app03 Thu, 18 Jun 2026 03:47:51 +0000 Fireside (https://fireside.fm) High Signal: Data Science | Career | AI https://highsignal.fireside.fm Wed, 17 Jun 2026 23:46:14 -0400 Welcome to High Signal, the podcast for data science, AI, and machine learning professionals. High Signal brings you the best from the best in data science, machine learning, and AI. Hosted by Hugo Bowne-Anderson and produced by Delphina, each episode features deep conversations with leading experts, such as Michael Jordan (UC Berkeley), Andrew Gelman (Columbia) and Chiara Farranato (HBS). Join us for practical insights from the best to help you advance your career and make an impact in these rapidly evolving fields. More on our website: https://high-signal.delphina.ai/ en-us © 2026 Delphina episodic Welcome to High Signal, where you’ll hear the best from the best in data science, machine learning, and AI. The goal of this podcast is to bring high signal, to help you advance your careers in data science, ML, and AI. Delphina Welcome to High Signal, the podcast for data science, AI, and machine learning professionals. High Signal brings you the best from the best in data science, machine learning, and AI. Hosted by Hugo Bowne-Anderson and produced by Delphina, each episode features deep conversations with leading experts, such as Michael Jordan (UC Berkeley), Andrew Gelman (Columbia) and Chiara Farranato (HBS). Join us for practical insights from the best to help you advance your career and make an impact in these rapidly evolving fields. More on our website: https://high-signal.delphina.ai/ false data, data science, machine learning, AI Delphina hugobowne@gmail.com Episode 41: The Verification Crisis: Why Trust Is the New Bottleneck in AI https://highsignal.fireside.fm/41 f4c6c44d-8665-4f29-93fd-a7e4956aea15 Wed, 17 Jun 2026 22:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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? 53:27 true 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's writing we discuss:

Books, essays, and reports mentioned:

]]> data science, ML, AI, GenAI 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's writing we discuss:

Books, essays, and reports mentioned:

]]> 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's writing we discuss:

Books, essays, and reports mentioned:

]]> https://fireside.fm/player/v2/l27Jx_CL+jFw6Xeox ]]> Episode 40: The Economic Reality of AI: Friction, Talent, and the Future of the Firm https://highsignal.fireside.fm/40 c622611a-5c91-4241-bdd2-30c325e36821 Mon, 25 May 2026 23:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 58:32 false 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

]]> data science, ML, AI, GenAI 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+U6Ytb5Va ]]> Episode 39: The 100-Year Lead: What Baseball Teaches Us About the Future of AI https://highsignal.fireside.fm/39 d594d185-0d1a-4d94-b5af-67645caddf1d Tue, 12 May 2026 00:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Chris Fonnesbeck, creator of the open-source Bayesian modeling library PyMC and veteran analyst for the Yankees and Mets, 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 arc runs from Henry Chadwick's 1860s box score, to FC Lane's linear run estimator decades before formal regression, to Bill James and Moneyball, to today's Hawkeye cameras logging six or seven terabytes per game, automated strike zones adjudicated through a game-theoretic challenge system, and a pitch-design industry that turns marginal pitchers into elite ones by tweaking grip and release. 56:07 false 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 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+_7Kgwmmq ]]> Episode 38: Why AI Won’t Fix Your Data Culture, It Will Only Amplify It (And What To Do About It) https://highsignal.fireside.fm/38 bf755203-17c8-400d-98dd-5790c5b0147d Thu, 16 Apr 2026 18:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 45:46 false 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

]]> data science, ML, AI, GenAI 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+07wUCQzX ]]> Episode 37: Engineered Intelligence and The Data Science Problem in AI https://highsignal.fireside.fm/37 0b920ae2-0e66-4b36-ae9f-a47f3180499f Thu, 02 Apr 2026 01:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 46:14 false 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

]]> data science, ML, AI, GenAI 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+OdJqWuxX ]]> Episode 36: AI and the Judgment Problem in Data Science https://highsignal.fireside.fm/36 a407c218-174c-40f7-96ce-f973cb7d8971 Thu, 19 Mar 2026 02:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Dawn Woodard (Distinguished Engineer, LinkedIn), Andrés Bucchi (LATAM Airlines), and Jeremy Hermann (CEO & Co-Founder, Delphina) join High Signal for a deep dive into the shifting architecture of data science & analytics in the era of AI. As the industry moves from static dashboards to vibe coding and conversational querying, this panel of industry veterans explores why traditional data fundamentals—strict cataloging, verifiable outputs, and a single source of truth—are suddenly the most critical bottlenecks in the AI era. 1:03:30 false Dawn Woodard (Distinguished Engineer, LinkedIn), Andrés Bucchi (LATAM Airlines), and Jeremy Hermann (CEO & Co-Founder, Delphina) join High Signal for a deep dive into the shifting architecture of data science & analytics in the era of AI. As the industry moves from static dashboards to vibe coding and conversational querying, this panel of industry veterans explores why traditional data fundamentals—strict cataloging, verifiable outputs, and a single source of truth—are suddenly the most critical bottlenecks in the AI era.

We dig into the sobering reality of the "source of truth" problem, where the speed of AI-generated code far outpaces our ability to define what "correct" actually looks like in a complex enterprise. The conversation reveals how AI is breaking legacy experimentation platforms, the transition of the data analyst into a "verifier" of AI-generated workflows, and why "headless" security architectures are essential for the next generation of autonomous agents. From the limitations of LLMs in causal reasoning to the challenges of integrating AI into "brownfield" enterprise codebases, this discussion provides a grounded framework for leaders navigating the gap between AI hype and operational reality.

LINKS

]]> data science, ML, AI, GenAI Dawn Woodard (Distinguished Engineer, LinkedIn), Andrés Bucchi (LATAM Airlines), and Jeremy Hermann (CEO & Co-Founder, Delphina) join High Signal for a deep dive into the shifting architecture of data science & analytics in the era of AI. As the industry moves from static dashboards to vibe coding and conversational querying, this panel of industry veterans explores why traditional data fundamentals—strict cataloging, verifiable outputs, and a single source of truth—are suddenly the most critical bottlenecks in the AI era.

We dig into the sobering reality of the "source of truth" problem, where the speed of AI-generated code far outpaces our ability to define what "correct" actually looks like in a complex enterprise. The conversation reveals how AI is breaking legacy experimentation platforms, the transition of the data analyst into a "verifier" of AI-generated workflows, and why "headless" security architectures are essential for the next generation of autonomous agents. From the limitations of LLMs in causal reasoning to the challenges of integrating AI into "brownfield" enterprise codebases, this discussion provides a grounded framework for leaders navigating the gap between AI hype and operational reality.

LINKS

]]> Dawn Woodard (Distinguished Engineer, LinkedIn), Andrés Bucchi (LATAM Airlines), and Jeremy Hermann (CEO & Co-Founder, Delphina) join High Signal for a deep dive into the shifting architecture of data science & analytics in the era of AI. As the industry moves from static dashboards to vibe coding and conversational querying, this panel of industry veterans explores why traditional data fundamentals—strict cataloging, verifiable outputs, and a single source of truth—are suddenly the most critical bottlenecks in the AI era.

