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I've watched discussions about AI swing from encouraging people to use more of it to worrying about the bill. The cost complaints are often justified. What bothers me is how little of that discussion gets into what people are actually doing with the tools, or what they're learning when an attempt goes wrong.
If someone hits their allowance, I'd want to look at a recent task before deciding what to change. Were they getting useful work done, or spending an afternoon asking an agent to repair its previous fix?
From tokenmaxxing to cost control
I understand paying for experimentation. People need room to try things that don't work. But tokenmaxxing turns that permission into an odd measure of achievement. I'd rather hear about a task someone managed to finish, including what they had to correct along the way.
The same blind spot survives when the goal switches to spending less. A cheaper model may be perfectly adequate for a task. If it needs more correction, though, the person doing that correction is paying part of the difference with their time. The review effort belongs in that comparison.
Sometimes the work is useful and the allowance is simply too small. I'd want to know that before lowering it further.
Help people work within the budget
I see working well with AI agents as an engineering skill in its own right, much like learning to use IntelliJ or VS Code well. When a company lowers the allowance, I think it owes people some help adapting.
That includes harness engineering: configuring the tools and feedback around an agent. Context engineering is part of it too, deciding what information the agent gets and how to keep it relevant to the task. I want room to experiment with those choices, compare the results, and share what we learn.
An AI enablement team should help people develop those skills and build shared workflows around them. That could include teaching Claude Code or adapting an existing workflow framework to the company's engineering practices, with reusable setups for planning, implementation and review.
Knowledge-transfer sessions and seminars give teams a place to explain the setups they've tried. I'd want practical guides alongside them, with enough detail for another engineer to try the same setup. Include the attempts that didn't help and the review work they created, so people can judge whether a setup fits their tasks and budget.
What I'd expect from a platform team
AI platform work is a different responsibility, though the same team may handle both.
When I built an internal documentation MCP server, I was dealing with deployment instructions scattered across repositories. At one point I had about 20 tabs open. Without those instructions, the assistant suggested fields and values that didn't fit our setup.
A spending cap wouldn't have supplied those instructions. I wanted the assistant to find them without me attaching them to every conversation. The AI platform team later asked me to make the server available to other teams. Their MCP registry required a remote server, so I moved it off my laptop.
The registry is part of the platform job, along with model access and serving, usage and cost visibility, and company-wide controls such as budgets and approved tools. I expect the platform team to keep those services reliable and look for ways to reduce their total cost, working with Finance and Security on spending and governance decisions.
Shared tools also need a named maintenance owner, whether that's a platform engineer or the team that built them. The team using a setup still decides whether its results are acceptable.
Where I'd like this to go
I'd like more developers to be able to build small tools around work they understand, using shared infrastructure without having to recreate it. I see those tools becoming part of workflows that keep moving when the developer steps away.
The platform needs to make those runs observable and keep their costs under control. Enablement should help engineers build workflows that can progress without constant prompting, including how to handle failed runs and where human review belongs.
When models or spending limits change, platform and enablement should work through the consequences together. Enablement can help teams revisit their workflows and update or retire the shared examples. What they learn should also influence platform decisions, whether that's fixing an integration or reconsidering an allowance.
Final thoughts
I expect more development work to run through agents around the clock. Companies will need dependable AI services and engineers who know how to build and improve the workflows running on them.
A spending cap sets a financial limit. It still leaves the company needing to understand what that money buys and help people produce better work with it. The engineering time for that belongs in the budget too.