The mythos of Mythos and Allbirds takes flight to the neocloud

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0:41Welcome to another episode of the Practical AI podcast. This is Daniel Whitenack. I am CEO at Prediction Guard. I'm joined as always by my co-host Chris Benson who is a principal AI and autonomy research engineer. How you doing Chris? Hey I'm doing great today Daniel. Looking forward to catching up on one of these fully connected episodes where we get to talk about kind of whatever we want to talk about. Whatever we want to talk about. No one I mean I guess normally we just talk about what we want to talk about but at least when we have a guest we try to center the conversation on maybe a few things they want to talk about.

1:19I'm pretty excited, Chris, because I just got a brand new pair of shoes. And I've been wearing my new shoes all week. And I didn't think that that would be a relevant topic to bring up with you on the Practical AI podcast, because I thought shoes really didn't have any overlap with the AI world. Although I guess this is not the topic we're going to talk about, but I did see a company that had like, like you take a picture of your foot and like the AI figures out the shape of your foot or whatever. And then like could, I guess, advise on shoes or something. Anyway, but speaking of shoes today, I didn't even see this, but folks in the office here at Prediction Guard were like, hey, did you hear about Allbirds?

2:15And I had not heard about Allbirds, but apparently Allbirds is now an AI company, which is quite interesting. So Chris, do you have a pair of Allbirds? I guess they're AI Allbirds now? I actually don't, but I got to say, that's a terribly interesting way of retreading your business model, you know. Yes. Yeah. Yeah. They really, yeah, kicked it, kicked it to the curb, I guess. So from, from, from shoes to AI data centers? Yeah. Well, I guess kind of background information for people here. I, I sort of only know this because my wife had, my, my wife was really into all birds she had a few pairs but um it seems like kind of 2016 to around 20 uh just after covid 2021 there was this you know huge rise of the all birds brand which was a favorite in terms of uh shoes that you would order online i think eventually they did have retail locations that sort of thing.

3:25But from 2022 to 2025, so through this kind of last year, they kind of consistently had a decline, all growth, margins kind of compressed, and their stock price declined, making the business distressed. And so I guess March of this year, so as we're recording this, were in April of 2026. In March of 2026 of this year, Allbirds exited the actual footwear shoe part of their business. So selling off all of those assets to American Exchange Group. Don't know a whole lot about them, but basically that sort of ends the shoe operation portion of Allbirds. They still, of course, had the shell of a company which had a name and an entity and a stock ticker, etc.

4:28And they had a bunch of cash, right? So what do you do with a bunch of cash? And I guess they did also raise additional cash. What do you do with a bunch of cash but buy GPUs? Of course. What else is there? Apparently what happened. Is that what you do with all your cash? shits yes rebranding as ai compute infrastructure which i'm wondering if they'll give me ai compute infrastructure for cheaper um that would be kind of nice you know it's amazing like you know we're joking about this being kind of the pivot you didn't see coming um and quite a pivot too but Their shares jumped at least 700 % based on what I'm looking at here, which is quite a jump in terms of the market not only accepting but endorsing that kind of a decision.

5:27You've got to be wondering if there aren't many, many boards out there and CEOs that are kind of going, we're in kind of a tough spot in our business things have been struggling recently you know maybe we go buy gpus and go into the ai business i mean it apparently is a is a perfectly legit business plan now yeah i guess on the positive side this could seem to be a rational allocation of capital right so i have a bunch of capital well i don't have that much capital i wish i had that much capital. But a party has that much capital. And, you know, if your core business is dying, and you're able to sell that off, you have a company, a stock ticker, then what, you know, what's what's the hot thing.

6:22And, and obviously compute is a, is a core part of the expansion of AI everywhere, the running of these models at scale. Many might not be self-hosting models, but they're certainly consuming models that are running on infrastructure somewhere. Right. And, and it's, yeah. So I guess from that perspective, it could be kind of seen as a very positive and useful kind of, kind of pivot. What's, what's your thought? Well, apparently so. I mean, the market is endorsing it. And I think this is like prior to this announcement, you know, this is the kind of thing nobody would have bought into. Like it would have been it would have been seen as a joke, you know.

