Are you token maxing or value maxing?

Over the last year, most of the advice about AI has been the same: adopt it, and adopt it broadly. That was the right call. Getting people using these tools, seeing what they can do, building the muscle, all of that matters.

But a pattern has set in that is worth naming. A lot of teams are now token maxing. They are using as much AI as they can, on as many things as possible, largely because it is fun and the friction is almost zero. And the bills are starting to land.

The bills are coming due

Two things changed at once. The price of the models started to become material, and access spread across the whole business. Leaders who began with a handful of users opened it up to everyone, which was the point, and usage climbed fast.

Then the invoices arrived. I have seen budgets consumed at a rate that surprised everyone involved. One organisation went through its entire annual AI budget in two weeks. Another large company burned through its allocation in a couple of months. At some point the finance conversation catches up with the enthusiasm, and someone asks the obvious question: what did we get for that?

The real risk is not cost. It is producing things nobody wants

Here is the part I want people to sit with. The danger is not just spend. It is that AI makes it so easy to produce work that you can become very efficient at making things nobody actually asked for.

A CEO I know keeps asking his team one question about all the output flowing out of these tools: who cares? Who wants this, and what decision does it change? It is a blunt question, and it is the right one. Lots of activity can look like progress without being progress.

What leaders should do differently

I am not telling anyone to slow down their experimentation. Keep experimenting, and keep doing it broadly, because that is how you discover what actually works.

The discipline comes at the next step. Distinguish experimentation from production. Scale the use cases that are clearly demonstrating an improvement, and leave the rest as experiments. In other words, experiment widely, but scale specifically. Token max to discover what is valuable, then value max to put resources behind it.

We have seen this before

If this feels familiar, it should. In the early web, every business rushed online and measured traffic and page views. It took a while for people to accept that eyeballs did not automatically translate into value. Moving online was necessary, but it was not sufficient on its own.

Tokens are this cycle's page views. They are evidence that something is happening. They are not proof that it is worth anything. The teams that did well in the web and cloud eras were the ones who moved past counting activity and started measuring outcomes. The same will be true here.

What this means if you sell to these teams

For the technology channel, this is an opening. Your customers are about to need help turning a pile of AI experiments into repeatable business value. If you can become the advisor who helps them find where the value actually is, and cut the spend that is not earning its keep, that is genuinely valuable work.

The takeaway

Broad adoption was right. But there is a difference between token maxing and value maxing, and the second is where the return comes from. Keep experimenting, get disciplined about what you scale, and keep asking who actually wants the output.

That is this week's episode of The AI Take. Sid and I get into the whole thing, including the examples and the practical rule, in about eight minutes.

Watch the full episode on YouTube: [YouTube link]

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About 1KE:

1KE is the presales autopilot for technical sales teams. It turns discovery calls, emails, and technical documents into structured requirements and customer-ready solutions in minutes, so teams can respond while the deal is still warm.

Learn more → https://1ke.ai

Michael → linkedin.com/in/michaelchanter/ · https://x.com/1keceo

Sidney → linkedin.com/in/sidneyminassian/

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