B (4:41)

Got it. So I think when I, when I read that quote, I was thinking it's not gonna be 9 to 5, meaning we might not be working that much. But you're saying it's, it's more just like an entrepreneurial way of working where it's, it's suffused throughout your life.

A (4:54)

Exactly.

B (4:54)

And that actually by the way, that

A (4:56)

also can be, in some cases you're just not working as much. I mean it's, it's much higher range

B (5:00)

if you're Tim Ferriss. Yes.

A (5:05)

He's already been doing that already.

B (5:06)

I know, right? Yeah. He's got to do a new four hour work week.

A (5:09)

Yeah, the future's already here, just unevenly distributed eg.

B (5:13)

Yeah, that actually makes me think of one of my hot takes for 2026. So I think we can, we can jump there real quick because I really want to know what you think. We've been on this trajectory of talking about technology and addicting technologies and social media and how social media breaks your brain. And I think we've put up on this pedestal. The act of creating things as something that is like, can be inherently good and not necessarily addicting. And my experience with Claude Code right now is I'm addicted to it. It's like, I cannot stop. I just want one more prompt. And I think, shockingly, the most addictive technology of 2026 and the, the narrative that we might be talking about at the end of the year is how addicting it is to just make things. And what's interesting is there's a certain class of people that know that already, and it's CEOs of startups who, who have that experience already because you're always looking at your, you know, your chat, your discord, your slack or whatever, and you're always like, oh my God, I need to do something else. But I think now that's like a broadly distributed thing where everyone's just going to be prompting cloud code.

A (6:29)

So to one, I definitely believe it can be addicting. And I think it's actually addicting for a much broader range of people than normally think. And it's partially because most people just don't have the experience of succeeding, creating. And once you have that, like the dopamine hit as you succeed at creating, and part of the thing that cloud code actually AI more generally, generative AI more generally, but it suddenly goes, oh my God, I can create something interesting. And I think that's the, like, it's actually a healthy dopamine hit. I mean, one of the things that's weird about the word addiction is, you know, you say, well, I'm addicted to breathing, you know, and so it's like, well, actually, in fact, that's a good thing. It's not. And so addiction has kind of negative overlay. But the fact that you, you, you get very committed to something, it's like, oh, is it unhealthy for you? And actually, in fact, in the creation thing, actually, in fact, it's not unhealthy. And if you're like, no, no, I actually am going a little bit more obsessive. I'm going like, I want to finish this, I want to make this. Am I going to make this really great? That's actually, in fact, part of where we you know, we get our, we, we explore our, our, our fuller potentials, our super agency, if you will. And it's, it, and it's that kind of thing that I think is actually really good. And I do think that it's part of the, the, the kind of generative AI revolution in ways that people go like, you know, I think the discourse is generally, you know, a quite mixed and negative and actually will be more intensely negative next year because of the, of the transformations and changes. But it's part of the reason why it's so important for people to go, go, oh, wait a minute, I can be so much more human doing this. And we can be collectively together. And so we need to sort out the fact that yes, it's going to be a turbulently created future, but like, we can do amazing things. And so I think that that kind of creative addiction, you know, creative commitment, you know, creative exploration is actually, in fact, you know, one of the actual really important things. And I think people have been discovering it not just with cloud code, but also like learning, you know, through, you know, prompting these agents, you know, creating images and, and, and you know, that's part of the reason why Sora, you know, kind of went to the moon in a couple weeks because it's like, well, wait, I can, I can make

B (8:56)

something here that I think that makes sense. I kind of want to know for like, one thing you said earlier is you think that there's going to be sort of a, I don't know if backlash is the right word, but negative, negative sentiment towards, towards tech will increase in 2026. Is that, is that one of your biggest hypotheses? So tell me about that.

A (9:15)

So, so we haven't. So while there's been a lot of discussion, the actual overall impacts of AI have been, you know, relatively more minimally felt. And most of the places where they're described as being felt are actually, in fact, you know, kind of fictional. Like for example, oh, AI is causing electricity prices to rise. And really, actually, in fact, I mean, a little bit here and there maybe in like certain grids, you know, certain, you know, power stations, but really it's old grids, old power stations, increasing cost of energy, you know, net impact of tariffs and other kinds of things. Like if you actually kind of do an analytic map, say, where are the data centers? That doesn't actually correlate to, oh, that's where the places where energy prices, like, you know, electricity prices gone up and not. But that's going to be the meme. And so the meme is like, oh, college students aren't going to be hired because of AI. The meme is going to be electricity prices are going up because of AI. The meme is going to be the price of eggs is going up because of AI. Like, and so because there's a lot of, of people who go, I'm looking around for something to blame for things being troubled, bad, different than I would like. And you know, it is going to be a very turbulent year. And so AI, you know, it's going to be almost like the, the Farmer McDonald song, you know, AI, AI, AI is going to be the, the, the, the. The. The way that this is going to play. And I think it's actually really important for people to understand, actually. In fact, you know, AI hasn't had any of that impact yet, but it's actually going to start like, I think it's gonna, like, for example, it's suddenly it's gonna be like, hey, I used to be really competent at my marketing job, etc. You know, I think it'll be the, hey, I only want to hire when it's part of an AI transformation, you know, a la Shopify and that kind of thing for doing this. It isn't going to be what a lot of the employment is, is a reworking of the COVID disaster and, you know, kind of mishiring, misorganization, etc. Know, for doing this. But actually it is going to start, you know, kind of impacting. And then, so it moves from, from call it 98, 99% fictional to 90% fictional. But that will intensify the desire to kind of say a whole bunch of negative things. Like, you know, for example, I've been surprised so far at, and I think it's just because people don't pay any attention online, you know, the creation of a, you know, kind of a Christmas record for my friends using AI. I haven't gotten a whole bunch of negative blowback of, oh, this is going to be terrible for artists and terrible for creatives and so forth. I think that will happen. Like, you know, I'm going to create some more records and I think that will be the case. And I actually think it's not the case. I just think you need to adjust to using it and, and creating that as a, as a, as a kind of a, you know, kind of as a new basis for your creativity, for your industry, for your work. And that transition is going to be what's difficult. But I think you're going to have, I think next year is going to be much more negative and I. Than actually this year in general popular discourse.

