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.”

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The US Federal Communications Commission (FCC) closed an AWS-3 re-auction with gross winning bids topping $3.5 billion in what was the agency’s first spectrum sale after a four-year lapse in activity.

Bidding concluded for Auction 113 yesterday (23 June) after 72 rounds for the 200 licences across the 1695-1710MHz, 1755-1780MHz and 2155-2180MHz bands.

All the licences received bids in the process, which had 17 qualified participants and began on 2 June.

The frequencies were originally auctioned to Dish Network, which is now part of EchoStar, in 2014 but never made it into service after a series of defaults and bid withdrawals left them sitting unused in the FCC’s inventory for more than a decade.

In 2015, Dish Network affiliates Northstar Wireless and SNR Wireless surrendered a number of spectrum licences worth $3.5 billion after a dispute with the FCC over discounts.

In April, the FCC and EchoStar reached an agreement which included the latter dropping a lawsuit it filed in a US Court of Appeals over the defaults by Northstar and SNR.

The financial total carries significant regulatory implications. Up to $3.3 billion of the proceeds will be legally directed to cover funds borrowed for the FCC’s Secure and Trusted Communications Networks Reimbursement Program, commonly known as the “rip and replace” initiative to swap out Huawei and ZTE gear from US networks.

Surpassing the $3.5 billion mark clears the $2.9 billion threshold required for EchoStar to avoid paying a potential shortfall payment to the regulator under a prior settlement framework.

While specific winning bidders remain anonymous under strict anti-collusion quiet period rules, AT&T, Verizon, T-Mobile US and SpaceX are widely expected to have driven the demand.

The event marks a milestone for FCC chair Brendan Carr, representing the return of the agency’s auction power.

“After years on the sidelines, FCC auctions are finally back,” Carr stated. “Today’s successful auction generated billions of dollars in competitive bids to put spectrum to effective commercial use, and it bolsters competition in the wireless marketplace.”

The FCC plans to carry the momentum into its upcoming Upper C-Band auction, tracking on schedule to meet a congressional deadline to auction at least 100MHz of the spectrum by July 2027.

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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 and telecom partners including SoftBank Corp and NTT Data plan to demonstrate a stack of data, models, simulation tools and secure runtimes at DTW Ignite 2026, aimed at helping operators move from task-based automation to fully autonomous network operations.

The company argues while generative AI has delivered strong returns in network management, customer care and back-office automation, most of the impact has been limited to speeding up predetermined steps while humans still correlate insights and direct next actions.

Nvidia’s claims automation is now “the launchpad to autonomy” rather than the end goal, with AI agents expected to proactively monitor for problems and coordinate changes across network, IT and business systems while keeping humans in control of policy.

Nvidia cites figures showing 54% of telecom operators view data-related issues as their biggest obstacle to building telecom-specific AI models, largely because the most valuable network and customer data is too sensitive to use directly for training.

SoftBank Corp is tackling the problem by using Nvidia’s NeMo Safe Synthesizer and NeMo Anonymizer tools to generate privacy-preserving synthetic datasets which mirror the structure of real network performance and configuration datasets.

Those datasets are being used to fine-tune its Large Telecom Model (LTM) and build specialised network agents.

On the runtime side, Nvidia stated its NemoClaw blueprints and OpenShell secure runtime are designed to give long-running agents policy-based guardrails and sandboxed access to telecom systems, allowing operators to expand agent responsibilities while keeping behaviour auditable and governed.

NTT Data is applying Nvidia’s open Nemotron models alongside NemoClaw to build agents which track long-term network performance trends and escalate anomalies to specialised research agents for deeper telemetry analysis.

Other partners building on the same framework include: AdaptKey, which is piloting self-healing 5G agents with operators; ServiceNow, which is bringing its Project Arc incident-response tooling to telecom network operations centres; and Tata Consultancy Services, which is developing a multi-fidelity AI sensor architecture for faster anomaly detection.

Simulation is the third pillar of the effort, intended to let agents test recommendations in near-real-time digital environments before acting on live systems.

Software company Forsk stated its GPU-accelerated radio propagation model now achieves ray-tracing-level accuracy up to 200 times faster than CPU-only systems, while Viavi Solutions reports order-of-magnitude gains in RAN simulation throughput after shifting workloads to Nvidia’s RTX Pro 6000 Blackwell GPUs.

