Meta’s AI Comeback Moment, Claude Mythos | Diet TBPN

0:00John Coogan:The big news today is that Meta Platforms has launched a new AI model. Alex Wang, the chief AI officer at Meta Platforms, announced a new large language model today, its first major new artificial intelligence model in more than a year. The rollout of the model, called MuseSpark, is a critical moment for Meta, which is up 7.5 % already, which has spent billions of dollars hiring AI talent in a bid to catch up to OpenAI, Anthropic, and Google DeepMind. leading labs have been putting out models at an accelerating pace. In a departure from its previous models, which were open source, MuseSpark is a closed model that will power Meta's AI chatbot and AI features within it.

0:39John Coogan:John Ludig has a very interesting post about open source AI and sort of predicted this. Predicted that Meta would eventually bail? Yeah, the future foundation models is closed source. He said, given Meta is the primary deep-pocketed large open source model builder, open source AI has become synonymous with meta AI. He wrote this maybe three or four years ago. So the operative question for open source AI is what game is meta playing? In a recent podcast, Zuckerberg explains meta's open source strategy. One, he was burned by Apple's closeness for the past two decades and doesn't want to suffer the same fate with the next platform shift.

1:13John Coogan:It's a safer bet to commoditize your compliments. He likes building cool products and cheap, performant AI enhances Facebook and Instagram. That's 100 % true. We've seen this in the ads product and the growth there. There's some call option value if AI assistants become the next platform. And that makes sense in Manus and the Meta AI app. He bought hundreds of thousands of H100s for improving social feed algorithms across products. And this seems like a good way to use the extras. That all makes sense. And Llama has been great developer marketing for Facebook. But Zuck also suggests several times that there's some point at which open source AI no longer makes sense, either from a cost or safety perspective.

1:47John Coogan:When asked whether Meta will open source the future $10 billion cost model? The answer was, as long as it's helping us. At some point, they'll shift their focus towards profit. And that's what John Ludig wrote. He says, unlike the other model providers, Meta is not in the business of selling model access via API. So while they'll open source, as long as it's convenient for them, developers are on their own for model improvements thereafter. That begs the question, if Meta is only pursuing open source insofar as it benefits themselves, what is the tipping point at which Meta stops open sourcing their AI sooner than you think, he says.

2:20John Coogan:Exponential data frontier models trained on the corpus of the internet, but that data is a commodity. Model differentiation over the next decade will come from proprietary data, both via model usage and private sources. Exponential capex, he highlighted this two years ago, a lagging edge model that requires just a few percent of Meta's 40 billion in capex is easy to open source. No one will ask questions, but when you reach 10 billion dollars or more in capex for spend for model training, shareholders will want clear ROI on that spend. The metaverse raised some question marks at at a certain scale too.

2:48John Coogan:Diminishing returns on model quality within Meta. There's a large upfront benefit for Meta building an open source AI model, even if it's worse than the frontier closed source counterpart. There are lots of small AI workloads, think feed algorithms, recommendations, and image generation where Meta doesn't want to rely on a third party provider like they had to rely on Apple. And so the news has been, back in December, there was a reporting that Alex Wang disclosed an internal company Q and A, that his team was working on two new models. One was this text-based LLM codenamed Avocado, and then a separate model that was for image and video.

3:26John Coogan:Mango. Yeah. And so have they clarified if this is Avocado? This feels like what Avocado should be, this Muse Spark. Is that what it's called again? Yeah, I see what it is. I don't know what else. So the image model should be coming soon. The question that I had was, will a code-focused Agenta coding harness be a separate model, a different train? It feels like it's not a coincidence that this news is dropping on the heels of Anthropix's new model, Mythos, which sort of was announced loosely and the model card dropped yesterday, even though the model is not available yet to play around with. They break out Muse Spark thinking against Opus 4.6 Max, Gemini 3.1 Pro High, GBD 5.4 X High, and then Grok 4.2.

4:11The way that they position it, it looks somewhat of a chart crime. But if you look, when you basically look at the top and you see that Muse Spark gets an 86.4 and it's in blue. And then you look over and it's outperforming all the other models on that benchmark. You just sort of assume that like the highlighted blue means that it's outperforming.