B (12:32)

So to, to repeat that back, I think you're saying so far it's, it's kind of a meme, like, AI is bad, and the meme, and to a lot, to a large extent, is making AI a little bit of a scapegoat for just anything bad. If you're laying people off, it's easy to say, like, because of AI. Um, and that that will probably continue. I, I do think that that's true. That's just going to continue and there's like, there will be increasing real negative impacts that people are going to have to deal with. So you're a programmer and you're coming into work and you're like, oh, man, my job just totally changed. It's like, I'm not in the code anymore. And that's going to be upsetting to people, and that's going to lead to changes in the way organizations are run and who gets hired and all that kind of stuff. And that's going to, that's going to make people upset. Um, what do you think is the right move for big AI companies in an environment like that and, and how they should be talking about it, how they should be positioning. And, you know, to some extent, it's, it's probably not even desirable to prevent any kind of backlash. Like, it's normal for people to have, like, bad feelings about new things. Um, but, yeah, how do you, how do you think. What's the right way to deal with that strategically?

A (13:43)

Well, the most substantive way is to make it pragmatically helpful to as many people and as many people as you can. It's part of the reason why, you know, the podcast you and I are doing and other things to say, hey, explore it, get a chance, use it. You can use it for personal things, like if you have a, you know, kind of any kind of serious medical question, you know, if you're not getting a second opinion from, you know, chat, CBT or favorite frontier model, you know, you and your doctor are both making mistakes and, you know, and then, you know, similarly to, okay, how do I use it to help me with my work? How do I use it to help me learn things? How do I help it? You know, help me be creative and, and if you can't in each of those areas, find something where it's actually, in fact, seriously, hopefully you're not, you're not trying hard enough, you're not looking, doesn't mean it's everything it's not the Swiss army knife for everything yet. There's many, many limitations, but it is norm. It is enormously kind of amplifying. And so I think that's the, and that's part of the reason why, you know, everything from, you know, not just writing Super Agency, but creating, you know, holiday Christmas gift records is kind of like showing, hey, that's a, that's, this is the kind of thing we can do now. Like, everyone can do this. This. Not using any, not using any tools that do this. And by the way, not only can everyone do it, but by the way, as the, the people who get more expert, like people who are much better at music than I am, which is, you know, 95% of the human race, you know, can then do much better. Right? It's, it's an amplifier for everybody. And I think that's the, the kind of most substantive thing. And then I think the question. That's the substantive thing. And then on the communications thing, you know, I think one of the thing that, you know, various very well meaning AI creators are kind of saying is like, oh my God, it's going to be a white collar bloodbath, et cetera.

B (15:51)

And you're like, well, I think I know one. I have one person in mind that you're talking about.

A (15:57)

And it's like, look it, like, I get it. You're trying to say, hey, guys, things are going to change a whole lot. Really pay attention. I'm ringing a bell to start adjusting to this. But that kind of ringing the bell is like, you know, kind of yelling fire in the movie theater. It's like, it's, it's, it doesn't create productive response. The, the important thing is to kind of be orienting towards a productive response doesn't mean to be papering over the difficulties of transition, but, but it's like, oh, you know, we're going to be going into like this intense, you know, category 10 rapids. And here is, here's the kind of paddles you need, and here's the kind of thing you should be doing as you're going into it.

B (16:39)

That's the thing you want to, if you're going to say we're going into the rapids, you want to offer the paddles too, you know, and if you're just saying we're going to the rapids, that's not really helpful in my view.

A (16:47)

Yes, exactly. And that's, that's, that's, I think, the comms part of it for Everybody.

B (16:53)

Yeah. If 2025 was the year of agents. What's 2026?