KDDI and KDDI Research, working with Nvidia, Keysight and Samsung Research America, are building a high-fidelity RAN digital twin aimed at the 6G era, allowing multiple autonomous agents to simulate scenarios for future radio conditions and traffic shifts.

The TM Forum’s DTW Ignite conference runs 23–25 June 2026 in Copenhagen.

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Mavenir unveiled an Agentic Service Assurance Framework designed to automate complex network operations across multiple domains while allowing operators to retain existing infrastructure.

The vendor stated the TM Forum-compliant, multi-agent system combines an intent orchestrator with a layered agentic ecosystem to help detect, diagnose, recommend and resolve network faults faster.

Mavenir positioned the framework as a step toward higher operator productivity and autonomous network operations.

The vendor said network operations across 5G, cloud-native and IP domains are becoming increasingly fragmented and expensive to manage at scale. Traditional assurance systems can identify alarms but often struggle to determine root causes, while the retirement of experienced engineers risks taking years of institutional knowledge out of operations teams.

Multi-vendor environments add another layer of complexity by creating data silos which individual tools cannot easily correlate, according to Mavenir.

At the heart of the framework is Mavenir’s Intent Ops engine, which observes how network operations centre teams work, learns patterns behind successful issue resolution and converts those workflows into repeatable automation templates with explainability and guardrails.

The company explained the workflows are validated against a human baseline before running autonomously, with vetted automations published to an expanding orchestration catalogue.

The framework supports cross-domain monitoring, correlation and root-cause analysis, with closed-loop remediation built on Mavenir products.

It also connects with existing OSS AI agents through the agent-to-agent protocol defined in TM Forum’s IG1453, which Mavenir stated enables operators to preserve prior technology investments while extending automation across the network stack.

Mavenir CEO Pardeep Kohli told Mobile World Live the foundation AI agents are designed to automate 60% to 75% of routine network operations work, to help operators lower costs, improve service quality and reduce the need to train staff on repetitive tasks.

“In a way, you have a kind of personal assistant always watching you, but this personal assistant is for the network,” he said.

Mavenir did not say when it will be available but a representative stated the initial release is being trialled in customer deployments and future feature releases will be delivered through software upgrades.

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Amazon is now water positive in India, meaning the company now returns more water to communities than it uses across its direct operations, including corporate offices, data centres and fulfilment centres.

The milestone arrives ahead of the company’s original 2027 target and marks a step towards its broader global water stewardship strategy.

The achievement is noteworthy in a country facing acute water stress. India holds 18% of the world’s population but only 4% of its freshwater resources.

The company’s approach rests on three pillars: reducing water use, reusing water through treatment and harvesting systems, and replenishing water in stressed communities.

By comparing water returned through these initiatives against water withdrawn for operations, the company says it returned 120% of the water it used in 2025, with progress verified through internal and third-party audits.

“Water is fundamental to the communities where we operate, and we recognise both the scale of India’s water challenge and the opportunity to make a meaningful difference,” stated Abhinav Singh, Amazon’s VP of operations for India, APAC, Middle East and Turkiye.

On the reduction side, Amazon’s data centres in India do not use water for cooling, while water use across fulfilment centres and offices is largely tied to restrooms, kitchens and drinking water, managed through low-flow fixtures and smart meters that detect leaks.

On reuse, on-site sewage treatment plants recycled an estimated 298 million litres in 2025 for flushing and irrigation, while rainwater harvesting pits collected roughly 178 million litres which went into local groundwater.

For replenishment, Amazon has committed more than $6.5 million to water stewardship projects across Maharashtra, Karnataka, Telangana, Delhi NCR, Andhra Pradesh and Tamil Nadu, which are expected to replenish more than 4 billion litres annually.

The work ties into Amazon Web Services’ 2022 global pledge to become water positive by 2030 across its data centre operations.

The company stated it is now 75% of the way to its goal, returning three litres for every four used in 2025.

Amazon continues to expand its footprint in India, with plans to invest more than $35 billion in the country by 2030 to boost AI capabilities and digital transformations.

