{"id":255434,"date":"2026-09-03T12:29:06","date_gmt":"2026-09-03T16:29:06","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=255434"},"modified":"2026-09-03T12:29:09","modified_gmt":"2026-09-03T16:29:09","slug":"is-ai-actually-creating-value","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/podcasts\/ibkr-podcasts\/is-ai-actually-creating-value\/","title":{"rendered":"Is AI Actually Creating Value?"},"content":{"rendered":"\n

AI is becoming a bigger part of how companies operate, but measuring its actual value is proving more complicated. Nasdaq\u2019s Michael Normyle joins the IBKR Podcast with Jeff Praissman to discuss how traditional software metrics are evolving in the age of agentic AI, what new measures could emerge, and whether productivity could become a key way investors evaluate AI-driven value.<\/p>\n\n\n\n

AUDIO PLACEHOLDER<\/p>\n\n\n\n

Summary \u2013 IBKR Podcasts Ep. 422<\/h2>\n\n\n\n

The following is a summary of a live audio recording and may contain errors in spelling or grammar. Although IBKR has edited for clarity no material changes have been made<\/em>.<\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

Hi, everyone. This is Jeff Praissman with Interactive Brokers.\u00a0It’s\u00a0my pleasure to welcome back to the IBKR Studio Michael Normyle from Nasdaq. Hey, Michael. How are you?\u00a0<\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Doing well, thanks. Glad to be back.\u00a0<\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

I have the pleasure today of going over another one of the great articles\u00a0that’s\u00a0on your website, “The Search for New Measures of Value in the AI Era<\/a>.”\u00a0As listeners remember, last month we did a podcast on a similar subject, but this is\u00a0sort of a\u00a0further take on it. So, Michael, historically, investors have focused on metrics like seat growth, ARR, and net revenue retention.\u00a0<\/p>\n\n\n\n

But this article brought out some pretty interesting points that these measures might become less meaningful in the agentic AI world. <\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah. Just to give everyone a quick overview here, this is our second entry in our AI series as part of the launch of the Nasdaq Economic Institute. In that first one that you referenced, we were talking about AI and how\u00a0it’s\u00a0impacting\u00a0entrepreneurship, and now\u00a0we’re\u00a0looking at how\u00a0it’s\u00a0impacting\u00a0the way that companies are explaining their value during earnings calls.\u00a0<\/p>\n\n\n\n

And this has been a big shift, especially for software companies, because historically, those software-as-a-service companies, or SaaS companies, defined their value by metrics like the number of seats. That’s basically the number of users, since that was directly linked to revenue, right? The more seats they had, the more revenue they had. <\/p>\n\n\n\n

They also reported annual recurring revenue, the ARR that you mentioned, which measured the recurring revenue over a calendar year, and then net revenue retention, which measures the percentage of recurring revenue retained and grown from existing customers over some period. All these help capture churn and upsells and stuff like that. <\/p>\n\n\n\n

In the agentic AI era, though, these measures become less relevant because the value provided\u00a0isn’t\u00a0necessarily tied to the number of users.\u00a0It’s\u00a0more about the outcomes and the productivity that the product creates.\u00a0So\u00a0it could actually be unrelated, or perhaps even inversely related, to the number of seats.\u00a0Maybe now\u00a0you have one finance professional who can run an AI agent to automate profit and loss statements, cash flow analysis, bookkeeping tasks, things that used to occupy an entire team. And so\u00a0that’s\u00a0precisely why we need new metrics for these companies to define the value that\u00a0they’re\u00a0creating.\u00a0<\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

Yeah, I should mention that the name of the article is “The Search for New Measures of Value in the AI Era,” and that will be linked to this podcast as well. As always, you can go to nasdaq.com and go into their Insights area, and\u00a0it’ll\u00a0be there as well.\u00a0<\/p>\n\n\n\n

But yeah, you kind of alluded to it a little bit here. The article describes the evolution from on-premise software to SaaS and now AI. Looking back, though, you sort of described how they were measured, but are there lessons that investors can learn from previous technology transitions when trying to evaluate AI businesses today? <\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah, and I think really the biggest one that\u00a0we’ve\u00a0seen in the past, and\u00a0we’re\u00a0already seeing now, is that it takes time to settle on metrics that are useful for investors.\u00a0In the piece, we talk about the internet era, for example, where some metrics persisted because they\u00a0directly linked\u00a0user activity to economic outcomes, like customer acquisition cost, customer lifetime value, conversion rates, and unique visitors.\u00a0Others, though,\u00a0kind of fell\u00a0by the wayside because they proved to be “attention dressed up as value,” as we put it in the article, with things like eyeballs.\u00a0So, loosely defined things that aren’t actually that helpful.\u00a0And right now, we’re still in the very, very early stages of this agentic era, and there’s a lot of different metrics being used, and a number are very unique to the company\u00a0reporting\u00a0them.\u00a0So\u00a0it’s\u00a0possible that some of these new measures\u00a0maybe won’t\u00a0prove enduring. The risk also is that if the value proposition related to AI becomes\u00a0really unique\u00a0to customers, then\u00a0it’s\u00a0going to be hard to land on universal metrics. So\u00a0that’s\u00a0something that\u00a0we’ll\u00a0see in time.\u00a0<\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

