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    <fireside:hostname>web01.fireside.fm</fireside:hostname>
    <fireside:genDate>Sat, 11 Apr 2026 12:21:03 -0500</fireside:genDate>
    <generator>Fireside (https://fireside.fm)</generator>
    <title>High Signal: Data Science | Career | AI - Episodes Tagged with “Ml”</title>
    <link>https://highsignal.fireside.fm/tags/ml</link>
    <pubDate>Thu, 02 Apr 2026 01:00:00 -0400</pubDate>
    <description>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/
</description>
    <language>en-us</language>
    <itunes:type>episodic</itunes:type>
    <itunes:subtitle>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.</itunes:subtitle>
    <itunes:author>Delphina</itunes:author>
    <itunes:summary>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/
</itunes:summary>
    <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
    <itunes:explicit>no</itunes:explicit>
    <itunes:keywords>data, data science, machine learning, AI</itunes:keywords>
    <itunes:owner>
      <itunes:name>Delphina</itunes:name>
      <itunes:email>hugobowne@gmail.com</itunes:email>
    </itunes:owner>
<itunes:category text="Technology"/>
<itunes:category text="Business"/>
<item>
  <title>Episode 37: Engineered Intelligence and The Data Science Problem in AI</title>
  <link>https://highsignal.fireside.fm/37</link>
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  <pubDate>Thu, 02 Apr 2026 01:00:00 -0400</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/0b920ae2-0e66-4b36-ae9f-a47f3180499f.mp3" length="90437984" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>Jordan Morrow, SVP of Data &amp; 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.</itunes:subtitle>
  <itunes:duration>46:14</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Jordan Morrow, SVP of Data &amp;amp; AI Transformation at AgileOne and the "Godfather of Data Literacy," joins High Signal to discuss the shift from being "data-driven" to becoming "AI-enabled." Jordan warns that many organizations are walking into the same traps that derailed the data science era a decade ago: prioritizing expensive tooling and hype over the cultural change and literacy required to actually move the needle. The pattern is already visible: enterprise AI projects are failing at around 90%, while individuals doing shadow AI are quietly thriving. The catch is that shadow AI brings its own risks, with people feeding sensitive data into public models without governance. He argues that because AI is probabilistic rather than deterministic, the bottleneck for success has shifted from technical coding ability to a user’s ability to apply "Engineered Intelligence," a blend of machine capability and human emotional intelligence.
LINKS 
Jordan's new book "Data and AI Skills: Gain the Confidence You Need to Succeed"  (https://www.koganpage.com/business-and-management/data-and-ai-skills-9781398624139) (also here on Amazon (https://www.amazon.com/Data-AI-Skills-Confidence-Succeed/dp/1398624136))
Jordan on LinkedIn (https://www.linkedin.com/in/jordanmorrow/)
High Signal podcast (https://high-signal.delphina.ai/)  
Watch the podcast episode on YouTube (https://youtu.be/JwS6B1FNF0A)  
Delphina's Newsletter (https://delphinaai.substack.com/) 
</description>
  <itunes:keywords>data science, ML, AI, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>Jordan Morrow, SVP of Data &amp; AI Transformation at AgileOne and the &quot;Godfather of Data Literacy,&quot; joins High Signal to discuss the shift from being &quot;data-driven&quot; to becoming &quot;AI-enabled.&quot; 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 &quot;Engineered Intelligence,&quot; a blend of machine capability and human emotional intelligence.</p>

<p><strong>LINKS</strong> </p>

<ul>
<li><a href="https://www.koganpage.com/business-and-management/data-and-ai-skills-9781398624139" rel="nofollow">Jordan&#39;s new book &quot;Data and AI Skills: Gain the Confidence You Need to Succeed&quot; </a> (also <a href="https://www.amazon.com/Data-AI-Skills-Confidence-Succeed/dp/1398624136" rel="nofollow">here on Amazon</a>)</li>
<li><a href="https://www.linkedin.com/in/jordanmorrow/" rel="nofollow">Jordan on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a><br></li>
<li><a href="https://youtu.be/JwS6B1FNF0A" rel="nofollow">Watch the podcast episode on YouTube</a><br></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>Jordan Morrow, SVP of Data &amp; AI Transformation at AgileOne and the &quot;Godfather of Data Literacy,&quot; joins High Signal to discuss the shift from being &quot;data-driven&quot; to becoming &quot;AI-enabled.&quot; 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 &quot;Engineered Intelligence,&quot; a blend of machine capability and human emotional intelligence.</p>

