High Signal: Data Science | Career | AI

• Episode 26: Gen AI's True Cost: Why Today's Wins Are Tomorrow's Debts

  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 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/)

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• Episode 25: How Data-Driven Growth Redefined a Media Giant

  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/)

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• Episode 24: Rebuilding an Airline for the 21st Century: LATAM's Data-Driven Transformation

  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/)

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• Episode 23: Why Most AI Agents Fail (and What It Takes to Reach Production)

  Anu Bharadwaj (President, Atlassian) joins High Signal to unpack how humans and AI agents will work together across the enterprise, and how that shift could change the very nature of teamwork. Atlassian employees have already built thousands of agents across product, marketing, engineering, and HR teams, while customers like HarperCollins are cutting manual work by 4x as industries from publishing to finance rethink their workflows. We dig into how Atlassian’s culture enables bottom-up experimentation, why grounding and reliability are critical for adoption, and how non-technical teams are often the ones creating the most useful agents. The conversation also looks ahead to the frontiers of multiplayer agent collaboration, proactive and ambient workflows, and the governance and compliance challenges enterprises will face as agents move from tools to teammates. LINKS Anu on LinkedIn (https://www.linkedin.com/in/anutthara/) 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) Watch the podcast episode on YouTube (https://youtu.be/898M86sKIi8?si=YGoekFzVJ0UH6pCJ) Delphina's Newsletter (https://delphinaai.substack.com/)

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• Episode 22: Why a Trillion Dollars of Market Cap Is Up for Grabs (and How AI Teams Will Win It)

  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/)

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