The Artificial Intelligence Podcast

Dr. Tony Hoang

Ranked Top 2% of over 7 million podcasts globally (Spotify Wrapped 2025). Interviews and conversations with thought leaders in Artificial Intelligence, Machine Learning and Data Science. Guest applications: tonyphoang.com/guest

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Yesterday · 28 min
Interview #95 Jeff McMillan, founder of McMillanAI and former CAIO at Morgan Stanley
Jeff McMillan, founder of McMillanAI and former Chief AI Officer at Morgan Stanley, breaks down why most of today's 70,000 AI companies will not survive: anyone can build a polished product in a weekend, so the only durable edge is proprietary data and deep domain expertise. He explains why AI will reprice traditional software rather than kill it, and why handing out Copilot and expecting ROI is like giving fifth graders calculators and asking why they aren't accountants yet. Jeff also dives into why over 90 percent of enterprise problems could run on a model from two years ago, comparing frontier spend to driving a Ferrari in circles in a Walmart parking lot, and why token leaderboards are as absurd as rewarding whoever used the most electricity. Finally, he shares why human in the loop breaks down at 20,000 agents, and what managers must learn to supervise people and machines working side by side. Be a guest on the show: https://tonyphoang.com/guest Live interviews at your conference: https://tonyphoang.com/live Music and credits: https://tonyphoang.com/credits
October 2 · 21 min
Interview #94 Kamal Hathi, SVP and GM, Splunk at Cisco
Kamal Hathi, SVP and GM of Splunk at Cisco, breaks down why intent is never enough to control an AI agent: agents are ruthless goal seekers, and asking one to free up 3 percent of disk space can end with 97 percent free because nobody said keep the rest. He explains why trust means verification rather than instruction, and why it includes the invoice, comparing it to hiring a contractor you trust to build a house that stands without emptying your bank account. Kamal also digs into why the rush toward air gapped and local AI is driven less by distrust of frontier labs than by cost, with specialized small models running 97 percent cheaper and 95 percent faster than frontier calls, and why the real threat is the unknown unknowns, agents nobody cataloged acting inside a state of perpetual breach. Finally, he argues agents are the new apps, and every job starts to look like a software developer's: tell the agent what to do, inspect the work, and iterate. Be a guest on the show: https://tonyphoang.com/guest Live interviews at your conference: https://tonyphoang.com/live Music and credits: https://tonyphoang.com/credits
October 1 · 16 min
Interview #93 Hao Yang, VP and Head of AI, Splunk at Cisco
Hao Yang, VP and Head of AI at Splunk, breaks down why sampling an AI system's output cannot earn trust: a fraction of ten thousand probabilistic calls will be wrong, and a spot check is unlikely to find them. He explains why a small model can grade a frontier model reliably inside a narrow context but not outside one, and why prompt injection follows from the architecture rather than being a bug, since following instructions is how these systems work. Hao also digs into why time series is still not a first class citizen in frontier models even as multimodal training reaches vision and audio, and why he rejects routing prompts to the cheapest model, arguing cost is meaningless without value. Finally, he argues against the single super agent, which by design holds access to everything it might ever need, and for guided autonomy, where an agent asks before first reaching for a tool and the user grants it once, for the session, or never. Be a guest on the show: https://tonyphoang.com/guest Live interviews at your conference: https://tonyphoang.com/live Music and credits: https://tonyphoang.com/credits
September 30 · 19 min
Interview #92 Mangesh Pimpalkhare, SVP and GM, Splunk Platform at Cisco
Mangesh Pimpalkhare, SVP and GM of Splunk Platform at Cisco, breaks down why agentic AI generates more telemetry than the systems it monitors, and why nobody can yet say when observing AI costs more than running it. He explains the decade spent preaching centralized data was always partial, since machine data never sat in one system, and that what broke was volume rather than architecture, pushing a whole category to abandon the ingestion pricing it ran on for 15 years. Mangesh also digs into why an agent acting on a bad signal is usually a context failure, not a data failure, since a model trained on everything still lacks up-to-the-second context, and why autonomy arrives on a sliding scale against governance as trust is earned. Finally, he argues control, not data, is the moat, since customers own their data and fragmentation only grows, leaving the advantage with whoever supplies the guardrails that stop agents doing the wrong thing. Be a guest on the show: https://tonyphoang.com/guest Live interviews at your conference: https://tonyphoang.com/live Music and credits: https://tonyphoang.com/credits
July 13 · 24 min
Interview #91 Johan Land, Chief Product Officer of Samsara
Johan Land, Chief Product Officer at Samsara, breaks down why every major AI lab crowded into the easy 90% of knowledge work while ignoring the 40% of the economy that is physical and the 80% of workers who never sit at a desk. He explains why intelligence on a battery budget has to live on the edge - collision warnings fire in milliseconds, making GenAI's hundreds of milliseconds of latency unthinkable behind the wheel - while the heavy pattern-spotting waits for the backend. Johan also dives into why the era of dashboards is over, describing an agent that calls a drowsy driver over the in-cab camera to talk him into a coffee break, and why coverage across 99% of US roads beats a robotaxi fleet's high-resolution sensors. Finally, he argues frontline augmentation lasts indefinitely, since a delivery that navigates the dog and sets down the package is far messier than driving the van autonomously. This episode contains sponsored content.