<p><strong>LINKS</strong> </p>

<ul>
<li><a href="https://www.koganpage.com/business-and-management/data-and-ai-skills-9781398624139" rel="nofollow">Jordan&#39;s new book &quot;Data and AI Skills: Gain the Confidence You Need to Succeed&quot; </a> (also <a href="https://www.amazon.com/Data-AI-Skills-Confidence-Succeed/dp/1398624136" rel="nofollow">here on Amazon</a>)</li>
<li><a href="https://www.linkedin.com/in/jordanmorrow/" rel="nofollow">Jordan on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a><br></li>
<li><a href="https://youtu.be/JwS6B1FNF0A" rel="nofollow">Watch the podcast episode on YouTube</a><br></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 36: AI and the Judgment Problem in Data Science</title>
  <link>https://highsignal.fireside.fm/36</link>
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  <pubDate>Thu, 19 Mar 2026 02:00:00 -0400</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/a407c218-174c-40f7-96ce-f973cb7d8971.mp3" length="123721065" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>Dawn Woodard (Distinguished Engineer, LinkedIn), Andrés Bucchi (LATAM Airlines), and Jeremy Hermann (CEO &amp; Co-Founder, Delphina) join High Signal for a deep dive into the shifting architecture of data science &amp; 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.</itunes:subtitle>
  <itunes:duration>1:03:30</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Dawn Woodard (Distinguished Engineer, LinkedIn), Andrés Bucchi (LATAM Airlines), and Jeremy Hermann (CEO &amp;amp; Co-Founder, Delphina) join High Signal for a deep dive into the shifting architecture of data science &amp;amp; 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 on LinkedIn (https://www.linkedin.com/in/dawn-woodard/)
Andrés on LinkedIn (https://www.linkedin.com/in/bucchi/)
Jeremy on LinkedIn (https://www.linkedin.com/in/jeremyhermann/)
Build Your Bracket with Data in the Delphina Sandbox (https://delphina.ai/NCAA)
High Signal podcast (https://high-signal.delphina.ai/)  
Watch the podcast episode on YouTube (https://youtu.be/oSqHw0nMo0E)  
Delphina's Newsletter (https://delphinaai.substack.com/) 
</description>
  <itunes:keywords>data science, ML, AI, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>Dawn Woodard (Distinguished Engineer, LinkedIn), Andrés Bucchi (LATAM Airlines), and Jeremy Hermann (CEO &amp; Co-Founder, Delphina) join High Signal for a deep dive into the shifting architecture of data science &amp; 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.</p>

<p>We dig into the sobering reality of the &quot;source of truth&quot; problem, where the speed of AI-generated code far outpaces our ability to define what &quot;correct&quot; 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 &quot;verifier&quot; of AI-generated workflows, and why &quot;headless&quot; 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 &quot;brownfield&quot; enterprise codebases, this discussion provides a grounded framework for leaders navigating the gap between AI hype and operational reality.</p>

<p><strong>LINKS</strong> </p>

<ul>
<li><a href="https://www.linkedin.com/in/dawn-woodard/" rel="nofollow">Dawn on LinkedIn</a></li>
<li><a href="https://www.linkedin.com/in/bucchi/" rel="nofollow">Andrés on LinkedIn</a></li>
<li><a href="https://www.linkedin.com/in/jeremyhermann/" rel="nofollow">Jeremy on LinkedIn</a></li>
<li><a href="https://delphina.ai/NCAA" rel="nofollow">Build Your Bracket with Data in the Delphina Sandbox</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a><br></li>
<li><a href="https://youtu.be/oSqHw0nMo0E" rel="nofollow">Watch the podcast episode on YouTube</a><br></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>Dawn Woodard (Distinguished Engineer, LinkedIn), Andrés Bucchi (LATAM Airlines), and Jeremy Hermann (CEO &amp; Co-Founder, Delphina) join High Signal for a deep dive into the shifting architecture of data science &amp; 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.</p>

<p>We dig into the sobering reality of the &quot;source of truth&quot; problem, where the speed of AI-generated code far outpaces our ability to define what &quot;correct&quot; 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 &quot;verifier&quot; of AI-generated workflows, and why &quot;headless&quot; 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 &quot;brownfield&quot; enterprise codebases, this discussion provides a grounded framework for leaders navigating the gap between AI hype and operational reality.</p>

<p><strong>LINKS</strong> </p>

<ul>
<li><a href="https://www.linkedin.com/in/dawn-woodard/" rel="nofollow">Dawn on LinkedIn</a></li>
<li><a href="https://www.linkedin.com/in/bucchi/" rel="nofollow">Andrés on LinkedIn</a></li>
<li><a href="https://www.linkedin.com/in/jeremyhermann/" rel="nofollow">Jeremy on LinkedIn</a></li>
<li><a href="https://delphina.ai/NCAA" rel="nofollow">Build Your Bracket with Data in the Delphina Sandbox</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a><br></li>
<li><a href="https://youtu.be/oSqHw0nMo0E" rel="nofollow">Watch the podcast episode on YouTube</a><br></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 35: Beyond Online Experimentation: Generative Software That Optimizes Itself</title>
  <link>https://highsignal.fireside.fm/35</link>
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  <pubDate>Wed, 04 Mar 2026 20:00:00 -0500</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/ffe555a5-d5b0-4dd6-85c8-12f5b502664b.mp3" length="107918861" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>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.</itunes:subtitle>
  <itunes:duration>55:11</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>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 on LinkedIn (https://www.linkedin.com/in/martintingley/)
Want Your Company to Get Better at Experimentation? by Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley (Harvard Business Review) (https://hbr.org/2025/01/want-your-company-to-get-better-at-experimentation)
Avoid the Pitfalls of A/B Testing by Iavor Bojinov, Guillaume Saint-Jacques and Martin Tingley (Harvard Business Review) (https://hbr.org/2020/03/avoid-the-pitfalls-of-a-b-testing)
Martin &amp;amp; Co.'s Seven Part Blog Series on Experimentation at Netflix (https://netflixtechblog.com/netflix-a-culture-of-learning-394bc7d0f94c)
Roberto Medri (Meta) on High Signal: The Incentive Problem in Shipping AI Products — and How to Change It (https://high-signal.delphina.ai/episode/roberto-medri-on-the-incentive-problem-in-shipping-ai-products----and-how-to-change-it)
Tim O’Reilly on High Signal: The End of Programming As We Know It (https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it)
Watch the podcast episode on YouTube (https://youtu.be/_hTZ1q0_JRM)
Delphina's Newsletter (https://delphinaai.substack.com/)
</description>
  <itunes:keywords>data science, ML, AI, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>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.</p>