7:10And so, but the fact that at least at this point, the market's doing that really does make such a pivot into a concern that companies may be evaluating. And, you know, in these articles, as they talk about Allbirds kind of, you know, once a point in time being the next Nike, you know, I've seen that bantered about in some of the articles. And it got me thinking for just a second there, like, what if Nike were to do the same thing? What if Nike were to pivot from shooting? Yeah, just do it. But I'm wondering, would they brand themselves as AI? Sorry. Yeah, AI Jordans. There you go. Yeah. Speaking of terms, I was running across this term, which, you know, sometimes we try to clear up jargon on the show.

8:02Pretty practical. And sometimes jargon doesn't make any sense to me. But this term neocloud, is this something that you've run across or is this new to you? This is new to me. So you'll have to take us into neocloud. So apparently, and this is related to the Allbirds thing, because apparently NeoCloud, or sometimes referred to as AI native cloud, is kind of a shift that we've seen recently where a NeoCloud is kind of cloud infrastructure that's built specifically for AI workloads, not general computing. So in that way, all birds kind of would be potentially putting together a NeoCloud. So like the kind of old cloud model is you have your web app infrastructure, you have databases, managed databases, you have managed storage of some type, you have some IT or logging, monitoring services.

9:08It's kind of general purpose, flexible, lots of different services. that sort of the idea of this neo cloud or ai native cloud think of other companies maybe like core weave or together ai or lambda labs right um is infrastructure that's built either for ai training inference or both um massive gpu workloads kind of gpu first not cpu first and this exists kind of because gpus are scarce also in the hyperscalers in the general cloud platforms uh the workloads are different right because maybe you are running a lot of things across many nodes uh running a large model across many nodes um uh lots of movement of of data often.

10:02And you're kind of supply chain constrained in terms of what you need to support. So that's this idea and kind of how it intersects here. This was a new one for me as I was looking a little bit at this story. I'm curious, do you have any insight into, like, if you're looking at neocloud companies and you're comparing them against kind of the traditional cloud players, you know, which are the alphabets, the Apple, the Microsoft, the metas, those. Are you, you know, how is the business model changing and how much is, is Neocloud eating in to that? You know, I mean, is it, are we seeing it very specialized or is it making kind of general market traction?

10:47I would, well, I don't have exact numbers like right, right in front of me. I think, you know, if our listeners do have that, Let us know, point us to those on social somewhere. But I do know that I'm seeing a lot of CoreWeave and other, I guess, other neocloud type of companies being talked about quite a bit. And I think that it is partially because there is this sort of, there can be this specialization towards the AI workloads and the specific compute there. And as you know, you know, going into a hyperscaler, if I go into AWS, or if I go into some of these platforms, you can do just about anything.

11:31and there isn't that focus, which is good in one sense because you can kind of support a lot of different types of things. But if you're an AI native, AI forward company and maybe you're quickly spinning up no code applications, you're not maybe doing a lot of that hosting and management in a traditional way, then maybe it makes sense for you to run a lot of that stuff serverless or otherwise and kind of have this pay as you go in AI specialized clouds, which is kind of interesting. I guess that's one of the things about the Allbirds case that you could talk about on maybe the negative side. My hot take on this is like there's no really what Allbirds is bringing here is a company shell and capital.

12:29right they're not bringing any domain expertise that maybe i'm aware of maybe there are some there's some domain expertise around supply chain and maybe manufacturing or or in just industrial settings like that they're bringing but they're not bringing ai specific expertise in terms of building this kind of neo cloud that's true the other thing is like approximately 50 million dollars, although that's much more money than I can imagine generally, is very much a drop in the bucket in terms of the AI data center market. So part of my question is like, okay, like create your little data center. It is very much a drop in the bucket in terms of whether you look at like what China is spending on data centers or just like companies in the U.S.

13:25investing billions of dollars in AI data centers. That maybe is the cynical take on this is like, okay, you have a little bit of this capital. You don't have the domain expertise in AI and you're going to what, spend$50 million on a little data center. How is that going to make a mark? And maybe part of it is like, this is the foothold and more capital will be infused and they'll figure it out. And I don't wish them bad or anything. It's just more of a skeptical take. Yeah. I mean, in another industry, that money would seem like quite a starter, but in this industry where the availability of GPUs in the ecosystem is already quite strained based on the demand.

