AI Math is not Adding Up - Where is the ROI?

**Ray Rike** (0:08)
Hello, everyone. Welcome to this week's AI to ROI, the Big Story episode of our podcast. I'm Ray Rike, founder and CEO of Benchmarkit, and I'm joined by my Big Story co-host, Peter Buchanan.

**Peter Buchanan** (0:23)
Yes, I'm Peter Buchanan. I am the managing partner of NewPlan. We work with young tech companies trying to get to the next level in a whole bunch of different ways, and most of them are AI.

**Ray Rike** (0:36)
Well, today's topic, very timely, was the focus of the newsletter that we published this morning, and we're recording this on Tuesday, June 2nd. So for everyone out there, if you want to read more details, just go to AI to ROI podcast on Substack and you'll find it.
But it's about all these recent stories that show how AI spending and token costs are accelerating sharply, while ROI visibility or, I'll even say, measurable ROI is not keeping pace. In fact, Peter, there was a recent Ramp Data Report that said that the average monthly AI token spend across its entire customer base, which I don't know the number, but I think it's more than 10,000.

**Peter Buchanan** (1:26)
50,000. 50,000 customers, yeah.

**Ray Rike** (1:30)
That token spend has risen 13x since January 2025
I know almost every CFO, I talk to, token costs so far in the first quarter and a half, almost two quarters of 26 is rising anywhere from 2 to 4x. But more importantly, only 27% of enterprise executives say that AI investments have met the return on investment expectations. So I thought that's what we're going to talk about in today's episode.

**Peter Buchanan** (2:05)
You bet. So the cost side of the equation, you're talking about it there, it's visible and it's growing fast. The other challenge is the value side is basically invisible to most corporate dashboards who could be there, but it doesn't show up in any place that anybody measures. So the episode today covers why the measurement problem exists, what it costs enterprises to ignore it, and what the companies are getting right if they're doing it effectively. So we're using ramp data, we're using our podcast interview that comes out next week with Russ Frayden, the CEO of Lariden.
We have sources like Exponential View, Semianalysis, and we have good case studies. So we're backing it all up with evidence today, Ray.

**Ray Rike** (2:54)
Well, to me, it's almost this AI measurement paradox, Peter.
The core argument is, come on, we're getting more productivity out over individual workers.
I've probably talked to 20 to 30 different companies a month, and almost everyone says that their X is being more productive or getting things done quicker. But it's not generating company level ROI.
So until we can actually translate these individual productivity gains into demonstrable positive impact on the financial reports, there's always going to be questions. I'll give you an example. We know that engineers are submitting a hell of a lot more pull requests, sometimes I'm seeing 2X to 3X. Sales reps are doing more outbound prospecting, are delivering more proposals. Analysts are conducting more research and doing it quicker. So that's a signal at the individual productivity level. But as one senior tech executive who's managing a thousand cloud code using engineers told Exponential View, we are seeing the use of AI coding tools where one plus one plus one plus one equals one and a half, not four. Now I think there's another kind of law at play here and it's called Parkinson's Law, Peter. And the core premise of Parkinson's Law is that work expands so as to fill available time left, even due to increased productivity. So in the workplace, if a worker now can do something in four hours versus eight hours, they find something to do with that remaining four hours that may or may not be that positive or increasing productivity or decreasing cost or increasing profitability. So I think that's a real problem. So I don't think it's a motivation problem. I think most companies, individuals using AI are really motivated. I think, and we're going to talk about it, I think it's a management issue, it's a measurement issue, and it's an organizational design problem.

**Peter Buchanan** (5:08)
Right, so Semianalysis calls this phenomenon AI dark output. So AI generates meaningful economic value, and it never shows up on the corporate dashboards to basically make people happy and make people believe that they're making the right investment. So there's an example in our newsletter. So if the cost of developing a legal document drops from $400 to $5, that's completely fantastic.
But the savings register as a token expense, so the cost of tokens goes up even though you've saved $395. That's the obvious expense.