High Signal: Data Science | Career | AI

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 The Evolution of the Modern Data Engineer: From Coders to Architects Why Most AI Agents Fail (and What It Takes to Reach Production) with Anu Brahadwaj (Atlassian) The End of Programming As We Know It with Tim O'Reilly The Incentive Problem in Shipping AI Products — and How to Change It with Roberto Medri (Meta) Andrej Karpathy — AGI is still a decade away Chris Child on LinkedIn High Signal podcast Watch the podcast episode on YouTube Delphina's Newsletter

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 Context Engineering for Agents by Lance Martin Learning the Bitter Lesson by Lance Martin Context Engineering in Manus by Lance Martin Context Rot: How Increasing Input Tokens Impacts LLM Performance by Chroma Building effective agents by Erik Schluntz and Barry Zhang at Anthropic Effective context engineering for AI agents by Anthropic How we built our multi-agent research system by Anthropic Measuring AI Ability to Complete Long Tasks by METR Your AI Product Needs Evals by Hamel Husain Introducing Roast: Structured AI workflows made easy by Shopify Watch the podcast episode on YouTube Delphina's Newsletter

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 Fei-Fei Li on Generative AI as a Civilizational Technology Tim O'Reilly on The End of Programming As We Know It Watch the conversation on YouTube Delphina's Newsletter

Andrés Bucchi (Chief Data Officer, LATAM Airlines) joins High Signal to unpack how a century-old airline reinvented itself with data and AI—and how that transformation is unlocking value from fuel efficiency to fraud detection. LATAM has built a massive data operation, experimenting across everything from pricing to operations, while customers benefit from a more reliable and secure travel experience. We dig into how LATAM fostered an experimentation culture, why existing data infrastructure is a critical asset, and how the biggest bottleneck in AI adoption isn't the technology itself, but human decision-making. The conversation also looks ahead to the future of generative AI as a software engineering problem, and the organizational changes needed to unlock its full potential. LINKS Andrés Bucchi on LinkedIn Tim O'Reilly on The End of Programming As We Know It, High Signal Watch the conversation on YouTube Delphina's Newsletter

Tomasz Tunguz (Theory Ventures) joins High Signal to unpack why a trillion dollars of market cap is up for grabs as AI reshapes enterprise software. He explains why workflows are now changing faster than packaged software can keep up, how “liquid software” is redefining CRM and marketing automation, and why background agents will require a new kind of “agent inbox.” We discuss the compounding errors that arise when tools are chained too finely, the hidden AI technical debt accumulating in today’s systems, and why modular stacks—mixing local and cloud models—will beat monolithic apps. The conversation also surfaces early memory architectures, what breaks when one IC manages 100 agents, and how these shifts change the real bottlenecks in scaling AI. LINKS Tomasz' Website (check out his blog!) Tomasz on LinkedIn Building effective agents by Erik Schluntz and Barry Zhang at Anthropic How we built our multi-agent research system by Anthropic Tim O'Reilly on The End of Programming As We Know It Delphina's Newsletter

Daragh Sibley, Chief Algorithms Officer at Literati and former Director of Data Science at Stitch Fix, joins High Signal to unpack how machine-learning moves from slide-deck promise to bottom-line impact. He walks through his shift from academic research on how kids learn to read to owning inventory and personalization algorithms that decide which five books land in every child’s box. We dig into the moment a data leader stops advising and starts owning P&L-critical calls, why some problems deserve simple analytics while others need high-dimensional models, and how to design workflows where human judgment and algorithmic predictions share accountability. Along the way we talk incentive design, balancing exploration and exploitation in inventory, and measuring success in dollars—not dashboards. LINKS Daragh on LinkedIn Eric Colson on Why 90% of Data Science Fails—And How to Fix It Sudarshan Seshadri on High-Stakes AI Systems and the Cost of Getting It Wrong Delphina's Newsletter

Sudarshan Seshadri—VP of AI, Data Science, and Foundations Engineering at Alto Pharmacy—joins us to explore what it takes to build high-stakes AI systems that people can actually trust. He shares lessons from deploying machine learning and LLMs in healthcare, where speed, safety, and uncertainty must be carefully balanced. We talk about designing AI to support pharmacist judgment, the shift from bottlenecks to decision backbones, and why great data leaders are really architects of how irreversible decisions get made. LINKS Suddu on LinkedIn Careers at Alto Pharmacy High Signal podcast Delphina's Newsletter