TSMC raised its investment budget in the US by a further $100 billion, driven by strong demand for AI technology through to 2030.

In an announcement tied to its quarterly results, the Taiwanese chipmaker revealed it would spend the sum in the US state of Arizona, adding to an investment of $165 billion which it has already committed.

TSMC has pledged to build eight manufacturing sites in Arizona, and with the additional investment, could now set up another four focused on manufacturing advanced 2nm chips, in addition to facilities for advanced packaging.

CEO CC Wei stated its raised outlook was based on demand mainly from cloud service providers, which provide a “strong signal and positive outlook”.

“Thus, our conviction in the multi-year AI mega-trend remains very high.”

He added the investment “will help further foster the development of the US semiconductor ecosystem, strengthen the supply chain and support an increasing number of high-tech, high-paying jobs” in the country.

In Q2 2026 the company reported a year-on-year revenue increase of 36% to TWD1.3 trillion ($40.4 billion), with net income increasing 77% to TWD706.6 billion.

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Thinking Machines Lab unveiled its first AI model more than a year after former OpenAI executive Mira Murati (pictured) founded the start-up, moving it from research previews to a full launch.

The Inkling model is designed to reason natively across text, images and audio, while balancing cost against performance through variable, adjustable effort.

Thinking Machines stated the model is trained to exhibit safe behaviour across all modalities.

The company noted Inkling is a mixture-of-experts transformer with 975 billion total parameters and 41 billion active parameters, supporting a context window of up to 1 million tokens.

Thinking Machines described it as the first in a family of models of varying sizes.

Rather than chasing state-of-the-art performance in a single domain, Thinking Machines said it trained Inkling for solid capabilities across agentic tasks, reasoning, coding, instruction-following, factuality, vision and audio, positioning it as a foundation model for future releases.

The company acknowledged Inkling is not the most performant model available today, closed or open.

Inkling is open-weight, meaning developers can download and customise the model without access to training data or source code.

It is available for fine-tuning on Tinker, Thinking Machines’ developer platform, alongside a so-called Inkling Playground for testing the model directly.

Bloomberg reported Thinking Machines is not aiming to monetise the model itself; the company currently generates revenue through Tinker, which it sells to customers including hedge fund Bridgewater Associates for financial-task fine-tuning.

The news outlet noted Murati’s company raised $2 billion at a $12 billion valuation last year and was reportedly in talks over a larger subsequent round, though it added several employees have since departed for firms including Meta Platforms and OpenAI.

Bloomberg also reported the launch could help fill a gap in the US open model market, as Meta shifts focus on proprietary offerings and rising AI spending pushes some users toward freely available Chinese models.

Thinking Machines was founded in early 2025 by Murati and a host of other former OpenAI employees, with the company initially focused on collaboration between the technology and people.

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Anthropic is reportedly seeking to meet with investors ahead of a potential mega-IPO, as the company prepares to join a wave of AI-driven stock market debuts.

Bloomberg reported the banks leading the offering are scheduling meetings between investors and the Claude chatbot maker in the coming weeks, citing people familiar with the matter. The news outlet previously stated Anthropic is considering an IPO as soon as October.

Deliberations remain ongoing and details including timing could still change, Bloomberg stated.

Moving ahead on the timeline would put Anthropic ahead of rival OpenAI, which is now eyeing a debut in 2027 after earlier targeting 2026.

The news site noted an IPO in October would also see Anthropic list before DeepSeek, the Chinese AI company which has been steadily gaining market share.

DeepSeek is reportedly preparing its own IPO and could file this year.

Bloomberg stated Anthropic is working with Morgan Stanley, Goldman Sachs and JPMorgan Chase on its IPO.

The AI player is also navigating uncertainty tied to its relationship with the Trump administration, which briefly imposed foreign access restrictions on two of Anthropic’s top models.

Anthropic separately sued the US Department of War after it declared the company posed a risk to the nation’s supply chain.

The company was valued at $965 billion following a funding round in May, making it one of the world’s largest private companies and pushing its valuation past OpenAI’s for the first time.

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The US government launched a cybersecurity coordination group allowing AI developers and service providers a formal way to work together to respond to threats emerging from the technology.

The White House stated it, along with the Department of the Treasury, Homeland Security, Department of War and the Cybersecurity and Infrastructure Security Agency worked closely with industry partners to detect threats faster and develop a rapid and prioritised response to cyber vulnerabilities across critical infrastructure sectors.

The group, called Gold Eagle, has been spearheaded by President Donald Trump as part of his vision to secure and accelerate American AI innovation.

It was first revealed as part of Trump’s executive order in June to promote advanced AI innovation and security, which includes plans for the release of a new operational model for cyber defence.

The model aims to tap into frontier AI capabilities to continue advancing faster than adversaries, reduce duplicative scanning efforts and “deliver prioritised and actionable threat and remediation information to defenders” across the government and private sector.

The White House statement explained Gold Eagle will help government and industry collectively identify risks, prioritise action and strengthen the resilience of systems that power the economy, national security and daily life.

Companies involved in the coordination group were not disclosed.

Trump’s administration has taken a hands-on approach to AI regulation.

In May it unveiled a legislative framework to create uniform security and safety guardrails. This followed another executive order at the end of 2025 to secure control of AI regulation away from individual states.

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OpenAI is reportedly preparing to enter the consumer hardware market with a portable, screen-free smart speaker designed to serve as an AI companion in the home.

