In 2023, General Cherry started making 10 drones a month. Today they're approaching 100,000. Marko Kushnir, communications director of one of Ukraine's top-five drone manufacturers, joins Craig Smith for one of the most operationally specific conversations available about what drone warfare looks like at industrial scale, from the daily feedback loops with front-line units that drive product iteration, to the $2,000 interceptor drone that can destroy a $100,000 Shahed, to the on-device AI targeting model that guides an interceptor to impact at 70% accuracy after the operator activates it and steps back.

The conversation's most important insights are structural rather than technical. Marko describes the fundamental asymmetry of the conflict with unusual precision: Ukraine's decentralized, startup-driven ecosystem produces new technologies faster than Russia's command economy, but Russia's vertical industrial structure copies and scales those technologies faster than Ukraine can stay ahead. He also expresses genuine alarm about fully autonomous AI drones, not from an ethical standpoint but from a practical one: any autonomous capability Ukraine deploys will be in Russian hands within weeks, making full autonomy a danger Ukraine would share immediately with its enemy.

The episode closes with his most far-reaching argument: just as the internet era created a cybersecurity industry that every organization eventually had to build, the drone era is now beginning, and every government, police force, and major corporation will soon need a drone security department to function safely in a world where drones are as common as smartphones.

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A Ukrainian entrepreneur who spent 14 years building cameras for pets pivoted to building cameras that down Shaheds, and is now building the autonomy software that could define how wars are fought for the next generation. Yaroslav Azhnyuk, co-founder of Fourth Law, joins Craig Smith in Kyiv to explain why Ukraine has become what he calls the Defense Valley or the Florence of Defense: a dense, fast-moving ecosystem of founders, engineers, and military operators who are building, testing, and iterating on autonomous drone systems in real combat conditions, with a feedback loop that no defense contractor in the West can currently match.

The conversation covers Azhnyuk's five-level autonomy framework for drones, the eight-dimensional model for what a fully autonomous battlefield ecosystem requires, and the economic math that he believes makes global rearmament inevitable: a $500 drone that can already destroy a $5 million tank becomes roughly 10,000 times more capable when full autonomy is added for a few hundred dollars more. The competitive landscape is mapped with unusual candor, an "Apple vs. Android" comparison between Eric Schmidt's vertically integrated interceptors and Fourth Law's modular platform approach, alongside two arguments that cut against the mainstream narrative. First, that within 5 to 10 years it may become unethical to use weapons without AI, because non-AI weapons cause more collateral damage, not less. And second, that the real AGI risk isn't Skynet, it's the subtle transfer of power that happens when 100 smarter advisors gradually stop waiting for the President to decide, and nobody notices until the President is no longer the one making decisions.

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The most consequential arms race in the world right now isn't nuclear, it's software. Craig Smith speaks with Alexander Palamarchuk, an engineer in the R&D department of Ukraine's Azov Brigade, calling in from approximately 18 kilometers from the front line in the Pokrovsk region. What emerges is one of the most technically candid accounts available of what drone warfare actually looks like from the inside: how Ukraine went from homemade reconnaissance drones in 2014 to AI-guided systems being developed and tested in real combat today, how the jamming arms race has forced his unit to develop custom frequency systems spanning 100 to 3,000 megahertz in a constant search for clean windows Russia hasn't yet closed, and why the tank - once the defining weapon of land warfare - has been reduced on the modern battlefield to a mobile jamming platform.

The most important distinction Alexander draws is one that rarely surfaces in mainstream coverage: AI already exists that can recognize and classify vehicles and people with high accuracy. The unsolved problem isn't recognition, it's discrimination, determining with certainty whether a recognized target is military or civilian. That gap is the only thing standing between today's AI-assisted drones and fully autonomous lethal systems, and Alexander puts the timeline for closing it at approximately two years. The US maintains an official policy of not developing fully autonomous lethal weapons for ethical reasons. On the battlefield 18 kilometers from where Alexander is speaking, that policy is being outpaced in real time, and he is clear that NATO's software advantage positions Western countries to win that race before anyone else gets there.

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It takes a few hours to build an AI agent. It takes six to seven months to get it into production. Manoj Saxena - the executive who commercialized IBM Watson - built TrustWise around one thesis: intelligence without control is not deployable. In this episode, he joins Craig Smith to explain why 95% of enterprise AI agent projects are stalling between pilot and production, and why the answer has nothing to do with the quality of the underlying models. The bottleneck, Saxena argues, is the absence of an entirely new class of infrastructure, something that can evaluate every tool call, every action, every output of every agent at runtime, in milliseconds, against the full stack of alignment requirements that govern what an AI is actually allowed to do inside a real enterprise.

TrustWise's AI Control Tower does that across all vendors and agent frameworks simultaneously, operating in live, sidecar, batch, or simulation mode and aligning agent behavior against six layers of requirements - from UN Human Rights frameworks down to individual customer SLA commitments - in 10 to 300 milliseconds per decision. The conversation covers demonstrated results (83% cost reduction, 40% safety improvement), the token consumption paradox that's making agentic AI far more expensive than expected even as token costs fall, and a milestone Saxena compares to the moment data traffic surpassed voice on AT&T's network: last month, for the first time ever, agent traffic on the internet exceeded human traffic. The episode closes with a preview of Genesis agents, TrustWise's next product, designed not just to prevent bad outcomes but to surface beneficial hypotheses by looking 95 moves deep into enterprise data, in domains like fraud detection and revenue leakage, in the way Deep Blue looked 95 moves deep in chess.

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Most humanoid robot companies are still running curated demos in replica environments. Foundation Future Industries is running 150 robots on real automotive production lines in Georgia, and heading to Ukraine this year to deploy on the battlefield. Mike LeBlanc, the co-founder of Foundation Future Industries - currently the only company supplying humanoid robots to the US Department of Defense, with contracts across the Army, Navy, and Air Force, joins Craig Smith to explain why the race is moving faster than almost anyone in the industry believes, and why the companies that are moving cautiously are about to be left behind. His frame is striking: he keeps a framed 1906 New York Times article on his office wall predicting that human flight would take between one million and ten million years. It was published three months before the Wright Brothers flew. He thinks humanoids are in exactly that moment right now.

The conversation covers the full operational picture: how Foundation trains robots using video rather than simulation; why the fry-cook robot that couldn't open the bag of fries is a perfect metaphor for everything wrong with how most companies approach go-to-market in this space; why the human form factor isn't a philosophical preference but an empirical fact,  humans are still doing every job in every factory that other robots can't, and that's the proof of concept; and why Mike LeBlanc isn't particularly worried about competing against Boston Dynamics backed by Google DeepMind, because they're still demoing in replica sites while Foundation is deploying on production lines.

The episode ends with a bet: LeBlanc tells Craig that in twelve months, he'll be back to report 10,000 robots deployed in the world. Craig says he remains cautious. One of them is going to be right.

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The AI chip market looks monolithic from the outside - NVIDIA dominates, and everyone else is fighting for scraps. But d-Matrix's CEO Sid Sheth argues that the market is quietly splitting into two distinct tiers, and the one that's exploding right now is the one NVIDIA's architecture isn't built for. In this episode, Sid joins Craig Smith to explain the "premium token economy": a new class of AI inference where interactivity is the product, users pay ten times more per million tokens for instant responses, and the memory bandwidth limits of GPU-based systems create a structural ceiling that purpose-built architectures don't have.

The conversation is unusually candid about what AI actually looks like at the executive level: Sid describes using Claude as a sounding board for M&A strategy, producing full integration plans in 15 minutes that used to require entire banking advisory teams, and watching AI shift from a tool that echoed his ideas back at him to one that genuinely disagrees, flags what he missed, and pushes back with enough confidence to be useful. He also makes the case that we're at the beginning of a shift from individual agents to what he calls "organizational AI" - teams of agents running entire company functions at a high level of abstraction - and that the infrastructure bet d-Matrix is making positions them directly in the path of that wave.

