Reuters reported the deal is worth about $10 billion dollars.

The vendor is now using Google Cloud’s Vertex AI platform portfolio and its Gemini AI large language models to power its own copilots, ensuring optimised performance, scale and reliability for customers. 

The expanded partnership unites Google Cloud’s advanced AI and infrastructure capabilities with Palo Alto Networks’ Prisma Airs AI security platform to empower enterprises to securely develop and deploy AI offerings.

According to Palo Alto Networks’ state of cloud report released in December, enterprises are dramatically increasing their use of cloud infrastructure to support new AI applications and services. However, 99 per cent of the surveyed organisations experienced at least one attack on their AI infrastructure over the past year.

The two companies stated the newly announced agreement addresses these challenges by offering enhanced go-to-market strategies and building security into every aspect of the AI lifecycle.

Google Cloud president Matt Renner stated the “latest expansion of our partnership will ensure that our joint customers have access to the right solutions to secure their most critical AI infrastructure and develop new AI agents with security built in from the start”.

Palo Alto Networks previously had more than 75 joint integrations with Google Cloud and $2 billion in sales through Google Cloud Marketplace.

The post Palo Alto strikes multibillion dollar deal with Google Cloud appeared first on Mobile World Live.

Mobile World Live (MWL) surveyed several industry analysts for their perspectives on what worked in 2025, what did not and where AI is headed in 2026.

This AI panel of experts includes AvidThink founder and principal Roy Chua, Scott Raynovich, founder and principal analyst at research company Futuriom, Appledore founder and principal analyst Patrick Kelly, and Matt Walker, chief analyst at MTN Consulting.

MWL: Is the much talked about AI bubble real or imagined?

Walker: “Most definitely this is a bubble and has been for several quarters. It hasn’t popped because people keep raising the stakes and nobody is willing to back away and pursue a different strategy. Unless they are forced to, as Chinese companies are due to supply chain constraints. That may be a hidden blessing.”

Chua: “There’s definitely a valuation bubble for specific AI companies”.  He noted OpenAI went from $300 billion valuation in March to $500 billion in October this year. “Since the end of 2022, AI-related equities have driven 75 per cent to 80 per cent of the S&P 500’s earnings and total performance. The Bank of England is worried, and even Sam Altman admits investors are ‘overexcited’.

“Nevertheless, the underlying technology demand is very real. Hyperscalers can’t get GPUs fast enough, and the infrastructure buildout looks like the 1990s internet boom again. Over five to ten years, AI will likely have more impact than the internet or mobile but expect some painful corrections along the way.”

Raynovich: “Somewhat yes. The near-term value as expressed in financial markets is overexuberant, but its long-term impact will definitely be profound.”

MWL: 2025 was supposed to be the year of agentic AI, or, more granularly AI agents. Did it live up to the hype?

Kelly: “Early deployments demonstrate that AI agents can deliver measurable improvements when applied to well-defined domains such as RAN optimisation, transport anomaly detection, and fibre fault resolution.

“However, widescale adoption remains constrained. Appledore Research finds that the primary barrier is not model performance but data accessibility and quality. Fragmented data across OSS, network domains, and IT systems limits an agent’s ability to reason accurately and act confidently.”

Raynovich: “I give it a C+. It’s definitely going to be a long-lived technology but there are many things to work out on the compliance and security side.”

Chua: “Not quite, but that’s okay. The ‘year of the agent’ delivered more of a reality check than a revolution. While many organisations experimented with agents, only 17 per cent reached full deployment, per PwC’s Agent AI survey in 2025.

“However, AI agents are rapidly improving, and the state-of-the-art as represented by coding agents has progressed tremendously through 2025.  In general, agents worked great in bounded domains. And we’re seeing them across most telco OSS and BSS systems performing limited tasks well.”

MWL: What are the roadblocks for large-scale AI deployments?

Walker: “It depends on what kind of AI. With generative AI, the predictive, probabilistic nature of the models means that results are different every time they are run.”

“That is a big roadblock for an industry used to certainty and five-nines of reliability.”

Raynovich: “Security, data privacy, governance, and sovereignty.”

Chua: “Data, talent and culture. Few business leaders think their data maturity can support AI at scale, and many we talk to say data management can’t keep pace. We keep hearing that telcos are sitting on decades of siloed systems that don’t talk to each other.”

“The talent gap is brutal, and telcos have a hard time finding the ‘purple squirrels’ who understand both AI and telco. The hyperscalers and model builders are outbidding everyone for pure AI talent.

“Then there’s cultural resistance. At TM Forum’s DTW, industry leaders called it the primary hurdle to network automation. Fix the foundations first, or you’re just doing AI theatre.”

MWL: Are operators deploying AI to make money or save money?

Chua: “Save money. Telco survival demands it. As I shared, the GSMA confirms 75 percent to 80 per cent of deployments target cost reduction.

“But the pivot is starting. Deutsche Telekom is building a €1 billion ‘AI Factory’ with Nvidia. SK Telecom is pushing GPU-as-a-service, and T-Mobile US is using AI-powered switching tools to poach competitors’ customers. I think the path is clear: Save first to fund the revenue experiments.”

Walker: “So far it is mostly about cost and time saving. Hopefully next year we will start seeing more revenue growth examples.”

Raynovich: “The initial lift will come on the savings side. The operators that can efficiently connect data centres will see lift on the revenue side as well.”

MWL: Which US operator had the most success deploying AI this year?

Walker: “I would say that T-Mobile US deserves some recognition given the DT group leadership.”

Chua: “Probably T-Mobile, given their much lauded OpenAI-powered IntentCX platform. The T-Life app’s ‘Easy Switch’ feature completes carrier switching in 15 minutes using AI-recommended plans. These efforts are likely contributing to T-Mobile’s continued postpaid net adds that outpace the competition.”

MWL: Which vendor ruled the AI roost in 2025?

Chua: “Nvidia, obviously. They own 80 per cent to 90 per cent of the AI accelerator market. And they are spreading excess cash to give them visibility and influence over the data centre, application, and telco markets. For example, the $1 billion infusion into Nokia and the partnership for 6G AI-RAN help bolster their telecom position.

“Broadcom is also notable for its custom ASICs for Alphabet and Meta, as well as an OpenAI deal that represents 10GW of AI accelerators between 2026 and 2029.”

Raynovich: “Ciena had an epic year.”

Walker: “Ciena is a strong contender. Even if there is an AI bubble, all of these new AI data centres and factories need fibre connectivity, and Ciena is a leader in the space. It has a strong relationship with Meta, for example.

“Plus, Ciena continues to be a leading player in optics/IP in the telco market.”

MWL: What’s next for AI vendors, enterprises and operators?

Raynovich: “We believe the next phase of growth in AI is implementing AI at the edge, implementing new AI-focused data sovereignty and security solutions, and helping enterprises manage their data and AI applications.”

Chua: “Physical AI and edge intelligence. The next killer apps are robots, drones, and autonomous systems that need real-time network connectivity because they can’t process everything on-device.