We dig into the sobering reality of the "source of truth" problem, where the speed of AI-generated code far outpaces our ability to define what "correct" actually looks like in a complex enterprise. The conversation reveals how AI is breaking legacy experimentation platforms, the transition of the data analyst into a "verifier" of AI-generated workflows, and why "headless" security architectures are essential for the next generation of autonomous agents. From the limitations of LLMs in causal reasoning to the challenges of integrating AI into "brownfield" enterprise codebases, this discussion provides a grounded framework for leaders navigating the gap between AI hype and operational reality.

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+f-23mSzF ]]> Episode 35: Beyond Online Experimentation: Generative Software That Optimizes Itself https://highsignal.fireside.fm/35 ffe555a5-d5b0-4dd6-85c8-12f5b502664b Wed, 04 Mar 2026 20:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina Martin Tingley, Head of Windows Experimentation at Microsoft and former Head of the Experimentation Platform Analysis Team at Netflix, talks about why humans are the bottleneck in experimentation, and how a five-level maturity framework points the way toward self-optimizing software. 55:11 false Martin Tingley, Head of Windows Experimentation at Microsoft and former Head of the Experimentation Platform Analysis Team at Netflix, talks about why humans are the bottleneck in experimentation, and how a five-level maturity framework points the way toward self-optimizing software.

Our conversation traces the path from basic hypothesis testing to a frontier where Generative AI creates, evaluates, and refines product variants in a closed loop. We explore the architectural shift required to move from testing single variants to optimizing entire parameter spaces, and how startups are already using AI to generate production-ready landing pages for Fortune 500 companies in hours rather than weeks. Tingley also shares a strategic lens on "experimentation programs," explaining how plotting the distribution of treatment effects across different product areas can serve as a powerful tool for capital allocation and high-level strategy.

LINKS

]]> data science, ML, AI, GenAI Martin Tingley, Head of Windows Experimentation at Microsoft and former Head of the Experimentation Platform Analysis Team at Netflix, talks about why humans are the bottleneck in experimentation, and how a five-level maturity framework points the way toward self-optimizing software.

Our conversation traces the path from basic hypothesis testing to a frontier where Generative AI creates, evaluates, and refines product variants in a closed loop. We explore the architectural shift required to move from testing single variants to optimizing entire parameter spaces, and how startups are already using AI to generate production-ready landing pages for Fortune 500 companies in hours rather than weeks. Tingley also shares a strategic lens on "experimentation programs," explaining how plotting the distribution of treatment effects across different product areas can serve as a powerful tool for capital allocation and high-level strategy.

LINKS

]]> Martin Tingley, Head of Windows Experimentation at Microsoft and former Head of the Experimentation Platform Analysis Team at Netflix, talks about why humans are the bottleneck in experimentation, and how a five-level maturity framework points the way toward self-optimizing software.

Our conversation traces the path from basic hypothesis testing to a frontier where Generative AI creates, evaluates, and refines product variants in a closed loop. We explore the architectural shift required to move from testing single variants to optimizing entire parameter spaces, and how startups are already using AI to generate production-ready landing pages for Fortune 500 companies in hours rather than weeks. Tingley also shares a strategic lens on "experimentation programs," explaining how plotting the distribution of treatment effects across different product areas can serve as a powerful tool for capital allocation and high-level strategy.

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+FDzu8hlT ]]> Episode 34: Duolingo and the Future of Personalized Education with AI https://highsignal.fireside.fm/34 04aee2b4-020d-41b4-b658-3e27f66e1e21 Tue, 10 Feb 2026 01:45:00 -0500 hugobowne@gmail.com (Delphina) full Delphina Bozena Pajak, VP of Learning at Duolingo, joins High Signal to discuss the evolution of AI at Duolingo: from personalized difficulty models to the current generative frontier where AI characters provide low-stakes and high impact conversational practice. We discuss the role of AI in overcoming one of the biggest hurdles in language acquisition, speaking anxiety. We also talk about how Bozena's team leverages agentic workflows to scale content and why the next wave of personalization involves shifting from difficulty levels to "thematic lenses" tailored to specific user interests. 45:39 false Bozena Pajak, VP of Learning at Duolingo, joins High Signal to discuss the evolution of AI at Duolingo: from personalized difficulty models to the current generative frontier where AI characters provide low-stakes and high impact conversational practice. We discuss the role of AI in overcoming one of the biggest hurdles in language acquisition, speaking anxiety. We also talk about how Bozena's team leverages agentic workflows to scale content and why the next wave of personalization involves shifting from difficulty levels to "thematic lenses" tailored to specific user interests.

LINKS

]]> data science, ML, AI, GenAI Bozena Pajak, VP of Learning at Duolingo, joins High Signal to discuss the evolution of AI at Duolingo: from personalized difficulty models to the current generative frontier where AI characters provide low-stakes and high impact conversational practice. We discuss the role of AI in overcoming one of the biggest hurdles in language acquisition, speaking anxiety. We also talk about how Bozena's team leverages agentic workflows to scale content and why the next wave of personalization involves shifting from difficulty levels to "thematic lenses" tailored to specific user interests.

LINKS

]]> Bozena Pajak, VP of Learning at Duolingo, joins High Signal to discuss the evolution of AI at Duolingo: from personalized difficulty models to the current generative frontier where AI characters provide low-stakes and high impact conversational practice. We discuss the role of AI in overcoming one of the biggest hurdles in language acquisition, speaking anxiety. We also talk about how Bozena's team leverages agentic workflows to scale content and why the next wave of personalization involves shifting from difficulty levels to "thematic lenses" tailored to specific user interests.

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+tekwHI31 ]]> Episode 33: Why Your AI Product Will Be Obsolete in Six Months (And What To Do About It) https://highsignal.fireside.fm/33 93f9c4c9-95da-4d56-8048-919b3674a38d Tue, 27 Jan 2026 14:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina Benn Stancil 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? 1:00:21 false 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

]]> data science, ML, AI, GenAI 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+SXE7lhZ3 ]]> Episode 32: The Post-Coding Era: What Happens When AI Writes the System? https://highsignal.fireside.fm/32 28326dfc-75d3-4fb4-81ff-0175920057e8 Mon, 12 Jan 2026 22:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina 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. 41:44 false 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, 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, 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

]]> https://fireside.fm/player/v2/l27Jx_CL+8fNkfIrg ]]> Episode 31: Why Data Governance In Your Org is Broken (And How to Fix It) https://highsignal.fireside.fm/31 7bfb563b-f650-4fab-b807-40e0a7474b90 Mon, 29 Dec 2025 19:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina 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. 47:00 false 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

]]> data science, ML, AI, GenAI 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, 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

]]> https://fireside.fm/player/v2/l27Jx_CL+WlCA91Vs ]]> Episode 30: The AI Paradox: Why Your Data Team’s Workload is About to Explode https://highsignal.fireside.fm/30 08ee712a-432a-4822-bdd9-8d37010009db Wed, 10 Dec 2025 23:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina 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. 50:18 false 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

]]> 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

]]> 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

]]> https://fireside.fm/player/v2/l27Jx_CL+GizlLZ-j ]]> Episode 29: Why AI Adoption Fails: A Behavioral Framework for AI Implementation https://highsignal.fireside.fm/29 a34c146e-1bb8-47e0-92eb-ffa801c2cb5b Thu, 27 Nov 2025 20:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina 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. 49:25 false 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

]]> data science, ML, AI, Nudge, GenAI 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