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The same tools that slowed the U.S. military down in Afghanistan (PowerPoint, Excel, email, and Word) are now slowing American businesses down in the AI race. Drew Cukor spent 30 years as a Marine intelligence officer, helped build Project Maven into a battlefield command and control system, served as Chief Data Officer at JP Morgan, and is now leading AI transformation at TWG AI. In this episode, he joins Craig Smith to make a case that most enterprise AI strategies are fundamentally broken, not because the technology isn't there, but because companies are storing their data in Microsoft file folders where it becomes inaccessible to AI, appointing AI officers who block progress rather than enable it, and mistaking chatbot deployments for transformation.

Cukor's prescription is specific: take a company's core workflows apart, how it acquires customers, delivers services, handles back office operations, and rebuild them from scratch with AI embedded throughout, protected inside Palantir Foundry, delivered within 36 months, with the CEO owning the outcome rather than delegating it to a CTO or a made-up AI officer role. The stakes, he argues, are not abstract: China is going AI-native from the start without the legacy infrastructure that's slowing American enterprise, token spend is approaching the cost of a human salary making poorly designed AI workflows as expensive as bad hiring decisions, and the window for acting is closing. The most important video he recommends any business leader watch isn't one where the AI wins, it's the footage of Lee Sedol losing to AlphaGo and realizing mid-game that he no longer understands how the game works. That moment, Cukor says, is coming for every legacy business that doesn't move now.

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Hundreds of billions of dollars are flowing into AI data centers right now, and Paolo Ardoino, CEO of Tether - the company behind the world's most widely used stablecoin with 573 million users - thinks that investment is going to age very badly. In this episode, he joins Craig Smith to explain QVAC, Tether's open-source platform for running AI on smartphones, laptops, and edge devices, and to make a case that within five years, 90% of what ordinary people use AI for will run entirely on consumer hardware, without touching a data center. The evidence is already there: Tether's team built a 4-billion-parameter medical AI model that outperforms Google's 27-billion-parameter MedGemma, running on a good smartphone, and a 1.7-billion-parameter version that runs on the average $80 smartphone available in Africa.

The deeper argument in this conversation is philosophical as much as technical. Ardoino applies the same disintermediation logic that made sending dollars to the world's unbanked free - zero transaction fees, revenue from treasury bill interest - to AI: "not your AI, not your intelligence." If you don't control how your AI runs and your data never leaves your device, the AI is genuinely yours. If it does, someone else is getting smarter with your information. He also makes a pointed economic argument: the AI companies currently charging $200 for subscriptions that cost $1,000 to $5,000 to deliver are subsidizing growth while private, and when they go public, retail investors will absorb the gap. His prescription isn't to stop building, it's to build differently, toward millions of small interacting models rather than trillion-parameter monoliths, toward devices that think locally rather than systems that route everything through Ireland and back.

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Most enterprise IT teams spend the majority of their time fighting the same fires repeatedly. BMC Helix is building the AI system that handles those fires automatically, detecting anomalies, tracing root cause through millions of asset relationships, generating remediation plans, and learning from every incident it resolves.

Craig Smith sits down with Erhan Giral, VP of AI Strategy and Innovation at BMC Helix, and Ryan Manning, Chief Product Officer at BMC Helix, to explain how agentic AI is transforming IT service management from a reactive, human-driven process into something closer to a self-healing system, and why doing that at enterprise scale requires a fundamentally different architecture than most AI deployments attempt.

The most technically interesting part of this conversation is where BMC Helix is headed: building "gyms", synthetic data center environments where AI agents deliberately break things and learn to fix them overnight, 24 hours a day, generating the bespoke operational training data that text-based foundation models can no longer provide.

Erhan describes an architecture of specialized sub-agents, anomaly detection, log analysis, root cause analysis, remediation planning, that work in a hierarchy, passing hypotheses between each other until they converge on an answer, fine-tuned to reason the way a specific enterprise's best IT engineer would rather than the way a generic documentation page reads. For customers, the results are measurable: 25 to 50% cost reduction, fewer recurring outages, and IT staff who can finally go home at a predictable time rather than spending their nights firefighting problems that could have been prevented.

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Most companies think they're transforming with AI. They're not, and the gap between what they believe and what's actually happening on the ground is costing them far more than they realize.

In this episode, Craig Smith sits down with Chris Blackburn, founder and CEO of Liatrio, a consultancy that has spent a decade embedding directly inside large enterprises to help them actually change how they work, not just what tools they use.

The conversation opens with a striking data point: the average enterprise Blackburn works with operates at just 5 to 6% efficiency, meaning employees spend only three to three-and-a-half hours per week on work that genuinely creates value, compared to Toyota's benchmark of 70%. The core argument is that AI is being applied to the wrong part of the problem: individual productivity gains don't flow through to the bottom line if the organizational system around the individual - the approvals, handoffs, bureaucracy, and middle management layers - stays exactly the same.

Blackburn introduces a concept he calls "strangling the enterprise": rather than trying to transform a 5,500-person organization all at once, build a small, low-bureaucracy unit inside it that operates with radical autonomy, proves the model works, and expands outward. 

The episode closes with a frank conversation about what real transformation actually costs: roughly half of total compensation spend across the organization, sustained for two years, a number Blackburn describes as "absolutely insane" and one he believes most CFOs aren't yet prepared to confront.

Key Topics Covered:

●     Why the average enterprise operates at 5-6% efficiency, and what Toyota's 70% benchmark reveals about the scale of the opportunity AI could unlock

●     The critical distinction between individual productivity gains and system-level improvement, and why saving an hour doesn't automatically improve the bottom line

●     "Strangle the enterprise": how to build a small, autonomous AI-native unit inside a large organization rather than trying to transform the whole thing at once

●     Why most CEOs are dangerously disconnected from the actual work being done, and what McKinsey says about how much time they should be spending on transformation

●     What AI transformation actually costs: roughly half of total compensation spend, sustained over two years, and why most CFOs aren't ready for that number

●     Why AI isn't just changing jobs but changing life - from shorter work weeks to longer health spans - and what the farming analogy reveals about how slowly societies absorb new productivity

As enterprises pour money into AI tools while reporting little bottom-line impact, this conversation offers the most operationally honest account available of why that gap exists, and what organizations that actually want to close it need to be willing to do differently.

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Nobody has ever built a cell from scratch - assembled entirely from purified molecules on a shelf - that can feed itself, grow, and split into daughter cells through its own genetic activity. Until now. Dr. Kate Adamala, a synthetic biologist and a professor of genetics at the University of Minnesota, whose lab just published a landmark paper on what she calls "spud cells," joins Craig Smith to explain what her team built, why it matters, and what it will take to go from proof of concept to a platform that could eventually replace every molecule civilization currently extracts from petrochemicals. The conversation is as philosophically rich as it is technically specific: Adamala argues that life has no magic ingredient, and that the universe itself is predisposed to give rise to it. She describes the spud cell not as a mic drop but as biology's Sputnik moment: proof that you can escape the gravity well of evolution and build lifelike systems from the ground up.

The episode also covers the most alarming biosecurity question in synthetic biology right now: mirror life - cells built from mirror-image molecules that would be invisible to every immune system on earth and potentially capable of replicating uncontrollably in the environment. Adamala led the global call to pause all mirror life research in 2024, and she explains both why that research was so dangerous and why the spud cell doesn't move the field any closer to that red line. Craig also asks the question nobody else thought to ask: could AI now simulate the billions of years of molecular evolution that a primordial sea would need millions of years to complete - running a few trillion iterations computationally to find what emerges? Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.

Every company is creating content that nobody reads, nobody watches, and nobody remembers, and the CEO of the AI platform that 90% of Fortune 100 companies use to fix that just explained what comes next.

In this episode, Craig Smith sits down with Victor Riparbelli, co-founder and CEO of Synthesia, to discuss the $4 billion company that is now redefining what video communication means for the enterprise.

The conversation opens with the founding insight that still drives the company: AI is going to drive the marginal cost of creating video to zero, which changes not just how content is produced but who can produce it and for whom. Victor describes how Synthesia found its first real market not in Hollywood - which rejected the technology as too low quality - but in corporate trainers and educators who were comparing it not to a film but to a 10-page PDF no one was reading. The most forward-looking section of the conversation covers Synthesia's next product: moving video from a one-way broadcast into a two-way interactive conversation, where an AI avatar can conduct a real-time sales demo, simulate a customer for sales training, draw graphs on screen to explain pricing, and score whether the person on the other side actually understood the content. Victor also makes a sharp prediction about where AI entertainment will actually emerge, not in cinemas or on Netflix, but from film students with laptops posting 17-minute short films on Instagram, the same way synthesizers didn't replace pianos but created entirely new genres of music.

