What's Up with Tech?

• Cisco’s Incubation Engine For Agentic AI And Quantum Networking

  Interested in being a guest? Email us at [email protected] if the most human-feeling teammates on your projects moved at machine speed and collaborated across every vendor you use? We sit down with Vijoy Pandey is GM and Senior Vice President of Outshift to unpack how agentic AI and quantum networking move from shiny demos to dependable, scaled systems that actually ship results.We start by reframing innovation horizons around risk instead of time. Technology risk is where quantum lives today, with foundational challenges from hardware to protocol. Market risk is where agentic AI thrives, as teams test real enterprise use cases beyond coding assistants. Platform risk is the mandate for a company like Cisco: open, interoperable architectures instead of brittle point tools. That lens shapes everything OutShift builds.You’ll hear a clear blueprint for the Internet of Agents: discover the right capabilities across vendors, grant task and transaction-based access that expires fast, enable real-time many-to-many messaging across voice and video, and close the loop with rigorous measurement that feeds reputation and policy. We spotlight Agency (AGNTCY), the Linux Foundation–hosted open source project backed by a broad industry coalition, designed to make multi-agent collaboration portable and trustworthy.The proof shows up in production. A healthcare triage flow uses a Webex voice agent that coordinates with insurance and diagnostics agents to verify claims and route patients to the right human expert faster. In networking, a multi-agent pipeline built with Swisscom stress-tests configuration changes like a chess engine, exploring outcomes, generating tests, and catching cascading failures before they hit production. On the horizon, quantum networking becomes a scale-out fabric that links quantum computers, data centers, and sensing devices—accelerating timelines by five to ten years while delivering near-term classical benefits in security and precision.If you care about enterprise AI, open standards, and the road to quantum-ready infrastructure, this conversation maps the path from uncertainty to impact. Subscribe, share with a colleague who’s building with agents, and leave a review with the one capability you want most from an Internet of Agents.Inspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifySupport the showMore at https://linktr.ee/EvanKirstel

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• From Surveillance To Smarter Stores With AI

  Interested in being a guest? Email us at [email protected] don’t just record anymore—they inform, alert, and help teams act before problems get expensive. We sit down with Jeff Corrall Chief Product Officer at March Networks, to unpack how AI-powered video is evolving from security footage to real operational intelligence you can search, measure, and scale without ripping out legacy systems.Jeff explains why continuous video analysis is costly and how an image-first approach delivers the wins that matter. By capturing snapshots at set intervals or when simple edge analytics detect an event, their platform sends images to the cloud for deeper interpretation. That means retailers catch blocked fire exits before fines hit. Managers get texts when queues spike. Regional leaders track trends across hundreds of sites in real time. And teams can literally “Google” their stores for spills, cash left out, or outdated signage, fixing issues at the speed of a search.We also dig into the AI3 camera, a 360-degree device that blends people counting, queue monitoring, and traffic flow with traditional video, replacing multiple sensors with one scalable unit. Beyond retail, transit agencies pair passenger counts with payment data to spot revenue leakage, while banks audit promotional compliance by uploading an example poster and finding any branch where it’s still displayed. The near-term horizon is especially exciting: fusing point-of-sale data with AI triggers to calculate conversion, surface anomalies with visual proof, and notify the right person to take action—before customer experience or revenue suffer.If you care about operational excellence, loss prevention, and smarter use of your existing cameras, this conversation is packed with practical insights. Follow the show, share it with a colleague who owns store ops or security, and leave a quick review to tell us what you’d automate first.Support the showMore at https://linktr.ee/EvanKirstel

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• 5G To Autonomous: Making Networks Think

  Interested in being a guest? Email us at [email protected] if your network could understand intent, adapt in real time, and deliver the exact experience you need—whether you’re at a packed concert, inside an ambulance, or running a factory floor? We dive into the shift from manual engineering to autonomous networks and how open standards are making AI practical, measurable, and global.Claudia Muñiz, Global Head of Sales for Cognitive Network Solutions at Ericsson, explains why 5G’s real promise is differentiated connectivity and how the Service Management and Orchestration (SMO) layer unlocks it. We trace the journey from early self-organizing networks to an open RAN ecosystem where developers ship RAN apps once and deploy everywhere. Expect clear examples of digital twins and multi-objective reinforcement learning tuning antennas across massive configuration spaces, with live results: higher throughput, faster speeds, fewer dropped calls, and better spectral efficiency—proof that AI at the radio layer moves the needle when paired with the right data and processes.We also separate hype from reality on large language models in telco. Lightweight, energy-aware models handle millisecond decisions at the edge, while LLMs shine in cloud workflows, documentation, coding, and natural language operations. With partnerships that blend domain expertise and elastic compute, engineers can ask plain-English questions and get guided actions, all built on a unified, secure data layer. The conversation lands on what matters most: time-to-first value, measurable gains in churn, revenue, energy, and effort; and the near future of intent-based networking that coordinates specialized agents to meet business goals across open interfaces.If you care about 5G, open RAN, AI-driven operations, developer ecosystems, and the real-world path to autonomous networks, this is your roadmap. Subscribe, share with a colleague who lives in dashboards, and leave a review with the one question you want us to tackle next.Inspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifySupport the showMore at https://linktr.ee/EvanKirstel