]]> 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

]]> https://fireside.fm/player/v2/l27Jx_CL+WwnxgYH- ]]> Episode 28: From Context Engineering to AI Agent Harnesses: The New Software Discipline https://highsignal.fireside.fm/28 006c093f-00a4-4bac-a222-3aaad753d41a Thu, 13 Nov 2025 00:15:00 -0500 hugobowne@gmail.com (Delphina) full Delphina 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. 50:34 false 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

]]> data science, ML, AI, GenAI 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+0-yOM0zH ]]> Episode 27: Why Your Data Team Doesn't Have a Seat at the Table (And How to Earn It) https://highsignal.fireside.fm/27 a6af4539-c257-4c88-9976-e94e44300700 Wed, 29 Oct 2025 20:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 41:35 false 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

]]> data science, ML, AI, GenAI 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 (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

]]> https://fireside.fm/player/v2/l27Jx_CL+nNZYRu19 ]]> Episode 26: Gen AI's True Cost: Why Today's Wins Are Tomorrow's Debts https://highsignal.fireside.fm/26 4801b29e-7d72-41e4-8125-7477c801d8bc Thu, 16 Oct 2025 01:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 43:14 false 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

]]> data science, ML, AI, GenAI 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+Rtnzd2YZ ]]> Episode 25: How Data-Driven Growth Redefined a Media Giant https://highsignal.fireside.fm/25 b23bb87f-cf9d-4870-b873-fe490007bce6 Thu, 02 Oct 2025 00:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 56:22 false 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

]]> data science, ML, AI, GenAI 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 (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

]]> https://fireside.fm/player/v2/l27Jx_CL+EzolfVtz ]]> Episode 24: Rebuilding an Airline for the 21st Century: LATAM's Data-Driven Transformation https://highsignal.fireside.fm/24 90bff920-3997-49ae-988f-b048c8b7df4a Mon, 15 Sep 2025 06:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 49:56 false 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

]]> data science, ML, AI, GenAI 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 (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

]]> https://fireside.fm/player/v2/l27Jx_CL+PtxtbsIv ]]> Episode 23: Why Most AI Agents Fail (and What It Takes to Reach Production) https://highsignal.fireside.fm/23 3531ded6-bf39-4eea-b697-b5aa6b41c6dd Tue, 02 Sep 2025 06:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 51:17 false 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

]]> AI, agents, machine learning 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+bWaGWlZ_ ]]> Episode 22: Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It) https://highsignal.fireside.fm/22 8a3700ce-2760-498a-8016-c03d1d88dc35 Tue, 19 Aug 2025 03:00:00 -0400 hugobowne@gmail.com (Delphina) full 1 Delphina 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. 46:50 false 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

]]> data science, ML, AI, GenAI 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+Xb1MCQZ9 ]]> Episode 21: Why Great Data Still Leads to Bad Decisions (And How to Fix It) https://highsignal.fireside.fm/21 4a212733-7547-488f-98b7-6f9b02eab933 Tue, 05 Aug 2025 10:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 50:38 false 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 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+zlPwG0EM ]]> Episode 20: Incentives, Accountability, and the Data Leader’s Dilemma https://highsignal.fireside.fm/20 0c911662-8c87-48c2-ac07-f65639d4a4e8 Mon, 21 Jul 2025 23:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Daragh Sibley, Chief Algorithms Officer at Literati and former data-science leader 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. 1:03:13 false 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 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+jqFQSMcO ]]> Episode 19: Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI https://highsignal.fireside.fm/19 ca0afd9b-0a7a-4be7-929b-5f3cd1a1c0e5 Thu, 03 Jul 2025 09:45:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 54:08 false 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

]]> data science, ML, AI, Nudge, GenAI 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

]]> 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

]]> https://fireside.fm/player/v2/l27Jx_CL+hiGkaJXa ]]> Episode 18: High-Stakes AI Systems and the Cost of Getting It Wrong https://highsignal.fireside.fm/18 f1d42a52-bd55-46fe-bb7a-87e96642a3e6 Thu, 19 Jun 2025 05:15:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 58:45 false 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

]]> data science, machine learning, AI, LLMs 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

]]> 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

]]> https://fireside.fm/player/v2/l27Jx_CL+km2LVzL1 ]]> Episode 17: The Incentive Problem in Shipping AI Products — and How to Change It https://highsignal.fireside.fm/17 cbd4221e-6ad5-4be3-88a7-e8696d8d47ad Thu, 29 May 2025 11:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina 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. 53:52 false 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

]]> data science, machine learning, AI 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 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

]]> https://fireside.fm/player/v2/l27Jx_CL+B5QWjMXK ]]> Episode 16: How Human-Centered AI Actually Gets Built https://highsignal.fireside.fm/16 d958ce1f-b476-4d3f-ad61-613669286f22 Tue, 13 May 2025 02:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Fei-Fei Li—co-director of Stanford’s Human-Centered AI Institute and one of the most respected voices in the field—reflects on AI’s evolution from the early days of ImageNet to the rise of foundation models. She explains why spatial intelligence may be the next major shift, how human-centered design applies in practice, and why AI should be understood as a civilizational technology—one that shapes individuals, communities, and society at large. 47:22 false Fei-Fei Li—co-director of Stanford’s Human-Centered AI Institute and one of the most respected voices in the field—reflects on AI’s evolution from the early days of ImageNet to the rise of foundation models. She explains why spatial intelligence may be the next major shift, how human-centered design applies in practice, and why AI should be understood as a civilizational technology—one that shapes individuals, communities, and society at large.

LINKS

]]> data science, machine learning, AI, LLMs Fei-Fei Li—co-director of Stanford’s Human-Centered AI Institute and one of the most respected voices in the field—reflects on AI’s evolution from the early days of ImageNet to the rise of foundation models. She explains why spatial intelligence may be the next major shift, how human-centered design applies in practice, and why AI should be understood as a civilizational technology—one that shapes individuals, communities, and society at large.

LINKS

]]> Fei-Fei Li—co-director of Stanford’s Human-Centered AI Institute and one of the most respected voices in the field—reflects on AI’s evolution from the early days of ImageNet to the rise of foundation models. She explains why spatial intelligence may be the next major shift, how human-centered design applies in practice, and why AI should be understood as a civilizational technology—one that shapes individuals, communities, and society at large.

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+moX-2gm_ ]]> Episode 15: Why Good Metrics Still Lead to Bad Decisions — and How to Fix It https://highsignal.fireside.fm/15 77774df9-3464-4d8c-a491-ff06643766f7 Thu, 24 Apr 2025 01:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Eoin O'Mahony—data science partner at Lightspeed, former Uber science lead, and co-designer of the system that kept NYC’s Citi Bikes available across the city—argues that positive metrics are meaningless if you don’t understand the mechanism behind them. At Uber, he was careful to make sure his launches both looked good on paper and made sense in practice. Now in venture, he’s applying that same rigor to unstructured data—using GenAI to scale a kind of work that’s long resisted systematization. 54:17 false Eoin O'Mahony—data science partner at Lightspeed, former Uber science lead, and one of the early architects of the system that kept NYC’s Citi Bikes available across the city—argues that positive metrics are meaningless if you don’t understand the mechanism behind them. At Uber, he was careful to make sure his launches both looked good on paper and made sense in practice. Now in venture, he’s applying that same rigor to unstructured data—using GenAI to scale a kind of work that’s long resisted systematization.

