Chain of Thought

AI is accelerating at a breakneck pace, but model quality isn’t the only constraint we face.. There are major infrastructure requirements, energy needs, security, and data pipelines to run AI at scale. This week on Chain of Thought, Cisco’s President and Chief Product Officer Jeetu Patel joins host Conor Bronsdon to reveal what it actually takes to build the critical foundation for the AI era.Jeetu breaks down the three bottlenecks he sees holding AI back today: • Infrastructure limits: not enough power, compute, or data center capacity • A trust deficit: non-deterministic models powering systems that must be predictable • A widening data gap: human-generated data plateauing while machine data explodesJeetu then shares how Cisco is tackling these challenges through secure AI factories, edge inference, open multi-model architectures, and global partnerships with Nvidia, G42, and sovereign cloud providers. Jeetu also explains why he thinks enterprises will soon rely on thousands of specialized models — not just one — and how routing, latency, cost, and security shape this new landscape.Conor and Jeetu also explore high-performance leadership and team culture, discussing building high-trust teams, embracing constructive tension, staying vigilant in moments of success, and the personal experiences that shaped Jeetu’s approach to innovation and resilience.If you want a clearer picture of the global AI infrastructure race, how high-level leaders are thinking about the future, and what it all means for enterprises, developers, and the future of work, this conversation is essential.Chapters:00:00 – Welcome to Chain of Thought0:48 - AI and Jobs: Beyond the Hype6:15 - The Real AI Opportunity: Original Insights10:00 - Three Critical AI Constraints: Infrastructure, Trust, and Data16:27 - Cisco's AI Strategy and Platform Approach19:18 - Edge Computing and Model Innovation22:06 - Strategic Partnerships: Nvidia, G42, and the Middle East29:18 - Acquisition Strategy: Platform Over Products32:03 - Power and Infrastructure Challenges36:06 - Building Trust Across Global Partnerships38:03 - US vs. China: The AI Infrastructure Race40:33 - America's Venture Capital Advantage42:06 - Acquisition Philosophy: Strategy First45:45 - Defining Cisco's True North48:06 - Mission-Driven Innovation Culture50:15 - Hiring for Hunger, Curiosity, and Clarity56:27 - The Power of Constructive Conflict1:00:00 - Career Lessons: Continuous Learning1:02:24 - The Email Question1:04:12 - Joe Tucci's Four-Column Exercise1:08:15 - Building High-Trust Teams1:10:12 - The Five Dysfunctions Framework1:12:09 - Leading with Vulnerability1:16:18 - Closing Thoughts and Where to ConnectConnect with Jeetu Patel:LinkedIn – https://www.linkedin.com/in/jeetupatel/ X(twitter) – https://x.com/jpatel41Cisco - https://www.cisco.com/Connect with ConorBronsdon Substack – https://conorbronsdon.substack.com/ LinkedIn – https://www.linkedin.com/in/conorbronsdon/X (twitter) – https://x.com/ConorBronsdon