Key Topics Covered:

●     How Synthesia found its first real market: corporate trainers creating content nobody was reading, who compared AI video not to Hollywood but to a PDF, and found it vastly superior

●     The transition from one-way video broadcast to two-way interactive avatar conversations, and what that means for sales demos, corporate training, and education

●     Why Hollywood will be the last industry to adopt AI video, and why the first AI-generated entertainment will come from broke film students on Instagram, not studios

●     Why AI content won't replace real video, it will become its own genre, the same way synthesizers didn't replace guitars but created electronic music

●     How the CEO uses Claude daily for strategic thinking, playing devil's advocate, and replacing the long memo with a voice note

As AI video tools proliferate, this conversation offers one of the clearest frameworks for understanding where the technology is actually headed, not toward Hollywood, but toward transforming the way every company communicates internally and externally, with interactive AI avatars replacing the static website as the primary interface between a business and its customers.

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Companies are spending billions building AI factories, but most of them can't tell you why their AI workloads are failing, whether their GPUs are actually being used, or what their infrastructure is going to cost them when agents start running at scale. Paul Appleby, CEO of Virtana, joins Craig Smith to discuss the findings of their AI Factory Reality Check study, a research report that reveals a striking and underappreciated gap between the pace of AI infrastructure investment and the governance needed to run it safely and efficiently. Six in ten enterprises, the study found, cannot automatically identify root cause when an AI workload fails, a problem that compounds fast once you're running critical services on AI infrastructure at scale.

The conversation covers the mechanics of Virtana's observability platform, capturing 20,000 metrics per second across the entire AI stack, correlating them in real time, and increasingly using agentic capabilities to remediate failures automatically, but its most important insights are structural. Appleby makes a sharp observation that cuts through a lot of AI optimism: token costs are falling, but token consumption is exploding, meaning the total cost of running agentic AI systems is still going up even as the per-unit price drops. He also tracks a cultural shift inside enterprises - IT resilience reporting that used to happen annually now happens weekly - as evidence that technology risk has become a board-level conversation in a way it simply wasn't before. The result is a conversation that's less about the promise of AI and more about what it actually takes to make it work at production scale.

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AI is moving faster than enterprise security systems were designed to handle. In this episode of Eye on A.I., Craig Smith speaks with Bradon Rogers, Chief Customer Officer at Island, Island about how companies are struggling to govern the rise of AI agents, browser-based workflows, and unsanctioned AI tools inside the workplace.

The conversation explores why traditional "block-and-control" security models are breaking down and how a new approach, embedding policy directly into the browser and user workflows, may offer a path forward. It also dives into emerging risks like prompt injection and autonomous agent behavior, and why enterprises are increasingly becoming multi-AI environments by default.

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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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Eighty percent of lung cancer cases are diagnosed too late, not because the signals aren't there, but because nobody was looking at the right moment. Prashant Warier, co-founder and CEO of Qure.ai, joins Craig Smith to explain how his company is changing that using a tool most people already encounter: the routine chest X-ray. Cure's Lung Nodule Malignancy Risk Score - validated in the CREATE study - analyzes X-rays people get for unrelated reasons, identifies high-risk nodules, and flags which patients need follow-up CT scans. The result is a detection rate of 54 positive patients out of 100 flagged as high-risk, compared to the 2 out of 100 found by standard CT screening programs. That's not a marginal improvement. That's a different category of outcome.

The conversation covers the full landscape of where AI diagnostics actually stands today: the 15 million TB screening X-rays that Cure reads autonomously every year across 70 countries with no radiologist in the loop, because in many of those countries there are only two radiologists for the entire nation; the 26 FDA clearances and 200-plus published studies that underpin the company's clinical credibility; and the regulatory barriers that currently prevent patients from uploading their own scans and getting an AI read directly. Warier also makes his sharpest prediction: within 5 to 10 years, primary care will be AI-first, the first conversation you have when something feels wrong won't be with a doctor, it will be with an AI. Based on what Cure is already doing at scale today, that timeline is harder to dismiss than it might sound.

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Genpact surveyed 500 senior executives to understand why companies are investing in AI but not seeing the value, and what they found was both clarifying and uncomfortable. Sanjeev Vohra, Genpact's Chief Technology and Innovation Officer, joins Craig Smith to share the results: only 12% of companies qualify as genuine AI leaders, meaning they're deploying AI in production environments, generating measurable business outcomes, and have the governance systems in place to actually assess that value. The other 88% are somewhere between experimenting and stalled, and the most common culprit isn't the technology or the C-suite. It's what Vohra and his clients call the "frozen middle", the operationally stretched middle managers who are too busy to lead the transformation and too central to the business to be bypassed.

The conversation covers the full landscape of what separates leaders from the rest: why co-pilots are a stepping stone that most companies are mistaking for the destination; why 99% of enterprises have no real AI governance program even as agents begin to proliferate; how Genpact's own CEO writing code on a Friday afternoon became the most powerful AI adoption signal in the company; and why Vohra's sharpest piece of advice is also the simplest, progress over perfection, because the companies still waiting for a complete roadmap before they start have already fallen behind. His formula for what's coming: engineers who are 10 times more productive, business professionals who are 3 times more capable, and organizations that treat that as a baseline expectation, not a stretch goal.

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The Global AI Summit just happened in New Delhi, and the message from India was clear: this country is no longer just writing code for the rest of the world. It's becoming an architect of the global AI order. Ivana Bartoletti, Chief Privacy and AI Governance Officer at Wipro and Council of Europe advisor, joins Craig Smith to unpack what that shift actually means. Her frame is the sharpest line of the episode: Europe writes the rules, the US writes the checks, and India is writing the code, in 22 languages. But she's careful to add that the AI race isn't just a technical one. It's about institutional capacity, the ability to absorb AI capability and drive it into real applications that serve real people at scale.

The conversation ranges across the full landscape of AI's global moment: why companies that announced 100% AI replacement in customer service quietly had to rehire the humans they let go; why the popular narrative of "Europe regulates, America innovates" is a myth that doesn't survive contact with California's actual AI rules; and why India's strategic choice may prove to be the most durable positioning in a field where trust is becoming the scarcest resource. Bartoletti speaks from a genuinely rare vantage point: a European executive, sitting in Germany, working for an Indian company, advising the Council of Europe, watching the geopolitical AI order reorganize itself in real time.

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One company now has more AI agents deployed in its organization than it has human employees. Slack's CMO Ryan Gavin dropped that stat into a conversation with Craig Smith, and then immediately identified the secondary problem it creates: when your digital workforce outnumbers your human one, how do employees know which agent to call for which task? That orchestration problem, and the conversational interface that solves it, is what this episode is really about. Gavin describes Slack bot's transformation from a notification tool into what he calls the ChatGPT moment for the enterprise, an AI that doesn't just understand the internet, but understands your business, your team, your customers, and your company's entire conversational history, all the way back to day one.

The conversation covers the full arc of what this shift means in practice: a Salesforce executive walking into an unfamiliar meeting and being praised for their questions, because Slack bot had prepared them in minutes using the team's full history; a marketer who built his own data scientist agent over a weekend and is now completely unshackled from the bottleneck that was slowing him down; and Gavin's most honest admission, that he's been saying for years that AI won't replace jobs, but this is the first time he actually believes it, because the soul-crushing "work of work" is finally shrinking, and what's left is the kind of creative, high-energy output that people actually want to do. The inbox, he says, is a deathtrap in the AI era. The companies that figure out how to move beyond it will outperform their competitors by multiples.

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Zendesk went private two weeks before ChatGPT launched, and the moment it came out, it was obvious that customer service would never be the same again. Shashi Upadhyay, head of product, engineering, and AI at Zendesk, joins Craig Smith to explain what the company has built since: a self-improving AI system that doesn't just resolve tickets but learns from every failure, studies what the human did to fix it, and gets measurably better over time. He calls it the resolution learning loop, and for Zendesk's best customers, it's already resolving 70 to 90% of incoming tickets autonomously, up from the 10 to 20% that chatbots managed just a few years ago.