LINKS

]]> data science, machine learning, AI, LLMs Eoin O'Mahony—data science partner at Lightspeed, former Uber science lead, and one of the early architects of the system that kept NYC’s Citi Bikes available across the city—argues that positive metrics are meaningless if you don’t understand the mechanism behind them. At Uber, he was careful to make sure his launches both looked good on paper and made sense in practice. Now in venture, he’s applying that same rigor to unstructured data—using GenAI to scale a kind of work that’s long resisted systematization.

LINKS

]]> Eoin O'Mahony—data science partner at Lightspeed, former Uber science lead, and one of the early architects of the system that kept NYC’s Citi Bikes available across the city—argues that positive metrics are meaningless if you don’t understand the mechanism behind them. At Uber, he was careful to make sure his launches both looked good on paper and made sense in practice. Now in venture, he’s applying that same rigor to unstructured data—using GenAI to scale a kind of work that’s long resisted systematization.

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+KqB-omJZ ]]> Episode 14: Why Most Companies Aren’t Actually AI Ready (and What to Do About It) https://highsignal.fireside.fm/14 d36785d0-49f4-46bd-af46-9bd7a69c82dd Wed, 09 Apr 2025 23:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Barr Moses—co-founder and CEO of Monte Carlo—thinks we’re headed for an AI reckoning. Companies are building fast, but most are still managing data like it’s 2015. In this episode, she shares high-stakes failure stories (like a $100M schema change), explains why full-stack observability is becoming essential, and breaks down how LLM agents are already transforming data debugging. From culture to tooling, this is a sharp look at what real AI readiness requires—and why so few teams have it. 51:58 false Barr Moses—co-founder and CEO of Monte Carlo—thinks we’re headed for an AI reckoning. Companies are building fast, but most are still managing data like it’s 2015. In this episode, she shares high-stakes failure stories (like a $100M schema change), explains why full-stack observability is becoming essential, and breaks down how LLM agents are already transforming data debugging. From culture to tooling, this is a sharp look at what real AI readiness requires—and why so few teams have it.

LINKS

]]> AI, LLMs, data science, machine learning, data science, GenAI Barr Moses—co-founder and CEO of Monte Carlo—thinks we’re headed for an AI reckoning. Companies are building fast, but most are still managing data like it’s 2015. In this episode, she shares high-stakes failure stories (like a $100M schema change), explains why full-stack observability is becoming essential, and breaks down how LLM agents are already transforming data debugging. From culture to tooling, this is a sharp look at what real AI readiness requires—and why so few teams have it.

LINKS

]]> Barr Moses—co-founder and CEO of Monte Carlo—thinks we’re headed for an AI reckoning. Companies are building fast, but most are still managing data like it’s 2015. In this episode, she shares high-stakes failure stories (like a $100M schema change), explains why full-stack observability is becoming essential, and breaks down how LLM agents are already transforming data debugging. From culture to tooling, this is a sharp look at what real AI readiness requires—and why so few teams have it.

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+FXt4AZmo ]]> Episode 13: The End of Programming As We Know It https://highsignal.fireside.fm/13 a660c513-d0cd-4f78-84f5-8375dd557adf Thu, 27 Mar 2025 01:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what it takes to learn, teach, and build responsibly in the age of AI. 1:23:09 false Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what it takes to learn, teach, and build responsibly in the age of AI.

LINKS

]]> data science, machine learning, AI, LLMs Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what it takes to learn, teach, and build responsibly in the age of AI.

LINKS

]]> Tim O’Reilly—founder of O’Reilly Media and one of the most influential voices in tech—argues we’re not witnessing the end of programming, but the beginning of something far bigger. He draws on past computing revolutions to explore how AI is reshaping what it means to build software, why real breakthroughs come from the edge—not incumbents—and what it takes to learn, teach, and build responsibly in the age of AI.

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+i-0wUnV2 ]]> Episode 12: Your Machine Learning Solves The Wrong Problem https://highsignal.fireside.fm/12 0be37662-1184-4a3f-bfcd-d65909a0eeec Thu, 13 Mar 2025 15:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work. 54:40 false Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.

LINKS

]]> data science, machine learning, causal inference, causal ML Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.

LINKS

]]> Stefan Wager—Professor at Stanford and expert on causal machine learning—has worked with leading tech companies including Dropbox, Facebook, Google, and Uber. He challenges the widespread assumption that better predictions mean better decisions. Traditional machine learning excels at prediction, but is prediction really what your business needs? Stefan explores why predictive models alone often fail to answer critical “what-if” questions, how causal machine learning bridges this gap, and provides practical advice for how you can start applying causal ML at work.

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+Abi-I1qh ]]> Episode 11: What Comes After Code? The Role of Engineers in an AI-Driven Future https://highsignal.fireside.fm/11 e5dd654b-0f0b-44f4-a232-cdd533e6bc88 Thu, 27 Feb 2025 01:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina Peter Wang—Chief AI Officer at Anaconda and a driving force behind PyData—challenges conventional thinking about AI’s role in software development. As AI reshapes engineering, are we moving beyond writing code to orchestrating intelligence? Peter explores why companies are fixated on models instead of integration, how AI is breaking traditional software workflows, and what this shift means for open source. He also shares insights on the evolving role of engineers, the commoditization of AI models, and the deeper questions we should be asking about the future of software. 1:05:44 false Peter Wang—Chief AI Officer at Anaconda and a driving force behind PyData—challenges conventional thinking about AI’s role in software development. As AI reshapes engineering, are we moving beyond writing code to orchestrating intelligence? Peter explores why companies are fixated on models instead of integration, how AI is breaking traditional software workflows, and what this shift means for open source. He also shares insights on the evolving role of engineers, the commoditization of AI models, and the deeper questions we should be asking about the future of software.

LINKS

]]> Peter Wang—Chief AI Officer at Anaconda and a driving force behind PyData—challenges conventional thinking about AI’s role in software development. As AI reshapes engineering, are we moving beyond writing code to orchestrating intelligence? Peter explores why companies are fixated on models instead of integration, how AI is breaking traditional software workflows, and what this shift means for open source. He also shares insights on the evolving role of engineers, the commoditization of AI models, and the deeper questions we should be asking about the future of software.

LINKS

]]> Peter Wang—Chief AI Officer at Anaconda and a driving force behind PyData—challenges conventional thinking about AI’s role in software development. As AI reshapes engineering, are we moving beyond writing code to orchestrating intelligence? Peter explores why companies are fixated on models instead of integration, how AI is breaking traditional software workflows, and what this shift means for open source. He also shares insights on the evolving role of engineers, the commoditization of AI models, and the deeper questions we should be asking about the future of software.

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+JuTvM1Vo ]]> Episode 10: AI Won't Save You But Data Intelligence Will https://highsignal.fireside.fm/10 2fe6cf02-566b-443b-aee3-0efdebceac69 Wed, 12 Feb 2025 18:15:00 -0500 hugobowne@gmail.com (Delphina) full Delphina Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps. 59:42 false Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps.

SHOW NOTES

]]> data science, machine learning, AI Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps.

SHOW NOTES

]]> Ari Kaplan—Global Head of Evangelism at Databricks and a pioneer in sports analytics—explains why businesses fixated on AI often overlook the real advantage: making better decisions with their own data. He shares lessons from his work building analytics teams for Major League Baseball, advising McLaren’s F1 strategy, and helping companies apply AI where it actually works—without falling into hype-driven traps.