Microsoft and Google have also announced significant data centre investments in India over the past year.

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Mavenir and Red Hat announced what they are calling an Integrated AI Platform, a joint offering designed to give mobile operators the infrastructure to sell services and capture revenue currently flowing to hyperscalers and frontier AI companies.

After making the decision to pivot away from open RAN last year, the vendor has turned its focus to AI, which is evidenced in its partnership with Red Hat.

The Integrated AI Platform blends Mavenir’s AI software with Red Hat’s enterprise Kubernetes and AI capabilities using the latter’s OpenShift and the former’s Digital Enablement (MDE) platform for billing and charging.

The result is a platform which enables operators to monetise AI through token-based consumption plans, with full control over pricing, SLAs and the large language models (LLMs) which power every interaction.

The two companies believe token-based AI consumption, the unit model powering every LLM interaction, mirrors the mobile data billing paradigm operators have long used.

Mobile operators spent decades mastering consumption-based data billing, and Mavenir CEO Pardeep Kohli believes the same model can now be applied to AI while also capturing AI revenue currently flowing to the likes of OpenAI, Anthropic and the major hyperscalers.

 “At the end of the day, most of the money is going to go to OpenAI and all these other players,” Kohli told Mobile World Live. “What we are hoping is that this time, there’s an opportunity for operators not only to use AI to cut their own cost and reduce opex and do more automation but also be a provider of AI services to enterprises or even to consumers.”

How it works
The AI platform operates across three deployment models: operator-branded AI services for their own subscribers; as the AI infrastructure layer powering operator AI grid deployments; and as a managed AI platform through which operators can offer the capabilities to enterprise customers on a consumption basis.

The architecture is designed as hybrid, using on-premises and open-source models to serve most of the traffic, while Mavenir’s model router provides policy-governed, metered access to external frontier models for use cases which require advanced reasoning or multimodal capability.

Kohli explained every generation of mobile technology, from 3G to 5G, saw the bulk of value flow to application developers and hyperscalers while operators became commoditised connectivity pipes.

He believes AI represents a rare chance to break that cycle, provided operators move fast enough to control the infrastructure layer. Kohli pointed to live deployments, including Turkcell running on-premises language models and Telefonica working with Mavenir on a range of use cases announced at MWC, as evidence the strategy is already in motion rather than theoretical.

Use cases
One example he provided MWL is real-time voice translation. An operator customer of Mavenir is deploying an agent which converts live calls between languages.

At a small scale, routing the workload through a hyperscaler is economically viable. At the scale of a major mobile network, it becomes prohibitively expensive unless the inference runs on infrastructure the operator controls directly.

The AI platform, as Kohli describes it, is the mechanism which makes the transition possible without rebuilding the operator’s entire operational model.

Kohli also said currently when a mobile operator customer sends a query to Gemini or OpenAI, it bypasses the operator entirely and then goes straight to the hyperscaler’s data centre with the operator earning nothing beyond the data connection.

His AI grid model changes the scenario by making the operator an intelligent routing layer. A simple query gets handled by a local on-premises model at low cost; a complex reasoning task gets routed to a frontier model; a non-urgent request could be sent to a cheaper, more distant data centre.

With the AI grid, the operator becomes a traffic controller in the AI value chain rather than, as Kohli put it, “a dump pipe”.

Sovereign angle
Regulated operators in markets with strict data residency requirements have ample reasons to prefer on-premises inference over sending subscriber data to hyperscaler data centres in unspecified jurisdictions.

The physical proximity argument of operators has merit as Kohli noted operators with urban data centres genuinely are better placed than hyperscalers whose capacity often sits in rural locations.

“We are going use case by use case, but the actual framework and platform is common, and it can be used for many applications,” he said.

Analyst outlook
AvidThink founder and principal Roy Chua raises several issues with the token-as-currency model. One of his points is a token is not a megabyte. A megabyte is a fixed, fungible unit of information.

He explained a token from a frontier reasoning model and a token from a fine-tuned small language model running on premises are not equivalent. One may resolve a complex problem immediately while the other “takes 10 times the tokens getting to a worse answer”.