Sources told Bloomberg the device will access ChatGPT’s AI capabilities to control smart home appliances, play media, answer questions and respond to messages. It will utilise OpenAI’s GPT-Live voice technology to listen and adaptively respond at the same time to support more natural conversations and incorporate AI models beyond those used in existing smart speakers.

In addition, the hardware will apparently feature a camera and sensors to enable the AI to read its surroundings. Unlike conventional smart speakers, the rechargeable device will be portable and feature moving mechanical elements.

It will also draw on user data such as emails to become increasingly personalised over time. OpenAI envisions the speaker developing a humanlike personality, proactively surfacing information, anticipating users’ needs and acting as a physical embodiment of ChatGPT.

OpenAI’s hardware unit is reportedly developing around five products in total, with the speaker set to be the first to market.

Last year, the company splashed $6.5 billion to acquire io Products, the device startup co-founded by former Apple design chief Jony Ive. Ive’s creative design studio LoveFrom is also working on the hardware line.

A hardware launch would put the ChatGPT-maker in direct competition with Apple, Amazon and Google in consumer devices.

Trade secrets
Last week, Apple filed a lawsuit against OpenAI and io Products, alleging “theft of trade secrets” obtained through former employees during hiring to accelerate the development of its AI devices. According to its lawsuit, more than 400 former Apple employees have joined OpenAI’s hardware effort.

Drew Pusateri, a spokesman for OpenAI, told the BBC OpenAI has “no interest in other companies’ trade secrets” and is “focused on building innovative technology that empowers people everywhere”.

Apple is seeking an injunction that could delay the AI giant’s device sales. According to Bloomberg, OpenAI is aiming to unveil the speaker later this year, with release targeted for 2027, subject to the lawsuit.

Meanwhile, Apple is reportedly developing its own AI-focused home devices including a smart home hub with a 7-inch display and facial recognition tied to the upcoming AI-powered Siri revamp in iOS 27.

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Google DeepMind CEO Demis Hassabis called for the establishment of a new US-led regulatory framework to govern frontier AI, proposing a self-regulatory structure which could bypass political opposition.

In a post on X, Hassabis warned the breakneck pace of commercial and geopolitical competition is currently outstripping the industry’s understanding of the technology.

He noted the arrival of Artificial General Intelligence (AGI) is likely only a few short years away.

To address looming technical, biological, and cybersecurity risks without stifling innovation, Hassabis proposed creating a new US standards body.

He modelled it after a self-regulatory organisation like the Financial Industry Regulatory Authority (FINRA), rather than a traditional government agency.

The structural choice comes amid heavy resistance to AI regulation from the tech industry and shifting policies from the Trump administration.

The FINRA-style blueprint would be staffed by leading technical experts and open-source representatives but primarily funded by the industry to secure the massive compute resources needed for testing.

TechCrunch reported the proposed system would replace the current, highly criticised ad hoc government reviews previously used for models like Anthropic’s Mythos and OpenAI’s Sol.

Those reviews drew fire from industry onlookers for a distinct lack of technical expertise and opaque decision-making, according to the news outlet.

Under Hassabis’s framework, the independent body would define thresholds to classify models as Frontier-Class. Organisations behind them, dubbed Frontier Labs, would eventually be required to pass the independent assessments to deploy in the US market.

Hassabis stated assessments would check for advanced threats, deception, and guardrail bypass attempts. The standards body could also outsource specific evaluations to a growing ecosystem of independent, third-party AI safety groups.

He argued the approach is designed to keep up with AI’s acceleration, allowing the industry to safely steer toward an era of scientific abundance while addressing the Trump administration’s concern over state-mandated red tape.

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New York made history by becoming the first US state to halt the construction of massive new data centres, imposing a one-year moratorium as concerns escalate over the environmental and financial toll of the AI boom.

The moratorium, outlined in an executive order signed by Governor Kathy Hochul (pictured), targets hyperscale operations by halting discretionary environmental permits for new facilities requiring 50 megawatts or more of power.

The rapid expansion of facilities housing thousands of computer servers has triggered intense community and political backlash across the US.

“This pause will remain in place for up to one year while New York establishes the strongest possible framework to protect our community’s guardrails to reduce the risk to our energy grid, minimize land disruption, noise pollution, and protect our natural resources, especially our water supply,” Hochul stated during a press conference. “We’re going to explore having hyperscale data centres pay into a larger fund to support our grid statewide.

“We need to invest in this, help defray costs, reduce bills for our rate payers,” she added. “We expect this process, which we already launched, to be completed within the year.

Once the policy is in place, the freeze will be reviewed and lifted, Hochul stated.

She also said the data centres should only be built in areas which want them and “they will never be exempt from local zoning, local approvals”.

Local populations are increasingly concerned the energy-hungry centres by companies including Alphabet, Microsoft, Meta, Amazon, and Oracle are inflating residential utility bills, straining electricity grids and consuming millions of gallons of water for cooling.

Hochul framed the aggressive intervention as a necessary protective measure for state residents, stating as data centre development threatens to hike utility bills and deplete natural resources, it is her responsibility to act.

She noted labour organisations will have a seat at the AI data centre discussions “so projects can include wage standards and labour agreements and prioritise local hiring”.

To further rein in the sector, Hochul announced plans to pursue legislation to repeal lucrative sales tax exemptions currently enjoyed by hyperscale data centre operators.