A (16:58)

Well, by the way, I think there's an interesting thing on this. So I don't think actually 2025 was fully the year of agent, so a lot of agentic development, but I think it was actually mostly only agents and code. Right? So you know, Claude Code, Codex, etc. Of which by the way, a relatively very small percentage of humanity actually in fact fully experienced. Right. Like if you go to the vast majority of the people you and I know, they're like, I don't know. You mean, you mean, you mean agents? Allah. I asked ChatGPT a few questions and had some dialogue and it's like, well, no, no, no, that's not actually really agents. And yes, there's a chatbot, but it's not really agents. Agents is doing stuff and doing it in parallel and doing it in amplification and so forth. So I think code had that. But what I think actually, in fact 26 will be is how we move from this kind of basis of agentic, you know, coding agents to agents and everything else. And actually, in fact, what I think that a. There's just going to be a whole bunch of that. Like, for example, like call it 10 to 100x. People will experience what it is to have a. Their computer running separately from them, doing something productive for them as they're walking away to go get their coffee and then, and then coming back, you know, whether it's, you know, Claude Minis, you know, Mac Minis running Claude cloud code or, or Codex. Different questions, but like that in applied to a lot of other things because that orchestration that allows the parallel, allows, you know, eight hours of work, allows, you know, that kind of thing. And I think that will be broader and then the more subtle thing, which I think will also be a really important part of 26 is orchestration, namely, like, okay, if we begin to have like, you know, hey, when I'm doing this particular form of intellectual knowledge, work, thinking work, cognition work, et cetera, and I now have agents working with me for me, et cetera, and then I'm orchestrating them. I think orchestration is, is the thing that will be, you know, I don't think it'll be March 26, I don't think it'll be more Q4, 26, or kind of growing into that, and then maybe even intensively 27.

B (19:19)

I totally agree with that. I think it's something we're starting to see already. And it actually, it, it brings me to perhaps my hottest, my hottest take that I would love your input on. And it starts with coding agents, which is. I think that OpenAI is currently missing the real coding market because they are not. And they are not really. When you think about orchestration, I think of orchestration as being something that's enabled by tools, but it's also enabled. It's a new skill. It's a new skill for programmers. And when I look at the stuff that OpenAI is producing, I think it's really made for programmers who use AI, like senior engineers who use AI, which are. Is different from AI native engineers who are like just in for quad code terminals and are never looking at the code. And it's really valuable. Like the models that they make are really good. If I have a really hard technical challenge, it is. I definitely go to codex to be like, okay, figure out this like crazy performance bug that I can't figure out. But I don't see them orienting toward this new skill of. It's not vibe coding, but it's not traditional engineering with. With AI added. It's this third thing that is. I'm. I've got four cloud tabs open. I never look at the code. I'm thinking about how to orchestrate, I'm thinking about how to plan. I'm doing all this stuff and, and I'm technical so I can. I could go down to the code, but I never do. And I think that's a really interesting thing that I, I'm kind of noticing and I. OpenAI is like not used to being behind and I'm very curious about how that's going to play out. What do you think?

A (21:01)

Well, I think, I think it's one of the skills that OpenAI is going to pick up to. Because part of what's happening, the thing that this will be great for media because each month in the horse race it'll be like, oh my God. Opus 4.5. Oh my God. GBD, Codex. Oh my God. Gemini. Oh my God. And because all of them are going to be developing and what that means structurally is that some of the things where, you know, as opposed to a couple years where it was literally just OpenAI blazing ahead and I think this is good for the world and everything else. Like there'll be areas where, for example, Anthropic just did super smart stuff in making Claude code and that and that iteration and took, you know, kind of as it were, less capital and less depth of computer, but still made stuff that was pretty amazing. And I think that OpenAI will. This is one of the benefits of how competition kind of benefits industry, benefits society. I think that will make them pick it up and go, okay, we can't be behind on this. We got to be learning to do this. We got to be making this happen. And I think that's what will happen. It'll be painful. Competition frequently actually is kind of painful as you push your way on this. But I think that's the, that's the, I have pretty strong belief that that will be the, the, the, the end result. Now I do do think that it's like you know, credit to Anthropic that the notion of focusing on code is not just a code product but an amplification of many, many other things. Implication of obviously, you know, AI progress and development, but also an amplification on, on frankly every other form of information, slash knowledge, work and maybe even much more, many more things. And I think that's one of the reasons why frankly every kind of major player actually in fact has to be, you know, kind of capable at minimum in code, if not leading.

B (23:10)

Yeah, I mean they did this. It's such an interesting point. They got to the general purpose agent architecture by just making a great coding agent that had all the right primitives. And I gotta tell you, if you look at the software that we developed over the last month or so at Every since Opus 4.5 came out, pretty much every new thing we're building and I built this entire end to end reading app, we have this AI paralegal we've been doing for a while that has just got a huge upgrade. Every single app is just cloud code in a trench code and it's just basically UI wired to. If you press a button, it hits a prompt that has an agent that has a bunch of tools that does the thing you want it to do. And it is the coolest way to build software because it's so much more flexible. Users can modify it. It's just like, it's, it's just exactly right. And it's so such a pleasure to see someone figure out those primitives.

A (24:00)

Yep. And, and, and massive credit to the Anthropic team for doing that. And basically, you know, everyone else, hey, you should be, you should be learning from it, building on top of it, trying to iterate to the next generation whole set.