Chua said the intelligence embedded in a token varies wildly between models, and tokenisation methods differ between model families. Until there is an established, auditable standard for what a token represents in terms of capability, he argues, “the token can’t function as the billing primitive”.

There is also the question of where developer loyalty sits today. Enterprises and software developers have built their AI workflows around the frontier labs and around aggregation layers which already solve model routing across providers.

“For developers and enterprises, trying to insert the operator into a value chain where most operators currently have no relationships and add little differentiated value is an uphill climb,” he said.

He stated a stronger move is not selling tokens as a currency, but selling sovereign, compliant, latency-advantaged AI services wrapped in outcomes, particularly for regulated buyers who need in-country control.

“Let me give credit where it’s due,” Chua said. “Pardeep Kohli and the Mavenir team have correctly identified the macro tailwind. The geopolitics around AI access are real, data sovereignty pressures are real, and the desire for operators to climb the value stack is older than the cloud itself.”

He continued: “However, token-as-a-service, isn’t a sellable product yet for most telcos, with very few exceptions. Someone like SoftBank could do it in the future.”

DTW Ignite
Kohli said Mavenir will have a booth for the first time at TM Forum’s DTW Ignite conference next week. Historically, he noted, DTW was the domain of operators’ IT and billing-side teams while Mobile World Congress served the network side.

Mavenir’s presence at DTW now reflects his view those two worlds are converging as operators build common platforms spanning both functions.

 “We have a booth now to explain how we can actually have a common platform, which can not only do billing and charging, customer onboarding and customer facing work, but also can manage your network,” he said. “And it learns about packet code, and it learns about voice calling, it learns about SMS, MMS and not just your bill.

“I believe this is a golden opportunity,” he added.

The post Analysis: Mavenir pitches AI tokens as a telco cash cow appeared first on Mobile World Live.

US President Donald Trump (pictured) stated Apple has struck a deal with Intel to produce computer chips inside the US, a move which marked a significant moment in his administration’s push to reshore semiconductor manufacturing.

“I decided to help Intel because we need to design and build our Chips right here in America,” Trump wrote on Truth Social today (18 June).

He added Apple’s agreement was the latest in a series of wins for his domestic chip strategy, following similar commitments from Nvidia and Elon Musk’s TeraFab project with Intel.

The Apple agreement marks another significant win for Intel. CEO Lip-Bu Tan, who spent much of last year shoring up the company’s balance sheet through major external investments, now appears to be delivering on operational improvements which seemed unlikely just a year ago.

For Apple, the partnership offers a chance to diversify a supply chain which currently depends heavily on TSMC to produce the processors powering its iPhones, iPads and Mac computers.

Any agreement will likely be a foundry deal, since Apple already designs its own silicon.

Apple price increases
Apple CEO Tim Cook told the Wall Street Journal price increases for its products are now unavoidable, citing soaring costs for memory and storage chips driven by the AI boom. He stated Apple had absorbed rising costs for as long as possible, but the pressure has become too great to avoid passing increases on to customers.

A domestic chip supply deal from Intel could give Apple more flexibility in an increasingly constrained global market, though analyst Ming-Chi Kuo has previously suggested any production through Intel’s advanced 18A-P process node is unlikely to reach mass scale before late 2027.

Trump’s post also gave him an opportunity to tout the government’s earlier bet on Intel. Last August, his administration converted roughly $8.9 billion in federal funding into an equity stake of around 10% in the company.

He noted in his post Intel was worth around $100 billion when the government made its offer.

“Now they are worth over 600 BILLION DOLLARS! Nine months, and they’ve increased in value over HALF A TRILLION DOLLARS. America’s stake is now over 60 Billion Dollars.”

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Anthropic CEO Dario Amodei and Google DeepMind’s Demis Hassabis reportedly lobbied for a US-led coalition to shape global AI rules, as Anthropic remained locked in negotiations with the Trump administration over export controls on its newest models.

CNBC reported that during a closed-door lunch at the G7 Summit attended by President Donald Trump and other world leaders, Amodei called for international cooperation to cover structured access to frontier models, a chip and critical components trade framework which excludes China and coordinated responses to AI risks in cybersecurity, bioterrorism and intelligence.

