Anthropic's $30B Ramp, Mythos Doomsday, OpenClaw Ankled, Iran War Ceasefire, Israel's Influence

David Sacks: I think Anthropic has proven that it's very good at two things. One is product releases. The second is scaring people. And we've seen a pattern in their previous releases of at the same time they roll out a new model or new model card, something like that. They also roll out some study showing really the worst possible implication of where the technology could lead. We saw this last year, about a year ago, they rolled out this blackmail study where supposedly the new model could blackmail users. There's been a whole bunch of these things. Actually, I went back to Grok and I just asked, Hey, give me examples where Anthropic has basically used scare tactics, and it's a pattern. Okay, it's a pattern. Okay. These guys, I'm not saying it's not sincere, but they have a proven pattern of using fear as a way to market their new products. And if you think back to, again, my favorite example is this blackmail study, where they prompted the model over 200 times to get the result they wanted, and that result was clearly reverse engineered, and it got them the headlines they wanted. And I would say the proof that it's reverse engineered is we're now a year later, there's a bunch of open source models out there that have the same level of capability that anthropic model had. And have you seen any examples of blackmail in the wild? I don't think so. So in other words, if that study were true in the sense of being a likely outcome of that model, I think you would see examples in the wild of that behavior, and we haven't seen any of that in the past year.

David Sacks: I think Anthropic has proven that it's very good at two things. One is product releases. The second is scaring people. And we've seen a pattern in their previous releases of at the same time they roll out a new model or new model card, something like that. They also roll out some study showing really the worst possible implication of where the technology could lead. We saw this last year, about a year ago, they rolled out this blackmail study where supposedly the new model could blackmail users. There's been a whole bunch of these things. Actually, I went back to Grok and I just asked, Hey, give me examples where Anthropic has basically used scare tactics, and it's a pattern. Okay, it's a pattern. Okay. These guys, I'm not saying it's not sincere, but they have a proven pattern of using fear as a way to market their new products. And if you think back to, again, my favorite example is this blackmail study, where they prompted the model over 200 times to get the result they wanted, and that result was clearly reverse engineered, and it got them the headlines they wanted. And I would say the proof that it's reverse engineered is we're now a year later, there's a bunch of open source models out there that have the same level of capability that anthropic model had. And have you seen any examples of blackmail in the wild? I don't think so. So in other words, if that study were true in the sense of being a likely outcome of that model, I think you would see examples in the wild of that behavior, and we haven't seen any of that in the past year. Now, let's talk about this specific example with cyber hacking. I actually think that this one is more on the legitimate side. I mean, look, the reason why I bring this up is anytime anthropic is scaring people, you have to ask, is this a tactic? Is this part of their Chicken Little routine? Or is it real? Are they crying wolf or not? I actually would give them credit in this case and say, this is more on the real side.

David Sacks: I actually think that this one is more on the legitimate side. I mean, look, the reason why I bring this up is anytime anthropic is scaring people, you have to ask, is this a tactic? Is this part of their Chicken Little routine? Or is it real? Are they crying wolf or not? I actually would give them credit in this case and say, this is more on the real side. It just makes sense, right? So that as the coding models become more and more capable, they're more capable of finding bugs. That means they're more capable of finding vulnerabilities. And like one of their engineers said, that means they're more capable of stringing together multiple vulnerabilities and creating an exploit. And so I do think that over, say, the next six months, we're going to have this call it one time period of catching up where AI-driven cyber is going to be able to detect a whole range of bugs that maybe have been dormant over the past 20 years across a wide range of systems. And so I do think that there is real risk here. And I do think, therefore, that having this pre-release period makes a lot of sense where they're giving the capability to all these software companies that have existing code bases to use the tool to detect the vulnerabilities for themselves so they can patch them before these capabilities are widely available. And by the way, it won't just be anthropic that makes these capabilities available. We know that like, let's say the Chinese open source models like Gimme K2, it's about six months behind. So we have a window here of maybe six months where we're still in this pre-release period where I think companies that have large code bases can get advanced access to this model.

Brad Gerstner: So I think you're right that they systematically copied feature by feature of OpenClaw, incorporated that into Claude, and then the coup de grace was basically cutting off OpenClaw. Can you just explain exactly what they did? Okay, very simply, when you buy a subscription to these services, they have blended your usage across many users. So there's, you know, 9 out of 10 users use less than the tokens they're paying for, and the top 10% use much more. When OpenClaw became a phenomenon, the number one open source project in history on GitHub, with all of this usage, people went crazy. And you heard me talking about how crazy I went for it. Those people with the $200 subscriptions were using 2000, $20,000 worth of tokens. So they said, you can no longer use your subscription to, you know, either your professional or enterprise subscription at $200 and plug that into your OpenClaw. You now have to go to the API and pay per usage. So no more like unlimited essentially. If you use Anthropic's own agent harness, are you part of the bundled flat rate? You can assume that that's what they'll do, which if you were thinking on an antitrust level, might be token dumping or price dumping. I'm not saying like I'm ratting them in with it. price dumping or bundling. When you price something under the market price in antitrust, that would be price dumping, right? And if you were to bundle, it would be like the bundling issue.

