SED News: Apple’s AI Problem, The Real Business Model of AI, and Token Cost Reckoning

18:58Gregor Vand:And then for our final headline this week, this is via TechCrunch. So Remote, which is a company that I think quite a few listeners might be aware of and may even be recipients of their payroll through Remote, they've been growing pretty dramatically. So they are a seven-year-old company based in Amsterdam. I didn't actually know that until I read this article. I thought they were American, but they've surpassed 300 million in ARR, which is pretty exceptional. I know compared to say an AI builder platform that says it's done 200 million in ARR in a year or something, but I think this is a slightly more durable business.

19:37Gregor Vand:I think people would agree. But what is interesting is if we look at what's been reported is their pair employee revenue, basically, but it's just the fact that they claim that they've pushed through this 300 million ARR, become cashflow positive, but they have not been hiring any more people. You could, of course, people could say this, but it sounds, this is CO saying that their headcount has stayed flat to get to this place. So at least in theory, that proves that this is possible. I think we're also going to, certainly if I've scat ahead to my own hacker news highlights, it is going to be a bit of this coming in, like AI and people, like, again, what does that mean?

20:15Gregor Vand:It should be more time spent at work, less time spent at work. at least i think you and i showed we're both seeing roughly the same thing in industry right now which is more time spent at work but okay maybe headcount does stay flat but that headcount

20:27Sean Falconer:is spending more time at work yeah you just you keep it flat but double the efficiency or double

20:33Gregor Vand:the output by having everyone work 80 hours yeah and it is exactly yeah i wonder if it's like sort of almost like group dynamics at play where people see other colleagues quote achieving more and it It just sort of keeps, when I say raising the bar, I'm not sure I want to say that in a positive sense. Like it just raises the expectation, I think is what I'm actually meaning. Like raises the expectation of output.

20:57Sean Falconer:There's probably some of that. It's like if you're on a team and everybody's working extra hours and you're the one person who isn't, then it could potentially create some issues because your teammates might be like, well, why am I working 12 hours a day and Gregor's getting away with less? so it can create some if only that was true but it's some social some social problems within the company but i also think that there's also like an addictive component to a lot of these ai tools i experienced it myself like using quad code or whatever your agentic engineering tool of choices like it is addictive it's like a swap machine you're putting your tokens in it's churning away you're getting some tokens out and you keep trying to get it to do more.

21:44Sean Falconer:And it feels like, Hey, if I'm not using my compute cycles right now to cycle through tokens and generate code or generate something, then I'm just wasting all this compute, like sitting there idle on my computer. So you want it running all the time. And it's like, it's taxing, but it's a different type of taxing than being in full focus mode in like locked in coding. Like what used to sort of be a requirement to really be efficiently generating code. Now it's become somewhat of like an asynchronous process that has kind of changed the mode of behavior. And maybe that leads to you being able to do this for longer sustained periods than what we've been able to do before as well.

22:22Sean Falconer:So there's a novelty factor. There's a lot of things I think going on that leads to the increase in people spending time. And it also is like a highly competitive market. People are getting laid off. There's pressure on companies to churn out more. It's got to come from somewhere. And then you have AI companies that seem to be moving incredibly fast and are very competitive against each other. Valuations are super high. There's lots of pressure all around. So I think that creates some of this as well.

22:48Gregor Vand:Yeah, absolutely. And investors, I'm not entirely sure of remote setup if they're fully VC backed or et cetera, but I'm sure they have to get to 300 million ARR. I assume they do have investment and they probably have investors looking quite closely at what does seem to be a bit of a new North Star, this per employee, revenue per employee. I think I've mentioned this before. I still find this funny because of running a service business for 10 years. And I would always look at revenue per employee. That's literally, you know, you're effectively selling time of a human to another business. And so you have to look at revenue per employee and then compare that to say one of our competitors who has 5X the number of employees.

23:27Gregor Vand:But if I did the maths on their reported revenue, I was like, oh, we're making a lot more revenue per employee. I would rather be doing that than hiring 50 more people. But it's now become very trendy in tech to be reporting that and actually gone are the low interest years of boasting about just how many employees you have and how much you've grown the headcount. And now it's almost the opposite. It's like boasting about keeping it flat or reducing. Yeah.

23:53Sean Falconer:I mean, that becomes the new sort of North Star, I guess.

