it is simultaneously possible to spend a lot on AI and still underuse it, esp in larger orgs
we're seeing this with meta, uber, and many other orgs instituting budgets
some factors are at play:
cost of the frontier comes at an enormous premium: fable -> glm 5.2 is a 10x dropoff in cost
tragedy of the commons, in large orgs, much safer to always default to larger model at a higher reasoning effort. ends up in a situation where most features/people are on too high of a setting, resulting in 2-3x more spend than needed
very easy for runaway automations, openclaw bros, subagent accidents, to create a lot of spend quickly
results in a very skewed distrubtion of usage with a small number of people/features with high usage
to counteract these issues, and avoid internal budgets (for now)
we changed defaults across the company to lower reasoning levels, across surfaces
thinking about the p50, p75, p95 session. cost to PR/cost for support ticket/cost for session, and actively compressing model tiers (gpt 5.1->5.4-mini) over time
banning automations from using frontier models, and high reasoning efforts, and using flex api tiers (adds up to 75%+ savings)
tldr before you institute budgets, try these first
more in the blog:
engineering.ramp.com/post/ai…
Link
You're Spending Too Much on AI. You're Also Using Too Little.
A big AI bill does not mean you are using too much AI. It means you are buying it wrong.
engineering.ramp.com