Tech workers are consuming massive amounts of AI computing in a race to code faster and automate more work. In Silicon Valley, this practice is known as “tokenmaxxing,” where employees push their use of ChatGPT and other large language models to the limit to maximize productivity.
This trend has accelerated alongside the emergence of AI coding tools and agents that can handle increasingly complex tasks. Unlike casually asking ChatGPT or Claude for help, generating code and running a proxy workflow consumes more tokens, or units of data, that AI models process when users enter prompts or generate responses. Within startups and technology companies, the use of tokens has increasingly become an indicator of how heavily employees rely on AI in their daily work, with some engineers even letting programming agents work overnight to speed up product development.
But not all enterprise AI leaders believe that more usage automatically translates into better results. “I think so [tokenmaxxing] “It’s going to be a short-lived hype cycle. There’s a bill to pay for these tokens,” Chris Bede, chief customer officer at ServiceNow, told the Observer at the ServiceNow Knowledge 2026 event this week.
ServiceNow is a premier cloud platform that helps organizations manage, automate, and architect workflows. Its products are used by nearly 90% of Fortune 500 companies, including Nvidia, AT&T, and Delta Air Lines. In the first quarter of 2026, ServiceNow generated subscription revenue of $3.67 billion, an increase of 19 percent year over year since making AI central to its business strategy.
One of its flagship products is the AI Control Tower, a platform that allows customers to oversee AI deployments, including tracking agent behavior and measuring return on investment (ROI). The company also offers an “autonomous workforce” of “AI-specialized” agents, which it has expanded to execute workflows across IT, CRM, security and risk, among other functions. It is also investing heavily in improving AI skills through ServiceNow University, a platform designed to train workers to use AI in their jobs.
As AI agents take on larger roles in the workforce, Bedi says ServiceNow aims to help customers maximize value without overspending. But in his conversations with enterprise clients, Tokenmaxxing was not top of mind. “When I talk to senior officials, I don’t see anything related to tokenmaxxing,” Bedi said.
This trend confuses activity with value, Bedi says. “It’s almost like measuring a restaurant based on how many ingredients they buy,” he said. “You don’t measure a restaurant that way. I don’t.” For example, a worker who asks an AI chatbot dozens of times to generate code may end up with the same result as someone who gets there in just a few prompts.
His doubts come as the use of artificial intelligence within technology companies increases. According to the New York Times, employees at AI companies are consuming staggering amounts of computing internally, allegedly by one Anthropic employee Collect a $150,000 bill in one month Using Cloud Code. Tokenmaxxing has become a symbol of how quickly the costs of experimenting with artificial intelligence can rise as workers face pressure to ramp up their workflow. As employers increasingly enforce the adoption of AI at work, token usage is expected to rise to greater levels.
This increase has been a boon for AI model providers who charge fees based on token consumption. OpenAI says its ChatGPT APIs Processing more than 15 billion codes per minute. Gemini Google Forms Now Processing more than 16 billion codes per minute– A 60 percent increase year-over-year, according to the latest earnings report. For AI providers, enterprise adoption creates a strong incentive structure: the more workers rely on AI, the more revenue these systems generate.
Some technology companies have encouraged employees to increase their use of the token internally. There is an employee dead Create internal leaderboards Track token usage and highlight top users, The Information reported in April. After the project leaked publicly and sparked controversy over the value of token consumption rankings, a leaderboard appeared It was taken down.
Generous token budgets are increasingly being treated like premium software salaries or free meals. At Nvidia’s annual GTC conference in March, CEO Jensen Huang said engineers should expect annual nominal budgets of roughly half their already high salaries, on top of their base salary, so their production can be amplified 10X.
However, a growing number of executives argue that token hype risks becoming another measure of Silicon Valley vanity. Yamini Ranjan, CEO of HubSpot, recently wrote on LinkedIn that “[Outcome maxxing >> token maxxing],“Which means that measurable business outcomes are more important than usage. Andrew Lau, CEO of engineering intelligence firm Jellyfish, shared a similar view, describing tokenmaxxing as a “Starting point“To amplify growth.
According to Bedi, the value of AI is best measured by whether it measurably improves performance. Companies still rely on familiar metrics: the amount of time workers save, the volume of output they produce, and whether AI improves operational efficiency.
Although less flashy than symbolic numbers, traditional business outcomes remain key to determining the size of AI ROI, Bedi said. “The overall goal is, how do I help my workforce be as empowered as possible with AI, and how do I help them feel comfortable using it?”