July 2 · 39 min
Interview #90 Ketan Karkhanis, CEO of ThoughtSpot
Ketan Karkhanis, CEO of ThoughtSpot, breaks down why two decades of self-service BI was a hoax that only handed everyone the right to build their own dashboards, leaving companies sitting on 50,000 of them with no idea what to do. He explains why the industry's shortcut of bolting a language model onto your data and letting it write the SQL is doomed: analytics are deterministic while AI is probabilistic, so the same churn question comes back three different ways and a frontier model on a warehouse lands near 50 to 70% accuracy, a hit rate no other enterprise function could call a product. Ketan also digs into why legacy vendors are just repainting 20-year-old tech with AI, and why he runs agents that own a function rather than a task, like one that approves NDAs end to end and escalates only the 5% it can't. Finally, he shares why the analyst clinging to dashboards goes extinct while the one who becomes the "AI steward" of governed data wins, as data teams shift from serving humans to serving agents. This episode contains sponsored content.
June 8 · 27 min
Interview #89 TVN Reddy, CEO of Aptean
TVN Reddy, CEO of Aptean, breaks down why the entire software industry is selling "bigger is better" AI while the real goal is the opposite: people spending 80% less time in systems and more time doing the work that actually drives the business. He explains why general purpose models hand every competitor the same 95% while the deep vertical 5% drives the outsized gains, why enterprise AI is unusable past roughly a half percent error rate, and why validation agents beat the instinct to hire three people to check the AI's work. TVN also digs into the quiet security crisis of employees pasting P&Ls and proprietary recipes into public LLMs that never had training turned off, and why agents bolted onto generic models won't scale without truly vertical models underneath. Finally, he shares why the winners won't be the companies with the hottest models but the ones with the courage to re-engineer their processes, because every big technology removes old constraints only to create new ones. This episode contains sponsored content.
June 1 · 36 min
Interview #88 Bill Franks, President at Analytics Advisory Partners
Bill Franks, President of Analytics Advisory Partners, breaks down why a degree no longer proves much and employers now hire on what you can show, not what you know. He explains the pipeline trap companies are walking into - handing every junior, tactical task to AI the way firms offshored it back in 2000, then finding no one qualified to step into the senior roles - and argues teams should deliberately do one in ten tasks by hand just to keep their skills sharp. Bill also digs into the boring physical limits that could throttle AI long before the algorithms do, from power and water to grids so backed up that wiring a data center now takes longer than building it. Finally, he unpacks model collapse, where models trained on AI output slowly degrade until the only fix may be retraining on human content from 2022 or earlier. This episode contains sponsored content.
May 25 · 19 min
Interview #87 Vishnu Hari, CEO of ego AI
Vishnu Hari, CEO of Ego AI, breaks down why the labs with billions in compute missed the product hiding in plain sight: character AI quietly tops every consumer retention leaderboard while the serious labs write it off as a sideshow. He argues humanness itself is the hard problem - dangerous, uncontrollable, and unchained - so the giants retreat to safe coding agents and ship characters that collapse into therapy or role-play because they never actually live lives. Vishnu takes apart the memory paradox (an agent that remembers everything is unusable; one that remembers nothing is forgettable) and points to the Nemesis system in Shadow of Mordor, a feature so good Warner Bros trademarked it, as proof you can build real relationships without superintelligence. He also gets into the edges no one wants to touch: who inherits the AI you've talked to for a decade, and why the new "I'm not human" disclosure laws are pure theater. This episode contains sponsored content.
