Quick answer
Run whoburnedmore with the CLI command npx whoburnedmore. Existing commands and links remain supported.
Search intent
The reader wants to create a team AI token leaderboard without creating bad incentives.
Best for
Team leads, hackathon organizers, and AI champions.
Start opt-in
Developers should choose whether to publish individual burn. Team aggregate views can still help leaders understand adoption without exposing everyone.
- Opt-in profiles
- Private mode
- Team aggregate
Collect usage, not code
The leaderboard does not need source code, prompt text, or file contents. Keep the collected data to usage totals and public handles.
- Tokens
- Costs
- Tool names
- Dates
Add outcome context
High usage can be impressive or wasteful. Notes about shipped work help the team understand what the burn bought.
- Shipped feature
- Incident fix
- Learning session
- Experiment
Use boards for local culture
Public global leaderboards are fun, but teams often need their own board for a hackathon, company, or friend group.
- Team board
- Hackathon board
- Friend board
Short answer for ai token leaderboard for teams
The practical answer is to measure the workflow before changing tools or plans. Use opt-in profiles, aggregate token totals, no source-code collection, team-level context, and outcome notes. Make the leaderboard fun, not punitive. Then review the result against the intended outcome: whether the work shipped, whether the agent got stuck in a loop, and whether the same task should use a smaller prompt, a cheaper model, or a different AI coding product next time.
This is also why the page links to authoritative external sources and to related whoburnedmore guides. Pricing pages explain the vendor unit; your local usage history explains what that unit means in practice. Keep both views together before making a budget, upgrade, or team-policy decision.
Mistakes to avoid
Optimizing before measuring
It is tempting to change plans, switch tools, or clamp down on usage as soon as ai token leaderboard for teams becomes a concern. That usually hides the real issue. Measure the current workflow first, then decide whether the problem is volume, scope, model choice, team policy, or one unusually expensive session.
Comparing vendor units directly
A request, credit, ACU, message, token, and quota are not interchangeable units. Convert each tool back to the work it produced: the feature, bug fix, review, prototype, or incident response. That makes cross-tool comparison fair enough to act on.
Treating high burn as automatically bad
A high-burn session can be waste, but it can also be the session that unblocked a release. Add outcome notes before judging the number. The goal is not low usage; the goal is useful, explainable usage that the team can repeat.
Practical playbook
What to measure first
Start with the signal most likely to change behavior for this topic: opt-in. For someone searching ai token leaderboard for teams, the useful answer is not a generic definition. It is a repeatable way to decide whether the current workflow is healthy, whether the cost is justified, and which next action will reduce waste without killing useful AI experimentation.
How to turn it into a habit
Use a simple weekly rhythm: measure the biggest burn, label the task, record whether it shipped value, and change one prompt or routing rule. The sections above cover start opt-in, collect usage, not code, add outcome context, and use boards for local culture. Those are the pieces that make the guide actionable instead of another pricing summary.
How whoburnedmore fits
whoburnedmore is the measurement layer, not the policy layer. It reads local AI coding-agent usage, keeps source code out of the upload path, and gives you a shared burn view. That means this guide can stay focused on decisions: when to upgrade, when to narrow context, when to switch tools, and when a high-burn session was actually worth it.
Decision checklist
Can you explain why ai token leaderboard for teams matters for a real task this week?
Do you know which tool, model, project, or workflow created the largest burn?
Is the next action a smaller prompt, a different tool, a plan change, or a team policy update?
Can you review the result without uploading source code or raw prompt content?