High Signal: Data Science | Career | AI

• Episode 30: The AI Paradox: Why Your Data Team’s Workload is About to Explode

  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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• Episode 29: Why AI Adoption Fails: A Behavioral Framework for AI Implementation

  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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• Episode 28: From Context Engineering to AI Agent Harnesses: The New Software Discipline

  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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• Episode 27: Why Your Data Team Doesn't Have a Seat at the Table (And How to Earn It)

  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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• Episode 26: Gen AI's True Cost: Why Today's Wins Are Tomorrow's Debts

  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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