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AI dev tooling at scale: pain points

Hey all 👋

Quick poll for an article I'm writing on deploying AI coding tools (Claude Code, Copilot, Cursor), from small teams to larger orgs. Takes 3 min max, and genuinely helps me calibrate what actually matters out there.

For context: I work in an early-stage company with a small senior team, so my day-to-day probably doesn't reflect what many of you run into. All team sizes welcome, from a 5-person SME to a 200-dev org. Your input helps me understand challenges across the board and keep building tools that are actually useful beyond my own context.

By Florian Bruniaux, for my blog.

I've identified a few common pain points with AI dev tooling at scale. Which ones do you actually run into?

🔒 Security: secrets/credentials leaking to APIs, unaudited MCPs, destructive commands an agent might execute

💸 Cost & visibility: opaque token spend, no way to know who consumes what, end-of-month billing surprises

💻 Machine power: laptops crawling with 3-4 parallel sessions, expensive fleet refresh just to run Claude Code

🏛️ Fleet governance: one person updates the config (hooks, skills, allowed MCPs), everyone on the team gets it automatically, no one diverges

🧠 Context loss / collaboration: no shared memory across sessions, everyone redoes the same plans, duplicated work

📋 License management & contractors: buying licenses in bulk, onboarding freelancers without a named account, revoking access when they leave

📊 Measuring ROI: no way to assess if AI tooling actually improves output, hard to justify the investment to management

Other

AI dev tooling at scale: pain points

I've identified a few common pain points with AI dev tooling at scale. Which ones do you actually run into?

Anything else you'd like to add? (optional)