“Telcos may own the beachfront property for supporting edge intelligence, but they should be cautious given past failings in MEC.

“Sovereign AI clouds are becoming real products. Deutsche Telekom, Orange, and others are wrapping Nvidia compute in national flags for regulated industries. Operators who figure this out could evolve into AI infrastructure providers, but the path will prove treacherous for all save the nimblest of telcos.”

Walker: “Not anytime soon, outside of attention-grabbing trials that don’t scale,” when asked about mass deployments of physical AI for robots or edge devices next year.

The post Analysis: Was AI overhyped or successful in 2025? appeared first on Mobile World Live.

China’s top tech giants, led by e-commerce giant Alibaba, earmarked at least CNY380 billion ($54 billion) in compute power and AI infrastructure this year.

While both countries are investing huge sums to emerge as a leader in the segment, they have vastly different goals.

Huawei CEO Ren Zhengfei last month pointed out the US is exploring artificial general intelligence (AGI) and artificial superintelligence to solve big issues, such as “what it means to be human”.

This is clearly demonstrated by Meta CEO Mark Zuckerberg’s bold ambition, outlined in July, to bring “personal superintelligence” to billions of users. It has allocated to up $27 billion to AI compute this year.

Real-world applications
In contrast, Ren said China is studying the adoption of AI in real-world scenarios, aiming to create more value and drive growth, with a focus on improving industrial efficiency, safety and profitability.

The CEO said the key technical requirement for AI is having sufficient electricity and a well-developed information network, two areas where China has a significant advantage over the US. “China has excellent power generation and grid transmission systems, and its communication network is the most advanced in the world.”

He told a gathering of students last month at its Lianqiu Lake Campus AI’s sensing and control capabilities will depend on data transmitted across thousands of kilometres, which will require advanced networks.

Leonardo Dinic, an adviser on geopolitics and international business, highlighted in an article in China-US Focus that while both countries’ AI strategies are globally ambitious, there are clear differences. 

He suggested the US “explicitly promotes the export” of end-to-end AI technology, covering hardware, models, software, applications and standards to allies and firm geopolitical partners, to prevent competing adversaries from free-riding on innovation. 

Meanwhile, China is positioning itself as a partner for Global South nations by emphasising “open technology exchange, cross-border cooperation”, which is less conditioned by each actor’s political alignment, Dinic noted.

Lagging in AI chips
Despite restricted access to the most advanced AI chips, Chinese AI companies, demonstrated by start-up DeepSeek introducing its R1 model in January, are keeping up with US leaders in many categories.

TrendForce analyst Frank Kung told Mobile World Live Chinese AI suppliers have adopted an approach of using a larger number of chips compared with US-based GPUs, such as Nvidia’s. 

He explained: “This entails disadvantages such as higher costs and a much larger data centre footprint when assembling equivalent systems, but it is also a breakthrough strategy that they are effectively compelled to pursue.”

The country’s AI suppliers face disadvantages in upstream supply, such as wafer fabrication and high-bandwidth availability, as well as in process technology, Kung added. 

As a result, their development path is mainly focused on two directions.

First, they adopt a server-cluster approach: while individual chips may underperform compared with leading alternatives, system-level performance is achieved by aggregating a larger number of chips.

Second, they place greater emphasis on AI inference applications. Although their capabilities in large-scale LLM training may be less competitive, they aim to compensate by developing more diversified AI application scenarios, he noted.

The pod advantage
Huawei rotating chair Eric Xu acknowledged at the chip level, the performance of an individual AI Huawei chip is not as good as Nvidia’s, “but at a super pod level, we can deliver the most powerful systems in the market”.

In September, he detailed the company’s roadmap for AI computing platforms, with plans to release the world’s most powerful single system in Q4 2026 and then double the performance with a new launch a year later.

Looking ahead, talk of an AI bubble rages on not only in the US but globally, with downside risks coming from over-investment (possibly repeating Meta’s metaverse overreach) to a lack of electricity to power data centres in some locations.

From a global perspective, Kung noted the major source of uncertainty stems from geopolitical risks. “An unstable political and economic environment – such as export controls, bans or tariffs – could lead enterprises to adopt a more cautious stance, increasing the risk of tightened procurement spending and slower adoption.”

Ren earlier suggested the development of AI will take decades, even centuries, advising “don’t worry.”

Such a “long march” view leaves plenty of time for shares of AI companies to soar to new peaks and for the market to sustain a wrenching correction, and the two countries to continue to battle for the claim of AI champion.

The post AI feature: Chinese practicality vs US idealism  appeared first on Mobile World Live.

According to an internal memo seen by Bloomberg, Yao will report directly to Tencent president Martin Lau and head the company’s newly formed AI Infrastructure Department.

The AI Infrastructure Department, Tencent’s new research and development unit, sits within the company’s Technology Engineering Group (TEG), which has established two fresh units to focus on large-model training, inference platforms and full-stack data and machine learning services as competition intensifies across the sector.

Yao was also appointed head of TEG’s large language model development, reporting to the group’s president Lu Shan. He reportedly joined Tencent earlier this year after a nearly two-year stint at OpenAI. Yao’s previous roles include positions at Google, Microsoft and Princeton University while his research background spans AI agents and large-scale systems.

South China Morning Post reported that as part of its AI overhaul, Tencent also dissolved its Machine Learning Platform department and redistributed staff across the newly created teams. In addition, the company renamed its existing data platform team and reassigned staff to better align data services with model training and deployment.

At a recent earnings call, Lau reportedly said Tencent was recruiting more top-level AI researchers to strengthen its Hunyuan model family.

Yao’s appointment comes as global competition for AI talent intensifies. Earlier this month, Meta Platforms made headlines for recruiting a senior Apple design expert while OpenAI nabbed a Google executive to lead as its VP of corporate development.

The post Tencent nabs OpenAI researcher as AI shake-up advances appeared first on Mobile World Live.

0:00 Intro
2:00 Was it a quiet year?
5:00 Nokia shake-up
9:00 US operators drop DEI
16:00 Satellite gets real
21:00 Smartphones get thin
29:00 Billions pumped into AI
33:00 Our 2026 predictions

The post Podcast: Nokia, Trump and AI billions – our 2025 review appeared first on Mobile World Live.

Osborne stated on LinkedIn he is now managing director and chief of its “OpenAI for Countries” project which includes the expansion of Stargate while ensuring AI systems are built on democratic values.

Osborne’s remit also includes supporting local innovation ecosystems, education and infrastructure. He starts his new job next month and will be based in London.

He served as the UK’s chancellor of the exchequer from 2010 to 2016.

Osborne’s appointment follows OpenAI rival Anthropic hiring former UK Prime Minister Rishi Sunak as a paid senior advisor in October.

OpenAI also hired former Google executive Albert Lee as its VP of corporate development as part of a plan to expand its AI efforts.

