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 are enterprises struggling to turn AI hype into real workplace transformation, and how is Lenovo using agentic AI to actually close that gap? In this episode of Eye on AI, host Craig Smith talks with Rakshit Ghura about how his team is reinventing the modern workplace with an omnichannel AI architecture powered by a fleet of specialized agents.


We explore how Lenovo has evolved from a hardware company into a global solutions provider, and how its Care of One platform uses persona based design to improve employee experience, reduce downtime, and personalize support across IT, HR, and operations. You will learn what enterprises get wrong about AI readiness, why trust and change management matter more than technology, and how organizations can design workplace stacks that meet employees where they are.


We also cover how Lenovo approaches responsible AI, how enterprises should think about security and governance when deploying agents, and why so many organizations are enthusiastic about AI but still not ready to adopt it. Rakshit shares real examples from retail, manufacturing, and field operations, including how AI can improve uptime, automate ticket resolution, monitor equipment, and provide proactive insights that drive measurable business impact.


You will also learn how to evaluate ROI for digital workplace solutions, how to involve employees early in the adoption cycle, and which metrics matter most when scaling agentic AI, including uptime, productivity improvements, and employee satisfaction.

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 inference becoming the new battleground for speed, cost, and real world scalability, and how are companies like Clarifai reshaping the AI stack by optimizing every token and every deployment?

In this episode of Eye on AI, host Craig Smith sits down with Clarifai founder and CEO Matt Zeiler to explore why inference is now more important than training and how a unified compute orchestration layer is changing the way teams run LLMs and agentic systems.

We look at what makes high performance inference possible across cloud, on prem, and edge environments, how to get faster responses from large language models, and how to cut GPU spend without sacrificing intelligence or accuracy. Learn how organizations operate AI systems in regulated industries, how government teams and enterprises use Clarifai to deploy models securely, and which bottlenecks matter most when running long context, multimodal, or high throughput applications.

You will also hear how to optimize your own AI workloads with better token throughput, how to choose the right hardware strategy for scale, and how inference first architecture can turn models into real products. This conversation breaks down the tools, techniques, and design patterns that can help your AI agents run faster, cheaper, and more reliably in production.


Stay Updated:
Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.

Visit https://agntcy.org/ and add your support.

How will AI evolve once it can understand and reason about the 3D world, not just text on a screen?

In this episode of Eye on AI, host Craig Smith speaks with Fei Fei Li about the rise of spatial intelligence and the world models that could transform how machines perceive, imagine, and interact with reality.

We explore how spatial intelligence goes beyond language to connect perception, action, and reasoning in physical environments. You will hear how models like Marble build consistent and persistent 3D spaces, why multimodal inputs matter, and what it takes to create digital worlds that are useful for robotics, simulation, design, and creative workflows. Fei Fei also explains the challenges of long term memory, continuous learning, and the search for training objectives that mirror the role next token prediction plays in language models.

Learn how spatial reasoning unlocks new possibilities in robotics and telepresence, why classical physics engines still matter, and how future AI systems may merge perception, planning, and imagination. You will also hear Fei Fei's perspective on the limits of current architectures, why true understanding is different from human understanding, and how world models could shape the next generation of intelligent systems.

Stay Updated:
Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.

Visit https://agntcy.org/ and add your support.

How could Karl Friston's Free Energy Principle become a blueprint for the future of AI?

In this episode of Eye on AI, host Craig Smith sits down with Karl Friston, the neuroscientist behind the Free Energy Principle and advisor to Verses AI, to explore how active inference and brain inspired generative models might move us beyond transformer based systems. They unpack how Axiom, Verses' new architecture, uses probabilistic beliefs and message passing to build agents that learn like brains instead of just predicting the next token.

We look at why transformers face scaling and reliability limits, how Free Energy unifies prediction, perception, and action, and what it means for an AI system to carry explicit uncertainty instead of overconfident guesses. Learn how active inference supports continual learning without catastrophic forgetting, how structure learning lets models grow and prune themselves, and why embodiment and interaction with the real world are essential for grounding language and meaning.

You will also hear how Axiom can sit beside or beneath large language models, how explicit uncertainty can reduce hallucinations in high stakes workflows, and where these ideas are already being tested in areas like logistics, robotics, and autonomous agents. By the end of the episode, you will have a clearer picture of how Karl Friston's Free Energy blueprint could reshape AI architectures, from enterprise planning systems to embodied agents that understand and act in the world.

