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00:46:14 · Sam, Angela, Kosta
[00:46:14] S1: Yeah. Yeah. Yeah. [00:46:15] S8: So it's a great way to get started. [00:46:16] S1: Yeah. Yeah. See. I see. These examples. I love that. That's a great answer. [00:46:18] S0: Within this, there there are two avenues I'd love to go down. You wrote in... I think it's a sixteen z twenty twenty six. This was published in December 2025, and it was basically a swath of predictions around what will happen in the future. There's a quote you have that says, If advancement stopped and AI got no better whatsoever, it would take five years for all of the large companies to adopt it. And so, the frontier intelligence could stop today, and we would have a five year time lag for enterprise CEOs, enterprise companies, can catch up to the frontier intelligence and use that internally. Can you talk to us a little bit about this delta between the frontier and the ways that enterprise companies are actually using AI today? Because we talk about it loads on the show. Some CEOs are always getting told by the board, you need to be using AI, you need a frontier model, you need to be using an Astra or a Sol to do your internal works. But the honesty within that is it's not really true. They need to be turning, putting $1 in and getting $2 back. This was a prediction you made a year ago. We're already seeing it happen today, which is amazing. Talk to us a little bit about this prediction. How can we change that? How can enterprise CEOs adapt their AI strategy for B2B enterprises today?
00:47:20 · Angela, Kosta, Sam
[00:47:20] S8: Yeah. So, I actually think my prediction of five years might have been a little optimistic. It's probably a lot longer than that. So I think what has changed is there are amazing use cases beyond... Like, for instance, customer service with Decagon is working very well, and lots of great use cases there. Right? There's lots of great uses in coding. Where it hasn't gone as fast is just general knowledge work. [00:47:44] S0: Yeah. [00:47:45] S8: But I think there is starting to be very good use cases with, like, you know, my mortgage example. But I think it's combo of one, pick something small and just get working. And so, think now what we're seeing with a lot of the... Like, we've got another company, Factor Labs. And they land... They use... Like, computer use has gotten very good. They can do work on top of your legacy systems. And so they've started with QA. So how do you know that there's... You know, like, you have armies of QA on top of legacy systems? Like, they can go into those point and click around and send you those reports. Right? You land with that. You can expand dramatically. So, I think enterprises are saying, I've got one specific problem. I'm just gonna get going. I'm gonna prove that it works and go from there. [00:48:27] S1: Yep. [00:48:28] S8: But, you quickly go outside of Silicon Valley, to other places in The US, to other places in Europe, and nobody's thinking about AI in the same way. So, I think, from a human perspective, it is still gonna take a while to spread. But we've got enough success cases to point to
00:48:28 · Angela, James Foster, Sam, Kosta, Fabian
[00:48:28] S8: But, you quickly go outside of Silicon Valley, to other places in The US, to other places in Europe, and nobody's thinking about AI in the same way. So, I think, from a human perspective, it is still gonna take a while to spread. But we've got enough success cases to point to [00:48:44] S7: that it's moving a lot faster. [00:48:45] S1: Yeah. And then, I think final question. Where is enterprise adoption at today? If you could kind of like quantify where it is in terms of people using AI in terms of the most leveraged way that you could use AI. Where do you feel like it is? What is the huge opportunity there? I'd love to understand like where your brain's at in terms of the adoption. [00:49:07] S8: Oh my god. I think we are 1% of the way there. Yeah. Yeah. Like, you just look at how powerful personal agents are. What's the limit? All of us thinking of what to do with them. Right? And then the... In the work... In the workflow... In the workplace, it's it's even more that way. And I think in many cases, we are just using the same processes and then just doing those processes with AI. Yeah. I think the opportunity is if you're gonna start from scratch, how would you build that? And we're seeing it with de novo companies. Like, AI native companies in the same industries are built entirely differently. There's a lot of transformation to go on in large ones. [00:49:42] S1: Very exciting. Anish, thank you so much. [00:49:43] S0: We appreciate it. Absolute pleasure. Awesome. [00:49:46] S6: Thank you. Appreciate it.