AI companies OpenAI, Microsoft, Meta Platforms and Google have engaged in an AI hiring spree this year, which has including attracting employees from each other.

The post OpenAI names former UK minister to lead Stargate push appeared first on Mobile World Live.

The study, which surveyed around 2,000 businesses and decision-makers across the US and Europe, found networks must evolve rapidly to keep pace with increasingly complex AI workloads. In the US, 88 per cent of respondents said infrastructure limitations could restrict AI’s scale, compared with 78 per cent in Europe.

According to Nokia, AI is “redefining network requirements”, with traffic patterns shifting away from traditional downlink-heavy consumer use towards uplink-intensive data flows. Applications such as autonomous vehicles, smart factories and remote healthcare generate large volumes of data at the edge that must be sent upstream for processing, placing pressure on networks originally designed for browsing and video streaming.

In Europe, 86 per cent of enterprises believe today’s networks are not yet ready for mass AI adoption. While two-thirds of enterprises surveyed already have AI in live use, more than half are already experiencing downtime, latency and throughput constraints. Security concerns are also rising as more than 80 per cent of businesses across sectors believe AI is introducing risks, with cybersecurity emerging as the top AI use case.

Competitiveness is another pressure point. Nearly 29 per cent of European enterprise leaders warned infrastructure constraints could force them to move AI workloads abroad, potentially undermining the continent’s digital sovereignty.

Reshaped industries
To address these concerns, respondents called for regulatory simplification, spectrum availability and greater investment in energy-efficient, AI-ready networks across the continent.

In the US, respondents highlighted the need to optimise bi-directional data flows, expand fibre capacity and deploy low-latency edge infrastructure. Despite leading global AI adoption, most US respondents remain concerned infrastructure upgrades may lag behind.

“The first wave of the AI supercycle has already reshaped industries and accelerated innovation,” noted Pallavi Mahajan, chief technology and AI officer at Nokia. According to her, the research shows “a clear understanding across the ecosystem that future waves will demand more advanced, AI-native networks”, with “connectivity, capacity, and low-latency performance” becoming increasingly critical.

Looking ahead, the Finnish vendor called for closer collaboration and more predictable regulation to enable timely network investment as AI demand accelerates.

The post Nokia warns AI boom is outgrowing digital infrastructure appeared first on Mobile World Live.

Google Gemini, for instance, faced criticism for generating historically inaccurate images, while xAI’s Grok was noted for producing politically slanted responses.

ChatGPT drew scrutiny over its susceptibility to reflect embedded prejudices in training data, sometimes resulting in problematic suggestions or offensive outputs.

The impact of these biases so-far has arguably been played down, a series of gaffes which are only natural as the systems learn and develop.

But, as the role and use of generative AI becomes more pervasive, the risk of these mistakes resulting in real-world harm grows.

Training
Inclusion and AI strategist Dr Patricia Gestoso noted bias has always existed, explaining AI is essentially just speeding the process of arriving at skewed conclusions.

In part, the problem relates to a perception fields including algorithms and mathematics are objective, she said.

“That’s not true”, Gestoso said, highlighting the outcome depends on the data inputted: “It’s like a recipe. An algorithm is a recipe”.

Gestoso (pictured, right) spent more than 15 years advising companies, non-government and government organisations about technology, science, diversity and inclusion, data analytics and customer experience, so her views carry clout.

She noted maths and statistics have long been “weaponised against groups”, with data manipulated to suit a particular narrative. In this context, AI simply makes the process easier, she argued.

If bias has always existed, does it matter if consumer AI exhibits the same traits?

Gestoso highlighted the employment market as one example where AI bias is problematic. The technology is used to write job descriptions then sort through applicants, a process with the potential to exclude people before they even apply for a role due to the use of certain linguistic traits, or dismiss their application based on the way data provided is employed.

AI is already offering different outcomes to men and women using it to research information on job roles and pay levels, or providing noticeably different outcomes when preparing curricula vitae (CVs) based on whether it is dealing with a male or female, Gestoso said.

There are deeper implications. Gestoso highlighted a growing role for AI in healthcare, a position with the potential to skew how certain groups or genders are treated.

Perhaps the greatest problem in Gestoso’s view is generative AI, the branch which creates content including text and images, is persuasive. People are inclined to believe the results it generates even though most companies developing the technology acknowledge it is not currently at that level and results should be verified.

For all that AI itself is causing problems, Gestoso indicated there are also shortcomings in the mostly trial-and-error approach to its development.

It leaves the door open to potential harms being inflicted while bugs are tackled and kinks ironed out, an approach Gestoso indicated would not happen in any other field: “we wouldn’t say to a doctor that you just try and give it a go about a new medicine”.

Gestoso acknowledged a potential link between the historic or unconscious biases of people developing AI and the technology’s preconceptions, though stopped short of saying this is the root of the problem.

Instead, she indicated people had bought into hype about a technology perhaps not quite as ready for the roles being promoted as those endorsing it might have us believe.

“We have a lot of what I call AI washing”, Gestoso said, pointing to cases where manual work remains de facto, or companies have backtracked on replacing support staff with chatbots and artificial agents.

Legality
Gestoso does not believe tackling AI bias necessarily requires fresh laws because much existing legislation can be applied to the digital world.

“We don’t need to do law for each new technology, because many existing laws and domain regulations can readily be applied to them.”

But she does believe platforms and intermediaries providing access to the technology have a role to play in terms of accountability, and suggested any laws removing such liability might not be helpful because they focus “only on the creator, or the person that is the harasser, or the person that is racist to me”.

Gestoso pointed out AI is a broad field which has been around for decades and some applications have already resulted in useful tools along with many promising developments, though cautioned against viewing the technology as a “magic bullet”.

The implications of AI-bias may be widespread but, given how far advanced deployments of the technology now are, can it still be guided towards fairer, more balanced outcomes?

Allison Koenecke, an Assistant Professor of Information Science at Cornell Tech (pictured, left), believes the answer is not a simple one.

The academic is focused on the point where economics and computer science meet, a field which incorporates the concept of algorithmic fairness. At a broad level this is a means of assessing if machine learning systems operate without bias.

Koenecke said there is a degree of fluidity to the definition, explaining it spans ensuring marginalised groups are fairly represented in the algorithms used, “all the way to the more theoretical computer scientists who are thinking about different mathematical definitions of fairness”.

The concept can also involve evaluating how well algorithms work “across different groups”.

Koenecke homed in on one of her specialities, speech-to-text transcriptions, to highlight how potential bias can occur.

If “there are very few black voices speaking African American English in the training data, the transcriptions on those voices are going to be worse downstream”.

People power
She noted there are “a lot of other reasons you might end up with biases”. The acoustic qualities of male voices, for example, can result in poorer quality transcriptions than for female speech, potentially skewing outcomes despite equality in the people training systems.

Koenecke said fixes in such cases “might be more about the modelling architecture and less about the training data”.

