Here is what readers should know about reid hoffman weighs in on the tokenmaxxing’ debate, in plain language. The wider context, the practical takeaway, and the parts that matter most for everyday decisions.
Days after Meta shut down its internal “tokenmaxxing” dashboard following news of the AI leaderboard leaking to the press, LinkedIn co-founder and venture capitalist Reid Hoffman came out in support of the concept that’s recently taken Silicon Valley by storm.
An AI token is a small chunk of data that an AI model processes when it’s trying to understand a prompt and generate a response. It’s also the unit that’s used to measure AI usage and determine how much AI services cost.
As a result, many companies have begun internally tracking which employees are using the most tokens as a proxy for understanding those who are more readily embracing AI tools. They’re calling this concept “tokenmaxxing” — the “maxxing” being Gen Z lingo for optimizing something, as you may have heard in other slang, like “looksmaxxing” or “sleepmaxxing.”
However, engineers at tech companies have been arguing whether or not this metric is a viable measure of productivity in the workplace, as it’s akin to ranking people based on who spends more money than others.
Hoffman, in an interview aired at Semafor’s World Economy summit this week, offered his advice for companies adopting AI, saying he had a favorable view of the practice. Though he didn’t refer to the metric in Gen Z-speak, he did express that tracking employee token spend was a good idea.
“You should be getting people at all different kinds of functions actually engaging and experimenting [with AI],” Hoffman said at the event. “Here’s one of the things that is a good dashboard to be looking at — doesn’t mean it’s a perfect example of productivity, but… how much token usage are people actually doing as they’re doing it?”
He went on to explain that some people may be using a lot of tokens, but in more random or exploratory ways, which is why you want to pair tracking the “tokenmaxxing” practice with an understanding of the things people are using their tokens to do.
“Some of it will be experiments that’ll fail — that’s fine. But it’s in that loop, and you want a wide variety of people using it essentially, collectively, and simultaneously,” Hoffman added.
Hoffman shared other advice to companies trying to figure out their AI strategies, too, suggesting that AI should be embedded across the entire organization. He also suggested regular check-ins to share what works with others.
“We should have, essentially, a weekly check-in. It doesn’t have to be everyone, all the time with each other –but a group check-in about ‘what did we try to do new this week, to use AI for both personal and group and company productivity, and what did we learn?’ Because what you’ll find, some of the things are really amazing,” Hoffman said.