<p>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 &quot;experimentation programs,&quot; 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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/martintingley/" rel="nofollow">Martin on LinkedIn</a></li>
<li><a href="https://hbr.org/2025/01/want-your-company-to-get-better-at-experimentation" rel="nofollow">Want Your Company to Get Better at Experimentation? by Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley (Harvard Business Review)</a></li>
<li><a href="https://hbr.org/2020/03/avoid-the-pitfalls-of-a-b-testing" rel="nofollow">Avoid the Pitfalls of A/B Testing by Iavor Bojinov, Guillaume Saint-Jacques and Martin Tingley (Harvard Business Review)</a></li>
<li><a href="https://netflixtechblog.com/netflix-a-culture-of-learning-394bc7d0f94c" rel="nofollow">Martin &amp; Co.&#39;s Seven Part Blog Series on Experimentation at Netflix</a></li>
<li><a href="https://high-signal.delphina.ai/episode/roberto-medri-on-the-incentive-problem-in-shipping-ai-products----and-how-to-change-it" rel="nofollow">Roberto Medri (Meta) on High Signal: The Incentive Problem in Shipping AI Products — and How to Change It</a></li>
<li><a href="https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it" rel="nofollow">Tim O’Reilly on High Signal: The End of Programming As We Know It</a></li>
<li><a href="https://youtu.be/_hTZ1q0_JRM" rel="nofollow">Watch the podcast episode on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>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.</p>

<p>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 &quot;experimentation programs,&quot; 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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/martintingley/" rel="nofollow">Martin on LinkedIn</a></li>
<li><a href="https://hbr.org/2025/01/want-your-company-to-get-better-at-experimentation" rel="nofollow">Want Your Company to Get Better at Experimentation? by Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley (Harvard Business Review)</a></li>
<li><a href="https://hbr.org/2020/03/avoid-the-pitfalls-of-a-b-testing" rel="nofollow">Avoid the Pitfalls of A/B Testing by Iavor Bojinov, Guillaume Saint-Jacques and Martin Tingley (Harvard Business Review)</a></li>
<li><a href="https://netflixtechblog.com/netflix-a-culture-of-learning-394bc7d0f94c" rel="nofollow">Martin &amp; Co.&#39;s Seven Part Blog Series on Experimentation at Netflix</a></li>
<li><a href="https://high-signal.delphina.ai/episode/roberto-medri-on-the-incentive-problem-in-shipping-ai-products----and-how-to-change-it" rel="nofollow">Roberto Medri (Meta) on High Signal: The Incentive Problem in Shipping AI Products — and How to Change It</a></li>
<li><a href="https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it" rel="nofollow">Tim O’Reilly on High Signal: The End of Programming As We Know It</a></li>
<li><a href="https://youtu.be/_hTZ1q0_JRM" rel="nofollow">Watch the podcast episode on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 31: Why Data Governance In Your Org is Broken (And How to Fix It)</title>
  <link>https://highsignal.fireside.fm/31</link>
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  <pubDate>Mon, 29 Dec 2025 19:00:00 -0500</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/7bfb563b-f650-4fab-b807-40e0a7474b90.mp3" length="91674741" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>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.</itunes:subtitle>
  <itunes:duration>47:00</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Cara Dailey, VP and Head of Data Strategy at Early Warning (the parent company of Zelle), joins High Signal to discuss the evolution of high-stakes data leadership and governance. From her early work in online advertising at DoubleClick to shaping data strategy at Nike and holding Chief Data Officer roles at Bank of the West and T. Rowe Price, Cara has seen every iteration of the data leader’s role. Now, she’s navigating her 'product era'—shaping the data strategy for Early Warning's Decisions Intelligence business, where she leverages rich financial data and data science to drive fraud monitoring and modeling.
In this episode, Cara shares her pragmatic 'progress over perfection' approach to governance, why she’s abandoning monolithic platforms in favor of incremental data products, and her 80/20 rule for balancing operational rigor with innovation. We also discuss why 'loving' data isn't enough—you have to actually 'take care' of it—and why AI is finally shining a spotlight on the often-neglected fundamentals of data stewardship and conversational BI.
LINKS
Cara Dailey on LinkedIn (https://www.linkedin.com/in/cara-dailey/)
Why AI Adoption Fails: A Behavioral Framework for AI Implementation, A High Signal Conversation with Lis Costa (Chief of Innovation, Behavioural Insights Team) (https://high-signal.delphina.ai/episode/why-ai-adoption-fails-a-behavioral-framework-for-ai-implementation)
Watch the podcast episode on YouTube (https://youtu.be/KphTkF_NrEA)
High Signal podcast (https://high-signal.delphina.ai/)
Delphina's Newsletter (https://delphinaai.substack.com/)
</description>
  <itunes:keywords>data science, ML, AI, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>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 &#39;product era&#39;—shaping the data strategy for Early Warning&#39;s Decisions Intelligence business, where she leverages rich financial data and data science to drive fraud monitoring and modeling.</p>

