Every time you type a question into ChatGPT, Claude, or Gemini, you’re spending something. Not dollars — not exactly. You’re spending tokens, the atomic units of computation that large language models consume to think, reason, and respond. And like any scarce resource that powers an economy, tokens are rapidly developing their own pricing dynamics, strategic implications, and geopolitical weight.
The analogy is striking and, once you see it, hard to shake. Tokens are mana.
That’s the argument advanced by Proof of Concept, a technology publication that recently drew a direct line between the resource management systems of fantasy role-playing games and the economic architecture of modern AI. In games like Magic: The Gathering or World of Warcraft, mana is the finite resource that constrains what a player can do on any given turn. You don’t just choose the best spell — you choose the best spell you can afford. Tokens work the same way. Every API call, every chain-of-thought reasoning step, every agentic loop that an AI system executes burns through tokens. And those tokens cost real money.
The comparison isn’t merely clever. It’s structurally precise. Mana systems in games create strategic depth by forcing resource allocation decisions. Do you spend your mana on a powerful but expensive action now, or conserve it for a more efficient series of smaller moves later? AI developers, product managers, and enterprise architects face an almost identical calculus every single day.
Consider the numbers. OpenAI’s GPT-4o charges $2.50 per million input tokens and $10 per million output tokens at current rates. Anthropic’s Claude 3.5 Sonnet runs $3 per million input tokens and $15 per million output tokens. Google’s Gemini 1.5 Pro comes in at varying rates depending on context window size. These aren’t trivial figures when you’re running AI agents that might execute dozens or hundreds of calls to complete a single complex task. A sophisticated agentic workflow — one that plans, searches, reasons, and iterates — can burn through tens of thousands of tokens before producing a final output. Multiply that by millions of users or thousands of enterprise deployments, and you’re talking about a resource consumption problem that looks a lot like energy economics.
Which is exactly the point.
The token economy has created a new class of optimization problems. Engineers now routinely make architectural decisions based on token efficiency — choosing when to use a smaller, cheaper model versus a larger, more capable one. This practice, sometimes called model routing, has become a core competency at companies building AI-native products. Not every query needs GPT-4. Some can be handled by GPT-4o-mini or Claude 3 Haiku at a fraction of the cost. The skill lies in knowing which is which. Automatically. At scale.
And the market is responding. A growing number of startups and infrastructure providers have built tools specifically to manage token consumption. Caching layers that store and reuse previous completions. Prompt compression techniques that reduce input token counts without sacrificing output quality. Routing engines that dynamically select the optimal model for each request based on complexity, latency requirements, and cost constraints. These aren’t nice-to-haves. For companies running AI at production scale, token management is now as fundamental as bandwidth management was in the early days of cloud computing.
Proof of Concept makes the case that this resource constraint is actually a feature, not a bug. In game design, unlimited mana produces boring gameplay. When everything is free, nothing requires strategy. The same principle applies to AI. Token costs force developers to think carefully about prompt engineering, context window management, and system architecture. They create pressure toward efficiency and elegance. They reward clever design.
But there’s a darker side to this analogy too. In games, mana regenerates. In the AI economy, tokens are purchased. This means access to AI capability is fundamentally gated by financial resources. Large enterprises with deep pockets can afford to run expensive reasoning models on every query, while smaller companies and individual developers must constantly optimize and compromise. The mana metaphor illuminates something uncomfortable: in the AI economy, the richest players get to cast the most powerful spells.
This dynamic is playing out in real time across the industry. OpenAI’s recent moves illustrate the tension perfectly. The company has been aggressively cutting prices — GPT-4o-mini launched at a fraction of GPT-4’s original cost — while simultaneously introducing more expensive reasoning models like o1 and o3 that consume far more tokens per query because they “think” before responding. The result is a widening spectrum of price-performance options. More choice, but also more complexity. The mana pool has different colors now, each with different costs and different powers.
Anthropic has taken a somewhat different approach, emphasizing efficiency and safety in its Claude models while competing aggressively on context window size — the amount of text a model can process in a single interaction. A larger context window means more tokens consumed per call, but it also means the model can reason over more information at once. It’s the AI equivalent of a bigger mana pool: more expensive per turn, but potentially more powerful in what you can accomplish.
Google, meanwhile, has been pushing Gemini’s context window to extraordinary sizes — up to two million tokens — while attempting to keep costs competitive. The strategy seems aimed at enterprise customers who need to process large documents, codebases, or datasets in a single pass. More mana. Bigger spells. Higher stakes.
So what happens when tokens become the primary unit of economic value in technology?