One overwhelming theme in the article is that AI creates value through work performed rather than users added, right?\u00a0So\u00a0it’s\u00a0the overall\u00a0results\u00a0versus some measurable number of users.\u00a0But how should, or how could, investors think about measuring, quote-unquote, “work performed” in a way that is both meaningful and comparable across different companies?\u00a0<\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah.\u00a0So\u00a0this is\u00a0definitely a\u00a0challenge of this era, at least so far. I think there will be a degree to which some companies can use universal metrics and be compared, but perhaps not as broadly as we saw in the SaaS era.\u00a0<\/p>\n\n\n\n

So for now, I think you can think about things like tasks completed without human intervention, for example, or the percent of time saved with the help of AI agents. So, relatively standardizable metrics that are probably applicable to a broad swath of agentic AI tasks. <\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

And AI is obviously a household word at this point. But one stat from the article that kind of surprised me is that\u00a0nearly four out of five\u00a0Nasdaq-100 companies are discussing AI on earnings calls.\u00a0So even companies that have nothing to do with AI on the forefront, like Nvidia or chips or anything like that,\u00a0it’s\u00a0involved with everything now. How can investors separate genuine AI-driven value creation from companies simply\u00a0participating\u00a0in the AI narrative?\u00a0<\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah, and I think for the full context of that stat that\u00a0we’re\u00a0providing, before ChatGPT was released at the end of 2022, only about one in five Nasdaq-100 companies were talking about AI on earnings calls. Now\u00a0it’s\u00a0four in five,\u00a0like\u00a0you said.\u00a0<\/p>\n\n\n\n

And you see this growth across any index, whether it’s the S&P 500, Nasdaq-100, Russell 3000, whatever you want to look at. <\/p>\n\n\n\n

And I think, unfortunately,\u00a0there’s\u00a0not necessarily a shortcut to recognizing which companies are merely talking about AI in passing during earnings and which are discussing genuine AI-driven value creation beyond looking at\u00a0it\u00a0company by company.\u00a0So, yeah, I don’t think there’s an easy shortcut, unfortunately.\u00a0I think\u00a0it’s\u00a0more representative of how pervasive AI has become as companies are trying to implement it either in their business or\u00a0on\u00a0their business.\u00a0So\u00a0it’s\u00a0obviously grown in leaps and bounds in just a few years.\u00a0<\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

You know, one thing the article did a really nice job of, too, is highlighting the wide range of emerging AI metrics, from token usage and API calls to hours saved on a job or duties, and probably most importantly, tasks completed autonomously.\u00a0<\/p>\n\n\n\n

Which, if any, of these categories do you believe are most likely to become standard metrics over time? <\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah, and with the metrics we were highlighting, we were selecting companies that we think are really at the forefront of measuring the value of agentic AI.\u00a0So\u00a0I think\u00a0there’s\u00a0a good chance that\u00a0any and all\u00a0of these that you mentioned do become\u00a0reasonably standard\u00a0metrics here.\u00a0And the way that we also thought about them in the article is that they fall broadly into two categories: inputs and outputs.\u00a0On the input side, you have token usage and API calls, plus LLM requests, which we also mention. And then on the output side, you have hours\u00a0saved\u00a0and tasks completed autonomously.\u00a0So\u00a0knowing both sides has value to investors because with inputs,\u00a0you’re\u00a0getting a sense of activity and potential costs to the company.\u00a0And with outputs,\u00a0you’re\u00a0seeing the value created. So, of course, both sides are relevant. You might be concerned if you see that the inputs are far surpassing the outputs, at least in a relative sense.\u00a0So ultimately, I\u00a0think you\u00a0probably do\u00a0need a mix of these input and output measures to get a sense of efficiency and value created by these AI products.\u00a0<\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

Yeah, and that kind of leads me right into my next question. All these companies are taking different approaches to reporting the value of AI, right?\u00a0So\u00a0is there a risk that investors just\u00a0won’t\u00a0be able to compare these companies if\u00a0everyone’s\u00a0developing their own AI performance metrics?\u00a0<\/p>\n\n\n\n

Will sector analysis be able to kind of bucket them and say, “Okay, we’re going to compare the best of token usage versus the best of output versus the best of time savings,” and so forth? <\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah, I think that’s definitely a challenge, and one that we highlight in the research, right?\u00a0So if agentic AI does not lend itself to universal metrics, since each company’s value proposition is relatively unique, then that does create a real challenge in comparing companies and can create some uncertainty about what the true value of a company should be.\u00a0<\/p>\n\n\n\n