<p>In this episode, Cara shares her pragmatic &#39;progress over perfection&#39; 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 &#39;loving&#39; data isn&#39;t enough—you have to actually &#39;take care&#39; of it—and why AI is finally shining a spotlight on the often-neglected fundamentals of data stewardship and conversational BI.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/cara-dailey/" rel="nofollow">Cara Dailey on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/episode/why-ai-adoption-fails-a-behavioral-framework-for-ai-implementation" rel="nofollow">Why AI Adoption Fails: A Behavioral Framework for AI Implementation, A High Signal Conversation with Lis Costa (Chief of Innovation, Behavioural Insights Team)</a></li>
<li><a href="https://youtu.be/KphTkF_NrEA" rel="nofollow">Watch the podcast episode on YouTube</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>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 &#39;product era&#39;—shaping the data strategy for Early Warning&#39;s Decisions Intelligence business, where she leverages rich financial data and data science to drive fraud monitoring and modeling.</p>

<p>In this episode, Cara shares her pragmatic &#39;progress over perfection&#39; 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 &#39;loving&#39; data isn&#39;t enough—you have to actually &#39;take care&#39; of it—and why AI is finally shining a spotlight on the often-neglected fundamentals of data stewardship and conversational BI.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/cara-dailey/" rel="nofollow">Cara Dailey on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/episode/why-ai-adoption-fails-a-behavioral-framework-for-ai-implementation" rel="nofollow">Why AI Adoption Fails: A Behavioral Framework for AI Implementation, A High Signal Conversation with Lis Costa (Chief of Innovation, Behavioural Insights Team)</a></li>
<li><a href="https://youtu.be/KphTkF_NrEA" rel="nofollow">Watch the podcast episode on YouTube</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 29: Why AI Adoption Fails: A Behavioral Framework for AI Implementation</title>
  <link>https://highsignal.fireside.fm/29</link>
  <guid isPermaLink="false">a34c146e-1bb8-47e0-92eb-ffa801c2cb5b</guid>
  <pubDate>Thu, 27 Nov 2025 20:00:00 -0500</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/a34c146e-1bb8-47e0-92eb-ffa801c2cb5b.mp3" length="96523642" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>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.
</itunes:subtitle>
  <itunes:duration>49:25</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Liz Costa of the Behavioral Insights Team returns to High Signal to deliver a critical behavioral science playbook for the AI era focused on human and business impact. We discuss why the potential of AI can only be fulfilled by understanding a single bottleneck: human behavior. The conversation reveals why leaders must intervene now to prevent temporary adoption patterns from calcifying into permanent organizational norms, the QWERTY Effect, and how to move organizations past simply automating drudgery to achieving deep integration.
We dig into why AI adoption is fundamentally a behavioral challenge, providing a diagnostic framework for leaders to identify stalled progress using the Motivation-Capability-Trust triad. Liz explains how to reframe AI deployment by leveraging Loss Aversion to bypass employee skepticism, and how to design workflows that improve human reasoning rather than replace it. The conversation provides clear guidance on intentional task offloading, the power of using AI to stress-test decisions, and why sanctioning employee experimentation is essential to discovering high-value use cases.
LINKS
AI &amp;amp; Human Behaviour: Augment, Adopt, Align, Adapt (https://www.bi.team/publications/ai-and-human-behaviour/)
Thinking Fast and Slow in AI (https://sites.google.com/view/sofai/home)
How does LLM use affect decision-making? (https://www.bi.team/wp-content/uploads/2025/09/How-can-LLMs-reduce-our-own-biases-Analysis-Report.pdf)
Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI with Lis Costa (High Signal) (https://high-signal.delphina.ai/episode/defaults-decisions-and-dynamic-systems-behavioral-science-meets-ai)
The Behavioral Insights Team (https://www.bi.team/)
Lis Costa on LinkedIn (https://uk.linkedin.com/in/elisabeth-costa-6a5b35248)
High Signal podcast (https://high-signal.delphina.ai/)
Watch the podcast episode on YouTube (https://youtu.be/dXId0BbcsSE)
Delphina's Newsletter (https://delphinaai.substack.com/) 
</description>
  <itunes:keywords>data science, ML, AI, Nudge, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>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.</p>

<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.bi.team/publications/ai-and-human-behaviour/" rel="nofollow">AI &amp; Human Behaviour: Augment, Adopt, Align, Adapt</a></li>
<li><a href="https://sites.google.com/view/sofai/home" rel="nofollow">Thinking Fast and Slow in AI</a></li>
<li><a href="https://www.bi.team/wp-content/uploads/2025/09/How-can-LLMs-reduce-our-own-biases-Analysis-Report.pdf" rel="nofollow">How does LLM use affect decision-making?</a></li>
<li><a href="https://high-signal.delphina.ai/episode/defaults-decisions-and-dynamic-systems-behavioral-science-meets-ai" rel="nofollow">Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI with Lis Costa (High Signal)</a></li>
<li><a href="https://www.bi.team/" rel="nofollow">The Behavioral Insights Team</a></li>
<li><a href="https://uk.linkedin.com/in/elisabeth-costa-6a5b35248" rel="nofollow">Lis Costa on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a></li>
<li><a href="https://youtu.be/dXId0BbcsSE" rel="nofollow">Watch the podcast episode on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>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.</p>