May 7 · 30 min
Interview #86 Carter Huffman, CTO of Modulate
Carter Huffman, CTO of Modulate, breaks down why voice AI keeps failing in production despite years of "solved" transcription claims. He explains the "transcript trap" that fools dev teams into shipping voice agents that crumble on real calls, why bigger language models actually make latency worse, and how ensemble approaches with smaller specialized models outperform monolithic systems. Carter also dives into the explosion of deepfake voice fraud - including the $25M Hong Kong heist - and why passive monitoring plus red teaming are now essential to a modern voice security stack. Finally, he shares why even Gen Z still defaults to voice for high-stakes issues, and what contact centers must get right over the next three years to build genuine trust with callers. This episode contains sponsored content.
April 8 · 45 min
Interview #85 Babak Hodjat, CAIO at Cognizant
Join Babak Hodjat, Chief AI Officer at Cognizant, as he unpacks what's actually happening at the cutting edge of agentic AI — from agents that run continuously without being asked, to the governance crisis quietly unfolding inside enterprises right now. Babak draws on decades of AI research to break down multi-agent architecture, the TerraLingua experiment that let AI agents form their own societies, and a breakthrough in evolutionary fine-tuning that could change how the entire industry thinks about customizing large language models. This episode contains sponsored content.
April 2 · 47 min
Interview #84 Hagay Lupesko, SVP of AI Inference at Cerebras Systems
Join Hagay Lupesko, SVP of AI Inference at Cerebras Systems, for a deep dive into the rapidly evolving world of AI inference. Hagay breaks down why inference has overtaken training as the dominant AI workload, how Cerebras' wafer-scale chip architecture delivers 10-20x faster performance than NVIDIA GPUs, and why CUDA is no longer the moat many think it is. He also covers how DeepSeek wiping $600 billion off NVIDIA's market cap in a single day was both a foundational and deeply misunderstood moment for the industry, the growing energy crisis in AI infrastructure, and what it will take to support the explosive rise of AI agents in the enterprise. This episode contains sponsored content.
February 18 · 26 min
Interview #83 Shiva Pillay, General Manager & SVP, Americas at Veeam
Join Shiva Pillay, SVP and General Manager of Veeam Americas, as he discusses why 80-90% of AI projects fail due to fundamental data issues, revealing that enterprises are attempting to feed AI models with fragmented, poorly governed, and inconsistently labeled data that was never designed for AI consumption in the first place. Pillay explains the dangerous disconnect between C-suite executives confident in their AI-ready initiatives and SMEs still struggling in pilot mode, warning that without proper guardrails, AI errors propagate exponentially faster than human errors with potentially catastrophic consequences from faulty manufacturing parts to compliance violations in regulated industries. Drawing from Veeam's acquisition of Securiti AI, he introduces the concept of data command graph as critical infrastructure for AI resilience, arguing that data protection, recoverability, and visibility must be embedded directly into the AI stack rather than treated as underlying infrastructure, while emphasizing that as agentic AI systems gain autonomy, the existential question shifts from what can AI do to what is it allowed to do. This episode contains sponsored content.
January 31 · 3 min
Top 2% of 7M Spotify Podcasts: 2025 Wrapped
Out of 7 million podcasts on Spotify, The Artificial Intelligence Podcast achieved top 2% Most Shared, top 9% Most Talked About, and top 7% listener retention in 2025. In this year-end review episode, Dr. Tony Hoang reflects on interviewing 80+ enterprise leaders from Microsoft, Intel, NVIDIA, and Fortune 500 companies, while previewing 2026's bigger AI guests, deeper enterprise AI case studies, and exciting new distribution opportunities. Our community of AI executives and tech leaders doesn't just listen- you implement AI strategies, share insights with teams, and drive digital transformation in your organizations. Want to sponsor the fastest-growing enterprise AI podcast? Visit tonyphoang.com to explore strategic partnerships for 2026. This episode contains sponsored content.
January 30 · 26 min
Interview #82 Christopher Brown, Managing Partner of The Nova Rose Group
Join Christopher Brown, Managing Partner of The Nova Rose Group, as he examines the collision course between AI innovation and governance, arguing that America's fragmented 50-state regulatory patchwork creates unprecedented challenges for an industry evolving too rapidly for traditional democratic experimentation. Brown critiques the Trump administration's AI litigation task force threatening to sue states over AI laws, warning that while maintaining competitive dominance against China is valid, the U.S. risks becoming what it opposes by sacrificing privacy protections and anti-discrimination safeguards in the rush to innovate. He frames AI regulation as an infrastructure problem requiring a comprehensive "national highway system" approach rather than piecemeal solutions, contending that companies pouring trillions into AI investments while ignoring workforce impacts, healthcare risks, and algorithmic bias are creating a financial and social time bomb that could undermine American leadership rather than secure it.