The conversation goes deep on the engineering decisions that actually matter: why hallucination is a feature, not a bug, and why the real challenge is knowing exactly when to switch from creative AI to deterministic code; why Zendesk acquired Forethought and what made their approach to going live in days rather than months so valuable; and why, despite all the momentum, Upadhyay estimates we are only about 5% through the adoption of AI in customer service. The bottleneck isn't the technology, it's the change management required to restructure how human and AI workforces operate together. His vision of the end state is striking: personal AI agents talking directly to enterprise AI agents, resolving 90% of issues instantly, while humans focus exclusively on the complex, high-value interactions that genuinely require them.

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AI agents can now connect to every tool your employees use. The problem is that connecting them and trusting them are two completely different things, and most enterprises have figured out the first without solving the second. Oren Michaels, co-founder and CEO of Barndoor AI, joins Craig Smith to explain why that gap is the defining challenge of the agentic enterprise era. His framework is simple and sharp: agents are like enthusiastic interns. They will absolutely do something when you ask them to. Whether it's what you intended is another matter, and when an agent can act across Salesforce, Slack, email, and calendar simultaneously, the blast radius of a misunderstood instruction is far larger than anything a human intern could cause.

The conversation covers the 100,000 agent problem - the reality that each agent handling a discrete task needs its own set of rules about what it's allowed to do, and that number scales to a size no human team can govern manually - and why traditional identity management systems were never built for the failure modes AI agents create. The new threat isn't bad actors getting in; it's authorized people using allowed tools with agents that still do the wrong thing. Barn Door's governance layer sits between the agent and the tools it can access, specifying exactly what each agent is permitted to do in each context, and Venn brings that same capability to individuals who want to understand what's possible before their organizations catch up. This is one of the most practically useful conversations available about what enterprise AI governance actually looks like.

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Every time you hit a phone tree or a chatbot with canned answers, you're experiencing the gap between what AI can already do and what most companies are still delivering. Craig Smith sits down with Tom Chen, Chief Product Officer at Aircall, to explore why that gap is closing fast, and what it means for any business that relies on voice as a customer communication channel. Tom makes a case that is both practical and counterintuitive: AI voice agents aren't better than your best human rep, but they are better than your average one. They never get frustrated. Their patience is infinite. Their tone never changes. And they can handle 100 concurrent calls at a fraction of the cost of a human operation, without lunch breaks, without bad days, and without going off script.

The conversation covers a finding that should change how any business thinks about AI adoption: when one of Aircall's customers gave callers the explicit choice between a human agent and a faster AI agent, far more people chose the AI than anyone expected, and satisfaction scores went up. Tom also identifies the real bottleneck that most businesses don't see coming: it's not the AI technology, which is increasingly commoditized. It's the tribal knowledge, the undocumented expertise that lives in the heads of long-tenured employees and never gets captured anywhere, that determines whether an AI agent performs well or not. Until that knowledge is surfaced, even the best voice agent will underperform.

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Most AI systems follow a gradient, a mathematical slope that tells them exactly how to improve, step by step, toward a known goal. Neuroevolution doesn't follow any gradient. Instead, it runs hundreds or thousands of competing solutions simultaneously, spreads them across the space of possibilities as broadly as possible, and lets the best ones recombine, the same logic that drives biological evolution. The result, as Risto Miikkulainen explains to Craig Smith, is creativity: solutions that no human designer would have anticipated, that emerge routinely from the evolutionary process.

Miikkulainen is a professor at UT Austin and VP of AI Research at Cognizant AI Labs, and he has been working on this field since the 1980s, which makes him both a historian of it and one of its most active frontiersmen.

The conversation covers a remarkable range: a mystery model that outperformed every competitor in a recent stock trading competition with forensic footprints pointing to neuroevolutionary AI; Sakana AI's system that autonomously designed experiments, wrote a paper, and had it accepted at a major machine learning conference; and a pandemic decision system that trained overnight and made country-specific recommendations by morning, with Iceland actually following some of them, all the way to the prime minister.

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One in four people over 65 will experience a fall, and for most of them, the technology designed to help is a device that hasn't meaningfully changed since the 1980s. Chia-Lin Simmons, CEO of LogicMark, joined Craig Smith to make the case that this gap is both unnecessary and solvable, and that AI is finally making it possible to shift personal safety from reactive to predictive. Her company's Freedom Alert Max doesn't just detect falls after they happen, it builds a personalized digital twin of each user, tracking steps, sleep patterns, and medication adherence over time to identify the subtle signs of health decline that even daily caregivers often miss.

The conversation is one of the most grounded and human discussions of applied AI you'll hear, covering why Apple Watch fall detection was engineered for crash detection, not elderly falls; why AI can flag a problem but a human needs to hear the breathing on the other end of the line; and why the 700,000 caregiver shortage in America makes technology like this not a luxury but a scaling mechanism. For anyone navigating aging parents, their own future, or the sandwich generation pressures in between, this episode is both practically useful and genuinely moving.

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Luiz Domingos has spent 25 years watching enterprise communications evolve, from IP telephony to cloud to AI, and his assessment of where things stand now is unusually concrete. Companies have moved past the strategy deck phase. AI is being embedded directly into contact centers, compliance workflows, and communication pipelines, and the question executives are asking has shifted from "which model is smartest" to "which deployment reduces friction and stays compliant." Domingos is direct about what gets in the way: you cannot pour AI into a legacy architecture and expect transformation, and cloud-only AI doesn't solve the latency or data sovereignty problems that regulated industries face every day.

In this conversation with Craig Smith, Domingos covers the practical mechanics of how Mitel is applying AI across its portfolio, from real-time transcription and sentiment analytics in contact centers, to agentic workflows that turn conversations into automated tickets and follow-ups. He draws a clear line between AI agents (which give recommendations) and agentic AI (which takes actions), a distinction the market consistently confuses. He also makes a prediction worth noting: within five years, voice will replace the traditional app interface as the primary way people interact with enterprise AI systems. For any CIO or CTO trying to move from experimentation to real ROI, his framework - start with workflow friction, not pilots - is the most actionable takeaway in the episode.

A fly with 100,000 neurons can fly, find food, and reproduce. A $100 million supercomputer cannot. Dr. Terry Sejnowski used that observation to silence a room full of MIT AI researchers in the 1980s, and it remains just as sharp today. Sejnowski is one of the foundational figures in the history of deep learning, co-inventor of the Boltzmann machine, and a professor at the Salk Institute who has spent his career studying both the brain and the machines we build to imitate it. In this conversation with Craig Smith, he turns that dual perspective on ChatGPT, and what he finds is something genuinely clarifying: not a human mind, not a threat to humanity, but an alien intelligence that has absorbed more knowledge than any brain ever could while remaining fundamentally empty when nobody is talking to it.

The conversation covers the full landscape of what current AI is missing - from goals and reinforcement learning to the constant self-generated flow of thought that defines consciousness - and why the word "understanding" is so ambiguous that even the world's top cognitive scientists can't agree on whether ChatGPT has it. Sejnowski also makes the case that hallucinations aren't a flaw to be engineered away but the flip side of creativity itself, that we are in a pre-Copernican era when it comes to understanding intelligence, and that the real future of AI lies not in scaling language models further but in looking at what nature has already solved, from field mice to fruit flies. His new book is written for the general public and available now.

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Your child's data profile doesn't start when they get their first phone. It starts before they're born, the moment a parent emails a gynecologist or visits a fertility clinic website. That's the core argument behind Born Private, Proton's new initiative that lets parents reserve an email address for their child at birth, anchoring their digital identity in a privacy-preserving ecosystem before the profiling machine gets started. Craig Smith sits down with Eamonn Maguire, Engineering Director, Machine Learning & AI at Proton, who has spent his career at the intersection of data, security, and visualization to explore what's really happening to our data and what, if anything, we can do about it.

The conversation covers the mechanics of how just three email sign-ups can allow Google to infer your age, politics, and religion; why OpenAI and Anthropic have shown "not much regard for the law" when it comes to training data and copyright; and why social media platforms are operating like unregulated gambling companies - engineering addiction with no structural incentive to stop. It's one of the most grounded, specific, and genuinely alarming conversations about digital privacy you'll hear, and it ends with a simple, actionable proposition: privacy should be a decision you make at birth, not a problem you try to solve after the damage is done.