14:15And if you look at the fact that, you know, kind of globally, there's basically half a dozen key players in the GPU ecosystem in terms of supply as you, as companies may pivot to this kind of business model, which is really, you know, NVIDIA, TSMC, AMD, Intel, Apple, and Qualcomm for the most part. And each of those is cranking out the types of chips in this capacity for AI purposes that they make. And so I can't help wonder, but if this becomes a trend where you see a lot of companies that are struggling pivoting into that, what does that chip supply chain start looking like? It gets even more strained going forward.

15:05So this will be a really interesting kind of see if this turns into a trend to watch and see what happens with that. Yeah. And what do you think, Chris? There's kind of two elements happening here. One is the centralization expansion of these very much centralized compute resources in data centers, which will grow. But there's also this push towards, I think, I don't know if this was one of the trends that we talked about at the beginning of the year for 2026. It's certainly one of the trends that I'm thinking about in terms of the market in general is this shift towards kind of physical or embedded AI, where AI is kind of living everywhere in a bunch of environments, whether that's like kiosks in a retail environment or, you know, actually on the manufacturing floor, not in a data center for a manufacturer, of course, in phones, or we just had the conversation with Kama AI, who has AI in these devices that they're putting in cars to make them self-driving.

16:12So yeah, what is your take on this and how people could think about the, is it both are increasing simultaneously, like we'll just see more data centers and we'll see more physical AI? Or is there a shift more towards that embedded edge centric model versus kind of everything being centralized in data centers? I mean, I think it'll be all of the above, in my view. But I think the giant growth area is going to be in what you might call far edge, meaning it because people define edge differently. You know, some people would say kind of the edge of the data center, edge of the cloud is edge. But if you're talking about embedded devices that are out on embedded in physical devices that are used that are not directly cloud connected or are, but are not relying on that for all of its functionality, then, I mean, there's huge, huge, huge growth potential in that across so many different industries and that's still in its infancy.

17:16But yes, I mean, I do think that, you know, that will go, you know, your notion of neocloud as you instructed us a few minutes ago is an opportunity that many, many companies will go at. My gut is that in the long run, that is not as profitable just because there's already huge players dominating that and as others fill in the niche. There'll be many, many players there. So it'll be interesting to see if that continues to be an amazing strategic opportunity versus specializing out in various devices that are embedded. Yeah. Yeah. Well, I guess that is your set of beliefs or assumptions about something, or shall I say your mythos about that, which brings us to an interesting topic of mythos.

18:20What's the right way to say it? I'm actually not 100 % sure. I've heard people say it both ways. So either one is fine for today. Okay. Unless folks have been under a rock than the last week, They hopefully have heard a bit about this already. Yeah, I'll maybe switch between the two. That way, at least for part of the time, I can seem smart. Yeah, I mean, go ahead. Mythos, mythos model from Anthropic has been in the news, or I guess the supposed mythos model that Anthropic has and is somewhere and not seen by people yet is in the news, I should say. Correct. So the short of it, there is a new frontier model that I think logically you would say is kind of the next thing past the Opus model, which has been, you know, the powerhouse driving Claude Code.

19:20We've talked a lot about that on Opus and Cloud Code on the show. And so the next generation being Mythos from Anthropic is a powerful model. But back before any of us had heard of it, I think what's been reported is that Anthropic had it in a sandbox environment. They discovered it was particularly adept at uncovering security vulnerabilities in just about every meaningful software package, arena, whatever that you could imagine. They discovered they claimed many thousands of vulnerabilities in every operating system and every browser. And they realized that it could have profound effects out there on its own.

20:12So they, instead of releasing it, as I think they had been planning, they kept it close hold. And they started a new project called Project Glasswing, which is a security project that is kind of closed. And they brought in apparently 40 companies, but only about a dozen of those companies are public. A number of companies are not. and those companies are being invited to use Mythos to make sure that their various systems are not exposed or give them time to fix those. So there's not a lot of information, as you would expect, about the specifics of that process. And so that is ongoing right now.