B (24:17)

Do you have a thought on why Opus is. Opus 4. 5 is so good? I'm assuming you think it's that good. I think it's, I think it's the best model I've ever used. It's like this crazy leap for me, I'm curious if you agree and if you do agree, do you have any thoughts on how they manage to do that?

A (24:34)

Well, I think it's amazingly good. I don't know if it's, if it's the everything model for me. I mean, I think to some degree, you know, kind of. I think GPT5Pro with Codex also is, you know, pretty amazing on a lot of levels. And by the way, like, you know, Gemini 3 on like science topics and so forth. So like, it's kind of. I still am kind of in a. Hey, I bring, you know, all three of them with me to various things I do. Now that being said, I am very curious about how they pulled 4.5 together. And one of the mistakes that outsiders think is they think, oh, you just apply scale and you know, you press play on compute and some of it works and some of it doesn't. And actually, in fact there is but both a lot of both science and art to do it. And it's one of the reasons why, you know, obviously, you know, Meta has needed to restart its kind of AI efforts because you can't just go, oh, I throw a whole bunch of compute at it and it works. It has to kind of like relearn these things in terms of how it's playing. So I'm, I'm, I think it'll come because, you know, one of the things is, you know, the techniques, you know, kind of spread out very quickly. So I think we'll learn, but I actually don't know what the, what the, what the, what the new, the new genius was in opus 4 or 5. Do you have any, do you, you have any hypotheses?

B (26:04)

I have no idea. I think the only thing that I can think of is recently we got a view of the underlying like, Soul document of, of Claude and the, the interesting thing that, that I feel from Opus and I agree, like, I use ChatGPT as my daily driver. To be clear, I use it for everything. But when I'm building software, except for like specific performance things or like hard bugs, I'm using Opus as my daily driver.

A (26:32)

Yep.

B (26:33)

I think that there's usually this trade off that you see a little bit with Codex where the better it is at programming, the less like empathetic it is. Like, it just feels a little bit more like a senior engineer. It's slightly more autistic or something like that. And Opus, they sort of figured out how to make it both sort of humanistic and understand users and what I might want and what I might mean and how interfaces work and what like a good interface is and it's a fantastic programmer and something about a SOUL document where it tells it, this is who you are and like what you care about and whatever. It's one example of, I think anthropic thinking about these things in a maybe a bit of a more holistic way way to, to create a being rather than a tool. And I, I think that that is actually going to be a big deal going forward.

A (27:24)

You know, it's interesting. You know, this is one of the things that inflection kind of started with kind of eq. And actually soul is a very natural, you know, because the inflection start and there's still a lot of ways in which PI is still, you know, amongst the leading of the kind of the, you know, having a, a richly textured, you know, kind of conversation agent, like focusing on, on EQ as much as iq. Like, no, not slouch on iq, but like putting the two together and actually a sole document, actually I think is, is maybe the next. You know, because this is what we learn and iterate is actually great and it's, and it, it's part of what of course makes Claude code work because it's actually in fact a really good human amplifier and like kind of what, what kind of how do you operate that way? And then, you know, you get better performance if you can interact in that way, the right way. So I think that's a, a good insight. I suspect there's other things. I think we both suspect there's other things too and we'll, we'll hopefully learn them in the next few months.

B (28:26)

That would be great. So last thing on the coding front. So you, you mentioned the horse race earlier and there's, everyone's going to be trading volleys and. But if we, let's say we want it, we don't want to be fooled by randomness. We don't want to like, you know, track every little change. We, we hit the snooze button and we come back at the end of 2026. Where do you see the landscape of who's winning in the coding agent race?

A (28:50)

Well, so I think it'll. I don't know who will be winning, but I think it'll be what I, what I would predict strongly is that the horses that are leading now will still be like neck and neck. It'll be like in, in the first hundred meters, this one's a little ahead, then the next hundred meters, that one's a little ahead. And you know, like I don't think that the horses that are in the race, any of them will particularly stumble. Right. So like you'll go wow. I thought you know, cursor was really, was really fantastic and it just gone like. I think that none of them will stubble now. I do think what will interesting is the folks who are not in this at all like you know, say like the easy one to pick on Apple, right. Despite the fact we use you know, Macs for our various things. But the AI part of it is you know, non existent is. Well I think the gap will be like even more stunning the fact that you actually haven't gotten what this coding amplification, everything else means. And I think that will be, will be playing out more but I think they'll all be in the mix. And the thing will be interesting will be not as much as which one will have stumbled out. But I'm really curious about like what are the one or two, you know, like superstars that will really, you know, get in the mix more, you know, will replit. Be more general? Will Loveable be more general? Like, like, like will those be or will it be something else? I mean and part of what's like like with some high probability something will surprise us here.

B (30:36)

Yeah, I don't know what it'll be but predicting surprise.

A (30:41)

Yes.