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Training a frontier AI model today requires hundreds of thousands of GPUs, months of compute time, and a budget that only a handful of companies on earth can afford. Steffen Cruz, co-founder and CTO of Macrocosmos, thinks that model is about to break, and he's spending his time building what comes next. His project IOTA, operating within the BitTensor blockchain ecosystem, uses distributed training to split large language models across thousands of devices located around the world, coordinated by blockchain, and powered by surplus cheap energy wherever it exists. After nine months of research, the system can reproduce baseline benchmark performance using what Cruz calls "wonky vegetables" - unreliable, churning, globally distributed compute - and turn it into something indistinguishable from centralized training if you use the right approach.

The conversation with Craig Smith covers the mechanics of how this actually works, why the blockchain's role is far narrower and more practical than most people assume, and why the Mac mini stockpiling trend creates an unexpected supply of distributed compute that can earn passive income when idle. Cruz's target: a 70 billion parameter model by mid-2025, trained at 10-20% of what it would cost through a hyperscaler, and aimed squarely at the legal firms, hospitals, and cash-strapped startups that have been waiting to train their own sovereign models but couldn't afford the price tag.

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Errol Gardner has spent 35 years advising the world's largest organizations through major technology transitions, and his assessment of where enterprise agentic AI actually stands is one of the most grounded you'll hear anywhere. His number: less than 1 out of 10 on a maturity scale. Not because the technology isn't ready, but because deploying agentic AI across an organization doesn't tweak how it works, it requires rebuilding how it works. And that is a fundamentally different kind of challenge than anything the AI hype cycle is currently acknowledging.

In this conversation with Craig Smith, Gardner walks through why cloud adoption still hasn't reached 7 out of 10, what that means for agentic AI timelines, why the single biggest barrier to adoption is human resistance rather than technical limitation, and why governments will ultimately have to step in to manage workforce displacement at scale. He also raises a question that almost nobody is asking: is the value exchange between the technology sector and traditional industries sustainable in the long run? It's a conversation that doesn't just describe where AI is, it explains why the gap between the narrative and the reality has never been wider.

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IBM's VP of Quantum Systems, Oliver Dial, has spent his career building quantum computers from the ground up, and he's unusually direct about what they can and can't do. In this conversation with Craig Smith, Oliver Dial walks through where the field actually stands in 2026: quantum utility was achieved in 2023, quantum advantage is the target for this year, and a fully error-corrected machine capable of tackling the hard problems is on IBM's roadmap for 2029. That last milestone, Dial says, now feels both achievable and terrifying.

The episode is worth your time because Dial doesn't hype. He explains why IBM built a 1,000-qubit computer and then took it apart almost immediately, why Google's quantum advantage claims remain scientifically contested, and how a new error-correcting code IBM developed just reduced the qubit overhead required for fault-tolerant quantum computing by an order of magnitude. For anyone trying to understand what quantum computing will actually mean for their industry, and when, this is the clearest map of the road ahead available right now.

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Kris Lovejoy, Global Strategy Leader at Kyndryl, has spent her career at the intersection of IT infrastructure and security. Right now, she's one of the people enterprises call when they want to move from AI experimentation to real deployment. Her diagnosis is clear: agentic AI is a bullet train sitting on tracks built for 30 miles per hour. The technology is ready. Most organizations aren't, and the gap between a successful pilot and a production system running at scale is far wider than the hype suggests.

In this conversation with Craig Smith, Lovejoy walks through why IT service management is the smartest entry point for agentic adoption, how cost savings of up to 90% in that area can fund broader modernization, and why the security risks in agentic systems are less about sophisticated hackers and more about misconfiguration, bad context, and human error. She closes with a specific prediction: half of traditional IT administration tasks will be handled by AI agents by 2031, and a surprising take on who will actually thrive in the agentic era: not coders, but people trained to ask the right questions.

For anyone making decisions about AI adoption, this is the most practical conversation available right now.

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AI has fundamentally changed the cybersecurity threat landscape, not by inventing new attack types, but by collapsing the timeline. The same tools that make software developers more productive are now being used by attackers to move from vulnerability disclosure to active exploit in a matter of hours. That shift, argues Loris Degioanni, CTO and founder of Sysdig, changes everything about how defense needs to work.

In this episode, Craig Smith talks with Loris Degioanni about why human-centered security is becoming a structural liability, what "headless cloud security" means in practice, and why the coding agent (tools like Claude Code or Codex) may become the new operating system through which all enterprise security workflows run. It's a conversation about architecture, urgency, and what it actually means to fight a tank when you've been trained to use a baseball bat.

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What if the most competitive exam in the world is also the most destructive?

In this episode of Eye on AI, Craig Smith sits down with Professor Andrew Thangaraj, faculty at the Department of Electrical Engineering at IIT Madras, to explore how one of India's most prestigious institutions is quietly dismantling the system it helped build.

Andrew lays out the honest reality of higher education in India. Two and a half crore kids reach college age every year. Only 90 lakh make it to college. And the IITs, the most coveted institutions in the country, take just 17,000. The competition to reach those seats has become so extreme that students are losing their childhoods, their development is stunted, and even those who make it through are often unemployable because the system rewards knowledge over skills.

Andrew walks through exactly how IIT Madras is responding. A full, IIT-branded undergraduate degree in data science delivered entirely online for under five lakhs, roughly $5,000. No JEE required. No elite school background needed. Just a 10th standard foundation and the willingness to do the work. The program flips the traditional model, putting hands-on skills and real projects before theory, building in multiple exit points for students who need to start earning before they finish, and scaling to over 40,000 active students through a hybrid of faculty-recorded lectures, full-time instructors, and a remarkably active student community.

We also get into the bigger picture. Why India's AI talent gap is as much a culture problem as a numbers problem. Whether India can leapfrog into AI leadership the way China did after rebuilding its research ecosystem. Where AI tools are already being tested inside the program and where they still fall short. And how AI deployed in Indian languages, in agriculture, and in the courts could drive the kind of societal change that no corporate productivity tool ever will. 

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(00:00) Introduction and Andrew Thangaraj's Background

(01:29) India's Higher Education Bottleneck

(03:45) Designing a $5,000 IIT Degree 

(09:27) Why Graduates Still Lack Skills

(12:31) When the Program Started and How It Got Approved

(13:56) Program Structure, Diplomas and Multiple Exit Points

(17:52) Who the Program Reaches and Surprising Student Stories

(24:57) Older Students, Working Professionals and International Enrollment

(29:55) Can India Leapfrog in AI

(34:03) Data Centers, Power and Infrastructure Gaps 

(40:57) How Involved Are the IITs in India's AI Mission

(46:00) AI for Languages, Farms and Courts

What does the quantum industry actually look like right now, beneath all the hype?

In this episode of Eye on AI, Craig Smith sits down with Celia Merzbacher, Executive Director of the Quantum Economic Development Consortium (QED-C), to break down the real state of quantum technology in 2025. From market growth and enterprise readiness to the growing intersection with AI, Celia brings a grounded insider perspective on where the industry stands and what comes next.

Celia explains why the quantum market is growing faster than even the companies inside it predicted, with revenues rising roughly 27% year over year and actual numbers consistently beating forecasts. She also makes clear that the future is not quantum replacing classical computers. It is hybrid systems combining both to solve problems that simply cannot be solved today, with early use cases already emerging in pharmaceuticals, energy, finance, and defense. 

We also get into quantum sensing, the most underrated corner of the quantum world. From biomedical imaging already in clinical trials to quantum clocks powering GPS and financial transaction timestamping, sensing is already partially commercialized and quietly reshaping industries most people have never connected to quantum at all.

Finally, Celia addresses the AI question directly. Will AI replace quantum? No. The two are complementary. AI is already accelerating quantum hardware design and algorithm discovery, and quantum may eventually improve how AI systems are trained. She closes with a clear message for enterprise leaders: the transition to quantum will not be a migration. It will be a paradigm shift, and the time to start preparing is now. 

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Timestamp:

(00:00) Introduction: What Is QED-C and Why Does It Exist?