21:01And so we'll see what happens. We don't really know what the future of mythos is, but I think I would finish by saying, you know, if you just kind of look at it in the same way that you and I are often talking among ourselves and with guests about these types of models, the fact that people know it's possible now means that it is probable that, you know, other people will be developing models and stuff, as we've seen in every case ever. on frontier models. And so it might bespeak of a very interesting future where we are seeing some tremendous capabilities from frontier models that are, once again, a significant step beyond the generation that's public.

21:49So who knows? But I think in the months ahead, I suspect this is a topic we will end up revisiting from time to time. Haven't we been here before, Chris? I mean, you and I have been doing this for a while, and it seems like this is the same conversation we've had with respect to some open AI model releases and gated releases of this and that because it's going to end the world or something. It was GPT-3, I believe. Am I remembering correctly? I believe it was. It was like an earlier one. Yeah, we talked about the whole gated release thing, right? Yes. And they were holding it. And then finally they were out and they didn't even try that on the four side.

22:33And now like you look at, you talk about GPT-3, even GPT-4 and feels like ancient history. That thing sucks. Yeah. So it's like at the time it was going to end the world, but it kind of sucks. I was talking to somebody the other day and they were using in their business, uh, GPT four Oh. And I literally said, why, why are you using that dinosaur? You know, why would, why would you do that to yourself? And so, uh, so yes, we're definitely, this is like definitely coming around to the same story. I'm not saying it's not better. I think my point is just, hey, I don't I don't think it's world ending.

23:19People can probably rest a little bit at night. I do think like I guess they emphasize the kind of that it is and this is I think from Reuters and a couple other places that it's you know especially strong maybe at discovering vulnerabilities exploiting those vulnerabilities and so this does expand I mean even already right I can use whatever models and agentic coding techniques to create malware just like I can use to create great software and I can exploit systems. So like there is definitely this narrowing or which maybe has always been the case in the cybersecurity world where like the threat actors get better and there's better availability of tools and that sort of thing.

24:11This is certainly a different level of that. I'm not saying it's equivalent. I'm sure it's a different level of that. Right. But, And, you know, when I look at this, like regardless of what mythos capabilities really are, like whether they're really high, low, whatever, I think that, you know, Anthropic has historically been kind of the a little bit lower key and kind of the safety oriented and and not quite as flamboyant and and kind of over the top as is the open AI. I, you know, folks have been, I mean, Sam Altman's known for his, you know, the kind of statements he makes all the time. And people over time have kind of learned to take those with a grain of salt.

24:58But it is, it's starting to look, you know, with some of the cloud code stuff and get into this, like maybe Anthropic has started taking a page from that playbook at OpenAI in terms of kind of the marketing, you know, aspect of this. because regardless of what Mythos capabilities are, whether they are amazing or less or just whatever, this is still an amazing amount. I mean, you and I are sitting here talking about it. We're contributing to that. They were all over the news. And so it's fantastic marketing strategy on their part, no matter what the reality is. And who knows how much is tied to also recent problems in terms of interactions with the government.

25:43I do think that this is, you know, and I would be lying if I did not have a personal bias and hope for this, but I do think that this emphasizes kind of a tailwind for governance and control capabilities within the AI world, which is of course an area where I work, but also like letting people know that there is a risk is very different than controlling that risk. And so whether it's like AI SOC that's using AI like within security operations kind of, you know, on the offensive side or defensive side, I think that like it ushers in great, you know, a tailwind for those companies, but also on the like, hey, if companies actually want to use a model like this, there's bad things that can happen as well as good things that can happen, which emphasizes kind of a push towards governance and control, regardless of what model stack you're using.

26:45And one of the, you know, shout out if you're out there and we'd love to have you on the podcast, but there's like things like the AI underwriting company and others that have received funding recently where they are actually trying to establish some of those auditable certifications for companies in terms of how they institute governance, what evidence there is for that. That's a very different thing than saying there is a risk, right? It is. It is. Yeah. But interesting. I look forward to trying it out whenever I get my hands on it. I'm not part of the, I don't have a golden ticket. So I'll have to wait with the other ones, I guess.

27:26Yeah, I'm right there with you. Until I go for token maxing my Mythos endpoint. Oh, you threw that out. Now we got to talk about that. I mean, that's like token maxing is the hottest new term over the last few weeks. First off, I feel really old because I just don't like the whole whatever maxing. I feel old talking about anything with that sort of term. But yeah, I guess there is the token maxing thing. Oh, OK. So for those who have not heard the term, again, like it's it's been all over the place recently. So, you know, stepping back and because I think this really ties back ironically into the anthropic conversation that we had.