SHOW NOTES

]]> https://fireside.fm/player/v2/l27Jx_CL+ZemsYZBP ]]> Episode 9: Why 90% of Data Science Fails—And How to Fix It -- With Eric Colson https://highsignal.fireside.fm/9 20a1123f-33fc-4849-a13f-55c4c3a5fbdb Thu, 30 Jan 2025 15:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—explains why most companies fail to fully leverage their data science teams. Drawing on his experience leading data functions at top tech companies, he shares how organizations can move beyond treating data science as a support function and instead empower data scientists to drive strategic impact through experimentation, iteration, and algorithmic decision-making. 1:09:40 false In this episode of High Signal, Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—breaks down why most companies fail to unlock the full potential of their data science teams. Drawing from years of experience leading data functions at top tech companies, Eric shares how organizations can shift from treating data scientists as a service function to empowering them as strategic drivers of business impact.

Key topics from the conversation include:

  • Data Science as a Strategic Function: Why many companies limit their data teams to answering business requests instead of leveraging their ideas for competitive advantage.
  • Beyond Skills—The Power of Cognitive Repertoires: How data scientists' unique ways of framing problems can lead to breakthrough innovations.
  • Trial and Error as a Competitive Advantage: Why most experiments fail—but scaling experimentation is the key to big wins.
  • Decoupling Algorithms from Applications: How separating data science from engineering enables rapid iteration and direct business impact.
  • Shifting from Cost Center to Revenue Generator: Practical steps for structuring data teams to drive measurable value and long-term success.

💡 Tune in to learn how leading companies structure their data teams for impact, why experimentation beats rigid planning, and how treating data science as a strategic function can unlock new business opportunities.

You can find more on our website: https://high-signal.delphina.ai/

SHOW NOTES

]]> data science, machine learning, AI In this episode of High Signal, Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—breaks down why most companies fail to unlock the full potential of their data science teams. Drawing from years of experience leading data functions at top tech companies, Eric shares how organizations can shift from treating data scientists as a service function to empowering them as strategic drivers of business impact.

Key topics from the conversation include:

  • Data Science as a Strategic Function: Why many companies limit their data teams to answering business requests instead of leveraging their ideas for competitive advantage.
  • Beyond Skills—The Power of Cognitive Repertoires: How data scientists' unique ways of framing problems can lead to breakthrough innovations.
  • Trial and Error as a Competitive Advantage: Why most experiments fail—but scaling experimentation is the key to big wins.
  • Decoupling Algorithms from Applications: How separating data science from engineering enables rapid iteration and direct business impact.
  • Shifting from Cost Center to Revenue Generator: Practical steps for structuring data teams to drive measurable value and long-term success.

💡 Tune in to learn how leading companies structure their data teams for impact, why experimentation beats rigid planning, and how treating data science as a strategic function can unlock new business opportunities.

You can find more on our website: https://high-signal.delphina.ai/

SHOW NOTES

]]> In this episode of High Signal, Eric Colson—former Chief Algorithms Officer at Stitch Fix and VP of Data Science and Machine Learning at Netflix—breaks down why most companies fail to unlock the full potential of their data science teams. Drawing from years of experience leading data functions at top tech companies, Eric shares how organizations can shift from treating data scientists as a service function to empowering them as strategic drivers of business impact.

Key topics from the conversation include:

  • Data Science as a Strategic Function: Why many companies limit their data teams to answering business requests instead of leveraging their ideas for competitive advantage.
  • Beyond Skills—The Power of Cognitive Repertoires: How data scientists' unique ways of framing problems can lead to breakthrough innovations.
  • Trial and Error as a Competitive Advantage: Why most experiments fail—but scaling experimentation is the key to big wins.
  • Decoupling Algorithms from Applications: How separating data science from engineering enables rapid iteration and direct business impact.
  • Shifting from Cost Center to Revenue Generator: Practical steps for structuring data teams to drive measurable value and long-term success.

💡 Tune in to learn how leading companies structure their data teams for impact, why experimentation beats rigid planning, and how treating data science as a strategic function can unlock new business opportunities.

You can find more on our website: https://high-signal.delphina.ai/

SHOW NOTES

]]> https://fireside.fm/player/v2/l27Jx_CL+IYzIDwbX ]]> Episode 8: From Zero to Scale: Lessons from Airbnb and Beyond https://highsignal.fireside.fm/8 59303699-9397-42d7-b581-75f3a71a0c3f Thu, 09 Jan 2025 00:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina Elena Grewal—former Head of Data Science at Airbnb, political consultant, professor at Yale, and ice cream shop owner—shares her journey of building data teams that scale across vastly different contexts. Drawing on her experiences in tech, consulting, and brick-and-mortar, Elena offers practical lessons on leadership, trust, and experimentation. 1:06:42 false In this episode of High Signal, Elena Grewal—former Head of Data Science at Airbnb, political consultant, professor at Yale, and ice cream shop owner—shares her journey of building data teams that scale across vastly different contexts. Drawing on her experiences in tech, consulting, and brick-and-mortar, Elena offers practical lessons on leadership, trust, and experimentation.

Key topics from the conversation include:

  • From Zero to Scale: How Elena built Airbnb’s data science function from the ground up, scaling it to a 200-person team while driving impact across the organization.
  • Trust and Team Culture: Why trust is foundational for building effective teams, fostering creativity, and empowering data scientists to drive results.
  • Applying Data Science Across Contexts: Lessons learned from using data to inform decisions in politics, academia, and even running an ice cream shop.
  • Experimentation and Iteration: Insights into tailoring experimentation methods to fit different scales, from small businesses to tech giants.
  • Critical Thinking and Data: How Elena equips the next generation of leaders at Yale to ask better questions, assess data quality, and think critically about evidence.

💡 Tune in to explore how data science principles can scale across industries, the leadership skills required to build impactful teams, and why experimentation is as relevant to ice cream as it is to AI systems.

You can find more on our website: https://high-signal.delphina.ai/

SHOW NOTES

]]> data science, machine learning, AI In this episode of High Signal, Elena Grewal—former Head of Data Science at Airbnb, political consultant, professor at Yale, and ice cream shop owner—shares her journey of building data teams that scale across vastly different contexts. Drawing on her experiences in tech, consulting, and brick-and-mortar, Elena offers practical lessons on leadership, trust, and experimentation.

Key topics from the conversation include:

  • From Zero to Scale: How Elena built Airbnb’s data science function from the ground up, scaling it to a 200-person team while driving impact across the organization.
  • Trust and Team Culture: Why trust is foundational for building effective teams, fostering creativity, and empowering data scientists to drive results.
  • Applying Data Science Across Contexts: Lessons learned from using data to inform decisions in politics, academia, and even running an ice cream shop.
  • Experimentation and Iteration: Insights into tailoring experimentation methods to fit different scales, from small businesses to tech giants.
  • Critical Thinking and Data: How Elena equips the next generation of leaders at Yale to ask better questions, assess data quality, and think critically about evidence.

💡 Tune in to explore how data science principles can scale across industries, the leadership skills required to build impactful teams, and why experimentation is as relevant to ice cream as it is to AI systems.

