ОБ ЭТОМ ЭПИЗОДЕ
Gartner expects forty percent of agentic AI projects to be canceled by the end of 2027, and the companies bucking that trend aren't the ones giving their agents the most freedom. They're the ones narrowing it. This week: seven diagnostic signs your data isn't AI-ready, straight from practitioners at Capital One and EY. Thomson Reuters' forty-million-dollar bet on its own legal AI model, and the honest limits the company admits sit underneath it. How Cloudflare turned its engineering standards into a system that's already flagged 230,000 violations. And the research explaining why narrow scope, pre-execution checkpoints, and real traceability, not more autonomy, are what separate a surviving AI deployment from a canceled one.
Voices in this episode are digitally rendered.
If you're building or scaling AI infrastructure and want to work through a delivery or governance bottleneck, book a call at https://calendar.app.google/YH7SA6Rrs3CbZRaw5, message Vladimir Cvijanovic on LinkedIn (https://www.linkedin.com/in/vladimircvijanovic/), or reach us at podcast@c42.services.
Voices in this episode are digitally rendered.
If you're building or scaling AI infrastructure and want to work through a delivery or governance bottleneck, book a call at https://calendar.app.google/YH7SA6Rrs3CbZRaw5, message Vladimir Cvijanovic on LinkedIn (https://www.linkedin.com/in/vladimircvijanovic/), or reach us at podcast@c42.services.
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ПОИСК ПРОШЛЫХ ЭПИЗОДОВ
Искать прошлые эпизоды AI In Production: Hack the Stack.
ДРУГИЕ ЭПИЗОДЫ В ЭТОМ ПОДКАсте
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Gartner projects $207 billion in enterprise spending on AI agent software this year, up 139% from $86.4 billion in 2025. Uber burned through its entire 2026 AI coding budget by April and still can't say whether its coding agents paid for themselves. This week, Vlad, Kate, and Zoltan dig into why: […
Senior IT and AI leadership keep hitting the same wall: the pilot works, the board gets excited, and six months later nothing's actually shipped to production. Hack the Stack is the weekly show that closes that gap, breaking down what it actually takes to run AI in production: architecture, governa…
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