“New Yorkers work hard and, on my watch, their tax dollars will not be used to benefit these large firms, these centres.”

Reuters reported Hochul directed state officials to develop a Generic Environmental Impact Statement. The comprehensive assessment will establish consistent operating and environmental standards for new facilities while examining the overall footprint of data centre construction and operation across the state.

The construction ban will be lifted once the rigorous regulatory guidelines are finalised.

The news outlet reported state lawmakers recently passed a bill targeting data centres consuming 20 megawatts or more of power, representing a wider scope than the governor’s executive order.

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Experts speaking at the inaugural AMR Technology Forum offered a picture of the road to deploying AI in enterprise which is not unlike the early days of Formula One and other motor racing series.

The core message from Aston Martin Aramco technology partners is the best way to employ AI is to use it.

It is an ethos which is arguably the very basis on which Formula One racing is built. Want to try a different valve configuration on your engine? Do it. Is the car better with the power unit behind the driver than in front? Let’s find out. Are six wheels better than four and so on.

AI strategy
Speakers from the AMR Network of technology partners essentially said the strategy for deploying AI is no different to those shifts and trials in car design. Devise a clear plan about what you want to achieve and just do it. Do not be fearful, go all in and make sure what you do helps the people with the real expertise to do their jobs better.

David Ingham, digital partner in media and entertainment with Cognizant (pictured, third from right), said any AI deployment needs “to be solving a problem”, so it makes sense the first step would be to identify what that is, then the rest “falls in line”.

Ingham explained Cognizant’s approach is born of its position as a company which brings “all the layers of the AI stack” together. He referred to chips, infrastructure and software as the elements in mind, explaining the primary goal is to combine them into “a coherent workflow”, a “process that adds value”.

He argued Aston Martin Aramco’s racing cars are a great example, because individual components “don’t add value, they don’t win a race” until they are brought together.

An obvious use for AI in any organisation is coping with vast amounts of data, an element Ryan Lewis, head of Cohere’s operation in the UK and Ireland (pictured, second from right), said is another in which Formula One is a fitting example due to the volume of information teams generate and rely on.

He pointed to telemetry, diagnostic and simulation data as examples from the sport which emphasise the challenges any organisation faces in making the most of their information.

Data is “sometimes really hard to access” due to being stored in different systems, modalities or even languages.

Lewis believes “making sense of the data wherever it’s sitting” to enable rapid decision making “in high-stakes environments” presents a “really exciting opportunity for AI just to take that off the table”.

He envisions a set-up where individual AI agents communicate directly to synthesise the insights, “reports and responses” staff can use to work more effectively.

For Lewis, the watchword of deploying AI in a business is empowerment.

Cohere is not inherently seeking to use AI to “replace the things that are high value in organisations”, instead it is driving to “give the people the power to do the things that are mundane and routine” more easily, in turn accelerating the decision-making process.

Teamwork
Aston Martin Aramco technology ambassador Eric Ernst (pictured, second from left) elaborated on the empowerment theme, explaining it is the experienced staff within the team which drive the value of AI usage.

He noted machine learning is well-established, meaning AI deployment strategies today are more about how best to employ generative iterations.

Aston Martin Aramco is deriving benefits by using AI to create and assess several possible strategies, which knowledgeable engineers can then pick from based on their real-world experience.

Ernst’s advice is humans must remain the foremost element in use of AI, interpreting recommendations and conclusions the technology produces to derive the smartest outcomes.

For a Formula One team like Aston Martin Aramco, this could be as simple as when drivers should go flat out and when they should be more conservative during a race.

The point about the human element was picked up in a later session by Jonathan Armstrong, senior director of Brand and Creative at Arm, who noted AI is good at deconstructing “complex problems” but argued the full benefits of the technology can only be reaped when it is combined with the vision, ingenuity and creativity of people.

“When you have technology and AI and human insight altogether, that’s a powerful weapon.”

He said Formula One teams are working to optimise the combination of AI and human abilities, an “exciting” period for the engineering sector.

Simon Cox, chief transformation officer at ServiceNow (pictured, far right), urged organisations not to be paralysed by worries over the accuracy of their operational data. He explained the companies making the most of their AI strategies “are the ones that embrace” the chaos of current information sources and adopt a “fix it as we go” mindset.

He argued the iterations of AI involved in the latest dawn of the technology deliver the capabilities to correct data anomalies on the fly and so make a success of the very approach which embodies the ethos of Formula One racing.

The technology today can “find those gaps and see through the noise” in a company’s data. The volume of information and challenges of employing it should not hinder companies, instead Cox believes the secret to success is to “just start, get going”.

Cox’s advice to take the plunge was echoed by Ben Richardson, head of CoreWeave International (pictured, third from left), who urged companies not to wait for the perfect AI or deployment strategy.

“Like anything, if you really want to use it you’ve got to take risks, you can’t sit there and you can’t just wait”, because rivals are not.

“People have to be brave and you have to be able to say this is happening, it is here, how can I start to capture it”?

Richardson’s view is cracking on and using AI holds the potential to create a virtuous circle, where the technology becomes more valuable based on how much it is employed. Or, as he more succinctly put it: “the more people that use it the more you get out of it”.

Restrictions
With the experts’ broadly agreeing the best strategy is to learn about AI by using it, what do they think is holding most organisations back?