You can find more on our website: https://high-signal.delphina.ai/

SHOW NOTES

]]> In this episode of High Signal, Elena Grewal—former Head of Data Science at Airbnb, political consultant, professor at Yale, and ice cream shop owner—shares her journey of building data teams that scale across vastly different contexts. Drawing on her experiences in tech, consulting, and brick-and-mortar, Elena offers practical lessons on leadership, trust, and experimentation.

Key topics from the conversation include:

  • From Zero to Scale: How Elena built Airbnb’s data science function from the ground up, scaling it to a 200-person team while driving impact across the organization.
  • Trust and Team Culture: Why trust is foundational for building effective teams, fostering creativity, and empowering data scientists to drive results.
  • Applying Data Science Across Contexts: Lessons learned from using data to inform decisions in politics, academia, and even running an ice cream shop.
  • Experimentation and Iteration: Insights into tailoring experimentation methods to fit different scales, from small businesses to tech giants.
  • Critical Thinking and Data: How Elena equips the next generation of leaders at Yale to ask better questions, assess data quality, and think critically about evidence.

💡 Tune in to explore how data science principles can scale across industries, the leadership skills required to build impactful teams, and why experimentation is as relevant to ice cream as it is to AI systems.

You can find more on our website: https://high-signal.delphina.ai/

SHOW NOTES

]]> https://fireside.fm/player/v2/l27Jx_CL+JFsDInwI ]]> Episode 7: What Lies Beyond Machine Learning and AI: Decision Systems and the Future of Data Teams https://highsignal.fireside.fm/7 ee1d663f-7a1f-4e4f-b7ef-20c9146f0810 Wed, 18 Dec 2024 22:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina In this episode of High Signal, Chris Wiggins—Chief Data Scientist at The New York Times, Professor at Columbia University, and co-author of How Data Happened—shares how organizations can move beyond prediction to actionable decision systems. Drawing on his work at The New York Times and in academia, Chris explains how to scale data teams, optimize systems, and align data science with organizational impact. 1:18:44 false In this episode of High Signal, Chris Wiggins—Chief Data Scientist at The New York Times, Professor at Columbia University, and co-author of How Data Happened—shares how organizations can move beyond prediction to actionable decision systems. Drawing on his work at The New York Times and in academia, Chris explains how to scale data teams, optimize systems, and align data science with organizational impact.

Key topics from the conversation include:
• From Prediction to Prescription: Why organizations need to focus on interventions that drive outcomes, illustrated with insights like, “Imagine a hospital prescribing treatments instead of just diagnosing conditions.”
• The AI Hierarchy of Needs: Foundational practices, such as data logging and engineering, that enable advanced machine learning and AI.
• Personalization and Optimization: How reinforcement learning and exploration-exploitation methods help optimize KPIs and adapt to user context.
• Scaling Data Teams: Strategies for attracting and retaining talent by emphasizing autonomy, mastery, and purpose.
• Empathy as a Data Science Skill: The importance of collaborating with other teams and understanding their goals to drive adoption and success.

🎧 Tune in to learn how to build decision systems, integrate causality into workflows, and develop scalable data science teams for real-world impact.

You can find more on our website: https://high-signal.delphina.ai/

LINKS

]]> AI, LLMs, data science, machine learning, data science, GenAI In this episode of High Signal, Chris Wiggins—Chief Data Scientist at The New York Times, Professor at Columbia University, and co-author of How Data Happened—shares how organizations can move beyond prediction to actionable decision systems. Drawing on his work at The New York Times and in academia, Chris explains how to scale data teams, optimize systems, and align data science with organizational impact.

Key topics from the conversation include:
• From Prediction to Prescription: Why organizations need to focus on interventions that drive outcomes, illustrated with insights like, “Imagine a hospital prescribing treatments instead of just diagnosing conditions.”
• The AI Hierarchy of Needs: Foundational practices, such as data logging and engineering, that enable advanced machine learning and AI.
• Personalization and Optimization: How reinforcement learning and exploration-exploitation methods help optimize KPIs and adapt to user context.
• Scaling Data Teams: Strategies for attracting and retaining talent by emphasizing autonomy, mastery, and purpose.
• Empathy as a Data Science Skill: The importance of collaborating with other teams and understanding their goals to drive adoption and success.

🎧 Tune in to learn how to build decision systems, integrate causality into workflows, and develop scalable data science teams for real-world impact.

You can find more on our website: https://high-signal.delphina.ai/

LINKS

]]> In this episode of High Signal, Chris Wiggins—Chief Data Scientist at The New York Times, Professor at Columbia University, and co-author of How Data Happened—shares how organizations can move beyond prediction to actionable decision systems. Drawing on his work at The New York Times and in academia, Chris explains how to scale data teams, optimize systems, and align data science with organizational impact.

Key topics from the conversation include:
• From Prediction to Prescription: Why organizations need to focus on interventions that drive outcomes, illustrated with insights like, “Imagine a hospital prescribing treatments instead of just diagnosing conditions.”
• The AI Hierarchy of Needs: Foundational practices, such as data logging and engineering, that enable advanced machine learning and AI.
• Personalization and Optimization: How reinforcement learning and exploration-exploitation methods help optimize KPIs and adapt to user context.
• Scaling Data Teams: Strategies for attracting and retaining talent by emphasizing autonomy, mastery, and purpose.
• Empathy as a Data Science Skill: The importance of collaborating with other teams and understanding their goals to drive adoption and success.

🎧 Tune in to learn how to build decision systems, integrate causality into workflows, and develop scalable data science teams for real-world impact.

You can find more on our website: https://high-signal.delphina.ai/

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+1hbkWUwJ ]]> Episode 6: What Happens to Data Science in the Age of AI? https://highsignal.fireside.fm/6 7cb9e213-8fe3-41d0-9632-522a6ce6a0e9 Wed, 04 Dec 2024 13:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina In this episode of High Signal, Hilary Mason—renowned data scientist, entrepreneur, and co-founder of Hidden Door—shares her unique insights into the evolving world of data science and generative AI. Drawing from her pioneering work at Fast Forward Labs, Bitly, and Hidden Door, Hilary explores how creativity, judgment, and empathy are reshaping the data landscape. 1:18:23 false In this episode of High Signal, Hilary Mason—renowned data scientist, entrepreneur, and co-founder of Hidden Door—shares her unique insights into the evolving world of data science and generative AI. Drawing from her pioneering work at Fast Forward Labs, Bitly, and Hidden Door, Hilary explores how creativity, judgment, and empathy are reshaping the data landscape.

Highlights from the discussion include:

  • Judgment as a Competitive Edge: Hilary emphasizes the enduring importance of human judgment in framing problems and evaluating AI outputs.
  • The Future of Generative AI: She discusses its transformative potential while cautioning against over-reliance on prompts, advocating for systems rooted in rich context.
  • Building for Creativity with Hidden Door: Hilary shares how her company turns generative AI’s liabilities into assets, creating immersive, bias-aware storytelling experiences.
  • The Shifting Role of Data Science Careers: With automation redefining entry-level roles, Hilary outlines how data professionals can focus on transferable skills to stay ahead.
  • Navigating AI Strategy in Leadership: She offers pragmatic advice on balancing the hype of AI with practical business impact, aligning leadership expectations with achievable goals.

