Episodes

  • Episode 32: The Post Coding-Era: What Happens When AI Writes the System?

    Episode 32: The Post Coding-Era: What Happens When AI Writes the System?
    Jan 13 2026
    Nicholas Moy, former Head of Research at Windsurf (formerly Codeium) now at Google DeepMind, joins High Signal to discuss the rapid evolution of AI-powered software engineering. We discuss Windsurf's journey from early prototypes that struggled with compounding errors to the successful launch of their agentic coding product. Nick explains that building a startup in the current climate requires a strategy of "disrupting yourself" to avoid the innovator’s dilemma; companies must be ready to pivot as soon as a new frontier model makes previously impossible features viable. He argues that traditional technical moats are increasingly fragile, and true defensibility now comes from real-world usage data, brand reputation, and a deep intuition for what users need at the frontier of these capabilities. The conversation also explores the shift from "co-driving" to a truly "agentic" era of development. While early AI tools required developers to monitor every line of code in real-time, the next phase involves assigning projects to agents that work independently and present their results for review later. Nick highlights the engineering challenge of writing software that remains useful even as the underlying models improve every few months, suggesting that builders should focus on "invariants" (the fundamental information needs and output forms of a task) rather than brittle, model-specific harnesses. LINKS Nicholas Moy on LinkedIn (https://www.linkedin.com/in/nicholas-moy/) Introducing Google Antigravity, a New Era in AI-Assisted Software Development (https://antigravity.google/blog/introducing-google-antigravity) “A Flash of Deflation - Gemini 3 Flash represents a step function increase in model deflation : a gauntlet thrown” (https://tomtunguz.com/gemini-3-flash-price-performance/) by Thomas Tunguz Tomasz Tunguz on Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It) (https://high-signal.delphina.ai/episode/why-a-trillion-dollars-of-market-cap-is-up-for-grabs-and-how-ai-teams-will-win-it) High Signal podcast (https://high-signal.delphina.ai/) Watch the podcast episode on YouTube (https://youtu.be/AT8iDaZP7zs) Delphina's Newsletter (https://delphinaai.substack.com/)
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    42 mins
  • Episode 31: Why Data Governance In Your Org is Broken (And How to Fix It)

    Episode 31: Why Data Governance In Your Org is Broken (And How to Fix It)
    Dec 30 2025
    Cara Dailey, VP and Head of Data Strategy at Early Warning (the parent company of Zelle), joins High Signal to discuss the evolution of high-stakes data leadership and governance. From her early work in online advertising at DoubleClick to shaping data strategy at Nike and holding Chief Data Officer roles at Bank of the West and T. Rowe Price, Cara has seen every iteration of the data leader’s role. Now, she’s navigating her 'product era'—shaping the data strategy for Early Warning's Decisions Intelligence business, where she leverages rich financial data and data science to drive fraud monitoring and modeling. In this episode, Cara shares her pragmatic 'progress over perfection' approach to governance, why she’s abandoning monolithic platforms in favor of incremental data products, and her 80/20 rule for balancing operational rigor with innovation. We also discuss why 'loving' data isn't enough—you have to actually 'take care' of it—and why AI is finally shining a spotlight on the often-neglected fundamentals of data stewardship and conversational BI. LINKS Cara Dailey on LinkedIn (https://www.linkedin.com/in/cara-dailey/) Why AI Adoption Fails: A Behavioral Framework for AI Implementation, A High Signal Conversation with Lis Costa (Chief of Innovation, Behavioural Insights Team) (https://high-signal.delphina.ai/episode/why-ai-adoption-fails-a-behavioral-framework-for-ai-implementation) Watch the podcast episode on YouTube (https://youtu.be/KphTkF_NrEA) High Signal podcast (https://high-signal.delphina.ai/) Delphina's Newsletter (https://delphinaai.substack.com/)
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    47 mins
  • Episode 30: The AI Paradox: Why Your Data Team’s Workload is About to Explode