Lewis believes many large organisations are simply not clear enough about what they want to achieve and so are struggling to develop a “cohesive enterprise AI strategy”.

Companies must consider the result of their AI deployments and doing so requires “a top-down perspective”, one which ties every element together.

Rushing headlong into deploying any technology is, of course, inherently risky. Richardson acknowledged as much and advised companies to build protections into their strategy “if that’s what you need” to deliver the confidence required to reap the AI benefits he outlined.

Cox noted governance is a decidedly unsexy element in any discussion of AI, but explained it is still necessary.

Experience from using data, cloud computing and more has taught ServiceNow the importance of installing controls “from the beginning”.

“Resilience by design can give you the control tower for your AI and then you’re able to fly.”

Governance “sounds dull” in any discussion of enterprise AI strategy best-practice, but is an element Cox argues is absolutely necessary.

The post Feature: Experts offer top tips for enterprise AI deployments appeared first on Mobile World Live.

Intel outlined a €5 billion capital investment in its Republic of Ireland campus to expand ‌European output to meet the increased demand for AI and high-performance computing.

The chipmaker stated the expansion at its facility in Leixlip, County Kildare, began earlier in the year and involves upgrading fabrication facilities and installing leading-edge manufacturing equipment to boost production of its Intel Xeon 6 and next-generation Xeon processors built on the advanced Intel 3 process node.

Intel stated the chips are increasingly critical to power AI factories.

As part of the infrastructure overhaul, Intel is expanding its “automated track system to integrate disparate campus modules” into a single, “high-velocity production environment”.

Intel explained the investment programme would create permanent high-tech jobs alongside engaging specialised building and equipment installation tradespeople.

Naga Chandrasekaran, EVP and GM of Intel Foundry, said the investment is a “definitive commitment to maximising capacity” for foundry customers.

Upgrading the site “with state-of-the-art technology and installing cutting-edge tools” would help keep the Republic of Ireland “at the forefront of the world’s most advance manufacturing ecosystems while strengthening” Europe’s position in “the global technology landscape”, he said.

The move aligns with broader European Union tech-sovereignty goals to establish a resilient, domestic supply chain for leading-edge processors.

Intel invested more than €30 billion in the Republic of Ireland since establishing operations in 1989. The Leixlip site currently employs 4,900 people.

The company is trying to revitalise its chip manufacturing business and become a serious player in the booming foundry industry.

In April, it signed up to Elon Musk’s Terafab AI chip project to create processors for orbital data centres and robots.

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Qualcomm struck a deal to acquire AI software company Modular for about $3.9 billion, in all stock-deal aimed at strengthening its software foundation for generative and agentic AI across data centre and edge environments.

The deal will pair Qualcomm’s silicon expertise with Modular’s AI-native software platform, which is designed to run AI models efficiently across CPUs, GPUs, NPUs and custom ASICs without requiring separate rewrites for each type of accelerator.

The chipmaker stated developers and enterprises will only need to build once ahead of deploying across multiple environments at a lower total cost of ownership.

Qualcomm framed the acquisition as a response to a shift in what is constraining AI deployment at scale. As AI use grows, the company argued, efficiency rather than raw capability becomes the limiting factor, since performance-per-watt drives inference costs and those costs determine what can scale.

Closing this gap, according to Qualcomm, requires software that connects system-level optimisation with heterogeneous, disaggregated compute, turning silicon performance into AI services which work reliably across different accelerators and use cases.

The deal is intended to expand Qualcomm’s data centre ambitions, supporting more efficient inference, orchestration and deployment in distributed AI systems while deepening relationships with model creators, developers, hyperscalers, and enterprise customers.

It is also expected to help the company achieve strong day-zero performance on new Qualcomm AI hardware as it rolls out.

Qualcomm President and CEO Cristiano Amon called the acquisition a pivotal moment for the AI industry. He stated agentic AI’s growth across data centres and edge environments is pushing the sector toward disaggregated, multi-vendor architectures which need a more open, modern software foundation.

He said Qualcomm believes the future belongs to developer-friendly platforms which can run across diverse compute environments, giving customers real choice in how and where they deploy AI.

“With Modular, we’re accelerating that shift, combining our scale and energy-efficient data center technologies with an open ecosystem approach to help drive the next chapter of AI,” Amon stated.

Modular co-founder and CEO Chris Lattner stated the company was built on the belief AI needs a more open and efficient software foundation spanning diverse hardware and deployment environments.

The transaction is expected to close in the second half of 2026, subject to customary closing conditions and regulatory approvals. Qualcomm plans to issue 19.2 million shares to Modular equity holders.

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Verizon’s road to Level 4 network autonomy was boosted by turning 33,000 employees into software developers earlier this year using Anthropic’s Claude model.

Verizon CTO Yago Tenorio (pictured) told Mobile World Live (MWL) the company gave about 500 application developers on his team access to Claude Code in December 2025.

“We started using it like there is no tomorrow,” he said. “In a few weeks we made everyone in technology proficient as software developers and then people started coding.

“There is a mantra now that says: ‘If you can imagine it, Claude can code it for you.’ It is amazing the massive amount of enablement, simplification and efficiency we achieved in a few months.”

He explained Verizon’s legacy as a long-established operator means its path to network autonomy is far more complex compared to a greenfield player. Years of M&A, overlapping legacy systems, merged networks and coexisting OSS platforms have created operational complexity which must be abstracted away before AI-driven automation can work at scale.