Stay Updated:
Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI

How are AI and telematics changing safety for fleets in the real world, and what does it take to get from basic recordings to true accident prevention?

In this episode of Eye on AI, host Craig Smith speaks with Hemant Banavar, Chief Product Officer at Motive, and Ryan Ennis, CIO at FusionSite Services, to explore how AI powered cameras and telematics are transforming safety, productivity, and profitability across the physical economy, from trucking and construction to field services.

We look at what makes safety AI trustworthy at scale, how to reduce false alerts that drivers ignore, and how to combine in cab coaching, human review, and rich telematics data to drive down risky behaviors. Learn how FusionSite Services cut unsafe events by more than ninety percent while tripling in size, slashed insurance claims and premiums, and used real time insights to tackle idling, under utilized assets, and the hidden costs of unsafe operations.

You will also hear how leading fleets run side by side vendor tests, design incentive programs that get drivers on board with cameras, and build a culture around zero preventable accidents. If you are responsible for safety, operations, or risk, this episode will show you how to evaluate AI and telematics platforms, which benchmarks to demand, and how to turn your data into safer roads and stronger unit economics.

Stay Updated:
Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI

How are Decision Intelligence and AI agents reshaping enterprise operations today? In this episode of Eye on AI, host Craig Smith sits down with Fred Laluyaux, CEO of Aera Technology, to unpack how organizations move from dashboards and ad hoc workflows to a system that senses, decides, and acts.

AI is not just about chatbots. At the heart of this transformation is decision intelligence: connecting data, analytics, AI, and automation to optimize decisions across the enterprise. Fred explains why this is becoming the operating backbone of the modern enterprise and how it accelerates the shift toward autonomous, self-driving businesses.

We look at how to build a decision intelligence stack end to end, how AI agents collaborate with people, and how to stand up a control room that monitors decisions across supply chain, finance, and customer operations. Learn how leading companies model decisions, govern them safely, and measure impact with clear metrics that matter, including service level, cost to serve, cash flow, inventory turns, and time to resolution.

Stay Updated:
Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI

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 do today's LLMs forget key details over long context, and what would it take to give them real memory that scales?

In this episode of Eye on AI, host Craig Smith explores Manifest AI's Power Retention architecture and how it rethinks memory, context, and learning for modern models. We look at why transformers struggle with long inputs, how state space and retention models keep context at linear cost, and how scaling state size unlocks reliable recall across lengthy conversations, code, and documents. We also cover practical paths to retrofit existing transformer models, how in context learning can replace frequent fine tuning, and what this means for teams building agents and RAG systems.

Learn how product leaders and researchers measure true long context quality, which pitfalls to avoid when extending context windows, and which metrics matter most for success, including recall consistency, answer fidelity, task completion, CSAT, and cost per resolution. You will also hear how to design per user memory, set governance that prevents regressions, evaluate LLM as judge with human review, and plan a secure rollout that improves retrieval, multi step workflows, and agent reliability across chat, email, and voice.

Stay Updated:

Craig Smith on X:https://x.com/craigss

Eye on A.I. on X: https://x.com/EyeOn_AI

This episode is sponsored by AGNTCY. Unlock agents at scale with an open Internet of Agents.

Visit https://agntcy.org/ and add your support.

How do the world's most powerful AI models get trained and trusted at scale, and what does that really take from data to deployment?

In this episode, Appen CEO Ryan Kolln joins Eye on AI to unpack how rigorous human evaluation, culturally aware data, and model-based judges come together to raise real-world performance.

In this episode of Eye on AI, host Craig Smith speaks with Ryan Kolln, CEO of Appen, about building evaluation systems that go beyond static benchmarks to measure usefulness, safety, and reliability in production. They explore how human raters and AI evaluators work in tandem, why localization matters across regions and domains, and how quality controls keep feedback signals trustworthy for training and post-training.

Ryan explains how evaluation feeds reinforcement strategies, where rubric-driven human judgments inform reward models, and how enterprises can stand up secure workflows for sensitive use cases. He also discusses emerging needs around sovereign models, domain-specific testing, and the shift from general chat to agentic workflows that operate inside real business systems.

Learn how leading teams design human-in-the-loop evaluation, when to route judgments from models back to expert reviewers, how to capture cultural nuance without losing universal guardrails, and how to build an evaluation stack that scales from early prototypes to production AI.

Stay Updated:
Craig Smith on X: https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI