In this episode
Dan Weirich, Chief Operating Officer and Chief Financial Officer of Treasure AI (formerly Treasure Data), explains how AI spend inside his own company went from open encouragement to cost rationalization in a matter of months, and why Treasure AI is moving its customers to outcome-based pricing where they pay only when the platform delivers value. Recorded live at Agentic World 2026 in Miami, the conversation with Greg Kihlström covers a real-time report whose real-time component cost about $450,000, the decision to give every department ownership of its AI spend, why Treasure AI moved marketing under product, and how a CFO weighs growth against profit when deciding where money goes.
Key takeaways
- AI spending flipped from encouragement to rationalization within months. Weirich says the start of 2026 brought mass encouragement to use AI, and by around May many companies had exhausted their AI budgets. Treasure AI spent the summer rationalizing usage and moving to more cost-effective models.
- Real-time output is expensive when nobody needs it in real time. One internal report was genuinely informative, but its real-time component cost about $450,000 for information the team only needed weekly or monthly.
- AI spend needs a single owner in each department. Effective July 1, the first day of its second fiscal quarter, Treasure AI moved all AI spend into the departments that use it, because people owned pieces of it but no one owned all of it.
- AI has changed how many people R&D needs. Weirich cites roughly a 10X improvement in R&D output, and says Treasure AI’s R&D spend is running behind budget because it doesn’t need as many people.
- Outcome-based pricing means customers pay only when value is delivered. Treasure AI has evolved from its predictability-focused No Compute model to pricing tied to whether the platform actually delivers value, and treats that pricing as a product differentiator.
- Owning the whole stack makes new pricing models possible. Because Treasure AI develops its underlying technology down to the database layer, it can manage pricing without involving third parties.
- Treasure AI moved marketing under product. At the start of its fiscal year, marketing began reporting to the company’s product leader, a structure Weirich says he hadn’t seen in more than 30 years of working.
- A point of growth is worth two and a half to three times a point of profit. Cutting costs is within a company’s control; growing revenue requires a counterparty to agree to buy, which makes it much harder.
- Data activation no longer requires deep technical skill. In automotive, Weirich says buyers once visited a dealer about six times before purchasing and now visit about 1.2 times, with roughly 85% of shopping happening online.
Chapters
- 01:05 — Who carries the risk when a martech vendor underperforms?
- 02:33 — Dan Weirich’s background: 8×8, three decades of cloud cycles, and the operational CFO
- 03:30 — From “token maxing” to tokenomics: why AI budgets ran out by May
- 05:59 — The $450,000 real-time report and giving AI spend an owner
- 07:12 — Privacy rules, internal training, and democratizing customer data
- 08:15 — From No Compute to outcome-based pricing
- 10:16 — Is outcome-based pricing where the industry is headed?
- 13:31 — How a CFO works with marketing: budgets, ownership, and no micromanagement
- 14:14 — Why Treasure AI moved marketing under product
- 17:14 — Capital discipline: regional P&Ls and why growth beats profit
- 19:29 — From six dealer visits to 1.2: activating data in automotive
- 21:05 — What Weirich is watching at Agentic World
- 21:49 — Staying agile: Carbide Ventures and investing in AI companies
Why finance started pumping the brakes on AI spend
Weirich describes a year that moved at the pace of the news cycle. At the start of 2026, the message inside many companies was simply to use AI. By around May, budgets for AI were exhausted. Treasure AI’s fiscal year starts April 1, so it was only about 45 days in when it hit that point, while companies on a calendar year were four and a half months in. The company spent the summer rationalizing usage and moving to more cost-effective models.
What a $450,000 report says about real-time AI
The savings were real: Weirich cites roughly a 10X improvement in R&D output, with R&D spend now behind budget because fewer people are needed. The waste was also real. One report produced genuinely useful information in real time, but the team only needed that information weekly or monthly, and the real-time component cost about $450,000. The lesson Treasure AI took was to match the cost of an output to how often anyone actually needs it.
Why AI spend needs a single owner
The underlying problem, Weirich says, was ownership. Different people owned elements of AI spend, but nobody owned all of it. Effective July 1, the first day of its second fiscal quarter, Treasure AI moved all AI spend into the departments that use it.