23:56Gregor Vand:Yeah. And it is hard to, I think for many companies, this is all just the classic tech noise that comes out in the news. And I think every company just has to make their own assessment. They shouldn't be like sheep following, just saying, oh, well, everyone's not hiring, so we shouldn't either or whatever. Every company should make their own judgment on that. That's a fairly utopian view from me. I think just rounding out with what you were saying, Sean, about just the actual filling of time that an employee has, it is, I guess, like it's Parkinson's laws, you know the time available whenever time is allotted will kind of always get filled and i think we're just seeing that 100 where despite the efficiencies of the tooling and enabling us to generate the output in theory faster and potentially quote better it just means we're being expected to output even more and also when we talk about input like everything you output at least and where I said everything that I output has to be taken in by somebody else in the team somewhere.

24:57Gregor Vand:And so we've got this debate going on right now of like, well, where's the line between you can't just keep churning out documents expecting a human to keep reading them? Or is it like AI versus AI on the input and the output or the output and the input?

25:10Sean Falconer:So yeah, you write a few short points and then use AI to explode it into like a document. Yeah, exactly. And then you give it to somebody and then that person puts it in AI to summarize it back into points so they can digested.

25:22Gregor Vand:Yep, definitely have seen that. So I think that's a good place to leave that. Very interesting. We'll sort of follow along with how other, I like that this was remote and not just yet another talking about meta or something. I think it's interesting to follow these companies that a lot of them are powering. I see a lot of the same names pop up, power a lot of other startups these days and remote definitely has played a big part in that. So yeah, very interesting. Moving to our main topic, we did touch a little bit on it in the last 10 minutes, but it really is just, okay, well, business models of especially coding agents.

25:56Gregor Vand:And this is based on Simon Willison's blog post. He put out something, I think it was yesterday. And for those that aren't familiar, Simon Willison, you'll see his post pop up on Hacker News quite frequently. These very, not always long, but just very detailed dives into how, especially at the moment, how certain models operate. He runs a bunch of experiments against them. and so on. But he declared yesterday, I think Anthropik and OpenAI have found product market fit. And this is based on the idea that subscriptions that certainly the consumer pays has absolutely no correlation with A, the cost of the business.

26:40Gregor Vand:But why is there product market fit? It's actually because enterprise is paying so much money for the use of these models. We have touched on this in the past a little bit, you know, sort of saying this all looks a bit strange. How can a$20 a month cloud subscription in any way be covering its costs? And why does my, you know, if I look at sort of our work subscription, like those numbers look very different. and Simon Willison saying, you know, he's got the$100 a month max plan from Anthropic and the$100 a month pro from OpenAI. He used a token usage tool over the past 30 days and he'd found he'd consumed 2 ,000 worth of credits or tokens.

27:23Gregor Vand:So that's just to do the math there, that's 2 ,000-ish worth of usage for 200. However, enterprise are paying far, far over that and that's the business model.

27:34Sean Falconer:Yeah, I mean, it's essentially you get people hooked on this. It's almost like a freemium model, or you can think of it, it's like open source or something like that, where people for in their own projects and on their own free time, they can use something that's less expensive. But then it's a way to get network effects across the industry, because anybody can try it. And then once they try it, because they have product market fit, like they're hooked, and people legitimately feel like they can't do their jobs now without these tools. I feel it myself. I depend on this so much now. So companies are willing to pay a lot for the enterprise licenses because their employees are pushing for it.

28:12Sean Falconer:And then additionally, had the market dynamics that we were talking about, like there's just a lot of pressure on companies to try to generate more efficiencies and show that they're more AI forward and, you know, whatever it happens to be. So I think there's multiple things going on where you have both the bottoms up effect of people clamoring for this because they're using it themselves. They're paying for it with these cheaper licenses and they're pushing their companies for it. But then the companies are also sort of top down want these things. So it's a very interesting dynamic, which, of course, creates like rocket fuel for all these companies.

28:45Sean Falconer:And I think in a lot of ways, like, I don't think this was necessarily expected that this was going to be where the model companies found their, like, true revenue driver. For a long time, long is relative in this world, but, like, not that long ago, it was all about, like, models. It was all about competing for the best model. And now that's really changed to competing for sort of the best agentic harness. The true moat has a lot to do with the context management of the information environment around the model rather than just the base model. And we've even found in our own company that we can get similar performance from some of the lower cost models available from, say, Anthropic versus the top tier models.