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• How A Decentralized GPU Network Beats The Cloud On Price And Flexibility

  Interested in being a guest? Email us at [email protected] happens when thousands of retired Ethereum GPUs meet a tidal wave of AI demand? We sit down with Hidde Hoogland Founder at Decent to unpack how a former mining operation evolved into a decentralized edge provider and why connecting to Aethir’s GPU marketplace changed the math on utilization, cash flow, and scale. It’s a candid look at turning stranded hardware into a real business with clearer ROI targets and a pricing story that can undercut hyperscalers by 60–80 percent for many workloads.We trace the journey from proof-of-work mining to a three-part operating model: sourcing hardware directly from vendors, hosting and maintaining systems, and owning the facility layer. Hine explains how decentralized GPU-as-a-service works for both sides of the market: buyers get location choice, hourly rentals, and on-chain payments; providers get a single platform that aggregates demand. The kicker is stability. Aethir’s static lease fees create a baseline even when GPUs sit idle, letting operators scale into actual demand rather than guessing. That foundation supports a portfolio approach to revenue: pay OPEX in fiat, stack tokens for growth, and invest in GPU SKUs with clear utilization histories and 12–24 month payback windows.We also zoom out to the European landscape. The Netherlands offers elite fiber and interconnect density but faces expensive power and a saturated grid. That reality shifts strategy toward storage-heavy growth while onboarding next-gen GPUs like 5090s where the numbers make sense. The medium-term vision is compelling: pair decentralized compute with distributed storage so teams can run inference, fine-tune LLMs, and manage data gravity without surrendering cost control or flexibility. If AI demand keeps rising, this “house of DPin” model could redefine how we think about cloud alternatives at the edge.If this conversation sparked ideas about your own GPU strategy or you’re curious about decentralized infra economics, follow the show, share it with a teammate, and leave a quick review to tell us what you want to hear next.Support the showMore at https://linktr.ee/EvanKirstel

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• How Agentic AI Turns Calls Into Continuous Learning

  Interested in being a guest? Email us at [email protected] don’t call to chat with a bot—they want outcomes. In my chat with Jim Palmer Dialpad's Chief AI Officer we dive into how Dialpad’s AI-first platform turns real-time voice, video, and digital interactions into agentic workflows that act on behalf of users, learn from every result, and hand off seamlessly to humans when it counts. No black boxes. No bolt-ons. Just a unified stack where speech recognition accuracy, in-domain adaptation, and transparent evaluations compound into trust you can measure.We talk through the nuts and bolts of building AI where it belongs: inside the media layer, not tacked on after the fact. That foundation unlocks higher-quality transcripts, reliable summaries, and precise suggestions that downstream models can use without drowning in errors. From there, in-domain training becomes the engine for better answers: when support questions share patterns across industries, the system adapts faster and resolves issues with fewer escalations. And when a case does escalate, the agent sees the distilled context—intent, key points, attempted steps, and recommended next actions—so the conversation keeps moving.The flywheel is the real breakthrough. As new issues surface in a rolling window of conversations, the platform quantifies impact and proposes automations. Humans review, approve, and refine. The system executes, measures completion and containment, and folds learnings back into models. Layer on governance—clear data policies, observability, and rigorous evaluation—and you get AI that leaders can trust and teams love to use. We also share how internal hackathons and applied research push practical features over the line, keeping innovation and execution in lockstep.If you care about customer experience, agent performance, and measurable ROI from AI, this conversation brings a clear blueprint. Subscribe, share with a colleague who owns CX or support, and leave a review with your biggest automation challenge—we might feature it next.Inspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifySupport the showMore at https://linktr.ee/EvanKirstel

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