How outcome-based pricing shifts accountability to the vendor
Treasure AI started roughly 12 years ago as a big-data-in-the-cloud service, and early query engines made customer costs hard to predict. Weirich’s analogy: the old engine was a 12-ounce can of soda, and the faster one was a Big Gulp with free refills, where it was unclear how much anyone was using. The company later introduced its No Compute model for predictability. Its newest model is outcome-based, so customers pay only when the platform delivers value, and attributing that value is the question Treasure AI says it focuses on.
Why Treasure AI moved marketing under product
Weirich runs leaders by giving them a budget and the authority to succeed or fail on their own, without micromanagement. At the start of the fiscal year, Treasure AI moved marketing underneath product, because the two have melded so closely in its category and at the speed AI is moving. From his seat, the result is that he hears about fewer issues, because fewer people are involved in resolving them.
How a CFO weighs growth against profit
Treasure AI runs separate P&Ls for its go-to-market teams in North America, Europe, and Asia (managed out of Japan), and allocates the rest of the business as a percentage of recurring revenue. Weirich says a percentage point of growth rate is worth two and a half to three times a percentage point of profit. Saving money is within a company’s control; growing revenue requires a counterparty who agrees to buy, which is why Treasure AI directs funds toward wherever it can get the highest growth rate.
FAQ
Why are CFOs scrutinizing AI spend in 2026? Dan Weirich says early-year pushes to use AI exhausted many companies’ AI budgets by around May. That led to a period of rationalization and a move to more cost-effective AI models.
What is Treasure AI’s outcome-based pricing? It is the successor to Treasure AI’s predictability-focused No Compute model. Customers pay only when the platform delivers value, and the company treats that pricing as a product differentiator.
How did Treasure AI assign ownership of its AI spend? Effective July 1, the first day of its second fiscal quarter, Treasure AI moved all AI spend into the departments that use it, because no one previously owned the whole of it.
Why did Treasure AI put marketing under product? Weirich says product and marketing have melded closely in Treasure AI’s category, especially at the speed AI is moving, so at the start of its fiscal year marketing began reporting to the product leader.
How does Treasure AI weigh growth against profit? Weirich values a percentage point of growth rate at two and a half to three times a percentage point of profit. Cutting costs is within a company’s control, while revenue growth needs a buyer to agree.
Is outcome-based pricing where the martech industry is headed? Weirich says companies have tried outcome-based pricing for years with mixed results. He expects Treasure AI to do well with it because it owns its technology down to the database layer and has no third parties to involve in pricing.
About Dan Weirich
Dan Weirich is Chief Operating Officer and Chief Financial Officer of Treasure AI (formerly Treasure Data), where he leads the company’s business and financial operations and sits on its board. He brings more than 25 years of finance and operations leadership at technology companies.
Dan first joined Treasure Data as CFO in 2014. After Arm acquired the company in 2018, he became Vice President of Operations for Arm’s data business. He returned as COO and CFO in 2021, when Treasure Data separated from Arm and joined SoftBank Vision Fund 2’s portfolio.
Before Treasure Data, Dan spent more than a decade at 8×8, Inc., as CFO and later President, and helped build its software-as-a-service business. Earlier, as Vice President of Business Development at iAsiaWorks, he expanded the company into 13 Asian countries and helped take it public on NASDAQ in 2000. He has also held roles at Qwest and Phoenix Network.
Dan is a General Partner at Carbide Ventures and holds a BS in International Business from the University of Colorado Boulder.
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Transcript
[00:01:05] Greg Kihlström: Hi, I’m Greg Kihlström, host of The Agile Brand, and here’s a question for you. There’s a lot riding on all aspects of your marketing, so when you sign a contract with a MarTech vendor, who’s actually carrying the risk if it doesn’t perform? Is it you or is it them? Today we’re recording live at Agentic World 2026 in Miami, and we’re gonna talk about how the real cost of running AI is reshaping the economics of marketing platforms from the inside out, and what capital discipline looks like in the middle of an AI hype cycle, and what it means for the future of the CDP category. To help me discuss these topics, I’d like to welcome Dan Weinrich, COO and CFO at Treasure AI. Dan, welcome to the show.
[00:02:25] Dan Weirich: Thank you for having me.
[00:02:26] Greg Kihlström: Yeah, looking forward to talking about this. before we dive in though, why don’t you give a little background on you and your role at Treasure AI?