The conversation concludes with Hilary’s optimistic take on how the data science community can continue to thrive by embracing creativity, empathy, and interdisciplinary collaboration.

🎧 Tune in to gain practical insights into building robust AI systems, navigating career shifts, and leveraging generative AI for meaningful innovation.

You can find more on our website: https://high-signal.delphina.ai/

LINKS

]]> data science, machine learning, AI, LLMs In this episode of High Signal, Hilary Mason—renowned data scientist, entrepreneur, and co-founder of Hidden Door—shares her unique insights into the evolving world of data science and generative AI. Drawing from her pioneering work at Fast Forward Labs, Bitly, and Hidden Door, Hilary explores how creativity, judgment, and empathy are reshaping the data landscape.

Highlights from the discussion include:

  • Judgment as a Competitive Edge: Hilary emphasizes the enduring importance of human judgment in framing problems and evaluating AI outputs.
  • The Future of Generative AI: She discusses its transformative potential while cautioning against over-reliance on prompts, advocating for systems rooted in rich context.
  • Building for Creativity with Hidden Door: Hilary shares how her company turns generative AI’s liabilities into assets, creating immersive, bias-aware storytelling experiences.
  • The Shifting Role of Data Science Careers: With automation redefining entry-level roles, Hilary outlines how data professionals can focus on transferable skills to stay ahead.
  • Navigating AI Strategy in Leadership: She offers pragmatic advice on balancing the hype of AI with practical business impact, aligning leadership expectations with achievable goals.

The conversation concludes with Hilary’s optimistic take on how the data science community can continue to thrive by embracing creativity, empathy, and interdisciplinary collaboration.

🎧 Tune in to gain practical insights into building robust AI systems, navigating career shifts, and leveraging generative AI for meaningful innovation.

You can find more on our website: https://high-signal.delphina.ai/

LINKS

]]> In this episode of High Signal, Hilary Mason—renowned data scientist, entrepreneur, and co-founder of Hidden Door—shares her unique insights into the evolving world of data science and generative AI. Drawing from her pioneering work at Fast Forward Labs, Bitly, and Hidden Door, Hilary explores how creativity, judgment, and empathy are reshaping the data landscape.

Highlights from the discussion include:

  • Judgment as a Competitive Edge: Hilary emphasizes the enduring importance of human judgment in framing problems and evaluating AI outputs.
  • The Future of Generative AI: She discusses its transformative potential while cautioning against over-reliance on prompts, advocating for systems rooted in rich context.
  • Building for Creativity with Hidden Door: Hilary shares how her company turns generative AI’s liabilities into assets, creating immersive, bias-aware storytelling experiences.
  • The Shifting Role of Data Science Careers: With automation redefining entry-level roles, Hilary outlines how data professionals can focus on transferable skills to stay ahead.
  • Navigating AI Strategy in Leadership: She offers pragmatic advice on balancing the hype of AI with practical business impact, aligning leadership expectations with achievable goals.

The conversation concludes with Hilary’s optimistic take on how the data science community can continue to thrive by embracing creativity, empathy, and interdisciplinary collaboration.

🎧 Tune in to gain practical insights into building robust AI systems, navigating career shifts, and leveraging generative AI for meaningful innovation.

You can find more on our website: https://high-signal.delphina.ai/

LINKS

]]> https://fireside.fm/player/v2/l27Jx_CL+2aHQzIQR ]]> Episode 5: The Hard Truth About Building AI Systems and What Most Leaders Miss About AI https://highsignal.fireside.fm/5 ca3782d4-c7a4-44d6-a7c3-176201c14f69 Wed, 20 Nov 2024 16:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina In this episode of High Signal, Gabriel Weintraub (the Amman Professor of Operations, Information, and Technology at Stanford Graduate School of Business), brings his expertise in market design, data science, and operations, enriched by his experience with global platforms like Uber and Mercado Libre, to a conversation that spans practical strategies, cultural insights, and global perspectives on data and AI. 1:02:06 false In this episode of High Signal, Gabriel Weintraub (the Amman Professor of Operations, Information, and Technology at Stanford Graduate School of Business), brings his expertise in market design, data science, and operations, enriched by his experience with global platforms like Uber and Mercado Libre, to a conversation that spans practical strategies, cultural insights, and global perspectives on data and AI.

Highlights from the discussion include:

  • Bridging the C-Level and Technical Divide: Gabriel emphasizes the importance of aligning leadership with on-the-ground teams to build effective, data-driven organizations.
  • Starting with the Basics: From building pipelines to identifying high-ROI projects, Gabriel outlines foundational steps for companies adopting data science and AI.
  • Cultural Transformation for Experimentation: He explains why fostering an experimentation culture, where negative results are valued for learning, is essential for success.
  • Opportunities in Latin America: Gabriel shares insights on the unique challenges and immense potential of the Latin American tech ecosystem, including the critical role of startups and the need for local innovation systems.
  • Generative AI’s Role in Driving Impact: Discussing generative AI’s transformative potential, Gabriel highlights its capacity to lower barriers for smaller teams while emphasizing the importance of problem-first approaches.

The conversation concludes with a forward-looking exploration of opportunities in government, education, and healthcare, and Gabriel’s optimism about building ecosystems where startups and local talent thrive.

🎧 Tune in to learn from Gabriel’s thoughtful perspectives on navigating the complexities of building data-driven cultures, the global AI landscape, and how to leverage data for impactful change.

You can find more on our website: https://high-signal.delphina.ai/

]]> data science, machine learning, AI In this episode of High Signal, Gabriel Weintraub (the Amman Professor of Operations, Information, and Technology at Stanford Graduate School of Business), brings his expertise in market design, data science, and operations, enriched by his experience with global platforms like Uber and Mercado Libre, to a conversation that spans practical strategies, cultural insights, and global perspectives on data and AI.

Highlights from the discussion include:

  • Bridging the C-Level and Technical Divide: Gabriel emphasizes the importance of aligning leadership with on-the-ground teams to build effective, data-driven organizations.
  • Starting with the Basics: From building pipelines to identifying high-ROI projects, Gabriel outlines foundational steps for companies adopting data science and AI.
  • Cultural Transformation for Experimentation: He explains why fostering an experimentation culture, where negative results are valued for learning, is essential for success.
  • Opportunities in Latin America: Gabriel shares insights on the unique challenges and immense potential of the Latin American tech ecosystem, including the critical role of startups and the need for local innovation systems.
  • Generative AI’s Role in Driving Impact: Discussing generative AI’s transformative potential, Gabriel highlights its capacity to lower barriers for smaller teams while emphasizing the importance of problem-first approaches.

The conversation concludes with a forward-looking exploration of opportunities in government, education, and healthcare, and Gabriel’s optimism about building ecosystems where startups and local talent thrive.

🎧 Tune in to learn from Gabriel’s thoughtful perspectives on navigating the complexities of building data-driven cultures, the global AI landscape, and how to leverage data for impactful change.

You can find more on our website: https://high-signal.delphina.ai/

]]> In this episode of High Signal, Gabriel Weintraub (the Amman Professor of Operations, Information, and Technology at Stanford Graduate School of Business), brings his expertise in market design, data science, and operations, enriched by his experience with global platforms like Uber and Mercado Libre, to a conversation that spans practical strategies, cultural insights, and global perspectives on data and AI.