B (30:44)

Yeah, I think that, I think that's interesting. One of the things I've been toying with is the stakes are so high and programming is such a obvious use case that is so economically valuable and it feels like everyone is just like it's now a knife fight for programming. And I wonder if there are, you know, you've been predicting AI will be used for more creative use cases for a while. I wonder if the surprise entrance comes from a place like that where we don't necessarily expect. Where it's not actually about programming. The one like caveat to that is like you said, Claude sort of invented this general agent by being good at programming. So there's you know, it's hard to say exactly but I wonder if that that is coming like it leaves them vulnerable to competitors from, from other places because they're just focusing on programming right now.

A (31:36)

Well, I definitely, so I definitely think the programming is part of the architecture for getting everything else. And like for example, part of the reason why coding is important is that even when we get to hey, how are you going to have a much better paralegal? I love what you're doing, among other things, better medical assistant, better tutor, etcetera I think coding will actually in fact be not just the amplifier of it, but the fitness function of, you know, how do we, how do we kind of like, you know, kind of go hey this is getting better, this is amplifying the work better, etc. That parallel, not just the foundations of coding, driving planning, you know, longer work, parallelization, orchestration, et cetera, but also like, well how does like a better legal document work will actually in fact also be coming out of it. And I think some of that will also be in creative like it's, it won't be surprising to me. Like obviously everyone's trying to figure out like okay, how do we, well not everyone, a number of people trying to figure out how do we take, you know, Veo Soro etc and then, and then go okay, can we create a 30 minute movie off it? And you know, the coding like pattern will be part of, of, of what happens there and so can be in those kind of creative. Now obviously, you know, some of the more interesting possible surprises are. Well because there's, there's, there's a number of different efforts trying to do this too. Well, could we get you know, raw Audi raw, raw ideation like better at science. So like we read a whole bunch of science papers and we can do scientific hypotheses. Now by the way, you begin to say well maybe that'll also be true of like AI research and ideas for doing this and suddenly it's doing idea generation in this kind of thing. And that's, that's definitely a whole bunch of projects trying to work at that. So the, the notion of hey, if you can think a lot better, you can then you can then apply that to this kind of creativity and this kind of new ideas. Those I think are much more speculative. Like it's an interesting hypothesis. There are people who hold them saying hey, we've just seen that with scale learning and compute and it's going to happen. And I'm like well look, it's crazy that everyone smart should assign a non zero hypothesis, you know, probability of that because that's really amplifying. But on the other hand I think it's like it's not clear that we're, we're, we're yet seeing any of that. Even when you see people like, you know, Terence Tao saying hey, I'm using, you know, generative AI to help me understand where I should be thinking in my, in my math analysis. And yes, but you know, I think 100% but of course Terence Tao is one of the most you know, genius mathematicians of our age and is providing a ton of the metacognition in this.

B (34:32)

That makes sense. Yeah, I, I think I, I'm trying to, I'm going back to your, your comment about no one stumbling and I'm trying to like one. I'm wondering who would stumble if there was a stumble. And I think my current feeling is I would, I would guess Cursor.

A (34:48)

Yeah, that's probably my highest likelihood.

B (34:51)

Not that they go away, they're obviously going to be a successful company, all that kind of stuff, but I think that they're caught a little bit in the same position that OpenAI is. But OpenAI has more flexibility here where Cursor. A lot of their business is built on traditional developers using IDEs inside of big companies with AI on the side. And they're sort of caught between that paradigm and this totally new 2e cloud code type paradigm. And they kind of have to do both. And I think that's going to hamper their product direction and velocity in a way that I would bet in a couple years we'll look back and be like, that was a interesting era and it's still like a widely distributed piece of software, but it's, it's not the next generation thing that we thought it was.

A (35:35)

That's a. I agree and that's one of the reasons I brought it up in the other one I've been thinking about that is like the, the, the hardest. And another angle of that is, you know, how are we going to be not just integrating the kind of the application functionality ui, but the, the underlying model and compute fabric capabilities. And you know, cursor is, is, is just beginning to do that kind of stuff. And you know what the shape of that is and does is going to have to be dual targeted, like you mentioned, or multi targeted. I think it's a, it's a, it's a harder slalom race for them.

B (36:13)

I think the narrative right now is that enterprise AI deployments are not doing as well as hoped as people hope. What do you think the narrative will be in the enterprise by the end of 2026?

A (36:24)

