By Liz Eversoll, CEO, and Joe Shepherd, CPO — Career Highways

Over the last month, two stories have surfaced in the AI market.  On the surface they look unrelated.  Read together, they expose the same problem.

The first is cost.  Reports from Microsoft, Uber, and other large adopters show AI usage costs climbing faster than anyone budgeted.  Uber’s CTO told The Information the company burned through its entire 2026 AI coding budget in four months.  The cost per token keeps falling, but total spend rises as usage scales — and Goldman Sachs projects agentic AI will drive a 24-fold increase in token consumption by 2030.  Cheaper to use, more expensive to operate.

The second is behavior.  Engineers at major technology companies have been encouraged — sometimes incentivized through internal leaderboards — to maximize token consumption as a proxy for AI adoption and productivity.  The practice has a name now: tokenmaxxing.  Cognizant CEO Ravi Kumar S. recently called it what it is: a vanity metric.

Both stories are being framed as an AI cost problem.  They are actually exposing something larger.  AI does not get expensive because you use too much of it.  It gets expensive when it runs without understanding the work — and without structured knowledge to operate against.

If you own the AI bill — and increasingly that is the CTO — this is the distinction that matters.  Cost is exploding and usage is exploding, and your CFO and CEO can see both.  What they want to know is whether it can be controlled.  It can.  But not by capping usage.  By fixing what the usage is spent on.

The Wrong Metric

For decades, companies measured labor utilization.  Now many are measuring AI utilization.  Neither tells you whether value is being created.

Token consumption is simply the latest case of Goodhart’s Law: when a measure becomes a target, it stops being a good measure.  More prompts, more agents, and more AI calls do not necessarily produce better decisions, better products, or better outcomes.

The real problem is not that organizations use too much AI.  It is that most cannot say what work should be done by people, what should be augmented by AI, and what should be automated outright.  Without that understanding, you optimize for activity — and activity, billed by the token, is expensive.

Context Is the New Advantage

Here is what the cost narrative misses.  AI is expanding access to problem-solving across the entire workforce.  People no longer need years of specialized training to analyze data, build a workflow, generate a solution, or ship working software.  The barrier to participation has dropped.

As access to answers goes up, something else becomes scarce and valuable: context.  Understanding the business problem.  Understanding how the work actually gets done.  Knowing what a good outcome looks like — and how to tell whether the AI produced one.  A model can generate an answer.  It cannot reliably tell you whether you are solving the right problem.

This is why a company like Cognizant is expanding hiring beyond traditional technical backgrounds.  Value is shifting toward people who understand the business, the customer, and the workflow — not only the technology.  AI widens who can participate; context determines who creates value.  The question is no longer who can perform the work.  It is who can frame the right problem, validate the solution, and put it into production.  Those are skills.

From Skills to Execution

Understanding the work, and the skills behind it, is the first step.  Most organizations still lack a systematic way to define that work, map the skills it requires, and apply that consistently across the enterprise.  That is the gap Career Highways closes — identifying the work that actually exists, defining the skills required to perform it, and connecting those skills to roles, workflows, and outcomes.  The result is a system of record for capability: what work needs to be done, and who can do it.

But a second constraint shows up the moment you try to operationalize it.  Even when the work and the skills are defined, AI still struggles to use that knowledge efficiently.

Why AI Keeps Rediscovering What You Already Know

Most enterprise knowledge already exists — policies, frameworks, data models, domain definitions.  It is just not structured in a way AI can reliably apply.  Documents describe intent but cannot enforce it.  Data may be structured, but the relationships and constraints that govern how it behaves are not.  Prompts try to fill the gap, and introduce inconsistency every time.

So agents retrieve information, but they do not apply it systematically.  They infer, approximate, and reinterpret the same logic on every run.  That is what drives both inconsistency and cost.  AI is expensive precisely because it has to rediscover context every time it runs.

Introducing SIGN

At Career Highways, we built SIGN (Sigil Intelligence Graph Notation) to solve exactly this.  SIGN is an open standard for expressing enterprise knowledge in a form agents can read, reason over, and act on — consistently, and with governance.

If JSON serializes data, SIGN makes knowledge executable.  More simply: SIGN is the Rosetta Stone of agentic knowledge — it unlocks the institutional knowledge an organization already holds, in a form every agent can read, apply, and be held accountable to.

It separates what is defined from how it is executed, so knowledge can be reused and enforced consistently across systems.  Instead of describing rules in prose or burying them in code, SIGN encodes them directly into the knowledge layer — defining what is true, what must be enforced, and how decisions are made.

“AI systems today spend most of their time reconstructing context that already exists in the enterprise.  SIGN flips that model.  It makes the knowledge itself executable, so agents don’t have to guess — they operate against defined logic.”  — Joe Shepherd, Chief Product Officer, Career Highways, and inventor of SIGN.

Why This Changes the Cost Equation

The AI cost problem is not only about scale.  It is about how inefficiently knowledge is represented and applied.  Most enterprise formats force AI to do unnecessary work — JSON adds structure without meaning, documents demand heavy context to interpret, prompts recreate the same logic over and over.  The result is longer prompts, repeated reasoning, inconsistent outputs, and higher token consumption.

SIGN removes that inefficiency by encoding meaning directly.  Rules, constraints, and inference logic are defined once and applied consistently — so AI executes against known logic instead of inferring what it should already know.

From Probabilistic Guessing to Deterministic Execution

In most systems today, reasoning is left to the model.  Because large language models are probabilistic, rules get applied inconsistently, constraints get missed, and outputs vary from run to run.

SIGN moves that responsibility out of the model and into the knowledge layer.  It defines what must happen, what cannot happen, and how conclusions are derived — so agents operate with governed logic instead of inference.  The result is a shift from probabilistic output to deterministic decisioning: ask the same question, get the same answer.

The Impact: Fewer Tokens, Better Outcomes

When knowledge is structured and executable, AI no longer has to reconstruct context or reinterpret logic.  Prompts get shorter.  Workflows become reusable.  Outputs become consistent.

We have measured this in our own platform.  Encoding our knowledge in SIGN cut our token consumption by roughly half for the same work.  Same knowledge.  Half the tokens.  Deterministic results.

The Bottom Line

The debate about AI costs is real — but most of it is aimed at the wrong problem.  AI does not get expensive because it is used too much.  It gets expensive when it is used without understanding the work, and without structured knowledge to operate against.

Career Highways defines the work and the skills.  SIGN makes that knowledge executable — so AI can operate with precision instead of guesswork.

For the CTO, the first move is small and measurable: take one high-cost agentic workflow, encode the knowledge that governs it in SIGN, and watch the token count.  Stop tokenmaxxing.  Start work-maxxing.  That is how AI stops being a cost and becomes a capability.

Sources

• “The Pulse: ‘Tokenmaxxing’ as a weird new trend” — The Pragmatic Engineer
• “Microsoft reports are exposing AI’s real cost problem: Using the tech is more expensive than paying human employees” — Fortune
• “Cognizant CEO is swimming against the tide on AI: he’s hiring over 20,000 graduates this year and says AI tokenmaxxing is a ‘vanity metric’” — Fortune
• “AI Agents Forecast to Boost Tech Cash Flow as Usage Soars” (24-fold token growth by 2030) — Goldman Sachs Research