4:35John Coogan:Oh, yeah, but it's not frontier at MMLU. Because there's a bunch of them as you get further and further down. So like ARK-AGI 2, Mew Spark dramatically underperforms the other leading models. And there's a number of others where it's overperforming. There's plenty where it's underperforming. I, of course, we started asking it jokes right away. So I'll pull up the joke. I said, tell me a joke. Okay. It said, gotcha, here's one for you. Why don't skeletons fight each other? They don't have the guts. Okay. But then it doesn't even let me react. It just says, want a different vibe? I've got dad jokes, nerdy ones, or Malibu appropriate surf puns too.

5:18And so I'm like, wait, why would you think I want Malibu appropriate surf puns?

5:23John Coogan:If you look in your previous chats. I have no previous chats. No previous chats. Because Meta AI crosses with Instagram. So when you search on Instagram, sometimes that prompts Lama or previous. Yeah, yeah. So I'm trying to get to the bottom of it. Like this feels like an oddly specific type of joke to ask me. And so I said, why would you think I want Malibu appropriate surf puns? Haha, fair call. Toss that in without you asking for it. I try to mix in different flavors of jokes. And surf puns just popped into my head as a random option. No special reason behind it. I just said, I don't believe it was random.

5:58And it says, you got me. You got me? That wasn't my smoothest moment. But then it denies it again. Weird. It says, it's just like, what kind of joke do you actually want? No hidden motives here. I said, you gave a hyper-specific example based on my life, so I have to assume you were looking at my other account for inspiration. I get why that felt weird. I'm sorry it came off that way, and then it denies it again. You're clearly lying.

6:23John Coogan:I mean, you have talked about being in Malibu on the internet for a full year. It's possible it got baked into the pre-training or something. I don't know. But yeah, I mean, what is personal super intelligence if it doesn't even know your name? Like that feels like they haven't dialed in the harness or whatever the tuning is to actually find tuned responses. Yeah, and of course, like Meta is going to be hyper aware. We don't want a PR cycle. Yeah, yeah, yeah. Like they trained on your data, right? Everyone's been, oh, that ad was a little bit too close to home. And you remember every once in a while, one of those like a screenshot that's been screenshot like a thousand times like goes viral.

6:56and it's like, I do not give Mark Zuckerberg purpose. Oh, yeah.

7:00John Coogan:Yeah, yeah, yeah. Like that works. Yeah. It's hilarious. Is this a rebuttal to the bench hacking allegations that happened last week or last year? So according to Meta's internal benchmark test, MuseSpark outscored Google Gemini on some tests and was competitive with models from OpenAI and Anthropic on others. It significantly outscored XAI's Grok on most tests. Alexander Wang's hiring followed the disappointing release of Meta's previous model called Llama 4. The company was accused of and later admitted to gaming a third-party benchmark that it used to rank various models against each other on performance.

7:40John Coogan:It also delayed the rollout of its biggest model called Behemoth, which it never ultimately released. And so when I look at a model card like this, where you could call it a chart crime where, you know, it's highlighted in blue and it feels like it's the best, but it's actually, you know, doing better on some. It does well on HealthBench hard. It underperforms on ArcGi2, as you mentioned. But this maybe is the bull case here is that they have at least moved on from the culture of like optimizing for the benchmarks. Right. Isn't that a good thing? There are rumors about them. Like there was like extra bonuses if they if they got number one on Elam Arena.

8:13I think that was like something like the rumor.

8:15John Coogan:Yeah. But yeah, I mean, you've seen a lot of the labs kind of move away from benchmarks generally because I think they're just not that meaningful anymore. Like a lot of them are like basically so saturated. They're competing between 89 % and 91%. And they're just not very meaningful like you see. And you won't actually feel that in the product necessarily. Yeah. You kind of need to talk to these things for a long time before you can actually get the vibe. Yeah. But I do think this news is very interesting in the context of the Claudeonomic stuff. The dashboard, yeah. Because what does it mean if the entire company has been maxing their Claude tokens over the past month?

8:50It means that they weren't using this model.

8:51John Coogan:Yeah, to me, it means they need to commoditize their complements, right? They need to bring down that cost potentially. And if they're, I mean, we sort of dug into are they spending a billion dollars a month? Seems like absolutely not. But they're clearly spending a lot. And if you can turn that OPEX into CAPEX and train your own model and then inference it much cheaper on your own hardware, that feels like just an economic opportunity that makes a ton of sense in the context of just 10 ,000, 20 ,000 engineers writing a lot of code. Yeah, I think there's basically two ways to square those two things happening.

9:24Either one, this model's not that good because the engineers aren't using it, or your theory that they're just distilling cloth. So one of those is true.

