IoT & AI Leaders

As AI accelerates cyber-attacks and intelligence moves to the edge, security economics are being rewritten.

In this episode of IoT and AI Leaders, Nick Earle is joined by Jasson Casey, CEO and co‑founder of Beyond Identity, to explore what happens when AI, IoT, and autonomous agents collide and why identity has become the weakest link in modern systems.

The conversation goes beyond device security into the deeper problem of movable credentials, AI‑powered attackers, and agent-driven systems operating at machine speed. From hacked robotic vacuums to compromised payment terminals, Jasson explains why most cyber incidents still share the same root cause and how immovable, cryptographically bound credentials change the game.

Key topics include:
Why AI makes speed more dangerous than sophistication in cyber attacks
How 80%+ of breaches trace back to identity and access failures
What “immovable credentials” really mean (and why chip & PIN got it right)
Real-world IoT security failures—and their systemic consequences
The rise of autonomous AI agents and “shadow AI” inside organisations

Tune in to hear the full conversation.

Key Topics & Chapters
(00:00) Introduction: AI, IoT and security
(02:10) Jasson Casey
(04:00) AI and the speed of cyber
(07:45) Movable credentials and breach
(11:20) Chip & PIN and immovable credentials
(15:30) IoT device failures and real-world risks
(21:10) Hacked vacuums and firmware trust
(24:30) Autonomous agents and shadow AI
(29:40) AI governance and data-flow control
(33:50) Humans, verification, and future skills
(38:10) Closing thoughts on security at machine speed

Artificial humans are already here. And most organizations are not prepared for what that means.

As AI becomes embedded across enterprise systems, the real shift is not just smarter software. It is the emergence of autonomous digital actors working alongside humans, powered by real-time streams of data from connected devices.

HiveMQ CEO and Chairman Barry Libert joins the podcast to explore what happens when IoT data streaming meets AI at scale, including:

• Why artificial humans are already working alongside real humans
• How data streaming becomes the foundation for AIoT systems
• Why IoT and AI are no longer separate technologies
• How ontologies and real-time operational intelligence reshape enterprise software
• Why the next wave of productivity will come from autonomous machines and devices

Tune in to hear why the convergence of AI, IoT, and data streaming will redefine how companies operate.

Key Topics & Chapters

(01:49) Barry Libert introduction
(03:46) Why the podcast pivoted
(04:30) AI needs IoT data
(05:24) Why HiveMQ matters
(06:08) Data streaming between devices
(07:20) Humans are already devices
(08:24) Blurring human device boundaries
(10:10) Data streaming changes business
(12:04) Data streaming drives AIoT
(13:08) Enterprise brain and dashboards
(15:23) Machines act autonomously
(16:40) HiveMQ builds operational ontology
(17:36) Claude Code inside HiveMQ
(18:47) Enterprise software faces disruption
(20:27) AI deprecates SaaS models
(22:28) SaaS versus AI battleground
(23:01) Ariba and SaaS lessons
(25:04) What happens to humans
(26:47) Why Barry remains optimistic
(27:27) Framing beats answering
(29:10) Artificial humans are here
(30:12) Another species enters work
(31:11) The jobs gap problem
(32:00) National winners and losers
(34:16) From outsourcing to AI sourcing
(35:42) Speed creates transition pain
(36:00) Customers now want ontology
(37:44) Healthcare process intelligence example
(40:42) Every company needs streaming
(41:42) Ontology leads to automation
(42:29) Closing reflections on HiveMQ

AI is getting smarter but it’s still thinking in the wrong place.

Currently too much intelligence sits in the cloud, leaving devices dependent, fragile, and slower than the real world can tolerate. If IoT is going to feed the next wave of AI, the model has to flip. Intelligence needs to move into the device, with the cloud supporting updates and orchestration, not doing all the thinking.

David Linthicum joins the podcast for one of our deepest conversations yet, exploring what it takes to rebuild AI for the edge, including:

• Why today’s “agents” are not truly autonomous
• The case for a client-server style architecture for AI
• How small, purpose-built models can live inside constrained IoT devices
• Why 5G will not solve latency, reliability, or physics
• Why device manufacturers will set the standard, not the cloud giants

Tune in to hear why edge intelligence is the reset AI and IoT both need.

Key Topics & Chapters

(01:58) David Linthicum background
(04:02) AI and IoT convergence
(07:00) Why AI isn’t at edge
(08:03) Problems with cloud dependency
(09:02) Small vs large models
(11:30) Client server architecture analogy
(14:02) Flaws in IoT architecture
(18:05) Inefficiency of cloud AI
(20:02) Why edge AI matters
(22:03) What drives the shift
(24:02) Rise of autonomous devices
(26:03) Why 5G isn’t enough
(28:32) Importance of system decoupling
(32:02) Who will drive innovation
(35:02) How standards will emerge
(36:25) AI impact on jobs
(38:32) Limits of AI replacement
(40:02) Short versus long term jobs
(42:02) Outlook on future work

IoT promised to transform the physical world. Ten years on, adoption still lags behind expectation.

