Are rising AI workloads pushing your infrastructure to the limit—and leaving you wondering whether cloud, edge, or on-prem is the smarter investment? As companies rush to deploy generative AI and analytics everywhere, leaders face mounting pressure to balance performance, cost, and reliability. This episode explores the hidden expenses of AI infrastructure and why simplicity, scalability, and smart architecture are key to long-term success.

In this episode of Full Tech Ahead, host Amanda Razani interviews Bruce Kornfeld, Chief Product Officer at StorMagic, about how organizations can optimize edge and on-prem environments to support AI without breaking the bank. Kornfeld shares practical insights on building simple, reliable systems, avoiding over-engineering, and using hyperconverged infrastructure to lower costs and latency. He also discusses the evolution of AI at the edge—from retail use cases to hybrid models that run inference locally while training in the cloud—and offers actionable guidance for IT leaders looking to achieve ROI and agility in their AI strategy.

TIMESTAMPS

[00:00] Introduction and Guest Overview

[01:16] Why Some Organizations Stay On-Prem

[02:33] Simplicity, Cost, and Reliability at the Edge

[04:17] Aligning Teams and Avoiding Miscommunication

[06:06] Cloud vs. Edge Architecture Decisions

[08:02] The Growing Role of AI in Infrastructure Planning

[08:27] Measuring ROI and Building a Sustainable Edge Strategy

[10:25] Edge AI in Action—Retail Use Cases

[12:33] Hybrid AI: Blending Cloud Learning and Edge Inferencing

[14:57] The Core Takeaway – Simple, Smart, and Scalable Edge

Quotes

• “Simplicity is king. If you try to build the edge like a data center, you’ll overspend and overcomplicate.”
• “The cloud can’t solve every problem—sometimes you need real-time performance that only the edge can deliver.”
• “AI doesn’t have to mean massive GPU farms. Smart architecture lets you do more with less.”
• “Leave the big models in the cloud; bring the intelligence you need to the edge.”
• “Building on-prem infrastructure for the edge doesn’t have to be expensive or complicated.”

Takeaways

1. Simplify your infrastructure: Avoid over-engineering; focus on easy, reliable systems suited for edge environments.
2. Adopt hybrid AI models: Keep training in the cloud but run inference locally for faster, cost-effective results.
3. Leverage HCI technology: Combine compute, storage, and networking into smaller, more efficient systems.
4. Measure total cost of ownership: Use ROI and TCO modeling tools (like stormagic.com/tco) before deploying AI infrastructure.

Find Amanda Razani on LinkedIn. https://www.linkedin.com/in/amanda-razani-990a7233/

AI and cloud computing are reshaping video surveillance, but are we prepared for what’s ahead? Traditional DVR systems are being replaced by intelligent, data-driven cloud platforms. Businesses and cities now face tough questions about privacy, data ownership, and the role of AI in predicting human behavior. How do we balance innovation with responsibility as technology becomes more powerful and accessible?

In this episode of Full Tech Ahead, host Amanda Razani interviews Greg Stone, Product Marketing Manager at Luminys. With over two decades of experience in physical security and cloud-based systems, Greg reveals how the industry has evolved from analog monitoring to predictive analytics. He explains how AI and metadata are transforming not only safety but also business operations and smart city development.

TIMESTAMPS
[0:02] Greg Stone’s Background and Role at Luminys
[1:58] How AI and Cloud Technology Are Changing Surveillance
[3:18] The Rise of Interactive and Predictive Search
[4:06] Early Detection and Preventive Security
[5:24] Privacy Concerns in a Data-Driven World
[6:05] The Growing Importance of Metadata
[7:25] Smart Cities and Their Positive Community Impact
[8:58] Balancing Safety, Privacy, and Legal Boundaries
[10:14] Commercial Use Cases Beyond Security
[12:02] Facial Recognition Regulations and Alternatives
[13:01] The Future of Predictive Video Surveillance

Quotes

• ‘’AI depends on accurate data, and the cloud is the engine that powers it. The more reliable your data, the better your AI performs.” – Greg Stone
• “Metadata has become the foundation of modern surveillance. It allows organizations to move from reactive monitoring to predictive insights.” – Greg Stone
• “Privacy isn’t just about avoiding cameras. It’s about making sure your data is used responsibly, not misused or manipulated to create false narratives.” – Greg Stone

Takeaways

• Adopt AI-driven cloud solutions to move from reactive surveillance to proactive protection.
• Treat metadata as a strategic resource that fuels predictive analytics and smarter operations.
• Address privacy early by creating transparent policies for data storage and usage.
• Prepare for the next decade of security by integrating predictive technologies that detect risks before they escalate.

Thanks to Luminys for sponsoring this episode
Visit https://www.luminyscorp.com/

Find Amanda Razani on LinkedIn. https://www.linkedin.com/in/amanda-razani-990a7233/

Legacy software keeps businesses running, but updating it is slow, risky, and expensive. What if AI could read, map, and modernize millions of lines of code in days instead of months? How can organizations move faster without breaking what already works?

In this episode of Full Tech Ahead, Amanda Razani talks with Ray Ploski, Vice President of Corporate Strategy and Marketing at CoreStory. Ray shares how his team is using AI to unlock the hidden value inside decades-old systems through their innovative “code-to-spec” technology. Drawing from years of experience in software strategy and AI-driven transformation, he explains how CoreStory is helping enterprises modernize complex codebases with speed, accuracy, and confidence—turning legacy software into a foundation for future growth.

TIMESTAMPS

[0:02] Ray Ploski’s Role and CoreStory’s Rebrand

[0:39] How “Code-to-Spec” Changes Software Development

[2:37] The Challenge of Legacy Systems and Modernization

[3:55] Why Context and Business Continuity Matter

[5:59] CoreStory’s AI-Powered Code Intelligence

[7:12] The Limits of ChatGPT for Enterprise-Grade Code

[9:11] Natural Language Programming: The Future of Development

[10:30] Preparing Businesses for the Shift to Spec-Driven Development

[11:24] Final Thoughts on AI’s Role in Modernization

[12:20] Closing Reflections and Takeaway

Quotes

• “Our ability to generate code outpaces our ability to understand it. CoreStory turns code into knowledge.” – Ray Ploski
• “Speed means nothing if it breaks what works. You can modernize fast, but if your business continuity suffers, you’ve lost the point of modernization entirely.” – Ray Ploski
• “AI hallucinations are real and dangerous. You need confidence, accuracy, and visibility into what AI can’t know.” – Ray Ploski
• “The next generation of software will use natural language. Systems will code themselves from clear goals.” – Ray Ploski

• Use AI to understand and document legacy systems before making changes.
• Prioritize accuracy and confidence in AI results over speed and automation.
• Prepare teams for a future where natural language drives software creation.

Find Amanda Razani on LinkedIn. https://www.linkedin.com/in/amanda-razani-990a7233/