Tantra's Mantra with Prakash Sangam

For many years, RFID technology has been used by leading retailers such as Walmart, Macy's, and Lowe's. CVS, Zara, and others for theft prevention. But now, it is ready to go beyond this limited use case and transform supply chain logistics. In the episode, I talk to Gagan Luthra, VP of Product and Strategy at RAIN RFID leader Impinj, about the history of technology, how it is currently being used, how it is evolving to support complex supply chain use cases, including Gen2X performance enhancements, new form factors, higher processing power, and more. We also delve into even more exciting opportunities, including using AI models for better forecasting, analytics, and trend analysis, as well as monetizing data through third-party players. Also, check out my EE Times article about RFID tackling food waste losses, utilizing Avery Dennison food labels: https://bit.ly/47APGav Index: 00:00 - Intro 02:13 - Guest intro (Gagan Luthra) 04:41 - History and current state of RFID, expanding use cases, beyond theft prevention 06:30 - RFIDs are much more than wireless barcodes - much more information and value, from "cradle-to-grave" of products 09:25 - How RFID works without batteries - Tags are energized by an RF signal, and the receiver "reads" the reflections from the tags for identification 11:32 - Automated and Real-time information, unlike barcodes, with static information, from manual operation 15:03 - Higher initial cost of RFID compared to barcode, but much lower opex, much higher utility, and better ROI 20:30 - RFID cost curve continuing to go down, with economies of scale, wider adoption, and improvement in silicon process technology 22:19 - RAIN Alliance, standards for interoperability, difference between RAIN RFID, and NFC 25:13 - Upgrades needed beyond RFID standards for complex supply chain use cases, details of Impinj's Gen2X enhancements to address those needs, Gen2X traction 29:07 - Full backward compatibility with RAIN standards, how that works 31:30 - Edge processing needs at RFID readers, Impinj's latest announcement about R700 enhancements for readers 33:45 - How AI can power next phase of RFID - real-world, physical data for AI models, Edge AI in readers for smart decisions, utilizing cloud for better forecasting, trend analysis, and more, monetization opportunities for anonymized data for third-party players 37:59 - "Crystal Ball" question, where is RFID headed in the next 3-5 years - wider adoption across many verticals, tagging almost anything, even very low-cost items, and adoption of AI 40:35 - Closing

Just like everything else, AI is profoundly transforming the security landscape. It is increasing the attack surface, sophistication, and impact. while also providing more potent tools to increase security. In enterprise PCs, security and device management apps (as part of the Corp Image) significantly reduce performance. AIPCs promise to run apps more efficiently on NPUs. In the episode, I talk to Nima Baiati, VP of Commercial Software and Security at Lenovo, about the evolving PC security landscape, the impact of AI, and the challenges of migrating security apps to the NPU. We also delve into how to incentivize ISVs to prioritize migration, how Lenovo can serve as a model for the industry, and the expected timelines for the NPU migration. Index: 00:00 - Intro 02:10 - Guest intro (Nima Baiati) 03:17 - Changing landscape of endpoint security, especially with the advent of AI 02:23 - How is Lenovo addressing the changing landscape, both from traditional and AI-enhanced threat vectors? - ThinkShield - a comprehensive platform ensuring security from the supply chain, hardware, and software 11:01 - AI: the double-edged sword- tremendous capability to both create & fight security risk 12:53 - AIPCs and the promise of NPU for running security and device management functions more efficiently 14:00 - NPU migration challenges - Three forks ISVs should run through: 1) Re-architecting and optimizing ML models; 2)Instruction set variability between NPU vendors; 3) Testing and performance optimization. How Lenovo helps ISVs in migration 16:36 - Awareness about the benefits NPU among the stakeholders (ISVs, CIO/CSO/IT Managers, OEMs) 19:20 - Business model challenge of ISV in migrating security applications to NPU - Lots of work but no new revenue. 22:26 - Do enterprises (CIO, IT Managers) have to do anything to accomplish migration to NPU? How Lenovo works closely with ISVs and enterprise customers to bring mutually beneficial solutions 25:31 - How to incentivize ISVs to prioritize migration, e.g., include it in their KPI? 28:03 - How Lenovo's size and scale uniquely position it to be the leader and drive this for the industry, and pave the road 29:01 - Should the NPU migration be driven as an industry initiative? Is there a need for standardization, etc.? 31:08 - How is Agentic AI affecting security? - Huge role for automation, rapid response, but agents completely taking over security is a fantasy. Human intervention 35:01 - Latest trends in the security landscape - AI privacy, AI Governance, automation detection and remediation, management and orchestration of devices and environments, etc. 36:47 - What is the timeline for the industry to substantially migrate security apps to NPU? 39:50 - Closing