She explained having a diverse pool of developers can help alleviate bias simply by bringing different points of view to the table. In speech-to-text, if “you never think to evaluate whether or not this particular tool works well on the deaf and hard-of-hearing population, then you’re just never going to know whether or not it works well”.

Koenecke acknowledged people are part of the problem when it comes to AI bias, emphasising awareness as a key weapon in tackling the issue.

This falls to people because the technology itself may not yet be capable of mulling matters over. “It might be difficult to actually train the model to account for those sorts of biases without first using human expertise to determine what kinds of biases are occurring.”

Koenecke differs from Gestoso by believing specific regulation might be needed to prevent AI bias having real-world harms. In healthcare, for example, there are potential dangers if one demographic is prioritised over another.

“I think these sorts of regulations likely have to be at the domain level, because the way that you would regulate something in the medical space is very different from” the recruitment sector.

She expects “many of these domains are going to have to grapple with very similar problems in terms of how much error are you willing to accept, how much can we have humans in the loop serving as experts overseeing AI output, and how much discrimination could arise” and be mitigated by regulations.

Dangerous
Koenecke noted you do not have to look far for examples of harm already being caused, pointing to suicides prompted by chatbots. She said this is a good reason for companies to prioritise safety themselves rather than wait to be told to.

Helene Molinier, adviser on Digital Cooperation at the UN, argues the way AI is trained lies at the heart of sexist, racist and misogynistic output.

She noted the majority of AI is “trained on enormous datasets that reflect centuries of inequalities”, highlighting sources including books, websites and images.

If this “data contain stereotypes, discrimination or under-representation, AI will replicate and sometimes amplify them.”

Molinier said examples of the discrimination which could impact AI are not hard to find: she explained women, “people of colour and people from the Global South are underrepresented in many training sets”, and many large language models (LLMs) “have been shown to associate men more often with leadership and women with caregiving professions”.

It is similar to the point Gestoso made regarding CVs, one she suggested is highlighted by switching the gender of the person involved while keeping all other details the same.

Molinier added image generators have been found to show similar gender bias by portraying men as politicians, professors or company CEOs.

She agreed with Koenecke’s view on the breadth of developer pools, emphasising additional reasons for AI bias.

Other factors include what data is selected to train LLMs, algorithmic design choices, institutional blindness, and structural and historical amplification of discrimination.

“This is not just a coding issue; addressing AI bias requires comprehensive, multi-layered approaches and having more gender parity in technical and decision-making roles.”

Molinier said various UN and “global normative frameworks” offer “a comprehensive roadmap” towards gender inclusivity and governance.

These include auditing procedures, improved source data, education and protecting rights, work she explained is necessary to avoid AI bias discouraging women from “entering and remaining in the tech sector”.

“When AI systems consistently undervalue women’s contributions, reproduce gender stereotypes or fail to serve women’s needs effectively, it sends a clear message about who technology is designed for and by.”

It makes addressing such bias an urgent matter, one which goes to the heart of digital justice and equality, Molinier said.

The post Feature: Does AI bias matter? appeared first on Mobile World Live.

The US Tech Force is hiring software engineers, data scientists, project managers and AI experts to work on modernisation projects across government agencies, according to its website.

Participants will work on high-impact technology initiatives including AI implementation, application development, data modernisation and digital service delivery across federal agencies.

Tech Force is designed as an ongoing initiative to continuously bring top technical talent into government service.

The government is partnering with at least 28 tech companies on the programme, including Apple, AMD. Amazon Web Services, Anduril, Dell Technologies, Box, Meta Platforms, Microsoft, Nvidia, OpenAI, Palantir, Oracle, Uber, ServiceNow, and xAI, among others. There are plans for more companies to be added over time.

The private companies can also nominate their employees to do stints of government service.

The US Office of Personnel Management oversees the early-career programme in partnership with other federal agencies such as the US Department of War, the Department of Homeland Security and the Department of Treasury.

Potential employees can seek to find permanent positions with the agencies or private companies once they complete their two-year terms.

Tech Force stated compensation varies based on experience level and agency placements. Annual salaries are expected to be in the range of $150,000 to $200,000, plus benefits.

The Trump administration is keenly focused on keeping the US ahead of other countries, such as China, on the development of AI.

In July, it unveiled its AI action plan, designed to solidify the US’ position as the global leader.

On 11 December, Trump signed an executive order to blocks states from enforcing their own AI regulations in favour of a single federal rule.

The post Trump launches Tech Force to groom AI talent appeared first on Mobile World Live.

Reuters reported Lee is starting his new role today (16 December), reporting to finance chief Sarah Friar.

The news agency cited comments from OpenAI explaining the move to bring Lee “on board is to ensure we have a senior leader with broad visibility across the company who is empowered to move quickly”.

Lee had an almost 15-year stint at Google, most recently as its senior director of corporate development for Google Cloud and Google DeepMind.

On his LinkedIn profile, Lee states he worked on more than 60 transactions during his career at Google, including strategic investments totalling more than $50 billion. He was involved in work for the company’s $32 billion deal to buy Israeli cybersecurity company Wiz in March.

Top AI companies OpenAI, Microsoft, Meta Platforms and Google have engaged in an AI hiring spree this year, which has including attracting employees from each other.

The post OpenAI snares Google exec to lead corporate development appeared first on Mobile World Live.

The operator explained in a statement the Agentic Callbot is a phone response service that uses AI technology to handle even complex queries naturally by understanding customer intentions and conversation context in real time. It added agentic callbots can understand various expressions and situations and control the system on their own without prior learning through large-scale language models and knowledge retrieval.

Jung Young-hoon, head of its enterprise AI business, noted it plan to introduce additional agentic AI call centre services by using various technologies.

An upgraded version, the Agentic Callbot Pro, is scheduled to be released next year with the stated aim of accelerating enterprise customers’ digital transformation.

Going forward, LG Uplus plans to introduce an agent service that links multiple LLMs and a speech-to-speech function that processes text-to-speech, voice recognition and reasoning through real-time APIs based on OpenAI’s multimodal LLM.

Last July, the company laid out an AI growth strategy for its enterprise business, with a focus on AI data centres and on-device AI; AI platforms using smaller large language models; and AI-powered services for B2B customers.

The post LG Uplus uses OpenAI models to automate customer support appeared first on Mobile World Live.

The Nemotron 3 family of models consist of three sizes: Nano, Super and Ultra. Nvidia claims they use a hybrid mixture-of-experts (MoE) design, enabling high throughput and scalability across multi-agent systems.

The models are optimised for low inference costs and high performance, making them ideal for tasks like software debugging, content summarisation, AI assistant workflows and information retrieval.

The company stated the launch of the Nemotron 3 models address the aggressive acceleration of industry adoption of agentic AI applications which require reasoning and tool orchestration.

Nvidia stated organisations are shifting away from single model chatbots to collaborative multi-agent AI systems.

Launching today (15 December), Nvidia explained Nano delivers four times higher throughput than its predecessor and excels in multi-agent tasks with a 1-million-token context window.