[00:02:33] Dan Weirich: yes, I joined the company in 2014, so I’ve been here around 12 years, when we were very small, with around a dozen people, and have grown, significantly, since then. Before this, I was, CFO and president of a NASDAQ-listed company named 8×8 for around 10 years, and so I’ve seen kind of all cycles of, kind of cloud adoption back when it used to be called, like kind of hosted software-
[00:03:03] Dan Weirich: … and then it became cloud and SaaS and, AI. And so I, I’ve seen it, it all of the last, it’s actually 30 years, as it’s kind of, evolved with the evolution of the internet. And, I’m a very much an operational, CFO. I, I took one accounting class in my life and, I actually understand accounting extremely well, but, it’s not my interest. It’s more the kind of the operational elements of businesses is kind of my strong suit.
[00:03:30] Greg Kihlström: Yeah. Yeah. Love it. So let’s dive in here, and we’re gonna talk about a few, few different things here, but I want to start with just this, what I teed up in the intro, this, the strategic shift to outcome-based models. And so I’ve been hearing a little bit about this just i- in the industry, and for those CMOs out there that have had some, let’s just say, token-related conversations with their CFOs in the last year, how have you seen the conversation shift from companies encouraging token maxing to, maybe pumping the brakes and now we’re talking tokenomics, and why did some of these very high-profile examples in some cases not see this coming?
[00:04:12] Dan Weirich: So at Treasure AI, we’ve internally have kind of followed the, the news cycle. We’re kind of like, a week ahead or a week behind, as the news cycle this year has been crazy.
[00:04:24] Dan Weirich: and at the beginning of the year, it was, mass encouragement of everyone to use AI and, I don’t think the token maxing term even existed then.
[00:04:35] Dan Weirich: And then, kind of in May timeframe, everyone’s realizing that their budgets for AI were exhausted by that point, and we were actually– We’re only in April 30, April 1st, fiscal year start, so we were only like 45 days in the fiscal year, and I think some other people were four and a half months in. And so we spent the summer kind of on rationalization and, looking at more cost-effective kind of, AI models and, it’s, it’s an ever-evolving experience. But, we had, instances where we’ve seen massive improvements, kind of like 10X improvement in our R&D organization in terms of, code that’s developed and kind of output. If you look at kind
[00:05:20] Dan Weirich: of our P&L now, we’re behind on R&D spend relative to our budget, just because we don’t need as many people. And so that’s kind of, like, beneficial because, one is we’re able to develop more product that I’m, very excited will be announced here in the next 48 hours here at, Agentic World, and, th- we’re able to do it more efficiently. We did have s- some instances where we had, a report that was generated and it was extremely, informational, but it was like a real time report, and if you really looked at what the report was generating, it was something you need, only needed to know like once a week or once a month or whatever.
[00:05:59] Dan Weirich: And that report ended up costing The real-time component ended up costing, like, $450,000. So we kind of, like, learned very quickly, like, “Oh yeah, that was ex- that was some nice info,” but it was really expensive. And so, we’ve seen kind of all spectrums of it, and we’ve, been kinda, like, busy actually, like, effective July 1, which was, the first day of our second physical quarter of the year. We actually put the AI spend, all of it, into the de- de- departments of the organization, because the issue that we are kind of experiences is there wasn’t, like, a true owner. There was owners of elements of it, but not all of it.
[00:06:41] Dan Weirich: And that, that… So it’s been, you know… I can’t believe that we’re only, like, 185 days into a physical year right now and, it feels like, five years-
[00:06:53] Greg Kihlström: Right. Right
[00:06:53] Dan Weirich: … in the last six months, but.
[00:06:54] Greg Kihlström: Well, I mean, and, and so much learning because, I mean, there are, there are some similarities to past cycles to, to your earlier point, but, but not… It’s not exactly the same, right? So I mean, a lot of organizations are learning right now just not only what they can do, but what they should do, right?
[00:07:12] Dan Weirich: Yes. I mean, a- and then you kind of couple that kind of on the, the privacy side, what we’ve had to do for our customers on, there’s, different rules and different geographies. We operate, in, North America, in Europe, in, in Asia, and, on the kind of privacy side, Europe is, is a little bit more on the stricter side than kind of the Americas and, in Japan. But, with AI it’s, it’s, effectively, making sure that our customers are kinda comfortable with the products that we’re essentially offering them b- but empowering their employees to use. So we’re kind of democratizing enormous amounts of information that they have, stored inside of Treasure AI and,
[00:07:58] Dan Weirich: there’s kind of control elements there. We have internal kind of trainings for our employees. There’s kind of, more elements of the standard kind of infosec training is kind of, getting longer [chuckles]-
[00:08:08] Greg Kihlström: Right. Right. Yeah
[00:08:08] Dan Weirich: … with, with these type things. So that, that’s what we’ve been very busy on working, in the last six months.
