The Artificial Intelligence Podcast

Somya Kapoor, CEO of IFS Loops, breaks down why most AI agent projects fail on change management rather than technology, and why a company that waits for perfect data will never get an agent deployed. She explains why building an agent is now easy enough for an 11-year-old while getting one into production is not, how an agent that starts at 40-50% accuracy can reach 80-85% in weeks with a human handling the exceptions, and why we demand perfection from machines when people are only about 70% productive at their jobs. Somya also dives into why every digital worker needs a supervisor agent keeping an audit trail, and who answers when an agent misprocesses a purchase order at 3 AM. Finally, she shares which jobs agents will take and which new ones they create, and why regulation, not technology, will keep a human signing off in industries where one outage can leave millions without power.

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Alex Salazar, Co-founder & CEO at Arcade.dev, breaks down why treating AI agents as teammates is an architectural dead end, and why they should be treated as software that is unpredictable enough to count as adversarial. He explains why security is usually the first team to say no, why an agent's own permissions are only half the problem when an intern could ask it for the CEO's pay, and what has to be true before a bank lets an agent wire money, from payee accounts registered ahead of time to checking the CRM so a refund is not hallucinated. Alex also dives into approval fatigue, research showing people check an agent's work less when they see it as a peer, and why no model should ever be trusted, open-weight or not. Finally, he shares why the vendors behind most agent strategies likely did not exist before 2024, and why agents moving money will feel mundane by the end of next year.

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Dor Skuler, CEO & Co-founder at Intuition Robotics, breaks down why AI labs pour billions into making models smarter and almost nothing into making them understand people. He explains why a companion AI should be rewarded only when a person's well-being improves, why it must never upsell or report what it hears without permission, and why the industry needs an outside auditor for AI ethics, the way VeriSign once vouched for websites. Dor also dives into AI that speaks first, using six levels of intrusiveness so it does not interrupt at the wrong moment, and why safety guardrails need plain if-statements behind the fine-tuned model, since a probabilistic filter will eventually let something slip. Finally, he shares what a home robot will get wrong on its first day, from taking orders from a child to stepping into an argument, and why one broken confidence can end trust for good.

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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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