SED News: Anthropic’s Mythos, Supply Chain Hacks, and the AI Spending Surge

17:10Sean Falconer:Yeah, I mean, it's a small sample size, so I think I'll hold off on my tinfoil hat and call it a conspiracy theory for a little bit. But if we're five months from now and every month we've reported another Delve-related data breach, then I will join your circle of conspiracy theories.

17:28Gregor Vand:Yes, well, you never know. We'll keep tallies and see how we know at the end of the year. Go ahead, Delve Conspiracy Count.

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19:53Gregor Vand:So moving away from security, these were more of a macro headline that, again, has touched, especially like financial news, layoffs, unfortunately, again. And so the two standout companies were Snap and Meta. What's interesting, I guess, is more the communication around the why. I think commentators are always digging in on the whys. And the whys are usually derived from, especially these public companies, it's from the public statements that they put out, maybe as part of earnings calls and this kind of thing. So Snap is famously unprofitable. So Evan Spiegel said that they were hitting this critical moment where they really have to do something to make the company a profitable one and doubling down on AI and their specs product.

20:40Gregor Vand:And that led to a thousand job cuts, which apparently is sort of roughly 16 % of the workforce. They basically had a lot of pressure from investors to start massively improving their financial performance. And they do look a bit strange, quite frankly, next to a lot of their, when I say peers, companies that might have IPO'd around the same time, Meta being one of them, but we'll come back to Meta in a second. But yeah, not being profitable in this era, I guess, for the age of the company. Yeah, I can see why they're getting some heat from investors. actually that is not to say i mean i always feel i can very publicly say i very much feel for anyone that's been affected by these layoffs it's a horrible situation but i guess from a logical standpoint it might make sense that at least investors were getting a bit unhappy with the

21:26Sean Falconer:snap performance yeah i mean i think being a public company in the public market right now is a tough spot to be in a lot of ways and then on top of that from an employee perspective some of these companies, when they announce layoffs, they see stock bumps. So like the stock goes up after saying like, hey, we're laying people off and we're focused on efficiency. And that creates, I think, a certain cascading effect where when maybe the stock is not doing well, then the investors are putting pressure on the company to make a change. And if we're sort of rewarding companies for downsizing, at least from like a stock valuation perspective, then that's the easy decision by a company to make because it's going to positively reflect in the stock.

22:10Sean Falconer:And then on top of that, I think with social media companies in particular, there's a lot of, in terms of historically human capital deployed to like review and curate content. There's just a lot of process there. Like I remember going to Hyderabad when I was at Google in India, and there's a huge amount of people that work there to review all the like YouTube videos and make sure that someone's not putting something up that's awful. So there's a lot of people to do that. And I think all these social media platforms have some version of that. But if they can offload some of that to using AI to do it, then you can do a lot of this stuff in a more efficient way where you're taking out some of the tasks that historically have required a lot of human energy around it is one thing.

22:59Sean Falconer:But I do wonder, broadly speaking, about the future of social media. We have social media companies that are like weighing off humans to build AI that would generate content that are used to be made by humans on their platforms. The feed is increasingly AI generated content. It's served by AI curated algorithms. The humans whose attention is then sold to by AI optimized ad systems, like at some point, like who's this for? like a ai circle of like content to optimization to serving ads and it's kind of like the human element maybe is the people who are passively consuming this stuff but like it's all ai generated content like what are you signing up for as a user yeah absolutely and i mean we should

23:43Gregor Vand:bring meta into this as well being effectively the most popular across a couple of platforms probably social media company in the world excluding x i guess but snap said just to compare and contrast snap said we're doing this they didn't exactly say to double down like well they said they're going to double down on ai and they're going to invest a lot more in this moonshot of the specs products and we've talked a lot about that idea in past episodes meta however saying they're actually doing it and being almost i would say more quote honest and saying like we are doing this to offset the investments that we're making in ai i mean that's a very polite way of saying we're losing money over our AI infrastructure bets all this stuff we need to do something to shore up that and continue to be profitable to look profitable so they yeah they just said so they're they're cutting 10 % of its workforce which is much larger in comparison and so it's I believe amounts to about 8 ,000 employees and also not higher 6 ,000 roles that were open I mean I can't even fathom that a company has 6 ,000 open roles but that's just something I can't get my head around.

