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Trust has become the entry ticket to the internet, and users are now choosing safety over price and speed. We sit down with Raj from Trua to dig into the 2025 Trust Survey and why verification has shifted from a nice-to-have to a must-have. From shocking stats on consumer expectations to the real reasons fear and fatigue are rewriting behavior, we map out what it takes to build platforms people will actually use—and pay for.

We walk through the practical playbook: make safety the first thing users see, put verified badges and last-checked timestamps front and center, and adopt “friction by design” to deter bad actors without punishing good ones. Raj breaks down progressive checks that scale with risk—identity and address for low-stakes, licenses and criminal screens when necessary, and liveness for high-impact flows like dating and hiring. He explains why trust tech is emerging as its own category and how a reusable, tokenized credential can follow you across apps, slashing repeated data collection while boosting conversion and confidence.

AI and deepfakes add urgency. With bots posing as people, human identity verification becomes essential for high-risk interactions, and continuous authentication helps separate real users from synthetic scams. We also explore the regulatory landscape, why co‑opting consumers is cheaper and more effective than piling on rules, and how brands like Apple translate privacy into loyalty. The gig economy emerges as the flash point: tens of millions move across platforms that need fast, safe onboarding and fewer data silos to breach.

If you care about growth, retention, and brand resilience, now is the time to operationalize trust. Subscribe, share this episode with a product or security leader, and leave a review with one change you’ll make to bring safety to the surface.

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What 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.

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Cameras 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.

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