AI: Trust but Verify

In 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.

Dominick “Dom” Romano is the founder and CEO of Drainpipe.io, an AI company focused on making AI systems trustworthy enough for regulated, high-stakes environments. Dom’s background spans video game development, casino gaming, advertising, real-time routing for hazardous payloads, mainframe engineering for IBM z/OS and banking systems, real-time transactions, and observability. Today, Drainpipe.io works with major manufacturers in Germany across pharmaceuticals, automotive, and chemical manufacturing, helping them deploy AI systems where the inputs and outputs must be verifiable—especially when AI touches critical regulatory data such as pharmaceutical dossiers submitted to health authorities.

• AI adoption in regulated industries requires trust, not just capability. Dom emphasizes that when AI touches critical workflows—especially in pharmaceuticals, compliance, or regulatory submissions—organizations need confidence that both the data going in and the AI-generated output are legitimate and trustworthy.
• AI can scale mistakes, bias, and discrimination. The conversation highlights how AI systems used in decisions such as lending or hiring can create large-scale harm if bias or unlawful discrimination goes undetected—particularly when companies cannot prove the model is not using impermissible factors.
• Cybersecurity has to come before “cool” AI features. Dom and Alec discuss how rapidly adopted AI tools can create serious security risks when they go viral without proper cybersecurity foundations. The OpenClaw example is used as a warning about software that provides value but fails to account for security from the start.
• AI is becoming a geopolitical and cultural force. Dom raises concerns about “digital colonialism,” where countries in the Global South may become increasingly shaped by Western AI models that do not represent their own cultures, languages, or values.
• The near-term AI risks may be more urgent than distant sci-fi scenarios. While existential risk is discussed, Dom argues that immediate threats—deepfakes, multimodal AI on devices, AI in armed conflict, and rapidly expanding cybersecurity vulnerabilities—deserve serious attention right now.

• Dominick “Dom” Romano — Founder & CEO, Drainpipe.io
• Website: https://drainpipe.io
• LinkedIn: linkedin.com/in/domromano
• X: https://x.com/dromanocpm
• Dom specifically points listeners to LinkedIn, X under the handle @drobmanocpm, and
• Alec Crawford — Founder & CEO, Verapath; host of the conversation linkedin.com/in/aleccrawford
• Verapath — Secure AI platform for financial institutions https://verapath.com
• Full Sail University — Dom’s educational background in video game development
• https://www.fullsail.edu
• IBM z/OS — Mainframe platform referenced in Dom’s background
• https://www.ibm.com/products/zos
• EU AI Act — European AI regulation discussed in the episode https://artificialintelligenceact.eu
• JP Morgan / JPMorgan Chase — Mentioned in the discussion of AI, attrition, and jobs
• https://www.jpmorganchase.com
• Anthropic — Mentioned in the discussion of AI existential risk https://www.anthropic.com
• Ray Kurzweil — Mentioned as one of the people concerned about AI risk https://www.kurzweilai.net
• Elon Musk — Mentioned in the discussion of AI risk https://x.com/elonmusk
• Marcus Hutter — AI superintelligence researcher mentioned by Alec https://www.hutter1.net
• Doraemon — Referenced as an example of Japan’s more positive cultural association with AI
• https://dora-world.com
• OpenClaw — Referenced as an example of a viral software/AI-adjacent tool with serious cybersecurity concerns
• Mythos — Referenced in the discussion of emerging cybersecurity threats and online infrastructure risk

In 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 .

• AI 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 .

• HTEC — Tim’s current company: https://htecgroup.com
• Groq — AI inference accelerator company Tim previously worked at: https://groq.com
• Morgan Stanley — where Alec and Tim worked during the dot-com era: https://www.morganstanley.com
• Goldman Sachs — where Alec and Tim first met: https://www.goldmansachs.com
• Target — where Tim worked with data science / engineering teams: https://www.target.com
• NVIDIA — discussed in the context of GPUs and AI compute constraints: https://www.nvidia.com
• Marcus Hutter — AI / AGI / superintelligence researcher mentioned in the episode: https://www.hutter1.net
• Reid Blackman — mentioned in the discussion of AI ethics: https://www.reidblackman.com
• Large Language Models / LLMs — central model architecture discussed: https://en.wikipedia.org/wiki/Large_language_model
• World Models — mentioned as a possible future AI architecture direction: https://en.wikipedia.org/wiki/World_model
• Quantum Computing — discussed as another major future technology wave: https://en.wikipedia.org/wiki/Quantum_computing
• Post-Quantum Cryptography — related to the “Q-Day” discussion about quantum computers breaking encryption: https://csrc.nist.gov/projects/post-quantum-cryptography
• Verapath — platform mentioned in the episode: https://www.verapath.com

In 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 [email protected], 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.