[00:08:15] Greg Kihlström: And so from a pricing standpoint as well for your customers, Treasure AI has evolved it- its pricing from a no compute model focused on predictability to a new engagement based model focused on accountability. What was the motivation behind this change, and how, how will this likely affect new and as well as existing customers?
[00:08:36] Dan Weirich: I mean, if, if you go back, like, 12 years ago or so, we, we were, like, Hadoop as a service, and it was kind of provided, like, big data in the cloud, like a data warehouse.
[00:08:47] Dan Weirich: And one of the query engines was Hadoop, and then we rolled out something called Presto, which was faster. And the anal- the only, like, basic analogy I could give you, if, like, you take a s- a can of soda that’s 12 ounces of liquid, and that was Hadoop. So that was kind of what you could run. And Presto was like, you went to, like, the corner convenience store or 7-Eleven and you just got, like, the Big Gulp cup and you went in and just started, getting free refills or whatever, and… But there’s a cost to that, and it’s just, so it’s unclear how much you’re kinda using. That was sort of what Presto was. And so, as we were doing that, if your cost structure is, is kind of, a little bit unknown, it, we kind of
[00:09:32] Dan Weirich: morph it so it works for the customers, but then, there were some whoopsies over the years, that, we kinda had to fine-tune. And, we’ve moved a long way since then, but the, the most recent one that, we’re launching is effectively, where it actually is true, like, outcome based. Like, you’re only paying if you’re, delivering value. And, and that’s been kind of, like, the question for many, many, many, kind of software solutions is, like, how do you attribute value that’s being delivered? And that’s, what we talk about all the time and, something that, we’re very excited morphing, the, the pricing element into kind of a differentiator in the product offering.
[00:10:16] Greg Kihlström: Well, yeah. I mean, because CMOs and, and marketing teams are getting held by their, stakeholders and, and execs- exec teams to more accountability from, ROI and things like that, so I mean, it, it makes sense that there… Do, do you think that this is kinda where the industry is headed or is… where, where do you see, where do you see the industry going with this or, or is this maybe a differentiator for Treasure AI?
[00:10:44] Dan Weirich: I mean, people have tried com- or companies have tried kind of outcome based pricing-
[00:10:51] Dan Weirich: … for years and years and years on different types of products, and some companies kind of nail it and do well and some don’t. And, I think that in this, I think we’re gonna do fabulous with it.
[00:11:04] Dan Weirich: But I think that others maybe won’t evolve or they’re going to, be a little bit slower. And, there’s still just a lot of, like, talk about things that I hear that we were doing, like, six months ago. And so we’ve always just been kind of, on the bleeding edge of, doing things and, kind of learning the, the hard way kind of as we’re kind of, like, out in the frontier kind of, trailblazing, and I think that, should give us a very, good advantage. And we, we own and develop all the underlying technology, even from the database layer, so it gives us n- a lot of ability to m- m- manage pricing in ways that you, we don’t have any third parties we have to involve. [outro music]
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[00:13:31] Greg Kihlström: So, I talk with a lot of CMOs on this show, but I don’t, I don’t talk as often with a CFO, so, or, or COO. What’s your perspective on the relationship between CFOs, COOs, and, and marketing over the last, let’s say five years or so? And, d- is the future on the same trajectory? Like, what, what are, what are you seeing in the, in, in this kind of relationship?
[00:13:56] Dan Weirich: I mean, the way that we, run, Treasury AI is we have very good leaders and we, effectively give them a budget and empower them to, succeed or fail on their own. And, and so that is, like, like even, I mean, with my direct reports, I don’t have marketing reporting to me.
