Atlassian has introduced AI wallets with monthly spending caps for its research and development team, setting limits between $500 and $2,000 per employee. The move comes as token costs surge across the industry and companies scramble to control employee AI use, a trend HR leaders will need to track closely.
The mechanics of AI wallets
Employees in Atlassian's R&D division can spend their allocated budget across four AI products. They receive a notification when they near the limit, and usage stops once the wallet is empty. Staff can request additional funds if needed. A company spokesperson told The Guardian that the wallet actually represents an increase in what employees can spend, as Atlassian repositions itself as an "AI-first" firm. "Atlassian provides a significant budget for our builders to leverage multiple AI tools," the spokesperson said. "AI tooling budgets are set by role based on how different teams work."
Tokenmaxxing drives up costs
The new budget controls arrive amid a wave of "tokenmaxxing" - the practice of maximising AI usage by consuming as many tokens as possible with autonomous agents. Tokens are the text units that AI models process; one token equals roughly four characters, and a single paragraph uses about 100 tokens. OpenAI's GPT-5.6 Sol charges $5 per million input tokens, while Anthropic's Claude Fable 5 and Claude Mythos 5 models cost $10 per million input tokens. Widespread adoption has pushed token expenses higher at many firms, prompting restrictions.
Other companies tighten controls
Amazon recently shut down an internal AI leaderboard that scored staff based on their activity on the Kiro AI platform, after reports of tokenmaxxing. Meta also warned employees that token budgets, allocations, and controls are underway, noting that internal AI use in 2026 alone could reach billions of dollars. For HR departments, these moves signal a need to build AI cost governance into workforce planning. Understanding how to set and monitor AI tool budgets is now a practical skill, and resources like AI for Human Resources can help teams develop those frameworks.
Why this matters for Human Resources
As AI becomes embedded in daily work, HR professionals must manage not only adoption but also the financial footprint. Atlassian's wallet model shows one approach: role-based budgets with hard caps and override requests. HR leaders can start by auditing current AI tool usage, defining token budgets for different roles, and educating managers on cost-aware AI practices. An AI Learning Path for HR Managers offers training on these topics, from tool selection to expense governance. Without clear policies, tokenmaxxing can quietly drain departmental budgets - a risk HR is uniquely positioned to mitigate.
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