Well, I think for sure there will be some intense usage. And the one that I've been predicting that I think a lot of enterprises will get out of their way on is just amplify coordination, you know, meetings, et cetera. Right. So a lot of them say like the obvious thing to do now is record every single meeting and run AI agents on it, not just to transcribe it, but to say, hey, what are like who are, who in the organization should be notified about stuff, who should be asked about stuff, where action items are following up on, you know, like a whole set of things. What, what, what, what, you know, team of agents should start working on some of this stuff and preparing for the next thing. What, what should be the briefing for the next meeting, you know, off this, all that stuff should be done. And I think that people aren't doing it because they're like, well, I'm worried, does it, does it get the legal liability? You know, you know, we never really recorded everything that was happening in this and someone made an off color joke and does that, does that have a problem? And you know, and I think actually in fact part of the unlock to this will be also using agents. So you go, okay, I'm worried about legal liability. Well, here's the legal liability check agent that, that can go, you know, because you're not, we're gonna scrub, you know, anything that, or change it. Anything that, that we think is actually in fact a is, is a real issue or things like that. And so what I would say is you, yes, it'll be much more intensely positive. And I think it'll be positive because we'll have two groups of things that will be now in real deployment. One is like, I think maybe by the end of 26, if you're not. Yeah, let me state this a little bit more crisply. If, if you, for a company to be a thriving, going and growing concern and evolving with the times, you will need to be recording every single meeting and using agents on it to amplify your work process. And by the end of 2026, if you're, if you're not doing it, that's because you're making excuses. And actually, in fact, it's a little bit like, hey, you know, these cars won't be a big thing. We can keep doing our horses and buggies. You know, that, that, that, that is, I think one and then two is that you will start systematically deploying groups of agents in various problems. And that's part of the reason why I tend to think that, you know, if you said, hey, I need to predict what the next thing is. It's orchestration because it's groups of agents doing things. And that's part of the reason why I don't think It'll kick off Q1 per se, but will grow through 26. And then whether or not 26 is orchestration year or 27 is orchestration year, that's the reason why you have a High prediction there.

B (39:36)

I totally agree with you. I think it's so clear to me that agents are going to reshape how we think about doing company operations. And one of my big proof points for that is just internally we did our 2026 planning with an agent and basically now we're like 20 people. So it's like the first time we have to do like a real planning type exercise for, you know, every department and budgets and like all that kind of stuff. And so Brandon, who's our coo, made this agent that for anyone in the company, it has access to all of our, you know, all of our notion and all of our data. Anyone in the company that, that has a, that is a leader in the company talks to the agent and asks them like, really interesting questions about, okay, how does this, you know, layer up to the overall company strategy which it has access to? What kind of resources do you need? Here are some tough questions to think about decisions you might need to make. And then basically we have this notion page now and it's just like every single department has this like, really crisp, really clean strategy document that, um, someone has gone through and it levers up into the, like the overall company strategy. And it. Then you can do all these amazing things. Like the first thing I did was I had Claude be like, okay, who's not talking to each other? That should talk to each other. And it found all of these strategies, strategy documents that like, I needed to get three people in a room together to just like figure that out. Or another one is, you know, you do a strategy document and then you forget about it. In Q1 you're making a decision and you forget about the overall strategy or what you said you were going to do. So one of the things I'm going to do over Christmas is I have, we have this, this Claude code in a trench coat running in our discord, which is, we use that as our internal chat. And it's called R2C2. And I'm going to basically have R2C2 listening in and anytime we're making a decision, I can just tag it and be like, hey, how does this, how does this lever layer up to the like 20, 26 strategy for this department and the whole org? And like, how would you think about it? And it's a, it's a sort of way to kind of make that, make those documents more alive and more like woven into the everyday of how you make decisions. And I think that's so important and exciting.

A (42:03)

Yep, I think that's exactly Right. And that's, that's the broader version of just doing the coordination on meeting is how, well, how does the coordination of the meeting also relate to, you know, strategy changing conditions in market, changing conditions and competitors, etc. And you know, like this, this is, this is the tangible substantiation of what ISAI mean is that you have intelligence at the scale and price of electricity. And so that means that you know, previously where you had to be extremely selective about where you applied intelligence because intelligence is always kind of, kind of through high priced, you know, kind of human talent, which by the way, I think will continue. But then you go, look, let's, let's, let's apply to all, and all these other places as well.

B (42:48)

Yeah, totally. And by the way, like once you have that free intelligence, you can put the information that you need everyone to consume in lots of different formats. Like we have a vibe coded 2026 strategy app that people can like click through and we're going to do a podcast and there's all this stuff where it's like, you don't want to read this long document, just listen to it on your run and it just helps make the whole company get on the same page in a new way.

A (43:12)

Yep. No, exactly.

B (43:15)

Okay, AGI timelines. Where are we going to hit AGI in 2026? If not, when are we going to hit AGI? Depending, like whatever your defer, whatever your definition of AGI is.

A (43:27)

