Today with David McCullagh
What is tokenmaxxing and why is it causing a problem for big tech?
10 Jun 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What is token maxxing and how does it affect big tech?
Now, last month, Amazon rolled out an internal AI product that lets employees create agents that can automate work tasks. Reports say internal targets were set by Amazon to encourage staff to use these AI tools, with usage being tracked by company leaderboards. But last week, Amazon announced they were shutting down the tracker after it was discovered employees...
were using the tool to unnecessarily boost their scores in a practice known as token maxing, which in turn was increasing the company's computing costs. To explain token maxing in more detail, I'm joined by Adam McGuire from the RT Business House. Morning, Adam. How are you? OK, tokens, what are they?
Yeah, so a token is a basic unit of measurement that represents the data that's processed by an AI system. So the likes of ChatGPT or Anthropix Cloud, because as people know, when these AI platforms, you know, they're hoovering up services. swathes of data from any source they can find. All of that information is then used to kind of train and teach the platform.
And not just the information itself, but the conclusions from the information, how things are worked out, how different things interact with each other, how sentences and conversations are structured and so on. And all of that then is supposed to be able to, you know, take all that in, understand it all. And then when we ask it a question, it can think or create an appropriate response.
answer or an accurate answer hopefully so every time you ask an AI platform something essentially it's doing thousands of searches around all of this data through all those conversations interactions with other users trying to figure out the most helpful user and the answer and the more complicated your query the more searches it has to do to come up with an answer and we need to measure that and that's how we get tokens so
To put it into a more human context, roughly speaking, a short sentence would represent something like 10 or 20 tokens. But of course, all of this data is hugely power hungry. It's why power demands from data centres is growing rapidly. It's the reason why AI and other tech firms are racing to build more data centres. It's the reason why chip makers like NVIDIA
are the few ones making money from AI because they're selling the processors that are processing all of this data. So more than just being a simple unit of measurement, a token is now becoming a basis for how companies are being charged for their use of AI. So Anthropic, for example, charges $5 per million tokens for its latest version of its cloud platform. And that's for input.
So the data you feed into the system, the questions you ask it and so on. Then it charges $25 for a million tokens of output. So that's what it turns back out at you is the output.
So if I'm a company boss and I'm being charged per token, why on earth would I encourage my employees to use lots of tokens?
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Chapter 2: What are tokens and why are they important in AI?
And that might be a leaderboard, like what Amazon have done and other companies have done. In some cases, there are reports that companies are linking token usage to bonuses. So it's part of the consideration with a bonus. So there's a financial incentive for you to use as many tokens as possible. But of course, there's more to it than that.
In some cases, companies are trying to build up more data around how people work. to get a better picture of what can be replaced by AI. Some are just trying to keep up with the Joneses because other companies are doing it. We should as well.
But at the same time, it's also a sales tactic because a lot of these tech companies that have introduced these targets and leaderboards are trying to develop AI platforms of their own. They're investing tens to hundreds of billions of dollars into AI in the hopes that it will cut costs and boost revenue.
And one way it's going to boost revenue is because they're going to sell their AI system to other companies and individuals who want to use it. But of course, it's going to be harder to convince investors that all of that AI spend is justified. And it's going to be harder to convince your customers they should buy your AI platform if your own staff aren't using it first.
So there's pressure coming from managers top down to get their teams to make, for better or worse, more use of AI. And that's led to creation of these leaderboards and reward systems and so on. And it's backfired.
Yeah.
It has, yeah. In the 1970s, a British economist, Charles Goodhart, coined Goodhart's Law, which said when a measure becomes a target, it ceases to be a good measure. And, you know, so you create a target, people will manipulate the system to hit it.
And that's exactly what's happened here, because that kind of blind pressure to use AI, you know, with a very kind of vague kind of metric and target, it's just how much data you're forced to use. It's been taken advantage of. Many developers... realised their managers didn't actually care how they were using AI or what results they were getting. They just wanted them to use it.
So it actually created a perverse incentive for workers because the ideal would be you ask a precise question and you get the right result. Suddenly now it's better to ask a very convoluted question, have lots of back and forth, use lots of tokens before you get to the same end goal. But even then, as you say, companies were using these AI agents
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