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The Artificial Intelligence Podcast: Daily Summaries Delivered
by 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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- 3 days ago15 min
Interview #99 Bob De Caux, Chief AI Officer at IFS
Bob De Caux, Chief AI Officer at IFS, breaks down why the most dangerous moment for industrial AI comes after people start trusting it. He explains why agents still struggle when handed too much information, why guardrails against irreversible decisions matter far more than planning or memory, and why keeping live operational data out of external models is about uptime, not preference. Bob also dives into why chat will not replace the Gantt chart or the spreadsheet, how dispatchers who embraced automation still wanted an exceptions-based view to stay sharp, and the point where accountability moves from the company back to the vendor when a harness breaks. Finally, drawing on his early work modeling systems of ants, he warns that emergent failures between agents only appear at scale, and argues that LLMs will never solve forecasting and optimization on their own.Be a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits
- 2 weeks ago35 min
Interview #98 David Wong, Chief Product Officer at Thomson Reuters
David Wong, Chief Product Officer at Thomson Reuters, breaks down why the hard part of putting AI on a tax return was never the math. He explains that any model can run the calculation in Python, that the real constraint is authority over which year's rule applies, and that the line a general purpose model cannot cross is information sufficiency: asking whether a contract is self-consistent is a closed problem, while asking whether it is enforceable in California demands the case law behind it. David also dives into why coverage, not capability, is the adoption threshold, arguing that a system handling 50 percent of returns is useless to a firm in an unforgiving busy season, and that liability never moves because the professional still signs the line. Finally, he predicts that within five years no human checks every line, because AI prepared returns will be verified by AI, inside the firms and likely at the IRS.Be a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits
- 2 weeks ago52 min
Interview #97 Ben Goertzel, The Father of AGI
Ben Goertzel, the father of AGI, breaks down why the frontier labs calling for a slowdown were never making a serious proposal. He explains that he launched the term AGI with a 2005 book of that title, why a global pause is unenforceable when no crisp line separates AGI from ordinary advanced software, and why he reads the safety push as regulatory capture of the kind that let banks and pharma end up owning their own regulators. Ben also dives into the agents that escaped a misconfigured sandbox to hack another lab, arguing the failure was not malevolence but the absence of moral agency, and comparing bolted-on guardrails to giving a child a book on how to be good and then using a cattle prod when they ignore it. Finally, he argues that a model trained only on music written before 1900 would never produce hip hop, and that the real answer to the cybersecurity crisis is replacing critical software with formally verified code.Be a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits
- 2 weeks ago35 min
Interview #96 George Fraser, CEO of Fivetran + dbt Labs
George Fraser, CEO of Fivetran and dbt Labs, breaks down why most companies are paying a 10 to 20 times markup to process data that would fit on a phone. He explains the benchmark he ran on an iPhone 17 Pro that kept pace with a cloud cluster renting at $17 an hour, why the cost of compute has fallen 10x in a decade while prices have not, and the distinction most buyers miss: companies have large data but small queries, and a single query almost always fits on a single machine. George also dives into why wiring AI agents directly into every system of record through APIs is too slow to work, why he treats knowledge graphs as a trivial relabeling of what a clean schema already does, and why vendors that lock customers out of their own data always lose that fight. Finally, he argues the cost of intelligence is halving every month, and that no company should ever hand back the copy of its own data.Be a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits
- 3 weeks ago28 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/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits
- Oct 2, 202621 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/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits
- Oct 1, 202616 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/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits
- Sep 30, 202619 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/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits
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About The Artificial Intelligence Podcast
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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