[00:14:14] Dan Weirich: But, some people I have direct one-on-ones with, like on a schedule basis, but some I don’t, and I’m just like, “If you need anything, call me.” And some of, sometimes I talk to them 12 times in one day, and sometimes I don’t talk to them for like two and a half weeks. and so, that, they tend to like that because there’s no micromanagement. And so on the, the marketing side, I mean, we’ve been evolving it massively. we did a change at the beginning of this physical year where we actually moved marketing underneath product. So Rafa, who runs kind of our product org, runs marketing as well. Because kind of product and marketing have melded so closely together, specifically in our kind of category and with kind of like the speed that AI is moving,
[00:14:59] Dan Weirich: we’ve just morphed them directly together.
[00:15:02] Greg Kihlström: Makes sense.
[00:15:02] Dan Weirich: And I’ve never seen that, like in an organization. I’ve been working 30 years, a little over 30 years, and I’ve never seen that, but we kind of did it in April. And, this is, we’ve done some smaller events. This is our first big event when, we’ve done it together, but so far so good.
[00:15:20] Greg Kihlström: Yeah, I mean, it seems like at the speed that you’re able to move, that, that tying product and, and marketing together actually makes a ton of… And in hindsight, it makes a ton of sense. I’m sure it took some decisions to, to get to that point. But yeah, it, it does seem… Because that, that to me, the product team and the marketing team, there, there was always a little bit of a gap in communication. the marketing team was trying to keep up, but they weren’t embedded with product, and so it, again, it, it makes a ton of sense.
[00:15:50] Dan Weirich: ‘Cause a lot of times you see like product is like, could be with like R&D and engineering or something like that.
[00:15:55] Dan Weirich: Or, sometimes standalone, but kind of working with them a l- like the developers a lot and, merging kind of product and marketing. I just, I actually from my seat, I actually just hear less like things.
[00:16:09] Greg Kihlström: Yeah. Yeah.
[00:16:09] Dan Weirich: Like t- times, most times when you hear things, there’s some issue or something rather.
[00:16:13] Greg Kihlström: Right. Yeah, yeah.
[00:16:14] Dan Weirich: And so when there’s less like kind of cooks in the kitchen, you know-
[00:16:16] Dan Weirich: … it’s like things are just getting done or they’re resolving it without having to, have like, arbitrators involved or whatever. [laughs]
[00:16:25] Greg Kihlström: Yeah. Right. Right. Yeah. Yeah. It just, again, it, it, it, it makes sense because there was, uh… Now, if you can solve the m- the marketing and sales, that, that stereotypical-
[00:16:37] Greg Kihlström: … relationship.
[00:16:37] Dan Weirich: Now that one is, yeah, I, I’m not so sure about that one-
[00:16:40] Greg Kihlström: Right. Right
[00:16:40] Dan Weirich: … being mastered yet. [laughs]
[00:16:41] Greg Kihlström: Right. Right. Well, all, all in good time, right? [laughs] So let’s talk a little bit about future, future outlooks. So, as you mentioned, you’ve been CFO for, a CFO for a while and seen s- several milestones with this, with this company alone. What does capital discipline look like for a well-funded vendor? And when you look at the industry in general, where do you see, maybe opportunities for, to decrease waste, whether that’s on the vendor side or in how enterprise buyers are spending?
[00:17:14] Dan Weirich: Yeah. So I mean, we are, just under a couple hundred million dollars in revenue. we’re actually quite profitable. we’re growing nicely and, we have go-to markets in three different geos. So we have North America, we have Europe, and then we have Asia that’s managed out of Japan. And they, all those orgs have their own P&L, and then effectively the rest of the organization is allocated as a percentage of recurring revenue is the allocations. But the, the go-to-market piece, which would be, sales and marketing is geographic specific, and they’re very different because things happen different in Japan than they have in here,
[00:17:59] Dan Weirich: for example, on go-to-market activities And so we, we run that as, like, a, a PNL. some markets make more money than others. Typically, the markets that don’t make as much money are actually growing faster, so there’s a heavier investment in sales and marketing. I mean, we just look at it that, like, if we can get, like, a percentage of, growth rate verse a percentage of profit, the value of the percentage of growth rate is, like, two and a half to three times more valuable than profit. I mean, you can always, like, save money. It maybe isn’t, like, the funnest thing in the world, but it’s within your control. Growing revenue is, is … You have to have a counterparty to, agree to, procure your services
[00:18:44] Dan Weirich: and use them and things like that. So that’s much harder. And so that’s the way we kind of, like, from our allocation standpoint, is we just look at, like, where can we move funds and resources so we can, get the, highest growth rate that we can. So that, that’s kind of, like, how we, we look at it, ourselves. And with our customers, I mean, they’re typically have the similar mindset. we sell across a whole bunch of different verticals. Some, are different. but effectively, we’re helping our customers gain much more, insight into their users to specifically in kind of the B2C companies. Actually, like, what they’ve been doing, what their interests might be and,
[00:19:29] Dan Weirich: kind of … Like, before it was called AI, like, three and a half years ago, we, we would have these, like, machine l- learning libraries for, like, the last decade and started, we started at first on the auto sector, and it was on, like, on the purchasing side, where, you used to go into a dealer, like, six times before you bought a car. Now it’s, like, 1.2 times, and that 85% of the shopping happens online. And, we had been doing that for years of kind of stitching it so that when the person got to the dealer and it synced in with the CRM and they figured out who you were, they knew that you were gonna buy a Subaru Outback. You’re not buying a pickup truck, and you’re literally down to, like, the blue color or the gray color or whatever.