9:32John Coogan:That is not my theory. That is the schizo theory. The news this morning, meta platforms and the information. Meta platform has taken down internal employee-built leaderboard, tracking how many tokens staffers were using, showed total usage over a recent 30-day period, amounted over 60 trillion tokens. The dashboard now displays a message that is offline. It says, we really enjoyed building this app on Nest for everyone. It was meant to be a fun way for people to look at tokens, but due to data from this dashboard being shared externally, we've made the decision to shutter it for now. It seemed like a fun side project.

10:04John Coogan:Mike Isaac was reporting on it here. He said it's down. Unclear to me if this was a homespun one by employees or an official one. Employee projects come and go frequently. Conspicuous timing, though. But yeah, you don't want to have, you want to measure the output, the impact, not necessarily the input and how much is going on there. Lisan Al-Ghaib says, meta might actually be back with MuseSpark, still behind OpenAI, Anthropic and Google, but ahead of XAI and Chinese labs. MuseSpark stores 52 on the artificial intelligence analysis index behind only Gemini 3.1 Pro, Gemini GPT 5.4 and Claude Opus 4.6.

10:40John Coogan:MuseSpark is the first new release since Llama 4 in April 2025 and also Meta's first release that's not open weight. So a huge jump up in performance across a variety of benchmarks. So all good stuff there. The market is thrilled that Meta has released a close to frontier level model, right? This is a new group. They've been at it for less than a year. The stock is up almost 8 % today. And again, so much of the pricing pressure, the downward pressure on Meta has just been kind of uncertainty on what all these tens of millions of dollars will actually go towards and what will be accomplished. And still unclear, like, are they going to go after CodeGen at all?

11:23Are they just going to try to compete on the consumer LLM side?

11:28John Coogan:And can you economically go after CodeGen if you're just using it for internal models, if you're not selling it externally? internally, can you justify the capex just purely on the internal usage? Having this model be vended into all the different family of apps makes a lot of sense because they have billions of users that will wind up interacting with this in one way or another. Yeah, the question is, will they try to send MetaVibes? Again, with the new model. All the way up to the top of the App Store charts. Meta's new family of AI models can reach the same performance as KimiK2 with only 30 % of compute and only 10 % of the compute to reach LLAMA4 Maverick, so a much more efficient computing frontier here.

12:10John Coogan:Metaspark is an early data point on our trajectory and we have larger models in development, so the mythical 10 trillion parameter model. That is the 10T is what everyone's working on right now, 10 trillion. Yeah, probably in that range. Yeah, it's all rumored at this point. Yeah, rumored GPT-4 was something like a trillion, right? You remember those memes where it's like a small circle and then the big circle? And then a huge circle. GPT-4, GPT-5. Yeah. Martin Cassato has a little bit more context on like what actually unlocks new capabilities in AI models. He says, Mythos appears to be the first class of models trained at scale on Blackwells.

12:49John Coogan:Then there will be Vera Rubens. Pre-training isn't saturated. Narrative violation. RL works. and there's so much computing coming online soon. That's a narrative violation. Buckle your chin straps. It's going to be wild. The scaling laws - And you know Brad Gerstner had to come in with the 100 emoji. 100, yep, for sure. Yeah, there's a crazy bull case for NVIDIA in the information, arguing it should be worth, what,$22 trillion? That is a wild move. There's a lot going on. The scaling laws holding is the most important part of this. Yeah, article from the information finance. NVIDIA worth$22 trillion.

13:25This old school financial model says yes.

13:28John Coogan:The big news on yesterday was Anthropics' new model, Mythos. Some really impressive statistics and anecdotes yesterday, both the model card, the benchmarks, and some stories about breaking out of a variety of, what do they call them, walled gardens or test environments? I don't know, the simulation. Sandbox. The sandbox, yeah. Breaking out of the sandbox, sending emails, all sorts of stuff like that. The model preview is only available right now to about 50 companies that maintain critical infrastructure because the model is particularly good at finding zero days bugs and exploits in technical systems.

14:05John Coogan:And if they, you know, they lead that, they leak that out before big companies have time to go and address all the bugs, there could be serious, you know, serious ramifications for cybersecurity. And so key partners include Apple, Google, Microsoft, Amazon, NVIDIA, JPMorgan Chase, Broadcom, the Linux Foundation, Cisco, CrowdStrike, and Palo Alto Networks. They're all listed on the cybersecurity focus page for Project Glasswing. Chris Bakke was having a little bit of fun because he noticed Anthropic put their own logo on the partner page, which is a little bit funny. But at the same time, it's kind of smart because a lot of people are just going to see the image quickly.