The AI autonomy revolution is well underway. In 2025, Verizon’s closed-loop automation platforms executed over 70 million network configuration changes autonomously.

“I feel like it’s making the network better. It is reacting in real time and it is freeing up hours of labour that can be then thrown on something that requires a human.”

But Tenorio draws a line between that kind of automation and what’s coming next. Those 70 million loops were largely programmatic, following decision trees and fixed logic which engineers wrote in advance, the network equivalent of a Python script running the same routine repeatedly.

The shift now underway replaces fixed logic with AI agents which can reason through a situation they were never explicitly programmed for because they are trained on the skills and judgment which make Verizon’s network teams effective, rather than coded with rules for every scenario.

Verizon is moving away from traditional hardware constraints to turn its infrastructure into a software-defined, self-organising reasoning engine which optimises itself in real time. The company describes this not as simply adopting tools, but as engineering an AI-native enterprise.

Level 4 network autonomy
In the TM Forum’s six-stage taxonomy for autonomous networks, Level 4 is a major shift from traditional automation to true intelligence. For this level, the network moves away from rigid, rule-based scripting and task-execution, transitioning instead to automating decisions.

CCS Insight research director Ian Fogg told MWL a part of an operator’s network may be at one level of autonomy, but other parts will likely be at a different level depending on how far along each operator’s suppliers and network teams are in modernisation.

“The barriers are that this is simply a very complicated area,” he said. “AI and especially generative AI and agentic AI are increasingly at the heart of autonomous network innovation as operators move to level 3 and beyond. 

“AI is an essential technology to enable the creation of autonomous networks,” he added.

Tenorio is careful not to overclaim on autonomy levels. He said Tesla represents something closer to Level 3, a Waymo represents Level 4, and Level 5 would be a car with no steering wheel at all.

He is not convinced the network needs to go Level 5. In his view, building a network with literally no human element anywhere in it is not necessarily the goal, even if individual domains or use cases can get there.

Across the board, in every domain and every use case, he considers Level 4 a reasonable target, while cautioning operators in the industry tend to generalise their autonomy successes across specific areas.

Rather than chase a number, Tenorio stated Verizon’s focus is on making autonomy matter operationally, whatever level it ends up being called.

He also pushes back on the idea that full autonomy means removing humans from the picture entirely. Even with what he calls a “totally dark” network operations center, people must still be dispatched to base stations and customer premises for hardware replacement or tuning which cannot be done remotely.

He argues agents are trained, not programmed, meaning the human expertise which has shaped a network for years does not disappear, it just gets transferred into the skills the agents learn from.

The loop which runs the network day-to-day can become fully closed, but the loop of deciding how those agents should behave still runs through human judgment.

AI models
To get there, Verizon has doubled down on generative and agentic AI. Tenorio describes Verizon’s approach as model-agnostic. For agents now running parts of its network, it is primarily using Anthropic’s Claude and Google’s Gemini.

Verizon is also optimising across model size and cost: simpler problems get routed to small language or cheaper models, while more complex tasks get the most capable ones available, including Claude Opus 4.7

Verizon had access to Anthropic’s Claude Fable 5 model prior to the US government shutting it down.

Tenorio used Fable 5 on a side project built with Claude Cowork and came away impressed, particularly by what felt like a broader contextual awareness and more lateral reasoning than he was used to. However, he is cautious about claiming a measurable gap versus Opus 4.8, since the same project has continued just as well on Opus 4.8 since Fable 5 access ended.

“I thought it was fantastic,” he said of Fable 5. “I was blown away by it.”

Starting in mid-January, the company expanded Claude access to its entire 33,000-person technology organisation over about six weeks, turning employees across modelling, capacity planning, capital deployment, and operations into capable software developers regardless of their formal coding background.

It now counts between 7,000 and 10,000 weekly active users of the tool. Tenorio frames this as a deliberate first step on the road to autonomy, not a side project.

Before trying to make the network autonomous, Verizon wanted to find out what happens when everyone in the organisation can write software.

Architectural blueprint
Verizon is currently conducting an autonomous network programme with intelligent agents running full-time, although Tenorio resists calling the effort a pilot.

He instead describes it as a deliberate, already-in-production evolution. The same 70 million automated loops from last year are progressively having reasoning and token-based decision-making layered on top of them, with the foundational pieces of true network autonomy now live rather than experimental.

He identifies two areas for genuine autonomous network operations.

The first is a common data layer. Since an autonomous network is only as good as the data feeding it, the telemetry needs to be shared across the organisation in near real time, and it needs to reflect actual customer experience rather than what the network itself reports.

Tenorio stated Verizon began rolling this out at the start of June and it is now in production.

The second is a common repository of skills, tools and agents, the framework by which years of collective engineering expertise and pattern recognition gets encoded into a form AI agents can draw on.

Once both pieces are in place, Tenorio stated deploying domain-specific agents becomes comparatively straightforward.

He said this approach has no off-the-shelf component and is not organised around a specific network domain or generation. It is not built around radio, transport, core, or optical separately, and it is not tied to 5G or 6G.

Instead, Verizon designed it to run across the network as a whole, starting with making every employee a developer rather than buying a vendor’s packaged autonomy product.

A master agent can immediately detect a localised anomaly, take control and instantly spin up a network of specialised sub-agents focused on the exact root cause, pinpointing it down to the precise network domain and specific element.