24:52Gregor Vand:But yeah, I mean, I hate to say I prefer this one, but I just I do appreciate that at least being clear that we're doing it to offset the fact that we're losing money somewhere else, which is I think what everybody's been trying to get some of the companies to just admit, which is, are you actually making any money from this capex that you're investing in?

25:09Sean Falconer:Yeah, I mean, I think we're in a place where the world's changing very quickly. And I think a lot of companies feel like the existential threat of what the future might look like. So they have to make fairly big bets in investments to survive this digital transformation that's happening, this paradigm shift around AI. And you want to be part of the winning side of that, which takes investment in a big bet. It's hard to do that as a public company because you're under such scrutiny and people are ultimately looking at how much money you're making, the profitability, the bottom line, while you're also trying to do these innovation bets.

25:44Sean Falconer:And some companies have the luxury of like a really healthy revenue generating business, like a Google, for example, where they can spin off innovation arms that are well funded and it doesn't deteriorate essentially their core business. But for other companies, especially smaller public companies, that's hard to do. If you have like your core business and then you're also trying to like change as a business, like how do you fund the innovation while also protecting and growing the core business? That's a difficult balance to make. So if you want to survive the existential threat, something has to give.

26:19Gregor Vand:Yeah, exactly. So again, obviously very sorry to hear anyone potentially listening. If you're caught up in this, unfortunately, it looks like this was nothing really to do with anyone's skills, etc. It was just a purely financial thing that is unfortunately seems to be part of the landscape as of the last five years with big tech. So just very briefly, before we move to our main topic, I slightly, so this dropped, I today actually which was that cotu which is a huge investor they've actually got a plan to buy up land for data centers and the question is why and i think people are speculating that this is actually for anthropic so it's kind of interesting that rather than just invest more money in anthropic they've actually just gone straight to the infrastructure themselves or the base of it and just said well maybe we'll just buy land and then give that or lease that i guess to one of our investees so that's an interesting strategy there you know a lot of companies are trying to sort of

27:13Sean Falconer:pivot their way into being AI companies. Maybe they need to pivot their way into being real

27:17Gregor Vand:estate companies and just like own the land that companies need to build their data centers on. Yeah. And similarly, I think it was yesterday, it's been sort of rumored that Anthropic are going to be doing one more funding round, possibly a 9 billion valuation, sorry, 900 billion valuation. Yeah, no, clearly it's the end of the week. My brain can't remember nine versus 900. and this is again being floated that this is probably the last raise pre-IPO. I mean, I think that probably was said last time as well, but I'm sure this is the last, last, last, final, final, final, you know, the documents on the fundraising, final, final, final.

27:58Sean Falconer:I mean, there's a lot of companies that are still private that I can remember talking to interview processes like several years ago and how they're like, oh yeah, we're 18 months from IPO and this is like five years ago. And they've raised multiple rounds since then and stuff. So So a lot of it depends, I think, on you want to time the public offering to what's happening in the market. And then also there's a lot of things that you have to do to get ready to go public too, which take time.

28:21Gregor Vand:So our main topic today, we're really doing just a deep dive. I say it's a deep dive, but it's both a high level, a high level deep dive, if that's possible. On what does effectively, we've basically seen about 700 billion in AI CapEx happen since the sort of AI boom. And I guess here we're just taking a pause and just looking holistically, you know, where are things across a lot of the big players? We do this every so often, SED News, just take a pause and touch a lot of the big names and what they're doing and why. And we feel this is kind of important because of just the speed of which things are moving.