28:17But stepping back a little point to us talking about Opus as the greatest thing since sliced bread and the fact that, as we have discussed, Opus combined with CloudCode made a substantial difference or change in how people were approaching coding and stuff. I know me coding last year with AI assistance in various forms versus me coding this year, the workflow is quite different. And so, and really, it really has accelerated in a lot of ways, aside from, you know, where the models are and stuff like that. So the tool set has been great. We've, and we've talked about this on the show fairly recently as well.

28:59So, you know, acknowledging this process, we've had a number of the kind of, especially like in the big traditional AI companies, especially like meta, you know, can't imagine the meta culture embracing this. meaning of course it has if you look at who's meta CEO is they have gamified the use of developers and they're trying to basically get them to spend as much as they possibly can on cloud code and other competing development tools to try to accelerate what any given developer can do to the point at levels that the rest of us look at and go, that's insane. You know, where it's like, you know, go spend, you, a developer, go spend hundreds of thousands of dollars on tokens that you're, you know, to accelerate your capability.

29:58And I guess this is trying to, you know, to kind of 10 times, you know, if you will, to use another buzzword, what any given developer is able to do from a, in terms of producing work and they're orchestrating teams around token maxing and stuff like that. And I know it meta, I don't know if it's still up or not, but they had a scoreboard that was kind of in one of their main areas where everybody could see who was token maxing the most. Uh, and then people were gaming the token maxing system. So they were, they would, they would, uh, actually, uh, spend tokens on kind of trivial things just to make sure that they were, uh, showing up on the scoreboard.

30:38So yeah, I mean, very like in those kinds of stories, absolutely doing it to excess. But that's trickled down. And so there are many other organizations that may not have the budgets of some of these top AI companies, but they're trying to figure out what can we afford for our developers to do, you know, in terms of spending money on tokens and what will that get us in terms of the production capability in our own businesses. So that's now another big thing that's out there in business right now. Yeah, I would say just anecdotally, I very much think that we are, we meaning the company that I'm leading, I don't think we are spending enough.

31:20We are not, we're not token maxing. We have no leaderboard, but I also don't think we are spending enough on AI usage. I think that certainly one of the things as a founder I think about and I'm pushing, I think very much like there's, uh, it, it kind of, it, it kind of reminds me, um, well, I don't know. There, there's all sorts of things obviously, and parallels you could draw with everything in moderation. Right. But certainly people have to push the boundary for us to know where the boundary really is. Right. So I don't, I don't think, I think there's probably abuses within that and there's inefficiencies within that and things that don't make sense.

32:11But also it makes sense to me that there would be a push towards figuring out where the proper boundary is, because I don't know if we totally, totally know that yet. I know even some folks, I think it was, I think it was Jensen from NVIDIA who obviously, obviously has a horse in the race in terms of token maxing, right? Maybe, maybe. As we talk about NeoCloud and GPUs, but I think he was saying like he would be very alarmed if there was an engineer that was making 500K and they weren't spending 250K on like half their salary on tokens. I don't know if that's where I land, But like I say, I think we were not spending enough on tokens.

33:06I don't think in terms of, and this is where I think in one conversation we're talking about, you always have an infinite engineering roadmap, right? So on the negative side, certainly you can just spend tokens on dumb things. I don't like, I'm willing to say that. But also, I think it is some indicator of how effective you're running an AI-driven engineering team in today's world. I think that is a very sensible approach in the sense of, I think what is unknown now is what the price to productivity translation really is. And I think if you look across many organizations, I suspect you'd find a very significant standard deviation in terms of what that variability is.

34:00And so with, I think going forward as this also matures, that we're going to see best practices. We're going to see there will be books being written that, you know, you'll start seeing in all the developer areas about how to do it efficiently. And so I think there's probably, I think we're just not there yet. I think there'll probably be some guidance developing over time about how to do it without just being Jensen's spend all the money on GPUs you possibly can approach, you know, which you would expect. Yeah. And I think maybe it's because we don't totally know the right metrics to optimize and max out right now.