Eye On A.I.

In this episode of Eye on AI, Craig Smith speaks with Jonathan Wall, founder and CEO of Runloop AI, about why AI agents require an entirely new approach to compute infrastructure.   Jonathan explains why agents behave very differently from traditional servers, why giving agents their own isolated computers unlocks new capabilities, and how agent-native infrastructure is emerging as a critical layer of the AI stack. The conversation also covers scaling agents in production, building trust through benchmarking and human-in-the-loop workflows, and what agent-driven systems mean for the future of enterprise work.   Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI   (00:00) Why AI Agents Require a New Infrastructure Paradigm (01:38) Jonathan Wall's Journey: From Google Infrastructure to AI Agents (04:54) Why Agents Break Traditional Cloud and Server Models (07:36) Giving AI Agents Their Own Computers (Devboxes Explained) (12:39) How Agent Infrastructure Fits into the AI Stack (14:16) What It Takes to Run Thousands of AI Agents at Scale (17:45) Solving the Trust and Accuracy Problem with Benchmarks (22:28) Human-in-the-Loop vs Autonomous Agents in the Enterprise (27:24) A Practical Walkthrough: How an AI Agent Runs on Runloop (30:28) How Agents Change the Shape of Compute (34:02) Fine-Tuning, Reinforcement Learning, and Faster Iteration (38:08) Who This Infrastructure Is Built For: Startups to Enterprises (41:17) AI Agents as Coworkers and the Future of Work (46:37) The Road Ahead for Enterprise-Grade Agent Systems

In this episode of Eye on AI, Craig Smith sits down with Anurag Dhingra, Senior Vice President and General Manager at Cisco, to explore where AI is actually creating value inside the enterprise. Rather than focusing on flashy demos or speculative futures, this conversation goes deep into the invisible layer powering modern AI: infrastructure. Anurag breaks down how AI is being embedded into enterprise networking, security, observability, and collaboration systems to solve real operational problems at scale.  From self-healing networks and agentic AI to edge computing, robotics, and domain-specific models, this episode reveals why the next phase of AI innovation is less about chatbots and more about resilient systems that quietly make everything work better. This episodeis perfect for enterprise leaders, AI practitioners, infrastructure teams, and anyone trying to understand how AI moves from theory into production. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) Why AI Only Matters If the Infrastructure Works (01:22) Cisco's Evolution (04:39) Connecting Networks, People, and Experiences at Scale (09:31) How AI Is Transforming Enterprise Networking (12:00) Edge AI, Robotics, and Real-World Reliability (14:18) Security Challenges in an Agent-Driven Enterprise (15:28) What Agentic AI Really Means (Beyond Automation) (20:51) The Rise of Hybrid AI: Cloud Models vs Edge Models (24:30) Why Small, Purpose-Built Models Are So Powerful (29:19) Open Ecosystems and Agent-to-Agent Collaboration (33:32) How Enterprises Actually Adopt AI in Practice (35:58) Building AI-Ready Infrastructure for the Long Term (40:14) AI in Customer Experience and Contact Centers (44:14) The Real Opportunity of AI and What Comes Next

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.  Visit https://agntcy.org/ and add your support. Most large language models today generate text one token at a time. That design choice creates a hard limit on speed, cost, and scalability. In this episode of Eye on AI, Stefano Ermon breaks down diffusion language models and why a parallel, inference-first approach could define the next generation of LLMs. We explore how diffusion models differ from autoregressive systems, why inference efficiency matters more than training scale, and what this shift means for real-time AI applications like code generation, agents, and voice systems. This conversation goes deep into AI architecture, model controllability, latency, cost trade-offs, and the future of generative intelligence as AI moves from demos to production-scale systems. Stay Updated: Craig Smith on X: https://x.com/craigssEye on A.I. on X: https://x.com/EyeOn_AI (00:00) Autoregressive vs Diffusion LLMs (02:12) Why Build Diffusion LLMs (05:51) Context Window Limits (08:39) How Diffusion Works (11:58) Global vs Token Prediction (17:19) Model Control and Safety (19:48) Training and RLHF (22:35) Evaluating Diffusion Models (24:18) Diffusion LLM Competition (30:09) Why Start With Code (32:04) Enterprise Fine-Tuning (33:16) Speed vs Accuracy Tradeoffs (35:34) Diffusion vs Autoregressive Future (38:18) Coding Workflows in Practice (43:07) Voice and Real-Time Agents (44:59) Reasoning Diffusion Models (46:39) Multimodal AI Direction (50:10) Handling Hallucinations

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.  Visit https://agntcy.org/ and add your support. Why are AI, biotechnology, and gene editing converging right now, and what does that mean for the future of humanity? In this episode of Eye on AI, host Craig Smith sits down with futurist and author Jamie Metzl to explore the superconvergence of artificial intelligence, genomics, and exponential technologies that are reshaping life on Earth. We examine the ethical and scientific realities behind human genome editing, the controversy around CRISPR babies, and why society is not yet ready to edit human embryos at scale. The conversation unpacks the complexity of biology, the risks of tech driven hubris, and why governance, values, and social norms must evolve alongside scientific breakthroughs. You will also hear a wide ranging discussion on health span versus longevity, AI and human decision making, education and inequality, and how these technologies could either unlock massive human flourishing or deepen existing global challenges depending on the choices we make today. Stay Updated: Craig Smith on X: https://x.com/craigss  Eye on A.I. on X: https://x.com/EyeOn_AI   

Try OCI for free at http://oracle.com/eyeonai  This episode is sponsored by Oracle. OCI is the next-generation cloud designed for every workload – where you can run any application, including any AI projects, faster and more securely for less. On average, OCI costs 50% less for compute, 70% less for storage, and 80% less for networking.  Join Modal, Skydance Animation, and today's innovative AI tech companies who upgraded to OCI…and saved. Why is AI moving from the cloud to our devices, and what makes on device intelligence finally practical at scale? In this episode of Eye on AI, host Craig Smith speaks with Christopher Bergey, Executive Vice President of Arm's Edge AI Business Unit, about how edge AI is reshaping computing across smartphones, PCs, wearables, cars, and everyday devices. We explore how Arm v9 enables AI inference at the edge, why heterogeneous computing across CPUs, GPUs, and NPUs matters, and how developers can balance performance, power, memory, and latency. Learn why memory bandwidth has become the biggest bottleneck for AI, how Arm approaches scalable matrix extensions, and what trade offs exist between accelerators and traditional CPU based AI workloads. You will also hear real world examples of edge AI in action, from smart cameras and hearing aids to XR devices, robotics, and in car systems. The conversation looks ahead to a future where intelligence is embedded into everything you use, where AI becomes the default interface, and why reliable, low latency, on device AI is essential for creating experiences users actually trust. Stay Updated: Craig Smith on X: https://x.com/craigss     Eye on A.I. on X: https://x.com/EyeOn_AI