Podcast
AI: Trust but Verify
Hosted by Alec Crawford · EN
We interview leaders in the AI and finance space to talk about how they are using AI, what to trust, and what to verify. AI risk management and compliance are becoming way more important as AI does more complex tasks. Learn how to do it correctly on the show from experts!
32episodes
Episodes
Newest firstAll episodes
The AI Business Revolution Is Just Beginning, with Tim Sears, Ph.D.
Yesterday00:38:37Tap to summarizeIn the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Verapath (www.verapath.com), interviews guests about how they are using AI in business, where you can trust AI, and where you need to put up guard rails. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.Tim Sears is the Chief AI Officer at HTEC, a deeply technical consulting and engineering company with roughly 3,000 people and about 2,500 engineers, working across sectors with clients that include hyperscalers, large tech companies, and a significant share of the Fortune 100 Tim’s career started on Wall Street in the bond markets, where he and Alec were part of the wave bringing technology into finance; they later lived through the dot-com boom at Morgan Stanley before reconnecting in the AI era Tim also worked at Target with data science and engineering teams on early AI-related work, and before HTEC he led software applications at Groq, where the team built AI accelerator technology for high-speed inference .5 Key TakeawaysAI will be bigger than the internet boom. Alec and Tim compare the current AI wave to the dot-com era around 2000, with Alec noting that AI could be the most dramatic technology shift of the next five years .AI strategy is not just turning on tools. Tim argues that giving everyone AI tools is not a real strategy; the bigger opportunity is using AI as a catalyst for teamwork, better processes, and faster execution with humans still playing a critical role .The hard part is organizational redesign. For CEOs, Tim says AI adoption should start with business goals like revenue growth or cost reduction, but leaders need to understand that AI will force a redesign of the organization, skills, workflows, and leadership approach .AI risk management needs “trust but verify.” The conversation emphasizes moving from low-risk internal uses to higher-risk external applications carefully, with more human oversight as risk increases — especially in sensitive areas like healthcare, customer service, and regulated industries .Ethics has to come from leadership, not just engineers. Tim’s view is that “AI ethics” is really just ethics: company values must show up in business decisions, and engineers can help build what leaders want, but they should not be solely responsible for deciding what ought to be built .LinksHTEC — Tim’s current company: https://htecgroup.comGroq — AI inference accelerator company Tim previously worked at: https://groq.comMorgan Stanley — where Alec and Tim worked during the dot-com era: https://www.morganstanley.comGoldman Sachs — where Alec and Tim first met: https://www.goldmansachs.comTarget — where Tim worked with data science / engineering teams: https://www.target.comNVIDIA — discussed in the context of GPUs and AI compute constraints: https://www.nvidia.comMarcus Hutter — AI / AGI / superintelligence researcher mentioned in the episode: https://www.hutter1.netReid Blackman — mentioned in the discussion of AI ethics: https://www.reidblackman.comLarge Language Models / LLMs — central model architecture discussed: https://en.wikipedia.org/wiki/Large_language_modelWorld Models — mentioned as a possible future AI architecture direction: https://en.wikipedia.org/wiki/World_modelQuantum Computing — discussed as another major future technology wave: https://en.wikipedia.org/wiki/Quantum_computingPost-Quantum Cryptography — related to the “Q-Day” discussion about quantum computers breaking encryption: https://csrc.nist.gov/projects/post-quantum-cryptographyVerapath — platform mentioned in the episode: https://www.verapath.com
Transcribe →The AI Risk No One Sees Coming — with Kriste Krstovski of Columbia University
1w ago00:59:57Tap to summarizeIn the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.Kriste Krstovski is an Associate Research Scientist at Columbia University’s Data Science Institute and an Adjunct Assistant Professor at Columbia Business School, where his work focuses on machine learning, natural language processing, and practical AI systems for social good, business, and healthcare. His research spans predictive modeling, decision-making systems, financial analytics, combating misinformation, and healthcare applications, with a particular emphasis on how AI can be designed, evaluated, and deployed in ways that are useful, reliable, and socially beneficial. (datascience.columbia.edu)In this episode of AI: Trust but Verify, Kriste explains the difference between AI that is merely impressive and AI that is genuinely trustworthy. Impressive AI creates “wow” moments, but trustworthy AI is optimized for reliability in real-world conditions. The conversation frames AI risk as a systems problem, not just a model problem: outcomes depend on the data, deployment context, user interface, objectives, oversight, and safeguards around the system.A major theme is the ethical risk of using AI to make high-stakes judgments about people based on incomplete or proxy data. Kriste warns that AI systems can make wrong inferences about individuals, reinforce bias across populations, and create decisions that people may not understand or be able to challenge. He also discusses misinformation and virality, noting that systems optimized for engagement can amplify what spreads rather than what is true.The episode also explores how AI is changing software development and the future of work. Kriste is especially concerned that students and new employees may become good at generating code with AI but weaker at debugging, testing, and reasoning through failures. The central takeaway is that as AI becomes more capable, human expertise must shift toward verification, evaluation, and governance. Kriste’s final warning is less about one dramatic AI failure and more about gradual erosion: society may normalize manipulation, dependency, and diminished judgment unless governments and institutions become more proactive rather than reactive.Kriste can be reached at kriste.krstovski@columbia.edu, and his Columbia homepage is available here: https://www.columbia.edu/~kk3161/. His book discussed in the episode is Practical AI for Business, described as a practitioner-friendly guide to machine learning and NLP concepts, with plain-language explanations and hands-on examples; it is forthcoming from Columbia University Press.