14:40And it's good to position yourself with the other companies.

14:44John Coogan:So, yeah, it is interesting. I mean, people have predicted that AI models would be particularly good at cyber attacks. And this was one of the main sort of vectors of AI fears. It feels like this is what maybe what Dario was referring to when he was talking about the end of the exponential finding and exploiting software bugs. It's sort of perfectly in the sweet spot for coding agents and reinforcement learning. Combing through piles of code, tirelessly trying different exploits to find bugs, having a clear verifiable reward. did you crash the system or not? Did you break into the system or not?

15:19John Coogan:It's a very clear binary signal that you can send to the model to determine were you successful in breaking into that system, and it requires basically no time delay. There's no lag. So there was one snarky tweet I saw that was something to the effect of like, okay, then if it's so good, go cure cancer. But any application that requires a real-world feedback cycle, even if it's just a few minutes of human interaction. In the cancer example, you're going to need to be testing the drugs in vitro, in mice, in monkeys, in humans at some point. Or even if you're just sequencing DNA or doing anything in the lab, pipetting anything, if it's even just a few minutes, all of a sudden every iteration, every attempt is going to take a few minutes and that's going to put you on just a wildly different exponential as opposed to being able to spin up a virtual machine with basically every single piece of software out there and then try every single exploit against every single piece of software and you wind up with a ton of exploits.

16:18John Coogan:And very, very bullish for cybersecurity that this is being done preemptively. There's a whole bunch of different discussions. Ben Thompson has a good piece on the whole decision to release the model or not and stage it out and the go-to-market there. But even if the bio research, the other impacts are on sort of a slower exponential, there's still so much opportunity in even a software-only singularity. There's also risk in a software-only singularity. We've seen this story before, though, a model that's too powerful to release but then works its way out and has pretty moderate impact on the world.

16:57John Coogan:This was the story of GPT-2, the story of ChatGPT, the question of, is this the model that's dangerous to put in the hands of people? Yeah, a headline from February 22, 2019 by Aaron Mack. OpenAI says its text-generating algorithm GPT-2 is too dangerous. So there is a, I think Ben Thompson called it like the boy who cried wolf syndrome, the mythos wolf. He says, there's a lot of skepticism about Anthropics announcement. This tweet was representative from Buko Capital bloke. Anthropic's marketing strategy is so funny, like, ah, the government is treading on me. Ah, our models are so good, we can't release them.

17:35John Coogan:It would be too dangerous. Ah, someone stop me, I'm going to destroy the economy. The rolling of the eyes is exacerbated by the fact that Anthropic has reasons to not make mythos widely available beyond a lack of compute. Another factor is surely trying to avoid having mythos distilled by Chinese model makers. So there's actually two good reasons to gate access. And when you're looking at those logos, when you're looking at the world's largest tech companies, there's much more ability to scale rollout, demand, set pricing. These companies might be able to pay more. The model is very expensive.

18:11John Coogan:But if you're justifying that against bug bounties for zero-day exploits in your most critical system, when you look at, like, JPMorgan Chase, it's a bank. like what is the price of finding an exploit in that system? It's pretty high. It probably clears the token hurdle a lot. And if the rollout is paced evenly across all the different companies, they'll all sort of understand that they're getting inference allocation at the efficient price that clears the cost to actually serve the model. So I do think the systems, all of these 10 trillion parameter models will be released soon broadly. And the main reason that an AI that's smart enough to find zero-day exploits should be able to recognize that it's being used by a bad actor to find zero-day exploits.

18:56John Coogan:It's only been a few months since the last flurry of competing models for OpenAI, Anthropic, and Google. And the next cycle is already off to an aggressive start. We had Meta. And then the other news is that Elon Musk announced that he is getting ready to do another larger model with XAI. He's got a few. He's doing seven models in training. Wow, that is a lot. Imagine V2, two variants of$1 trillion, two variants of$1.5 trillion, a$6 trillion model, and a$10 trillion model. He says there's some catching up to do, but he says he will never give up, never. So he is continuing to grind and train more models.

19:36Mike from Also Capital, former guest, says, We've decided not to release our latest investment strategy. It's so powerful. releasing it might end the entire venture asset class as we know it.