Mike Sievert cut his last ties with T-Mobile US, resigning from the board and stepping down as vice chair a little over a year after he gave up the CEO job.

In a filing with the US Securities and Exchange Commission (SEC) Sievert (pictured) told the board he would resign from it and from its Transaction Committee on 1 November.

T-Mobile noted the resignation is not the result of any disagreement with the company over operations, policies or practices.

Recon Analytics founder and analyst Roger Entner noted on X Sievert’s exit echoes the departure of his predecessor John Legere years earlier.

The timing matches the expiry of an amendment to his employment agreement and made in September 2025 and completes a handover process begun when T-Mobile confirmed then COO Srini Gopalan would succeed Sievert as CEO.

A contract struck with Sievert in 2023 would have seen him remain CEO until 2028. He was appointed to the post in 2020 when T-Mobile closed its merger with Sprint.

He took on the vice chair post to stay involved in the management of the company and on the board.

Gopalan became T-Mobile’s COO in March 2025 after heading Deutsche Telekom’s German business.

He joined the US operator’s board in 2022.

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T-Mobile US CTO John Saw revealed it is testing whether humanoid robots could respond faster and rely less on onboard computers by running some of their AI at its network edge.

In a blog, Saw explained T-Mobile is working with robotics company Figure AI to test if its systems can run on nearby computing infrastructure without slowing the machines’ responsiveness.

The systems involve controlling robots’ movement, and interpreting visual information and language instructions.

Saw wrote shifting some processing off the robots could reduce battery use, computing demands, heat, weight and cost. It could also let them run more capable AI without needing more hardware or power.

AWS
T-Mobile is working with AWS on the first deployments of Kinetic Edge, the operator’s distributed computing infrastructure for physical AI.

They are using T-Mobile’s 5G-Advanced network and distributed edge along with AWS’ cloud orchestration and AI services to push “deterministic connectivity, compute and intelligence closer to where machines operate”, Saw wrote.

He explained reliable connections and localised computing could help businesses take physical AI beyond individual demonstrations, highlighting developers are putting vision and reasoning models at the network edge to make robots, cameras and sensors less dependent on a distant cloud.

“This early work is helping us explore the capabilities that could make physical AI possible at scale, while opening the door to entirely new applications and opportunities.”

Machines require consistent connectivity when moving, sufficient uplink capacity to send sensor data, low latency and wide-area coverage, Saw wrote. Wi-Fi and satellite have a role, but he argued advanced terrestrial wireless networks are better suited to meeting the needs.

Human interaction
Saw noted employing people to assist robots would become challenging when thousands of machines are involved: T-Mobile is working with Serve Robotics to evaluate the feasibility of conducting monitoring at the edge to assess which problems can be solved by humans and which need more AI input.

The executive also highlighted work with Nvidia, Nokia and a growing group of developers to run physical AI applications across distributed edge networks, along with the recent completion of an AI-RAN call involving the vendors.

Saw explained T-Mobile is working with the city of San Jose in the state of California and specialist companies to develop a way to create an overview of traffic using edge AI to combine the views of separate 5G cameras.

They are testing if AI can connect occurrences at different junctions and if providing a big-picture could help people spot traffic patterns sooner.

The operator is also exploring “how AI and network workloads can share accelerated computing infrastructure” through partnerships with Nvidia, Nokia and Ericsson.

Saw said T-Mobile must develop ways to deliver “data and workloads” to “where intelligence needs to run”, whether it be on devices, the edge or cloud.

He said the direction “is becoming clearer” and T-Mobile’s network would “be the connective tissue” between the layers.

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Mo Katibeh posted on LinkedIn he has left his roles of EVP and CMO for T-Mobile US Business after nearly three years.

Katibeh (pictured) stated former VP of business marketing and strategy Michael Lubunski would take on the T-Mobile for business product, marketing and operations leadership role.

A T-Mobile representative told Mobile World Live Labunski’s new role is SVP, business marketing and product for T-Mobile for Business.

Katibeh joined the operator in April 2024 following a 20-year career across various executive roles at rival AT&T. He was also COO and president of cloud platform company RingCentral for nearly two years.

Labunski was RingCentral’s chief data officer during Katibeh’s stint.

“I can honestly say it’s been some of the best and happiest years in my professional life” Katibeh wrote. “I’m incredibly proud of what our T-Mobile for Business team has accomplished.”

During his tenure, T-Mobile launched T-Priority for first responders, SuperMobile networking slicing for businesses, SuperBroadband with Starlink and ANS Edge Control.

In July, T-Mobile revealed chief business and product officer Mike Katz had stepped down, taking an advisory role until December.

At the same time, the operator appointed former AT&T executive Chris Sambar as chief enterprise officer.

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T-Mobile US is testing AI to allocate radio resources on its 5G-Advanced network, with Ericsson trial results showing the biggest gains during peak traffic.

The operator is trialling AI-Managed Beamforming on live commercial traffic at scale. Chief network officer Ankur Kapoor (pictured) outlined the results in a blog post. The work focuses on sounding reference signals (SRS), short transmissions from phones that help the network measure the path between a device and a cell site and direct beams more accurately.

Due to those resources being limited, they have traditionally been allocated under fixed rules. T-Mobile instead uses a supervised-learning model trained on its network data and running on Ericsson’s RAN compute and silicon to predict where demand is rising and redirect resources accordingly.

Kapoor explained training the model on T-Mobile’s data gives it an advantage on the operator’s network over networks which do not contribute their own tuning data.

The results came from newly activated sites in New York City, but the blog did not say how many were included or give a baseline for the trial.

T-Mobile reported the biggest gains during peak hours, including about a 50% increase in advanced beamforming using full-band SRS, a 16% improvement in downlink user throughput, 12% better spectral efficiency and a 10% increase in downlink data volume.

In May, the companies reported close to 10% better spectral efficiency from a separate AI-native scheduler with link adaptation on live traffic.

Kapoor said the technology suits T-Mobile’s network because beamforming works particularly well in TDD spectrum, where uplink and downlink share the same band, and the operator has a leading position in midband TDD.

He linked the trial to rising network demand, citing Ericsson’s June 2026 Mobility Report, which reported global mobile data traffic growth of 22% year-on-year. Video accounted for about 75% of traffic and 5G subscriptions topped 3.1 billion.

Jaco du Plooy, SVP and head of the T-Mobile customer unit at Ericsson Americas, stated the trial marked another step toward networks which respond more intelligently to demand, enabled by Ericsson’s silicon.

Kapoor said 6G networks will need to sense, learn and respond to support applications such as distributed AI, robotics, vision AI and other forms of physical AI, describing T-Mobile’s nationwide 5G-Advanced network as its proving ground.

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