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  "path": "/article/4153278/tokenomics-why-it-leaders-need-to-pay-attention-to-ai-tokens.html",
  "publishedAt": "2026-04-01T17:01:11.000Z",
  "site": "https://www.networkworld.com",
  "tags": [
    "Artificial Intelligence, Business, Careers, Enterprise, Industry, IT Skills and Training, Markets",
    "Artificial intelligence",
    "enterprise deployment",
    "currency by which AI services are delivered",
    "tokens are the “currency” that AI models use",
    "grow dramatically"
  ],
  "textContent": "Artificial intelligence has moved rapidly from test beds to enterprise deployment, and AI service providers are popping up daily. As organizations race to embed AI into workflows—everything from market analysis and reporting to automation and digital agents—a new concept has entered the enterprise IT vocabulary: tokenomics.\n\nWhile the term originated in the cryptocurrency world, tokenomics now refers to the economics around running AI models, particularly large language models (LLM). For enterprise IT leaders, understanding tokenomics is becoming essential because tokens are going to be the currency by which AI services are delivered.\n\n## What is a token?\n\nFirst, let’s start at the beginning: What is a token in the context of AI? At the most basic level, tokens are the “currency” that AI models use. When a user enters text, audio, video, or other forms of input, that information is converted into tokens, which the model then processes to generate an output. How many tokens are consumed varies based on the task being performed as well as the model and language.\n\nA single AI interaction typically involves three categories of tokens:\n\n  * Input tokens: representing the user’s query or task.\n  * Reasoning tokens: used internally by the model as it analyzes and processes the request.\n  * Output tokens: which make up the response returned to the user.\n\n\n\nFor simple queries, the number of tokens is trivial. But as AI systems evolve in complexity of task and output, the number of tokens consumed per request can grow dramatically. In enterprise environments, where AI may be used continuously and at scale, token consumption directly translates into performance demands and operational costs.\n\nToken consumption can be significant when it’s human-to-AI interaction, but with agentive AI, token use increases considerably because tokens are used every step of the way in a process, said Dave Salvatore, director of accelerated computing products in the accelerated computing group at Nvidia.\n\n“Not only do you know you need the fast throughput, but you need the fast response time, because ultimately that end-to-end operation is going to define how long it takes you to get back your full answer,” he said.\n\n## How tokens drive enterprise decisions\n\nTokens are used as the currency for public AI products, such as text to image or image to video conversions. Within an enterprise, this situation is different. Token consumption is commonly expressed in cost per million tokens.\n\nIn enterprise settings, this often translates to two approaches: metered usage models, where departments or applications consume tokens against a defined budget; and enterprise or site licenses, where organizations negotiate volume-based pricing to manage costs at scale.\n\nSome enterprises may allocate token budgets to departments, setting soft or hard limits to control usage. Others may rely on centralized licensing to simplify governance and cost management. Either way, tokenomics becomes a core part of financial planning for AI initiatives.\n\n“Selling” tokens to your employees may seem strange, but Salvadore said employees will not be given a blank check to use enterprise AI applications or public AI services. “[IT] has to think about how can we do this in a way that’s going to get our organization the capabilities that they need to really make a good use of authentic AI, while at the same time balancing the ability for individual users to be able to get what they need quickly enough, while also balancing cost,” he said.\n\nVirtually all of the public API services providers offer tiered usage. For example, ChatGPT has four pricing plans, from free to Pro, which runs for $200 per month but offers considerably more services than the free version. Through tokenomics, enterprises can buy Pro-level services but limit their use or availability.\n\n## OPEX reduction\n\nThe enterprise value of tokenomics is not just about controlling spend, it’s about return on investment through process expedience, something that has been elusive in the rush to embrace AI.\n\nWhen AI is applied to mundane business processes, tasks that once took days, weeks, or months — such as market analysis, business reporting, or medical coding — can often be completed in hours or even minutes. From an OPEX perspective, these time savings translate directly into cost reductions and productivity gains.\n\n“If you think about it from an OPEX perspective, if that task used to take, say, a month to generate and it can now be done in about an hour, that has some pretty obvious OPEX implications from how the business is run from day to day,” said Salvatore.\n\n## AI token growth\n\nAs AI services mature, the market is likely to become increasingly segmented. Free or low-cost tiers will likely remain available, but advanced capabilities—such as large-scale reasoning models, multi-agent systems, and AI-to-AI interactions—are expected to sit behind premium or enterprise pricing.\n\nSalvatore sees a future scenario where departments are given a token budget where the department gets so many tokens to play with, and if you exceed that limit, there’s some incremental cost or you face a hard limit.\n\n“From a policy perspective, it’s going to vary from organization to organization,” he said. “That said, a lot of the AI providers have enterprise site licenses. It helps them manage cost and gives their employees the capabilities they need to actually get the most out of AI.”",
  "title": "Tokenomics: Why IT leaders need to pay attention to AI tokens"
}