29:28Sean Falconer:So in a lot of ways, the underlying models have kind of been at a performance level good enough for almost a year since about November of 2025, I'd say. was when they kind of became good enough for tool differentiation. But the biggest change that's happened has been really the management of that information environment around coding tasks, which has completely transformed the developer experience over the last half a year or whatever. So that's kind of a very different competition landscape than it being about bigger model wins, which is interesting. I don't think it's something that anybody necessarily saw coming.

30:07Sean Falconer:I also think that the other interesting bit about this is if you thought about like OpenAI and their strategy initially was maybe like, hey, we're going to own a sort of like become the face of the Internet, the way that Google became the front door of the Internet. That's a much different business model. Google is a volume based business, at least from like search and ads and stuff like that. They get a ton of searches, they got a ton of users, and they make a little bit of money off of every user. And that leads to like a gigantic business. But if the way that I think there's like 900 million users of ChatGPT and only like a single digit percentage of them pay, like that's an OK business, but that's not like trillion dollar valuation business.

30:51Sean Falconer:And I think that where they're now seeing this is more of the B2B play. that Anthropic, I think, really figured that out even before OpenAI did. Now, I think the comparison, at least from a business model perspective, is not Anthropic, OpenAI, or the new Google. They're more like the new Salesforce or SaaS. You're selling seats and licenses. You're doing B2B enterprise sales. That's a completely different motion and completely a different muscle, which is also interesting. It's kind of a deviation from maybe where people expected the market to go.

31:23Gregor Vand:Yeah, there was a lot of, popped up in many news outlets, the Uber CTO, you know, saying he'd maxed out the full year AI budget in just a few months in 2026. And that's, you know, that's definitely done the rounds, I think, especially in enterprise CFOs, CTOs saying exactly, look, you know, we've got to be super careful. Like this is Uber saying that they've ridden to their budget. I think the subtext there is, I don't think Uber would be saying that if they were worried, that's what had happened. I think they're kind of saying it more like, this is only positive that actually we just made a miscalculation on how valuable these tools are.

32:01Gregor Vand:And we happen to have spent what we predicted, but that's fine. Like we're just going to keep paying these companies. And I think that's where, especially, you know, we might touch on it a bit, but you know, the IPO is probably coming up with Anthropic and SpaceX, which doesn't include, you know, Grok, for example. The way that Simon Millicent put it sort of, I think was very helpful, you You know, he says this isn't an AI failure story. It's just that a budget set in 2025, which is what happens at companies like an Uber of that size. Like the budget for 26 is set far in advance in 25, basically.

32:31Gregor Vand:Failed to predict how indispensable these tools would become. And I think just also tied into this is the model selection. And to your point, Sean, that actually the bit I'm curious about is that the models, as you say, the choice is less of a factor now. like how, what are the splitting hairs and like what's the quality of output, you know, let's just say on Opus versus Sonic. Of course, a lot of people say Opus is far and away better and maybe there are some specific tasks that yes, it is. But Sonic can still get you very, very, very far as it should be able to. But especially in enterprise, it's very interesting.

33:06Gregor Vand:I don't think a lot of people understand the default settings on like their Cloud code or even when they go and just use Cloud, you know, interface, there'll be some non-technical users that are effectively given access to the enterprise account and they're just running everything through Opus and not really paying attention. So like maybe the average employee doing just some knowledge work through Opus is like racking up, I don't know,$200 a month of usage. But we've got developers who basically want to say, look, I'm only going to use the best model because why would I use anything less? And then racking up like thousands and thousands and thousands pair of themselves.

33:43Gregor Vand:And I think companies, they're not saying, don't do this like don't use this but they're definitely starting to say hang on we gotta keep looking at this because we want to make sure that at least use a cheaper model if you can but like i'm not fully clear like on what the best way to address that is because you're kind of saying if you think you can figure out that you'll still do as good work with a cheaper model then go ahead but if not it's fine use the expensive model now you're seeing more and i actually talked to the

34:12Sean Falconer:one of them today is like more startups that are focused on dynamic cost optimization where they're routing prompts to the correct or less expensive lms you know based on understanding sort of which models are good at certain tasks or looking at the history of interactions and so forth within the company so i think there'll be more and more like there's gonna be a lot of cost optimization stuff but i feel like at some point especially if the company with open ai and anthropics or stage to become public. At some point, someone's going to pay for this, basically. Right now, it's early innovation mode.