The Super model combines 100 billion parameters with 10 billion active parameters. It is designed to provide mid-range intelligence for multi-agent applications.

Finally, Ultra, a large reasoning engine with 500 billion parameters with 50 billion active parameters, delivers more powerful advanced reasoning for complex workflows.

The Super and Ultra models are expected to be available in the first half of 2026

Early adopters include Accenture, Deloitte, EY, Oracle, Palantir, Perplexity, ServiceNow, Siemens, Synopsys, and Zoom. Those companies are using the Nemotron models for AI workflows in manufacturing, cybersecurity, software, media, and communications.

Reuters reported the new family also positions Nvidia to better compete against a growing number of open-source models from companies such as DeepSeek, Alibaba and Moonshot AI.

Meta Platforms has been a big proponent of open source through its Llama large language models (LLMs) but Bloomberg reported it is mulling a shift to towards closed-source models.

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Paramount launches $108B WBD bid to rival Netflix

What happened: Paramount Skydance launched a hostile $108.4 billion bid to acquire the entirety of WBD, including its Global Networks segment and traditional TV assets, days after Netflix agreed an $82.7 billion deal for parts of the company.

Why it matters: In a statement, Paramount described its proposal as a “superior alternative” to Netflix’s transaction, arguing it delivers $18 billion more in cash and avoids “inferior and uncertain value”. It also warned Netflix’s deal could face a “protracted multi-jurisdictional regulatory clearance process”. Dave Ellison, chairman and CEO, added Paramount provides a “more certain and quicker path to completion”. The bidding war for WBD intensifies regulatory scrutiny around competitiveness and consolidation in streaming. US President Donald Trump stated the Netflix, WBD tie up “could be a problem” given Netflix’s hefty market share, while US Senator Mike Lee cautioned the deal would “raise serious competition questions”. Roger Entner, founder and analyst at Recon Analytics, told MWL a combined group would become the “dominant gateway for both production and distribution of premium filmed entertainment”, with “spillover effects on adjoining markets”.

Australians seek alternatives as social media ban takes hold

What happened: Australia’s ban on under-16s using major social media platforms came into force this week, prompting a surge in alternative app downloads as younger users sought ways around the new rules.

Why it matters: Demand for VPN services also spiked, with Top10VPN reporting a 103 per cent rise compared with the previous 28-day average. Under the law, platforms including TikTok, Instagram and Facebook must block underage accounts or face penalties of up to AUD49.5 million ($33 million). Australian Prime Minister Anthony Albanese argued the move is a “profound reform” aimed at “pushing back against big tech”, framing the legislation as a response to families affected by serious online harms. He added “the world is watching and following”, stating that the move will “continue to reverberate around the world in coming months”. Although some social media platforms have complied, Reddit filed a free speech lawsuit against the country. Researchers have also warned of potential risks; Professor Jessica Ringrose of University College London argued an outright ban is “problematic” because it “neglects young people’s rights and voice” and may erode trust, making children “less able to access support”. She said stronger media-literacy policies, supported jointly by schools and parents, are better suited to addressing fast-moving issues such as AI, privacy and consent.

Nvidia H200 China chip sales cleared

What happened: In a regulatory U-turn, the US government cleared Nvidia to resume exports of its H200 AI chips to approved customers in China, imposing a 25 per cent levy and vetting by the Department of Commerce to ensure national security.

Why it matters: The decision reverses earlier restrictions on advanced AI chips and follows a recent easing in US-China tech tensions. President Donald Trump announced the move on Truth Social, stating the US would permit sales under conditions ensuring “continued strong national security”. The approval excludes Nvidia’s latest Blackwell and Rubin lines, but also covers products from AMD and Intel. The H200 offers significantly more memory bandwidth than Nvidia’s previously permitted H20, giving Chinese buyers access to faster AI processing while remaining below top-tier US hardware. Indeed, the shift creates a major commercial opening for Nvidia, which had removed China-related H200 revenue from recent financial outlooks. The Wall Street Journal estimated Nvidia could stand to gain up to $5 billion in quarterly orders without geopolitical restrictions. The clearance may also slow Chinese adoption of domestic alternatives from vendors such as Huawei. However, some critics argue the policy risks accelerating China’s progress toward advanced AI systems. According to Reuters, US senator Elizabeth Warren warned the decision “risks turbocharging China’s bid for technological and military dominance and undermining US economic and national security”. This week, Nvidia also rebuffed media reports claiming Chinese AI start-up DeepSeek had used banned Blackwell chips to train its latest model.

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Markus noted fine-tuned SMLS are key for unlocking value in mature agentic offerings and stated businesses “will understand the importance of their own data in driving AI value”.

“The large language and reasoning models will often handle the master control of an agentic workflow, but the purpose-built SLMs very adequately deliver the required accuracy and efficiency when trained for their dedicated job within the agentic workflow,” he explained.

Markus provided several AI-related predictions for next year in a blog post, all of which are largely due to the combination of AI agents and AI-fuelled coding.

He stated AI-fuelled coding will be the next major methodology for developers, enabling them to cut development phases from weeks to minutes.

“With AI-fuelled coding, developers will start to wear multiple hats in the lifecycle, from product owners to architects, reducing cycle times and time to operation,” Markus stated.

It also helps non-technical teams to develop apps by using plain language prompts to build software prototypes.

Internally, AT&T has used AI coding to curate data products withing 20 minutes while also training it to “adhere to our rigorous code discipline for quality, security and compliance”.

On-demand apps
Also on the 2026 horizon, businesses will start to build on-demand apps supported by AI agents.

“AI-fuelled coding dramatically accelerates software development cycles, making it feasible for a company to build on-demand apps,” Markus said. “Autonomous agents can even independently adapt to new requirements, making redevelopment faster than traditional app cycles.”

He stated while traditional apps will remain, they will be supplemented by agile, AI-driven offerings.

Mobile operators will also offer more AI services, such as fine-tuning, to enterprise customers. Markus noted operators have a long history of working with cloud providers and AI software companies, “giving us a strong foundation in AI services like fine-tuning”.

His last prediction is metrics for AI accuracy, cost and speed will become the focus across every business sector.

“It’s not enough to use AI tools. They have to use them well, with measurable AI results. Accuracy is what drives value, and optimisation is what’s needed for generative and agentic AI to measure up across any use case,” he said.

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In addition to the $1 billion sum, Walt Disney will receive warrants to purchase additional equity.

As part of the landmark agreement, OpenAI’s text-to-video Sora AI model will enable users to generate and share short, user-prompted social videos featuring more than 200 characters from Disney, Marvel, Pixar, and Star Wars. 

The videos will include costumes, props, vehicles, and iconic environments, but exclude talent likenesses and voices, in a move to perhaps stave off protests from actors’ unions.

ChatGPT Images will allow users to create content from Disney’s intellectual property using simple prompts while selected fan-inspired Sora short-form videos will be available for streaming on Disney+.