OpenAI CEO Sam Altman also called for a formal international forum to establish globally accepted testing standards, provide impartial analysis of AI capabilities and risks, and serve as a venue for cooperation among nations, according to an OpenAI briefing.

The outlet reported Canadian Prime Minister Mark Carney agreed the US could lead such a coalition, according to people familiar with the discussions.

In addition to Trump, Treasury Secretary Scott Bessent, Commerce Secretary Howard Lutnik and Secretary of State Marco Rubio were also in attendance.

Anthropic ban
The backdrop of the AI discussions was the export controls imposed by the Trump administration on Anthropic’s Fable 5 and Mythos 5 models, which went into effect on 12 June after the government cited national security concerns.

News sites have reported Anthropic is currently in negotiations with the Trump administration over the directive.

Financial Times (FT) reported the US decision alarmed European governments and Silicon Valley alike, while raising concerns even allied nations could be cut off from cutting-edge AI.

French President Emmanuel Macron stated the Anthropic dispute clarified what was at stake, warning if the US could “from one day to the next turn off the switch” it would ultimately damage the multitrillion-dollar US companies leading the global AI race.

Macron called for stronger AI regulation and a platform for democratic cooperation to establish common standards. Indian Prime Minister Narendra Modi stated democratic nations needed access to frontier models to protect critical infrastructure, according to people familiar with the discussions.

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Consulting company Deloitte is set to launch an AI Studio at its London campus in partnership with Google Cloud, a co-innovation hub designed to support the UK’s transition towards agentic AI.

Opening in late July, the studio will serve clients across financial services, the public sector, retail, healthcare and life sciences, and technology, media and telecommunications.

Deloitte is pitching the facility as a place where production-ready agentic solutions are built and delivered. Innovation labs hosted at the studio will allow clients to prototype and validate agentic AI systems in as little as four weeks, the company stated, compressing time-to-market for complex deployments.

For its retail clients, the aim is to move beyond generative AI chat interfaces toward sophisticated systems capable of modernising the customer journey end-to-end.

In healthcare and technology, media and telecommunications, the studio will focus on how agentic AI can reshape traditional business models and daily workflows rather than simply augmenting them.

Alongside the studio launch, Deloitte announced an upskilling plan to train 1,000 members of its UK AI and data workforce on Google Cloud’s Gemini Enterprise. The certification programme is designed to ensure Deloitte’s engineers are equipped to implement Google’s most advanced agentic architecture, giving UK clients access to what Deloitte described as one of the largest pools of certified AI talent in the region.

“Our clients are looking for more than just productivity gains — they want AI that can take action and drive real-world business outcomes,” stated Hayley McKelvey, chief AI officer for Deloitte UK, speaking at the Google Cloud London Summit. “This is about moving from curiosity to a new era of autonomous business operations.”

The London studio is in addition to Deloitte’s existing AI Experience Centres in Egypt and India.

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Alibaba Cloud launched a cloud region in France featuring two availability zones in Paris as part of a plan to launch a suite of enterprise-grade agentic AI services in European markets later this year.

The move marks the Chinese giant’s third European hub following infrastructure builds in Germany and the UK.

The fully operational region brings enterprise-grade cloud services to European customers spanning computing, storage, containerisation, networking, security, databases, and developer tools.

The launch extends Alibaba Cloud’s global network to 105 availability zones across 32 regions.

The France deployment builds on European countries seeking to host their own data centres and cloud regions instead of relying on US companies such as Google, Microsoft and Amazon Web Services.

Built with data sovereignty in mind, Alibaba Cloud stated the Paris infrastructure is designed to comply with European regulatory frameworks covering data privacy, cybersecurity and resilience.

Alibaba Cloud noted the new region in France comes as European demand accelerates across sectors including retail, software development, AI-powered creative platforms and sports tech.

The company’s suite of agentic AI services includes AgentRun, a one-stop development platform for AI agents; STAROps, an intelligent operations platform; and ACS Agent Sandbox, which provides hardware-level security isolation while reducing operational costs.

The company is also bringing Agent Security Centre, AI Security Guardrails 2.0, and Agentic SOC to the region. Together, the portfolio is designed to cover the full agent lifecycle from development and debugging through deployment and security.

