AI ‘Tokenomics’ is a trap. CIOs should avoid it at all costs.

According to a quartet of Accenture intellectual heavyweights, what CIOs need now to account for and control AI costs is tokenomics, sweet tokenomics.

The idea of linking AI spend to business outcomes may be a noble one, but “Tokenomics” only pretends to do so, unless you think tallying costs and measuring business outcomes are one and the same thing.

But before we get to Tokenomics we need to set the stage.

Start with Function Point Analysis (FPA), a venerable and mostly forgotten discipline, which should say something about its efficacy.

The folly of cost metrics you can’t understand

Back in the day, IT leaders worried that boards of directors worried that business executives worried about the cost of information technology and how to manage it.

IBM introduced Function Point Analysis in 1979 to solve this issue by breaking a software application into chunks — function points. Analysts could then sum these function points to compare the application’s overall complexity — and therefore its cost and risks — with that of any other piece of software.

When presented with the number of function points a proposed software project would require, your average, hopelessly naïve business leader asked IT to point to a few of the project’s function points, expecting mocked-up screenshots, sample reports, and so on.

Sadly, IT had to decline because, the design analysts would explain, a function point is (as Google’s AI Overview explains) “an abstract, synthetic unit of measurement rather than a physical object or a single piece of code.”

The Function Point Analysis value proposition, then, was that business leaders could manage IT costs and risks better by relying on cost metrics they couldn’t understand.

Function points for the AI era

Fast-forward to today, and the equivalent function-point cost metrics for AI are tokens — the chunks of text that a large language model reads, processes, and generates. Which, in case you missed the briefing, supposedly makes tokenomics a way to manage the costs and risks of AI.

As the aforementioned Accenture gurus “explain” (scare quotes intended): “Enterprise AI is creating a new cost of doing business: tokens. As consumption scales, token economics is becoming a material risk and opportunity, drawing increasing attention across the C-suite and the board.”

Which raises the question: Where are our tokenomics pundits finding C-suite and board members who give even an infinitesimal damn about tokens? They’re probably the ones who still pay attention to function points, too.

A short lesson in Brickonomics

But before we delve deeper into Tokenomics, capital T, we need to understand building maintenance and the critical role Brickonomics plays in it.

It’s like this: Brick walls are made of bricks. So if a brick wall needs attention, a building management project estimator should need to understand the costs and risks associated with managing a standard brick in the crumbling wall they’re supposed to fix. Multiply the standard management cost of a standard brick (a brickon?) by the number of bricks in the wall, and voilà! Building management knows …

Well, it knows less than it would have if had it instead invested in a conventional tuckpointing analysis. Among the missing pieces: In tuckpointing analysis, what usually needs and gets attention is the mortar between the bricks, not the bricks themselves.

Putting it all together — because you will have to

Now we’re ready for Tokenomics, and its conjecture that dividing an LLM into tokens and assigning a cost to each of those tokens will prove more useful than dividing software into function points or a wall into individually managed bricks.

The next questionable aspect of Tokenomics is that it depends on the proposition that all AI implementations are built on large language models or similar architectures.

To be fair, Tokenomics doesn’t have to be limited to analyzing LLMs. It can be adapted to any collection of thingies that’s composed of chunks that can be parameterized.

But that really isn’t good enough, is it? Because given the current pace of AI innovation, assuming that anything about it is stable, especially that its future core architecture will be tokenizable, is a shaky assumption at best.

It’s a mortar versus bricks challenge.

Then there are those pesky function points. Just because AI is involved in an implementation, that doesn’t mean software isn’t also involved. Which would seem to suggest that Tokenomics will have to integrate Function Point Analysis into its methods and practices as well.

Imagine you’re the one who has to explain this mess to those boards and business leaders who are, supposedly, clamoring for it. Because, as CIO, you will be.

Chargebacks and culture

We haven’t even gotten to the other big risk of Tokenomics: that it focuses decision-makers on AI’s costs, with only token attention — no pun intended — paid to its benefits.

Deep down inside, Tokenomics’ value depends on its use for chargebacks and showbacks. The logic for imposing these is superficially appealing — it makes business owners take responsibility for the AI spend driven by their consumption.

Those CIOs who dove into the chargeback rabbit hole discovered just how futile chargebacks can be. That’s because of a time-honored practice updated to the digital age: arguing about the bill.

Because that’s all a chargeback is: a bill. It’s a statement of how much money will change hands so that someone who has provided a good or service is reimbursed for the value they’ve created.

By the numbers: (1) a chargeback is a bill; (2) the point of Tokenomics is to support chargebacks; and (3) no business leader will be able to understand the bill they’re supposed to pay given Tokenomics impenetrable vocabulary and construction.

But what about showbacks, then? Letting business leaders understand the costs they’re driving would seem harmless and potentially useful.

And in fact showbacks can be harmless and useful, so long as they’re applied in the right business culture. That’s a culture in which managers consider themselves to be leaders of the whole company, basing their AI utilization decisions just as they base their other decisions — on the extent to which they benefit the whole company.

With a culture like this, IT can limit its application of tokenomics, little t, to its technical architecture practices. This way, the CIO can inform business managers of AI’s costs just as the CIO informs business managers of the other costs of running IT.

Where, the business manager might ask, did these cost numbers come from?

“You have two choices,” the CIO might reply. “You can trust me, or I can explain it to you.”

Now there’s a threat worth paying attention to.