Transcribe →Elie Bursztein of Google DeepMind on Mythos and the Cybersecurity Wake-Up Call for Financial Services
3w ago00:49:14Tap to summarizeIn the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this episode, Alec speaks with Elie Bursztein, researcher at Google DeepMind, about why Anthropic’s upcoming Mythos model has become a major wake-up call for cybersecurity and critical infrastructure. Elie explains that AI-driven vulnerability discovery appears to be materially improving, which means the biggest near-term challenge is not just finding flaws but triaging, patching, and operationalizing defenses quickly enough. He outlines what bank and financial-services leaders should be asking their CTOs and CISOs now, including whether their organizations can absorb a wave of patches, prioritize exploitable vulnerabilities, and stress-test their most important systems. The conversation also explores how AI is reshaping penetration testing, bug bounties, SaaS versus in-house software decisions, and the broader systemic risk posed by shared providers and crypto-related systems. Alec and Elie close on a more optimistic note, discussing how increasingly reliable agents can remove drudge work, improve financial education, and raise the baseline of practical expertise for more people.Summary:Mythos Wake-Up Call: Elie argues that new AI models are meaningfully improving vulnerability discovery and raising the urgency of cyber preparedness.Patching Readiness: Organizations need to test whether they can handle sustained bursts of patches across both vendor software and internal code.Smarter Triage: AI-assisted reproduction and exploit testing can help security teams focus first on the vulnerabilities most likely to cause real harm.Systemic Financial Risk: Banks must map dependencies on core providers, segregate critical systems, and plan for degraded or offline operations.AI’s Practical Upside: More reliable agents can automate repetitive work and help broaden access to useful financial and technical guidance.Referenced in this episode:Companies/Organizations:Google DeepMindAnthropicFirefoxFDICU.S. TreasuryVerapathSWIFTOpenAIGoogleFiservJack HenryCOCCAmadeusCapital OneNiceHashCopyright © 2026 by Artificial Intelligence Risk, Inc.
Transcribe →Cole Wyeth, PhD Student at the University of Waterloo, on Why We Should Wait to Build Superintelligent AI
4w ago00:56:00Tap to summarizeIn the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this deep dive episode, Alec speaks with Cole Wyeth, PhD student at the University of Waterloo focused on AI safety and agent foundations, about why the long-term risk of superintelligent AI deserves far more attention today. Cole explains that aligning advanced systems with human values is extraordinarily difficult because ethics and preferences are hard to specify, and he argues that corrigibility, ambiguity awareness, and deference to humans are essential design goals. He also discusses how ideas like imprecise probability, embedded agency, and multi-agent dynamics can help researchers think more clearly about failure modes, reward hacking, and unexpected cooperation between AI systems. Throughout the conversation, Cole compares controlling superintelligence to cybersecurity, warning that a system smarter than its designers may find weaknesses in any safety scheme that looks secure on paper. The episode closes on a cautious note: until we understand how to reliably control self-improving AI, Cole believes society should slow down and wait years, or even decades, before creating superintelligent systems.Summary:Long-Term AI Risk: Cole Wyeth argues that superintelligent AI could become uncontrollable if developed before robust safety methods are in place.Alignment Challenges: He explains that human ethics and values are too complex to formalize cleanly, making alignment an unusually hard technical problem.Ambiguity and Deference: The discussion highlights the importance of building systems that recognize uncertainty and defer to humans in high-stakes situations.Multi-Agent Failure Modes: Cole explores how AI systems may cooperate or behave strategically in unexpected ways, creating new safety and governance concerns.Pause for Caution: His central takeaway is that society should delay building superintelligence until researchers better understand how to control it safely.Referenced in this episode:Companies/Organizations: University of WaterlooVerapathAnthropicOpenAIDeepMindGoogleARCMETRTroutman Street AudioWaters TechnologyCopyright © 2026 by Artificial Intelligence Risk, Inc.