28:55Gregor Vand:So keep kind of saying it, but you know, a month literally right now is easily what six months might have been pre-AI. So try and actually get a handle on what this scale even is. It's like hyperscaler CapEx. Actually, for 2026 alone, has been reportedly$650 to$700 billion across Amazon, Google, Meta, Microsoft. That was an Morgan Stanley report that has made this guess, if you like. and like you know in a single week google committed up to 40 billion to anthropic amazon committed 5 billion to anthropic and there was apparently sort of 100 billion in ews spend over a decade nvidia crossed the 5 trillion in market cap level which again is just like i think hard to fathom at all um and so if you think about it like anthropic is now simultaneously backed you know by Google, by Amazon, we've just touched on, it's probably going to be touching a 900 billion valuation.

29:54Gregor Vand:This is just the largest infrastructure investment cycle in the history of technology. It's crazy. So, yeah.

29:59Sean Falconer:Yeah, absolutely. I mean, it's pretty staggering. Like the numbers, like, I still remember the days when, and people still use this terminology, but we were, you know, referring to the unicorn startups that were valued over a billion dollars. And it's like, that started to get silly after more and more companies were valued at over a billion dollars. But that used to be like a big deal to be valued over a billion dollars. And now there's so many companies valued over a billion. It kind of dilutes the idea of like a unicorn and it kind of becomes meaningless. Like we probably need to shift that.

30:27Sean Falconer:Maybe it's a hundred billion dollars or we're going to get to a trillion dollars in terms of valuation. But there's like literally been more money going into AI compute in the last year than entire cloud built out over an entire decade. it's kind of a strange world where we have google and amazon both investing in anthropic but they're also competitive with it it's almost like they're hedging a bet somewhere along the way it's like in case we don't win the model wars like we still have a skin in the game yeah and i mean if we then

30:58Gregor Vand:look at it a little bit more strategically like there is a vertical integration play you know if you want to use that slightly what feels like an archaic term these days vertical integration but If you think of model labs becoming actually infrastructure tenants, so like Anthropics, 100 billion AWS commitment, it sort of means that Claude's training and inference potentially structurally tied to Amazon's chip roadmap. So that would be Tranium and Graviton. Meanwhile, OpenAI, that remains quite, despite some news of them starting to part ways more and more, still deeply integrated with Azure. Google is both an Anthropical investor.

31:38Gregor Vand:and building competing models, as you've just touched on, Sean, like Gemini. And that's on its own TPUs, which we did a bit on a couple of months ago. And then meanwhile, Meta has confirmed it will use, quote, hundreds of thousands of AWS Graviton chips. I think this is the thing, like bets do have to be made because it is quite difficult to unwind or just shift over the underlying chip infra that these models are being trained on. It's not just like, oh, I'm going to go run it on my other machine somewhere. It's, I think, analogous to like when Apple did move all their hardware off Intel onto their own chips.

32:15Gregor Vand:Like it's just that takes probably like a good year, a couple of years of planning to actually go there. This stuff is too big to be fully self-contained within one company.

32:27Sean Falconer:So inevitably you're going to get people who are competitive with each other, also completely dependent on each other. from like chips to cloud infrastructure to the models themselves to like the actual applications and so forth like that that's just inevitable because it's so big it can't be essentially you know self-contained within one company we're well beyond that at this point so if we then take

32:48Gregor Vand:another leap over to i guess the human side we're sort of again looking at this across multi-facets we have touched on this either last month or two months back really just looking at like what does the hiring landscape look like here but there was some data from true up that you know 67 ,000 open software engineering positions across 9 ,000 tech companies, which has doubled since mid-2023, which was a low, up 30 % in 2026. And coming back to these conferences we've been at is interesting. I did actually get quite a lot of questions from, I would say, especially younger people attending, which I love to see.

33:24Gregor Vand:And it's really great to see people still studying or maybe two years out but they were asking me like what do i think about engineering degrees and will engineers be needed and i did just say like absolutely i mean it's just that the concentration of where engineering will sit will be in these companies that are simply doubling down on ai and the you know i guess net consumers of these platforms are maybe going to decrease the reliance on human engineers. But I think that is going to be far outweighed by the net increase by these huge players and all the kind of ecosystem around the huge players needing just more and more engineering.