Despite proven technology and successful pilots, most IoT projects never make it to scale, and the reasons are not what many expect.

IoT product expert and author Afzal Mangal joins the podcast to challenge how the industry thinks about IoT adoption, and to explore whether AI could finally unlock its potential, including:

• Why the device remains the biggest single point of failure in IoT projects
• How firmware, not connectivity, determines long-term success
• The awareness and cultural gaps still blocking enterprise IoT adoption
• Why AI has reached the mainstream while IoT remains invisible
• Whether an AI-first approach could finally make IoT stick

Tune in to hear why rethinking IoT through an AI lens may be the reset the industry needs.

Key Topics & Chapters

(04:01) Cisco roots and telco beginnings
(06:17) Launching narrowband IoT networks
(07:16) Early global IoT developer demand
(08:34) B2B onboarding breaks IoT scale
(10:14) Pilots succeed, organizations resist
(11:05) Education missing from IoT adoption
(13:29) IoT innovation demands device failure
(14:37) Hardware failure destroys time and capital
(15:35) Device failure breaks entire IoT stack
(16:19) Firmware audits before global connectivity
(17:17) Firmware governs SIM and modem behavior
(18:10) Awareness blocks enterprise IoT progress
(21:37) Proven IoT solutions remain unknown
(24:18) AI awareness versus IoT invisibility
(26:01) AI prepares workers, IoT surprises them
(27:44) Fifty billion things prediction missed
(28:43) AI has consumed everything apart from IoT data
(29:56) Sound sensors gain meaning with AI
(31:40) Can IoT companies afford AI
(32:38) AI-first healthcare transformation model
(33:31) Smart hospitals track patients, staff, and assets
(34:24) AI exposes hospital process delays
(35:37) Do AI builders understand IoT
(37:24) Can AI simplify IoT integration?
(39:12) Humans still define data connections
(40:19) LLMs ignore IoT use cases
(41:03) AI quality depends on device data
(42:27) Selling IoT through AI consultants

Show Links
• Follow Afzal Mangal on LinkedIn
• Follow Nick Earle on LinkedIn
• Visit our website

AI is moving fast. And most enterprises are not ready for what comes next.

As organizations rush to deploy AI, the real constraint is no longer algorithms or compute. It is whether they have the right data, architecture, and operating model to turn intelligence into outcomes.

IDC Research Director Rob Tiffany joins the podcast to explain why private IoT data is becoming the foundation of enterprise AI:

Why IoT data gives AI real-world context that scraped content never can
The rise of private AI and IDC’s concept of the enterprise brain
Why most enterprise data remains on-prem and what that means for AI infrastructure
How IoT data feeds AI factories, vector databases, and real-time decision systems
Why IoT leaders now sit at the center of AI-driven competitive advantage

Tune in to hear how IoT data unlocks enterprise intelligence and reshapes the future of AI.

Key Topics and Chapters

(01:25) —IoT and AI Leaders Podcast rebrand
(03:48) — Rob Tiffany introduction
(04:16) — Navy submarines and special operations experience
(06:38) — IDC analyst role covering cloud
(08:03) — First IoT exposure via submarine sensors
(08:54) — Early IoT vending machines in 1994
(09:32) — Microsoft era and smartphone revolution
(10:21) — Building Azure Cloud and Azure IoT
(10:27) — Industrial digital twins at Hitachi
(12:32) — Why AI concentrates in hyperscale clouds
(13:48) — ChatGPT’s unexpected industry impact
(14:14) — Elon Musk rapidly launches xAI
(16:25) — Edge computing promise remains unmet
(17:32) — Enterprise brain concept explained
(19:04) — Most IoT happens indoors
(21:18) — AGVs reveal need for indoor cellular
(23:39) — Rise of enterprise hybrid AI data centers
(24:27) — Samsung data leak into ChatGPT
(25:22) — Growing interest in private enterprise AI
(27:14) — Fine-tuning AI with company data
(28:27) — Building the enterprise brain
(29:23) — Hybrid AI and competitive advantage recap
(35:28) — Enterprises downloading pretrained LLMs
(37:14) — Jensen Huang’s AI factory vision
(38:08) — Small language models for domains
(41:39) — ServiceNow and agent-driven automation
(44:27) — Will agents replace applications?
(47:12) — Graduate unemployment and future of work
(53:58) — AI disruption moves exponentially
(57:51) — AI gives IoT professionals new relevance
(58:16) — IoT data powers AI vector databases

Show Links

Follow Rob Tiffany from IDC on LinkedIn
Follow Nick Earle on LinkedIn
Follow Eseye on LinkedIn