[00:20:14] Dan Weirich: And now, we’ve made that so it’s kind of, faster and smoother, but, wrapping in kind of, like, the service, the maintenance. That’s a big cash cow is kind of for dealers and, getting the, people to bring the cars back. And then, autos actually track churn rates, which is … I kind of was, like, interested to learn that because when you start out with a Subaru and it’s time to get a new car, they want you to buy another Subaru. And so, that, that’s just, like, an auto example, but it’s stuff that we’ve been doing kind of for, like, over a decade, but it’s getting to where one is, is, people without te- deep technical skills can activate the data very easily and very quickly and, you know,
[00:20:59] Dan Weirich: do many, many things that, just technical skilled people used to be able to do.
[00:21:05] Greg Kihlström: Yeah. Yeah, love it. Well, Dan, thanks so much for joining today. Got two last questions for you as we wrap up here. the first one, so Agentic World technically kicks off tomorrow, so, what are you looking forward to most?
[00:21:17] Dan Weirich: I’m looking forward to the kind of the new product announcements. I’ve heard them a little bit in passing, but I’ve, I’ve haven’t, like, seen the full pitch, so I will be sitting there-
[00:21:27] Dan Weirich: … in the audience just along everyone else, hearing, kind of what the, customers’ and prospective customers’ kind of feedback is and, and, hearing kind of what the partners’ interest is, is in, continuing to grow the market.
[00:21:42] Greg Kihlström: Nice. Love it. And last question for you. What do you do to stay agile in your role, and how do you find a way to do it consistently?
[00:21:49] Dan Weirich: so I’ve been here for 12 years. I did take a little break. we sold the company to Arm in 2018 and I left and, and with the chairman of, of Treasure AI, Hiroyoshi Kah, he and I started a venture capital firm called Carbide Ventures. And, so we have a, a s- two funds. our, our second fund we just closed, recently, and we have an office in Palo Alto, California, and we have an office in Tokyo. And, we have, five partners, and we have, about 35 portfolio companies. We invest in kind of the seed and, series A stage, and we’ve had, the 35, we’ve had two companies have been purchased,
[00:22:34] Dan Weirich: two have closed down, and we have 30, 31 live, and we have, a lot of money to invest in new opportunities. So I’m, investing in, in companies and sitting on boards of companies and all over the world, and, so that is keeping me very, busy and agile and young.
[00:22:53] Greg Kihlström: Yeah. Thir- 31 companies, that’s gonna keep you agile for- [laughs]
[00:22:57] Dan Weirich: Yeah. And if you look, like, our vintage of our first fund was 2021 vintage and our vintage of our second fund is, this year, and i- we did an early close in, in ’25 on the second fund, so we started to make some investments from that. And if you look at the ’21 vintage, it’s, it’s kind of more traditional SaaS type companies.
[00:23:16] Dan Weirich: Fortunately, we didn’t have too many C, C-based kind of price, pricing model companies. And then our, second fund is, is all AI companies, so it’s, it’s totally different. And, it’s just, like, the, the … One is just the growth elements is, is just so much faster and bigger and, kind of the problems that they’re tackling are just frankly much more exciting than companies were, tackling in the ’21, ’22 timeframe, so.
[00:23:45] Greg Kihlström: Love it. Wow. Sounds exciting. [laughs]