34:48Sean Falconer:We've talked about companies are under pressure. They're not worried about the token costs. They're more worried about the public perception that they're a dinosaur and they're not adopting these AI tools. But it's like suddenly all your employees are having a $25 ,000 token bill per month. What's that mean to your business? Like you're essentially getting like some sort of AI compute tax on every head that you hire within the company that you have to factor in. It's almost like in the US, like factoring in like, okay, well, we fully loaded employee, we got to pay for insurance, we got these other expenses that we have to, now you're going to have like, like a line item that's like, what is the token usage of like a senior engineer versus, I don't know, some other role, product marketing or something like that.

35:36Sean Falconer:Like you're going to have to be thinking about that as a company as well to factor this stuff in. So I think at some point, like there's going to be some impact, ultimate impact to either these companies are going to have to charge more under public, similar to like what we saw with Uber or Lyft for a while, for a long time, like the ride shares were sort of VC subsidized. and at some point there's no free lunches like someone's gonna have to pay for this and then that's gonna also lead to i think a lot more cost optimization which might start with sort of independent players and eventually become some of these companies we saw the same thing with warehouses as well like for the early days of like when snowflake was growing like crazy people weren't really worried about sort of their warehouse bill as much but then when the market dynamics changed and things became a lot less about like growth at all costs and more around cost optimization and margin optimization and stuff like that then suddenly people were started being very concerned about their gigantic you know snowflake bill and other similar products so then a bunch of companies and consulting services spun up around like cost optimization and eventually snowflake in order to reduce the risk of like churning people built a lot of that tooling functionality actually directly into snowflake so that people have more controls of how spend gets allocated and stuff so i could certainly see things like that coming as well but those are all like signs of like a more mature market and we're just in like early early days so

37:03Gregor Vand:people are just kind of spending blindly and i guess tying it back to the headline about remote saying you know we kept headcount flat and it's because of ai they obviously haven't well i say they obviously haven't but we didn't see any breakout from them of but what are they spending on AI. If you're spending, I'm just picking numbers out here, but let's say as a business, you have 100 employees and then you're spending 10K a month on these tools. Well, okay, what's that? That's at least three to four potential employees you might have had otherwise. A bit less, maybe two or three, it depends on the company, but that probably still makes a ton of sense.

37:41Gregor Vand:Don't employ two to three people. Do put that kind of money into tooling for the other 100. Surely that makes sense. But as you say, Sean, we definitely haven't reached the real inflection point or the next stage in the journey, which is when do these costs start to come under scrutiny and when the cost optimization come in. With cloud, cloud is still the one that there's a ton of tools out there helping teams optimize their cloud bills on AWS, etc. But if you look at the numbers, AWS still just keeps growing revenue. And okay, people are much better optimizing costs but like for example at super base we have a team of two to three who their whole job is to optimize costs on aws yeah so and i know that we're i guess we're a little bit unusual in that respect that we like that makes a lot of sense for us but equally that's just symptomatic of the problem like you need to have three full-time employees if you use all of aws to make sure you're not like spending money where you shouldn't need to spend it so yeah i think

38:42Sean Falconer:companies will probably reach a similar spot with the model and token costs or use and stuff like that. What do you think all this might mean for the open weight models? If the llamas of the world, essentially, if the real value prop or moat is sort of this agentic harness in the way you manage the context and information environment and not necessarily just the model, then what's the play for the open weight models? Do they have to go and find a similar sort of business to go after? Or can they survive just as a model alone?

39:16Gregor Vand:Yeah, well, I guess to plug an episode, spoke to Benny at fireworks.ai. They run open weight models. And, you know, e.g., they run an open weight model for Cursor. That's not all of, obviously, as people that I think that know use Cursor, it's not all their options. When you ask it to do something, there's sort of like an auto mode, which, yes, does run on the sort of tuned open weight models and then you can of course choose your specific non-open weight models i think it's gonna be really interesting space because i think when more companies realize they could probably get the same outputs but then rather than paying a sas bill to just straight to anthropic what if you set up your own again call it like cloud instance on something like of fireworks where you say hey we know that this open weight model actually would be the best all-rounder for the kind of work we do and we would end up spending 20x less over a year if you guys just run it for us i think that's a very interesting space yeah yeah and the other thing

40:25Sean Falconer:too is these the total tam of the space is gigantic so there's going to probably be some model winner that or a collection of model winners that kind of open own the open weight model inference market.

