The Artificial Intelligence Podcast

Zachary Hanif, VP of Data and AI at Twilio, brings a wealth of experience leading AI initiatives in both regulated financial services and communication platforms. He dives into the delicate balance between explainable AI and high-performing black box models, stressing the importance of tailored governance frameworks. Hanif discusses the challenges of moving AI from proof-of-concept to production, with 80% of pilots failing. He emphasizes the role of privacy-by-design principles and collaboration between tech teams and domain experts for successful implementations.

Join Santosh Kaveti, CEO of ProArch, as he addresses the critical gap between AI ambition and execution in enterprise environments, where despite widespread C-suite commitment, only a quarter of organizations achieve meaningful AI implementation. Kaveti outlines his four-pillar framework for AI operationalization, emphasizing that AI adoption is fundamentally a people and culture problem rather than a technology issue, with 63% of companies lacking basic AI governance policies. He discusses the growing challenges of shadow AI usage, the convergence of IT and operational technology creating new security vulnerabilities in critical infrastructure, and how organizations can build compliance frameworks that won't become obsolete as AI regulations continue to evolve rapidly.

Join Suman Kanuganti, CEO of Personal AI, as he discusses the shift away from the one-size-fits-all approach of large language models toward specialized personal language models that capture individual decision-making patterns and expertise. Kanuganti explains how artificial personal intelligence differs from artificial general intelligence, focusing on creating AI personas that can run efficiently on edge devices rather than requiring massive cloud infrastructure while maintaining privacy-by-design architecture. He examines the future of distributed AI systems and how smaller, specialized models can deliver superior performance for specific use cases while addressing the fundamental scalability and cost challenges facing the current AI industry dominated by power-hungry large language models.

Join Jay Dawani, CEO of Lemurian Labs, as he discusses the critical infrastructure challenges facing AI development and his company's efforts to rebuild the AI software stack from the ground up. Drawing from his experience as a former NASA AI advisor working on Mars Rover navigation and exoplanet research, Dawani explains how current AI systems are plagued by massive inefficiencies, with some data centers operating at only 10-15% utilization despite consuming enormous amounts of energy. The conversation explores how the industry must shift from kernel-based programming models designed for single GPUs to dynamic runtime systems that can efficiently manage communication and memory across hundreds of thousands of processors, ultimately making advanced AI more accessible and sustainable.

Join Richard White, CEO of Fathom, as he discusses how AI is transforming workplace productivity through intelligent meeting transcription and note-taking solutions. White explains how modern AI can now write better notes than most humans within 30 seconds of a meeting ending, while also enabling companies to capture and disseminate organizational knowledge more effectively by analyzing patterns across thousands of hours of meetings. The conversation explores the evolution from basic transcription services to sophisticated AI workflows that can generate action items, update CRMs, and provide executives with real-time alerts about important developments across their teams, ultimately reshaping how organizations communicate and collaborate in an AI-first workplace.

Join Nadia Harhen, General Manager of AI Simulation at SandboxAQ, and Jordan Crivelli-Decker from the Biosim team as they discuss how Large Quantitative Models (LQMs) are revolutionizing drug discovery and materials science by generating synthetic data through physics-based computational chemistry rather than just predicting outcomes like traditional language models. They explain how this approach accelerates drug development timelines, reduces animal testing needs, and enables breakthrough solutions for complex molecular problems that conventional software cannot handle, including work with Nobel laureate Dr. Stanley Prusiner that moved from research to clinical trials in just 18 months. The conversation explores how combining quantum mechanics principles with machine learning creates novel molecular IP across industries from pharmaceuticals to defense applications, bridging the gap between AI capabilities and practical scientific breakthroughs.

Debo Dutta, Chief AI Officer at Nutanix, discusses the critical challenges enterprises face when scaling generative AI beyond proof-of-concepts, identifying skills gaps, unclear implementation pathways, and data governance concerns as primary barriers. He emphasizes that successful AI transformation requires a holistic approach addressing people, processes, and technology, with particular focus on data cleaning and creating "gold standard" datasets as foundational competitive advantages. Looking ahead, Dutta predicts the emergence of AI agents operating with human oversight rather than fully autonomous systems, while highlighting that clean, private enterprise data will become the primary moat as AI models become increasingly commoditized.

Dr. Michael Wu, Chief AI Scientist at PROS, discusses his transition from computational neuroscience research to applied AI in industry, emphasizing how his perspective without domain-specific baggage has enabled innovative problem-solving approaches. He explores the evolving landscape of AI agents and tools, highlighting the importance of Model Control Protocol (MCP) as a bridge between passive language models and actionable AI systems that can interact with existing enterprise tools. Wu emphasizes the need for organizations to create fail-safe environments that encourage AI experimentation while maintaining security, and advocates for balancing innovation speed with responsible development practices that prioritize safety, privacy, and legal compliance from the outset.

Maddie Daianu, Head of Data & AI at Intuit Credit Karma, focuses on building sophisticated recommendation systems that personalize financial offerings for over 100 million members by creating unified consumer profiles that track complete financial journeys. She emphasizes the critical importance of maintaining strict compliance requirements in the FinTech industry while leveraging both traditional machine learning for core systems and selective use of generative AI for contextualization. Her approach includes rigorous evaluation frameworks with five key metrics—product alignment, safety, compliance, data accuracy, and system integration—ensuring all AI implementations meet the high standards required for financial services.

Eleanor Lightbody, CEO of Luminance, explains how generative AI transformed legal work by providing 10x improvements over traditional machine learning systems, enabling end-to-end contract automation rather than just document review. She emphasizes that trustworthy AI in legal contexts requires multiple models checking each other's work to ensure consensus, with systems reverting to human oversight when confidence thresholds aren't met, particularly crucial for high-stakes deals and regulatory compliance. Lightbody discusses how AI agents will evolve beyond assistants to proactively complete work and seek approval, fundamentally changing how professionals interact with software and potentially disrupting traditional workflows while creating new opportunities.