(01:57) Celia Merzbacher on Her Background and Role

(04:32) Annual Market Survey: How Fast Is Quantum Actually Growing?

(09:10) Where Quantum Revenue Is Coming From Today

(11:11) Timeline and the Race to Utility-Scale Quantum Computing

(13:23) Early Use Cases: Pharma, Energy, Finance and Hybrid Computing

(16:14) What Is Quantum Sensing and Why It Matters

(20:39) The Three Pillars: Hardware, Error Correction and Algorithms

(27:40) How Enterprises Should Start Preparing for Quantum Now (38:39) AI and Quantum: Allies Not Competitors

What if you could train a frontier AI model without building a single data centre?

In this episode of Eye on AI, Craig Smith sits down with Steffen Cruz, co-founder and CTO of Macrocosmos, to explore a radical alternative to the way AI models are built today. Instead of billion-dollar GPU warehouses, Steffen is training large language models using idle compute from devices distributed around the world, coordinated through the Bittensor blockchain.

Steffen breaks down why the centralised data centre model is heading toward a wall. Projects like Stargate and Colossus cost tens of billions of dollars, and as appetite for larger models grows, the economics simply stop making sense. He explains how distributed training flips this on its head, tapping into surplus energy, underutilised GPUs, and even consumer devices like Mac Minis to train models at a fraction of the cost.

We also get into IOTA, Macrocosmos's flagship technology, an orchestration layer that takes compute nodes scattered across the globe and makes them act like a single supercomputer. No single device runs the full model. Instead, each one carries a small slice, a technique called model parallelism, and together they can train frontier-scale models that would otherwise be out of reach for startups, researchers, and enterprises.

Finally, Steffen shares what he's building toward: 70 billion parameter models trained at 10 to 20 percent of centralised costs, a two-sided marketplace for compute, and a future where anyone with a spare GPU or Mac Mini can earn passive income while contributing to the democratisation of AI.

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Timestamp:

(00:00) Introduction: The Problem With Blockchain AI Projects

(06:39) Meet Steffen Cruz: From Subatomic Physics to Decentralised AI

(09:16) What Is a Bittensor? The Blockchain Built for AI

(11:53) How the Blockchain Actually Works: Registry, Clock, and Rewards

(15:08) Why Data Centres Are Hitting a Wall

(22:01) Distributed Training vs Federated Learning: What's the Difference?

(27:47) Train at Home: Turning Your Mac Mini Into a Passive Income Machine

(32:49) IOTA Explained: Building a Global Supercomputer From Spare Parts

(39:43) How the Network Scales: From 256 Nodes to Limitless Compute

(44:39) The Road Ahead: 70B Parameter Models and the Future of Affordable A

What if your child already has a data profile, and they haven't even been born yet?

In this episode of Eye on AI, Craig Smith sits down with Eamonn Maguire, Director of Engineering for AI and ML at Proton, to explore one of the most urgent and underappreciated questions in the age of AI: who owns your data, who is building a profile on you, and what can actually be done about it?

Eamonn brings a rare combination of depth and range to this conversation. With a PhD from Oxford, a postdoc at CERN, and years at Facebook engineering ML systems to detect internal and external threats, he now leads Proton's AI efforts, including Lumo, their end-to-end encrypted alternative to ChatGPT. He makes a compelling case that the surveillance economy is not just a privacy problem but a behavioral one, where the systems profiling you are not only observing who you are but actively shaping who you become.

We get into how just three data points are enough for advertisers to infer your age, political leanings, religion, and spending habits. We discuss why trusting mainstream AI platforms with sensitive data is a structural problem, not just a policy one, and why the AI labs with the best models got there by acquiring the most data, often with little regard for copyright law. Eamonn also breaks down the difference between truly open models and open washing, and explains how Proton builds AI that is genuinely private by design, with local indexing, encrypted memory, and user-controlled data sharing.

Then there is Born Private, Proton's initiative to give children a private digital identity from birth. It sounds simple on the surface, but the conversation it opens up is anything but. Data collection on your child begins before they are born, the moment a parent emails a gynecologist or a fertility clinic. Eamonn argues that until we start thinking about privacy the way we think about other rights, from the very beginning, the surveillance machine will always have a head start.

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Timestamp:

(00:00) Introduction and Meet Eamonn Maguire

(00:38) From Bioinformatics to CERN to Facebook: Eamonn's Career Arc

(05:23) How Proton Started in the CERN Cafeteria

(09:23) What Mainstream AI Platforms Actually Do With Your Data

(13:00) Copyright, Training Data, and Why Big Labs Can't Be Trusted

(15:10) Open Models vs Open Washing: What Truly Open AI Looks Like

(24:22) How Lumo Works: Encrypted Memory and No Data Leakage

(31:18) Born Private: Reserving a Private Email Address at Birth

(33:00) How Data Profiling Starts Before Your Child Is Born

(34:26) How Three Data Points Become a Complete Profile

(39:07) Molly Russell and the Consequences of Algorithmic Profiling

(53:55) The Full Proton Ecosystem: Mail, VPN, Drive, Lumo, and Workspace

What if the country that trains the world's engineers finally built the infrastructure to match its talent?

In this episode of Eye on AI, Craig Smith sits down with Amith Singhee, Director of IBM Research India and CTO of IBM India and South Asia, to explore where India actually stands in the global AI race and what it will take to close the gap.

Amith gives an honest, ground-level assessment of why India has been slow to compete. The talent has always been there. But until recently, the investment, the compute infrastructure, and the institutional intent hadn't come together in a sustained, coordinated way. That's changing, and Amith explains exactly what's different now.

He walks through IBM Research India's 27-year presence in the country, the research it's doing on foundation models, hybrid cloud AI deployment, agentic systems, and quantum computing. He also explains why building AI from India doesn't just help India. Working with less data, less compute, and more linguistic diversity forces better engineering and makes IBM's models more generalizable for the entire world.

We also get deep into the technical frontier. Why catastrophic forgetting is one of the key unsolved problems standing between current AI and anything more capable. How IBM is already shipping continual learning in practice through its COBOL modernization tools, helping enterprises decode decades of legacy code before the engineers who wrote it are gone. And why agentic AI, for all the hype, still has a mountain of unglamorous enterprise engineering left to climb before it becomes truly reliable.

Plus, what Amith would tell an 18-year-old engineer in India today about what skills will actually matter in an AI-driven world.

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

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(00:00) Introduction and Amith Singhee's Background 

(06:26) Why IBM Set Up Research in India 

(11:45) Can India Compete in AI 

(15:18) How IBM Collaborates With Indian Universities 

(19:25) Why India Has Been Slow in AI 

(24:50) IBM's Hybrid Cloud AI Research Focus 

(27:34) How Data Scarcity in India Makes Better AI 

(31:18) Fine-Tuning Models Without Losing General Knowledge 

(35:03) Continual Learning and Catastrophic Forgetting 

(38:25) COBOL and Legacy Code Modernization 

(42:11) Agentic AI Hype vs Enterprise Reality 

(48:09) What Young Engineers Should Study Today 

What does it actually take to prove that AI delivers real value in the industries that keep the world running?

In this episode of Eye on AI, Craig Smith sits down with Debdas Sen, CEO of TCG Digital and Joint Managing Director of Lummus Digital, to explore what serious enterprise AI looks like when it is applied to some of the most complex, high-stakes problems on the planet. Problems like compressing years of catalyst research into weeks, predicting refinery failures before they happen, and accelerating drug development timelines that could determine how long a life-saving medicine takes to reach patients.

Debdas has spent nearly 30 years in data and AI, living through every hype cycle from the data warehousing era of 1997 to today's agentic revolution. He makes a compelling case that the AI community has one defining job right now: prove the ROI, or risk another AI winter.

We also get into what makes TCG Digital's platform mcube™ different. It is not a horizontal tool. It is a domain-first, agentic AI ecosystem built for the kinds of massive, multi-variable problems that horizontal platforms cannot touch. Debdas breaks down how mcube™ bridges legacy enterprise infrastructure with cutting-edge agentic systems, why hybrid modeling beats pure AI in energy and life sciences, and how the platform keeps private enterprise data protected while still drawing on the best of what public LLMs have to offer.