40:39Gregor Vand:Yeah. And at least from what I understand at present day, I think the thing that's interesting about open weight models is that just the tense, especially the bigger, more general ones, the majority are kind of coming out of China, basically. Mio Lama, yes, is a standout from US, but how much more effort does Mesa put into that? I'm not totally sure. It's definitely a big play, you know, and all of these models coming from China, which underpin a lot of like some of the offerings from safe fireworks and we actually talk about that in the episode i think there's just a lot to keep an eye on there and maybe actually that's a topic for a future sed news for we sort of look at how the open weight models are doing competing so hopefully thanks to simon willison for his very good article on on that i think it's you know it's very present as we say looking at you know ipos coming up so we expect to see you know probably the S1 document, you know, which is what gets produced before an IPO to attract investors.

41:38Gregor Vand:We expect to see that from Anthropic probably in the next few weeks, maybe months. Why is that interesting? Because that should have, you know, numbers break down. What is the actual revenue coming in from, maybe they won't break it down precisely your enterprise versus consumer, but like we'll get to start to see at least what is the revenue coming in from usage and we can, and what are the costs involved with running such services. And that'll be the SpaceX one. We definitely don't have time to dig into that one today, but like that's the problem with that one is it's a lot of financial chicanery.

42:11Gregor Vand:So it's a bit harder to dig into that one, at least from the AI perspective of like, what does it actually cost to run and how much revenue are they making? Just a sidebar is apparently the Starlink is actually the most profitable piece of SpaceX or the only profitable piece of SpaceX is actually Starlink. So that's already an indicator of that one. But yeah, definitely do have time to dig into that crazy-ass one today, but maybe another time. It's that time of the show. Hacker News highlights some of our favorites that have popped up. Oh, I see Doom. Doom's in there again. I love it.

42:45Sean Falconer:Yeah. Yeah, I couldn't resist. You always got to highlight Doom on everything. But yeah, so this was running Doom on a travel router with touch. so it's a essentially called a glinet slate 7 pro travel router it's like a networking device with a 2.8 inch touchscreen apparently it runs some version of linux and it has root ssh access so to get doom on there i guess was relatively straightforward but if you dig into the comments there's just some pretty funny ones like someone claims that they actually did this like 10 months you know previously but i think one was uh there's kind of like an earnest thread about whether humanity should have picked a less violent game for this like tradition of you know porting this one game across like every type of device should it be doom or should it be something that's like

43:34Gregor Vand:maybe a little less silent maybe someone needs to kick off like a mario or something like you know

43:39Sean Falconer:like mario on something yeah like the original mario yeah exactly this didn't get very high on

43:45Gregor Vand:haggard news but it did the rounds and it's related uh there's now something called doom bench so it's like can your stack run doom it's so it's a performance analyzer of as it sounds can your stack run doom so check that out as well if you're thinking of uh trying to port doom onto something but yeah i love the travel router with the little screen that's uh that's very fun my first one is this was at the top of hacker news before we were recording today it's literally just called can we have the day off and i thought this was a very timely it's quite a short little article blog posts by somebody called mike and this was posted by a user called mlsu it's basically just sort of well if we're all getting 10x productivity from ai can i just take friday off now is that okay i think probably a lot of us are feeling that at times and this person also talks about like they're paying six thousand dollars a month for child care in california so like can i not just go into the office for four days and save some money on my childcare as well.

44:49Gregor Vand:So yeah, I think it's just, I like these little kind of zeitgeist articles that do actually make it up where I think it's, it's usually a lot of what we're thinking in our own lives. And someone just says it. So.

45:01Sean Falconer:Yeah. I think, you know, we talked about this throughout the episode, but I think there's probably a lot of people feeling like they're all running sprints right now and how long can you sustain the sprint?

45:11Gregor Vand:So yeah, a marathon of sprints right now. Exactly. Yeah, exactly.

45:14Sean Falconer:It's a never ending sprint. The other one I had was YouTube is automatically labeling AI generated videos now. So before I think things were basically like an honor system. So like a creator could say that was AI generated and now they switching to automatic. So they're going to detect sort of photorealistic AI generated content, auto apply labels, even if the creators don't disclose it. It makes a lot of sense to me. Like I think we talked about this, like particularly with social media. if all the social media is generated by AI and then you're using AI to kind of like optimally show it to somebody and stuff like that, like at some point, like, you know, who's this for?

