Interview #100 Peter Evans, CEO at Xtract One Technologies

Andrew Moore, Co-founder and CEO of Lovelace, breaks down why an AI answer that merely sounds right is dangerous in banks, hospitals and defense, where one question can touch hundreds of thousands of facts. He explains how an AI that read local newspapers saw a fleet of ships leaving a Chinese port for Taiwan as the start of fishing season rather than an invasion, why bigger context windows cannot keep up with problems that grow exponentially, and how a leading question lets a model build a convincing case for almost any hunch. Andrew also dives into why entity resolution and data entitlements stall more projects than the models do, and why a $25 answer has to fall to about a cent before it can run every five minutes. Finally, he shares why defense AI deserves the same years of testing as a new helicopter, and why independent corroboration is the judgment machines most need to learn.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits

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.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits

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.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits

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.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits

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.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits

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.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits

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.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe 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.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe 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.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe 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.Free AI lessons for leaders, built on these interviews: https://tonyphoang.com/learnBe a guest on the show: https://tonyphoang.com/guestLive interviews at your conference: https://tonyphoang.com/liveMusic and credits: https://tonyphoang.com/credits