<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.bi.team/publications/ai-and-human-behaviour/" rel="nofollow">AI &amp; Human Behaviour: Augment, Adopt, Align, Adapt</a></li>
<li><a href="https://sites.google.com/view/sofai/home" rel="nofollow">Thinking Fast and Slow in AI</a></li>
<li><a href="https://www.bi.team/wp-content/uploads/2025/09/How-can-LLMs-reduce-our-own-biases-Analysis-Report.pdf" rel="nofollow">How does LLM use affect decision-making?</a></li>
<li><a href="https://high-signal.delphina.ai/episode/defaults-decisions-and-dynamic-systems-behavioral-science-meets-ai" rel="nofollow">Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI with Lis Costa (High Signal)</a></li>
<li><a href="https://www.bi.team/" rel="nofollow">The Behavioral Insights Team</a></li>
<li><a href="https://uk.linkedin.com/in/elisabeth-costa-6a5b35248" rel="nofollow">Lis Costa on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a></li>
<li><a href="https://youtu.be/dXId0BbcsSE" rel="nofollow">Watch the podcast episode on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 28: From Context Engineering to AI Agent Harnesses: The New Software Discipline</title>
  <link>https://highsignal.fireside.fm/28</link>
  <guid isPermaLink="false">006c093f-00a4-4bac-a222-3aaad753d41a</guid>
  <pubDate>Thu, 13 Nov 2025 00:15:00 -0500</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/006c093f-00a4-4bac-a222-3aaad753d41a.mp3" length="99715558" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>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.</itunes:subtitle>
  <itunes:duration>50:34</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Lance Martin of LangChain joins High Signal to outline a new playbook for engineering in the AI era, where the ground is constantly shifting under the feet of builders. He explains how the exponential improvement of foundation models is forcing a complete rethink of how software is built, revealing why top products from Claude Code to Manus are in a constant state of re-architecture simply to keep up.
We dig into why the old rules of ML engineering no longer apply, and how Rich Sutton's "bitter lesson" dictates that simple, adaptable systems are the only ones that will survive. The conversation provides a clear framework for leaders on the critical new disciplines of context engineering to manage cost and reliability, the architectural power of the "agent harness" to expand capabilities without adding complexity, and why the most effective evaluation of these new systems is shifting away from static benchmarks and towards a dynamic model of in-app user feedback.
LINKS
Lance on LinkedIn (https://www.linkedin.com/in/lance-martin-64a33b5/)
Context Engineering for Agents by Lance Martin (https://rlancemartin.github.io/2025/06/23/context_engineering/)
Learning the Bitter Lesson by Lance Martin (https://rlancemartin.github.io/2025/07/30/bitter_lesson/)
Context Engineering in Manus by Lance Martin (https://rlancemartin.github.io/2025/10/15/manus/)
Context Rot: How Increasing Input Tokens Impacts LLM Performance by Chroma (https://research.trychroma.com/context-rot)
Building effective agents by Erik Schluntz and Barry Zhang at Anthropic (https://www.anthropic.com/engineering/building-effective-agents)
Effective context engineering for AI agents by Anthropic (https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents)
How we built our multi-agent research system by Anthropic (https://www.anthropic.com/engineering/multi-agent-research-system)
Measuring AI Ability to Complete Long Tasks by METR (https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/)
Your AI Product Needs Evals by Hamel Husain (https://hamel.dev/blog/posts/evals/index.html)
Introducing Roast: Structured AI workflows made easy by Shopify (https://shopify.engineering/introducing-roast)
Watch the podcast episode on YouTube (https://youtu.be/2Muxy3wE-E0)
Delphina's Newsletter (https://delphinaai.substack.com/) 
</description>
  <itunes:keywords>data science, ML, AI, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>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.</p>

<p>We dig into why the old rules of ML engineering no longer apply, and how Rich Sutton&#39;s &quot;bitter lesson&quot; 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 &quot;agent harness&quot; 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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/lance-martin-64a33b5/" rel="nofollow">Lance on LinkedIn</a></li>
<li><a href="https://rlancemartin.github.io/2025/06/23/context_engineering/" rel="nofollow">Context Engineering for Agents by Lance Martin</a></li>
<li><a href="https://rlancemartin.github.io/2025/07/30/bitter_lesson/" rel="nofollow">Learning the Bitter Lesson by Lance Martin</a></li>
<li><a href="https://rlancemartin.github.io/2025/10/15/manus/" rel="nofollow">Context Engineering in Manus by Lance Martin</a></li>
<li><a href="https://research.trychroma.com/context-rot" rel="nofollow">Context Rot: How Increasing Input Tokens Impacts LLM Performance by Chroma</a></li>
<li><a href="https://www.anthropic.com/engineering/building-effective-agents" rel="nofollow">Building effective agents by Erik Schluntz and Barry Zhang at Anthropic</a></li>
<li><a href="https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents" rel="nofollow">Effective context engineering for AI agents by Anthropic</a></li>
<li><a href="https://www.anthropic.com/engineering/multi-agent-research-system" rel="nofollow">How we built our multi-agent research system by Anthropic</a></li>
<li><a href="https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/" rel="nofollow">Measuring AI Ability to Complete Long Tasks by METR</a></li>
<li><a href="https://hamel.dev/blog/posts/evals/index.html" rel="nofollow">Your AI Product Needs Evals by Hamel Husain</a></li>
<li><a href="https://shopify.engineering/introducing-roast" rel="nofollow">Introducing Roast: Structured AI workflows made easy by Shopify</a></li>
<li><a href="https://youtu.be/2Muxy3wE-E0" rel="nofollow">Watch the podcast episode on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>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.</p>