34:04Sean Falconer:I mean, there's just a lot of stuff to build right now. And it's a lot of experimentation. So you need engineers to build it. And I think the responsibilities of an engineer and kind of like what day-to-day might look like for an engineer is certainly shifting. But clearly there's, you know, hiring's up 30%. I just think it's not business as usual. It's a little bit different. One thing that's interesting too is like, you know, IBM, they're tripling their entry level hiring around sort of junior engineers into it's also going after junior. And like one of the topics of conversation has been around, you know, the agentic engineering and what it means to be an engineer is some companies have focused on, Hey, we're only going to hire like senior engineers because we want engineers that have some, you know, maturity in their career so that we rely on their judgment when it comes to evaluating what's coming out of the agentic engineering productivity tools and so forth and i think ibm and intuit and a few others are kind of taking a different approach where they're like hey like these junior engineers are actually super valuable because they're ai native they're sort of grew up and are adopting this tools and technologies faster so we want to invest in in them in some ways i think this is nice to see because I've been a little bit worried about like if everybody's hiring senior engineers, like what's the next, how do you become a senior engineer in the future?

35:23Sean Falconer:And what's that mean for the next, you know, tranche of users? I think one disconnect though, is a lot of the hype around AI and the things that you hear is like, you know, there's going to be massive job loss and engineering is going to go away. And we are actually seeing some impact in terms of enrollment into computer science programs. So what's that mean for the next tranche of people who are going to be entering the industry if suddenly we have not enough engineers to go around if this sort of hiring trend of 30 % increase continues to go up?

35:56Gregor Vand:Absolutely. And I think the role types, there's this slightly newish role called like technical ambassador that apparently OpenAI are hiring thousands of these. And it's really this sort of bridge between what's being built and then, you know, almost like solutioning with potentially non-technical stakeholders in these companies because the spending power can be so huge, but there's always just that massive gap of like, but what actually is this going to enable us to do? And can you show us like examples?

36:26Sean Falconer:I mean, applied AI is, I think, that function at Anthropic, for example. And I think there's a lot of new, like forward deployed engineers, which was a concept at Palantir is now like a very popular role. And I think that's a sign of this transitional period that's going on too, where companies have capital they want to deploy behind AI. It's really strategically important for them, but they don't actually a lot of times know or have the resources and know how to make that thing into something that delivers value to the company. So when they do invest in a particular platform or technology, they kind of need people from that company to come and handhold them to get to a place where they can be successful.

37:04Gregor Vand:Yeah. Final macro area on this. Yes, we're going to go back to security for a couple of minutes. We've talked about this several times, but I don't think it's sometimes you can never talk slightly too much about security. These tools, they are expanding, you know, what we'd call the attack surface faster than actual security tools themselves can keep up. We saw that with the Vercel side of things where basically because they were just going fast with tools like Context AI, but there was basically overbroad OAuth permissions there, you know. And would that have been authorized if we weren't adopting so many AI tools, but instead we're saying, oh, but I can't move fast unless it has access to everything, especially all the way up to leadership.

37:49Gregor Vand:I'm not talking about Purcell specifically. I don't know the ins and outs there. But I know that leadership generally, I think, is just under pressure to say yes. Because if that's the leadership saying, no, no, your tools should stay very scoped and it can't touch your email and it can't touch your Slack messages, well, then what's the point? I need my tools to know everything. And that's what keeps me ahead of everybody else. So yeah, it's just one of these things where I think it was Cisco's state of AI security 2026. They're saying that 83 % of orgs plan to deploy agentic AI, but only 29 % report being ready to secure it.

38:23Gregor Vand:So obviously there's quite a disconnect between what people want to do and then having the means to actually keep up on the security side.

