Eye On A.I.

Most AI is built for people sitting at desks. Kriti Sharma builds it for the people who work in refineries, aircraft hangars, and utility networks responding to wildfires at 4 a.m. and she spends weekends on-site with them to make sure what she builds actually holds up. In this episode, Kriti joins Craig Smith to discuss what industrial AI really looks like when failure genuinely isn't an option, and why the gap between an impressive AI pilot and a production-grade AI system is so much wider in the physical world than most technology companies appreciate.
The conversation is grounded in three specific products from Nexus Black, the elite AI unit Kriti leads inside IFS. The first is Resolve, a predictive maintenance platform built in close collaboration with William Grant's - the distillery behind Glenfiddich and Hendricks Gin - that is projected to save £8.4 million per year at a single factory by reading complex engineering schematics, identifying failure patterns before they occur, and giving frontline technicians step-by-step guidance on their phones without requiring them to remove a safety glove to type. The second is an airworthiness compliance tool for commercial airlines that automates a process currently consuming weeks of human engineering time, where a single mistake carries regulatory fines of up to $20 million and grounding a fleet costs $140 million per day. The third is a disaster response coordination system for utilities, built in partnership with Anthropic, designed to help field crews coordinate during wildfires, hurricanes, and grid outages in ways that, as a California disaster responder told Kriti directly after the most recent wildfire season, will get communities back online and hospitals lit up faster than ever before.
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What is an AI agent, really? Strip away the hype, and it's a model with access - to tools, APIs, databases, email, anything that lets it take real action instead of just generating text. That access is exactly where the risk lives, and Devvret Rishi, GM of AI at Rubrik, and former co-founder & CEO of Predibase, joins Craig Smith with a string of real-world incidents that make the case concrete: AWS reporting four major outages in 90 days after deploying coding agents, a Meta-related agent that deleted someone's emails while they were actively asking it to stop, and Rubrik's own internal pilot catching incidents that, without governance in place, would have gone unnoticed.
The conversation lays out the impossible choice most enterprises are facing right now - block AI agents and forfeit the ROI boards are demanding, or grant access and hope nothing breaks - and walks through how Rubrik's approach uses small, fine-tuned AI models to enforce plain-English security policies on every single agent action in real time. It closes on one of the most underexamined risks ahead: as agents increasingly talk to other agents to get work done, a layer of activity is forming that no human is watching, and the question of who's accountable when something goes wrong in that layer is only getting more urgent.
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It costs up to $2 billion and fifteen years to develop a drug, and big pharma still fails half the time at the final stage. BullFrog AI founder, Chairman, and CEO Vin Singh joins Craig Smith with a clear diagnosis of why: the industry keeps picking the wrong drug target from the beginning, and no amount of downstream optimization fixes a fundamentally wrong starting point. Built on AI technology originally developed at Johns Hopkins' Applied Physics Lab, BullFrog has assembled a three-stage platform that cleans messy clinical data, runs causal analysis to map disease pathways, and then ranks competing drug targets using a competitive framework that removes the subjectivity most pharmaceutical decision-making still relies on.
The most striking results in this conversation come from two case studies: work with the Lieber Institute for Brain Development - analyzing thousands of post-mortem brains - that led to the identification of potential driver genes for depression, bipolar disorder, and schizophrenia in months from data that researchers had spent fifteen years studying, and a pancreatic cancer trial where BullFrog's platform identified a patient subgroup with survival rates three times higher than the study average. Vin also delivers a candid assessment of the broader AI-pharma landscape: more than 90% of AI deals in the space are missing their milestones, most companies are wrapping open-source tools rather than building genuine technology, and the shakeout between players and pretenders is already well underway.
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After two years of AI pilots, enterprises are finally diagnosing what went wrong, and the answer keeps coming back to data. Alberto Pan, CTO of Denodo, joins Craig Smith to walk through the findings of the company's AI Trust Gap Report: a survey of 850 enterprise data leaders that reveals the dominant failure modes of enterprise AI agents are almost never the model's fault. They're caused by stale data, missing context, and inconsistent semantics across the hundreds of data sources agents need to access to do real work.
Pan explains why traditional data warehouse and lake house architectures - built for analytics, not real-time decision-making - are creating an invisible ceiling on AI performance, and how Denodo's logical data management approach lets agents query data where it lives without centralizing it first, while enforcing consistent governance across every source in one place. The conversation also identifies two specific traps most organizations fall into as they try to scale AI - over-centralizing data into a single system, or building custom ad hoc data layers for every agent - and why both approaches collapse in a multi-agent world where agents need to cooperate, share context, and work from a common semantic foundation.
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What if consciousness isn't a byproduct of complex brains, but a fundamental feature of reality itself, present, in some rudimentary form, all the way down to electrons and quarks? Philip Goff, a philosopher at Durham University and one of panpsychism's leading contemporary advocates, joins Craig Smith to make that case, arguing that modern science's founding move - separating the mathematical world physics studies from the subjective experience we know only from the inside - solved one problem while quietly creating another we've never resolved.
The conversation inevitably turns to AI: could a large language model ever be conscious? Goff's answer is a careful, well-reasoned no, not because he thinks consciousness is magical, but because his framework treats it as something closer to the physical substance of reality than an abstract computation, making him skeptical that anything resembling current AI architecture could cross that threshold. Along the way, he tackles one of the genuine open mysteries in his field: if natural selection only cares about behavior, why did evolution bother making us conscious at all, and what would it even mean to find experimental evidence for an answer.
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