Show Notes
Agentic AI has taken off in software engineering, but most CIOs still cannot make agents work in everyday knowledge work in the enterprise. Aaron Levie, co-founder and CEO of Box, explains why that gap exists and what enterprises must change to close it. Drawing on what Box sees across its enterprise customer base, including 68% of the Fortune 500, Levie covers data access, verification, budgets, architecture, and the new roles required to realize real value from enterprise AI agents.
====== This episode is brought to you by Gartner IT Symposium/Xpo™: https://cxo.news/KGg8XY ====== YOU'LL DISCOVER ✅ Why agentic coding raced ahead while knowledge work agents lag, across three properties: text based work, verifiability, and data access ✅ The "AI psychosis" pattern Levie says makes CEOs overestimate agents, and why distance from the last mile of work distorts executive judgment ✅ Why you should retry a failed AI project roughly every six months as frontier models keep improving ✅ The forward-deployed engineer role, internal and external, and why it becomes essential to enterprise AI adoption ✅ Why your IT and data architecture, not the model you pick, often determines what you actually get from agents ✅ The end of venture-subsidized tokens, and why the line of business, not just IT, now has to own the AI budget ✅ Why Levie says you should not vibe-code core systems of record like ERP or CRM, and where agent value actually accrues ✅ Value maxing versus token maxing: how to judge AI ROI and avoid a surprise overnight token bill
⏱️ TIMESTAMPS 0:00 The promise of agentic coding 5:11 Why knowledge work resists agents 8:52 The AI psychosis trap for CEOs 14:57 Be ambitious, then retry in six months 17:25 The rise of the forward-deployed engineer 21:09 Frontier models need your data architecture 27:14 The end of subsidized tokens 31:18 How knowledge workers should prepare 36:37 Where software value shifts 39:03 Reimagining workflows around abundance 43:03 Value maxing versus token maxing 49:46 Advice for CIOs
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