What's Up with Tech?

Interested in being a guest? Email us at [email protected]
Cloud was supposed to simplify everything. Instead, a lot of CIOs are staring at bills that are far higher than anyone forecast, feeling locked into hyperscalers, and wondering where the business value went. I sit down with David Linthicum, former Deloitte chief cloud strategy officer turned tech influencer, to give an unvarnished reality check on cloud computing costs, cloud repatriation, and what “pragmatic architecture” looks like when budgets are real and timelines are slow.

We also get blunt about enterprise AI. David explains why so many AI-driven transformations stall out on two constraints: money and talent. We dig into why AI can cost 10 to 20 times more than traditional software, why “AI-first enterprise” messaging can be dangerous, and how leaders can pick high-impact use cases instead of trying to bolt generative AI onto everything. Along the way, we talk about how AI is reshaping SaaS economics as agents start using systems on behalf of humans, and what that means for vendors and buyers.

Then we tackle the loudest buzzword of the moment: agentic AI. Where does it shine as a productivity force multiplier, and where is it mostly hype when you try to deploy it at enterprise scale? We round out with underhyped edge computing opportunities and the growing backlash around data centers, power, and the grid. If you care about enterprise architecture, cloud strategy, generative AI, and what’s actually deployable right now, you’ll get a clear set of takeaways you can use this week. Subscribe, share this with a CIO or architect, and leave a review with the most overrated tech trend you want us to challenge next.
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected]
Your backup system is sitting on a gold mine and most companies are treating it like a fire extinguisher behind glass. From the floor of Dell Technologies World, we talk with Simon Taylor CEO of HYCU about their next chapter: HYCU Air, an AI resiliency platform designed to turn SaaS backup data into something you can actually interrogate, learn from, and use to stay ahead of cyber risk. 

We dig into the big idea that the most valuable asset is not the LLM itself, but the unique datasets inside your systems of record. HYCU Air pairs a knowledge graph and context engine with an LLM so you can ask natural-language questions of your backup history, the same way you would investigate a security camera recording. That reframes data protection from “pay for recovery” to “use backup data every day” across modern cloud applications, collaboration tools, and enterprise SaaS sprawl. 

Then we get practical: cybersecurity posture management when AI agents and integrations are “running amok,” spotting policy drift, and using data classification to find sensitive data like PII that never should have been where it ended up. We also share what we’re hearing from customers, why demand is accelerating, and how this approach starts to look like the “brain of an organization” by connecting corporate memory across dozens of SaaS services. 

If you want to see where AI resiliency is heading, hit play, share this with a security or IT leader, and leave a review with the one question you wish your backups could answer.
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected]
The fastest way to break a modern network isn’t your Netflix download, it’s the quiet, constant upload from sensors, logs, and telemetry that nobody ever reads. We sit down with Julien Dersey from AtomBeam to unpack why data efficiency is suddenly a front-line issue for IoT networking, edge computing, and cloud operations, even in a world with 5G and new satellite options like Starlink. The uncomfortable reality is that bandwidth grows, then data expands to fill it, especially once cybersecurity teams demand near real-time visibility into who connected to what, from where, and when.

We get concrete about the uplink bottleneck that hits IoT deployments first, and why “just filter the data” is a risky workaround. Julian shares a field deployment with an oil and gas fracking operator transmitting over Starlink, where compaction reduced traffic dramatically and kept gigabytes per day flowing reliably for months, while also helping identify odd behavior coming from a sensor. From there, we explore how AtomBeam’s lossless “compaction tunnel” differs from traditional compression, how it can run with extremely low CPU and memory, and why keeping applications unchanged is a big deal for real teams.

We also dig into enterprise and operator integrations: testing with Ericsson over a 5G router and SD-WAN style network bonding, the latency and performance questions engineers always ask, and the security posture using TLS 1.3 with an added obfuscation effect. Finally, we widen the lens to point-of-sale receipt transmission at scale, disaster recovery replication speedups, and what’s coming as connected vehicles, smart meters, and smart grid AMI 2.0 generate even more machine data.

If you care about IoT bandwidth, edge efficiency, secure data transport, and the future of connected devices, subscribe, share this with a colleague, and leave a review. What’s the single noisiest data stream on your network right now?
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected]
ANZ moves fast, and that speed exposes the difference between “cloud by default” and communications that actually hold up under pressure. We sit down with Mitel and Ethan Global to unpack what it takes to deliver unified communications, contact center, and customer experience platforms across Australia, New Zealand, and the South Pacific where geography is huge, budgets demand efficiency, and resilience is non-negotiable.

We talk about why Australia and New Zealand are early adopter markets, how hybrid work has shifted to a role-based model, and why complex verticals like government, healthcare, education, emergency services, mining, and transport can’t rely on one-size-fits-all deployments. You’ll hear what customers are asking for right now: redundancy, tight integration into core business systems, managed services, and clear answers on data sovereignty and regulatory expectations.

Then we get into the AI reality check. Instead of vague hype, we focus on what’s delivering immediate value in CX and contact centers, including agent assist, conversation summarization, quality monitoring, and AI that improves IT operations through faster issue resolution and smarter provisioning. We also explore cloud-first mandates, the surprising rise of cloud repatriation when organizations move too quickly, and why the partner ecosystem now drives innovation as much as the platform itself.

If you’re planning a UCaaS or CCaaS modernization, building an AI roadmap for customer experience, or supporting a hybrid workforce at scale, this conversation will help you pressure-test your strategy. Subscribe, share with a colleague, and leave a review with your biggest question about cloud, AI, or enterprise communications.
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected]
Your AI assistant is only as smart as the mess behind your dashboards. When business data lives across CRM, accounting, HR, and job systems, “connect ChatGPT to our data” quickly turns into rate limits, broken joins, confusing IDs, and answers nobody trusts.

We sit down with Craig Morrall, co-founder of Tugger, to unpack a practical architecture for enterprise AI that actually holds up in the real world: pulling data from many platforms into a warehouse, then layering on a semantic model that explains what the data means and how records connect across systems. That extra context is what turns a chatbot into something you can rely on for revenue questions, profitability analysis, and cross-platform reporting without spending months on custom pipelines.

Craig also shares what customers are doing once the foundation is in place, including building interactive dashboards in minutes and generating repeatable board packs that used to take finance teams hours. We dig into time to value, early ROI stories, and how Tugger approaches security and governance with ring-fenced data storage, ISO 27001 certification, and guidance on using business-grade LLM plans to reduce training risk.

If you’re evaluating enterprise AI, data warehousing, semantic layers, or secure analytics with Claude or ChatGPT, this conversation will help you separate real capability from hype. Subscribe for more practical AI stories, share this with a friend building on enterprise data, and leave a review with the biggest data problem you want AI to solve.
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify
Support the show
More at https://linktr.ee/EvanKirstel