And that’s one of the ultimate functions of the stock market, to have this price discovery happening all the time. If this makes it harder to do so, that might create some, like I said, uncertainty about the true value of a company. <\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

Yeah. And I think, really, out of the metrics we\u00a0discussed\u00a0and the article discussed, productivity gain seems like a key outcome, right? The whole point of AI at the end of the day is to be more productive.\u00a0<\/p>\n\n\n\n

So do you think that could really be the next major valuation metric, compared to everything else, kind of like recurring revenue became the key SaaS metric? <\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah, I think that does make sense, since\u00a0it’s\u00a0the most obvious value creation piece here, right? But we still\u00a0don’t\u00a0necessarily have a universal measure even with that, because already\u00a0we’re\u00a0talking about some saying time saved, some saying tasks completed, and those are two different measures of productivity.\u00a0So that’s\u00a0kind of the\u00a0market, the earnings element of it. And then from the macro piece, you have the customer who needs to take that time saved and do something else with it. Because if that time saved\u00a0isn’t\u00a0translating to increased output, then the total output\u00a0remains\u00a0unchanged.\u00a0Of course,\u00a0that’s\u00a0incumbent on the customer to do something with it and is unrelated to the value that the company\u00a0we’re\u00a0talking about here is creating. But it is a\u00a0bigger-picture\u00a0AI\u00a0question.\u00a0And I think there’s interesting research recently from the Bank of Korea that showed that AI was saving people an hour and a half a week in Korea, but in general, they weren’t using that extra hour and a half to do anything productive, so total output was flat.\u00a0<\/p>\n\n\n\n

So that’s really a macro question and not a market question, but it’s something that remains to be seen because ultimately, the hope is that AI is this driver of productivity. <\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

Yeah, so it would be great if every company let the employees leave an hour and a half early, but I highly doubt\u00a0that’s\u00a0going to happen at most places, at least.\u00a0<\/p>\n\n\n\n

Michael, consumption-based pricing models have become increasingly common in software. How does that trend help pave the way for the AI era of business models and valuation frameworks? <\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah.\u00a0So\u00a0I think this\u00a0is a bit of a predecessor to charging for token usage, for example. Instead of\u00a0charging for\u00a0the number of users,\u00a0you’re\u00a0charging for\u00a0the amount of use.\u00a0And we noted in the article that this is something a handful of companies helped normalize in recent years, predating agentic AI.\u00a0And with that groundwork laid, we then see a study that we highlight from Metronome that, as of 2025, 85% of SaaS companies surveyed were experimenting with this consumption-based pricing.\u00a0So\u00a0I think this\u00a0helped create some understanding of this pricing method and how it worked before it was adopted by these agentic companies.\u00a0<\/p>\n\n\n\n

And so, in turn, I think it’s helping analysts and investors understand its meaning for valuation purposes by having some track record for a few years before it’s been adopted more widely for these AI-related reasons. <\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

Michael, this has been great. I\u00a0kind of just\u00a0want to wrap it up, summing up everything we talked about. Once the dust settles, what do you think success looks like for AI metrics?\u00a0<\/p>\n\n\n\n

Do you think it’s going to be a handful of standard measures like we use now, or will it be more siloed, where each company has its own way of demonstrating AI-driven value creation, or a hybrid of both? <\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah, I think success would be metrics that\u00a0provide\u00a0real insight into the value a company’s product\u00a0creates\u00a0and that are also relatively straightforward for investors to understand. Something like hours saved or tasks completed, those are\u00a0kind of intuitive\u00a0metrics.\u00a0<\/p>\n\n\n\n

And then I think beyond that, the understanding will be helped if companies can land on those standardized measures, because even if ARR or net recurring revenue weren’t the most intuitive things in the world, by virtue of becoming standardized measures, that helped people understand them.\u00a0And\u00a0perhaps within\u00a0this kind of framework for success, companies can then supplement those standardized measures with more custom ones that are specific to their company. That could hopefully help with proper valuations of companies along the way, while enabling some comparability across companies as well.\u00a0<\/p>\n\n\n\n

So that’s my hope for a win-win for everybody. <\/p>\n\n\n\n

Jeff Praissman<\/strong><\/h3>\n\n\n\n

Thanks again, Michael, for stopping by the IBKR Podcast Studio. Again, the name of the article is “The Search for New Measures of Value in the AI Era.” It can be found linked to this podcast, as well as on nasdaq.com.\u00a0<\/p>\n\n\n\n

And until next time, Michael, I look forward to continuing the discussion. <\/p>\n\n\n\n

Michael Normyle<\/strong><\/h3>\n\n\n\n

Yeah, thanks.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"

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