Sora and ChatGPT Images are expected to launch the fan-inspired videos with Walt Disney’s licensed characters in early 2026.

Walt Disney will use OpenAI’s APIs to build new products and experiences, including for Disney+, and will deploy ChatGPT for its employees.

Walt Disney CEO Robert Iger stated the agreement provides the company with new methods for sharing its content with the world through generative AI but noted it will respect and protect content creators and their works.

Bloomberg reported OpenAI conducted talks with other large film studios such as Comcast’s Universal Pictures and Warner Bros. Discovery about pairing Sora with their content but noted they are hesitant to partner with an AI company.

The deal is subject to final agreements, board approvals and standard closing conditions.

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In its latest Online Nation 2025 report, the regulator found that visits to ChatGPT increased by 156 per cent year-on-year in August, contributing to 1.8 billion UK visits in the first eight months of 2025, up from 368 million in same period of 2024.

Rival AI chatbots also saw traction, though from smaller bases.

Google’s Gemini saw UK visits rise 146 per cent, while Anthropic’s Claude grew 138 per cent and Perplexity increased 100 per cent in the year to August 2025. Ofcom did not break out how many visits these figures equated to.

Ofcom said search practices are moving away from conventional link-based pages toward AI-powered, chat-style interactions, finding early evidence that users who see AI summaries are less likely to click through to original content, reducing publisher traffic.

In addition, the adoption of AI summaries from services such as Google’s AI Overviews has become embedded into mainstream search use. Ofcom noted that 30 per cent of keyword searches now deliver AI-supported responses, encouraging passive user adoption.

Looking ahead, the regulator predicted genAI “will continue to become an increasingly important component of search” as “rapidly evolving AI-tools, fast user adoption and unprecedented levels of investment” spur growth and innovation.

Online activity
Overall online engagement also continues to rise as UK adults spent an average of four and a half hours online each day in 2025, up 10 minutes from last year. More than half of that time was spent within services owned by Alphabet and Meta Platforms.

Yet, Meta edged ahead of Alphabet in total time spent, with online adults averaging 1 hour 10 minutes per day on Meta-owned apps. Facebook and Messenger were used by 93 per cent of online adults and WhatsApp by 90 per cent, while Instagram continued to grow in reach and engagement.

However, Alphabet-owned YouTube remains the dominant individual platform across the country. Users accessed the video platform for an average of 51 minutes per day on smartphones, tablets and PCs, with Ofcom noting YouTube overtook Google Search in reach.

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In an emailed statement sent to Mobile World Live, Nvidia responded to a report from The Information, based on unnamed sources, which was cited by Bloomberg claiming Blackwell chips were smuggled into China through countries that permit their sale.

The AI startup apparently used chips that were installed in data centres in the undisclosed countries, then proceeded to dismantle and ship to China after clearing inspection by companies developing server equipment.

An Nvidia spokesperson said it hadn’t seen “any substantiated or received tips of ‘phantom data centres constructed to deceive us or our OEM partners, then deconstructed, smuggled, and reconstructed somewhere elsewhere.”

The statement continued: “While smuggling seems farfetched, we pursue any tip we receive.”

The news comes the same week US President Donald Trump gave Nvidia permission to ship H200 AI accelerators to China.

However, a ban on the more powerful Blackwell chipsets is still in force.

The US has enforced a block on advanced AI semiconductors being shipped to China, resulting in AI developers accessing hardware through data centres located outside of the country, Bloomberg reported.

Domestic push
DeepSeek rocked the global AI market at the start of 2025 when it showed off its R1 AI model, which experts stated had been built at the fraction of the cost of rival options such as OpenAI’s ChatGPT.

The company was founded by Chinese hedge fund High-Flyer, which amassed 10,000 Nvidia GPUs in 2021, before US export rules came into force, Bloomberg’s report added.

Despite Trump easing chip rules this week, Chinese authorities are still pushing companies to use domestic technologies in their AI pushes. DeepSeek last released a new model in September and stated it was working with domestic companies on the offering.

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In a stock market filing, the chipmaker stated it is “considering various measures to enhance corporate value, but nothing has been finalised at this time”.

Analysts suggest the potential listing of its treasury shares on a US stock exchange would be via American Depositary Receipts, which are issued by US depositary banks, allowing foreign shares to trade in the US like domestic stocks.

The filing added it will make a further announcement when specific details are finalised, or within one month.

In its Q3 earning call at end-October, the company said due to surging demand for AI memory it had secured orders for its entire DRAM and NAND production in 2026.

At that time, the company noted it will increase capex in 2026 to boost capacity, as it registered record profit and revenue in Q3, driven by rising prices of memory products. It plans to increase the capacity at its next-generation fabrication plant in Cheongju, dedicated to producing advanced DRAM and high bandwidth memory (HBM).

The company hasn’t issued a capex guidance for 2025, but industry sources suggest it could increase 30 per cent year-on-year. Last year’s outlay was KRW16 trillion ($10.9 billion), up 90 per cent from 2023.

SK Hynix led the global DRAM market in Q3 with a 33.2 per cent share, marginally ahead of rival Samsung with a 32.6 per cent share, data from TrendForce showed.

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At the Amazon Smbhav Summit in New Delhi, the e-commerce giant stated with the planned investment by 2030 it aims to accelerate digital transformation, strengthen infrastructure and support innovation across the country.

Amazon SVP for emerging markets Amit Agarwal noted it is “excited to continue being a catalyst for India’s growth, as we democratise access to AI for millions of Indians”.

The company said it has pumped a total of $40 billion in India. Agarwal added the company has invested in “growing the physical and digital infrastructure for small businesses in India, creating millions of jobs”.

The commitment comes a day after Microsoft announced plans to invest $17.5 billion in the country between 2026 and 2029, with a focus on hyperscale capacity.

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Through the formation of the Accenture Anthropic Business Group, approximately 30,000 Accenture professionals will be trained on the AI startup’s Claude AI technologies. The two companies stated the project will boost productivity, streamline code generation and improve software quality.

The multiyear initiative aims to accelerate enterprise AI adoption by providing customers with immediate access to skilled Claude practitioners, reducing deployment risks and speeding up the transition from proof-of-concept projects to full-scale production.

The companies are also developing a joint offering tailored for CIOs which is focused on scaling AI-powered software development across organisations. Key features of the programme include advanced productivity measurement tools, workflow redesign strategies and change management support.

The tie-up follows Accenture’s recent arrangement with Anthropic rival OpenAI to train thousands of the consulting company’s employees on ChatGPT Enterprise.

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Starting with India, the four-year investment runs from 2026 to 2029 and marks Microsoft’s largest commitment in Asia. It builds on a previous $3 billion investment announced in January, which is expected to be completed by the end of 2026.

The announcement followed a meeting between Microsoft chairman and CEO Satya Nadella (pictured, left) and Prime Minister Narendra Modi (pictured, right) to discuss India’s AI roadmap. Microsoft said its strategy aligns with the government’s ambition to move from digital public infrastructure to AI public infrastructure at national scale.

