Enterprise AI leaders will dive deep into why token costs are killing projects before production and how to architect for economics from day one—covering GPU decisions, data architecture, and orchestration strategies that actually survive budget reviews.
Token costs have overtaken hallucinations as the top reason enterprise AI projects die before reaching production; it’s cited by 29% of technical leaders as the primary production killer, ahead of every reliability failure combined. The teams seeing success don't treat inference cost as an after-the-fact line item. They architect for token costs from the beginning. This event examines what that shift actually looks like in practice: GPU memory and data architecture decisions that determine cost per token served and the orchestration choices that separate a project that survives its second budget review from one that doesn't. We'll open with new VentureBeat Pulse Research on how enterprises are — and aren't — measuring the economics of the AI they're already running. This isn't a panel. It's a small group of peers, over drinks, working through the hardest question in enterprise AI right now. An evening conversation and drinks with a small number of enterprise AI leaders, kept intentionally close to keep the conversation substantive. Applications reviewed on a rolling basis — apply today to request one of the remaining seats
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