High Signal: Data Science | Career | AI

Benn Stancil, writer and co-founder of Mode, joins High Signal to ask some uncomfortable questions about the current AI moment. Is now actually a terrible time to start a company? If the tools you build on today are obsolete in six months, at what point does the head start stop mattering? Is all that context engineering you're doing a waste of time, destined to go the way of Boolean search syntax in the 90s?

Benn argues that AI is turning us all into Steve Jobs, not the visionary who delegated, but the one who berated people over pixel placement. As AI takes over the doing, our job becomes obsessing over the polish. He makes the case that technical debt may be self-healing: if future models can untangle the mess today's models made, then messy code isn't debt…it's a spec for a clean rewrite.

We also dig into why Claude Cowork can't work. AI has these uncanny ticks you can't beat out, so anything it writes "as you" will smell like AI. The solution isn't better AI writing—it's to stop pretending we write to each other at all. Benn envisions a future where communication is radically intermediated: I dump facts into a shared repository, your AI reads them, and nobody bothers with the social decoration in between.

LINKS

Benn’s blog on Substack

Benn.website, with links to all everything else Benn related

Will there ever be a worse time to start a startup? Today's frontier is tomorrow's tech debt.

Why Cowork can’t work: The future isn’t collaborative.

Producer theory: Platforms are overrated.

Tim O’Reilly on High Signal: The End of Programming As We Know It

Watch the podcast episode on YouTube

Delphina's Newsletter

Nicholas Moy, former Head of Research at Windsurf & now at Google DeepMind, joins High Signal to discuss the shift from "co-driving" to a truly "agentic" era of development. 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.

LINKS

Nicholas Moy on LinkedIn

Introducing Google Antigravity, a New Era in AI-Assisted Software Development

“A Flash of Deflation - Gemini 3 Flash represents a step function increase in model deflation : a gauntlet thrown” by Thomas Tunguz

Tomasz Tunguz on Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It)

High Signal podcast

Watch the podcast episode on YouTube

Delphina's Newsletter

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

Why AI Adoption Fails: A Behavioral Framework for AI Implementation, A High Signal Conversation with Lis Costa (Chief of Innovation, Behavioural Insights Team)

Watch the podcast episode on YouTube

High Signal podcast

Delphina's Newsletter

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

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