Work which used to take engineering teams hours to manually diagnose and correct is now identified and resolved in less than two minutes, long before the end user ever experiences a dropped call or a lag in their data connection.

An autonomous system, however, is only as good as the live data feeding it. Traditional network KPIs tend to skew toward favourable conditions and do not always fully capture the true, localised end-user experience.

Verizon’s new AI-driven models aim to close the telemetry gap. By using aggregated, anonymised network performance signals, including floor-by-floor indoor environments, the company builds an accurate, hyper-granular picture of service quality, feeding the common data layer described above.

The rich physical telemetry, fused with existing network data, feeds a continuous optimisation loop which detects and resolves coverage and capacity issues proactively, often before a customer is affected.

Tenorio said the end-result is near real-time visibility into in-building performance environments, enabling automated alerting and network tuning at a level of granularity the company did not previously possess.

“We started by making everyone a software developer,” he said. “I don’t know if other companies are doing it.”

The post Feature: How Verizon is crafting its network for the agentic AI era appeared first on Mobile World Live.

SK Hynix outlined plans to go public in the US to raise KRW45.5 trillion ($29.4 billion), which would be the second-biggest IPO in history after SpaceX’s listing earlier this month.

The South Korean high bandwidth memory (HBM) chip company filed for the proposed IPO with the Securities and Exchange Commission to list American Depositary Receipts on the Nasdaq.

In total it plans to issue 17.79 million new shares to raise more than $29 billion, giving it a company valuation of $1.2 trillion. It expects to start trading on 10 July.

It will use the cash to fund the buildout of chip factories in South Korea and acquire chipmaking equipment including extreme ultraviolet lithography (EUV) scanners. It already has an order in place with ASML worth KRW12 trillion.

A supplier to Nvidia and Google, SK Hynix has been a big winner of the AI boom, with its shares quadrupling in value this year. It competes in the HBM segment with Samsung Electronics and US-based Micron.

Indrani De, global head of investment research at financial company FTSE Russell, told Bloomberg SK Hynix’s outperformance reflects its early move in HBM and strong demand for its chip.

“Supply shortages in HBM where Korean firms dominate with 80% market share are expected to last for years,” she said.

The size of the proposed IPO would see SK Hynix surpass Saudi company Aramco’s IPO in 2019, when it raised $25.6 billion, which is currently the second largest behind SpaceX’s $75 billion.  

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Industry associations the GSMA and TM Forum agreed a collaboration to aid adoption of practical AI systems across a range of use cases within the telecoms industry.

In a joint statement, GSMA director of AI initiatives Louis Powell and TM Forum EVP products and services Andy Tiller explained the organisations would work together to help create open AI models and frameworks able to meet the industry’s very specific needs.

“Networks underpin the cloud, device and edge capabilities on which AI depends,” the two argued.

“Yet telcos themselves have not fully captured the value of AI within their own operations”. 

“If the industry is to accelerate towards more autonomous, AI-native networks, it must align on shared assets, common standards and practical implementation pathways”.

The organisations address complementary areas towards the common goal. The GSMA is working on relevant models and agentic systems through the Open Telco AI initiative and TM Forum is focusing on frameworks and standards to align the industry and ensure interoperability.

They labelled the collaboration as being “about connecting the AI stack end-to-end, combining shared models, common frameworks and practical deployment pathways, with the trust required for critical network environments”.

Problem
Explaining the rationale for the closer ties between the organisations, Powell and Tiller argued adoption of AI is more complex in telecoms than other industries, but is a board-level priority in many organisations.

Among the difficulties cited is the multi-vendor nature of network architecture, with fragmented, siloed data and “very little tolerance for error”.

They also noted frontier AI models are not designed for operators and have not been trained on industry-specific data, leaving deployments limited to areas outside of the network layer such as customer experience.

Data published by GSMA Intelligence noted 16% of AI deployments target network use cases, despite networks accounting for 34% of opex.

The organisations believe the telecoms industry needs to “align to solve these challenges for itself, building AI approaches that reflect the complexity, economics and operational realities of the industry”.

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Orange lined up Usman Javaid (pictured) as its next AI chief, tasking the current chief product and marketing officer of its business unit with delivering on ambitious revenue targets for the technology.

Javaid is scheduled to succeed Steve Jarrett on 1 September, a little over three years after taking on his current post within Orange Business.

Orange is in the throes of implementing a strategy intended to derive more than €600 million in value from AI by 2028. It is also working to expand internal use of the technology and stated implementing agentic iterations would be a key element in Javaid’s remit.

During an Orange Business event in March, Javaid told Mobile World Live past experience in adapting to emerging technologies would serve the company well in the AI era, explaining the focus is on how to employ advances to benefit customers.

Orange credited Jarrett with having laid the foundations of its use of AI and establishing its credentials in the field.

There is a circularity to Orange’s appointment given Javaid began his career with the company in 2005. The executive also racked up experience in various roles held with Vodafone Group and AWS spanning cloud, IoT and network strategy.

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LIVE FROM MWC SHANGHAI 2026: AI naturally dominated the opening keynote session of this year’s event, with China Mobile and Filipino operator Globe Telecom offering insight into consumer benefits of the tech and how it is driving growth at both companies.

Carl Cruz, CEO and president of Globe Telecom (pictured), outlined how his business is arguably one of the most advanced operators globally in terms of implementing AI for its own employee work practices as well as customer use cases.

