Stop Measuring How Much AI You Use

Recently, I came across an interesting word being discussed in the AI world: Tokenmaxxing.

For the past couple of years, one way companies measured AI adoption was surprisingly simple: How much AI are people using?

How many employees are using it? How many prompts are being sent? How many tokens are being consumed?

There was some logic behind this. When AI was new, usage itself was a sign of adoption.

But I think we are quickly moving beyond that stage.

The better question is no longer:

How much AI are you using?

It is:

What are you getting from every token you use?

This connects closely to something I wrote about recently: Loop Engineering.

A good AI system should not simply take a prompt, generate an answer, and stop.

It should understand the goal, try an approach, evaluate the result against clear acceptance criteria, learn what didn't work, and try again.

Prompt → Execute → Evaluate → Learn → Repeat → Outcome.

But there is an important point here.

The objective of the loop is not to create more loops.

And the objective of AI is certainly not to consume more tokens.

The objective is to achieve the business outcome.

This distinction matters enormously for how we are building SnapLedger.

We want our users to benefit from increasingly sophisticated AI techniques — Loop Engineering, Context Engineering, agentic workflows, model routing, memory and evaluation — without having to understand any of them.

A small business owner shouldn't need to become an AI engineer to use AI effectively.

They shouldn't have to learn how to write the perfect prompt.

They shouldn't need to decide which model to use, how much context to provide, what should go into memory, when an agent should retry, or how to evaluate its result.

SnapLedger should do that work for them.

More importantly, SnapLedger has an advantage that a general-purpose AI assistant does not naturally have:

we are focused on some of the most important information inside a business.

Transactions.

Invoices.

Contracts.

Customers.

Suppliers.

Tax identities.

Accounting treatments.

Bank accounts.

Business conversations.

And increasingly, the history of decisions made around all of them.

Over time, Snappy doesn't have to rediscover the business from scratch every time someone asks a question.

It can continuously learn what matters.

That changes the economics of AI.

A token spent with the right business context is much more valuable than a token spent trying to reconstruct that context.

A reasoning loop with a clear acceptance goal is much more valuable than ten open-ended prompts.

And an AI that already understands your company can often get to the right answer faster than an AI that has to interview you every time.

So in a sense, we do want to maximize our users' tokens.

But not by maximizing how many tokens they consume.

We want to maximize the value produced by every token.

This is why I increasingly think one of SnapLedger's jobs is to hide the complexity of modern AI engineering from small business owners.

Behind Snappy, we can continuously adopt better models, better loops, better context management, better memory and better evaluation techniques.

The user doesn't need to know.

They just need to see that Snappy understands their business better, requires less explanation, and gets more things done.

Traditional SaaS tried to make software easier to use.

I think AI-native SaaS has a bigger opportunity:

make sophisticated AI easier to benefit from.

That is a very different definition of usability.

And perhaps, in the AI era, the most important metric isn't how many tokens your business consumes.

It is how much business value every token can create.