Tantra's Mantra with Prakash Sangam

Prakash Sangam
  • 5.0 (4)
  • Management
  • Updated Biweekly

The podcast that goes behind and beyond the technology news headlines to explore the unheard and unseen. The topics include 5G, AI, IoT, Smartphones, Networking, Intellectual Property and others.

  1. Jul 13

    Ep. 71: Apple vs. Open AI – Case Analysis

    On Jul 10th 2026, Apple sued OpenAI alleging theft of confidential information. The court filing claims that two OpenAI employees were stealing and encouraging Apple employees who are pursuing employment at OpenAI to steal Apple trade secrets. These individuals, both non-profit and for-profit entities of OpenAI, as well as IO Products, the company founded by legendary designer Jony Ive, acquired by OpenAI, are named as defendants in the case.   In this episode, Christie Theone, Principal at CTG Advisory, and Ex. VP of Qualcomm Policy and Legal comms and I discuss the specifics of Apple’s allegations, OpenAI’s statement, and likely response, as well as possible next steps. We also delve into how this will affect OpenAI’s plan to introduce highly anticipated personal AI devices, who has better leverage, the remedies Apple is requesting, and what some considerations might be for settlement. Index: 00:00 - Intro 01:43 - Guest intro (Christie Thoene Theone, Principal, CTG Advisory) 02:28 - Apple's complaint: Two employees of OpenAI + OpenAI (pro & non-profit) + IO Products (Founded by #JonyIve) 04:05 - Apple's attempt to set a narrative - All OpenAI innovations are tainted by the theft of Apple's trade secrets, even what IO Products might be working on 05:05 - Although complaints are centered around two employees (only one is a top-level executive), liability will be borne by the company 07:56 - Apple's complaints are well structured, presented "as a matter of fact," without inflammatory language, making the case very obvious and appearing to be "open and shut." 10:33 - Jony Ive's name never appears in the complaint. Probably out of respect, and not to tarnish his name 12:06 - OpenAI's statement is pretty standard PR response 14:42 - Next steps - Likely request for an extension to file the reply. The question is whether they just reply to Apple's allegation, or countersue to get some leverage 17:02 - Discovery is going to be interesting, with many trade secrets at play. Details will come out if there are conflicts between the companies. Can it be motivation for settlement? 21:05 - As things stand today, OpenAI is at a weaker place, as this case affects its product plans and IPO plans, and time is on Apple's side 22:45 - Apple's remedy requests are not very clear, not sure what "enjoining" in this case means. Injunctions are very hard to come by 25:15 - OpenAI can keep working on whatever they are doing till there is an order from the judge (e.g. 25:33 - The first target for Apple is to seek an early decision to stop OpenAI from whatever they are working on, and the target for OpenAI is to seek early dismissal of the case. Both are hard to come by 29:12 - Closing

  2. Jul 7

    Ep. 70: State of Enterprise AI - Discussion with Lenovo & TCS

    This is a special episode produced by the collaboration between TiE San Diego and Tantra's Mantra. In this episode, I speak to Mark Budgen of Lenovo and Rajnikant Gupta of TCS on the current status of AI adoption in enterprises. We start by discussing which segments and which functions within organizations have seen deeper adoption of AI, whether AI projects have moved from PoC and trials to production, what the major challenges are in that transition, how the thinking and approach of enterprises toward AI have changed over the short period, the need for “Human in the loop,” and the necessity of Hybrid AI. We discuss key takeaways from Lenovo’s “CIO Playbook 2026” report and how enterprises are now more focused on improving and accelerating business outcomes rather than simply improving productivity. Finally, we delve into what to expect in the near and far future, moving from Generative and Agentic AI to Physical and Embodied AI. Index: 00:00 - Intro 01:05 - Guest intro (Mark Budgen, CTO, Global Technology Partners at Lenovo, & Rajnikant Gupta, Global Head - Partner Ecosystems and Alliances, TCS) 02:28 - Current status of AI adoption in Enterprises, how AI can improve business, not just efficiency, risk considerations 06:30 - Difference in level of AI adoption between various functional groups within Enterprises - IT, Coding, Security, Supply Chain, Sales & Marketing 13:30 - Evolution of Human involvement in AI processes. Humans in the Agentic AI loop will be required for a long time, because of the risk of failure and its costs 16:56 - Key takeaways from Lenovo's "CIO Playbook 2026" report: AI is not the entire toolbox, but one of the tools; focus is on how to use AI to improve and accelerate business and finally the outcomes; mapping IT KPIs to Business KPIs 22:16 - Current status of AI projects, in "Production" vs. "PoC" or "Trials" stage. The majority still in the process of moving to Production. Execs now better understand their business value; Focus on platforms to scale rather than simple point solutions 28:13 - Challenges in implementing AI: Employee fear of replacement, lack of domain experts with AI knowledge, 31:56 - Hybrid AI: How to decide where to run AI: Cloud or Edge? Dependencies: Sovereignty, access to data, power, and cooling; Token use optimization 38:27 - What to expect to see in the near and far future: Physical AI, Embodied AI, AI becoming ubiquitous and 41:20 - Closing

