Cameron Otsuka

Tokenmaxxing as Management Theory

Metadata
  • Description: or, I sure hope finance doesn't this bill...
  • Publication: Inference Draft 2026-23
  • Published:
  • Last Modified:
  • Type: newsletter
  • Tags: ai
  • POSSE: Substack 
Corporate tech hub in cyberpunk workspace

Rolling out new technologies, procedures, and patterns within a company is historically difficult. An NBER study found that by the end of 2000, 88.6% of companies had adopted the internet into their business, but only 12.6% found it enhanced their business processes.

This process is playing out once again with AI, with similar levels of companies adopting AI and varying reports of enhancement: 5%, 15%, etc. Just like the internet era, companies have a desired outcome: use the new technology in ways that increase productivity, improve quality, and develop new ideas.

If you are management at one of these companies, how do you push the necessary tool, process, and hierarchy changes to enable the enhancement? One way is to announce to all employees that they need to be “tokenmaxxing,” or using as many tokens as possible with the hopes that it leads to enhancement. An Anthropic employee reportedly used $150k of tokens in a month. Jensen Huang wants $500k employees to be spending at least $250k in tokens per year. Disney and JPMorgan have AI usage dashboards.

But what do you do once your company has fully adopted? Now you need to focus on whittling down the use cases to those that enhance productivity and quality. And this is where we’re starting to see tokenmaxxing fatigue, with companies accidentally blowing $500MM, Salesforce attempting to create a metric to measure tokens and compute in relation to work completed, and Uber’s CTO realizing they already blew their 2026 budget four months into the year, while Uber’s COO is not seeing productivity gains.

We’ve known measuring developer productivity by lines of code is a poor measurement since the 80s and more broadly, managing via output metrics is a surefire way to mistake the output for the outcome. Some Amazon employees put together an informal leaderboard to track token usage and what do you know: the company asked them to deprecate it.

The strange part of the last wave of AI buzz is that companies seem to have temporarily forgotten about comparative advantages. “Anyone can write software now! Software companies are dead,” misses the point that the software was just one part of the actual product these companies sell: there is ongoing support, feature development, integrations, and 100% focus on the software. Companies did the cost-benefit analyses and decided that paying a third-party to hire engineers and develop the software was cheaper than attempting to do it in-house.

While AI has certainly brought the cost of building software internally down, there is evidence that models provide better answers to more sophisticated users. Specialists will continue to extract more value per token from these models, and if the studies hold true, they should diverge even more because these specialists will know which questions can be routed towards cheaper models versus frontier models, improving cost efficiencies.

So ultimately, the tokenmaxxing craze will likely be a passing fad. Companies needed to quickly adopt AI and rather than cautiously analyze the highest value use cases, they pushed their teams to just. get. on. board. now. As inference costs add up, the pendulum is likely to swing back towards the cost-benefit analyses: is the token spend justified by the outcomes?


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