Finally, Debdas shares where he sees the industry heading next, a future where agents from different providers can reason together in a neutral space, where inference and reasoning keep improving, and where the companies that go deepest into domain will pull furthest ahead.

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TCG Digital Website: https://www.tcgdigital.com/

TCG Digital on LinkedIn: https://www.linkedin.com/company/tcgdigital/ 

(00:00) Introduction and Meet Debdas Sen

(01:30) 30 Years in Data and AI: From Data Warehousing to Agentic Systems

(03:02) What TCG Digital Actually Does (04:32) Inside mcube™: How the Platform Works

(10:06) Domain vs Horizontal: Why Specificity Wins in Enterprise AI

(18:29) Catalyst R&D: Collapsing 12 Months of Research Into One

(30:38) Predicting Plant Failures Before They Happen

(36:51) Solving the Trust and Hallucination Problem in Enterprise AI

(44:51) The Six-Layer Architecture of mcube™

(47:05) What Is Genuinely New About Agentic AI

(49:22) What Young People Should Study to Work in Serious AI

(53:14) Velocity to Value: Why ROI Must Be Tracked From Day One

What if the country that produces the world's top AI talent finally figured out how to keep it?

In this episode of Eye on AI, Craig Smith sits down with Professor Mausam, one of India's leading AI researchers, AAAI Fellow, and founding head of the Yardi School of Artificial Intelligence at IIT Delhi, to get an honest and unflinching diagnosis of why India has fallen so far behind the US and China in artificial intelligence and what it will actually take to close that gap.

Mausam breaks down the structural story behind India's deficit. A pipeline of world-class students that gets exported abroad the moment it graduates. A professor shortage so severe that IIT Delhi's entire School of AI has hired only five new faculty members in five years. A government AI mission with the right instincts but not enough speed or boldness. And a brain drain made worse by the very thing India is proud of, its English fluency, which makes its talent the easiest in the world to absorb and the hardest to bring back.

Mausam walks through the full picture. How China built its research dominance not through students but through aggressively repatriating senior researchers with real salaries, real lab resources, and real authority to build research cultures from scratch. Why the AlexNet moment in 2012 was actually an equalizer that gave China's fledgling ecosystem a surprise advantage over more established Western research groups. How India's JEE coaching culture and IIT bottleneck are symptoms of a scarcity of quality institutions rather than a broken exam. What the government's AI mission is getting right on compute, data, and sectoral focus, and where the critical gaps remain. And why Mausam believes that bringing one hundred top professors back to India would do more for the country's AI future than any single government program or funding initiative.

We also get into the harder questions. Whether AI degrees belong at the undergraduate level or should sit on top of a computer science foundation. Why Mausam no longer holds an optimistic view on AI's impact on software jobs and why he thinks Geoff Hinton's point about plumbers has merit. And what it would actually take for a democracy of 1.4 billion people to stop training the world's AI leaders and start keeping them.

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(00:00) Introduction: India's AI Gap and Professor Mausam's Background
(02:30) Building the Yardi School of AI at IIT Delhi
(07:44) How Far China Has Pulled Ahead in AI Research
(12:55) Why India Could Not Follow China's Playbook
(29:18) The JEE System, Coaching Culture, and the IIT Bottleneck
(30:37) AI Degrees, Job Market Realities, and the Future of Work
(44:18) The Real Problem Is Professors, Not Students
(48:07) Big Tech Labs in India: Helpful but Not at Scale
(51:46) The Government AI Mission: Progress and Gaps
(55:20) The Compute and Data Infrastructure Problem
(59:54) Can India Close the Gap Before It Is Too Late 

Why IBM Is Betting Everything on Small AI Models

In this episode of Eye on AI, Craig Smith sits down with Sriram Raghavan, Vice President of AI at IBM Research, to explore one of the most important debates in enterprise AI right now. Do you actually need a massive model to get world class results? IBM's answer is no, and Sriram breaks down exactly why.

Sriram explains why IBM chose to train its Granite models directly using reinforcement learning rather than distilling from larger models like most of the industry. The reason goes beyond performance. It comes down to data lineage, safety alignment, and a belief that small, efficient models are the only sustainable path for enterprises running AI across hybrid cloud environments.

We get into the full technical stack behind that bet. How data quality has replaced model size as the real competitive advantage. Why parameter count is becoming the wrong metric entirely. How IBM's inference time scaling techniques allow an 8 billion parameter model to match the performance of GPT-4o and Claude 3.5 on code and math benchmarks. And why IBM is pioneering a new concept called Generative Computing, which treats AI models not as prompt receivers but as programmable computing elements with runtimes, modular LoRA adapters, and proper programming abstractions.

Sriram also shares where IBM Research is headed next, including breakthroughs in continuous learning, agent orchestration, and making unstructured enterprise data actually usable at scale.

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(00:00) Why IBM Skips Distillation and Trains Small Models Directly 

(04:50) Did We Even Need Giant AI Models in the First Place?

(08:12) How Data Quality Became the New Competitive Moat

(11:54) Why Parameter Count Is the Wrong Way to Measure a Model

(15:36) Reinforcement Learning Without Losing Broad Capabilities

(22:05) Inference Time Scaling: Getting Big Model Results From Small Models

(28:12) Generative Computing: Treating AI as a Programming Element

(36:40) Why IBM Open Sources and How Small Models Make It Sustainable

(41:25) The Path to Continuous Learning Without Rewriting Weights

(51:00) IBM's Full Roadmap: Models, Data, and Agents

What if the country that trained the world's engineers finally decided to keep them?

In this episode of Eye on AI, Craig Smith sits down with Abhishek, the civil servant leading India's $1.2 billion national AI Mission, to explore how one of the world's largest and most diverse nations is mounting a serious challenge to US and Chinese dominance in artificial intelligence.

Abhishek breaks down the honest story behind India's late start. World-class talent, but no research ecosystem to retain it. Digitization without AI-usable data. Compute so scarce that the entire country had fewer than 500 GPUs just two years ago. And a brain drain so severe that the engineers India trained are now running the biggest tech companies in the world, just not from India.

Abhishek walks through exactly how the mission is tackling each of those gaps. A subsidized compute program that gives researchers and startups access to 38,000 GPUs at under a dollar per hour. AI Kosh, a national data platform pulling public and private sector datasets into a single AI-ready repository. Centers of Excellence connecting IITs around domain-specific research in agriculture, healthcare, education and mobility. And a sovereign LLM program, with four models already in development and eight more on the way, built specifically for India's languages, voices and needs.

We also get into the geopolitics. Where India stands as the US and China carve out competing AI spheres of influence. Why Abhishek is pushing for a UN-led governance framework rather than aligning with either bloc. And what it would actually take for a country of 1.4 billion people to not just catch up, but leapfrog.

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(00:00) Introduction and Abhishek's Background 

(02:43) What the India AI Mission Is and How It Started 

(04:53) The $1.2 Billion Budget and Total AI Investment in India 

(06:36) Data Center Build-Out and the Road to 7 Gigawatts 

(08:11) AI Kosh: India's National Data Platform 

(10:50) Subsidized GPUs and How Researchers Access Compute 

(12:41) Brain Drain, Reverse Migration and Retaining Top Talent 

(17:24) Centers of Excellence Across IITs and Key Sectors 

(19:21) Expanding Fellowships and Training the Next Generation 

(20:11) Why India Started Late and What Changed 

(21:48) Sovereign LLMs Built for Indian Languages and Needs 

(22:42) The Diversity Challenge and Culturally Relevant AI 

(23:16) Government Funding for Foundation Model Development 

(24:12) The AI Impact Summit and India's Role on the Global Stage 

(24:52) India, China, the US and the Battle for AI Governance 

(29:37) The UN Framework and India's Third Way 

(31:16) India-China Relations and New AI Partnerships 

(32:01) How the $1.2 Billion Budget Was Decided 

(33:31) Can India Actually Catch Up With the US and China

Most enterprises are excited about agentic AI. But very few are actually deploying it in production.

In this episode of Eye on AI, Craig Smith sits down with Adi Kuruganti, Chief AI and Development Officer at Automation Anywhere, to break down why agentic AI is so hard to get right in the enterprise and what it actually takes to move from a promising pilot to a mission-critical deployment.