“This expansion, alongside the introduction of our agentic AI services to Europe, aligns with our broader strategy to bring our full-stack AI+Cloud ecosystem to global customers as we enter the agentic era,” stated Alibaba Cloud CTO and president of international business Dr. Feifei Li.

Last year, Alibaba Group CEO Eddie Wu committed to building “full stack technology capabilities” across AI chip design, infrastructure, cloud services and model development.

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Verizon unveiled a sweeping overhaul of its consumer offerings, scrapping activation and upgrade fees alongside launching two new plans designed to cut through what the US operator described as years of industry complexity and friction.

Verizon CEO Dan Schulman stated the industry has for too long burdened customers with complex plans and forced upgrades when announcing the new offerings and loyalty programme.

“We’re fundamentally reshaping Verizon inside and out to put the customer at the centre of everything we do,” he stated. “We’re listening, designing for them, and moving faster than we ever have before.”

The $40 activation fee, which Verizon raised from $35 as recently as last August, and the $40 upgrade fee charged when switching to a new device are both being eliminated.

However, customers will need to sign up for Verizon’s new free loyalty programme to benefit from the fee removal. The programme draws obvious comparisons to T-Mobile Tuesdays, which has been in operation for a decade.

The loyalty programme, which is rolling out this week, has two components. Verizon Dollars kicks back 3% of a customer’s bill in a currency redeemable for devices, accessories or partner rewards including Sephora, Hilton, Marriott and Starbucks.

The second element, Verizon Shine, offers weekly prize draws for experiences including tickets to the FIFA World Cup 2026 Final with David Beckham and a trip to Australia for the NFL’s first game there, alongside daily drops of lower-value benefits such as free Starbucks coffee and Amazon gift cards.

Verizon simplicity
The centrepiece of the announcement is Verizon Simplicity, priced at $45 per line with a promotional rate of $30 for switchers. Every Simplicity customer receives full access to Verizon’s 5G network with no upcharge, along with 10GB of mobile hotspot, roaming in Canada and Mexico and satellite texting.

Customers can add optional bundles and two upgrade tiers, Simplicity Plus at $35 extra per month and Simplicity Pro at $50, though the plan excludes multiple-line discounts and customers with remaining device trade-in credits would forfeit them by switching.

Also launching is Verizon One, a combined home and mobile plan priced at $70 a month with taxes and fees included, aimed at new customers only. The bundle pairs either a 500 Mb/s Fios fibre connection or 5G Home fixed wireless service with an unlimited 5G wireless plan, though hotspot use is not included.

The US operator is promoting the changes through an ad campaign featuring Dr. Evil from the Austin Powers films. In a video, Mike Myers’ Dr. Evil proposes to further confuse consumers through his “menace mobile” plans, which are shot down by members of his team in favour of Verizon’s new offerings.

The moves come after Verizon reported its first increase in post-paid subscriptions since 2013 in Q1 with 55,000 net additions.

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Meta Platforms’ Threads app has reached 500 million monthly active users, marking a significant milestone for the platform as it moves to deepen engagement through a suite of new community tools and personalised feed controls.

The social media giant stated the growth has been driven largely by communities, which are groups of users sharing interests spanning books, basketball, parenting, and music.

To mark the milestone, Meta is graduating its communities feature out of beta and launching a series of updates aimed at making those groups more visible and easier to navigate.

The communities hub is being added to the main menu to the left of the feed, making it easier for users to find and switch between groups.

Communities will also get their own distinct icons to make them more recognisable across the platform, alongside a progress indicator showing users how close a topic is to achieving full community status and what they can do to help it get there.

Champion recognition is being expanded to more users across communities, with local communities launching in Japan, Korea and Taiwan with native-language tags.

Meta stated Live Chats will expand to more communities in the coming weeks, with co-hosting and the ability to quote moments directly to a user’s feed.

Your Algo
Alongside the community updates, Meta is introducing Your Algo, a new personal feed setting which builds on the Dear Algo feature launched in February.

While Dear Algo allowed users to tell the Threads algorithm what they wanted to see more or less of, Your Algo adds a private layer of control, letting users filter specific topics from their feed and set how long a request lasts across one, three or seven days. Both tools will be manageable from a single hub.