A central focus will be hyperscale capacity. Microsoft plans to bring its India South Central cloud region in Hyderabad online by mid-2026, which the company said will be its largest data centre region in the country. It will also expand its existing data centre regions in Chennai, Hyderabad and Pune.

Beyond infrastructure, the company also plans to accelerate AI adoption on public platforms. It will integrate advanced AI capabilities into government platforms, extending AI-enabled services such as multilingual access and AI-assisted job matching to more than 310 million informal workers.

In addition, Microsoft is scaling up workforce development, doubling its AI skilling target to 20 million people by 2030. Union minister of electronics and information technology Ashwini Vaishna said, “Microsoft’s landmark investment signals India’s rise as a reliable technology partner for the world.”

Canadian ambitions
In Canada, Microsoft’s CAD19 billion investment runs from 2023 to 2027, with more than CAD7.5 billion earmarked for the next two years.

The company will expand its Azure Canada Central and Canada East data centre regions, strengthening domestic data residency and sovereign cloud capabilities for government and enterprises.

The company also unveiled a five-point digital sovereignty plan focused on cybersecurity defence, ensuring data sovereignty, strengthening privacy protections, supporting domestic AI developers and ensuring the continuity of cloud services amid geopolitical uncertainty.

As part of this effort, Microsoft has now launched a dedicated Threat Intelligence Hub in Ottawa, bringing together threat intelligence experts and applied AI security research to work closely with Canadian government and law enforcement partners.

Microsoft expects the new digital and AI capacity in the country to begin coming online in the second half of 2026. Brad Smith, vice chair and president, said “we believe Canada has what it takes to help lead the world in responsible AI innovation and adoption”.

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The operator stated it would use Amdocs’ amAIz offering to integrate genAI services, utilising agents and skills to “improve business efficiencies, reshape customer experience and interactions and increase revenue opportunities in multiple telco domains”.

Amdocs will offer a telco specific guardrail control mechanism with logging and auditing capabilities, role-based access control and privacy compliance measures to deliver “a safe and secure approach to genAI integration”.

The project is supported through Amdocs’ partnership with Nvidia, which utilises the chip giant’s AI platforms to build, customise and deploy genAI models.

Marwan Bin Shakar, acting CTIO at e& said the partnership with Amdocs makes it one of the first service providers to integrate genAI, helping to lead the industry’s transformation toward an AI driven future.

“We are deploying telco-specific agents with built-in guardrails to enhance customer care, retail, and network operations, delivering faster resolution times, smarter recommendations, and clear efficiency gains,” he said.

At the recent MWC25 Doha, e& group CEO Hatem Dowidar also outlined the company’s commitment to an AI-driven transformation, while highlighting the need for the wider industry to adopt the technology.

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The two aim to develop “AI experiences” they describe as simple and personal, comprising communication and everyday productivity applications for consumers and enterprises. Pilots are expected to begin in Q1 2026.

In its statement on the multi-year deal, Deutsche Telkom explained it aimed to “bring privacy-first AI” to millions on the continent.

As part of the pact the operator will be among the first to gain access to an OpenAI alpha-phase model.

The operator’s board member for product and technology Abdurazak Mudesir emphasised this was “not a typical vendor relationship” but a “strategic collaboration helping shape the future of AI in Europe”.

“We are focused on making AI intuitive, secure, and meaningful in everyday life for our customers, employees, and our networks,” he added. 

In its statement, OpenAI explained the partnership would “help make AI more helpful, secure, and accessible” in the region.

As well as collaborating on products for third parties, Deutsche Telekom is set to make OpenAI’s ChatGPT Enterprise available across the business.

It noted this would help “teams work more effectively, improve customer service, and spend more time on innovation”. The operator also plans to up use of AI in its networks as it works towards a goal of increased autonomy.

Medical tests
The move is the operator’s latest related to the highly-hyped technology, with the company contributing to a number of initiatives on infrastructure and showcasing advanced use cases for AI in Germany and beyond.

Earlier today (9 December) Deutsche Telekom released details on a partnership with research organisation Fraunhofer IAIS and heath care provider Kliniken der Stadt Koln to develop AI agents designed to support accident and emergency departments.

The trio are currently developing a prototype based on information from a simulated medical facility. There, the agent is assessing medical information being verbally communicated by healthcare professionals.

It then analyses and enriches the information before presenting in a structured graphical format for use later in the treatment journey.

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For many telecoms, AI still lives in isolated pilots – technically successful but disconnected from broader business strategy. The question now isn’t if AI should be pursued, but how it should be embedded into the enterprise in a way that delivers sustained value.

Scaling Is Still the Struggle

A recent AI roundtable Bounteous ran with senior telecom leaders surfaced a familiar issue: most AI programs are making progress, but few have scaled successfully.

This finding aligns with recent research: 88% of telecom firms report expected better returns from AI initiatives. Yet only 12% are building engineered AI products. Most investment remains focused on embedded tools or team enablement – automating tasks, summarizing calls, or tagging sentiment.

These wins are real, but they’re not transformative. Without a strategy to scale, telecoms risk stopping at efficiency instead of driving growth.

Why Pilots Stall

Telecom organizations face two core blockers to AI scale: fragmented ownership and lack of strategic alignment.

Our research found 50% of AI responsibility still sits with the CIO, while only a small portion of firms have cross-functional governance in place. This siloed ownership model makes it difficult to align AI initiatives with broader business goals.

At the same time, AI efforts are often disconnected from KPIs that matter at the enterprise level. When automation delivers cost savings but no visibility into customer impact or revenue outcomes, the business case weakens over time.

“We’re automating the edges,” one executive said during the roundtable, “but not transforming the core.”

The Maturity Gap

Self-reported AI maturity varies widely across the sector. While 36% of telecom leaders consider their organizations “expert”, 22% still place themselves at the beginner level – twice the cross-industry average.

This divide reflects deeper operational challenges. In some firms, AI is fully embedded in workflows. In others, adoption remains inconsistent across teams or functions. Additionally, 15% of telecom leaders expressed low confidence in AI’s impact, often citing limited training, poor cross-team collaboration, or concern about job displacement.

These aren’t technical problems – they’re cultural and structural.

What’s Working, and What Comes Next

The most effective telecom AI use cases today are not flashy, but focused:

• Enhancing billing accuracy through automation
• Real-time transcription and call summarization
• Triggering proactive workflows based on sentiment analysis

These efforts succeed because they’re well-scoped, measurable, and tied to real operational challenges. However, most remain department specific.

The next step is scaling those capabilities across the organization. That requires:

• Centralized AI ownership with cross-functional accountability
• Shared KPIs that link AI outcomes to business value
• Training programs that bring consistency and confidence to adoption

The Path Forward

Telecom firms are clearly moving in the right direction. Leaders are investing, experimenting, and learning. But transformation will only happen when AI is treated not as a toolset, but as a capability – owned strategically and built deliberately into the business model.