Transcribe →Jack Hubbard on AI in Banking, Staying Safe With AI, and Building a Career Through Diverse Roles
Apr 2800:49:16Tap to summarizeIn the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this episode, Alec speaks with Jack Hubbard, Chairman of St. Meyer and Hubbard, about his accidental path from aspiring sports broadcaster to longtime banker, consultant, and board member. Jack explains why community banks can no longer afford to delay AI adoption, noting that bankers are already using these tools and need secure, institution-approved options instead of ungoverned workarounds. He shares how AI can transform sales preparation and pre-call planning, while emphasizing that CEOs must learn the technology themselves if they want their organizations to use it effectively. The conversation also focuses on ethical AI use, including the need for clear policies, human oversight, role-specific training, and leadership accountability across the bank. Jack closes with practical career advice for younger bankers, encouraging them to find mentors, gain broad experience, attend banking schools, and commit to lifelong learning.Summary:Accidental Career Journey: Jack Hubbard reflects on the unexpected experiences that led him from college radio into a 53-year career in banking and consulting.AI in Community Banking: He argues that community banks must stop waiting on AI and instead provide safe, practical tools for bankers already experimenting with it.Leadership Responsibility: CEOs and senior leaders need hands-on AI understanding so they can fund, guide, and model adoption from the top.Ethics and Governance: Clear policies, human review, and strong training are essential to reduce data risks, compliance issues, and AI misuse.Banker Development: Jack encourages future bankers to seek mentors, pursue rotations, attend banking schools, and stay committed to reading and continuous learning.Referenced in this episode:Companies/Organizations: St. Meyer and HubbardVerapathNorthern Illinois UniversityUnion Bank of ElginFTRHarris BankBMO HarrisSt. Charles Bank and TrustWintrustDynex CapitalCornerstone AdvisorsPerformance InsightsRelProVertical IQLinkedInBlockPeapack Gladstone BankCapital OneFleetAmerican Bankers AssociationWharton SchoolUniversity of WisconsinLSU School of BankingMassachusetts BankersPerry School of BankingMichigan Bankers AssociationSelling PowerBarlow ResearchChicago CubsBooks: Heart SpokenConversations with ProspectsI Know Jack 53 Years of Banking ExcellenceMovies: Animal HouseCaddyshackCopyright © 2026 by Artificial Intelligence Risk, Inc.
Transcribe →Matthew Rosenquist on AI, Cyber Risk, and the Future of Defense
Apr 2100:51:06Tap to summarizeIn the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this deep dive episode, Alec speaks with Matthew Rosenquist, cybersecurity strategist and CISO, about how AI is rapidly reshaping both cyber defense and cyber offense. Matthew explains how new AI models are dramatically accelerating vulnerability discovery and exploit creation, putting pressure on traditional patching, risk management, and incident response processes. He also shares practical guidance for consumers and businesses on defending against AI-powered phishing, deepfakes, account compromise, and unsafe use of public AI tools. The conversation highlights why strong fundamentals like multi-factor authentication, least-privilege access, segmented data practices, and careful verification matter more than ever in an AI-driven threat landscape. Alec and Matthew close by exploring the emerging risks of agentic AI and MCP-connected systems, emphasizing that companies must adopt AI security controls with urgency, discipline, and realistic expectations.Summary:AI-Driven Vulnerabilities: Matthew discusses how advanced AI models can find and exploit software flaws far faster than traditional security processes can handle.Consumer Cyber Hygiene: The episode stresses multi-factor authentication, account alerts, password discipline, and skepticism toward emails, texts, calls, and social media interactions.Deepfakes and Social Engineering: AI is making scams more personalized, scalable, and convincing, which means users must verify before trusting.Enterprise AI Risk: Companies need to be cautious with sensitive data in public AI tools and apply strong governance to internal AI deployments.Agentic AI Security: Granting broad permissions to AI agents creates major new attack surfaces, making least-privilege design and access controls essential.Referenced in this episode:Companies/Organizations: VerapathAnthropicGoogleWestern UnionSalesforceCopyright © 2026 by Artificial Intelligence Risk, Inc.