It is a journey which started in 2024 when Globe Telecom established a group to lead its adoption of AI technology and created the position of chief AI officer (CAIO). The role was taken up by Anton Bonifacio and since widened to become Chief Intelligence and Trust Officer (CITO), leading the integrated structure of key technology and enabling groups across AI, data, cybersecurity and digital policy.

“Our goal was simple,” declared Cruz. “Democratise technology so innovation could come from anywhere. We see AI not as a threat but as a tool for power and growth.”

To that end, Cruz said Globe Telecom had witnessed more than 80 citizen-developed use cases across the business using a hub and spoke operating model. He cited other statistics claiming AI is now embedded in 90% of Globe’s business practices, with more than 260 relevant projects running across all business units.

Cruz put the AI focus on four main areas: hyper personalisation; customer experience; autonomous networks; and the creation of new products and services.

He gave the example of how Globe Telecom used AI to build a new billing agent: “It used to take days, even weeks, to reconcile billing. Today it takes seconds and minutes.”

“It’s about deepening customer relevance, unlocking new value and creating new growth engines for Globe.”

Cruz also talked up Globe Telecom’s AI Fiesta initiative, an affordable aggregator platform which bundles leading global AI models including ChatGPT, Claude, Gemini Pro, Perplexity, DeepSeek and Grok under a single, budget-friendly subscription. It is designed to democratise AI access for students, creators and small business owners.

And the operator has launched Globe Cloud, a personal cloud storage and file-backup service. Running Synchronoss technology, the app allows users to free up device space, sync media across devices, and use AI tools for editing and transforming images.

“AI at Globe is not an end to itself, it is a means to serve customers better, unlock new opportunities and growth,” Cruz said. “It’s not about using AI everywhere, it’s about using AI where it matters most; most importantly the communities we serve”.

“We are only scratching the surface of what’s possible.”

China Mobile: AI for All
The world’s largest operator China Mobile opened the keynote with a presentation tagged Mobile AI – AI for All.

Chair Zhongyue Chen highlighted how AI is enabling a hearing-impaired man to carry out everyday tasks.

“AI should not only be serving the elite or remain the domain of the masses,” Chen said.

The company claimed it is open to partnerships to further boost its AI offering. “We are not building a closed garden service for ourselves.”

And its AI for All mantra encompasses a variety of sectors, with Chen highlighting a focus on consumer services, showcasing how the technology is being used to supercharge the TV viewing experience in China of the FIFA World Cup; industries, for example in steel manufacturing where AI is helping reduce exposure to dangerously high temperatures; and infrastructure, where AI is measuring the power of individual models and also the capacity of platforms.

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SoftBank Group CEO Masayoshi Son reportedly rejected the case put forward by Elon Musk’s SpaceX to build AI data centres in space, arguing the economics favour terrestrial infrastructure.

Bloomberg cited comments made by Son at SoftBank’s annual shareholder meeting, who stated the “the main advantage” of eliminating electricity costs by putting AI data centres in space is outweighed by “higher fees to transport everything into space, maintenance and communication delays”.

Son was apparently responding to a shareholder who asked whether SoftBank would pursue a strategy similar to that proposed by the SpaceX chief, who filed for US regulatory approval in February to deploy up to 1 million solar-powered satellites designed to operate as AI data centres. Musk argues persistent access to solar power will “achieve transformative cost and energy efficiency”, adding “in the long term, space-based AI is obviously the only way to scale”.

While describing the tech mogul as a “remarkable agent of change”, Son reportedly said SoftBank would instead focus on building “formidable” data centre capacity on Earth. “In the battle for AI, the next few years will be far more important than what might happen a decade or so from now,” he noted, adding “he who strikes first wins”.

Despite intensifying competition, Son argued AI has scope for “ten-fold, a hundred-fold” growth, adding there was ample room for OpenAI and rivals including Anthropic and Google to expand amid accelerating AI demand.

The remarks put Son’s strategy at odds with Musk’s long-term vision for AI. SpaceX launched a record-breaking $75 billion IPO earlier this month, with proceeds funnelled towards funding its space-based AI initiative alongside the expansion of its Starlink satellite network.

Meanwhile, SoftBank has committed around $65 billion to OpenAI despite reports the investments were straining its balance sheets, and pledged to invest hundreds of billions of dollars in terrestrial AI infrastructure and data centres globally.

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The Five Eyes intelligence agency called for urgent action to respond to the evolving AI landscape, warning while the technology would help improve defences over time, it also accelerates the speed, scale and sophistication of attacks.

Five Eyes is a global intelligence-sharing alliance between the UK, Canada, US, Australia and New Zealand: it stated frontier models are expected to exceed current industry expectations, transforming offensive and defensive cyber capabilities.

The timeline for this “is not years, it is months”, meaning action must be swift “to remain ahead”.

“In this environment, cyber resilience is integral to advancing business continuity, market confidence and long-term value,” it stated.

As part of a number of recommendations, the alliance urged leaders to understand and assess, risk, readiness and accountability; prioritise foundational cybersecurity practices and controls; empower cyber leaders with authority and resources; and actively engage as threats evolve.

“Success will come from getting the basics right, acting quickly and integrating cybersecurity into core business strategy. Those that do not will face growing operational and strategic disadvantage.”