<p>We dig into why the old rules of ML engineering no longer apply, and how Rich Sutton&#39;s &quot;bitter lesson&quot; 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 &quot;agent harness&quot; 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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/lance-martin-64a33b5/" rel="nofollow">Lance on LinkedIn</a></li>
<li><a href="https://rlancemartin.github.io/2025/06/23/context_engineering/" rel="nofollow">Context Engineering for Agents by Lance Martin</a></li>
<li><a href="https://rlancemartin.github.io/2025/07/30/bitter_lesson/" rel="nofollow">Learning the Bitter Lesson by Lance Martin</a></li>
<li><a href="https://rlancemartin.github.io/2025/10/15/manus/" rel="nofollow">Context Engineering in Manus by Lance Martin</a></li>
<li><a href="https://research.trychroma.com/context-rot" rel="nofollow">Context Rot: How Increasing Input Tokens Impacts LLM Performance by Chroma</a></li>
<li><a href="https://www.anthropic.com/engineering/building-effective-agents" rel="nofollow">Building effective agents by Erik Schluntz and Barry Zhang at Anthropic</a></li>
<li><a href="https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents" rel="nofollow">Effective context engineering for AI agents by Anthropic</a></li>
<li><a href="https://www.anthropic.com/engineering/multi-agent-research-system" rel="nofollow">How we built our multi-agent research system by Anthropic</a></li>
<li><a href="https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/" rel="nofollow">Measuring AI Ability to Complete Long Tasks by METR</a></li>
<li><a href="https://hamel.dev/blog/posts/evals/index.html" rel="nofollow">Your AI Product Needs Evals by Hamel Husain</a></li>
<li><a href="https://shopify.engineering/introducing-roast" rel="nofollow">Introducing Roast: Structured AI workflows made easy by Shopify</a></li>
<li><a href="https://youtu.be/2Muxy3wE-E0" rel="nofollow">Watch the podcast episode on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 26: Gen AI's True Cost: Why Today's Wins Are Tomorrow's Debts</title>
  <link>https://highsignal.fireside.fm/26</link>
  <guid isPermaLink="false">4801b29e-7d72-41e4-8125-7477c801d8bc</guid>
  <pubDate>Thu, 16 Oct 2025 01:00:00 -0400</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/4801b29e-7d72-41e4-8125-7477c801d8bc.mp3" length="85413811" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>Vishnu Ram Venkataraman (Generative AI Executive &amp; 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.</itunes:subtitle>
  <itunes:duration>43:14</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Vishnu Ram Venkataraman (Generative AI Executive &amp;amp; Entrepreneur; former AI Leader at Credit Karma and Intuit) joins High Signal to unpack the true cost of generative AI. Having scaled AI solutions impacting over 140 million users, Vishnu reveals why the ease of shipping Gen AI prototypes often masks significant operational and engineering debts, challenging the conventional wisdom of rapid deployment.
We dive deep into the strategic shift from traditional ML to Gen AI, discussing why the shelf value of code is dramatically falling, how to design new organizational triads for continuous iteration, and the critical differences in testing probabilistic AI systems. The conversation also explores how to manage risk with sensitive data, the power of synthetic data in early development, and which mature ML practices remain indispensable in the new AI era.
LINKS
Vishnu on LinkedIn (https://www.linkedin.com/in/vishnuvram/)
Fei-Fei Li on Generative AI as a Civilizational Technology (https://high-signal.delphina.ai/episode/fei-fei-on-how-human-centered-ai-actually-gets-built)
Tim O'Reilly on The End of Programming As We Know It (https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it)
Watch the conversation on YouTube (https://youtu.be/vDQdCl_EOKg)
Delphina's Newsletter (https://delphinaai.substack.com/) 
</description>
  <itunes:keywords>data science, ML, AI, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>Vishnu Ram Venkataraman (Generative AI Executive &amp; 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.</p>

<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/vishnuvram/" rel="nofollow">Vishnu on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/episode/fei-fei-on-how-human-centered-ai-actually-gets-built" rel="nofollow">Fei-Fei Li on Generative AI as a Civilizational Technology</a></li>
<li><a href="https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it" rel="nofollow">Tim O&#39;Reilly on The End of Programming As We Know It</a></li>
<li><a href="https://youtu.be/vDQdCl_EOKg" rel="nofollow">Watch the conversation on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>Vishnu Ram Venkataraman (Generative AI Executive &amp; 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.</p>

