High Signal: Data Science | Career | AI

Noah Smith, economist and author of Noahpinion, joins High Signal to look at what AI is already changing… and what it isn’t. The conversation moves beyond the usual productivity hype to ask harder questions: Is agentic coding actually increasing revenue per hour worked? Will software remain a high-margin business if AI makes it easy to clone? And what happens when generating content, code, vendors, applications, and companies becomes much cheaper than verifying them? Noah argues that one of the biggest near-term shifts is not simply automation, but trust. AI is beginning to replace parts of the internet’s knowledge infrastructure — search, Stack Overflow, Reddit, and how-to content — while also flooding markets with new forms of slop. For AI builders and leaders, the central challenge may become less about producing more and more about knowing what is real, valuable, and worth trusting…. In a word, verification. LINKS Noah Smith on X Noahpinion — Noah's newsletter Noah's writing we discuss: You Are What You Consume by Noah Smith How Much More Software Do We Really Need? by Noah Smith What If a Few AI Companies End Up With All the Money and Power? by Noah Smith My Thoughts on AI Safety by Noah Smith Updated Thoughts on AI Risk by Noah Smith Salarymen, Specialists, and Small Businesses by Noah Smith Books, essays, and reports mentioned: Status and Culture by W. David Marx (Viking, 2022) If Anyone Builds It, Everyone Dies by Eliezer Yudkowsky and Nate Soares (2025) Machines of Loving Grace by Dario Amodei (2024) All Watched Over by Machines of Loving Grace by Richard Brautigan (poem, 1967) The Bitter Lesson by Rich Sutton (2019) Forecasting the Economic Effects of AI by the Forecasting Research Institute (2026) The Orthogonality Thesis (Arbital) Herbert Simon on the economics of attention (Attention economy) High Signal podcast Watch the podcast episode on YouTube Delphina's Newsletter

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 Thinking Fast and Slow in AI How does LLM use affect decision-making? Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI with Lis Costa (High Signal) The Behavioral Insights Team Lis Costa on LinkedIn High Signal podcast Watch the podcast episode on YouTube Delphina's Newsletter

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 Insight Extractor, Paras' blog on analytics, data science, and business intelligence Watch the conversation on YouTube Delphina's Newsletter

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 Watch the conversation on YouTube Delphina's Newsletter

Anu Bharadwaj (President, Atlassian) joins High Signal to unpack how humans and AI agents will work together across the enterprise, and how that shift could change the very nature of teamwork. Atlassian employees have already built thousands of agents across product, marketing, engineering, and HR teams, while customers like HarperCollins are cutting manual work by 4x as industries from publishing to finance rethink their workflows. We dig into how Atlassian’s culture enables bottom-up experimentation, why grounding and reliability are critical for adoption, and how non-technical teams are often the ones creating the most useful agents. The conversation also looks ahead to the frontiers of multiplayer agent collaboration, proactive and ambient workflows, and the governance and compliance challenges enterprises will face as agents move from tools to teammates. LINKS Anu on LinkedIn Building effective agents by Erik Schluntz and Barry Zhang at Anthropic How we built our multi-agent research system by Anthropic Watch the podcast episode on YouTube Delphina's Newsletter

Amy Edmondson (Harvard Business School) and Mike Luca (Johns Hopkins) join High Signal to unpack what actually drives good decisions in data‑rich organizations. Using contrasts like the Bay of Pigs vs. the Cuban Missile Crisis and product cases such as Airbnb’s work on measuring discrimination, they show how decision quality tracks conversation quality—framing options, surfacing uncertainty, and challenging assumptions. We cover common failure modes (correlation vs. causation, anchoring, hierarchy, false precision), practical meeting designs that raise the signal, and where algorithms and LLMs help or hinder human judgment. LINKS Amy on LinkedIn Mike on LinkedIn Where Data-Driven Decision-Making Can Go Wrong: Five pitfalls to avoid by Michael Luca and Amy C. Edmondson Many Analysts, One Data Set: Making Transparent How Variations in Analytic Choices Affect Results Trillion Dollar Coach by Eric Schmidt, Jonathan Rosenberg, and Alan Eagle Delphina's Newsletter

Lis Costa, Chief of Innovation and Partnerships at the Behavioural Insights Team, joins High Signal to explore how behavioral science is reshaping public policy, digital platforms, and machine learning. She explains how defaults influence behavior at scale, why personalization and chatbots are unlocking new kinds of interventions, and what happens when AI systems meet real-world complexity. We also discuss the limits of nudging, the promise of boosting, and why building for human decision-making requires more than just good models. 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. Lis 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 The Behavioral Insights Team Lis Costa on LinkedIn High Signal podcast Delphina's Newsletter

Roberto Medri, VP of Data Science at Instagram, explains why most experiments fail, how misaligned incentives warp product development, and what it takes to drive real impact with data science. He shares what teams get wrong about launches, why ego gets in the way of learning, and how Instagram turned Reels from a struggling product into a global success. A candid look at product, data, and decision-making inside one of the world’s most influential platforms. LINKS Roberto on LinkedIn High Signal podcast Delphina's Newsletter