45:53Sean Falconer:So at least I don't think they're changing the serving of it. So they'll still serve you AI generated content, but they're at least letting you know that it's AI generated content. And I'm sure maybe there'll be more controls or something like that over time. But at some point you're going to end up with more content, not just on YouTube, but more content that exists on the internet that's been generated by AI than generated by humans. And that may or may not be majorly problematic.

46:20Gregor Vand:Yeah, that's interesting. I would like to see that on Instagram. I haven't spent a ton of time on Instagram these days, but it is still where I probably of any network where I go to just see what various people I do know are up to as well as just random people I follow. But the AI thing is annoying because I see a bunch of stuff and I do have to kind of go to the comments to check Like if it's people say, obviously, this is AI. I'm into bird watching, for example. And like there was a period when all these sort of AI birds started appearing. And I'm like, that's not a real bird. Just no, you know, voting it down.

46:53Gregor Vand:So I would just like a label and I can just filter out anything within certain topics that is AI. That would be nice.

46:59Sean Falconer:Yeah, I know Dr. Christian Hubecki, who's been a guest on the podcast a couple of times. sometimes and i don't know if he's still doing this but for a while on twitter he would debunk like fake robotics videos because people would always show some robot doing something ridiculous and stuff like that and he would post his takes on it like of why this is like a fake

47:18Gregor Vand:video and things like that yeah especially robotics i guess you sort of get into the physics of it as well like there's no robot that could possibly do this or something to that yeah Yeah, exactly. So cool. My second one is, I feel it's like quite vanilla, but I quite like it. It's SimCity 3000 in 4K. So someone that has put together a nice port of what it sounds like, SimCity 3000. And you go to, you know, GOG, which is, you know, a sort of game, has game EXEs that you can grab. And you grab that and you do a bunch of tweaks that this person has put together. And then it runs in 4K, which is super nice.

47:56Gregor Vand:so you run it on your nice wide screens etc it's interestingly i always like this genre but i ended up playing it's a game called cities skylines i've never quite understood why it's not city skylines but i believe it's like a finnish developer so it ended up being called cities skylines but it was an excellent sort of the first one wasn't 4k i believe there's now a second cities skylines 2 which is 4k but then it is nice if we can go back and play these classics that we're so used to, but just with uprated graphics. As a sidebar, I did end up downloading Metal Gear Solid for the PS4, which is like the original.

48:32Gregor Vand:And they actually have two modes. Like you can run it in original graphics mode and improved graphics mode. And I've got to say, I actually almost prefer the original. Just it's like this kind of fun, blocky experience that reminds you of how PlayStation used to be. So each to their own on the, whether they want to uprate, see their classics uprated or just play it as they were. So then, yeah, just any thoughts for what we might see across the next month when we check in next?

49:00Sean Falconer:The only thing that jumps to mind is, and I think maybe next month is probably too fast, but I do think that we're going to see a lot more around like token cost optimization. I think maybe you look ahead six months. I think that's where we'll start to see that, where companies will become either more concerned about it or we're going to see a lot more sort of startups coming on the scene that will help you sort of manage your token costs and token bill that you're now spending on Claude. Because at some point, can you really justify, I don't know, a$25 ,000 or whatever it is per month bill per head of everybody in your company?

49:38Sean Falconer:Maybe, maybe not. I guess it depends on what the efficiency gain in the ROI is on that, but there's a pretty high bar for getting ROI from everybody at that level.

49:47Gregor Vand:Yeah, I guess sort of related, but maybe I'm taking a slightly alternate take. And I'm probably ahead of my time at this one, but S1, an S1 from Anthropic, I think we might see that. And if we do see it, I'm going to stick my neck out and say people will be very surprised positively at like how much money they're making. and say that this is like the next Microsoft or even the next NVIDIA to some degree. Sort of, yes, the R &D costs have been massive, but this is clearly going to be where all enterprise money ends up for the next 10 years. Yeah. Yeah, let's see what happens. As usual, thank you everybody for tuning in.

50:24Gregor Vand:We've had a few notes from various people that this is SED news is what they look out for on a monthly basis. So we really appreciate that. Both Sean and I, I think, have had separate anecdotes from people we've met. So anything you would like to see covered, always do just drop us a message on various socials and you can find SE Daily on LinkedIn, X and all the usual places. So do give us feedback. We love it.

50:47Sean Falconer:Yeah, absolutely. Feedback's welcome.

50:49Gregor Vand:Yeah, cool. All right. Well, until next time, we'll catch everybody on next month's SED News.

50:54Sean Falconer:Thanks, everyone.

51:05Thank you.