<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/vishnuvram/" rel="nofollow">Vishnu on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/episode/fei-fei-on-how-human-centered-ai-actually-gets-built" rel="nofollow">Fei-Fei Li on Generative AI as a Civilizational Technology</a></li>
<li><a href="https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it" rel="nofollow">Tim O&#39;Reilly on The End of Programming As We Know It</a></li>
<li><a href="https://youtu.be/vDQdCl_EOKg" rel="nofollow">Watch the conversation on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 25: How Data-Driven Growth Redefined a Media Giant</title>
  <link>https://highsignal.fireside.fm/25</link>
  <guid isPermaLink="false">b23bb87f-cf9d-4870-b873-fe490007bce6</guid>
  <pubDate>Thu, 02 Oct 2025 00:00:00 -0400</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/b23bb87f-cf9d-4870-b873-fe490007bce6.mp3" length="110793043" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>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.</itunes:subtitle>
  <itunes:duration>56:22</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Sergey Fogelson (VP of Data Science, Televisa Univision) joins High Signal to reveal how the world’s largest Spanish-language media company built a sophisticated data engine from the ground up. This transformation fueled a tenfold expansion of its digital streaming business by redefining how the company connects with 300 million viewers worldwide. At the heart of this success is a proprietary household graph that creates a single, privacy-first view of a massive and culturally diverse audience.
We dig into the journey from basic data unification to building production-ready recommendation engines, how his team uses embeddings on user behavior to uncover surprising connections in content consumption, and the trade-offs between investing in internal data tools versus direct revenue-driving products. The conversation also explores a pragmatic framework for AI adoption, showing how foundational machine learning often outperforms chasing the latest trends and where LLMs can deliver real, measurable value.
LINKS
Sergey Fogelson on LinkedIn (https://www.linkedin.com/in/sergeyfogelson/)
Watch the conversation on YouTube (https://youtu.be/f9R8mGcwygU)
Delphina's Newsletter (https://delphinaai.substack.com/) 
</description>
  <itunes:keywords>data science, ML, AI, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>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.</p>

<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/sergeyfogelson/" rel="nofollow">Sergey Fogelson on LinkedIn</a></li>
<li><a href="https://youtu.be/f9R8mGcwygU" rel="nofollow">Watch the conversation on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>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.</p>

<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/sergeyfogelson/" rel="nofollow">Sergey Fogelson on LinkedIn</a></li>
<li><a href="https://youtu.be/f9R8mGcwygU" rel="nofollow">Watch the conversation on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 24: Rebuilding an Airline for the 21st Century: LATAM's Data-Driven Transformation</title>
  <link>https://highsignal.fireside.fm/24</link>
  <guid isPermaLink="false">90bff920-3997-49ae-988f-b048c8b7df4a</guid>
  <pubDate>Mon, 15 Sep 2025 06:00:00 -0400</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/90bff920-3997-49ae-988f-b048c8b7df4a.mp3" length="97710398" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>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.
</itunes:subtitle>
  <itunes:duration>49:56</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Andrés Bucchi (Chief Data Officer, LATAM Airlines) joins High Signal to unpack how a century-old airline reinvented itself with data and AI—and how that transformation is unlocking value from fuel efficiency to fraud detection. LATAM has built a massive data operation, experimenting across everything from pricing to operations, while customers benefit from a more reliable and secure travel experience.
We dig into how LATAM fostered an experimentation culture, why existing data infrastructure is a critical asset, and how the biggest bottleneck in AI adoption isn't the technology itself, but human decision-making. The conversation also looks ahead to the future of generative AI as a software engineering problem, and the organizational changes needed to unlock its full potential.
LINKS
Andrés Bucchi on LinkedIn (https://www.linkedin.com/in/bucchi/)
Tim O'Reilly on The End of Programming As We Know It, High Signal (https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it)
Watch the conversation on YouTube (https://youtu.be/U_eaOmt-Rw4)
Delphina's Newsletter (https://delphinaai.substack.com/)
</description>
  <itunes:keywords>data science, ML, AI, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>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.</p>

<p>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&#39;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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/bucchi/" rel="nofollow">Andrés Bucchi on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it" rel="nofollow">Tim O&#39;Reilly on The End of Programming As We Know It, High Signal</a></li>
<li><a href="https://youtu.be/U_eaOmt-Rw4" rel="nofollow">Watch the conversation on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>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.</p>

