Eye On A.I.

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#316 Robbie Goldfarb: Why the Future of AI Depends on Better Judgment Jan 23, 2026

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AI is getting smarter, but now it needs better judgment.

In this episode of the Eye on AI Podcast, we speak with Robbie Goldfarb, former Meta product leader and co-founder of Forum AI, about why treating AI as a truth engine is one of the most dangerous assumptions in modern artificial intelligence.

Robbie brings first-hand experience from Meta's trust and safety and AI teams, where he worked on misinformation, elections, youth safety, and AI governance. He explains why large language models shouldn't be treated as arbiters of truth, why subjective domains like politics, health, and mental health pose serious risks, and why more data does not solve the alignment problem.

The conversation breaks down how AI systems are evaluated today, how engagement incentives create sycophantic and biased models, and why trust is becoming the biggest barrier to real AI adoption. Robbie also shares how Forum AI is building expert-driven AI evaluation systems that scale human judgment instead of crowd labels, and why transparency about who trains AI matters more than ever.

This episode explores AI safety, AI trust, model evaluation, expert judgment, mental health risks, misinformation, and the future of responsible AI deployment.

If you are building, deploying, regulating, or relying on AI systems, this conversation will fundamentally change how you think about intelligence, truth, and responsibility.

Want to know more about Forum AI? Website: https://www.byforum.com/ X: https://x.com/TheForumAI LinkedIn: https://www.linkedin.com/company/byforum/

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(00:00) Why Treating AI as a "Truth Engine" Is Dangerous (02:47) What Forum AI Does and Why Expert Judgment Matters (06:32) How Expert Thinking Is Extracted and Structured (09:40) Bias, Training Data, and the Myth of Objectivity in AI (14:04) Evaluating AI Through Consequences, Not Just Accuracy (18:48) Who Decides "Ground Truth" in Subjective Domains (24:27) How AI Models Are Actually Evaluated in Practice (28:24) Why Quality of Experts Beats Scale in AI Evaluation (36:33) Trust as the Biggest Bottleneck to AI Adoption (45:01) What "Good Judgment" Means for AI Systems (49:58) The Risks of Engagement-Driven AI Incentives (54:51) Transparency, Accountability, and the Future of AI

#315 Jarrod Johnson: How Agentic AI Is Impacting Modern Customer Service Jan 21, 2026

Show notes

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

#314 Nick Pandher: How Inference-First Infrastructure Is Powering the Next Wave of AI Jan 17, 2026

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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

#313 Evan Reiser: How Abnormal AI Protects Humans with Behavioral AI Jan 16, 2026

Show notes

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

#312 Jonathan Wall: AI Agents Are Reshaping the Future of Compute Infrastructure Jan 11, 2026

Show notes

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

#311 Anurag Dhingra: Inside Cisco's Vision for AI-Powered Enterprise Systems Jan 07, 2026

Show notes

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

#310 Stefano Ermon: Why Diffusion Language Models Will Define the Next Generation of LLMs Jan 04, 2026

Show notes

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

#309 Jamie Metzl: Why Gene Editing Needs Governance Or We Lose Control Dec 24, 2025

Show notes

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.

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#308 Christopher Bergey: How Arm Enables AI to Run Directly on Devices Dec 19, 2025

Show notes

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.

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#307 Steven Brightfield: How Neuromorphic Computing Cuts Inference Power by 10x Dec 16, 2025

Show notes

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 is AI so powerful in the cloud but still so limited inside everyday devices, and what would it take to run intelligent systems locally without draining battery or sacrificing privacy?

In this episode of Eye on AI, host Craig Smith speaks with Steve Brightfield, Chief Marketing Officer at BrainChip, about neuromorphic computing and why brain inspired architectures may be the key to the future of edge AI.

We explore how neuromorphic systems differ from traditional GPU based AI, why event driven and spiking neural networks are dramatically more power efficient, and how on device inference enables faster response times, lower costs, and stronger data privacy. Steve explains why brute force computation works in data centers but breaks down at the edge, and how edge AI is reshaping wearables, sensors, robotics, hearing aids, and autonomous systems.

You will also hear real world examples of neuromorphic AI in action, from smart glasses and medical monitoring to radar, defense, and space applications. The conversation covers how developers can transition from conventional models to neuromorphic architectures, what role heterogeneous computing plays alongside CPUs and GPUs, and why the next wave of AI adoption will happen quietly inside the devices we use every day.

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