The gap between early success and scalable impact is closing. The telecoms that move faster to align strategy, ownership, and outcomes will lead the next phase of industry innovation.

By Sanjeev Kumar, Executive Vice President, General Manager, TMT – BOUNTEOUS

Sanjeev Kumar bio:

Sanjeev Kumar is Executive Vice President of Telecom & Media at Bounteous, where he leads strategy, growth, and global delivery for clients across North America and EMEA. With more than 25 years of experience in the telecom and media industry, he advises enterprise leaders on driving transformation through AI, automation, platform modernization, and customer experience innovation.

Sanjeev has worked with some of the world’s largest telecom providers to navigate complex change, deliver measurable outcomes, and scale digital capabilities across the organization. He brings a pragmatic, forward-looking perspective to industry challenges and is a frequent contributor to conversations on how telecoms can move from experimentation to enterprise-wide impact.

Bounteous boiler plate:

Bounteous is a premier end-to-end digital transformation consultancy dedicated to partnering with ambitious brands to create digital solutions for today’s complex challenges and tomorrow’s opportunities. With uncompromising standards for technical and domain expertise, we deliver innovative and strategic solutions in Strategy, Analytics, Digital Engineering, Cloud, Data & AI, Experience Design, Digital Experience Platforms, and Marketing. Our Co-Innovation methodology is a unique engagement model designed to align interests and accelerate value creation. Our clients worldwide benefit from the skills and expertise of over 5,000+ expert team members across the Americas, APAC, and EMEA. By partnering with leading technology providers, we craft transformative digital experiences that enhance customer engagement and drive business success. Discover more about our impactful work and expertise by visiting www.bounteous.com and following us on X, LinkedIn, Facebook, and Instagram. 

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Following a recapitalisation initiative in June, the vendor restructured its organisation into two segments: packet core, messaging and security; and voice, radio access technologies.

Previously, Mavenir stated it was scaling back its efforts on radio unit hardware due to the slow uptake of open RAN.

The company’s new roadmap is designed to accelerate the transition from AI-integrated networks where AI augments specific functions, to AI-native networks. The latter includes embedding intelligence into the core architecture for full automation and real-time optimisation.

As part of the new operating model, Mavenir appointed employees to key roles effective immediately: Uli Dopfer as EVP and CFO, overseeing finance, strategic planning, and HR; Michael Cooper,  EVP and GM of packet core, security and messaging, and digital enablement R&D; Santhosh Kumar, EVP and GM of platform and cloud, platform engineering and cloud transformation; Sachin Karkala, EVP and GM of IMS and RAN with a focus on AI-native evolution; Dejan Leskaroski, EVP and CRO; Michael Cooper, EVP and GM of packet core, security and messaging and digital enablement R&D; Santhosh Kumar, EVP and GM of platform and cloud, leading platform engineering and cloud transformation; and Sachin Karkala, EVP and GM of IMS and RAN.

Mavenir also noted EVP and CFO Terry Hungle; Bahram Jalalizadeh, EVP, head of sales and marketing; Ashok Khuntia, president of core networks; and Anup Mahajan, SVP of engineering, will leave their jobs at the end of January 2026.

Hungle will become special advisor to the board of directors while Jalalizadeh will continue as a consultant. 

Mavenir president and CEO Pardeep Kohli stated with the new executives and sharper strategic focus the company is “well-positioned to deliver innovation and exceptional value for our customers”.

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In a statement, IBM indicated the purchase would provide the means for it to deliver an end-to-end platform for businesses to connect, process and govern data used in systems.

It added Confluent provides a “leading open-source enterprise data streaming platform” which can conduct its activities in real time.

IBM chair, president and CEO Arvind Krishna highlighted its post-completion offering would “enable enterprises to deploy generative and agentic AI better and faster by providing trusted communication and data flow between environments, applications and APIs”.

“Data is spread across public and private clouds, data centres and countless technology providers,” he added. “With the acquisition of Confluent, IBM will provide the smart data platform for enterprise IT, purpose-built for AI”.

Deal
Under the terms of the agreement, IBM will pay $31 per share for the company. The deal was already approved by each company’s board and Confluent’s largest investors, which represent 62 per cent of shareholder votes.

IBM expects completion in mid-2026, subject to regulatory approvals and other closing conditions. It also has to go through a full Confluent shareholder vote.

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The pair signed a partnership agreement covering navy- and maritime-specific AI products, with agentic elements involved. Thales stated the intended move would bring its expertise and corAIx incubator together with Cohere’s large language model capabilities.

Thales Canada CEO Ian Krepps said the company expects to be able to “rapidly deploy new, sovereign AI” capabilities for customers “including the Canadian Armed Forces”.

The arrangement demonstrates Thales’ “commitment” to providing customers with “agile, future ready” products.

Cohere co-founder Ivan Zhang said the partnership is a “strategic leap” in employing AI “for national defence”. The companies will provide systems which “analyse complex naval environments in real time”, delivering “actionable intelligence” to improve military decision making.

Zhang said the deal is also a boost for Canadian AI ambitions.

Thales stated the decision-making capabilities to be provided by the agentic AI systems would benefit projects due to launch imminently.

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“I will be doing a one rule Executive Order this week,” he stated in a Truth Social post. “We are beating all countries at this point in the race, but that won’t last long if we are going to have 50 states, many of them bad actors, involved in rules and the approval process.

The Financial Times reported Trump is facing opposition from members of his own political party and supporters over the order, who contend the President is favouring big tech such as Apple, Meta Platforms and Amazon which have donated to various projects.

“You can’t expect a company to get 50 Approvals every time they want to do something,” Trump stated. “That will never work!”

While he did not provide any additional details on his forthcoming executive order, Reuters reported Trump has threatened to withhold federal funding from states which do not comply or file lawsuits against them.

The news agency noted President Trump’s order will likely face opposition from various states.

In July, the Trump administration revealed its AI action plan, designed to solidify the US’ position as the global leader in the face of competition from countries such as China.

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The social media giant is partnering with major news outlets including CNN, Fox News, Fox Sports, Le Monde Group, People, The Washington Examiner, and USA Today to provide global breaking news, entertainment and lifestyle stories on its Meta AI chatbot.

Meta Platforms plans to add news partnerships and features in the future.

The company stated in a blog when users ask Meta AI questions, they receive direct links to partner articles “to help you discover timely and relevant content tailored to your interests”.

“We’re committed to making Meta AI more responsive, accurate and balanced.”

“Real-time events can be challenging for current AI systems to keep up with, but by integrating more and different types of news sources, our aim is to improve Meta AI’s ability to deliver timely and relevant content and information with a wide variety of viewpoints and content types.”

During the company’s Q3 earnings call in October, CEO Mark Zuckerberg said the chatbot had more than 1 billion active monthly users and he expects usage to increase as it improves its AI models.

Rival OpenAI has also struck various content deals to train ChatGPT to answer questions and provide summaries of news articles.

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