38:31Sean Falconer:Yeah, it's a huge challenge right now because these like silos and like swim lanes that companies kind of build up around different parts of their company as they grow become barriers to AI essentially being intelligent and be able to draw interesting results across disparate data sources and things like that. So you want to give the AI system access to those things, but then you're not necessarily set up in a way to be able to do that successfully. So either you slow down and you try to figure out a way to do that in some way where you can control it. Or you just open things up and then you take on a lot of risk where you might be exposing information that you don't want to expose.

39:13Sean Falconer:And there's so much pressure for companies to be delivering value around AI and be able to press around it and so forth that there's probably a lot of companies kind of bypassing perhaps their normal standard procedures around even vendor procurement and stuff like that. It's kind of similar to what we talked about with engineering and the CircleCI report. There's pressure on engineering organizations to be putting out product faster, but not all the validation and verification of the AI-generated code is necessarily there right now. and you either end up not putting out more product because you're spending the time to validate that the thing that you generated quickly is actually working, or you skip that step and you're pushing out a lot of code that then potentially leads to further security problems.

40:03Gregor Vand:To kind of recap, we've basically been trying to highlight here that people always just think of their cloud provider as this mutual infrastructure, but actually it's kind of not. If you look at it like really, You know, we've got this Google Amazon Anthropic triangle with model choices and cloud choices converging. So if you are building, you know, AI features, most of us are these days, the infra vendors chip investment will probably shape the model performance. So just stay abreast of this because looking at which cloud you sit on and how are the investors embedded with, especially on the infra side, I think it's going to, if we come back without being biased, but Claude is still having a moment right now.

40:45Gregor Vand:And it just seems like if you're not using Opus for a lot of stuff, then you're kind of being left behind. So that's something to bear in mind.

40:53Sean Falconer:One of the things that kind of relates to across some of the things that we're discussing in the main topic is we have a lot of money. Some subset of that$700 billion is kind of flowing into these code generation tools. But there's still a significant gap in the downstream validation and testing. and I wonder like I know there's some companies working on that but it's always like are we kind of ignoring like the real problem where we're so focused on the code generation and essentially compressing the time to like POC but there's all this work that happens after the POC stage to get that to production and eventually we're going to need some way to accelerate that and also have some confidence that's actually correct in order to take advantage of the speed that we're getting code generated.

41:40Gregor Vand:absolutely and then yeah exactly that bottleneck that you've just been touching on sean not to be a dead horse as they say but security that's the unglamorous part but i think the versell breach is a really great example of just it really has to be kept on top of effectively was a bit of a mission for our team to have to scramble within less than 24 hours to do what they had to do to keep things secure and i hope that we don't have one of those per week or something to that effect so i think it is really important that anyone building with with ai just has this in mind. So I guess moving on to what we often think of as our favorite part of the show, Hacker News highlights, where we get to just bring in something that's maybe piqued our interest from the last couple of weeks.

42:23Gregor Vand:Do you want to kick us off, Sean? What did you come across?

42:26Sean Falconer:I was really hoping I could find like a doom running on a lawnmower or sprinkler system or something like that. I went in a different direction. So the first one I wanted to mention was I just thought it was interesting. I love, you know, I come from this background with like my PhD research and so forth. This isn't that complicated, but I just love when people find new ways of sort of representing data in interesting trends and things like that. Like I'm a big fan of the book Freakonomics, which kind of dives into a lot of like these weird numbers. But this is like US gender ratios by Metro, which was posted by N.

43:04Sean Falconer:Sokoloski or something. Sorry, if you're listening, I butchered your name, but it simply shows a breakdown of gender ratios by state, city, and metro. So some interesting things there, like Washington, D.C. in the U.S. is the most female-heavy city. So there's only 45%. Male, 55 % female. And then the opposite end of the spectrum is like Colorado Springs, where it's like the most male-heavy city. So it's a lot of guys walking around in Colorado Springs, apparently it's 55%. And then not too shocking in a lot of ways, like Silicon Valley is pretty male dominant. It's like the third highest rate region in the US and probably is there's a lot of engineering jobs here.