This year's MWC took place as the telecom industry is at a crossroads, with additional monetization of 5G beyond mobile broadband less certain, smartphone growth flattening, AI influence increasing, and more questions than answers about the future. In this episode, Neil Shah of Counterpoint Research, Leonard Lee of Next Curve, and I discuss our experience at the event, and analyze the traction and monetization of 5G Advanced, Autonomous Networks, compare and contrast the progress of Western and Asian markets, the opportunity for AI for telecom, early use cases, and the prospect of RAN for AI. 6G and more. We also delve into whether telcos are better positioned for the sovereign AI and Data Center market. Index: 00:00 - Intro 00:35 - Guest intro (Neil Shah, Leonard Lee) 01:10 - MWC attendance 02:23 - Major themes of the event - 5G Advanced, AI, Autonomous Networks, 6G 07:00 - AI Ops for operators (AI for Telco) - Customer Care, Marketing, Billing, HR, Network Management, etc. 14:43 - AI for Networks Ops - Challenges,(data for AI) opportunities, and progress so far 17:38 - Autonomous Networks - Chinese operators at Level-4 (pockets), others at Level-2/2.5 21:30 - Current status of AI - Frank talk by Samsung Network executives on the current status 23:14 - Challenges of extending Autonomous Networks beyond China (by Chinese vendors), need for monetization opportunities 25:56 - Can the 5G Advanced monetization use case, successful in China, work in the US/Europe? 29:42 - RAN for AI, feasibility of GPU at Base stations, and challenges (power, weight, space) 33:29 - 6G - Qualcomm sensing demos, Ericsson/Apple - 5G/6G spectrum sharing (MRS) demo, uplink, need for monetization going beyond wireless service 41:25 - Are operators better positioned to offer Sovereign AI Data Centers - Deutsche Telekom's strategy, similar approach by Middle East /Korea, Is sovereignty is about data or also includes models? 50:50 - Did MWC 2026 move the needle for operators? 52:35 - Closing

Telecom was one of the earliest users of AI, long before its marketing hype and even before it was known by that name. The recent advancements are further propelling AI's use. In this episode, I talk to Dan Warren, Director of Communications Research at Samsung, regarding the role of AI in telecom networks. We discuss its role in network operations, AI for RAN, AI with RAN, and AI on RAN concepts; how software-based networking and virtualization (vRAN/Open RAN) enable AI; who will develop and implement AI models; the scope of standardization; and more. We also delve into the investment challenges for 5G operators to leverage AI, whether AI will encourage a larger role for Hyperscalers in telecom, and whether large-scale AI implementation can start with 5G/5G Advanced or will have to wait for 6G. Highlights: 00:00 - Intro 02:10 - Guest intro (Dan Warren) 03:22 - Samsung Network's categorization of the role of AI - Networks for AI, AI for networks 04:44 - Current interesting use cases of AI - RAN energy efficiency, complex capacity, and coverage optimization 06:40 - The current status of use of AI in telecom, major industry focus areas - optimizing opex and capex, experience enhancement 90:15 - Critical role of software-based networking and virtualization in enabling AI 12:48 - Are legacy networks w/o software-based networks out of luck for AI? 15:40 - How to decide where to run AI workload - where is the AI compute needed? 19:05 - Who will develop AI models for telecom? - property vs. standard, differentiation etc. 24:35 - Dichotomy between AI differentiation, open networking, and offering AI as a service layer 26:55 - Samsung's approach to AI as a service/software leveraging its software-based networking legacy. Might be different for more established players? 29:06 - Does today's architecture (e.g., SMO) have hooks for managing AI end-to-end? 30:01 - Data challenge of operators for AI - different forms, formats, sources, granularity, etc. Will things like NWDAF solve it? 31:50 - Operator understanding of the AI challenges ahead - transforming from a hardware operator to a software management company 34:36 - Is the investment needed for AI an impediment to its adoption? Worries of the fast-moving and changing AI landscape 38:46 - AI on RAN - Samsung Network's views 40:43 - AI on RAN - operators moving from telecom service providers to AI (edge) Data Center/infra providers - Does it make sense? 43:19 - AI timing - Will large-scale deployments wait for 6G, or can start with 5G? 45:16 - Closing

The mind-numbing investment in AI and the sky-high valuations of the companies involved have led many to wonder whether AI has become a bubble. In this episode, I talk to Dr. Riad Hartani, who is an advisor, investor, and a longtime time industry participant, on what is driving the investment - real market demand or FOMO, who is making those them, ongoing circular investment and risks, especially in Neoclouds, RoI concerns, consumer vs. enterprise AI dynamics, whether AI is a bubble, what could be early indicators of a burst, if any, who will be worst affected, and more. We also compare and contrast the current AI situation with the Dot-com bubble.