  3. Jun 19

    Ep. 69: Lenovo VP on QIRA – Cross Device/Platform/OS Personal AI Agent

    At CES 2026, Lenovo showcased an exciting new AI innovation called QIRA, redefining what personalized technology can offer. This powerful, system-level feature works seamlessly across devices and platforms, enhancing user experience like never before, and it’s now being rolled out on select Lenovo devices. In this episode, I had the privilege of speaking with Jeff Snow, the Head of Product and AI Ecosystem at Lenovo. We explored the inspiring vision behind QIRA, its capabilities, and how Lenovo is uniquely positioned to offer such an experience. Join us as we dive into how QIRA gathers insights, develops its extensive knowledge base, maintains synchronization across devices, and implements robust measures to protect user privacy and security. Index: 00:00 - Intro 02:00 - Guest intro (Jeff Snow, VP & Head of Product & AI Ecosystem) 02:50 - What is QIRA - Cross-device, local-first personal intelligence 03:42 - The vision of QIRA - Uniform "Lenovo AI Experience" across devices 05:13 - Organizationally, one team is working on QIRA, implemented on all Lenovo devices 06:12 - Examples of what it can do today: Catch me up - Summarize what happened across devices, Pay attention - Summarize meetings, and make it part of the knowledge base across devices, more 08:17 - QIRA learns from experiences - Implicitly and explicitly (with user permission), gathers and personal knowledge & insights 09:59 - It is more than an OTT app - App interface, but tightly integrated into the machine, even connecting with the OS, utilizes local AI models, and can be surfaced in any other apps 12:29 - The inputs to QIRA - Multimodal, text, images, voice, additional context (emails, chats, etc.), system health and telemetry, cross-device artifacts (e.g. notifications) 14:00 - Explicit user permission is required before saving any inputs, and it can be revoked at any time. "Humans are always in the loop" 15:06 - ALL the data generated by the device is stored locally on that specific device, the vectorized and encrypted data is shared across devices to keep all the devices in sync 18:16 - The "learning" happens and is stored in the individual device, and synced across devices 20:10 - Hybrid and agile model selection: mix of local, cloud, own, and third-party, with always a local-first approach 21:56 - User option "Local-only" or "Hybrid." Even in local-only mode, sync across devices can be enabled 23:56 - Most QIRA actions are user-initiated or part of an experience or workflow. 23:27 - It can surface suggestions, but not directly take action without direct user intervention 28:49 - QIRA can be the primary AI assistant interface on your Lenovo device 32:20 - Lenovo is exploiting its unique position, with pocket-to-cloud offerings, to provide a more personalized and well-rounded AI experience to its users 32:49 - Key challenges Lenovo faced in bringing QIRA to market: changing the mindset of the team to be software-focused, and moving quickly 34:99 - Defenses against accessing QIRA-related data when the device is stolen 36:17 - Ability to migrate personal knowledge base from old to new device 37:20 - No QIRA knowledge base vector data cloud back-up 38:30 - Different peer product in China, QIRA is for the rest of the world 39:33 - Evolution of QIRA: More devices, third-party devices, even richer knowledge base & orchestration, enterprise solution 39:48 - Portability of QIRA vector database 41:20 - Closing

  4. Apr 20

    Ep. 68: Impinj VP on RFID Transforming Supply Chain Logistics

    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

  5. Apr 7

    Ep. 67: Lenovo VP on Endpoint Security, AIPCs and NPU

    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

  6. Mar 16

    Ep. 66: Mobile World Congress 2026 - Recap and Analysis

    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

  7. 12/08/2025

    Ep. 65: Samsung on Role of AI in Telecom, AI RAN and More

    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

  8. 12/02/2025

    Ep. 64: Is AI a bubble? - Analyst Discussion

    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.

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The podcast that goes behind and beyond the technology news headlines to explore the unheard and unseen. The topics include 5G, AI, IoT, Smartphones, Networking, Intellectual Property and others.

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