The AI Business Revolution Is Just Beginning, with Tim Sears, Ph.D.

Dominick Romano: Watch Out for Foreign Influence in Our AI

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. AI: TRUST BUT VERIFY — WITH DOMINICK ROMANO ABOUT THE GUEST 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. TOP 5 TAKEAWAYS * 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. PEOPLE AND ORGANIZATIONS MENTIONED * Dominick “Dom” Romano — Founder & CEO, Drainpipe.io * Website: https://drainpipe.io [https://drainpipe.io/] * LinkedIn: linkedin.com/in/domromano [https://www.linkedin.com/in/domromano/?lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_contact_details%3BFSOLYJ%2FCRGe9fDPi6ph4Xw%3D%3D] * X: https://x.com/dromanocpm [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 [linkedin.com/in/aleccrawford] * Verapath — Secure AI platform for financial institutions https://verapath.com [https://verapath.com/] * Full Sail University — Dom’s educational background in video game development * https://www.fullsail.edu [https://www.fullsail.edu/] * IBM z/OS — Mainframe platform referenced in Dom’s background * https://www.ibm.com/products/zos [https://www.ibm.com/products/zos] * EU AI Act — European AI regulation discussed in the episode https://artificialintelligenceact.eu [https://artificialintelligenceact.eu/] * JP Morgan / JPMorgan Chase — Mentioned in the discussion of AI, attrition, and jobs * https://www.jpmorganchase.com [https://www.jpmorganchase.com/] * Anthropic — Mentioned in the discussion of AI existential risk https://www.anthropic.com [https://www.anthropic.com/] * Ray Kurzweil — Mentioned as one of the people concerned about AI risk https://www.kurzweilai.net [https://www.kurzweilai.net/] * Elon Musk — Mentioned in the discussion of AI risk https://x.com/elonmusk [https://x.com/elonmusk] * Marcus Hutter — AI superintelligence researcher mentioned by Alec https://www.hutter1.net [https://www.hutter1.net/] * Doraemon — Referenced as an example of Japan’s more positive cultural association with AI * https://dora-world.com [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

The AI Business Revolution Is Just Beginning, with Tim Sears, Ph.D.

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

The AI Risk No One Sees Coming — with Kriste Krstovski of Columbia University

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 [https://datascience.columbia.edu/people/kriste-krstovski/]) 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/ [https://www.columbia.edu/%7Ekk3161/]. 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.

Elie Bursztein of Google DeepMind on Mythos and the Cybersecurity Wake-Up Call for Financial Services

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.

Cole Wyeth, PhD Student at the University of Waterloo, on Why We Should Wait to Build Superintelligent AI

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 [https://verapath.com/] * Anthropic * OpenAI * DeepMind * Google * ARC * METR * Troutman Street Audio * Waters Technology Copyright © 2026 by Artificial Intelligence Risk, Inc.