Insiders React: Anthropic’s Fable, Mythos explained + SpaceX’s record-breaking IPO

**Chris Saad** (0:00)
My real concern is that this is gonna break the stock market.

**Yaniv Bernstein** (0:02)
OpenAI still has far more use than Anthropic, but it doesn't matter because that's a bunch of teenagers cheating on their high school assessments.

**Chris Saad** (0:10)
I love SpaceX. Watching that rocket flip over and land on its arse or land in those tongs, it gives me goose bumps. I only wish that Elon was focused on it, and the thing that was listing on the stock market was SpaceX. It's not SpaceX. It is a roll up of all of Elon's dead bodies.

**Yaniv Bernstein** (0:28)
If anything was gonna make it game over, then this is the thing. You're listening to The Startup Podcast.

**Chris Saad** (0:36)
This is a REACT's episode. Hey, I'm Chris Saad.

**Yaniv Bernstein** (0:41)
And I'm Yaniv Bernstein. Holy shit, did I just hear correctly? Chris, are you back?

**Chris Saad** (0:45)
I'm back. I'm back for this episode at least, and I'm trying to make it back for more episodes. But I'm very excited to get back on the pod and do a REACT's, which we haven't done for a while. This week, we're gonna talk about Anthropic taking the lead from OpenAI across pretty much every dimension you can measure. We're gonna talk about what happened, why it happened, and are they gonna be the ultimate winner of the AI race? We're also gonna talk related to that about Mythos and Fable and how those Anthropic models are absolutely crushing it. Have they developed a soul? Yaniv, answer that question maybe.
And then finally, we're gonna talk about SpaceX and the AI super cycle in the stock market. Is SpaceX gonna be the thing that takes us to the moon, literally and financially? Or is it gonna be the thing that craters the entire stock market with a 90x multiple?
The answer may surprise you. Listen all the way to the end to find out. All that and much, much more and other clickbaity things on this week's show right after the break.

**Yaniv Bernstein** (1:43)
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**Chris Saad** (2:30)
So the first thing I thought we could talk about is Anthropic taking the lead by vibe, by valuation, by revenue, by momentum over OpenAI.
Yaniv, I think it's pretty clear to everybody, there was a moment where OpenAI was the leader of everything, much like maybe Netscape back in the day. And then seemingly out of nowhere, Anthropic just started releasing a bunch of amazing products and models, right? Their enterprise focus, co-work, Opus 4.8, Mythos, and the whole, oh my god, we created a monster, we're only going to give it to a few people. And now most recently, Fable, which is another interesting story, I think, and we can unpack all of these. But I think that the general topic of Anthropic coming from behind to usurp OpenAI's position as the perceived and actual leader is big news over the last three to six months.

**Yaniv Bernstein** (3:32)
Absolutely. And you know, sort of quite a surprising turnaround. And I think to me, Chris, there are two big parts to this story. The first is one that's as old as time and that we've discussed on this podcast before, which is around product discipline. OpenAI got a bit complacent, got a bit flabby, got a bit distracted. They were like going to be the AI everything company, right? So instead of focusing on models and, you know, making sure you have the best capability, they were like, oh, we're going to have like a Sora social network, whatever it is. We're going to have like apps inside ChatGPT. And, you know, and we're going to have advertising and we're going to make sex bots and like whatever. And I think what that meant is it took away from their focus on building the best product. Now the other side of it, which was maybe a little bit harder to predict, although it seems obvious in hindsight, is all about coding, right? So large language models can do all sorts of things. And for a long time, the nexus of competition was like, you know, which one is best at doing writing, which one's best at doing analysis and aggregation and so on. And it was sort of purely about the large language models. And then, you know, it became clear that they were competent at coding. You sort of moved from GitHub Copilot to Cursor, and it started to become useful. But what wasn't perhaps obvious is that coding isn't just another domain in which large language models can be effective, but that it's kind of this meta domain, right? Because even if LLMs are not good at various things, if they're good at writing code, they can then write programs that are able to cover for gaps in the same way that as humans, we build tools and then we use those tools, and that's how we've become so dominant as a species. What AI can do is by writing code, then it can become really, really effective.