00:49:46 · Fabian, Kosta, Sam, James Lowe, S5
[00:49:46] S6: Thank you. Appreciate it. [00:49:47] S0: It's all for fun, guys. Really appreciate it. Can I bring [00:49:49] S1: Anish to the show? And then we're here. [00:49:52] S0: And then we wrap. Thank you very much. See you soon. Thank you so much. We love to do it again. [00:49:56] S4: Of course. Legend. [00:49:57] S0: And I love the shoes. So cool. [00:50:19] S1: Great story. [00:50:19] S4: That's a story for the show. [00:50:21] S6: To see you. [00:50:22] S0: Here he is. How are we, sir? [00:50:23] S1: I love this jumper. [00:50:24] S0: Hey, dude. [00:50:25] S4: We're just in [00:50:25] S0: today. Style [00:50:26] S1: pod by the a16z team right now. Although we do have the we do have the AC. [00:50:31] S6: To be [00:50:32] S0: fair, yeah, we're actually starting but We're in the [00:50:34] S1: Yeah. Yeah. [00:50:34] S0: Dude, the timeline loved this. I've been telling everyone I threw a photo up last night. I woke up, and it has a 155,000 views. And everyone's everyone's lovely. Everyone's saying, where's Gabe, actually? So Gabe's fans. He's everywhere, man. [00:50:46] S1: Yeah. I'm sure he's on a plane somewhere. [00:50:51] S0: Percent. Five in the next trillion dollar founder. [00:50:53] S5: He's he's just got a printer in his backpack. He's printing the turbinates out one at a time.
00:50:53 · S5, Kosta, William Loveday Powell, Fabian
[00:50:53] S5: He's he's just got a printer in his backpack. He's printing the turbinates out one at a time. [00:50:57] S0: Yeah. Just... He's probably throwing them out the airplane at this point. Very good. Thank you so much for joining us, Anish. It's always a pleasure to sit down with you. I'd love to start by just asking, I would love to get your take on the market right now in general. Just and I'm not just talking about like capital markets. I'm just talking about consensus around AI. We're seeing the market is reacting to general things that are happening in the space. I think we saw rumors of maybe Anthropic slashing their valuation. Saw OpenAI cut their funding round. [00:51:26] S2: By [00:51:26] S0: 10 bill. By 10 bill. I'm seeing takes online around. Maybe this is a slight correction, but we are only 1% into the journey of what can possibly happen here or there. I'd love to get your thesis, just high level. Where do think things are at? [00:51:39] S5: Yeah, it's interesting to sort of frame this against what was happening five years ago, you know, because if the closer you look with this product cycle, the more substantial all of this stuff is. [00:51:48] S6: Mhmm.
00:51:48 · Fabian, S5, Sam
[00:51:48] S6: Mhmm. [00:51:48] S5: Whereas I think five years ago when you... You didn't wanna look too closely. You know? Right? It was like, we had specs. We had all of this sort of distortions that felt like they were transient. And here, actually, the sustainability and the sort of substantiality feels really, really high. So I think that there's gonna be this choppiness and noise, and I would not wanna have to be my job be, you know, predicting memory prices in three months. Like, I think that's really difficult. But, certainly, I think the trend that's occurring is irreversible. And there's actually two huge breakthroughs that have happened in the last few months that I think are under discussed. You know, one, of course, is everything that happened with agent swarms or, as Sam would prefer, agent teams. [00:52:24] S1: We have this written down a lot, mate. [00:52:26] S5: Yeah. I think that, like, there's a white hat version of that that's super interesting. What Michele sort of called another scaling law last night. And then the other thing is this paper that was released on Monday about having models edit the weights of other models Yes. Which gives you continuous learning opportunity. And it was released, and there was enthusiasm, and then it was criticized. But I think it's a bigger deal than people appreciated.