    Episode 30: The AI Paradox: Why Your Data Team’s Workload is About to Explode
    Dec 11 2025
    Chris Child, VP of Product, Data Engineering at Snowflake, joins High Signal to deliver a new playbook for data leaders based on his recent MIT report, revealing why AI is paradoxically creating more work for data teams, not less. He explains how the function is undergoing a forced evolution from back-office “plumbing” to the strategic core of the enterprise, determining whether AI initiatives succeed or fail. The conversation maps the new skills and organizational structures required to navigate this shift. We dig into why off-the-shelf LLMs consistently fail to generate useful SQL without a semantic layer to provide business context, and how the most effective data engineers must now operate like product managers to solve business problems. Chris provides a clear framework on the shift from writing code to managing a portfolio of AI agents, why solving for AI risk is an extension of existing data governance, and the counterintuitive strategy of moving slowly on foundations to unlock rapid, production-grade deployment. LINKS * MIT Technology Review Report: Redefining Data Engineering in the Age of AI (https://www.snowflake.com/en/redefining-data-engineering-in-the-age-of-ai/) * The Evolution of the Modern Data Engineer: From Coders to Architects (https://www.snowflake.com/en/blog/evolution-of-the-data-engineer/) * Why Most AI Agents Fail (and What It Takes to Reach Production) with Anu Brahadwaj (Atlassian) (https://high-signal.delphina.ai/episode/anu) * The End of Programming As We Know It with Tim O'Reilly (https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it) * The Incentive Problem in Shipping AI Products — and How to Change It with Roberto Medri (Meta) (https://high-signal.delphina.ai/episode/roberto-medri-on-the-incentive-problem-in-shipping-ai-products----and-how-to-change-it) * Andrej Karpathy — AGI is still a decade away (https://www.dwarkesh.com/p/andrej-karpathy) * Chris Child on LinkedIn (https://www.linkedin.com/in/chrischild/) * High Signal podcast (https://high-signal.delphina.ai/) * Watch the podcast episode on YouTube (https://youtu.be/aZvZsXo7bu0) * Delphina's Newsletter (https://delphinaai.substack.com/)
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    50 mins
  • Episode 29: Why AI Adoption Fails: A Behavioral Framework for AI Implementation

    Episode 29: Why AI Adoption Fails: A Behavioral Framework for AI Implementation
    Nov 28 2025
    Liz Costa of the Behavioral Insights Team returns to High Signal to deliver a critical behavioral science playbook for the AI era focused on human and business impact. We discuss why the potential of AI can only be fulfilled by understanding a single bottleneck: human behavior. The conversation reveals why leaders must intervene now to prevent temporary adoption patterns from calcifying into permanent organizational norms, the QWERTY Effect, and how to move organizations past simply automating drudgery to achieving deep integration. We dig into why AI adoption is fundamentally a behavioral challenge, providing a diagnostic framework for leaders to identify stalled progress using the Motivation-Capability-Trust triad. Liz explains how to reframe AI deployment by leveraging Loss Aversion to bypass employee skepticism, and how to design workflows that improve human reasoning rather than replace it. The conversation provides clear guidance on intentional task offloading, the power of using AI to stress-test decisions, and why sanctioning employee experimentation is essential to discovering high-value use cases. LINKS AI & Human Behaviour: Augment, Adopt, Align, Adapt (https://www.bi.team/publications/ai-and-human-behaviour/) Thinking Fast and Slow in AI (https://sites.google.com/view/sofai/home) How does LLM use affect decision-making? (https://www.bi.team/wp-content/uploads/2025/09/How-can-LLMs-reduce-our-own-biases-Analysis-Report.pdf) Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI with Lis Costa (High Signal) (https://high-signal.delphina.ai/episode/defaults-decisions-and-dynamic-systems-behavioral-science-meets-ai) The Behavioral Insights Team (https://www.bi.team/) Lis Costa on LinkedIn (https://uk.linkedin.com/in/elisabeth-costa-6a5b35248) High Signal podcast (https://high-signal.delphina.ai/) Watch the podcast episode on YouTube (https://youtu.be/dXId0BbcsSE) Delphina's Newsletter (https://delphinaai.substack.com/)
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    49 mins
  • Episode 28: From Context Engineering to AI Agent Harnesses: The New Software Discipline

    Episode 28: From Context Engineering to AI Agent Harnesses: The New Software Discipline
    Nov 13 2025
    Lance Martin of LangChain joins High Signal to outline a new playbook for engineering in the AI era, where the ground is constantly shifting under the feet of builders. He explains how the exponential improvement of foundation models is forcing a complete rethink of how software is built, revealing why top products from Claude Code to Manus are in a constant state of re-architecture simply to keep up. We dig into why the old rules of ML engineering no longer apply, and how Rich Sutton's "bitter lesson" dictates that simple, adaptable systems are the only ones that will survive. The conversation provides a clear framework for leaders on the critical new disciplines of context engineering to manage cost and reliability, the architectural power of the "agent harness" to expand capabilities without adding complexity, and why the most effective evaluation of these new systems is shifting away from static benchmarks and towards a dynamic model of in-app user feedback. LINKS Lance on LinkedIn (https://www.linkedin.com/in/lance-martin-64a33b5/) Context Engineering for Agents by Lance Martin (https://rlancemartin.github.io/2025/06/23/context_engineering/) Learning the Bitter Lesson by Lance Martin (https://rlancemartin.github.io/2025/07/30/bitter_lesson/) Context Engineering in Manus by Lance Martin (https://rlancemartin.github.io/2025/10/15/manus/) Context Rot: How Increasing Input Tokens Impacts LLM Performance by Chroma (https://research.trychroma.com/context-rot) Building effective agents by Erik Schluntz and Barry Zhang at Anthropic (https://www.anthropic.com/engineering/building-effective-agents) Effective context engineering for AI agents by Anthropic (https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) How we built our multi-agent research system by Anthropic (https://www.anthropic.com/engineering/multi-agent-research-system) Measuring AI Ability to Complete Long Tasks by METR (https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/) Your AI Product Needs Evals by Hamel Husain (https://hamel.dev/blog/posts/evals/index.html) Introducing Roast: Structured AI workflows made easy by Shopify (https://shopify.engineering/introducing-roast) Watch the podcast episode on YouTube (https://youtu.be/2Muxy3wE-E0) Delphina's Newsletter (https://delphinaai.substack.com/)
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    51 mins
  • Episode 27: Why Your Data Team Doesn't Have a Seat at the Table (And How to Earn It)

