Hack the Stack #004: Same AI Spend, Opposite Outcomes

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: [Uber](https://venturebeat.com/orchestration/companies-are-spending-millions-rewiring-how-ai-gets-used-almost-none-can-prove-its-working) capped AI coding spend at $1,500 per employee per month after the money was already gone, while [Everlaw](https://venturebeat.com/orchestration/companies-are-spending-millions-rewiring-how-ai-gets-used-almost-none-can-prove-its-working) turned a $3,500 token spend into a 9.5-to-2.5-engineer-month reduction on a Java infrastructure project by measuring before it spent.
Then, a five-part architecture for making retrieval-augmented generation systems auditable, from structured retrieval logging to treating retrieved content as data instead of instructions, [from The New Stack](https://thenewstack.io/building-trust-agentic-rag/). And [Figma's](https://www.infoq.com/news/2026/09/figma-security-agents/) security team explains how three specialized AI agents, built around institutional memory and a "precision before recall" tuning discipline, deliver 70% faster alert resolution, a 20% drop in on-call pages, and over 100 previously unknown vulnerabilities caught.
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.