High Signal: Data Science | Career | AI cover art

High Signal: Data Science | Career | AI

High Signal: Data Science | Career | AI

Written by: Delphina
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 Welcome to High Signal, the podcast for data science, AI, and machine learning professionals. High Signal brings you the best from the best in data science, machine learning, and AI. Hosted by Hugo Bowne-Anderson and produced by Delphina, each episode features deep conversations with leading experts, such as Michael Jordan (UC Berkeley), Andrew Gelman (Columbia) and Chiara Farranato (HBS). Join us for practical insights from the best to help you advance your career and make an impact in these rapidly evolving fields. More on our website: https://high-signal.delphina.ai/© 2026 Delphina Economics
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
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