The Artificial Intelligence Podcast

Peter Evans, CEO at Xtract One Technologies, breaks down why many AI weapons scanners at school and stadium entrances still measure the wrong things. He explains why most "AI-enabled" detectors rest on the same physics as a walk-through metal detector, why knives and laptops expose the limits of that physics, and why a claim of 3,000 people an hour means little if a weak model sends most of them to a bag check. Peter also dives into how to estimate the weapons a system misses when nobody reports them, who should own the sensitivity dial, and what should have happened before police drew weapons on a teenager after a Maryland AI camera mistook a bag of chips for a gun. Finally, he shares why mandates in California and Georgia are racing ahead of testing standards, what an entrance should never be allowed to know about you, and why a 3D printed gun is still not invisible.

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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.

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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.

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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.

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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.

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