Stop Being Permanent Underclass. Implement a Token Factory.
Technology, software, and financial industries are all operating in headless-chicken mode. The pendulum swung from tokenmaxxing or PIP’ed in Q1 2026 to the opposite extreme of token budgets and throttling AI usage today. Everyone is getting whiplash from reactionary policies instead of pursuing a forward-looking AI transformation.
Use lower-cost AI models!
Don’t have the AI model think as much!!
Don’t spend too many tokens doing non-critical work!!!
These budget-conscious directives are among the gravest mistakes companies can make in AI transformation. AI transformation is about moving an organization into the next era, not cost control. Meanwhile, the AI gods have descended from the frontiers of human intelligence and are expanding aggressively into every downstream vertical. And we are sending a bunch of poor human souls armed with a $200 monthly token budget to defend against armies of Claude and Codex agents with unlimited tokens.
Good luck winning that battle.
The AI equivalent of guns, germs, and steel is tokens, talent, and data.
But what about costs?
Not everyone has the seemingly blank-check budgets of frontier AI companies. That is why every company should be implementing—not building—a token factory today.
A token factory is a centralized service where employees dispatch AI agents to complete tasks on their behalf by spending tokens. Each agent acts on behalf of a user, inheriting that user’s permissions to ensure compliant access to company systems and data. And this is not for engineers only. The tasks should include coding, operating, taking notes, authoring PowerPoint decks, redlining Word documents, conducting research, and completing almost any other productivity task or workflow.
This differs drastically from employees using local copies of Claude Desktop, ChatGPT, Microsoft Copilot, or equivalent apps. In that model, every user must configure the connector, choose the model, decide how much thinking to allow, and try whichever god-awful influencer prompt tricks appeared on LinkedIn that morning. All of this is cognitive overhead that users don’t need.
Agents inside a token factory are already equipped with the connectors, tools, context, and permissions required to complete the work. The supposedly complex decisions are abstracted away.
The user assigns a task. The agent completes it on the user’s behalf. That’s it.
Cost optimizations can then be managed centrally. The platform can route simpler tasks to lower-cost models, reserve expensive reasoning for harder work, implement proper prompt caching, terminate dead loops, and govern the cost of each completed task. Instead of another expensive policy U-turn, the company develops permanent productivity capacity with controlled cost.
The data collected—specifically, the traces and trajectories—can be used to optimize the system.
Implement, don’t build
Most companies should implement a token factory. Almost none should build one.
Be honest with your assessments. If your company does not already have a mature machine-learning infrastructure platform team operating recommender systems, search, or another ML product at scale with mature MLOps practices, buy a platform. Most companies ain’t Google, Netflix, Meta, or Spotify.
Token factories are complex data and AI operations. They are not pretty frontends used to fool non-technical executives into approving another budget. They require identity delegation, model routing, sandboxed execution, connector management, observability, budget controls, and, most importantly, data operations.
Several companies are already building pieces of this, including Cognition (Devin), Factory, OpenHands, and Glean. Anthropic’s Claude Managed Agents recently joined the party, although it currently does not support zero data retention (ZDR) or HIPAA coverage, making it unavailable for many enterprise customers.
In the right hands, the operational data collected by the factory becomes the foundation for continuous learning. Every task produces a trace with the user prompt, reasoning, tools used, failures, cost, whether the user accepted the output, and what finally worked. This data can establish evaluation suites, which can then improve model routing, tools, prompts, policies, and eventually the agents themselves and your company’s sovereign model, as Satya Nadella describes it. The factory will have higher utilization and throughput the more your organization uses it, while today’s disconnected desktop apps merely produce thousands of isolated chat histories.