The transformer architecture has dominated AI since 2017, but it’s not the only approach to building LLMs - and new architectures are bringing LLMs to edge devicesMaxime Labonne, Head of Post-Training at Liquid AI and creator of the 67,000+ star LLM Course, joins Conor Bronsdon to challenge the AI architecture status quo. Liquid AI’s hybrid architecture, combining transformers with convolutional layers, delivers faster inference, lower latency, and dramatically smaller footprints without sacrificing capability. This alternative architectural philosophy creates models that run effectively on phones and laptops without compromise.But reimagined architecture is only half the story. Maxime unpacks the post-training reality most teams struggle with: challenges and opportunities of synthetic data, how to balance helpfulness against safety, Liquid AI’s approach to evals, RAG architectural approaches, how he sees AI on edge devices evolving, hard won lessons from shipping LFM1 through 2, and much more. If you're tired of surface-level AI takes and want to understand the architectural and engineering decisions behind production LLMs from someone building them in the trenches, this is your episode.Connect with ⁨Maxime Labonne⁩ :LinkedIn – https://www.linkedin.com/in/maxime-labonne/ X (Twitter) – @maximelabonneAbout Maxime – https://mlabonne.github.io/blog/about.html HuggingFace – https://huggingface.co/mlabonne The LLM Course – https://github.com/mlabonne/llm-course Liquid AI – https://liquid.ai Connect with ⁨Conor Bronsdon⁩ :X (twitter) – @conorbronsdonSubstack – https://conorbronsdon.substack.com/ LinkedIn – https://www.linkedin.com/in/conorbronsdon/00:00 Intro — Welcome to Chain of Thought 00:27 Guest Intro — Maxime Labonne of Liquid AI 02:21 The Hybrid LLM Architecture Explained 06:30 Why Bigger Models Aren’t Always Better 11:10 Convolution + Transformers: A New Approach to Efficiency 18:00 Running LLMs on Laptops and Wearables 22:20 Post-Training as the Real Moat 25:45 Synthetic Data and Reliability in Model Refinement 32:30 Evaluating AI in the Real World 38:11 Benchmarks vs Functional Evals 43:05 The Future of Edge-Native Intelligence 48:10 Closing Thoughts & Where to Find Maxime Online

Most AI agents are built backwards, starting with models instead of system architecture.Aishwarya Srinivasan, Head of AI Developer Relations at Fireworks AI, joins host Conor Bronsdon to explain the shift required to build reliable agents: stop treating them as model problems and start architecting them as complete software systems. Benchmarks alone won't save you. Aish breaks down the evolution from prompt engineering to context engineering, revealing how production agents demand careful orchestration of multiple models, memory systems, and tool calls. She shares battle-tested insights on evaluation-driven development, the rise of open source models like DeepSeek v3, and practical strategies for managing autonomy with human-in-the-loop systems. The conversation addresses critical production challenges, ranging from LLM-as-judge techniques to navigating compliance in regulated environments.Connect with Aishwarya Srinivasan:LinkedIn: https://www.linkedin.com/in/aishwarya-srinivasan/Instagram: https://www.instagram.com/the.datascience.gal/Connect with Conor: https://www.linkedin.com/in/conorbronsdon/00:00 Intro — Welcome to Chain of Thought00:22 Guest Intro — Ash Srinivasan of Fireworks AI02:37 The Challenge of Responsible AI05:44 The Hidden Risks of Reward Hacking07:22 From Prompt to Context Engineering10:14 Data Quality and Human Feedback14:43 Quantifying Trust and Observability20:27 Evaluation-Driven Development30:10 Open Source Models vs. Proprietary Systems34:56 Gaps in the Open-Source AI Stack38:45 When to Use Different Models45:36 Governance and Compliance in AI Systems50:11 The Future of AI Builders56:00 Closing Thoughts & Follow Ash OnlineFollow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

This week, we're doing something special and sharing an episode from another podcast we love: The Humans of AI by our friends at Writer. We're huge fans of their work, and you might remember Writer's CEO, May Habib, from the inaugural episode of our own show.From The Humans of AI:Learn how Melisa Russak, lead research scientist at WRITER, stumbled upon fundamental machine learning algorithms, completely unaware of existing research — twice. Her story reveals the power of approaching problems with fresh eyes and the innovative breakthroughs that can occur when constraints become catalysts for creativity.Melisa explores the intersection of curiosity-driven research, accidental discovery, and systematic innovation, offering valuable insights into how WRITER is pushing the boundaries of enterprise AI. Tune in to learn how her journey from a math teacher in China to a pioneer in AI research illuminates the future of technological advancement.Follow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)Check out Writer’s YouTube channel to watch the full interviews. Learn more about WRITER at writer.com. Follow Melisa on LinkedInFollow May on LinkedInCheck out Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

