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

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Delphina's Newsletter

Steve Tadelis, Professor of Economics at UC Berkeley and former senior economist at eBay and Amazon, joins High Signal to bridge the gap between economic theory and the high-stakes reality of data science and AI. Drawing on his experience at the forefront of the world’s largest marketplaces, Steve discusses the "invisible friction" that prevents organizations from acting on data: a combination of misaligned incentives, organizational inertia, and the "Upton Sinclair problem," where leaders are effectively paid not to understand new paradigms.

The conversation moves from the "frustratingly obvious" opportunities left on the floor during eBay’s early years to the relentlessly scientific culture of Amazon. Steve explains why surface-level metrics like conversion rates often mask underlying rot in user retention and how rigorous experimentation, such as his famous $20 million search-ad experiment, can expose the difference between genuine growth and mere navigational intent. We also explore the structural shifts of the AI era, where Steve offers an important counter-narrative: rather than leveling the playing field, AI may act as an "unequalizer" that exponentially rewards those with the deepest critical thinking skills.

LINKS

Steve on LinkedIn

Consumer Heterogeneity and Paid Search Effectiveness by Blake, Nosko, and Tadelis (Econometrica, 2015)

The Limits of Reputation in Platform Markets by Nosko and Tadelis (NBER, 2015)

Information Disclosure as a Matching Mechanism by Tadelis and Zettelmeyer (AER, 2015)

The Anatomy of a Large-Scale Hypertextual Web Search Engine by Brin and Page (with Appendix A: Advertising and Mixed Motives)

Freakonomics Radio Ep 441: Does Advertising Actually Work? (Part 2: Digital)

High Signal podcast

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Delphina's Newsletter

Chris Fonnesbeck, veteran analyst for the Yankees and Mets and creator of the open-source Bayesian modeling library PyMC, joins to unpack why baseball has been a leading indicator for data science and analytics for over a century, and why builders and AI leaders need to pay attention now. The reason it has led is simple: huge incentives and a culture that treats decisions as quantifiable. With wins worth about eight to ten million dollars apiece and front offices built around probabilistic reasoning, baseball has had every reason to push the methods further and faster than industry.

The skillset and culture that built this lead is what AI teams now need to adopt more of: probabilistic thinking, hierarchical models, integrating expert judgment, reasoning carefully under uncertainty, and increasingly causal inference. The conversation traces the throughline from those early statistical innovations to the decisions driving multi-million dollar contracts today, with concrete patterns AI builders can take back to their own work: how to handle small samples and high stakes, why outcomes are the wrong thing to measure, what changes when you push uncertainty all the way through your model, and why robust causal inference needs to be the next frontier.

LINKS

Chris on LinkedIn

The Signal and the Noise: Why So Many Predictions Fail--But Some Don't by Nate Silver

Superforecasting: The Art and Science of Prediction by Tetlock and Gardner

The Book: Playing the Percentages in Baseball by Tango, Lichtman, and Dolpin

High Signal podcast

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Delphina's Newsletter

Noah Bruegmann, President of Data CRT, joins High Signal to discuss how to move your data function from a cost center to a strategic "value center". He explains how AI amplifies your existing data culture, the importance of "no-assistance" reporting, and how rebranding documentation as "Context" can finally secure executive buy-in. Drawing on 15 years of experience spanning trading floors and Silicon Valley startups, Noah argues that for too long, data teams have been submerged under an "iceberg" of invisible data preparation. He details how the arrival of LLMs and agentic tools is fundamentally shifting this landscape, automating technical drudgery and allowing data professionals to transition into what he calls "Jack Ryan" mode: acting as high-level intelligence analysts rather than mere number crunchers.

We dig into the architectural and psychological shifts required to navigate this new era and why the most valuable skill in an AI-augmented world is no longer mastering SQL syntax, but "problem framing": the ability to reduce business ambiguity into high-leverage insights. Noah cautions that while AI offers a dopamine hit of instant answers, it demands a new discipline of rigorous verification to avoid automated hallucinations. The conversation provides a clear directive for executives: move past the "ticket-taker" model and start treating the data team as the essential "left-side brain" for organizational decision-making.

LINKS

Noah on LinkedIn

High Signal podcast

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Delphina's Newsletter

Jordan Morrow, SVP of Data & AI Transformation at AgileOne and the "Godfather of Data Literacy," joins High Signal to discuss the shift from being "data-driven" to becoming "AI-enabled." Jordan warns that many organizations are walking into the same traps that derailed the data science era a decade ago: prioritizing expensive tooling and hype over the cultural change and literacy required to actually move the needle. The pattern is already visible: enterprise AI projects are failing at around 90%, while individuals doing shadow AI are quietly thriving. The catch is that shadow AI brings its own risks, with people feeding sensitive data into public models without governance. He argues that because AI is probabilistic rather than deterministic, the bottleneck for success has shifted from technical coding ability to a user’s ability to apply "Engineered Intelligence," a blend of machine capability and human emotional intelligence.

LINKS

Jordan's new book "Data and AI Skills: Gain the Confidence You Need to Succeed" (also here on Amazon)

Jordan on LinkedIn

High Signal podcast

Watch the podcast episode on YouTube

Delphina's Newsletter