Yes, well, to start with, what is AGI? And you know, my usual joke here is AGI is the AI we have invented yet. So, so each, each year we're not going to hit there because in one sense we have created AGI already. Like if you say, hey, if AGI is that you have a variety of tasks where the, the AI is, is, is substantially better than your average human. The answer is already like, for example, in writing, AI is better than most human beings at writing in various ways in terms of the vast majority. Now you say the good writers know well, the good writers, it's a little bit more mixed. Although good writers should be using AI to amplify themselves, et cetera, et cetera, et cetera. And there's a bunch of areas where it was already super intelligent. It has a breadth of knowledge, it has an ability to work at a speed that, that human beings simply can't. So if you say, hey, I'd like a, I'd like a report on this, or I'd like to understand this kind of thing, it can work at a speed that a human Being can't, which is part of the reason why they that needs to be used in ampler now we've always had, you know, speed multipliers, planes, cars, etc. This is just cognitive. So it's weird and new and all the rest now. So I think, you know, we've got forms of superintelligence already, we have forms of AGI already. So they go, okay, what's the definition for what will be 26? Now a little bit of that is I think what we will see more of in 26 is a combination of parallelization, longer workflows and orchestration. Which means that the notion of I now kind of. And that's part of the reason why getting more to the realization of what agents are. I think we'll see more of that and so it'll play more towards the oh like I don't think we'll have the press button get, you know, full human capable software engineer who's like I'm ready to do the thing that you've asked me to do, which is I think what you know, the sci fi and kind of thing that people are looking for. But I do think you'll see kind of much more of the hey, I come in as a human engineer and it's like I'm only really capable of. I've got my, my, my, my team agent set tool set that I'm deploying on various things. And the way that I do them is not just kind of like looking at the suggestion for inclusion for the type ahead in my code, but as you were mentioning is like hey look, I, I set this one and this one and this one and this one. And I actually, in fact, because part of what I have agents doing is I have them cross checking each other's code. So I'm not actually even necessarily like I'm running a bunch of it where I actually haven't looked at it, right? Partially because I'm like oh, if something breaks then I'll look at it. Or I'm also expecting to have my, my coding crosscheck agents going hey, you might want to pay attention to this. I go okay, I'll go look at that. You know, and that kind of thing. As I think the, the, the sort of AGI we're going to have applied to a, to a broader range of topics. And so it'll be more in the hey, this is actually doing real work in a more broad sense than just the, you know, the coding application we've had.

B (46:59)

If you, if we listed out the holy Commandments of AI. So thou shalt always scale, compute and data, or thou shalt always align your models and make sure they do exactly what you expect them to do, as much as, as much as possible. And they're probably more. Which holy commandment do you think will need to be broken or will turn out to be misapplied or irrelevant? So I'll give you an example. I feel like all of the alignment, the way that we do alignment has turned like has. Has created models that are sycophantic and kind of do. They're people pleasers. They do what we want them to do, more or less. And if you really want a good engineer, we're going to find that allowing models to have their own opinions and values and desires that are distinct from humans is actually an important part of creating models that can do more in the world and be more autonomous. And the trade off is that they don't always do exactly what you want. And that's a, that's a new thing that we have that we're going to have to get used to. That I think is against the received wisdom of, of how you should build AI.

A (48:13)

Yeah, obviously that's tricky because you don't want them to, you know, a la the old paperclip problem. You don't want them to be misaligned in ways that are serious, like, in ways that are like, hey, I know what you want better than what you think you want and look at what I've delivered is better. That is kind of what you want. You don't want the, oh, what I really want to do is strip mine. Your erase your hard drive. And for example, you say, well, I think what you really need is more time outside. So I'm going to actually lock you out from your computer and your devices for the next three hours just to make sure that you go get that time outside. And you're like, no, no, no, no, don't want that. So, so that's tricky, I would say. Let's see. It's interesting, the change of commitments. I mean, I, what I've been, my head has been mostly wrapped around is what does it mean? Like, almost goes back to this iconic Marvin Minsky book. Society of mind is it's, you know, tribes of agents. And so I tend to think a little bit about how you get opinionated is, is like you set up agents that are deliberately like, like debating intention, opponent, processors. Yes, Opponent process. And that's actually part of how you're solving things and it's part of how you get that, that, that more variation and it, I guess I would tend to think that you'd still want the orchestrator not to be sycophantically aligned but to have a very good sense of what it is you're trying to do. Right. And even if you have, if you're fuzzy about it or you're wrong about it, it's actually in fact helping you be better about that kind of thing as opposed to ultimately going well I'm going to go in direction X when you think yes. And so I'm not sure I buy into the orchestrator thing the way you do, but I guess, you know what I might, What I might say is kind of an interesting question is maybe where the notion of kind of like, like this might be. And I'm a little worried about this one too. So even like giving this one, I'm not sure I would want this one to be exact. It has a similar shape which is like currently we have a very natural thing where we try to say hey look, we're trying to get as much interpretability the agents we want like one of the sci fi worry cases is they start speaking in languages to each other that we don't understand and what, what does that mean? And that becomes further out of control and may get more in the paperclip direction and a set of things to kind of pay attention to that. And I, you know, I think those are good questions and should be paid attention to. They're not. They're not. I don't have the 10 out of the fire, the five fire alarm construction of them. But I do think it's an important something that could go seriously wrong and is worth paying attention to. Now maybe what I think is the thing is to say actually in fact what we want is we want a speed of coordination between the agents and a communication where this, that where what might be tolerable and allowed and shaped in certain ways is the same way that when you have these generative AI models where you say well I can't look under the hood and know what's going. Like I can't know. I can't look at that and prove that it's not paper clipping the world or something as a way of doing it. That may also be true of the comms fabric of how they're coordinating and kind of what the kind of the fitness and, and part of. Because I want the speed of coordination, the speed of learning between them to be such that I'll accept parameters of lack of interpretability there and that's super scary in some ways. So I'm not like, it's. I, I say this like, ah, how would we shape it and what parameters would be okay. And so, but I do think we will tend that direction. So that would be an area. It's a little bit like actually maybe one of the things that also might be is like another commandment was don't do self improvement, don't allow these self improving. And yet in many ways we are doing forms of self improvement. Not just the kind of data modeling, but like coding and that, wrapping back and so forth. And that's going to continue in certain shapes. And so what shapes is that okay? And what shapes is that not okay? Is I think, you know, where the commandments at least changing.