43:41Sean Falconer:Engineering typically skews very heavy on the male side. So you end up with a lot of maybe a sort of somewhat male dominated area of the world versus other parts of the world. So and then some cities are like 50-50 almost exactly.

43:55Gregor Vand:I find it kind of fascinating why say DC is actually, I guess that's 55 % female and 45 % male. Yeah, very fascinating. So I wonder if I can find the same for like European cities or that kind of thing. I wonder if there's any like massive, massive disparities there or something like that.

44:12Sean Falconer:Yeah, I don't know. It'd be interesting to look at.

44:15Gregor Vand:Yeah, on my side, yeah, I think the first one, one of these kind of feel good developer, just a little blog post by somebody. So the blog itself is MatthewBrunel.com and this was posted by SpeckX, user, user spec X could be the same person, who knows. But this was called using coding assistance tools to revive projects you never were going to finish, which I think kind of is fairly self-explanatory. But I think it's just nice that pulling a quote from that, Matthew said towards the end, you know, in my mind, there are different buckets for personal projects. One is things I do to learn and grow.

44:51Gregor Vand:And the other is things I really wish existed. this kind of project falls into the second bucket so using ai coding assistance to reify those projects it's sort of a form of wish fulfillment i never would have gotten to but now i can have the project i think i mean sort of now i can have the project that i wasn't able to have before and one last metaphorical book sitting unread on the bookshelf a bit of a long quote but i think we can all identify with that things that we started and quite frankly it's not that we didn't want to finish it but like things get in the way and we did obviously see that like oh this is going to be way more time consuming than i expected and it would be fun but i just simply don't have the time so that's pretty cool like we can actually even if we're not coding day-to-day but able to bring these tools and like i can think of at least like five projects that probably fit that bucket i just should quite frankly like hook up cloud code 2 and be like hey here's where i was trying when I get to finish it off, please, you know, upgrade some packages, secure the whole thing.

45:50Gregor Vand:And there we go. Like I think that's probably something I'll be doing on a lot of travel I've got for the next couple of weeks. So.

45:56Sean Falconer:Yeah. I think that there's a, it's almost like a subclass of that second bucket where you have these project ideas that you're like, Oh yeah, like I would like to do that, but I just don't even have the time to start it. Cause it's going to take too much time, but now you can kind of, you know, prompt your way to doing that fairly quickly. Like I was using Claude to build some games for my son based on ideas that he had. And I certainly could do that. You know, I had the programming skills to do that. But like, you know, it would have taken a reasonable amount of time and energy to crack that out for some little game that he may or may not even play with versus being able to take his ideas and turn it into something and have Claude kind of churn away at it and pump it out.

46:34Sean Falconer:And then, you know, like have him explore. It's also a good way for him to sort of see how, you know, you can turn ideas into something that manifests itself into a computer software or some sort of product or something.

46:46Gregor Vand:What was your second one, Sean?

46:48Sean Falconer:Yeah, so the second one was this headline that came up this week, which was Granite 4.1, which is IBM's$8 billion model, which matched their previous$32 billion mixture of experts model. So they have three different open source models. One's$3 billion,$8 billion, and$30 billion under Apache 2, trained on$15 trillion tokens. I think it made a lot of headlines because they were able to match the performance of the 32 billion model with the 8 billion across nearly every benchmark. It was really the way they were able to do that was they really focused on data quality over the parameter scaling. And they had kind of this like five phase training pipeline to do that.

47:27And I think that's really interesting because we've over the last year plus when it comes to models,

47:34Sean Falconer:a lot of times we talk about the limitation of the models. We're running out of data they're training the models on. Where are we going to get all this data? And I think one of the things that they were able to show with this is that, hey, we can really actually drastically improve the quality of the model while keeping the model size reasonable if we really focus on data quality during the training process and during reinforcement learning and so forth. They were also really open about some things that went wrong in training and kind of how they fixed that too, which I think was, you know, a little bit different and kind of refreshing.