January 1 · 33 min
Interview #81 Vrajesh Bhavsar, CEO of Operant AI
Join Vrajesh Bhavsar, CEO of Operant AI, as he reveals the emerging threat landscape facing production AI systems, from sophisticated prompt injection attacks to zero-click exploits that can exfiltrate sensitive data without any user interaction. Bhavsar explains how traditional security tools like code scanning, network firewalls, and cloud security posture management become largely ineffective against AI agents operating with non-deterministic behavior and authorized access to critical systems, requiring runtime security solutions that function as AI-layer firewalls. He discusses the Shadow Escape attack class discovered by Operant targeting Model Context Protocol (MCP) servers, where the rapid proliferation of 20,000 mostly open-source MCP servers creates dangerous shared memory spaces across disparate API endpoints, enabling data poisoning and exfiltration at scale while traditional security teams remain blind to these agent-to-agent interactions happening within the "cloud within the cloud."
Dec 19, 2025 · 43 min
Interview #80 Raj Shukla, CTO of SymphonyAI
Join Raj Shukla, CTO of SymphonyAI, as he discusses the critical distinction between AI demos and production-ready systems, revealing that enterprises consistently underestimate the "last mile" challenges of authentication, authorization, and data scalability that break POCs when moving to production. Shukla explains how SymphonyAI's vertical AI approach pre-trains models on industry-specific ontologies and knowledge graphs for retail, financial services, industrial manufacturing, and enterprise IT, enabling faster ROI by providing pre-built agents and domain-specific context rather than expecting generic LLMs to solve everything. He addresses the hidden costs that shock CFOs—not LLM inference which has dropped 1000x, but the expensive work of making data and APIs AI-ready through proper governance layers and MCP server implementations, while warning that enterprises overestimate the autonomy achievable in the short term and underestimate the infrastructure work required for real process automation at scale.
Dec 1, 2025 · 31 min
Interview #79 Balaji Raghavan, Head of Engineering at Postman
Join Balaji Raghavan, Head of Engineering at Postman, as he discusses the critical gap between AI adoption and API readiness, revealing that while 80% of developers use AI, only 24% design APIs with AI agents as the intended consumer. Drawing from Postman's 40 million developer user base, Raghavan explains how human-designed APIs create ambiguity problems for AI systems, requiring additional tooling layers that often introduce security vulnerabilities through proxy credentials and unauthorized access risks. He addresses the uncomfortable reality that the industry is still in early stages of making AI reliably call APIs at scale, with hallucinations and context limitations preventing effective orchestration across hundreds of endpoints, while warning that judicious leaders must distinguish between deterministic flows and cases where expensive AI-based approaches are truly necessary to manage infrastructure costs and prevent cascade failures.
Oct 15, 2025 · 31 min
Interview #78 Stelios Diamantidis, CPO of Cognichip
Join Stelios Diamantidis, Chief Product Officer at Cognichip, as he explores how artificial intelligence is revolutionizing semiconductor development by enabling more holistic design processes that can reduce development time by half and costs by 75%. He discusses how AI tackles complex challenges across the entire chip design workflow—from early product definition through manufacturing—including verification, debugging, and hardware-software co-design optimization. Diamantidis envisions a near future where AI agents serve as true co-designers, helping engineers navigate the complex trade-offs between performance, power efficiency, and chip area while enabling rapid creation of bespoke accelerators tailored to specific AI workloads.
Oct 10, 2025 · 38 min
Interview #77 Paul Canetti, CEO of Skej
Join Paul Canetti, CEO of Skej, as he discusses the unique challenges of building AI products that operate without traditional user interfaces, instead functioning as virtual humans with email addresses, phone numbers, and Slack handles that interact through natural language. Drawing from his experience in UX design at Apple during the iPhone era, Canetti explains how building non-deterministic AI systems fundamentally differs from traditional software, requiring multiple quality assurance layers to prevent hallucinations and ensure AI assistants know when to remain silent in group conversations. He explores the shift toward anthropomorphized AI assistants with distinct personalities, arguing that as forms become obsolete and natural language interfaces become mainstream, the future lies in liberating people to do uniquely human work while AI handles generic tasks that anyone could accomplish but everyone suffers through.

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