Fireside Chat: The Past, Present, and Future of AI with Igor Jablokov and Mark Hinkle

Igor Jablokov, Founder, Pryon and Mark Hinkle, CEO and Co-Founder, Peripety Labs

Long before generative AI captured the world’s attention, Igor Jablokov was quietly building the technologies that would reshape how we interact with machines. Best known as the father of Amazon’s Alexa, Igor brings a rare depth of perspective to the AI conversation — one rooted in decades of hands-on work in voice intelligence, natural language processing, and product development at the frontier of what’s possible.

In this fireside chat with Mark Hinkle, CEO of Peripety Labs and co-founder of All Things AI, Igor shares candid reflections on the evolution of the AI industry — from its early winters to today’s explosive growth — and what he sees coming next. The conversation explores what it was like building AI when few believed in its commercial potential, how the landscape has shifted, and where the real opportunities lie for entrepreneurs and enterprises alike.

Perhaps most compelling is Igor’s bold thesis: that North Carolina is poised to produce its first trillion-dollar startup, fueled by the state’s growing concentration of AI talent, world-class research universities, and a business-friendly ecosystem that’s increasingly attracting top-tier investment and innovation.

Whether you’re a founder, investor, enterprise leader, or technologist, this conversation offers a front-row seat to the thinking of one of AI’s true pioneers — and a vision for why the Triangle may be the next epicenter of transformative AI innovation.

When AI Meets Quantum: Expanding the Boundaries of Machine Intelligence

whurley, CEO, Strangeworks

Artificial intelligence has become remarkably capable, but it is still bounded by the assumptions of classical computation. Today’s AI systems learn, reason, and generate within frameworks that depend on approximation, statistical shortcuts, and ever-increasing scale. As models grow larger and problems more complex, these constraints are becoming more visible. The question is no longer how big AI can get, but how much further intelligence itself can be extended.

Quantum computing introduces a fundamentally different way of processing information, one that challenges the classical limits underlying modern AI. Rather than acting as a faster processor, quantum systems offer new methods for representing uncertainty, exploring vast possibility spaces, and modeling complex correlations that are difficult or impossible to capture classically. When combined with AI, this shift opens the door to new forms of machine learning and reasoning that go beyond incremental performance gains.

This talk explores the convergence of quantum computing and artificial intelligence as the emergence of a new computational paradigm. We focus on how quantum approaches can augment core AI capabilities such as learning from sparse or noisy data, probabilistic inference, generative modeling, and reasoning in high-dimensional environments. The emphasis is not on replacing existing AI systems, but on extending them through hybrid architectures where classical models and quantum processors operate together as a unified intelligence stack.

Attendees will gain a clear, practical understanding of what quantum-enhanced AI means today, where real progress is being made, and where expectations should remain grounded. We will separate near-term reality from long-term speculation, providing a mental model that AI practitioners, researchers, and decision-makers can use to evaluate this rapidly evolving field.

How the Oldest Principle in Engineering Will Reshape AI 

Luis Lastras, Director, Language and Multimodal Technologies, IBM

A fundamental principle in engineering—the decomposition of complex systems into modules with well-defined interfaces—has long enabled the transition from prototypes to production. Generative AI has yet to fully make this shift. Today’s agentic systems often remain monolithic and fragile, embedding logic, evaluation, and recovery into a single end-to-end prompt.

We propose treating both AI models and applications as structured, extensible systems rather than monolithic artifacts. By adopting composable architectures, capabilities can be added, tested, and evolved independently. As modularity replaces monolithic design in AI, it will become the foundation for greater reliability, scalability, and collaborative development.

AI’s Impact on the Startup Ecosystem

Chris Heivly, Author, co-founder of MapQuest, angel investor, Techstars, dubbed the “Startup Whisperer”

Abstract coming soon!

From Chaos to Coordination: The Executive Playbook for Managing an AI Agent Portfolio

Ben Heller, Field CTO, Microsoft

The challenge for today’s executives is no longer whether to build AI agents, but how to manage many of them at once—across departments, timelines, and levels of maturity—without losing control or value. This session introduces a pragmatic AI agent program operating model, designed for leaders overseeing concurrent initiatives. We’ll examine how successful organizations track agents in different lifecycle stages, standardize what should be shared, and intervene early to prevent duplication and fragmentation. Executives will gain clarity on how to structure ownership, decision rights, and success metrics so agent development remains aligned to enterprise priorities and delivers measurable outcomes to both internal and external stakeholders.