Your Algo rolled out in the US, Canada, UK, Australia, and New Zealand.

Meta stated the updates are the beginning of continued investment in the platform based on user feedback.

It launched Threads in July 2023 in a bid to compete against Elon Musk-owned X.

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Nokia highlighted a major expansion of its advanced test and packaging operations in the US state of Pennsylvania, in a move designed to scale production of optical networking technologies which underpin AI infrastructure.

The Finnish vendor stated the investment will increase the facility’s production capacity by up to 10 times its current level, with the extended capability expected to be commercially available by the end of Q3.

Nokia said the expansion in Allentown, Pennsylvania, is expected to nearly double the workforce there to more than 500 across engineering, manufacturing and R&D, with a projected economic impact of more than $500 million over the next five years.

It noted the Allentown site is one of only a few facilities in the US providing advanced test and packaging of photonic chips into optical modules for use in AI and telecoms infrastructure.

Nokia stated less than 2% of global semiconductor advanced test and packaging currently takes place in the US, making domestic capacity a strategic priority. The company claimed its optical technologies can reduce energy usage in AI communications by as much as 75%.

The expansion is part of Nokia’s multi-year plan to invest $4 billion in research and manufacturing in the US for AI-ready network connectivity.

The Allentown project involves approximately $30 million in total investment, including around $4 million in assistance from the state of Pennsylvania and approximately $10 million in federal CHIPS and Science Act tax credit.

Nokia CEO Justin Hotard said the “AI supercycle” is fundamentally reshaping network and infrastructure requirements and noted the Allentown expansion was a direct investment in that future.

“It also reflects the strong partnership between Nokia, the United States, and the Commonwealth of Pennsylvania to support advanced manufacturing, create jobs, and strengthen US technology leadership and global competitiveness,” he said.

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Google started rolling out Android 17 on its Pixel devices, though some of its headline AI features will not arrive for another few months.

The initial version of Android 17 became available yesterday (16 June) on its devices, with other hardware brands to follow throughout 2026.

A new feature available in the launch is Bubbles, a multitasking tool which lets users collapse any app into a floating window which stays visible on screen, Google stated in a blog post.

Bubbles can be stacked, dragged to position and resized as required. On large-screen and foldable devices, bubbles are docked in a dedicated bar at the bottom of the display.

Google positions the feature as useful for referencing maps, writing notes during a call or keeping texts visible without opening the app switcher.

Screen recording has been updated with a new toolbar and feature called Screen Reactions, which lets users record themselves via the selfie camera while simultaneously capturing what’s on their phone screens.

Bloomberg reported the tool is Google’s response to the growing popularity of reaction-style video across social media, building the format directly into Android to reduce reliance on third-party apps such as CapCut and Instagram Edits.

For foldable device owners, Android 17 introduces a dedicated gaming mode with an optimised 50/50 layout placing a game view on top and a dynamic gamepad below.

Google has also reduced frame drops and improved memory cleanup for high-definition gaming, with native controller remapping available for external controllers.

Security
On the security and privacy front, users can now grant apps one-time location access rather than making a permanent decision and share only specific contacts rather than their full address book.

Lost Mode has been tightened to require biometric authentication alongside a PIN, making it harder for thieves to access a missing device even if they know the passcode.

Live Threat Detection has also been improved to block a greater range of suspicious apps and scams, and enhanced Advanced Protection mode helps keep users safe from sophisticated threats.

To stop bad actors trying to guess their way into users’ phones, Google reduced the number of times someone can guess a PIN and added longer wait times between failed attempts.

Many of Android 17’s more advanced features will follow later this year. Gemini Intelligence, redesigned emoji, smarter voice dictation and vibe-coded widgets will arrive soon, coming first to the Pixel 11 series and upcoming Samsung devices.

Google is also updating Wear OS 7 with longer battery life, better interoperability with connected devices including smart glasses, and Gemini Intelligence features including multi-step app automations.

The Android updates came a week after Apple unveiled a long list of AI and Siri improvements due in the coming months, highlighting the intensifying rivalry between the two mobile platforms.

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SpaceX has agreed to acquire startup Cursor in a $60 billion deal, the latest move in Elon Musk’s push to close the gap with rivals on AI-powered coding tools.