The incredible velocity of AI coding tools has shifted the critical bottleneck in software development from code generation to code reviews. Greg Foster, Co-Founder & CTO of Graphite, joins the conversation to explore this new reality, outlining the three waves of AI that are leading to autonomous agents spawning pull requests in the background. He argues that as AI automates the "inner loop" of writing code, the human-centric "outer loop"—reviewing, merging, and deploying—is now under immense pressure, demanding a complete rethinking of our tools and processes.The conversation then gets tactical, with Greg detailing how a technique called "stacking" can break down large code changes into manageable units for both humans and AI. He also identifies an emerging hiring gap where experienced engineers with strong architectural context are becoming "lethal" with AI tools. This episode is an essential guide to navigating the new bottlenecks in software development and understanding the skills that will define the next generation of high-impact engineers.Follow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)Connect with Greg on LinkedInFollow Greg on XGraphite Website: graphite.devCheck out Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

What’s the first step to building an enterprise-grade AI tool? Malte Ubl, CTO of Vercel, joins us this week to share Vercel’s playbook for agents, explaining how agents are a new type of software for solving flexible tasks. He shares how Vercel's developer-first ecosystem, including tools like the AI SDK and AI Gateway, is designed to help teams move from a quick proof-of-concept to a trusted, production-ready application.Malte explores the practicalities of production AI, from the importance of eval-driven development to debugging chaotic agents with robust tracing. He offers a critical lesson on security, explaining why prompt injection requires a totally different solution - tool constraint - than traditional threats like SQL injection. This episode is a deep dive into the infrastructure and mindset, from sandboxes to specialized SLMs, required to build the next generation of AI tools.Follow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)Connect with Malte on LinkedInFollow Malte on X (formerly Twitter)Learn more about VercelCheck out Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

The technological moat is eroding in the AI era, what new factors separate a successful startup from the rest?Aurimas Griciūnas, CEO of SwirlAI, joins the show to break down the realities of building in this new landscape. Startup success now hinges on speed, strong financial backing, or immediate distribution. Aurimas warns against the critical mistake of prioritizing shiny tools over fundamental engineering and the market gaps this creates.Discover the new moats for AI companies, built on a culture of relentless execution, tight feedback loops, and the surprising skills that define today's most valuable engineers.The episode also looks to the future, with bold predictions about a slowdown in LLM leaps and the coming impact of coding agents and self-improving systems.Follow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)Connect with Aurimas on⁠ ⁠⁠LinkedIn⁠Aurimas' Course: ⁠End-to-End AI Engineering BootcampCheck out Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

As we enter the era of the AI engineer, the biggest challenge isn't technical - it's a shift in mindset. Hamel Husain, a leading AI consultant and luminary in the eval space, joins the podcast to explore the skills and processes needed to build reliable AI. Hamel explains why many teams relying on vanity dashboards and a "buffet of metrics" experience a false sense of security, which is no substitute for customized evals tailored to domain-specific risks. The solution? A disciplined process of error analysis, grounded in manually looking at data to identify real-world failures This discussion is an essential guide to building the continuous learning loops and "experimentation mindset" required to take AI products from prototype to production with confidence. Listen to learn the playbook for building AI reliability, and derive qualitative insights from log data to build customized quantitative guardrails. Follow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)Connect with Hamel on LinkedInFollow Hamel on X/TwitterCheck out his blog: hamel.devCheck out Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

What if your next competitor is not a startup, but a solo builder on a side project shipping features faster than your entire team? For Claire Vo, that's not a hypothetical. As the founder of ChatPRD, formerly the Chief Product and Technology Officer at LaunchDarkly, and host of the How I AI podcast, she has a unique vantage point on the driving forces behind a new blueprint for success.She argues that AI accountability must be driven from the top by an "AI czar" and reveals how a culture of experimentation is the key to overcoming organizational hesitancy. Drawing from her experience as a solo founder, she warns that for incumbents, the cost of moving slowly is the biggest threat and details how AI can finally be used to tackle legacy codebases. The conversation closes with bold predictions on the rise of the "super IC" - who can achieve top-tier impact and salary without managing a team - and the death of product management. Follow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)Connect with Claire on LinkedInFollow Claire on X/TwitterClaire’s podcast How I AICheck out Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