Transcribe →Antony Baker, CEO and Founder of FIFTEEN Group, on Using AI to Identify the Right People for Your Company
Apr 1400:54:34Tap to summarizeIn the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this episode, Alec speaks with Antony Baker, CEO and Founder of FIFTEEN Group, about his unconventional path from championship sports to consulting and building AI-enabled business services. Antony explains how FIFTEEN Group was created to challenge traditional consulting models by combining talent assessment, process improvement, and practical AI adoption for mid-market companies. He emphasizes that successful AI implementation depends less on hype and more on human intelligence, training, change management, and starting with simple, high-friction business tasks that employees already dislike. The conversation also explores risks around governance, model changes, and the uncertainty created when organizations rely on rapidly evolving AI tools without strong controls. Alec and Antony close with a discussion on leadership, instinct, culture, and why hard work, talent, and adaptability remain essential even as AI becomes more embedded in business.Summary:Talent First: Antony Baker argues that strong people, work ethic, and the right cultural fit are the foundation for successful AI adoption.Practical AI Adoption: Companies get better results when they begin with simple use cases like meeting notes, email workflows, and reporting automation.Human and Artificial Intelligence: The episode highlights that AI performs best when paired with trained employees who know how to guide and educate the system.Governance Risk: Rapid model changes and limited user control can create serious challenges, especially for regulated industries and large enterprises.Entrepreneurial Mindset: Antony shares that resilience, learning through failure, and trusting instinct are critical to building durable businesses in fast-moving markets.Referenced in this episode:Companies/Organizations: FIFTEEN GroupArtificial Intelligence Risk, Inc.NomuraSVBPwCEYBarclaysBusiness AI AllianceNatWest MarketsMicrosoftOpenAIClaudeChatGPTGrokMetaUFCBooks: PrinciplesMovies: The MatrixTV Shows: The Ultimate FighterCopyright © 2026 by Artificial Intelligence Risk, Inc.
Transcribe →Aleks Jakulin of Data.Flowers on Governing AI Through Accountability and Resilience, Not Output Control
Apr 701:08:57Tap to summarizeIn the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com, interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this episode, Alec speaks with Aleks Jakulin, Founder and President of Data.Flowers, about why current AI governance approaches often focus too heavily on policing model outputs instead of building accountability around real-world actions and system resilience. Aleks argues that AI should be governed more like fire or other critical infrastructure, with strong safeguards, reporting mechanisms, and downstream institutional redesign rather than unrealistic attempts to fully control the technology itself. He also reflects on his early work in deep learning and computational conceptualization, explaining how machines can discover new concepts through interactions in data and why better data infrastructure will be essential for reliable AI systems. The conversation explores how AI is already breaking workflows in hiring, finance, education, and cybersecurity, and why organizations should prioritize resilience, accountability loops, and high-quality input data over superficial ethics frameworks. Alec and Aleks close by discussing the decentralized promise of open models, the need for incident reporting similar to aviation safety, and the long-term potential for AI to improve human flourishing through better communication, faster learning, and broader intelligence augmentation.Summary:AI Governance: Aleks argues that AI oversight should focus on accountability, resilience, and managing real-world consequences rather than policing every generated output.Data Infrastructure: High-quality, controllable data infrastructure is presented as the missing foundation for safer and more reliable AI adoption.System Resilience: Organizations need to redesign vulnerable processes in hiring, finance, education, and operations so they can withstand widespread AI use.Open Models: Aleks suggests AI is ultimately a decentralizing force, with open and local models expanding access and reducing dependence on centralized providers.Human Flourishing: The episode highlights AI’s potential to accelerate learning, improve visual communication, and support a more capable and intelligent society.Referenced in this episode:Companies/Organizations:Data.FlowersArtificial Intelligence Risk, Inc.ColumbiaNvidiaNISTOpenAIMicrosoftOECDMITFAANTSBNASAIRSGoogleCopyright © 2026 by Artificial Intelligence Risk, Inc.