A regulatory filing today (16 June) with the US Securities and Exchange Commission (SEC) confirmed Cursor investors will receive SpaceX stock based on the implied $60 billion equity value of the startup.

The transaction, which converts an earlier option into a binding merger agreement, is expected to close in Q3. After gaining regulatory approvals, Cursor will become a wholly owned subsidiary of SpaceX. The filing was signed by SpaceX CFO Bret Johnsen.

The deal came a week after SpaceX completed the largest initial public offering in history.

Bloomberg reported the Cursor acquisition is central to SpaceX’s ambitions for its AI business.

The news outlet noted Musk has publicly acknowledged xAI, which is now part of SpaceX’s operations, trails rivals including Anthropic and OpenAI on coding capabilities.

Bloomberg also reported SpaceX’s AI unit has been hit by internal turmoil, with dozens of departures across engineering and data training and ongoing difficulty recruiting top talent, prompting Elon Musk to bring in leadership from Starlink to steady the business.

It also noted US-based Cursor, whose AI coding assistant launched in 2023, has become one of the fastest-growing coding startups by helping developers write and debug software through chatbot prompts.

Before SpaceX moved to acquire the company, Cursor was reportedly in talks for a funding round that would have valued it at more than $50 billion, according to Bloomberg.

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A US federal judge reportedly dismissed xAI’s trade secret lawsuit against rival OpenAI with prejudice, dealing another legal blow to Elon Musk in his widening battle with Sam Altman’s company.

Reuters reported Elon Musk-owned xAI failed to demonstrate OpenAI induced former xAI engineer Xuechen Li to misappropriate trade secrets, or that Li disclosed any confidential information during a presentation he gave while OpenAI was recruiting him.

In the order, the judge wrote xAI essentially equated asking a job candidate about prior work experience with encouraging a potential employee to divulge trade secrets obtained during that work.

The news agency stated the dismissal with prejudice means xAI cannot raise the same claims again.

The judge already dismissed an earlier version of the lawsuit in February, giving xAI an opportunity to strengthen its case. Having found xAI failed to cure the shortcomings in its amended complaint, the judge ruled it would be futile to allow further attempts.

The lawsuit was originally filed in September 2025, alleging OpenAI engaged in a coordinated campaign to hire away xAI employees to access trade secrets related to its Grok chatbot, including source code and data centre deployment strategies.

Musk’s AI company claimed former employees took confidential material when they left for roles at OpenAI.

Reuters noted OpenAI previously called the lawsuit baseless and part of Musk’s ongoing campaign of harassment against the company.

The case is part of a broader legal battle between Musk and OpenAI, which he co-founded alongside OpenAI CEO Sam Altman before departing.

Musk is separately suing OpenAI over its conversion to a for-profit company, while the AI firm has countersued Musk for harassment.

xAI also sued Apple in federal court, alleging it conspired with OpenAI to suppress rival platforms.  

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AT&T Business expanded the reach of its 400G wavelength connectivity into 16 additional US metropolitan areas, extending the service as enterprises face mounting pressure to support AI-era traffic at scale.

With the 400G wavelength edgeless handoff capability now live across 440,000 properties serving more than 2.3 million business tenants, the mobile operator is targeting rapid deployment within 15 days where eligible.

AT&T stated the 400G rollout is designed to connect AI workloads from any location to the cloud, with security embedded in the network rather than added on top.

The company’s edgeless design approach anchors 400G at key interconnection hubs where cloud, network and enterprise traffic converge. It is currently in more than 130 carrier hotel locations nationwide.

The architecture is intended to deliver consistent handoff and predictable performance without requiring traditional network edge builds at every location or data centre.

Amrit Singh, industry principal for information communications technology at Frost & Sullivan, stated AT&T’s wavelength services reflect the infrastructure rigour required for the AI era, citing the company’s fibre reach and a successful 1.6 Tb/s single-carrier wavelength trial.

For enterprises scaling AI across multiple sites, AT&T noted the ability to avoid stitching together multiple smaller circuits as workloads grow is a key benefit for the service.

Branch locations, campuses and edge environments requiring high-capacity connectivity to centralised compute and cloud services are among the primary use cases for the service.

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