Decoding the Token Economy
How Should Enterprises Train Employees to Use AI?
Uber once burned through a full year's AI budget in just four months; Meta consumes hundreds of millions of dollars in compute in a single month. As token consumption grows exponentially, the question enterprises really need to ask isn't "should we use AI," but how to train employees to use it well — and redesign the organization to capture that efficiency dividend.
Decoding the token economy: consumption grows faster than you'd think
A token is the smallest unit of data an AI model processes — covering text, code, instructions, and images. Now that we're in the era of agentic AI, agents no longer just passively answer questions; they read data, send instructions, and run computations on their own — driving token consumption to grow exponentially.
- Uber burned through what was supposed to be a full year's AI budget in just four months.
- Meta's monthly AI compute consumption alone reaches hundreds of millions of dollars.
For enterprises, tokens are no longer just a technical detail behind the scenes — they're a production cost that needs to be actively managed.
Three keys for enterprises to use tokens well
Facing rapidly accumulating token costs, what really determines the return on investment is whether an enterprise can master these three keys:
- Find the right pain points and use cases — The most effective deployments have AI assistants handle tedious, repetitive communication and verification work for office staff. For example, one software company had AI automatically read emails, cross-check them against inventory and production-line data, and auto-generate documents — cutting the workload for procurement staff by nearly half.
- Redesign processes to free up high-value productivity — For example, a Taiwanese telecom equipped employees with AI assistants for development, customer service, and data analysis, letting store staff and front-line employees save time on repetitive work and focus instead on understanding customer needs and delivering service.
- Evaluate your compute service model — Choosing between building in-house, renting, or connecting via API depends on project type, real-time requirements, security regulations, and usage frequency — this is how you effectively control token costs.
Reverse division of labor: how AI is reshaping the organization
Traditional management logic holds that finer division of labor means higher efficiency. But when division of labor gets too fine, meetings multiply, approvals slow down, and departmental silos grow taller — becoming a bottleneck for the organization instead.
The change AI brings makes reverse division of labor possible — one person can now do what used to take an entire team, making the organizational structure flatter, with fewer departments and leaner processes.
A data-dashboard build that used to take weeks can now go from "pitched in the morning" to "delivered by the afternoon."
Three core roles in the enterprise of the future
Reverse division of labor is reshaping not just processes, but the functional division of labor within an organization. The enterprise of the future will gradually converge around three core roles:
- Independent Contributors — Make up the majority of the enterprise, using the company's AI brain and agent assistance to achieve output equivalent to "one person doing the work of ten."
- Directly Responsible Individuals (DRIs) — Understand enterprise goals, engage directly with customer needs, and coordinate resources and teams to get things done.
- Player-Coach Managers — The manager role transforms, shifting its center of gravity from assigning work to helping the first two roles grow, ensuring quality, and keeping action aligned with overall company strategy.
The challenge for new graduates: entry-level roles are disappearing
AI has absorbed a large share of foundational work, taking away the traditional "training ground" new graduates used to rely on — entry-level positions that once built experience by processing large volumes of repetitive tasks are rapidly shrinking.
According to job-market data from June 2026, monthly hiring demand for software and internet engineers in Taiwan has fallen from around 80,000 two years ago to roughly 63,000.
A new answer for individual competitiveness: from T-shaped to comb-shaped talent
The talent paradigm is shifting: from a single specialty, to T-shaped or π-shaped talent with one area of deep expertise paired with broad general knowledge — and now, one step further, to comb-shaped talent.
What defines comb-shaped talent is using AI tools to quickly grow one new capability "tooth" after another, moving into new domains at will, without having to spend years building up each new specialty the way people used to.
In this shift, what really separates the winners isn't the number of hard skills they hold — it's soft skills. Staying curious and open-minded is the fundamental capability that lets a person keep learning alongside AI.
This article reflects SecuAgent's internal industry observations and practical recommendations. It does not constitute a guarantee or commitment of any kind — please evaluate your organization's actual adoption strategy based on your own circumstances.
Controlling the token economy is an enterprise's first line of defense in adopting AI
Uber and Meta's cases show that unmanaged token consumption can burn through an entire year's budget in just a few months. SecuAgent's smart routing and hybrid cloud-edge compute scheduling let enterprises dynamically allocate compute resources based on query complexity — training employees to use AI well while keeping token costs firmly under control.
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