B (53:03)

Yeah. We're going to have to do some legalistic, you know, interpretation of the commandments. So all of our Talmud scholars are going to be newly employed as AI researchers. I love that. I think that's so right. The first people to, to just take the risk to be like, you can communicate in ways we don't understand. I think. Yeah. There's so many gains to that. And it's so anathema to AI safety that I think it's, it's really been a commandment. And, and I, I bet there are ways to make it like make the boundaries of that safe.

A (53:36)

Yes. So we'll need to work on making the boundaries. It's safe, but I think that will happen.

B (53:41)

Yeah. One thing that I actually think, going back to the previous point is about AI that doesn't do what you say. And, and that being kind of my, my contention is it's, I think that that actually may be really useful for autonomy and doing interesting things that we wouldn't predict. And I think your contention is that's a horrible user experience. One way to potentially square that circle is once you have an orchestrator that is aligned with you and you do trust, it's okay if the orchestrator is using an agent that's a pain in the ass because it could be like, I don't care what you say, orchestrator, I'm gonna go off for three months and do this thing. And the orchestrator is like, fine. Like, I'll get most of it done with this other set of agents that actually follow my instructions, but this one is just off doing its thing. And every once in a while it comes back with something brilliant and that's actually valuable and important. And having a good enough orchestrator allows us to move in that direction because the human doesn't have to deal with the bullshit.

A (54:37)

Yep, that's what I was gesturing at. That's the reason why the orchestrator needs some deep alignment. But the orchestrator might have agents that are like, hey, I think everything you think is bozo and I'm going to go try something else. Okay, go ahead, don't just go do it, bring it back to me. But you know, go, go research it. That's great.

B (54:56)

Okay, we're almost out of time, so I've got one last question for you. What is the most important undersung category in AI that we're not talking about right now that we will be talking about at the end of 2026? And I want to put some restrictions around this. So a couple of the categories that may come to mind are like robotics or science or something like that. But I want to get more specific and have like a really specific concrete reason that you think that that thing will be valuable and important and something that we talk about a lot in 2026.

A (55:32)

Well, I'll choose one that's a little. It's just because I'm close to it. It's not really self serving, but it's close to it, I think. So right now the, the vast majority of stuff we're doing is, is extremely close to human language. So it's either obviously human language itself or coding or kind of equivalent. And I think we will be doing a lot more in depth models of things that are not close to human language. So for example, biology. And this is kind of part of the reason that's because of all the work that we've been doing with, you know, Manus AI with Siddhartha Mukherjee and Ujwal Singh and kind of understanding that it's a frequent trope to say biology is a language. It's actually one of the reasons why I'm kind of focusing on it because if you kind of go world of atoms and bits, bio is kind of not fully atoms and closer to bits and has a kind of a programmability kind of compute characteristic to it. You know, exactly how it's compute is still a little dvd. You get people like, you know, Penrose, you know, arguing what's unique about human cognition is is, is quantum computing effects and so forth. And it's an interesting question. And then, you know, what's the borderline between being able to simulate quantum and genuinely quantum is, you know, what is what comes of that is all kind of interesting questions. But I think what this results to is what I think we will see is things that where the generative AI model building out of data and prediction and everything else will be out of. Call it computational sets or language sets that is further afield from human language. And obviously, I think biology is probably the most natural one where that would come out of. And obviously, you know, I've been working on that and thinking about that intensely. Of course, because of Manus.

B (57:25)

And what's the big concrete impact that will have in 2026 that will cause us to be talking about it a lot?

A (57:31)

Well, the one we're going for is amazing new, you know, biological therapeutics or, you know, kind of understanding. I don't know if 26 will be the full. Full hit there. I mean, there's a probabilistic curve, but it wouldn't surprise me if, you know, you know, would you get the equivalent of kind of a move 37 in something around biology. Right. And might be it's a molecule that makes a, you know, that makes a massive difference, you know, Manus trying to cure cancer, et cetera. And we discovered something that was not like what I would hope maybe is a reasonably high probability is we discover a research possibility. Like I. Oh, this might be one of those things. This might be. It's like, you know, probability 27% that this is a move 37 in this. This arena. Maybe that's the 26.

B (58:35)

That would be amazing. Reid, always a pleasure. This is so fun.

A (58:39)

Likewise, Dan. I look forward to seeing you in the new year.

B (58:42)

Sounds good.

C (58:51)

Oh, my gosh, folks, you absolutely, positively have to smash that, like, button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure, unadulterated knowledge. Bombs About ChatGPT Every episode is a rollercoaster of emotions, insights, and laughter that will leave you on the edge of your seat craving for more. It's not just a show. It's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor. Hit like Smash, subscribe, and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely, hopelessly in love with you.