<p>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&#39;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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.linkedin.com/in/bucchi/" rel="nofollow">Andrés Bucchi on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it" rel="nofollow">Tim O&#39;Reilly on The End of Programming As We Know It, High Signal</a></li>
<li><a href="https://youtu.be/U_eaOmt-Rw4" rel="nofollow">Watch the conversation on YouTube</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 22: Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It)</title>
  <link>https://highsignal.fireside.fm/22</link>
  <guid isPermaLink="false">8a3700ce-2760-498a-8016-c03d1d88dc35</guid>
  <pubDate>Tue, 19 Aug 2025 03:00:00 -0400</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/8a3700ce-2760-498a-8016-c03d1d88dc35.mp3" length="91236448" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:season>1</itunes:season>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>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.</itunes:subtitle>
  <itunes:duration>46:50</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Tomasz Tunguz (Theory Ventures) joins High Signal to unpack why a trillion dollars of market cap is up for grabs as AI reshapes enterprise software. He explains why workflows are now changing faster than packaged software can keep up, how “liquid software” is redefining CRM and marketing automation, and why background agents will require a new kind of “agent inbox.” We discuss the compounding errors that arise when tools are chained too finely, the hidden AI technical debt accumulating in today’s systems, and why modular stacks—mixing local and cloud models—will beat monolithic apps. The conversation also surfaces early memory architectures, what breaks when one IC manages 100 agents, and how these shifts change the real bottlenecks in scaling AI.
LINKS
Tomasz' Website (check out his blog!) (https://tomtunguz.com/)
Tomasz on LinkedIn (https://www.linkedin.com/in/tomasztunguz/)
Building effective agents by Erik Schluntz and Barry Zhang at Anthropic (https://www.anthropic.com/engineering/building-effective-agents)
How we built our multi-agent research system by Anthropic (https://www.anthropic.com/engineering/multi-agent-research-system)
Tim O'Reilly on The End of Programming As We Know It (https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it)
Delphina's Newsletter (https://delphinaai.substack.com/) 
</description>
  <itunes:keywords>data science, ML, AI, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://tomtunguz.com/" rel="nofollow">Tomasz&#39; Website (check out his blog!)</a></li>
<li><a href="https://www.linkedin.com/in/tomasztunguz/" rel="nofollow">Tomasz on LinkedIn</a></li>
<li><a href="https://www.anthropic.com/engineering/building-effective-agents" rel="nofollow">Building effective agents by Erik Schluntz and Barry Zhang at Anthropic</a></li>
<li><a href="https://www.anthropic.com/engineering/multi-agent-research-system" rel="nofollow">How we built our multi-agent research system by Anthropic</a></li>
<li><a href="https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it" rel="nofollow">Tim O&#39;Reilly on The End of Programming As We Know It</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://tomtunguz.com/" rel="nofollow">Tomasz&#39; Website (check out his blog!)</a></li>
<li><a href="https://www.linkedin.com/in/tomasztunguz/" rel="nofollow">Tomasz on LinkedIn</a></li>
<li><a href="https://www.anthropic.com/engineering/building-effective-agents" rel="nofollow">Building effective agents by Erik Schluntz and Barry Zhang at Anthropic</a></li>
<li><a href="https://www.anthropic.com/engineering/multi-agent-research-system" rel="nofollow">How we built our multi-agent research system by Anthropic</a></li>
<li><a href="https://high-signal.delphina.ai/episode/tim-oreilly-on-the-end-of-programming-as-we-know-it" rel="nofollow">Tim O&#39;Reilly on The End of Programming As We Know It</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
<item>
  <title>Episode 19: Defaults, Decisions, and Dynamic Systems: Behavioral Science Meets AI</title>
  <link>https://highsignal.fireside.fm/19</link>
  <guid isPermaLink="false">ca0afd9b-0a7a-4be7-929b-5f3cd1a1c0e5</guid>
  <pubDate>Thu, 03 Jul 2025 09:45:00 -0400</pubDate>
  <author>Delphina</author>
  <enclosure url="https://aphid.fireside.fm/d/1437767933/7dfbb66c-ee57-4196-9f73-43348bd083a5/ca0afd9b-0a7a-4be7-929b-5f3cd1a1c0e5.mp3" length="106123679" type="audio/mpeg"/>
  <itunes:episodeType>full</itunes:episodeType>
  <itunes:author>Delphina</itunes:author>
  <itunes:subtitle>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.
</itunes:subtitle>
  <itunes:duration>54:08</itunes:duration>
  <itunes:explicit>no</itunes:explicit>
  <itunes:image href="https://media24.fireside.fm/file/fireside-images-2024/podcasts/images/7/7dfbb66c-ee57-4196-9f73-43348bd083a5/cover.jpg?v=1"/>
  <description>Lis Costa, Chief of Innovation and Partnerships at the Behavioural Insights Team, joins High Signal to explore how behavioral science is reshaping public policy, digital platforms, and machine learning.
She explains how defaults influence behavior at scale, why personalization and chatbots are unlocking new kinds of interventions, and what happens when AI systems meet real-world complexity. We also discuss the limits of nudging, the promise of boosting, and why building for human decision-making requires more than just good models.
We dig into why AI adoption is fundamentally a behavioral challenge, providing a diagnostic framework for leaders to identify stalled progress using the Motivation-Capability-Trust triad. Lis explains how to reframe AI deployment by leveraging Loss Aversion to bypass employee skepticism, and how to design workflows that improve human reasoning rather than replace it. The conversation provides clear guidance on intentional task offloading, the power of using AI to stress-test decisions, and why sanctioning employee experimentation is essential to discovering high-value use cases.
LINKS
The Behavioral Insights Team (https://www.bi.team/)
Lis Costa on LinkedIn (https://uk.linkedin.com/in/elisabeth-costa-6a5b35248)
High Signal podcast (https://high-signal.delphina.ai/)
Delphina's Newsletter (https://delphinaai.substack.com/)
</description>
  <itunes:keywords>data science, ML, AI, Nudge, GenAI</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>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.</p>

<p>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.</p>

<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.bi.team/" rel="nofollow">The Behavioral Insights Team</a></li>
<li><a href="https://uk.linkedin.com/in/elisabeth-costa-6a5b35248" rel="nofollow">Lis Costa on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>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.</p>

<p>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.</p>

<p>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.</p>

<p><strong>LINKS</strong></p>

<ul>
<li><a href="https://www.bi.team/" rel="nofollow">The Behavioral Insights Team</a></li>
<li><a href="https://uk.linkedin.com/in/elisabeth-costa-6a5b35248" rel="nofollow">Lis Costa on LinkedIn</a></li>
<li><a href="https://high-signal.delphina.ai/" rel="nofollow">High Signal podcast</a></li>
<li><a href="https://delphinaai.substack.com/" rel="nofollow">Delphina&#39;s Newsletter</a></li>
</ul>]]>
  </itunes:summary>
</item>
  </channel>
</rss>