    Episode 27: Why Your Data Team Doesn't Have a Seat at the Table (And How to Earn It)
    Oct 30 2025
    Paras Doshi (Head of Data, Opendoor; former data leader at Amazon) joins High Signal to unpack the playbook for building an indispensable data function. He shares his experience tackling the classic scaling challenge of fragmented data at Opendoor, where rapid growth led to inconsistent metrics across the business, and turning the data function into a centralized strategic asset. We dive deep into how to earn a true seat at the table, why he believes AI is creating the "100x individual contributor," and how the principles of agency, autonomy, and adaptability are the new essentials for data careers. The conversation also explores the pragmatic divide between batch and real-time ML, how to identify a truly data-led company, and why leaders must shield their top talent to unlock disproportionate impact. LINKS Paras Doshi on LinkedIn (https://www.linkedin.com/in/doshiparas/) Insight Extractor, Paras' blog on analytics, data science, and business intelligence (https://insightextractor.com/) Watch the conversation on YouTube (https://youtu.be/DDSKxL_JeLc) Delphina's Newsletter (https://delphinaai.substack.com/)
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    42 mins
  • Episode 26: Gen AI's True Cost: Why Today's Wins Are Tomorrow's Debts

    Episode 26: Gen AI's True Cost: Why Today's Wins Are Tomorrow's Debts
    Oct 16 2025
    Vishnu Ram Venkataraman (Generative AI Executive & Entrepreneur; former AI Leader at Credit Karma and Intuit) joins High Signal to unpack the true cost of generative AI. Having scaled AI solutions impacting over 140 million users, Vishnu reveals why the ease of shipping Gen AI prototypes often masks significant operational and engineering debts, challenging the conventional wisdom of rapid deployment. We dive deep into the strategic shift from traditional ML to Gen AI, discussing why the shelf value of code is dramatically falling, how to design new organizational triads for continuous iteration, and the critical differences in testing probabilistic AI systems. The conversation also explores how to manage risk with sensitive data, the power of synthetic data in early development, and which mature ML practices remain indispensable in the new AI era. LINKS Vishnu on LinkedIn (https://www.linkedin.com/in/vishnuvram/) Fei-Fei Li on Generative AI as a Civilizational Technology (https://high-signal.delphina.ai/episode/fei-fei-on-how-human-centered-ai-actually-gets-built) Tim O'Reilly on The End of Programming As We Know It (https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it) Watch the conversation on YouTube (https://youtu.be/vDQdCl_EOKg) Delphina's Newsletter (https://delphinaai.substack.com/)
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    43 mins
  • Episode 25: How Data-Driven Growth Redefined a Media Giant

    Episode 25: How Data-Driven Growth Redefined a Media Giant
    Oct 2 2025
    Sergey Fogelson (VP of Data Science, Televisa Univision) joins High Signal to reveal how the world’s largest Spanish-language media company built a sophisticated data engine from the ground up. This transformation fueled a tenfold expansion of its digital streaming business by redefining how the company connects with 300 million viewers worldwide. At the heart of this success is a proprietary household graph that creates a single, privacy-first view of a massive and culturally diverse audience. We dig into the journey from basic data unification to building production-ready recommendation engines, how his team uses embeddings on user behavior to uncover surprising connections in content consumption, and the trade-offs between investing in internal data tools versus direct revenue-driving products. The conversation also explores a pragmatic framework for AI adoption, showing how foundational machine learning often outperforms chasing the latest trends and where LLMs can deliver real, measurable value. LINKS Sergey Fogelson on LinkedIn (https://www.linkedin.com/in/sergeyfogelson/) Watch the conversation on YouTube (https://youtu.be/f9R8mGcwygU) Delphina's Newsletter (https://delphinaai.substack.com/)
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    56 mins