The Data Exchange with Ben Lorica

Ameet Talwalkar, Carnegie Mellon ML professor and Chief Scientist at Datadog, joins Ben Lorica to trace time series foundation models from skepticism to Datadog’s Toto V1 and V2. 
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Detailed show notes and transcript, can be found on The Data Exchange web site.

Ben Lorica sits down with Evangelos Simoudis to unpack why open-weights models are closing the gap with frontier labs and pushing enterprises toward “corporate AI sovereignty.” They then apply an attenuation-funnel lens to AI coding tools — more code, but far less shipped software and usage growth — before turning to “neoconsulting,” the outcomes-based services model reshaping Accenture, Palantir, and the big Indian IT firms.
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Detailed show notes and transcript, can be found on The Data Exchange web site.

Manos Koukoumidis, CEO of Oumi, returns to the podcast to make the case that most enterprises are wildly inefficient, using bloated generic AI models for tasks that demand specialized ones. He argues that companies should stop renting intelligence from frontier labs and start owning, customizing, and compounding it.
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Detailed show notes and transcript, can be found on The Data Exchange web site.

Andrew Moore, CEO of Lovelace, former head of Google Cloud AI, and former dean of Carnegie Mellon’s School of Computer Science, joins the podcast to discuss YottaGraph, a knowledge graph growing by a billion facts a week that serves as a context engine for enterprise AI agents. He explains why fully automatic knowledge graph construction is the only viable path at scale, why entity resolution remains a brutal engineering problem, and how graph theory tricks make million-node queries answerable in under a second.
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Detailed show notes and transcript, can be found on The Data Exchange web site.

Ben Lorica speaks with Helen Gu, CEO of InsightFinder AI and professor at North Carolina State University, about why reliability is becoming a central challenge for enterprise AI. They discuss AI incident response, monitoring agents in production, domain-specific small language models, causal inference for operational data, and why the “last 1%” of accuracy and reliability is often the hardest part of deploying AI systems. 
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Detailed show notes and transcript, can be found on The Data Exchange web site.