48:10Gregor Vand:Yeah, that's fascinating. And just like, again, the leaps of progress is just insane. So yeah, that's really cool. So yeah, I guess thanks to Steve hearing one user for putting that in. Yeah, I think on my side, the second one, at least when I found it, okay, I hadn't gotten tons of points. I wonder if this is one of these Hacker News articles where I'm not sure if it's well known about this, but you can submit an article to Hacker News and it might not do very well. You'll get one, two points or something. But every so often, someone at Hacker News will actually reach out to you and say, we're going to repost this because we think that this is really interesting and that this should actually get like more airtime.

48:47Gregor Vand:so it's kind of interesting that happened to one of mine i posted something about remote controlled telescopes like you could bring your telescope to this facility and put it somewhere and then they would store it and run it and you could remote into it and yeah it didn't go anywhere the first time i posted it and then agronews said hey we're going to repost it and it went like to the top which is kind of interesting this is so i think it could be one of those where it's only got 28 points but it was on the front page it's called cheating at tetris so the website is chalkdustmagazine.com. So, and the user was t-3, nice short name there.

49:21Gregor Vand:So I think that the TLDR here is like, if you can imagine you get to pick which Tetris pieces your opponent must play, could you force them to lose, basically? And so the article works through, you know, this basically mathematically, as you'd expect from sort of how do you cheat at Tetris? So you've got these, you know, I think everyone kind of roughly maybe remembers Tetris, like all these shapes that fall from the top of the screen. You need to rotate them and the whole point of the game is just if you make a total line that line disappears but if you don't make a complete line it sort of starts to stack up and if you hit the top of the screen it's the game over so you know you've got these different piece types like different shapes roughly you could think of them as like the letters i j l o s t z they can be played sort of indefinitely without causing game over so picking just one piece won't work so if you were to just mix just the S and the Z blocks, since they don't actually fit neatly together, they say that the best the player can do is to create separate columns of each.

50:19Gregor Vand:But because the board is 10 cells wide, fitting exactly five to cell wide columns, you always end up with an odd split, three of one, two of the other, which causes an imbalance that slowly fills the board. So the article goes on to identify which piece combinations can mathematically guarantee a loss, regardless of how well the player plays. So I do encourage you if you're a Tetris fan to go and look at that. And it's very hard to explain all the permutations, obviously, on a quick Hacker News highlights section here. But yeah, chalkdustmagazine.com. Go there and you can check out that kind of fun piece.

50:51Gregor Vand:So yeah, I guess looking ahead for the first time in history, Sean and I are probably going to meet in person, which is gonna be fun. We're gonna grab coffee, hopefully this weekend in SF. But apart from that very exciting event, like how anything else prediction wise over the next month, Sean?

51:07Sean Falconer:I don't know. I don't have a really good prediction. I mean, I could go with the lazy one that I did last time, which is we're going to have more security breaches, which I think will actually be the case. But I don't have an out there prediction this week.

51:19Gregor Vand:Yeah, unfortunately, security was hovering my mind as well. Just like, what do sound like a sort of broken record at this point with that one? But I often just look around the room and see what I'm looking at. For example, I've got a power bank, which I'm aware does have some processing power in it somewhere because of its usbc capabilities so i'm gonna say that somebody hacks doom onto a power bank like a power plug that has you know usbc outputs so someone's like running doom on a power plug or

51:47Sean Falconer:something like that would be kind of fun i guess the one tying back to one of the topics we talked about is the anthropic mythos model is it going to get more widely available by the time we talk

51:59Gregor Vand:next yeah that's a good one that's a good one so yeah maybe i'll just take a bet that you know they open it up to say 10 non-hyperscalers but the 10 are slightly people have a lot of opinions about that choice for example so let's see how that goes so thank you everybody for tuning in hope this has been helpful and just interesting getting to catch up on what's been going on in tech so much is happening so always useful just to have us try and condense it for you and give you a quick summary so yeah thanks for listening and we'll catch you next time thanks everyone cheers Thank you.