Eye On A.I.

In this episode of Eye on AI, Craig Smith sits down with Jarrod Johnson, Chief Customer Officer at TaskUs, to unpack how agentic AI is changing customer service from conversations to real action. 
 
They explore what agentic AI actually is, why chatbots were only the first step, and how enterprises are deploying AI systems that resolve issues, execute tasks, and work alongside human teams at scale. 
 
The conversation covers real-world use cases, the economics of AI-driven support, why many enterprise AI pilots fail, and how human roles evolve when AI takes on routine work. 
 
A grounded look at where customer experience, enterprise AI, and the future of support are heading.



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(00:00) Jarrod Johnson and the Evolution of TaskUs
(03:58) Why AI Became Core to Customer Service
(06:07) Humans, AI, and the New Support Model
(07:16) What Agentic AI Actually Is
(11:38) TaskUs as an AI Systems Integrator
(14:59) How Agentic AI Resolves Customer Issues
(19:52) Workforce Impact and the Human Role
(23:26) Why Most Enterprise AI Pilots Fail
(30:32) Real Client Case Study: Healthcare Impact
(36:34) Why Customer Service Still Feels Broken
(38:49) The End of IVR Menus and Legacy Systems
(42:25) AI Safety, Compliance, and Governance
(49:38) Training Humans for AI and RLHF Work
(54:34) The Future of Agentic AI in Enterprise

Inference is now the biggest challenge in enterprise AI.

In this episode of Eye on AI, Craig Smith speaks with Nick Pandher, VP of Product at Cirrascale, about why AI is shifting from model training to inference at scale. As AI moves into production, enterprises are prioritizing performance, latency, reliability, and cost efficiency over raw compute.

The conversation covers the rise of inference-first infrastructure, the limits of hyperscalers, the emergence of neoclouds, and how agentic AI is driving always-on inference workloads. Nick also explains how inference-optimized hardware and serverless AI platforms are shaping the future of enterprise AI deployment.
 
If you are deploying AI in production, this episode explains why inference is the real frontier.
 

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(00:00) Preview
(00:50) Introduction to Cirrascale and AI inference
(03:04) What makes Cirrascale a neocloud
(04:42) Why AI shifted from training to inference
(06:58) Private inference and enterprise security needs
(08:13) Hyperscalers vs neoclouds for AI workloads
(10:22) Performance metrics that matter in inference
(13:29) Hardware choices and inference accelerators
(20:04) Real enterprise AI use cases and automation
(23:59) Hybrid AI, regulated industries, and compliance
(26:43) Proof of value before AI pilots
(31:18) White-glove AI infrastructure vs self-serve cloud
(33:32) Qualcomm partnership and inference-first AI
(41:52) Edge-to-cloud inference and agentic workflows
(49:20) Why AI pilots fail and how enterprises succeed

In this episode of Eye on AI, we sit down with Evan Reiser, co-founder and CEO of Abnormal AI, to unpack how AI has fundamentally changed the cybersecurity landscape.
 
We explore why social engineering remains the most costly form of cybercrime, how generative AI has lowered the barrier for sophisticated attacks, and why humans have become the primary attack surface in modern security. Evan explains why traditional, signature-based defenses fall short, how behavioral AI detects threats that have never existed before, and what it means to build security systems that understand how people actually work and communicate.
 
The conversation also looks ahead at the AI arms race between attackers and defenders, the economics driving cybercrime, and what it truly means to be an AI-native company operating at scale.
 
This episode is a deep dive into the human side of AI security and why the future of cybersecurity depends less on code and more on behavior.



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(00:00) Abnormal AI's origin
(02:31) Why phishing is still the biggest threat
(05:57) How attackers manipulate human trust
(10:05) The true cost of social engineering
(11:58) Vendor account compromise explained
(15:02) How AI changed cyber attacks
(16:28) Behavioral security vs traditional defenses
(19:55) Where Abnormal fits in the security stack
(22:24) Human psychology as the attack surface
(24:01) Why cyber defense is asymmetric
(28:48) Humans as the new zero-day
(31:01) Why attackers target people, not systems
(33:21) Behavioral modeling from ads to security
(36:10) Why money drives almost all attacks
(40:06) What happens after credentials are stolen
(42:18) Text scams and lateral movement
(43:55) What it means to be AI-native
(47:13) How Abnormal uses AI internally

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.
 
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(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.

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(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