Adi explains why the future of enterprise automation is not agentic AI alone, but the combination of deterministic and agentic systems working together, and why companies that treat AI as a technology problem instead of a business outcomes problem are setting themselves up to fail.

They dig into how Automation Anywhere is orchestrating agents across legacy systems, healthcare platforms, and financial services workflows, why governance and compliance are the first questions every enterprise asks, and how their Process Reasoning Engine is continuously improving agent performance using metadata from over 400 million running processes.

The conversation also covers the real timeline to a fully autonomous enterprise, why the POC to production gap is the biggest failure point in enterprise AI today, and what companies that wait too long risk losing to competitors who started the journey earlier.

If you want to understand where enterprise AI actually stands today and what it takes to deploy it responsibly at scale, this episode gives you a clear and grounded perspective.

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(00:00) Why Enterprises Are Struggling With Agentic AI

(02:39) What Automation Anywhere Does and the APA Category Explained

(08:01) Deterministic vs Agentic AI: Why You Need Both

(10:59) How Human in the Loop Works in Enterprise AI

(17:16) The Mozart Orchestrator and Process Reasoning Engine

(23:50) How AI Is Upgrading and Replacing Classic RPA

(27:31) How Automation Anywhere Works With Enterprise Customers

(31:53) The Biggest Challenges of Scaling Agentic AI

(41:10) The OpenAI Partnership and What It Means

(47:06) Training Staff and Building AI Literacy at Scale

(51:39) Staying Close to Customers as the Technology Shifts

(53:17) Is the Autonomous Enterprise Actually Coming

What happens when AI writes code faster than anyone can test it?

In this episode of Eye on AI, Craig Smith sits down with Dan Faulkner, CEO of SmartBear, to explore one of the most underappreciated risks of the AI coding boom. As tools like Claude Code and Codex push software development to unprecedented speed, the systems built to validate that software are being left behind. Dan makes a distinction that every engineering leader needs to hear: clean code passing unit tests is not the same as an application that actually works.

Dan introduces the concept of application integrity, continuous and measurable assurance that your software does everything it was intended to do and nothing it was not. He explains why the gap between what AI builds and what teams actually validate is already creating hidden risk in production, and why that risk compounds the faster you ship.

We also get into the new failure modes that agentic AI is introducing. Slop squatting, instruction inversion, cascading errors. These are not theoretical. They are happening now, at scale, in codebases that no human has fully read.

Dan also walks through SmartBear's autonomy ladder framework and their newest product BearQ, a team of AI agents that explores your application, builds a knowledge graph, authors tests, runs them, and updates everything as your app evolves. The key distinction: it is built to augment human teams, not replace them.

Finally, Dan shares his honest take on the future of software engineering. The fallacy was always that coding was the hard part. The hard part is knowing what to build. That skill is not going anywhere.

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(00:00) Introduction and Dan Faulkner's Background 

(01:05) What SmartBear Does: Testing and API Lifecycle Management 

(03:27) AI Is Outpacing Application Testing 

(07:51) Slop Squatting, Instruction Inversion and New AI Failure Modes 

(17:31) Black Boxes, Technical Debt and the Expertise Crisis 

(22:00) How to Avoid Self-Validating AI Systems 

(24:11) The Autonomy Ladder and BearQ 

(31:30) Why Testing Must Be Continuous and Everywhere 

(36:31) Infrastructure Risk and Automation Bias 

(44:11) The Future of QA and New Specialist Roles 

(50:44) How Teams Use SmartBear Tools Today 

(58:57) The Future of Software Engineering and Human Roles

This episode is sponsored by Modulate.
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What does it actually mean to build a foundation model for robots?

In this episode of Eye on AI, Craig Smith sits down with Sergey Levine, co-founder of Physical Intelligence and professor at UC Berkeley, to explore a fundamentally different approach to building robots, one inspired not by programming a single perfect machine, but by training AI on the broadest and most diverse data possible so robots can learn, adapt, and operate in the unpredictable real world.

Sergey explains why the secret to general-purpose robots isn't perfecting one single machine, but training on massive, diverse data from all kinds of robots and even humans. The more variety the model sees, the better it gets. Just like ChatGPT learned from all the text on the internet, robotic foundation models learn from every robot that has ever moved, grabbed, or interacted with the real world.

We also get into the big humanoid robot debate. Are they the future, or is it mostly hype? Sergey gives an honest and technical take on why the form factor conversation is changing now that foundation models exist, and why that actually opens the door for more creativity, not less.

Finally, Sergey shares what he's most excited about next, building a true data flywheel where robots get smarter the more they are deployed, creating a continuous learning cycle that could change everything.

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(00:00) Introduction: What Are Foundation Models for Robots?

(01:44) Meet Sergey Levine: Physical Intelligence and UC Berkeley

(02:51) Breaking Down Foundation Models for Non-Technical People

(06:46) Why Real World Data Beats Simulation

(15:00) Building a Broad Robotics Foundation From Scratch

(24:00) The Open World Problem in Robotics

(40:00) Generalist vs Specialist Robots: Which Wins?

(47:00) Humanoid Robots: Real Innovation or Just Hype?

(55:10) The Future: Continuous Learning and the Data Flywheel

(56:23) Guilty Pleasure: Sci Fi and Thinking Beyond the Limits

AI has been trained like software.

But what if it should be grown like life?

In this episode of Eye on AI, Craig Smith sits down with Sebastian Risi, professor and leading researcher in neuroevolution and artificial life, to explore a fundamentally different approach to building intelligence, one inspired by how nature evolves, grows, and adapts.

Sebastian explains why traditional AI systems are limited by fixed architectures and one-time training, and how evolutionary algorithms can create systems that continuously learn, self-organize, and even grow their own neural structures over time.

They dive into concepts like plastic neural networks that keep updating during their lifetime, AI systems that can recover from damage, and models that develop from a single "cell" into complex structures, similar to biological organisms.

The conversation also explores how combining large language models with evolutionary search could unlock more creative and open-ended problem solving, from merging specialized models to building AI systems capable of generating and testing scientific ideas.

If you want to understand where AI is headed beyond today's transformer models, and why the future may look more like living systems than software, this episode offers a clear and thought-provoking perspective.

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(00:00) Why copy nature's evolution for AI

(01:20) What neuroevolution actually means

(05:52) How evolutionary search replaces gradients

(08:03) Plastic neural networks and continuous learning

(11:53) Growing neural networks like living systems

(18:08) Scaling challenges and limits of growth

(23:16) Can evolving systems replace LLM training

(27:28) Continual learning and model merging

(30:27) Artificial life, self-repair, and resilience

(35:10) AI scientists and evolution with LLMs

Quantum computing has been "5 years away" for decades.

So what's actually holding it back?

In this episode of Eye on AI, Craig Smith sits down with Izhar Medalsy, Co-founder & CEO of Quantum Elements, to break down the real bottleneck in quantum computing today and why the future of the industry may depend more on classical systems and AI than quantum hardware itself.

Izhar explains how digital twins of quantum systems are being used to simulate real hardware, generate massive amounts of training data, and solve one of the biggest challenges in the field: noise and error correction.

They dive into how his team improved Shor's Algorithm from 80% to 99% accuracy on IBM hardware, without changing the hardware itself, and what that means for the future of quantum performance.

The conversation also explores how AI is being used to optimise quantum systems, why classical computing will continue to play a central role in quantum development, and what milestones to watch as the industry moves closer to real-world applications.

If you want to understand where quantum computing actually stands today and what will unlock its next phase, this episode gives you a clear, grounded perspective.

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(00:00) The 99% Accuracy Breakthrough (Quantum's Turning Point) 
(01:03) Why Quantum Hardware Alone Isn't Enough 
(03:50) Digital Twins Explained (The Missing Layer) 
(08:09) The Real Problem: Noise, Instability & Environment 
(15:43) From 80% to 99% on Shor's Algorithm 
(26:36) How AI Is Fixing Quantum's Biggest Bottleneck 
(33:53) Inside the Platform: From Circuit to Optimization 
(40:51) Logical Qubits & Scaling Quantum Systems 
(43:34) The Limits of Simulation vs Real Quantum Hardware 
(54:29) When Quantum Becomes Useful (Real Timeline)