Ep 868: Tokenmaxxing is over: The New Era of Token Efficiency and how Your Company Should Adapt (Start Here Series Vol 27

Jordan Wilson (0:00)

Welcome to the Everyday AI Podcast. My name is Jordan Wilson, and for the past three and a half years, we've put out more than 800 episodes. Yet, one of the most common questions I get, I didn't really have an answer for. Where do I start on the Everyday AI Podcast? And that's why we started the Start Here series. And with the fall now back in full swing, the Everyday AI Podcast is going back to school and playing back the entire Start Here series from front to back. We've hit pause on our normal Monday to Friday programming to run back our most popular series ever for the next 30 days. We made the Start Here series for beginners and AI champions alike. So whether you're just trying to get a grasp on large language models or grappling with the best coding harness for multi-agentic workflows, the Start Here series covers it all. Plain language, no jargon and easy to follow along each day. So make sure to subscribe to the podcast and check back each day for new insights, day by day. The series is a culmination of spending more than 10,000 hours covering generative AI over the past three and a half years. So you don't want to miss a single episode of the Start Here series. Let's get into it.

There's been this pseudo badge of AI honor over the past six months that makes no sense. And I think it has led a lot of enterprise companies astray when choosing the right AI strategy.

What is that thing? It's token maxing. So token maxing is the corporate practice where companies push AI token consumption and correlate that AI usage as a direct metric for employee productivity or pointing to the fact that they're doing something positive with their AI spend. Like, if you're using billions of AI tokens a day, that means that you're living inside of large language models and clearly you are creating huge value for the company because more prompts has to mean more ROI, right? But that strange AI power flex of early 2026 has quickly revealed itself to be a and sometimes dangerous metric by mid 2026 because it turns out all that token maxing leads to is a competition to see who can run the longest agentic loops regardless of if it actually accomplishes a company's goal. So what's the new metric? What should your company be looking at? That's token efficiency. In other words, getting the most bang for your AI token usage buck means not just burning AI usage for the sake of using it, but instead aligning your company's AI strategy with the right model, use the right way for the right job. Weird. Common AI sense pays off more than a leaderboard showing who is using the most tokens. So on today's show, we're going to break down the rise and fall of token maxing. Not only showing you what tokens are and how they work, but also uncovering the proper best practices to put your tokens toward actual measurable outputs versus just lighting millions of dollars on fire. So here's the big picture.

For the past six-ish months, companies have treated heavy AI token use as proof of productivity. That's because token prices themselves have fell like 200 times, right? If you remember back the earlier versions of the GPT technology, were like $30 per million input and $60 per million token output. Now it's like three and eight, right? So token prices have fallen significantly, but the token usage has also gone up like 100 to 200x, as models get more agentic. And now that models are reasoning by default, AI agents make the number of tokens per task absolutely explode. So I think now, in mid-2026 and through the rest of the year, I think the goal, if it hasn't already in your organization, shifted away from token maxing and toward token efficiency. So stick with us on today's show and you're going to learn what token maxing is and how it led companies to waste a ton of money. You're going to learn even more fundamentally what a token is and the four main ways that AI models gobble those tokens up. You're going to know why companies are shifting from token volume to token efficiency and how to turn AI spending into real measurable results. Let's get into it and start here with the basics. Yes, this episode is part of our Start Here series, the Essential series to both learn the AI basics and to double down on your AI knowledge. So if that's what you're trying to do, make sure you go to starthereseries.com. That is going to give you free access to our exclusive Inner Circle community. It's free. There's no other way to get access aside from going to starthereseries.com. And once you sign up, you will be inserted in to our Start Here Series space where every single episode is there for you. You can digest them all very easily. We have a playlist there as well that we keep updated. So, Start Here Series, if you missed our last episode, we talked about the 2026 LLM cheat code and 10 essential steps to get the most out of any AI chatbot. And if I'm being honest, that's probably one of the best shows we've ever done here out of 790 episodes. So make sure you check that one out. That's volume 26 and episode 786 But today, let's talk TokenMaxing. So TokenMaxing is, well, thinking that how many tokens you spend means something. And in rare cases, it might. But I'd say in most cases, it just means that employees have been given a green light to use as much AI as possible. They're probably not closely monitoring what their agents are doing. And in some cases, it's more of people trying to round their chest and talk about how many tokens they're using. That's because companies, I think, especially when it came to Claude Code, in Claude Cowork of late 2025 and early 2026, that was kind of the ushering in of the new era of these agents that can run all the time. So you saw people sharing, I had my agent run for 30 minutes, for 60 minutes, for five hours. And then there was this rush for enterprises to, well, let's do that. Because clearly that has to mean something. And it was the assumption that if you use a lot of tokens or, you know, if your agents were constantly running around the clock, that means that the company was making money. But obviously those two things do not correlate. And I think some of the big companies set a bad example. One of the most prominent examples, well, we saw some reporting that Meta reportedly built an internal leaderboard ranking employees by tokens used. So one engineer reportedly ran 281 billion tokens in a single month.