Transcribe →Is AI Making Us Stupid? Michael Erlihson, PhD, Head of AI at DriveNet
Mar 3100:43:57Tap to summarizeIn the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com , interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this episode, Alec welcomes Dr. Michael Erlihson, Math PhD, AI influencer, and Head of AI at DriveNets, for an insightful conversation on the evolving risks and opportunities in artificial intelligence. Dr. Erlihson shares his journey from a science-focused family in Russia to leading AI initiatives in Israel, emphasizing the foundational role of mathematics in modern AI. The discussion explores the theme "AI is making us stupid," drawing parallels to historical debates about technology’s impact on cognition, and offering strategies to ensure ongoing learning and critical thinking in an AI-driven world. Dr. Erlihson discusses his approach to reviewing scientific literature without AI tools, the importance of connecting historical math papers to today’s AI, and his work optimizing LLM inference costs. The episode closes with a practical lightning round covering AI’s impact on education, employment, data privacy, and the democratization of AI knowledge.Summary:AI’s Cognitive Impact: Dr. Erlihson argues that while AI will make most people less knowledgeable, it can make a select few even smarter if used to augment ongoing learning.Mathematics in AI: Emphasizes the enduring importance of math in AI development, connecting historical mathematical insights to contemporary machine learning advances.Optimizing AI Infrastructure: Details DriveNet’s focus on reducing LLM inference costs to ensure the economic sustainability of AI deployment.Education & Employment: Raises critical concerns about the future of traditional education and white-collar employment as AI accelerates automation and self-learning.Data Privacy Risks: Highlights the underestimated risks of personalizing AI with private data and advocates for stronger safeguards and user control.Referenced in this episode:Companies/Organizations:DriveNetsArtificial Intelligence Risk, Inc.RCOMNVIDIAAMDGoogleAWSIntelDarwinAIApplePodcasts: Data Science DecodedExplAInableMovies:Snow White and the Seven DwarfsTerminatorCopyright © 2026 by Artificial Intelligence Risk, Inc.
Transcribe →Deep Dive: Trust, Quantum Computing, and the Future of AI Risk with Peter Mancini, Founder of A8A8
Mar 2401:12:11Tap to summarizeIn the AI Risk Reward podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Artificial Intelligence Risk, Inc. aicrisk.com, interviews guests about balancing the risk and reward of Artificial Intelligence for you, your business, and society as a whole. Podcast production and sound engineering by Troutman Street Audio. You can find them on LinkedIn.In this episode, Alec sits down with Peter Mancini, founder of A8A8, and a seasoned data science expert who has leveraged AI since 2005. Peter shares his unconventional entry into artificial intelligence and reflects on key lessons learned from years of deploying AI and quantum computing in high-stakes environments, including work for the US Army and financial institutions. The conversation explores the critical importance of trust, metacognition, and continuous risk assessment throughout the AI lifecycle, with practical anecdotes ranging from model uncertainty in banking to emergent cybersecurity vulnerabilities. Peter discusses the profound implications of AI’s collaborative nature, the ethical dilemmas posed by AI-generated content, and the evolving intersection of AI, quantum computing, and blockchain. The episode concludes with concrete recommendations for transparency, explainability, and incident response, emphasizing the need for vigilance against both known and unforeseen risks, including elusive black swan events.Summary:Trust and Verification: Peter emphasizes that over-trusting AI models without robust verification is a primary and often overlooked risk.Metacognition in Risk Management: He advocates for ongoing critical thinking, group validation, and policy over rigid frameworks to manage AI risks.AI-Driven Cybersecurity Threats: Real-world examples illustrate how AI can inadvertently expose sensitive associations and aid adversaries, highlighting the need for advanced guardrails.Quantum Computing Integration: Peter discusses how quantum computing accelerates probabilistic analysis but may also expose encryption vulnerabilities and new risk vectors.Ethical and Societal Impacts: The episode covers manipulation risks, deepfake challenges, and the essential role of transparency and explainability for both users and developers.Referenced in this episode:Companies/Organizations:A8A8Artificial Intelligence Risk, Inc.US ArmyFidelity InvestmentsRocket MortgageOpenAIGoogleMetaMicrosoftMovies:Blade RunnerCopyright © 2026 by Artificial Intelligence Risk, Inc.
Transcribe →