The Artificial Intelligence Podcast

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

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

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

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

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

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

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

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. Be a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/live

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. Be a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/live

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. Be a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/live