Eye on A.I. is a biweekly podcast, hosted by longtime New York Times correspondent Craig S. Smith. In each episode, Craig will talk to people making a difference in artificial intelligence. The podcast aims to put incremental advances into a broader context and consider the global implications of the developing technology. AI is about to change your world, so pay attention.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI. Craig Smith on X: https://x.com/craigss EYE On A.I. on X: https://x.com/EyeOn_AI Connect with Chris Blackburn LinkedIn: https://www.linkedin.com/in/chrisblackburn
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI. Craig Smith on X: https://x.com/craigss EYE On A.I. on X: https://x.com/EyeOn_AI Connect with Victor Riparbelli LinkedIn: https://uk.linkedin.com/in/victorriparbelli
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI. Craig Smith on X: https://x.com/craigss EYE On A.I. on X: https://x.com/EyeOn_AI Connect with Victor Riparbelli LinkedIn: https://uk.linkedin.com/in/victorriparbelli
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.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.v
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
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. Subscribe to Eye on A.I. for weekly conversations with the people building and deploying the future of AI.