In the AI: Trust but Verify podcast, our host, Alec Crawford (@alec06830), Founder and CEO of Verapath (verapath.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 Kriste Krstovski, Associate Research Scientist at Columbia University, about what it really means for AI to be trustworthy rather than merely impressive. Krstovski explains why AI risk should be treated as a systems problem, highlighting how deployment context, proxy data, evaluation standards, and human oversight all shape whether an AI system is reliable in the real world. He also discusses ethical concerns around bias, transparency, virality, high-stakes decision-making, and the pressure organizations face to adopt AI quickly before they fully understand its failure modes. The conversation explores how AI is changing software development, why verification and adversarial evaluation matter more than ever, and how open-source model access may require new governance approaches. Krstovski also shares insights from his forthcoming book, Practical AI for Business, which is designed to help non-technical practitioners understand and apply core AI methods in a practical way.

Summary:

• Trustworthy vs. Impressive AI: Krstovski distinguishes flashy AI demos from systems that are dependable, robust, and useful in real-world settings.
• AI as a Systems Problem: Risk comes not just from the model, but from data, interfaces, deployment choices, objectives, and oversight.
• Ethics and Proxy Data: He warns that AI can make harmful inferences from incomplete data, especially in areas like hiring, lending, and healthcare.
• Verification Over Generation: As AI writes more code, the critical human skill becomes testing, debugging, and verifying correctness under failure conditions.
• Governance and Gradual Harm: The episode argues that competitive pressure and weak safeguards may normalize manipulation, dependency, and poor judgment before dramatic failures occur.

Referenced in this episode:

Companies/Organizations:

• Verapath
• Columbia University
• Data Science Institute
• Columbia Business School
• AT&T
• Boston Dynamics
• European Union
• China
• Colorado
• Netflix
• Anthropic
• TSA
• FAA
• NTSB
• Columbia University Press
• Troutman Street Audio

Books:

• Practical AI for Business

Kriste's publications

Copyright © 2026 by Artificial Intelligence Risk, Inc.

In 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 DeepMind
• Anthropic
• Firefox
• FDIC
• U.S. Treasury
• Verapath
• SWIFT
• OpenAI
• Google
• Fiserv
• Jack Henry
• COCC
• Amadeus
• Capital One
• NiceHash

Copyright © 2026 by Artificial Intelligence Risk, Inc.

In 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 Waterloo
• Verapath
• Anthropic
• OpenAI
• DeepMind
• Google
• ARC
• METR
• Troutman Street Audio
• Waters Technology

Copyright © 2026 by Artificial Intelligence Risk, Inc.

In 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 Hubbard
• Verapath
• Northern Illinois University
• Union Bank of Elgin
• FTR
• Harris Bank
• BMO Harris
• St. Charles Bank and Trust
• Wintrust
• Dynex Capital
• Cornerstone Advisors
• Performance Insights
• RelPro
• Vertical IQ
• LinkedIn
• Block
• Peapack Gladstone Bank
• Capital One
• Fleet
• American Bankers Association
• Wharton School
• University of Wisconsin
• LSU School of Banking
• Massachusetts Bankers
• Perry School of Banking
• Michigan Bankers Association
• Selling Power
• Barlow Research
• Chicago Cubs

Books:

• Heart Spoken
• Conversations with Prospects
• I Know Jack 53 Years of Banking Excellence

Movies:

• Animal House
• Caddyshack

Copyright © 2026 by Artificial Intelligence Risk, Inc.

In 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:

• Verapath
• Anthropic
• Google
• Western Union
• Salesforce

Copyright © 2026 by Artificial Intelligence Risk, Inc.

In 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 Group
• Artificial Intelligence Risk, Inc.
• Nomura
• SVB
• PwC
• EY
• Barclays
• Business AI Alliance
• NatWest Markets
• Microsoft
• OpenAI
• Claude
• ChatGPT
• Grok
• Meta
• UFC

Books:

• Principles

Movies:

• The Matrix

TV Shows:

• The Ultimate Fighter

Copyright © 2026 by Artificial Intelligence Risk, Inc.

In 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.Flowers
• Artificial Intelligence Risk, Inc.
• Columbia
• Nvidia
• NIST
• OpenAI
• Microsoft
• OECD
• MIT
• FAA
• NTSB
• NASA
• IRS
• Google

Copyright © 2026 by Artificial Intelligence Risk, Inc.