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Pinching pennies on AI tokens is how AI transformation fails. Michael Laudon, Chief Product and Technology Officer at Quest Software, argues the real question is whether that spend produces measurable output, and no single metric answers it. Michael joins Russ to unpack how Quest runs its AI transf…
Tope Iluyomade has been working in AI since 2004, starting with robotics and reinforcement learning, and has spent two decades advising and leading technology inside large enterprises as both CIO and CISO before moving into executive advisory work. In this conversation, he breaks down why most AI g…
Coinbase drove its token usage up while cutting inference costs by more than half. Kyle Cesmat, who runs Coinbase's Agent Experience team, explains the architecture behind it, from an internal LLM gateway that routes their inference to cache hit rates held above 85 percent. He walks Ameya through h…
Most AI deployments stall because leaders treat the tool as the whole investment. Priyanka Vergadia, argues technology is only 20 percent of the equation, and the returns show up once you fund people, training, and change management. Priyanka, Senior Director of AI Transformation at Microsoft, brea…
Iain Hassall sorts AI spend into three buckets, and the trickiest is AI embedded in your own product, where cost basis and margins get murky. His warning to finance leaders is to lock in pricing and packaging before shipping AI features, or risk buying a dollar of inference and selling it for 80 ce…
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