We’re already seeing the answer. Infrastructure providers are building token metering and billing systems that look remarkably like utility meters for electricity or water. Cloud providers are bundling token allocations into enterprise agreements. Venture capitalists are evaluating AI startups partly on their token efficiency — how much value they extract per token spent. Some investors have started referring to “token unit economics” as a key metric, right alongside customer acquisition cost and lifetime value.
The implications extend beyond business strategy into geopolitics. The compute required to generate tokens depends on GPU availability, which depends on semiconductor manufacturing, which depends on a supply chain concentrated heavily in Taiwan and a handful of other locations. Token costs are ultimately a function of chip supply, energy costs, and data center capacity. When NVIDIA’s stock price moves, it’s partly because investors understand that GPU supply constrains the global production of tokens, and tokens are what power the AI economy. The mana supply chain is physical, fragile, and enormously consequential.
Recent developments have only intensified these dynamics. As AI agents — autonomous systems that can plan, execute multi-step tasks, and interact with external tools — have moved from research demos to production deployments in 2025, token consumption per user session has increased dramatically. An agent that browses the web, writes code, runs tests, and iterates on results might consume 50 to 100 times more tokens than a simple question-and-answer interaction. Companies building agentic products are discovering that their token bills scale nonlinearly with capability. The more useful the agent, the more expensive it becomes to run.
This has created a fascinating tension in product design. Users want powerful, autonomous AI agents that can handle complex tasks end-to-end. But every additional reasoning step, every tool call, every self-correction loop costs tokens. Product teams must decide: Do we let the agent think longer and produce better results, or do we cap its reasoning to control costs? It’s the mana dilemma in its purest form.
Some companies have started passing these costs directly to users through token-based pricing. Others absorb the costs and hope that scale economics will eventually bring prices down. Still others have adopted hybrid models — offering a base level of capability for free or cheap, with premium features that consume more tokens available at higher price tiers. The parallels to mobile data plans from a decade ago are hard to miss. Except the resource being metered isn’t bandwidth. It’s intelligence.
The Proof of Concept piece argues that understanding this mana-like dynamic is essential for anyone building with or investing in AI. It’s not enough to evaluate a model’s capabilities in isolation. You have to understand the token economics — what it costs to deploy those capabilities in the real world, at real scale, for real users. A model that scores highest on benchmarks but costs five times more per token than its nearest competitor may not be the right choice for most applications. The best model is the one that delivers sufficient quality at sustainable cost. Optimization, not maximization.
This is a lesson game designers learned decades ago. The best mana systems don’t just constrain players — they create strategic richness. The best token economies won’t just limit what AI can do — they’ll force developers to build smarter, leaner, more thoughtful systems. The constraint is the catalyst.
But constraints also create winners and losers. Companies that master token efficiency will have structural cost advantages. Those that don’t will find their margins compressed or their products priced out of the market. The AI industry is entering a phase where raw model capability matters less than the ability to deploy that capability economically. We’ve seen this pattern before in technology — the shift from “can we build it?” to “can we build it profitably?” It happened in cloud computing. It happened in mobile. Now it’s happening in AI.
And the stakes are enormous. Morgan Stanley estimated earlier this year that enterprise spending on AI inference — the process of running trained models to generate outputs, which is where tokens are consumed — could exceed $150 billion annually by 2027. That’s a lot of mana. The companies that control the supply, set the prices, and build the most efficient systems for consuming it will shape the next era of technology.
The token, in other words, isn’t just a technical abstraction. It’s becoming the fundamental unit of a new economic order. Every AI interaction, every agent task, every automated workflow burns through tokens the way factories burn through electricity. And just as the companies that mastered energy economics in the 20th century built industrial empires, the companies that master token economics in the 21st will build the next generation of technology giants.
There’s one more dimension to the mana metaphor worth considering. In most games, players get better at managing their mana over time. They learn which spells are worth casting and which are wasteful. They develop intuitions about resource allocation that make them more effective with the same budget. The same learning curve is happening now across the AI industry. Early adopters wasted tokens profligately — long, unoptimized prompts, unnecessary API calls, brute-force approaches to problems that could be solved more elegantly. The industry is growing up. Prompt engineering has become a discipline. Token budgeting has become a practice. And the organizations that learn fastest will have the sharpest competitive edge.
So the next time you ask an AI model a question — simple or complex, trivial or consequential — remember that you’re spending something finite and valuable. You’re casting a spell. And the cost of that spell, multiplied by billions of interactions per day across every AI system on the planet, is shaping the economics of an entire industry.
Tokens are mana. The game is just getting started.
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