at its discounted valuation.

AMD

Advanced Micro Devices is currently riding two of the hottest trends in AI: inference and agentic AI. The company's chip offerings make it much better positioned to take a larger slice of the AI inference pie, and it already has large GPU deals in place with OpenAI and Meta Platforms.

Expand NASDAQ : AMD Advanced Micro Devices Today's Change ( -3.40 %) $ -18.61 Current Price $ 529.52 Key Data Points Market Cap $894B Market cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded shares. Implied market cap may vary. Day's Range $ 509.63 - $ 558.89 52wk Range $ 149.22 - $ 584.73 Volume 957.6K Avg Vol 36.4M Gross Margin 47.09%

Inference is much more about fast memory access than raw compute power, and this is where AMD has focused its efforts. Its chiplet design allows its GPUs to be packaged with more memory, while its recent acquisition of memory optimization platform MEXT will allow it to virtually expand memory capacity without sacrificing performance, helping customers reduce costs.

At the same time, the company is set to ride a powerful wave in agentic AI. While other types of AI workloads have largely needed GPUs to provide their processing power, agentic AI workflows require more participation from central processing units (CPUs).

AMD has long been a leader in data center CPUs, and as AI agents proliferate, the need for CPUs is expected to grow rapidly. In fact, the GPU-to-CPU ratio in new data centers is expected to shrink from 8 to 1 for training to 1 to 1 for agentic AI. AMD has projected that the data center CPU market will double in size to $120 billion by 2030. It is already developing CPUs specifically for agentic AI.

With huge revenue growth ahead of it, AMD is a top stock to buy after its sell-off.

Broadcom

Broadcom has been one of the biggest beneficiaries of the trend among hyperscalers to deploy custom AI accelerators to help save costs. It helped Alphabet develop its Tensor Processing Units (TPUs), and with the search giant set to spend up to $190 billion on AI infrastructure this year, Broadcom is set to see rapid growth. Adding to that, Alphabet has agreed to sell Anthropic $21 billion worth of TPUs.

Expand NASDAQ : AVGO Broadcom Today's Change ( 1.28 %) $ 4.99 Current Price $ 394.10 Key Data Points Market Cap $1.9T Market cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded shares. Implied market cap may vary. Day's Range $ 386.79 - $ 398.86 52wk Range $ 273.00 - $ 495.00 Volume 690K Avg Vol 26.3M Gross Margin 65.66% Dividend Yield 0.65%

The success of TPUs led other hyperscalers to turn to Broadcom for help in developing custom AI chips. It expects this to grow into a more than $100 billion business in its fiscal 2027, while Citigroup has projected that Broadcom's AI revenue could rise to $180 billion in its fiscal 2028. The company also has a fast-growing data center networking business, and after signing a $30 billion deal with Apple (AAPL +3.95%), its non-AI chip business also looks set for a turnaround.

The stock is trading at a forward P/E of just 20 times fiscal 2027 estimates. Given its potential explosive growth, that's too cheap, and makes it an attractive buy.

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