It emphasised cyber risk cannot be treated purely as a technical issue, but rather a core business risk, arguing ensuring systems are secure by design should become standard industry practice, resilience should depend on a single solution or technology and there must be an understanding fresh vulnerabilities would emerge as AI systems evolve.

Five Eyes also called for better processes to swiftly patch faulty software, address legacy systems, and limit unnecessary system access and external connectivity.

On the flip side, the alliance stated AI should be used to bolster defences, explaining organisations integrating AI tools into security operations can detect vulnerabilities faster, improve quality, monitor unusual behaviour and respond to threats swiftly.

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SpaceX reportedly signed a significant computing power deal with Reflection AI, making the open-source start-up the latest outside company to tap Elon Musk’s Colossus infrastructure.

CNBC reported Reflection will get access to Nvidia’s GB300 chips, the company’s top-tier hardware for training and running advanced AI models, housed at SpaceX’s Colossus 2 facility in Memphis, Tennessee.

Reflection agreed to pay SpaceX $150 million per month starting 1 July through 2029, with total payments reaching roughly $6.3 billion if the agreement runs its full term.

Either party can terminate the contract with 90 days’ notice after the first three months. Bloomberg reported Reflection will pay SpaceX’s AI division, SpaceXAI, under the arrangement.

Colossus was originally built in part to power Grok, Musk’s AI chatbot as a rival to ChatGPT, but SpaceX has increasingly opened the infrastructure to outside AI companies seeking compute capacity.

The deal is smaller than two of SpaceX’s other compute agreements. According to TechCrunch, SpaceX’s existing contracts with Anthropic and Google cost those companies $1.25 billion and $920 million per month, respectively, with both agreements also running through July 2029.

CNBC reported SpaceX has also struck a computing power-related deal with Cursor prior to buying the company for $60 billion earlier this month.

In October 2025, Nvidia led a $2 billion funding round for the open-source start-up, directly contributing $800 million of the total to value Reflection AI at $8 billion.

Reflection AI was founded in 2024 by Misha Laskin and Ioannis Antonoglou, both alumni of Google’s DeepMind lab.

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Groq raised $650 million in funding as the company revamps itself from a chip startup into a data centre operator focused on AI inference.

The round was led by growth-focused investment company Disruptive and hedge fund Infinitum, both of which already hold board seats at Groq.

The funding follows a non-exclusive licensing agreement Groq struck with Nvidia in December 2025. At Nvidia’s GTC event earlier this year, the chipmaker unveiled its next-generation LPX platform, which incorporates Groq’s inference technology.

US-based Groq stated it plans to use the new cash to expand its network of 13 data centres in North America, Europe, the Middle East and Asia-Pacific, and quadruple total capacity to 200 megawatts by the end of 2027.

The new capital will also fund the fit-out of its existing footprint with Groq’s latest inference technology, including Nvidia’s new LPX system.

In September 2025 Groq raised $750 million, which valued it at $6.9 billion. Its backers include KDDI, Samsung Electronics, Cisco Systems and BlackRock.

Groq chair Alex Davis stated the company spent years building the technology, infrastructure and operational expertise needed for AI’s next phase, adding it is positioned to become a foundational layer of the AI economy.

Groq also announced several executive additions alongside the funding news. Alan Rice, who previously worked on xAI’s Colossus data centre project and Meta data centres, is now COO.

Sinclair Schuller will start as CTO in July, with Rakesh Malhotra joining as chief product officer the same month. Schuller and Malhotra previously worked together at enterprise cloud platform Apprenda and later co-founded a separate software engineering firm.

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Nvidia unveiled what it described as the industry’s first full-stack safety platform for physical AI designed to accelerate the deployment of humanoid robots in industrial environments.

The company said its expanded Halos platform combines AI compute, system software, sensors and inspection tools into a single safety architecture aimed at helping robotics companies build and certify autonomous systems operating alongside human workers.

The chipmaker explained as robots become more autonomous and increasingly rely on AI foundation models, distributed sensors and accelerated computing, “scaling these systems requires a full-stack safety architecture” before they can be trusted to work alongside humans in factories, warehouses and logistics operations.

The stack includes Nvidia’s IGX Thor industrial computing platform, Holoscan Sensor Bridge for sensor connectivity and Halos OS software stack.

Nvidia also opened its Halos AI Systems Inspection Lab to robotics partners, aiming to help them complete pre-certification testing and validate safety systems before approaching regulators.

Digit
Humanoid robotics company Agility Robotics is the first partner to adopt elements of the platform into the proprietary safety system for its Digit humanoid robot, which is already being deployed for industrial applications with customers including Amazon, Schaeffler and Toyota Motor Manufacturing Canada.

The robotics player will also use Nvidia’s Inspection Lab to validate Digit’s safety-related software, AI components and cybersecurity protections against strict standards.

Agility Robotics CEO Peggy Johnson said: “For humanoids to deliver value at scale, safety has to be built into the robot and validated across the entire system.”

She added the partnership with Nvidia “extends our leadership in responsible automation”, describing it as “a non-negotiable requirement for bringing humanoids safely into industrial workflows”.

Deepu Talla, VP of robotics and edge AI at Nvidia said: “Physical AI is transforming how factories, warehouses and logistics operations work. He added developers will be able to “harness Nvidia’s proven autonomous vehicle safety foundation to develop safer robots faster” and deploy systems “alongside workers with greater confidence”.

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