Jason Calacanis: But the question I'm specifically asking is whether they're giving themselves a price advantage. Because I think Brad is giving the most generous interpretation. You're taking a more cynical one. I'm with you, Sacks. I'm 100% on the cynical side. Open Claw is so powerful. It's got so much momentum that not only is Anthropic trying to ankle it. I believe when Sam Altman bought it, was he didn't buy OpenClaw itself. He hired, Aqua hired Peter. I believe it was to subvert the open source project to get Peter's next set of genius ideas inside of OpenAI as opposed to letting them go there. People are going to say I'm a conspiracy theorist. But this is the number one focus. And let me just give you a list of who is trying to kill OpenClaw slash compete with them. Obviously, you have Anthropic, but also Perplexity Computer launched. It's awesome. I've been using it. Anthropic has this Claude Manage Agents. They dropped that on Wednesday, April 8th, yesterday. Today's Thursday when we tape, you guys listen on Fridays. And then you have Hermes. Agent, that was released on February 25th. That's also open source and very good. So that's in the open source camp. Alibab is coming out with one. That's going to be based on their Quinn model. Then you have Elon who said he's got something called Grok Computer coming out of MacroHard, which is a play on words for Microsoft. In addition to that, Amazon and Apple are preparing new releases of their retard maxing assistants, Alexa and Siri, that will be less retarded in this new version.

Chamath Palihapitiya: But that belies the more important point, which is AI-enabled coding, I think, is still 5% of the broad market. So it's kind of a nothing burger. Yes, they're leading, but they're leading in something that isn't that big yet. Now, you would say, how could it not be big? And what I would say is, because most of the stuff that's being written is still white sheet de novo code. And I think the ugly truth is, I don't care what model you have, but the long horizon ability for any of these models to actually build enterprise-grade software is still shit. S-H-I-T shit. And that's the actual lived experience. Not for me, but when I call on our customers, half a trillion dollar banks, 100 billion dollar insurance companies, none of these guys are like, wow, it just works out-of-the-box. It doesn't work. So most of it is still hand-tuned. So until I can honestly tell you that we can point a model at this with the right guardrails, which I can't today, what I would say is it's a small market that will become large as these models become better. But we are in the world where we have 50 years of accumulated tech debt as a world. And I suspect when you enumerate the number of lines that represents, it's hundreds of trillions of lines of just pretty marginal mediocre code to bad code. On top of that, we have all these legacy languages. I'll tell you one of our customers, they have to go and get 60-year-old pensioners to come into the office to interpret cope. No, I'm not joking. This is a COBOL, Fortran. This is a $100 billion a year revenue company. And that's how they solve these problems. It's not Opus just solves it. So I would just keep in mind that most of the tech debt in the world that exists, 99% of it is still poorly addressed by these models. We are untying this Gordian knot, it's going to take decades to do it right.

Jason Calacanis and Chamath Palihapitiya: I'll tell you about one, we talked about BitTensor, TAO on this program a couple of weeks ago when we had the Jensen interview, you brought it up, actually, Chamath, there's a project that's subnet 62, it's called Ridges AI. And what they're doing is a competitor that is not only open source, but anybody can contribute to it. They spent about $1,000,000 in TAO, like rewards, and in 45 days, they hit 80% of what Claude 4 is. And they did that in under 45 days. The way that works is they give rewards for people who, and they can do this anonymously, make that coding product, which is like Codex or Claude Code better. That flywheel is racing right now with participation in the same way Bitcoin is. So you're going to see a lot of open source and these crypto open source combinations. And anybody who's not investigated this, I highly recommend you investigate this. I do think you're right about one specific thing. I would put zero, literally the probability 0 of any important company worth anything more than a dollar, having and outsourcing their production code to an open source project. That'll never happen. However, what will happen though, is when you look at the cost of training this 10 trillion parameter model on Blackwell, and when you look in the future, let's just say in six or nine months, that a 15 or 20 trillion parameter model is going to get trained on Vera Rubin, I think, Jason, where you are right, I have zero, and just to be clear, I have no investments in this at all. I do, to be super clear. I'm just observing, because another project other than BitTensor that someone brought up to me is Venice. The concept of open source training and orchestration is a hugely disruptive idea, which is the complete orthogonal attack vector to this idea that you have to raise 10s and 10s of billions of dollars to train your models. Because if the capital markets run out of 10 and $20 billion checks to give people, the only solution is to be totally distributed. So I tend to agree with you, Jason, that there is going to be, at some point, a very successful open source project for pre-training.