How do you build an AI-native company to a 7ドルM run rate in just six months?According to Marcel Santilli, Founder and CEO of GrowthX, the secret isn't chasing the next frontier model, it's mastering the "messy middle." Drawing on his deep experience at Scale AI and Deepgram, Marcel joins host Conor Bronsdon to share his framework for building durable, customer-obsessed businesses.Marcel argues that the most critical skills for the AI era aren't technical but philosophical: first-principles thinking and the art of delegation.Tune in to learn why GrowthX first focused on services to codify expert work, how AI can augment human talent instead of replacing it, and why speed and brand are a startup's greatest competitive advantages. This conversation offers a clear playbook for building a resilient company by prioritizing culture and relentless shipping.Follow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)Connect with Marcel on LinkedInFollow Marcel on X (formerly Twitter)Learn more about GrowthXCheck out Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

AI isn't just changing healthcare; it's providing the essential help needed to unlock a trillion-dollar opportunity for better care.Andreas Cleve, CEO & Co-founder of Corti, steps in to shed light on AI's immense, yet often misunderstood, transformative potential in this high-stakes environment. Andreas refutes the narrative of healthcare being slow adopters, emphasizing its high bar for trustworthy technology and its constant embrace of new tools. He reveals how purpose-built AI models are already alleviating the "pajama time" burden of documentation for clinicians, enabling faster and more accurate assessments in various specializations. This quiet, impactful adoption is seeing companies grow "like weeds" beyond common expectations.The conversation addresses how AI can tackle the looming global shortage of 10 million healthcare professionals by 2030, reallocating a trillion dollars worth of administrative work back into care. Andreas details Corti’s approach to building invisible, reliable AI through rigorous, compliance-first evaluation, ensuring accuracy and efficiency in real-time. He emphasizes that AI's true role is not replacement, but augmentation, empowering professionals to deliver more care, attract talent, and drive organizational growth.Follow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)LinkedIn: linkedin.com/in/andreascleveX (formerly Twitter): andreascleveCorti Website: corti.aiCheck out Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

AI agents offer unprecedented power, but mastering agent reliability is the ultimate challenge for agentic systems to actually work in production.Mikiko Chandrashekar, Staff Developer Advocate at MongoDB, whose background spans the entire data-to-AI pipeline, unveils MongoDB's vision as the memory store for agents, supporting complex multi-agent systems from data storage and vector search to debugging chat logs. She highlights how MongoDB, reinforced by the acquisition of Voyage, empowers developers to build production-scale agents across various industries, from solo projects to major enterprises. This robust data layer is foundational to ensure agent performance and improve the end user experience.Mikiko advocates for treating agents as software products, applying rigorous engineering best practices to ensure reliability, even for non-deterministic systems. She details MongoDB's unique position to balance GPU/CPU loads and manage data for performance and observability, including Galileo's integrations. The conversation emphasizes the profound need to rethink observability, evaluations, and guardrails in the era of agents, showcasing Galileo's family of small language models for real-time guardrailing, Luna-2, and Insights Engine for automated failure analysis. Discover how building trustworthiness through systematic evaluation, beyond just "vibe checks," is essential for AI agents to scale and deliver value in high-stakes use cases.Follow the hostsFollow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Atin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Vikram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Yash⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Today's Guest(s)Connect with Mikiko on LinkedInFollow Mikiko on X/TwitterExplore Mikiko's YouTube channelCheck out Mikiko's ⁠SubstackConnect with MongoDB on LinkedInConnect with MongoDB on YouTubeCheck out Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard