# Enterprise AI is shifting from usage targets to ROI

Markdown copy of https://realalpha.app/s/enterprise-ai-is-shifting-from-usage-targets-to-roi/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Matan Grinberg](https://realalpha.app/builders/matan-grinberg/) · Matan Grinberg is cofounder of Factory.
- Host: [Sabrina Halper](https://realalpha.app/people/sabrina-halper/) · Host, Sabrina Halper Show · Has worked as an investor at HOF Capital and founded SHS Media.
- Show: Sabrina Halper Show
- Episode: [Factory’s Matan Grinberg on Recursive Self-Improvement, Chinese Models & Lessons From String Theory](https://realalpha.app/v/-QQkpdS-Qjo/), Sep 2, 2026
- Moment: 23:56 to 28:28 of the episode (5 min), playing on https://realalpha.app/s/enterprise-ai-is-shifting-from-usage-targets-to-roi/; the episode from there: https://realalpha.app/v/-QQkpdS-Qjo/?t=1436

## Quote

> “We're now entering phase three, which is like, okay, we have people using AI. Now we need to actually **measure the ROI**”
> — [Matan Grinberg](https://realalpha.app/builders/matan-grinberg/) at [24:50](https://realalpha.app/v/-QQkpdS-Qjo/?t=1490) · Sabrina Halper Show

## Takeaways

1. Early token-maxing can help teams break old habits.
2. Measure the return on each incremental token.
3. Relationship-driven work often needs cheaper models.

## About this segment

Matan Grinberg describes enterprise AI moving from board pressure, to token-maxing, to measuring ROI per token. The shift helps leaders allocate compute where it beats hiring or cheaper models.

From “Factory’s Matan Grinberg on Recursive Self-Improvement, Chinese Models & Lessons From String Theory”, Sep 2, 2026. [Watch the full episode](https://realalpha.app/v/-QQkpdS-Qjo/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[24:08](https://realalpha.app/v/-QQkpdS-Qjo/?t=1448) So typically, what happens from what I've seen is there's like, there was a phase one, where basically, AI is all over the news, you know, Andrej Karpathy tweets about it and says models are good. So then every board is like, guys, models are good, adopt AI. And so that phase one is board yells at CEO and is like, adopt AI, like, what's our AI strategy, you know, make a put AI in our product somewhere, like make us an AI company.

[24:30](https://realalpha.app/v/-QQkpdS-Qjo/?t=1470) And then CEO kind of freaks out and is like, hey, CTO, like, what are we doing about AI? Like, we need to make sure people adopt. We need to become AI native. And then that's where some of the token maxing kind of emerged, which is like adoption by any means necessary. Let's start measuring performance of every person by how much AI they're using. Now, long term, that's not the optimal strategy. But if you want to kickstart the engines and just get people to, like, break down their old habits and build something new, I actually think it's worth going over budget for a period of time just to like break the old habits. We're now entering phase three, which is like, okay, we have people using AI. Now we need to actually measure the ROI and make sure we're doing it in a manner that makes sense. And this is what we spend a lot of time with our customers on, which

[25:14](https://realalpha.app/v/-QQkpdS-Qjo/?t=1514) is not only making sure people use agents, but measuring the agent effectiveness. So basically for every token that you use, how much ROI are you getting? You know, for every part of your org, the answer might be different. Like CIOs are going to need to decide who gets what token budget. It's not just going to be token maxing forever. But in order to decide that you need to understand if I give an incremental token to this part of the org, what ROI do we get? There may be some parts of the org where actually that incremental dollar is better spent on headcount and not tokens. There might be other parts of the org where actually you don't need headcount at all and you can just have tokens do the whole thing if you just I know it's probably so specific to each company but if you just had to get a gut check like what what orgs need need the best

[26:00](https://realalpha.app/v/-QQkpdS-Qjo/?t=1560) like frontier model and what really doesn't yeah I mean sales is fundamentally like a human driven thing at least for enterprise sales so like tokens maybe help to like give you a good overview before your meeting to make sure you like remember everything but you can use a pretty cheap model like you don't need the frontier of human intelligence to summarize 10 emails yeah um so i think you know any like relationship driven stuff generally there's some amount of tokens after which you don't get any more roi and then there are things like a product marketing data science engineering where i actually think you can do a lot with more tokens you can do more a b testing you can understand your users better you can improve the system itself that's where we're seeing like uh a lot of roi come from tokens and then beyond that it also comes down to the people

[26:46](https://realalpha.app/v/-QQkpdS-Qjo/?t=1606) like depending on the personnel you have on these different teams there might be some people who are very wasteful of their tokens and some people who are actually very token efficient and that's where our router comes in which is like because like i heard that there was a point where people were getting paid on like how much ai they were using yes yeah or getting judged on that yeah it makes no sense yeah well initially to whatever kickstart the engines yes but like long term i mean if you want me to spend a million dollars I can do that very easily it just might not produce anything useful yeah and on a broader scale like do you think there's certain sectors that are adapting to this really well and like adopting these technologies is there anything surprisingly slow that has that like hasn't been the biggest thing that I've learned is it's really in the

[27:27](https://realalpha.app/v/-QQkpdS-Qjo/?t=1647) hands of every individual and every leadership team because my understanding of cloud like I was a physicist during the cloud so you know I'm not I wasn't that close to it but my understanding is there were sector by sector like some sectors were early some were late here it's really like leadership team by leadership team there are some banks that are super forward and some that are super behind and it's like it so not seeing it um you know categorized like that so i think the empowering thing is it's like you know for any enterprise leaders out there it is literally in your hands which is pretty exciting and i want to ask you about um Bye-bye.