Highlights from the discussion include:

  • Bridging the C-Level and Technical Divide: Gabriel emphasizes the importance of aligning leadership with on-the-ground teams to build effective, data-driven organizations.
  • Starting with the Basics: From building pipelines to identifying high-ROI projects, Gabriel outlines foundational steps for companies adopting data science and AI.
  • Cultural Transformation for Experimentation: He explains why fostering an experimentation culture, where negative results are valued for learning, is essential for success.
  • Opportunities in Latin America: Gabriel shares insights on the unique challenges and immense potential of the Latin American tech ecosystem, including the critical role of startups and the need for local innovation systems.
  • Generative AI’s Role in Driving Impact: Discussing generative AI’s transformative potential, Gabriel highlights its capacity to lower barriers for smaller teams while emphasizing the importance of problem-first approaches.

The conversation concludes with a forward-looking exploration of opportunities in government, education, and healthcare, and Gabriel’s optimism about building ecosystems where startups and local talent thrive.

🎧 Tune in to learn from Gabriel’s thoughtful perspectives on navigating the complexities of building data-driven cultures, the global AI landscape, and how to leverage data for impactful change.

You can find more on our website: https://high-signal.delphina.ai/

]]> https://fireside.fm/player/v2/l27Jx_CL+HtCsUJKR ]]> Episode 4: How to Build an Experimentation Machine and Where Most Go Wrong https://highsignal.fireside.fm/4 1dedb130-8a6b-4a8d-890a-8d8b41cbd651 Thu, 07 Nov 2024 07:00:00 -0500 hugobowne@gmail.com (Delphina) full Delphina Ramesh Johari (Stanford, Uber, Airbnb, and more) explores the art and science of online experimentation, especially in the context of marketplaces and tech companies. 51:16 false Ramesh Johari (Stanford, Uber, Airbnb, and more) explores the art and science of online experimentation, especially in the context of marketplaces and tech companies.

Ramesh shares insights on how organizations evolve from basic experimentation practices to becoming fast, adaptive, and self learning organizations. We dive into challenges like the risk aversion trap, the importance of learning from negative results, and how generative AI is reshaping the experimentation landscape.

We also talk about common failure modes and the types of things you're probably doing wrong, along with strategies to avoid these pitfalls. Plus, we discussed the role of incentives, the necessity of data driven decision making, and what it means to experiment in high stakes environments.

]]> Ramesh Johari (Stanford, Uber, Airbnb, and more) explores the art and science of online experimentation, especially in the context of marketplaces and tech companies.

Ramesh shares insights on how organizations evolve from basic experimentation practices to becoming fast, adaptive, and self learning organizations. We dive into challenges like the risk aversion trap, the importance of learning from negative results, and how generative AI is reshaping the experimentation landscape.

We also talk about common failure modes and the types of things you're probably doing wrong, along with strategies to avoid these pitfalls. Plus, we discussed the role of incentives, the necessity of data driven decision making, and what it means to experiment in high stakes environments.

]]> Ramesh Johari (Stanford, Uber, Airbnb, and more) explores the art and science of online experimentation, especially in the context of marketplaces and tech companies.

Ramesh shares insights on how organizations evolve from basic experimentation practices to becoming fast, adaptive, and self learning organizations. We dive into challenges like the risk aversion trap, the importance of learning from negative results, and how generative AI is reshaping the experimentation landscape.

We also talk about common failure modes and the types of things you're probably doing wrong, along with strategies to avoid these pitfalls. Plus, we discussed the role of incentives, the necessity of data driven decision making, and what it means to experiment in high stakes environments.

]]> https://fireside.fm/player/v2/l27Jx_CL+UkHArV7R ]]> Episode 3: Data Science Meets Management: Teamwork, Experimentation, and Decision-Making https://highsignal.fireside.fm/3 c6ce415b-4f49-41ae-b3db-700040f0674d Sat, 19 Oct 2024 12:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Chiara Farronato (Harvard Business School) discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer marketplaces and the evolving role of data in decision-making. This episode offers key insights for business leaders working with technical teams and navigating platform-based innovation. 52:12 false Chiara Farronato (Harvard Business School) discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer marketplaces and the evolving role of data in decision-making. This episode offers key insights for business leaders working with technical teams and navigating platform-based innovation.

]]> Chiara Farronato (Harvard Business School) discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer marketplaces and the evolving role of data in decision-making. This episode offers key insights for business leaders working with technical teams and navigating platform-based innovation.

]]> Chiara Farronato (Harvard Business School) discusses how digital platforms like Airbnb and Uber have transformed industries. She explores the challenges of fostering collaboration between managers and data scientists, bridging communication gaps, and building data-driven cultures. Chiara also delves into the complexities of managing peer-to-peer marketplaces and the evolving role of data in decision-making. This episode offers key insights for business leaders working with technical teams and navigating platform-based innovation.

]]> https://fireside.fm/player/v2/l27Jx_CL+cC20LuDa ]]> Episode 2: Fooling Yourself Less: The Art of Statistical Thinking in AI https://highsignal.fireside.fm/2 44cfa4bc-192f-4325-90d2-13b7b9e1d781 Sat, 19 Oct 2024 11:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causal inference’s critical role in decision-making. This episode offers insights into balancing statistical theory with applied data analysis, making it a must-listen for both data practitioners and those interested in how statistics shapes our world. 1:00:51 false Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causal inference’s critical role in decision-making. This episode offers insights into balancing statistical theory with applied data analysis, making it a must-listen for both data practitioners and those interested in how statistics shapes our world.

]]> Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causal inference’s critical role in decision-making. This episode offers insights into balancing statistical theory with applied data analysis, making it a must-listen for both data practitioners and those interested in how statistics shapes our world.

]]> Hugo Bowne-Anderson welcomes Andrew Gelman, professor at Columbia University, to discuss the practical side of statistics and data science. They explore the importance of high-quality data, computational skills, and using simulation to avoid misleading results. Andrew dives into real-world applications like election predictions and highlights causal inference’s critical role in decision-making. This episode offers insights into balancing statistical theory with applied data analysis, making it a must-listen for both data practitioners and those interested in how statistics shapes our world.

]]> https://fireside.fm/player/v2/l27Jx_CL+ciZi2YGl ]]> Episode 1: The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale https://highsignal.fireside.fm/1 c4068f54-90b5-4e7a-b036-07a08ac9e813 Sat, 19 Oct 2024 09:00:00 -0400 hugobowne@gmail.com (Delphina) full Delphina Michael Jordan (UC Berkeley) on the future of machine learning as it extends to a planetary scale in "The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale." In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI can scale to address planetary-level challenges. 1:15:12 false Michael Jordan (UC Berkeley) on the future of machine learning as it extends to a planetary scale in "The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale." In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI can scale to address planetary-level challenges.

]]> Michael Jordan (UC Berkeley) on the future of machine learning as it extends to a planetary scale in "The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale." In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI can scale to address planetary-level challenges.

]]> Michael Jordan (UC Berkeley) on the future of machine learning as it extends to a planetary scale in "The Next Evolution of AI: Markets, Uncertainty, and Engineering Intelligence at Scale." In this episode, Mike speaks with Hugo about the evolution of AI, the importance of integrating machine learning, computer science, and economics, and how AI can scale to address planetary-level challenges.

]]> https://fireside.fm/player/v2/l27Jx_CL+B1kEA9Rr ]]>