In early 2026, Meta reportedly created an internal token-spend leaderboard called “Claudeonomics,” ranking employees by how many AI tokens they consumed.
The predictable result was enormous consumption. When token usage becomes the metric, people optimize for token usage.
Meta may have been an extreme example, but the underlying idea was everywhere for a while. Across the technology industry, employees were being encouraged to experiment aggressively with AI, agents, automation, and new workflows.
Use AI.
That was often the entire strategy.
Perhaps you had a similar experience. I know I did.
Riding the Adoption Wave
I was happy to take my organization up on the offer.
These were powerful new tools, becoming more capable almost weekly. I wanted to understand them, so I dove in.
My workflows changed repeatedly. I adopted tools, abandoned others, experimented with models and agent harnesses, learned MCPs and Skills, and started integrating AI into the infrastructure and operational work I was already doing.
The objective seemed obvious:
These tools aren’t going away. Learn how to use them.
For me, that meant leaning even harder into Infrastructure-as-Code and AI-assisted operations. Services became reproducible installs committed to Git, documented, idempotent, and accompanied by health checks and operational tooling.
AI wasn’t replacing the work. It was changing how I approached the work.
And for a while, many organizations seemed to want exactly that.
The mythical “10x engineer” was back, this time carrying a token budget.
Then somebody saw the bill.
Coin a Phrase “Token-Min’ing”
Fast-forward a few months.
Token budgets were reduced. Monthly quotas appeared. Approved-model lists became smaller. Suddenly the employees who had been encouraged to integrate AI deeply into their workflows were being asked to economize.
Thus began the age of token-min’ing.
Tools and techniques appeared specifically to reduce context, shrink prompts, compress agent communication, and squeeze more work out of fewer tokens.
And that is not inherently bad.
Frontier models cost real money. Efficient use of them matters. Learning how to accomplish the same task with fewer tokens is itself an important engineering skill.
But the rapid swing from token-maxxing to token-min’ing raises a more important question:
What did organizations build while everyone was learning?
Did they develop internal AI capability?
Or did they simply develop dependency on external AI providers?
And if an organization’s AI strategy stops working the moment token budgets are reduced, was it really an AI strategy?
Adoption Was Never Supposed to Be the Destination
The early AI adoption wave should have been a discovery phase.
Give engineers access. Let them experiment. Find out what works. Learn which workflows generate real value.
Then turn those discoveries into organizational capability.
That could mean:
- shared inference infrastructure;
- model gateways and routing;
- internal knowledge and memory systems;
- RAG;
- reusable Skills and MCP integrations;
- standardized agent workflows;
- security and secrets management;
- governance and guardrails;
- selectively hosted internal models;
- and business-specific automation built around APIs and existing systems.
Not every company needs racks full of GPUs.
In many cases, the valuable intellectual property isn’t the model anyway. It is the workflow surrounding the model: the integrations, business logic, knowledge, guardrails, prompts, Skills, agents, and automation that allow employees to solve actual problems.
Some organizations understood this.
Others treated AI primarily as another SaaS product to provision.
That distinction matters now.
Organizations that used the experimentation period to identify useful workflows can move those capabilities into shared platforms, optimize model selection, route workloads intelligently, and decide which workloads belong on frontier models and which can run elsewhere.
Organizations that didn’t are left with a much simpler lever:
Reduce the token budget.
Employees Have Already Made a Different Decision
There is another problem.
The people who successfully incorporated AI into their work aren’t necessarily going backward.
AI subscriptions are rapidly becoming another part of the professional toolkit — not fundamentally different, from the employee’s perspective, from buying a better monitor, keyboard, IDE, or productivity application.
I see this in my own experimentation.
As I write this, I have OpenCode working through custom firmware for an open-source ESP32 watch using a locally hosted model running on an AMD GPU.
At home I experiment with local inference, different models, agent harnesses, routing, MCPs, Skills, and the infrastructure required to support them.
I don’t use personal systems for non-personal work, but they are increasingly where I continue learning.
That creates an uncomfortable problem for employers.
If employees reach their corporate AI quota and simply move some of their experimentation to personally funded tools, the organization hasn’t eliminated AI usage.
It may simply have eliminated its visibility into it.
That is a considerably worse outcome for governance, security, knowledge sharing, and organizational learning.
Once someone has experienced a meaningful productivity improvement from AI, telling them to stop using it doesn’t necessarily eliminate the demand.
It can simply make the usage invisible.
The Incentive to Hide AI Productivity
Now consider a different problem.
Suppose an employee discovers a workflow that lets them accomplish eight hours of work in six.
What incentive do they have to announce that?
If the likely response is:
- “Great, here are two more hours of work,”
- “Maybe we don’t need as many people,”
- “Everyone should now achieve this output,”
- or “This is simply the new baseline,”
then the rational behavior may be to say nothing.
The employee becomes invisibly more productive.
They continue delivering good work, but the organization never learns exactly why.
The employee keeps the workflow.
The company gets the output.
And the larger organization loses the opportunity to learn from the person who figured it out.
That is a terrible trade.
Harvest the Gain or Reinvest It
Organizations have a choice in what they do with AI-driven productivity improvements.
They can harvest the gain, or they can reinvest it.
Harvesting is easy.
An employee automates 25% of their workload, so management fills that newly available 25% with more tickets, meetings, projects, and responsibilities.
The reward for becoming more efficient is a larger workload.
There is another option.
Let some of that recovered capacity be reinvested into:
- finding the next automation opportunity;
- improving existing workflows;
- turning personal tooling into reusable organizational tooling;
- teaching coworkers;
- creating shared Skills and integrations;
- documenting successful patterns;
- experimenting with new use cases;
- and identifying where additional AI investment would produce the greatest return.
Consider a team whose job is to catch fish.
One person discovers better fishing holes, develops better techniques, and builds better lures.
Management can respond:
“You catch twice as many fish, so your quota is now twice as high.”
Or it can ask:
“Why are you catching twice as many fish, and how do we teach everyone else to do it?”
The first approach harvests the individual’s productivity.
The second creates organizational capability.
And if you consistently choose the first, don’t be surprised when your best fisherman stops telling you where the fishing holes are.ly how all organizations work.
The Employee Who Automates Their Job May Have Found Their Next Job
This may be the largest missed opportunity of all.
Some employees are naturally good at spotting repetitive work, undocumented processes, disconnected systems, and information that should flow automatically but doesn’t.
AI dramatically increases what those people can do.
If someone repeatedly automates parts of their own job, the best use of their newly available capacity may not be assigning them more of the same work.
It may be asking them to automate parts of everyone else’s.
Those employees can become force multipliers: building integrations, reusable agents, internal platforms, operational tooling, and workflows that turn individual productivity gains into organizational ones.
That requires organizations to think differently about AI productivity.
The objective shouldn’t be to maximize tokens.
It shouldn’t be to minimize tokens either.
It should be to maximize capability created per token spent.
The organizations that understand that distinction will come out of this period with more than a collection of AI subscriptions.
They’ll come out of it with people, platforms, workflows, and institutional knowledge that didn’t exist before.
And that was probably the real opportunity all along.
